From 31764419a0da4079e55bec0f2f5bb25f87d19467 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alicia=20Tom=C3=A1s?= Date: Fri, 6 Feb 2026 03:28:13 -0600 Subject: [PATCH 01/28] =?UTF-8?q?Agregando=20ensayo=20de=20investigaci?= =?UTF-8?q?=C3=B3n=20en=20LaTex?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tareas/ensayo.tex | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tareas/ensayo.tex diff --git a/tareas/ensayo.tex b/tareas/ensayo.tex new file mode 100644 index 0000000..e69de29 From 49f9cebf599c2780de935be3637a4dd9b7398315 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alicia=20Tom=C3=A1s?= Date: Fri, 6 Feb 2026 03:39:34 -0600 Subject: [PATCH 02/28] =?UTF-8?q?Ahora=20s=C3=AD=20con=20contenido=20real?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tareas/ensayo.tex | 105 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 105 insertions(+) diff --git a/tareas/ensayo.tex b/tareas/ensayo.tex index e69de29..6416631 100644 --- a/tareas/ensayo.tex +++ b/tareas/ensayo.tex @@ -0,0 +1,105 @@ + +\documentclass[12pt]{article} + +% --- Página y tipografía --- +\usepackage[letterpaper,margin=2.5cm]{geometry} +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} % si compilas con pdfLaTeX +\usepackage{lmodern} +\usepackage{microtype} + +% --- Imágenes y color --- +\usepackage{graphicx} +\usepackage{xcolor} + +% --- Control fino de espacios --- +\usepackage{setspace} +\setlength{\parindent}{0pt} + +\begin{document} +\thispagestyle{empty} + +% ===== Encabezado con logos + texto ===== +\begin{minipage}[c]{0.18\textwidth} + \centering + % Cambia por tu logo izquierdo + \includegraphics[width=0.95\linewidth]{logo_usac.jpeg} +\end{minipage} +\hfill +\begin{minipage}[c]{0.60\textwidth} + \small + Universidad de San Carlos de Guatemala\\ + Escuela de Ciencias Físicas y Matemáticas\\ + Nombre estudiante\\ + Carnet: \\ + Programación 1\\ +\end{minipage} +\hfill +\begin{minipage}[c]{0.18\textwidth} + \centering + % Cambia por tu logo derecho + \includegraphics[width=1.4\linewidth]{logo_ecfm.jpg} +\end{minipage} + +\vspace{0.5cm} + +% Línea horizontal superior (gruesa) +\noindent\rule{\textwidth}{1.2pt} + +\vspace{0.2cm} + +% ===== Título ===== +\begin{center} + {\Large\scshape Ensayo}\\[0.3em] +\end{center} + +\vspace{0.1cm} + +% Fecha +\begin{center} + \small\scshape viernes 06 de febrero de 2026 +\end{center} + +\vspace{0.2cm} + +% Línea horizontal inferior (gruesa) +\noindent\rule{\textwidth}{1.2pt} + +\vspace{0.6cm} + +% ===== Caja de resumen ===== +\noindent +\colorbox{gray!36}{% + \parbox{\textwidth}{% + \vspace{0.6em} + \textbf{Resumen}\\[0.4em] + \small +El presente ensayo se enfoca en dara respuesta a las siguientes preguntas: ¿cuál sería tú área de investigación en Física? y ¿explica por qué la programación te sería útil en dicho campo?, se realizá una descripción para cada pregunta, desde el punto de vista de un estudiante de física. + \vspace{0.8em} + }% +} + + +\section{Pregunta No. 1} +\subsection{¿Cuál sería tú área de investigación en Física?} + +Es un tema facinante, si fuera un investigador de física, el área de investigación sería la Física de Radiociones, ya que es un área que conbina las leyes naturales y la vida y poder contribuir a savar las mismas.\\ + Y en esta misma área el interés en la investigación sería en Radioterapia que es el uso de radiación ionizante para tratar tumores (física de partículas, dosis que se requiere a un paciente).\\ + De acuerdo al siguiente texto "La radiación ionizante es la liberación de electrones mediante la emisión de energía en forma de ondas y partículas que se producen de forma natural en los materiales que conocemos como radiactivos (suelo, agua y vegetación) y de forma artificial en isótopos creados por el hombre, tal como los equipos que producen Rayos-X" (ATSDR en Español, 2016).\\ +Por tanto comprender la liberación de los electrones puede alterar o modificar la estructura molecular de un ser vivo. + + +\section{Pregunta 2} +\subsection{¿Explica por qué la programación te sería útil en dicho campo?} + +En física médica, no siempre se puedes experimentar directamente con humanos por razones éticas y de seguridad.\\ + +¿Para qué sirve? Se usan algoritmos (como Geant4 o MCNP) para simular millones de trayectorias de partículas (fotones, electrones, protones) golpeando un tejido.\\ + +La programación te permite: Definir la geometría del paciente y calcular exactamente cuánta energía se deposita en cada milímetro cúbico. + + + +\end{document} + +\end{document} \ No newline at end of file From 0a98ebdf64ceff034924a34b1119ce96f1680c4c Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Sat, 14 Mar 2026 01:19:25 -0600 Subject: [PATCH 03/28] Guardando cambios antes de sincronizar --- Dockerfile | 4 + autograder/README.md | 119 +++++++++++++++ autograder/autograder.bat | 87 +++++++++++ autograder/autograder.sh | 138 ++++++++++++++++++ .../tareas/contar_hasta_10/enunciado.md | 33 +++++ .../tareas/contar_hasta_10/solucion.cpp | 9 ++ autograder/tests/contar_hasta_10/caso1.in | 1 + autograder/tests/contar_hasta_10/caso1.out | 10 ++ autograder/tests/ordenamiento/caso1.in | 2 + autograder/tests/ordenamiento/caso1.out | 1 + autograder/tests/ordenamiento/caso2.in | 2 + autograder/tests/ordenamiento/caso2.out | 1 + autograder/tests/ordenamiento/caso3.in | 2 + autograder/tests/ordenamiento/caso3.out | 1 + hola_mundo.cpp | 6 + 15 files changed, 416 insertions(+) create mode 100644 Dockerfile create mode 100644 autograder/README.md create mode 100644 autograder/autograder.bat create mode 100755 autograder/autograder.sh create mode 100644 autograder/tareas/contar_hasta_10/enunciado.md create mode 100644 autograder/tareas/contar_hasta_10/solucion.cpp create mode 100644 autograder/tests/contar_hasta_10/caso1.in create mode 100644 autograder/tests/contar_hasta_10/caso1.out create mode 100644 autograder/tests/ordenamiento/caso1.in create mode 100644 autograder/tests/ordenamiento/caso1.out create mode 100644 autograder/tests/ordenamiento/caso2.in create mode 100644 autograder/tests/ordenamiento/caso2.out create mode 100644 autograder/tests/ordenamiento/caso3.in create mode 100644 autograder/tests/ordenamiento/caso3.out create mode 100644 hola_mundo.cpp diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..ab457d5 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,4 @@ +FROM gcc:latest +WORKDIR /usr/src/app +COPY . . +CMD ["tail", "-f", "/dev/null"] diff --git a/autograder/README.md b/autograder/README.md new file mode 100644 index 0000000..a28b4e8 --- /dev/null +++ b/autograder/README.md @@ -0,0 +1,119 @@ +# Autograder — Programacion 1, ECFM + +Herramienta para evaluar tareas de C++ contra casos de prueba automaticamente. +Compatible con **Windows, Linux y macOS** — solo requiere tener `g++` instalado. + +--- + +## Estructura de directorios + +``` +autograder/ +├── autograder.bat # Windows +├── autograder.sh # Linux / macOS +├── tests/ +│ └── / +│ ├── caso1.in # Entrada del caso de prueba +│ ├── caso1.out # Salida esperada +│ ├── caso2.in +│ └── caso2.out +└── tareas/ + └── / + └── enunciado.md +``` + +--- + +## Uso + +### Windows + +```bat +autograder.bat +``` + +### Linux / macOS + +```bash +chmod +x autograder.sh # solo la primera vez +./autograder.sh +``` + +--- + +## Ejemplo: "Contar hasta 10" + +### 1. Lee el enunciado + +``` +tareas/contar_hasta_10/enunciado.md +``` + +### 2. Escribe tu solucion en C++ + +Guarda el archivo como `solucion.cpp`: + +```cpp +#include +using namespace std; + +int main() { + for (int i = 1; i <= 10; i++) { + cout << i << "\n"; + } + return 0; +} +``` + +### 3. Ejecuta el autograder + +```bat +:: Windows +autograder.bat contar_hasta_10 solucion.cpp +``` + +```bash +# Linux / macOS +./autograder.sh contar_hasta_10 solucion.cpp +``` + +### 4. Lee el resultado + +``` + .:#@@@@+. _____ ____ _____ __ __ + :%@@@@@@%: | ____/ ___| ___| \/ | + *@@@@@@@@* | _|| | | |_ | |\/| | + :%@@@@@@%: | |__| |___| _| | | | | + '-#@@@@-' |_____\____|_| |_| |_| + Autograder — Programacion 1 F12 + +════════════════════════════════════════ + Tarea : contar_hasta_10 + Archivo: solucion.cpp +════════════════════════════════════════ + [INFO] Compilacion exitosa + [PASS] caso1 + + Resultado: 1 / 1 casos pasados +════════════════════════════════════════ +``` + +--- + +## Como agregar una nueva tarea + +1. Crea la carpeta `tests//` +2. Por cada caso agrega: + - `casoN.in` — texto de entrada (puede estar vacio) + - `casoN.out` — salida esperada exacta +3. Opcionalmente crea `tareas//enunciado.md` + +--- + +## Requisitos + +| Sistema | Instalacion de g++ | +|---------|--------------------| +| Linux | `sudo apt install build-essential` | +| macOS | `xcode-select --install` | +| Windows | [MinGW-w64](https://www.mingw-w64.org/) — asegurarse de agregar `bin/` al PATH | diff --git a/autograder/autograder.bat b/autograder/autograder.bat new file mode 100644 index 0000000..43794d1 --- /dev/null +++ b/autograder/autograder.bat @@ -0,0 +1,87 @@ +@echo off +setlocal enabledelayedexpansion + +:: ── Banner ─────────────────────────────────────────────────────────────────── +echo .:#@@@@+. _____ ____ _____ __ __ +echo :%%@@@@@@%%: ^| ____/ ___^| ___^| \/ ^| +echo *@@@@@@@@* ^| _^|^| ^| ^| ^|_ ^| ^|\/^| ^| +echo :%%@@@@@@%%: ^| ^|__^| ^|___^| _^| ^| ^| ^| ^| +echo '-#@@@@-' ^|_____\____^|_^| ^|_^| ^|_^| +echo Autograder -- Programacion 1 F12 +echo. + +:: ── Argumentos ─────────────────────────────────────────────────────────────── +if "%~2"=="" ( + echo Uso: autograder.bat ^ ^ + echo. + echo Ejemplo: + echo autograder.bat contar_hasta_10 solucion.cpp + echo autograder.bat ordenamiento solucion.cpp + exit /b 1 +) + +set TAREA=%~1 +set ARCHIVO=%~2 + +:: Verificar extension +if /i not "%ARCHIVO:~-4%"==".cpp" ( + echo [FAIL] Solo se aceptan archivos .cpp + exit /b 1 +) + +set BINARIO=%TEMP%\programa_ecfm.exe +set SALIDA_TMP=%TEMP%\salida_ecfm.txt +set TOTAL=0 +set PASADOS=0 + +echo ════════════════════════════════════════════════════════ +echo Tarea : %TAREA% +echo Archivo: %ARCHIVO% +echo ════════════════════════════════════════════════════════ + +:: ── Compilar ───────────────────────────────────────────────────────────────── +g++ -std=c++17 -O2 -Wall -o "%BINARIO%" "%ARCHIVO%" 2>"%TEMP%\ecfm_compile_err.txt" +if errorlevel 1 ( + echo [FAIL] Error de compilacion: + type "%TEMP%\ecfm_compile_err.txt" + exit /b 1 +) +echo [INFO] Compilacion exitosa + +:: ── Correr casos de prueba ──────────────────────────────────────────────────── +set TEST_DIR=%~dp0tests\%TAREA% + +if not exist "%TEST_DIR%" ( + echo [FAIL] No se encontro la carpeta de tests: %TEST_DIR% + exit /b 1 +) + +for %%F in ("%TEST_DIR%\*.in") do ( + set /a TOTAL+=1 + set CASO=%%~nF + set EXPECTED=%%~dpnF.out + + if exist "!EXPECTED!" ( + "%BINARIO%" < "%%F" > "%SALIDA_TMP%" 2>nul + + fc /w "%SALIDA_TMP%" "!EXPECTED!" >nul 2>&1 + if not errorlevel 1 ( + echo [PASS] !CASO! + set /a PASADOS+=1 + ) else ( + echo [FAIL] !CASO! + echo Esperado: + for /f "usebackq tokens=*" %%L in ("!EXPECTED!") do echo %%L + echo Obtenido: + for /f "usebackq tokens=*" %%L in ("%SALIDA_TMP%") do echo %%L + ) + ) else ( + echo [INFO] Sin .out para !CASO! - omitido + ) +) + +echo. +echo Resultado: %PASADOS% / %TOTAL% casos pasados +echo ════════════════════════════════════════════════════════ + +endlocal diff --git a/autograder/autograder.sh b/autograder/autograder.sh new file mode 100755 index 0000000..244e834 --- /dev/null +++ b/autograder/autograder.sh @@ -0,0 +1,138 @@ +#!/bin/bash +# Autograder — Programacion 1, ECFM +# Evalua tareas de C++ y Python contra casos de prueba + +set -euo pipefail + +# ── Banner ──────────────────────────────────────────────────────────────────── +banner() { + echo " .:#@@@@+. _____ ____ _____ __ __ " + echo " :%@@@@@@%: | ____/ ___| ___| \\/ |" + echo " *@@@@@@@@* | _|| | | |_ | |\\/| |" + echo " :%@@@@@@%: | |__| |___| _| | | | |" + echo " '-#@@@@-' |_____\\____|_| |_| |_|" + echo " Autograder — Programacion 1 F12" + echo "" +} + +# ── Colores ─────────────────────────────────────────────────────────────────── +GREEN='\033[0;32m'; RED='\033[0;31m'; YELLOW='\033[1;33m'; RESET='\033[0m' +pass() { echo -e " ${GREEN}[PASS]${RESET} $1"; } +fail() { echo -e " ${RED}[FAIL]${RESET} $1"; } +info() { echo -e " ${YELLOW}[INFO]${RESET} $1"; } + +# ── Configuracion ───────────────────────────────────────────────────────────── +TIMEOUT=5 # segundos maximos por caso de prueba +TEMP_DIR=$(mktemp -d) +trap "rm -rf $TEMP_DIR" EXIT + +TOTAL=0; PASSED=0 + +# ── Compilar C++ ────────────────────────────────────────────────────────────── +compile_cpp() { + local src="$1" + local bin="$TEMP_DIR/programa" + if g++ -std=c++17 -O2 -Wall -o "$bin" "$src" 2>"$TEMP_DIR/compile_err"; then + echo "$bin" + else + echo "" + cat "$TEMP_DIR/compile_err" >&2 + fi +} + +# ── Ejecutar un caso de prueba ──────────────────────────────────────────────── +# run_test +run_test() { + local cmd="$1" + local input="$2" + local expected="$3" + local name="$4" + + TOTAL=$((TOTAL + 1)) + + local actual + actual=$(timeout "$TIMEOUT" bash -c "$cmd" < "$input" 2>/dev/null) || true + + if diff -q <(echo "$actual" | tr -s ' ' | sed 's/[[:space:]]*$//') \ + <(cat "$expected" | tr -s ' ' | sed 's/[[:space:]]*$//') > /dev/null 2>&1; then + pass "$name" + PASSED=$((PASSED + 1)) + else + fail "$name" + echo " Esperado:" + cat "$expected" | sed 's/^/ /' + echo " Obtenido:" + echo "$actual" | sed 's/^/ /' + fi +} + +# ── Evaluar tarea ───────────────────────────────────────────────────────────── +grade() { + local tarea="$1" # ej: tareas/tarea1 + local archivo="$2" # archivo del estudiante (.cpp o .py) + + local ext="${archivo##*.}" + local cmd="" + + echo "" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + echo " Tarea : $(basename $tarea)" + echo " Archivo: $archivo" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + + if [[ "$ext" != "cpp" ]]; then + fail "Solo se aceptan archivos .cpp (recibido: .$ext)" + return + fi + + local bin + bin=$(compile_cpp "$archivo") + if [[ -z "$bin" ]]; then + fail "Error de compilacion" + return + fi + cmd="$bin" + info "Compilacion exitosa" + + # Correr cada caso de prueba definido en tests// + local test_dir="tests/$(basename $tarea)" + if [[ ! -d "$test_dir" ]]; then + fail "No se encontraron tests en $test_dir" + return + fi + + for input_file in "$test_dir"/*.in; do + [[ -f "$input_file" ]] || continue + local base="${input_file%.in}" + local expected_file="${base}.out" + local case_name=$(basename "$base") + + if [[ ! -f "$expected_file" ]]; then + info "Sin archivo .out para $case_name — omitido" + continue + fi + + run_test "$cmd" "$input_file" "$expected_file" "$case_name" + done + + echo "" + echo " Resultado: $PASSED / $TOTAL casos pasados" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" +} + +# ── Main ────────────────────────────────────────────────────────────────────── +banner + +if [[ $# -lt 2 ]]; then + echo "Uso: $0 " + echo "" + echo "Ejemplo:" + echo " $0 tareas/ordenamiento solucion.cpp" + echo " $0 tareas/busqueda solucion.py" + exit 1 +fi + +TAREA="$1" +ARCHIVO="$(realpath "$2")" # convertir a path absoluto antes del cd +cd "$(dirname "$0")" # siempre ejecutar desde autograder/ +grade "$TAREA" "$ARCHIVO" diff --git a/autograder/tareas/contar_hasta_10/enunciado.md b/autograder/tareas/contar_hasta_10/enunciado.md new file mode 100644 index 0000000..fe523f2 --- /dev/null +++ b/autograder/tareas/contar_hasta_10/enunciado.md @@ -0,0 +1,33 @@ +# Tarea: Contar hasta 10 + +Escribe un programa que imprima los números del 1 al 10, uno por línea, usando un ciclo `for`. + +## Entrada +Ninguna. + +## Salida +``` +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +``` + +## Ejemplo de solución en C++ +```cpp +#include +using namespace std; +int main() { + for (int i = 1; i <= 10; i++) { + cout << i << "\n"; + } + return 0; +} +``` + diff --git a/autograder/tareas/contar_hasta_10/solucion.cpp b/autograder/tareas/contar_hasta_10/solucion.cpp new file mode 100644 index 0000000..bb8250c --- /dev/null +++ b/autograder/tareas/contar_hasta_10/solucion.cpp @@ -0,0 +1,9 @@ +#include +using namespace std; + +int main() { + for (int i = 1; i <= 10; i++) { + cout << i << "\n"; + } + return 0; +} \ No newline at end of file diff --git a/autograder/tests/contar_hasta_10/caso1.in b/autograder/tests/contar_hasta_10/caso1.in new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/autograder/tests/contar_hasta_10/caso1.in @@ -0,0 +1 @@ + diff --git a/autograder/tests/contar_hasta_10/caso1.out b/autograder/tests/contar_hasta_10/caso1.out new file mode 100644 index 0000000..f00c965 --- /dev/null +++ b/autograder/tests/contar_hasta_10/caso1.out @@ -0,0 +1,10 @@ +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 diff --git a/autograder/tests/ordenamiento/caso1.in b/autograder/tests/ordenamiento/caso1.in new file mode 100644 index 0000000..0236378 --- /dev/null +++ b/autograder/tests/ordenamiento/caso1.in @@ -0,0 +1,2 @@ +5 +3 1 4 1 5 diff --git a/autograder/tests/ordenamiento/caso1.out b/autograder/tests/ordenamiento/caso1.out new file mode 100644 index 0000000..b8a5a7e --- /dev/null +++ b/autograder/tests/ordenamiento/caso1.out @@ -0,0 +1 @@ +1 1 3 4 5 diff --git a/autograder/tests/ordenamiento/caso2.in b/autograder/tests/ordenamiento/caso2.in new file mode 100644 index 0000000..86722ce --- /dev/null +++ b/autograder/tests/ordenamiento/caso2.in @@ -0,0 +1,2 @@ +4 +9 2 7 3 diff --git a/autograder/tests/ordenamiento/caso2.out b/autograder/tests/ordenamiento/caso2.out new file mode 100644 index 0000000..58e611f --- /dev/null +++ b/autograder/tests/ordenamiento/caso2.out @@ -0,0 +1 @@ +2 3 7 9 diff --git a/autograder/tests/ordenamiento/caso3.in b/autograder/tests/ordenamiento/caso3.in new file mode 100644 index 0000000..192e9d9 --- /dev/null +++ b/autograder/tests/ordenamiento/caso3.in @@ -0,0 +1,2 @@ +1 +42 diff --git a/autograder/tests/ordenamiento/caso3.out b/autograder/tests/ordenamiento/caso3.out new file mode 100644 index 0000000..d81cc07 --- /dev/null +++ b/autograder/tests/ordenamiento/caso3.out @@ -0,0 +1 @@ +42 diff --git a/hola_mundo.cpp b/hola_mundo.cpp new file mode 100644 index 0000000..4631985 --- /dev/null +++ b/hola_mundo.cpp @@ -0,0 +1,6 @@ +#include + +int main() { + std::cout << "Hola Mundo desde Docker!" << std::endl; + return 0; +} From 42e2dfb84754f74d5d95ed2b84c39bf9a9b46617 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alicia=20Tom=C3=A1s?= Date: Fri, 20 Mar 2026 11:21:00 -0600 Subject: [PATCH 04/28] Guardando autograder y archivos de fisica en tarea1 --- tareas/ensayo.tex | 105 ---------------------------------------------- 1 file changed, 105 deletions(-) delete mode 100644 tareas/ensayo.tex diff --git a/tareas/ensayo.tex b/tareas/ensayo.tex deleted file mode 100644 index 6416631..0000000 --- a/tareas/ensayo.tex +++ /dev/null @@ -1,105 +0,0 @@ - -\documentclass[12pt]{article} - -% --- Página y tipografía --- -\usepackage[letterpaper,margin=2.5cm]{geometry} -\usepackage[T1]{fontenc} -\usepackage[utf8]{inputenc} % si compilas con pdfLaTeX -\usepackage{lmodern} -\usepackage{microtype} - -% --- Imágenes y color --- -\usepackage{graphicx} -\usepackage{xcolor} - -% --- Control fino de espacios --- -\usepackage{setspace} -\setlength{\parindent}{0pt} - -\begin{document} -\thispagestyle{empty} - -% ===== Encabezado con logos + texto ===== -\begin{minipage}[c]{0.18\textwidth} - \centering - % Cambia por tu logo izquierdo - \includegraphics[width=0.95\linewidth]{logo_usac.jpeg} -\end{minipage} -\hfill -\begin{minipage}[c]{0.60\textwidth} - \small - Universidad de San Carlos de Guatemala\\ - Escuela de Ciencias Físicas y Matemáticas\\ - Nombre estudiante\\ - Carnet: \\ - Programación 1\\ -\end{minipage} -\hfill -\begin{minipage}[c]{0.18\textwidth} - \centering - % Cambia por tu logo derecho - \includegraphics[width=1.4\linewidth]{logo_ecfm.jpg} -\end{minipage} - -\vspace{0.5cm} - -% Línea horizontal superior (gruesa) -\noindent\rule{\textwidth}{1.2pt} - -\vspace{0.2cm} - -% ===== Título ===== -\begin{center} - {\Large\scshape Ensayo}\\[0.3em] -\end{center} - -\vspace{0.1cm} - -% Fecha -\begin{center} - \small\scshape viernes 06 de febrero de 2026 -\end{center} - -\vspace{0.2cm} - -% Línea horizontal inferior (gruesa) -\noindent\rule{\textwidth}{1.2pt} - -\vspace{0.6cm} - -% ===== Caja de resumen ===== -\noindent -\colorbox{gray!36}{% - \parbox{\textwidth}{% - \vspace{0.6em} - \textbf{Resumen}\\[0.4em] - \small -El presente ensayo se enfoca en dara respuesta a las siguientes preguntas: ¿cuál sería tú área de investigación en Física? y ¿explica por qué la programación te sería útil en dicho campo?, se realizá una descripción para cada pregunta, desde el punto de vista de un estudiante de física. - \vspace{0.8em} - }% -} - - -\section{Pregunta No. 1} -\subsection{¿Cuál sería tú área de investigación en Física?} - -Es un tema facinante, si fuera un investigador de física, el área de investigación sería la Física de Radiociones, ya que es un área que conbina las leyes naturales y la vida y poder contribuir a savar las mismas.\\ - Y en esta misma área el interés en la investigación sería en Radioterapia que es el uso de radiación ionizante para tratar tumores (física de partículas, dosis que se requiere a un paciente).\\ - De acuerdo al siguiente texto "La radiación ionizante es la liberación de electrones mediante la emisión de energía en forma de ondas y partículas que se producen de forma natural en los materiales que conocemos como radiactivos (suelo, agua y vegetación) y de forma artificial en isótopos creados por el hombre, tal como los equipos que producen Rayos-X" (ATSDR en Español, 2016).\\ -Por tanto comprender la liberación de los electrones puede alterar o modificar la estructura molecular de un ser vivo. - - -\section{Pregunta 2} -\subsection{¿Explica por qué la programación te sería útil en dicho campo?} - -En física médica, no siempre se puedes experimentar directamente con humanos por razones éticas y de seguridad.\\ - -¿Para qué sirve? Se usan algoritmos (como Geant4 o MCNP) para simular millones de trayectorias de partículas (fotones, electrones, protones) golpeando un tejido.\\ - -La programación te permite: Definir la geometría del paciente y calcular exactamente cuánta energía se deposita en cada milímetro cúbico. - - - -\end{document} - -\end{document} \ No newline at end of file From 412381e5fb10e249df698d8f4d27c44cd27bf9c2 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Sat, 14 Mar 2026 01:19:25 -0600 Subject: [PATCH 05/28] Guardando cambios antes de sincronizar --- Dockerfile | 4 + autograder/README.md | 119 +++++++++++++++ autograder/autograder.bat | 87 +++++++++++ autograder/autograder.sh | 138 ++++++++++++++++++ .../tareas/contar_hasta_10/enunciado.md | 33 +++++ .../tareas/contar_hasta_10/solucion.cpp | 9 ++ autograder/tests/contar_hasta_10/caso1.in | 1 + autograder/tests/contar_hasta_10/caso1.out | 10 ++ autograder/tests/ordenamiento/caso1.in | 2 + autograder/tests/ordenamiento/caso1.out | 1 + autograder/tests/ordenamiento/caso2.in | 2 + autograder/tests/ordenamiento/caso2.out | 1 + autograder/tests/ordenamiento/caso3.in | 2 + autograder/tests/ordenamiento/caso3.out | 1 + hola_mundo.cpp | 6 + 15 files changed, 416 insertions(+) create mode 100644 Dockerfile create mode 100644 autograder/README.md create mode 100644 autograder/autograder.bat create mode 100755 autograder/autograder.sh create mode 100644 autograder/tareas/contar_hasta_10/enunciado.md create mode 100644 autograder/tareas/contar_hasta_10/solucion.cpp create mode 100644 autograder/tests/contar_hasta_10/caso1.in create mode 100644 autograder/tests/contar_hasta_10/caso1.out create mode 100644 autograder/tests/ordenamiento/caso1.in create mode 100644 autograder/tests/ordenamiento/caso1.out create mode 100644 autograder/tests/ordenamiento/caso2.in create mode 100644 autograder/tests/ordenamiento/caso2.out create mode 100644 autograder/tests/ordenamiento/caso3.in create mode 100644 autograder/tests/ordenamiento/caso3.out create mode 100644 hola_mundo.cpp diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..ab457d5 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,4 @@ +FROM gcc:latest +WORKDIR /usr/src/app +COPY . . +CMD ["tail", "-f", "/dev/null"] diff --git a/autograder/README.md b/autograder/README.md new file mode 100644 index 0000000..a28b4e8 --- /dev/null +++ b/autograder/README.md @@ -0,0 +1,119 @@ +# Autograder — Programacion 1, ECFM + +Herramienta para evaluar tareas de C++ contra casos de prueba automaticamente. +Compatible con **Windows, Linux y macOS** — solo requiere tener `g++` instalado. + +--- + +## Estructura de directorios + +``` +autograder/ +├── autograder.bat # Windows +├── autograder.sh # Linux / macOS +├── tests/ +│ └── / +│ ├── caso1.in # Entrada del caso de prueba +│ ├── caso1.out # Salida esperada +│ ├── caso2.in +│ └── caso2.out +└── tareas/ + └── / + └── enunciado.md +``` + +--- + +## Uso + +### Windows + +```bat +autograder.bat +``` + +### Linux / macOS + +```bash +chmod +x autograder.sh # solo la primera vez +./autograder.sh +``` + +--- + +## Ejemplo: "Contar hasta 10" + +### 1. Lee el enunciado + +``` +tareas/contar_hasta_10/enunciado.md +``` + +### 2. Escribe tu solucion en C++ + +Guarda el archivo como `solucion.cpp`: + +```cpp +#include +using namespace std; + +int main() { + for (int i = 1; i <= 10; i++) { + cout << i << "\n"; + } + return 0; +} +``` + +### 3. Ejecuta el autograder + +```bat +:: Windows +autograder.bat contar_hasta_10 solucion.cpp +``` + +```bash +# Linux / macOS +./autograder.sh contar_hasta_10 solucion.cpp +``` + +### 4. Lee el resultado + +``` + .:#@@@@+. _____ ____ _____ __ __ + :%@@@@@@%: | ____/ ___| ___| \/ | + *@@@@@@@@* | _|| | | |_ | |\/| | + :%@@@@@@%: | |__| |___| _| | | | | + '-#@@@@-' |_____\____|_| |_| |_| + Autograder — Programacion 1 F12 + +════════════════════════════════════════ + Tarea : contar_hasta_10 + Archivo: solucion.cpp +════════════════════════════════════════ + [INFO] Compilacion exitosa + [PASS] caso1 + + Resultado: 1 / 1 casos pasados +════════════════════════════════════════ +``` + +--- + +## Como agregar una nueva tarea + +1. Crea la carpeta `tests//` +2. Por cada caso agrega: + - `casoN.in` — texto de entrada (puede estar vacio) + - `casoN.out` — salida esperada exacta +3. Opcionalmente crea `tareas//enunciado.md` + +--- + +## Requisitos + +| Sistema | Instalacion de g++ | +|---------|--------------------| +| Linux | `sudo apt install build-essential` | +| macOS | `xcode-select --install` | +| Windows | [MinGW-w64](https://www.mingw-w64.org/) — asegurarse de agregar `bin/` al PATH | diff --git a/autograder/autograder.bat b/autograder/autograder.bat new file mode 100644 index 0000000..43794d1 --- /dev/null +++ b/autograder/autograder.bat @@ -0,0 +1,87 @@ +@echo off +setlocal enabledelayedexpansion + +:: ── Banner ─────────────────────────────────────────────────────────────────── +echo .:#@@@@+. _____ ____ _____ __ __ +echo :%%@@@@@@%%: ^| ____/ ___^| ___^| \/ ^| +echo *@@@@@@@@* ^| _^|^| ^| ^| ^|_ ^| ^|\/^| ^| +echo :%%@@@@@@%%: ^| ^|__^| ^|___^| _^| ^| ^| ^| ^| +echo '-#@@@@-' ^|_____\____^|_^| ^|_^| ^|_^| +echo Autograder -- Programacion 1 F12 +echo. + +:: ── Argumentos ─────────────────────────────────────────────────────────────── +if "%~2"=="" ( + echo Uso: autograder.bat ^ ^ + echo. + echo Ejemplo: + echo autograder.bat contar_hasta_10 solucion.cpp + echo autograder.bat ordenamiento solucion.cpp + exit /b 1 +) + +set TAREA=%~1 +set ARCHIVO=%~2 + +:: Verificar extension +if /i not "%ARCHIVO:~-4%"==".cpp" ( + echo [FAIL] Solo se aceptan archivos .cpp + exit /b 1 +) + +set BINARIO=%TEMP%\programa_ecfm.exe +set SALIDA_TMP=%TEMP%\salida_ecfm.txt +set TOTAL=0 +set PASADOS=0 + +echo ════════════════════════════════════════════════════════ +echo Tarea : %TAREA% +echo Archivo: %ARCHIVO% +echo ════════════════════════════════════════════════════════ + +:: ── Compilar ───────────────────────────────────────────────────────────────── +g++ -std=c++17 -O2 -Wall -o "%BINARIO%" "%ARCHIVO%" 2>"%TEMP%\ecfm_compile_err.txt" +if errorlevel 1 ( + echo [FAIL] Error de compilacion: + type "%TEMP%\ecfm_compile_err.txt" + exit /b 1 +) +echo [INFO] Compilacion exitosa + +:: ── Correr casos de prueba ──────────────────────────────────────────────────── +set TEST_DIR=%~dp0tests\%TAREA% + +if not exist "%TEST_DIR%" ( + echo [FAIL] No se encontro la carpeta de tests: %TEST_DIR% + exit /b 1 +) + +for %%F in ("%TEST_DIR%\*.in") do ( + set /a TOTAL+=1 + set CASO=%%~nF + set EXPECTED=%%~dpnF.out + + if exist "!EXPECTED!" ( + "%BINARIO%" < "%%F" > "%SALIDA_TMP%" 2>nul + + fc /w "%SALIDA_TMP%" "!EXPECTED!" >nul 2>&1 + if not errorlevel 1 ( + echo [PASS] !CASO! + set /a PASADOS+=1 + ) else ( + echo [FAIL] !CASO! + echo Esperado: + for /f "usebackq tokens=*" %%L in ("!EXPECTED!") do echo %%L + echo Obtenido: + for /f "usebackq tokens=*" %%L in ("%SALIDA_TMP%") do echo %%L + ) + ) else ( + echo [INFO] Sin .out para !CASO! - omitido + ) +) + +echo. +echo Resultado: %PASADOS% / %TOTAL% casos pasados +echo ════════════════════════════════════════════════════════ + +endlocal diff --git a/autograder/autograder.sh b/autograder/autograder.sh new file mode 100755 index 0000000..244e834 --- /dev/null +++ b/autograder/autograder.sh @@ -0,0 +1,138 @@ +#!/bin/bash +# Autograder — Programacion 1, ECFM +# Evalua tareas de C++ y Python contra casos de prueba + +set -euo pipefail + +# ── Banner ──────────────────────────────────────────────────────────────────── +banner() { + echo " .:#@@@@+. _____ ____ _____ __ __ " + echo " :%@@@@@@%: | ____/ ___| ___| \\/ |" + echo " *@@@@@@@@* | _|| | | |_ | |\\/| |" + echo " :%@@@@@@%: | |__| |___| _| | | | |" + echo " '-#@@@@-' |_____\\____|_| |_| |_|" + echo " Autograder — Programacion 1 F12" + echo "" +} + +# ── Colores ─────────────────────────────────────────────────────────────────── +GREEN='\033[0;32m'; RED='\033[0;31m'; YELLOW='\033[1;33m'; RESET='\033[0m' +pass() { echo -e " ${GREEN}[PASS]${RESET} $1"; } +fail() { echo -e " ${RED}[FAIL]${RESET} $1"; } +info() { echo -e " ${YELLOW}[INFO]${RESET} $1"; } + +# ── Configuracion ───────────────────────────────────────────────────────────── +TIMEOUT=5 # segundos maximos por caso de prueba +TEMP_DIR=$(mktemp -d) +trap "rm -rf $TEMP_DIR" EXIT + +TOTAL=0; PASSED=0 + +# ── Compilar C++ ────────────────────────────────────────────────────────────── +compile_cpp() { + local src="$1" + local bin="$TEMP_DIR/programa" + if g++ -std=c++17 -O2 -Wall -o "$bin" "$src" 2>"$TEMP_DIR/compile_err"; then + echo "$bin" + else + echo "" + cat "$TEMP_DIR/compile_err" >&2 + fi +} + +# ── Ejecutar un caso de prueba ──────────────────────────────────────────────── +# run_test +run_test() { + local cmd="$1" + local input="$2" + local expected="$3" + local name="$4" + + TOTAL=$((TOTAL + 1)) + + local actual + actual=$(timeout "$TIMEOUT" bash -c "$cmd" < "$input" 2>/dev/null) || true + + if diff -q <(echo "$actual" | tr -s ' ' | sed 's/[[:space:]]*$//') \ + <(cat "$expected" | tr -s ' ' | sed 's/[[:space:]]*$//') > /dev/null 2>&1; then + pass "$name" + PASSED=$((PASSED + 1)) + else + fail "$name" + echo " Esperado:" + cat "$expected" | sed 's/^/ /' + echo " Obtenido:" + echo "$actual" | sed 's/^/ /' + fi +} + +# ── Evaluar tarea ───────────────────────────────────────────────────────────── +grade() { + local tarea="$1" # ej: tareas/tarea1 + local archivo="$2" # archivo del estudiante (.cpp o .py) + + local ext="${archivo##*.}" + local cmd="" + + echo "" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + echo " Tarea : $(basename $tarea)" + echo " Archivo: $archivo" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + + if [[ "$ext" != "cpp" ]]; then + fail "Solo se aceptan archivos .cpp (recibido: .$ext)" + return + fi + + local bin + bin=$(compile_cpp "$archivo") + if [[ -z "$bin" ]]; then + fail "Error de compilacion" + return + fi + cmd="$bin" + info "Compilacion exitosa" + + # Correr cada caso de prueba definido en tests// + local test_dir="tests/$(basename $tarea)" + if [[ ! -d "$test_dir" ]]; then + fail "No se encontraron tests en $test_dir" + return + fi + + for input_file in "$test_dir"/*.in; do + [[ -f "$input_file" ]] || continue + local base="${input_file%.in}" + local expected_file="${base}.out" + local case_name=$(basename "$base") + + if [[ ! -f "$expected_file" ]]; then + info "Sin archivo .out para $case_name — omitido" + continue + fi + + run_test "$cmd" "$input_file" "$expected_file" "$case_name" + done + + echo "" + echo " Resultado: $PASSED / $TOTAL casos pasados" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" +} + +# ── Main ────────────────────────────────────────────────────────────────────── +banner + +if [[ $# -lt 2 ]]; then + echo "Uso: $0 " + echo "" + echo "Ejemplo:" + echo " $0 tareas/ordenamiento solucion.cpp" + echo " $0 tareas/busqueda solucion.py" + exit 1 +fi + +TAREA="$1" +ARCHIVO="$(realpath "$2")" # convertir a path absoluto antes del cd +cd "$(dirname "$0")" # siempre ejecutar desde autograder/ +grade "$TAREA" "$ARCHIVO" diff --git a/autograder/tareas/contar_hasta_10/enunciado.md b/autograder/tareas/contar_hasta_10/enunciado.md new file mode 100644 index 0000000..fe523f2 --- /dev/null +++ b/autograder/tareas/contar_hasta_10/enunciado.md @@ -0,0 +1,33 @@ +# Tarea: Contar hasta 10 + +Escribe un programa que imprima los números del 1 al 10, uno por línea, usando un ciclo `for`. + +## Entrada +Ninguna. + +## Salida +``` +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +``` + +## Ejemplo de solución en C++ +```cpp +#include +using namespace std; +int main() { + for (int i = 1; i <= 10; i++) { + cout << i << "\n"; + } + return 0; +} +``` + diff --git a/autograder/tareas/contar_hasta_10/solucion.cpp b/autograder/tareas/contar_hasta_10/solucion.cpp new file mode 100644 index 0000000..bb8250c --- /dev/null +++ b/autograder/tareas/contar_hasta_10/solucion.cpp @@ -0,0 +1,9 @@ +#include +using namespace std; + +int main() { + for (int i = 1; i <= 10; i++) { + cout << i << "\n"; + } + return 0; +} \ No newline at end of file diff --git a/autograder/tests/contar_hasta_10/caso1.in b/autograder/tests/contar_hasta_10/caso1.in new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/autograder/tests/contar_hasta_10/caso1.in @@ -0,0 +1 @@ + diff --git a/autograder/tests/contar_hasta_10/caso1.out b/autograder/tests/contar_hasta_10/caso1.out new file mode 100644 index 0000000..f00c965 --- /dev/null +++ b/autograder/tests/contar_hasta_10/caso1.out @@ -0,0 +1,10 @@ +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 diff --git a/autograder/tests/ordenamiento/caso1.in b/autograder/tests/ordenamiento/caso1.in new file mode 100644 index 0000000..0236378 --- /dev/null +++ b/autograder/tests/ordenamiento/caso1.in @@ -0,0 +1,2 @@ +5 +3 1 4 1 5 diff --git a/autograder/tests/ordenamiento/caso1.out b/autograder/tests/ordenamiento/caso1.out new file mode 100644 index 0000000..b8a5a7e --- /dev/null +++ b/autograder/tests/ordenamiento/caso1.out @@ -0,0 +1 @@ +1 1 3 4 5 diff --git a/autograder/tests/ordenamiento/caso2.in b/autograder/tests/ordenamiento/caso2.in new file mode 100644 index 0000000..86722ce --- /dev/null +++ b/autograder/tests/ordenamiento/caso2.in @@ -0,0 +1,2 @@ +4 +9 2 7 3 diff --git a/autograder/tests/ordenamiento/caso2.out b/autograder/tests/ordenamiento/caso2.out new file mode 100644 index 0000000..58e611f --- /dev/null +++ b/autograder/tests/ordenamiento/caso2.out @@ -0,0 +1 @@ +2 3 7 9 diff --git a/autograder/tests/ordenamiento/caso3.in b/autograder/tests/ordenamiento/caso3.in new file mode 100644 index 0000000..192e9d9 --- /dev/null +++ b/autograder/tests/ordenamiento/caso3.in @@ -0,0 +1,2 @@ +1 +42 diff --git a/autograder/tests/ordenamiento/caso3.out b/autograder/tests/ordenamiento/caso3.out new file mode 100644 index 0000000..d81cc07 --- /dev/null +++ b/autograder/tests/ordenamiento/caso3.out @@ -0,0 +1 @@ +42 diff --git a/hola_mundo.cpp b/hola_mundo.cpp new file mode 100644 index 0000000..4631985 --- /dev/null +++ b/hola_mundo.cpp @@ -0,0 +1,6 @@ +#include + +int main() { + std::cout << "Hola Mundo desde Docker!" << std::endl; + return 0; +} From dad33e7ee5c674739e885b59c045723ceb9676f1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alicia=20Tom=C3=A1s?= Date: Fri, 20 Mar 2026 17:39:22 +0000 Subject: [PATCH 06/28] Sincronizando desde VS Code --- TRABAJOS_ENTORNO/calcular_fuerza | Bin 0 -> 16176 bytes TRABAJOS_ENTORNO/fisica.cpp | 15 +++++++++++++++ TRABAJOS_ENTORNO/prueba.txt | 1 + index.html | 1 + mi_app | Bin 0 -> 16024 bytes 5 files changed, 17 insertions(+) create mode 100755 TRABAJOS_ENTORNO/calcular_fuerza create mode 100644 TRABAJOS_ENTORNO/fisica.cpp create mode 100644 TRABAJOS_ENTORNO/prueba.txt create mode 100644 index.html create mode 100755 mi_app diff --git a/TRABAJOS_ENTORNO/calcular_fuerza b/TRABAJOS_ENTORNO/calcular_fuerza new file mode 100755 index 0000000000000000000000000000000000000000..d6d497ed02d265b5fbcea4f474c8ddd829de1ab5 GIT binary patch literal 16176 zcmeHOU2Ggz6~4Q6Vu$>A?KF+sq-0VW;SjR%UtFt^$=J?16Eh-P?0U?2SD29g!p~PuWlA=na{0Jjn3Zmes&^n>5qDo{r=iYPH zG?g2V%wW6hj%dzEes9JSgHGB1gMb(y6HU3U&yH zxriwl?bczDWI^L&UQhjG&Uz((8X5y>x@62}sAJ6icG#6``<42&E@j8OK>p72V5naR zqumnOEs-5_L3YeJp4bc?ox9;AxxR*LQiARi589m|yA!*0sllA}nR9nX-trAz(z_7M+23ctjxH@}d^qFFJWT#7U58GJCv&=-;y@-n-qC&_liHt2=cZ@& z&vbU|?`V&^`FN{zjMElli2ABuv1mlxC$SBHeFP5o`I_kNf4#e5+qXCMf>@UR0BA#* z8ti{L{Dlg5x&nT>0*?LG;0V$V0Lu9t1H4`|im`4TN^sv&p!Pr+^_ycloLA{rP(*!Q z*CiMuePNoD*?i7)J`HY*u(umh?=Te#ez>CI+ylN$;yn||DSnFj)Jd)=Fq2;(yl$3pY~u}&|;Ftm9?M;AiG{Fxo;oN znhq4RrP5>)R8eX{JgM-b1N}X{W^25yUC82z6J}eyJ>J&p7a^KeaP#9|HOxL$ib5$O z8X(3vZ!|c5`pxHIT2_gj6nE5o3vj+KdTze}GrwAVoNTLu#QB*c>u6rofPnW4Q>_n&4wfk9!|~o@YV_g!tiU?Whog^> z!U%*B2qO?iAdEm5fiMDL1XKi!Gk>o&o>}+1140;Q7rkilx^d3n*jCcH?C!{}5$N)oy$jbn#1x?NuZb>;uEUpykj`A$gmj4nKT zThF5w4r9Yb1d4A=oVz^XH_*iessH7-C7WL{F1>x&xOBV9h+HvVzvI=HTBxBGg7*Kc z`{)OAU0p5UXnN}t#{8Shr(qOV*6jl%@`cNH`9uA;zK)6zY3)L-ri+W1D=+`%PO)fw zBe7!4U%7}$^6~-z7qn9SPgMPKss1?tZq>z(t-vXx{rCW9NiQ5;tOD1z@GXIM4 zOyc&0QI}gZ=6}EZEVlZ2P%-AOE}JOen;Ht^AjsjqidS3CgEdzkV;NEyfiMDL1i}b} z5eOp?Mj(tp7=bVXVFbbm{J%s1zY8Jt=O!K7wY7|;Wi8jzn!Y%>_c3h^ZYAxE?O4fl zKBqNh54caZqQ-!wO-$R)X-fn9>5OOPJX^C}sL=-Pr@Va56_FiPhnm0#zV!e6onjH+ z=69|Xi}3m`27niUM{gF3KLUOq97vJV!y+;hi|p7`Q+qB_6T|OTxEA0#*guR4K;?DS zH!jruE$H&Mt4Ba?#sjc1;9^UxZ%gCjbsL|mofC&2JoJgSM`W{TcLI(<=+{fA*S#h7 z)oAbL_0bohCkQCQv0$I@yVlXcCq#5kEF#AKxE^J!N5X%CsAx+uJcSVmBM?R)j6fKH zFalu&!U%*B2qO?i;GQBd1|Lk3nu+s!2(I}S z4N881WM0pV>qe3I73o1GitDs~8Q-grxc`=6;Wn9PrObZebyCU;R8P(P&sb^wc)()^UEe<9pCvv){1owknK;_p`SR@e+clX*eDhQ{n|zNN&s&qobL+|B^hvz z!0egn(WOk&N{mW;YiZufN;rK94)n|S8RK7r0<63hd>=6WeJGS0Z>4elShiCqns@8m zM2VMc=c26LB6!_AEB_h_xDGvdz5gij`%Cu&>)(VzOl%jp9>PyuFmW}&n*#W4P^yRB zAH4qo|IdL`0|9J+_~60~q?hCwo+A>!dwuu_;Puh%qB+q180tqi34YE{Gh%|;VVU_E zslQRYOxGPmN#CiU-wor!e$J8p!=%4ZLI3><_*QHu3QqxQhnf)&0FL7d#&ZwiW!A$V z0vzKXjI$x}Mlsi;bJLO^Bb?V;bLC0E8(8r1vw)XdxBnvGnop05Kd8`-NIG6iJP*s< zt?{(!S(6C3-ZU()Ns5&1*pq43vtgNj)=XydIok!zRNkD-K>`KIMK)~spgJZDx+I9Ap+Q`6b(ESUHtxpsf8Y8rM0Sn*_`0GfTn z-NzDUV(ekN5SBBw!~&ch5iqEbNr! zS;Fi;4r}^T>6|(3+9@Fy_TT&V1l_l;ABXSe%_$4^IoLuD8=$}@0%f4~1emFe>DZZk z((-KFXW+pGh(a1nOIs+`HV-=??qN$qppC&1+>*hYDArcu)(PnH=&@doGVa^J-4JW_ zOpK)383AFz?bx34xdr?X0Cc}%(gzk?>0@oqN z`Dr6<#u-BpC>TE;e-i4@z6X_nINzNi`xymGdmIbq|ty1Z{xlhW!bXukkeetXW_dH-n8KG^?7u-^g)&ri;qd4FcmAIr>N z1KT>EJ-=UG#|Hxt>nUkfW_$@MaPG2y&Xak+EeD4C&2~JG{sgud|B^kuUoIU05Qz1Z zbhrP%LWSR+-%ppxUS~t%?4S8N0ej9Ho0^qDCI8&)dr-i(xc{6-c9Ol$6N3zN;>_^> z2iSj7XIQX(Fb73lBtH+hko7~<;1YEVe`l~Q9ydRK@c!h!OG7#}wX4F#K;!I}b?c!} Q&i-#5N~0=Z5D>+`0Y6v0Z~y=R literal 0 HcmV?d00001 diff --git a/TRABAJOS_ENTORNO/fisica.cpp b/TRABAJOS_ENTORNO/fisica.cpp new file mode 100644 index 0000000..c2161e1 --- /dev/null +++ b/TRABAJOS_ENTORNO/fisica.cpp @@ -0,0 +1,15 @@ +#include +using namespace std; + +int main() { + double masa, aceleracion, fuerza; + cout << "Ingrese la masa (kg): "; + cin >> masa; + cout << "Ingrese la aceleracion (m/s^2): "; + cin >> aceleracion; + + fuerza = masa * aceleracion; + + cout << "La fuerza resultante es: " << fuerza << " Newtons" << endl; + return 0; +} diff --git a/TRABAJOS_ENTORNO/prueba.txt b/TRABAJOS_ENTORNO/prueba.txt new file mode 100644 index 0000000..a19abfe --- /dev/null +++ b/TRABAJOS_ENTORNO/prueba.txt @@ -0,0 +1 @@ +Hola diff --git a/index.html b/index.html new file mode 100644 index 0000000..2d3959e --- /dev/null +++ b/index.html @@ -0,0 +1 @@ +

Docker Funciona

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Avanzar de dos en dos + while (i < n && lista[i] < objetivo) { + i += 2; + } + + // 2. Si nos pasamos o llegamos al final, retrocedemos uno + // Caso A: i >= n (se salió de rango) + // Caso B: lista[i] >= objetivo (encontramos el límite) + + // Verificamos la posición actual (i) si está en rango + if (i < n && lista[i] == objetivo) { + return i; + } + + // Verificamos la posición anterior (i-1) si es válida + if (i - 1 >= 0 && i - 1 < n && lista[i - 1] == objetivo) { + return i - 1; + } + + // Si no está en ninguna, no existe + return -1; +} + +int main() { + std::vector datos = {10, 20, 30, 40, 50, 60, 70}; + int buscar = 40; + + int resultado = busquedaDosEnDos(datos, buscar); + + if (resultado != -1) { + std::cout << "Elemento encontrado en el indice: " << resultado << std::endl; + } else { + std::cout << "Elemento no encontrado." << std::endl; + } + + return 0; +} diff --git a/autograder/ejercicio2.cpp b/autograder/ejercicio2.cpp new file mode 100644 index 0000000..e69de29 diff --git a/autograder/ejercicio3.cpp b/autograder/ejercicio3.cpp new file mode 100644 index 0000000..e69de29 diff --git a/autograder/ejercicio4.cpp b/autograder/ejercicio4.cpp new file mode 100644 index 0000000..e69de29 From 8499ed613ba6e53fdcb055400792b7a8272c97ab Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alicia=20Tom=C3=A1s?= Date: Sun, 22 Mar 2026 21:13:26 +0000 Subject: [PATCH 08/28] =?UTF-8?q?Tareas=20completadas:=20Primos,=20Vocales?= =?UTF-8?q?,=20Suma=20y=20B=C3=BAsqueda?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tareas/ensayo.tex | 105 ++++++++++++++++++ autograder/busqueda_dos_en_dos.cpp | 53 +++++++++ autograder/contar_vocales.cpp | 27 +++++ autograder/ejercicio1.cpp | 45 -------- autograder/ejercicio2.cpp | 0 autograder/ejercicio3.cpp | 0 autograder/ejercicio4.cpp | 0 autograder/numero_primo.cpp | 40 +++++++ autograder/suma_digitos.cpp | 20 ++++ autograder/tests/busqueda_dos_en_dos/caso1.in | 1 + .../tests/busqueda_dos_en_dos/caso1.out | 1 + autograder/tests/contar_vocales/caso1.in | 1 + autograder/tests/contar_vocales/caso1.out | 1 + autograder/tests/numero_primo/caso1.in | 1 + autograder/tests/numero_primo/caso1.out | 1 + autograder/tests/numero_primo/caso2.in | 1 + autograder/tests/numero_primo/caso2.out | 1 + autograder/tests/numero_primo/caso3.in | 1 + autograder/tests/numero_primo/caso3.out | 1 + autograder/tests/suma_digitos/caso1.in | 1 + autograder/tests/suma_digitos/caso1.out | 1 + autograder/tests/suma_digitos/caso2.in | 1 + autograder/tests/suma_digitos/caso2.out | 1 + 23 files changed, 259 insertions(+), 45 deletions(-) create mode 100644 tareas/ensayo.tex create mode 100644 autograder/busqueda_dos_en_dos.cpp create mode 100644 autograder/contar_vocales.cpp delete mode 100644 autograder/ejercicio1.cpp delete mode 100644 autograder/ejercicio2.cpp delete mode 100644 autograder/ejercicio3.cpp delete mode 100644 autograder/ejercicio4.cpp create mode 100644 autograder/numero_primo.cpp create mode 100644 autograder/suma_digitos.cpp create mode 100644 autograder/tests/busqueda_dos_en_dos/caso1.in create mode 100644 autograder/tests/busqueda_dos_en_dos/caso1.out create mode 100644 autograder/tests/contar_vocales/caso1.in create mode 100644 autograder/tests/contar_vocales/caso1.out create mode 100644 autograder/tests/numero_primo/caso1.in create mode 100644 autograder/tests/numero_primo/caso1.out create mode 100644 autograder/tests/numero_primo/caso2.in create mode 100644 autograder/tests/numero_primo/caso2.out create mode 100644 autograder/tests/numero_primo/caso3.in create mode 100644 autograder/tests/numero_primo/caso3.out create mode 100644 autograder/tests/suma_digitos/caso1.in create mode 100644 autograder/tests/suma_digitos/caso1.out create mode 100644 autograder/tests/suma_digitos/caso2.in create mode 100644 autograder/tests/suma_digitos/caso2.out diff --git a/ tareas/ensayo.tex b/ tareas/ensayo.tex new file mode 100644 index 0000000..6416631 --- /dev/null +++ b/ tareas/ensayo.tex @@ -0,0 +1,105 @@ + +\documentclass[12pt]{article} + +% --- Página y tipografía --- +\usepackage[letterpaper,margin=2.5cm]{geometry} +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} % si compilas con pdfLaTeX +\usepackage{lmodern} +\usepackage{microtype} + +% --- Imágenes y color --- +\usepackage{graphicx} +\usepackage{xcolor} + +% --- Control fino de espacios --- +\usepackage{setspace} +\setlength{\parindent}{0pt} + +\begin{document} +\thispagestyle{empty} + +% ===== Encabezado con logos + texto ===== +\begin{minipage}[c]{0.18\textwidth} + \centering + % Cambia por tu logo izquierdo + \includegraphics[width=0.95\linewidth]{logo_usac.jpeg} +\end{minipage} +\hfill +\begin{minipage}[c]{0.60\textwidth} + \small + Universidad de San Carlos de Guatemala\\ + Escuela de Ciencias Físicas y Matemáticas\\ + Nombre estudiante\\ + Carnet: \\ + Programación 1\\ +\end{minipage} +\hfill +\begin{minipage}[c]{0.18\textwidth} + \centering + % Cambia por tu logo derecho + \includegraphics[width=1.4\linewidth]{logo_ecfm.jpg} +\end{minipage} + +\vspace{0.5cm} + +% Línea horizontal superior (gruesa) +\noindent\rule{\textwidth}{1.2pt} + +\vspace{0.2cm} + +% ===== Título ===== +\begin{center} + {\Large\scshape Ensayo}\\[0.3em] +\end{center} + +\vspace{0.1cm} + +% Fecha +\begin{center} + \small\scshape viernes 06 de febrero de 2026 +\end{center} + +\vspace{0.2cm} + +% Línea horizontal inferior (gruesa) +\noindent\rule{\textwidth}{1.2pt} + +\vspace{0.6cm} + +% ===== Caja de resumen ===== +\noindent +\colorbox{gray!36}{% + \parbox{\textwidth}{% + \vspace{0.6em} + \textbf{Resumen}\\[0.4em] + \small +El presente ensayo se enfoca en dara respuesta a las siguientes preguntas: ¿cuál sería tú área de investigación en Física? y ¿explica por qué la programación te sería útil en dicho campo?, se realizá una descripción para cada pregunta, desde el punto de vista de un estudiante de física. + \vspace{0.8em} + }% +} + + +\section{Pregunta No. 1} +\subsection{¿Cuál sería tú área de investigación en Física?} + +Es un tema facinante, si fuera un investigador de física, el área de investigación sería la Física de Radiociones, ya que es un área que conbina las leyes naturales y la vida y poder contribuir a savar las mismas.\\ + Y en esta misma área el interés en la investigación sería en Radioterapia que es el uso de radiación ionizante para tratar tumores (física de partículas, dosis que se requiere a un paciente).\\ + De acuerdo al siguiente texto "La radiación ionizante es la liberación de electrones mediante la emisión de energía en forma de ondas y partículas que se producen de forma natural en los materiales que conocemos como radiactivos (suelo, agua y vegetación) y de forma artificial en isótopos creados por el hombre, tal como los equipos que producen Rayos-X" (ATSDR en Español, 2016).\\ +Por tanto comprender la liberación de los electrones puede alterar o modificar la estructura molecular de un ser vivo. + + +\section{Pregunta 2} +\subsection{¿Explica por qué la programación te sería útil en dicho campo?} + +En física médica, no siempre se puedes experimentar directamente con humanos por razones éticas y de seguridad.\\ + +¿Para qué sirve? Se usan algoritmos (como Geant4 o MCNP) para simular millones de trayectorias de partículas (fotones, electrones, protones) golpeando un tejido.\\ + +La programación te permite: Definir la geometría del paciente y calcular exactamente cuánta energía se deposita en cada milímetro cúbico. + + + +\end{document} + +\end{document} \ No newline at end of file diff --git a/autograder/busqueda_dos_en_dos.cpp b/autograder/busqueda_dos_en_dos.cpp new file mode 100644 index 0000000..56ba6bc --- /dev/null +++ b/autograder/busqueda_dos_en_dos.cpp @@ -0,0 +1,53 @@ +#include +#include +#include // Para usar std::max y std::min + +int busqueda_dos_en_dos(const std::vector& lista, int n, int objetivo) { + int i = 0; + + // 1. Avanzar de 2 en 2 mientras el elemento sea menor al objetivo + while (i < n && lista[i] < objetivo) { + i = i + 2; + } + + // 2. Retroceder 1 posicion (el objetivo puede estar en i-1 o i) + i = i - 1; + + // 3. Revisar hasta 2 posiciones a partir de la nueva i + // Usamos max y min para no salirnos de los bordes del vector + int inicio = std::max(0, i); + int fin = std::min(i + 1, n - 1); + + for (int j = inicio; j <= fin; j++) { + if (lista[j] == objetivo) { + return j; // Retorna la primera ocurrencia + } + } + + return -1; // No encontrado +} + +int main() { + int n, objetivo; + + // Leer cantidad de elementos + if (!(std::cin >> n)) return 0; + + // Leer la lista ordenada + std::vector lista(n); + for (int i = 0; i < n; i++) { + std::cin >> lista[i]; + } + + // Leer el valor a buscar + std::cin >> objetivo; + + // Ejecutar búsqueda e imprimir resultado + std::cout << busqueda_dos_en_dos(lista, n, objetivo) << std::endl; + + return 0; +} + + + + diff --git a/autograder/contar_vocales.cpp b/autograder/contar_vocales.cpp new file mode 100644 index 0000000..670543b --- /dev/null +++ b/autograder/contar_vocales.cpp @@ -0,0 +1,27 @@ +#include +#include +#include // Necesario para tolower() + +int main() { + std::string linea; + // Usamos getline para leer toda la frase, incluyendo espacios + if (!std::getline(std::cin, linea)) return 0; + + int contador = 0; + + // Recorremos la cadena carácter por carácter + for (char c : linea) { + // Convertimos a minúscula para comparar más fácil + char letra = std::tolower(c); + + // Verificamos si es una vocal + if (letra == 'a' || letra == 'e' || letra == 'i' || letra == 'o' || letra == 'u') { + contador++; + } + } + + // Imprimimos solo el número final + std::cout << contador << std::endl; + + return 0; +} diff --git a/autograder/ejercicio1.cpp b/autograder/ejercicio1.cpp deleted file mode 100644 index d28a8ca..0000000 --- a/autograder/ejercicio1.cpp +++ /dev/null @@ -1,45 +0,0 @@ -include -#include - -// Función de búsqueda de 2 en 2 con retroceso -int busquedaDosEnDos(const std::vector& lista, int objetivo) { - int n = lista.size(); - int i = 0; - - // 1. Avanzar de dos en dos - while (i < n && lista[i] < objetivo) { - i += 2; - } - - // 2. Si nos pasamos o llegamos al final, retrocedemos uno - // Caso A: i >= n (se salió de rango) - // Caso B: lista[i] >= objetivo (encontramos el límite) - - // Verificamos la posición actual (i) si está en rango - if (i < n && lista[i] == objetivo) { - return i; - } - - // Verificamos la posición anterior (i-1) si es válida - if (i - 1 >= 0 && i - 1 < n && lista[i - 1] == objetivo) { - return i - 1; - } - - // Si no está en ninguna, no existe - return -1; -} - -int main() { - std::vector datos = {10, 20, 30, 40, 50, 60, 70}; - int buscar = 40; - - int resultado = busquedaDosEnDos(datos, buscar); - - if (resultado != -1) { - std::cout << "Elemento encontrado en el indice: " << resultado << std::endl; - } else { - std::cout << "Elemento no encontrado." << std::endl; - } - - return 0; -} diff --git a/autograder/ejercicio2.cpp b/autograder/ejercicio2.cpp deleted file mode 100644 index e69de29..0000000 diff --git a/autograder/ejercicio3.cpp b/autograder/ejercicio3.cpp deleted file mode 100644 index e69de29..0000000 diff --git a/autograder/ejercicio4.cpp b/autograder/ejercicio4.cpp deleted file mode 100644 index e69de29..0000000 diff --git a/autograder/numero_primo.cpp b/autograder/numero_primo.cpp new file mode 100644 index 0000000..e079095 --- /dev/null +++ b/autograder/numero_primo.cpp @@ -0,0 +1,40 @@ +#include +#include + +int main() { + int n; + if (!(std::cin >> n)) return 0; + + // Casos especiales + if (n < 2) { + std::cout << "no primo"; + return 0; + } + if (n == 2) { + std::cout << "primo"; + return 0; + } + if (n % 2 == 0) { + std::cout << "no primo"; + return 0; + } + + // Algoritmo eficiente: revisar hasta la raíz de n + bool es_primo = true; + int limite = std::sqrt(n); + + for (int i = 3; i <= limite; i += 2) { + if (n % i == 0) { + es_primo = false; + break; + } + } + + if (es_primo) std::cout << "primo"; + else std::cout << "no primo"; + + return 0; +} + + + diff --git a/autograder/suma_digitos.cpp b/autograder/suma_digitos.cpp new file mode 100644 index 0000000..6064d10 --- /dev/null +++ b/autograder/suma_digitos.cpp @@ -0,0 +1,20 @@ +#include + +int main() { + long long n; + // Leer el número entero positivo + if (!(std::cin >> n)) return 0; + + long long suma = 0; + + // Mientras n sea mayor a 0, seguimos extrayendo dígitos + while (n > 0) { + suma += (n % 10); // Sumamos el último dígito (el residuo de n/10) + n = n / 10; // Eliminamos el último dígito (división entera) + } + + // Imprimimos el resultado final + std::cout << suma << std::endl; + + return 0; +} diff --git a/autograder/tests/busqueda_dos_en_dos/caso1.in b/autograder/tests/busqueda_dos_en_dos/caso1.in new file mode 100644 index 0000000..f599e28 --- /dev/null +++ b/autograder/tests/busqueda_dos_en_dos/caso1.in @@ -0,0 +1 @@ +10 diff --git a/autograder/tests/busqueda_dos_en_dos/caso1.out b/autograder/tests/busqueda_dos_en_dos/caso1.out new file mode 100644 index 0000000..3a2e3f4 --- /dev/null +++ b/autograder/tests/busqueda_dos_en_dos/caso1.out @@ -0,0 +1 @@ +-1 diff --git a/autograder/tests/contar_vocales/caso1.in b/autograder/tests/contar_vocales/caso1.in new file mode 100644 index 0000000..95e53aa --- /dev/null +++ b/autograder/tests/contar_vocales/caso1.in @@ -0,0 +1 @@ +murcielago diff --git a/autograder/tests/contar_vocales/caso1.out b/autograder/tests/contar_vocales/caso1.out new file mode 100644 index 0000000..7ed6ff8 --- /dev/null +++ b/autograder/tests/contar_vocales/caso1.out @@ -0,0 +1 @@ +5 diff --git a/autograder/tests/numero_primo/caso1.in b/autograder/tests/numero_primo/caso1.in new file mode 100644 index 0000000..7f8f011 --- /dev/null +++ b/autograder/tests/numero_primo/caso1.in @@ -0,0 +1 @@ +7 diff --git a/autograder/tests/numero_primo/caso1.out b/autograder/tests/numero_primo/caso1.out new file mode 100644 index 0000000..9ef3b7f --- /dev/null +++ b/autograder/tests/numero_primo/caso1.out @@ -0,0 +1 @@ +primo diff --git a/autograder/tests/numero_primo/caso2.in b/autograder/tests/numero_primo/caso2.in new file mode 100644 index 0000000..f599e28 --- /dev/null +++ b/autograder/tests/numero_primo/caso2.in @@ -0,0 +1 @@ +10 diff --git a/autograder/tests/numero_primo/caso2.out b/autograder/tests/numero_primo/caso2.out new file mode 100644 index 0000000..c9341ab --- /dev/null +++ b/autograder/tests/numero_primo/caso2.out @@ -0,0 +1 @@ +no primo diff --git a/autograder/tests/numero_primo/caso3.in b/autograder/tests/numero_primo/caso3.in new file mode 100644 index 0000000..b1bd38b --- /dev/null +++ b/autograder/tests/numero_primo/caso3.in @@ -0,0 +1 @@ +13 diff --git a/autograder/tests/numero_primo/caso3.out b/autograder/tests/numero_primo/caso3.out new file mode 100644 index 0000000..9ef3b7f --- /dev/null +++ b/autograder/tests/numero_primo/caso3.out @@ -0,0 +1 @@ +primo diff --git a/autograder/tests/suma_digitos/caso1.in b/autograder/tests/suma_digitos/caso1.in new file mode 100644 index 0000000..190a180 --- /dev/null +++ b/autograder/tests/suma_digitos/caso1.in @@ -0,0 +1 @@ +123 diff --git a/autograder/tests/suma_digitos/caso1.out b/autograder/tests/suma_digitos/caso1.out new file mode 100644 index 0000000..1e8b314 --- /dev/null +++ b/autograder/tests/suma_digitos/caso1.out @@ -0,0 +1 @@ +6 diff --git a/autograder/tests/suma_digitos/caso2.in b/autograder/tests/suma_digitos/caso2.in new file mode 100644 index 0000000..ea90ee3 --- /dev/null +++ b/autograder/tests/suma_digitos/caso2.in @@ -0,0 +1 @@ +45 diff --git a/autograder/tests/suma_digitos/caso2.out b/autograder/tests/suma_digitos/caso2.out new file mode 100644 index 0000000..ec63514 --- /dev/null +++ b/autograder/tests/suma_digitos/caso2.out @@ -0,0 +1 @@ +9 From 661bf7457d8e4052353b961e7bbade141171beb8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alicia=20Tom=C3=A1s?= Date: Sun, 22 Mar 2026 21:41:55 +0000 Subject: [PATCH 09/28] Tareas_Entregadas --- autograder/{ => Tareas_Entregadas}/busqueda_dos_en_dos.cpp | 0 autograder/{ => Tareas_Entregadas}/contar_vocales.cpp | 0 autograder/{ => Tareas_Entregadas}/numero_primo.cpp | 0 autograder/{ => Tareas_Entregadas}/suma_digitos.cpp | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename autograder/{ => Tareas_Entregadas}/busqueda_dos_en_dos.cpp (100%) rename autograder/{ => Tareas_Entregadas}/contar_vocales.cpp (100%) rename autograder/{ => Tareas_Entregadas}/numero_primo.cpp (100%) rename autograder/{ => Tareas_Entregadas}/suma_digitos.cpp (100%) diff --git a/autograder/busqueda_dos_en_dos.cpp b/autograder/Tareas_Entregadas/busqueda_dos_en_dos.cpp similarity index 100% rename from autograder/busqueda_dos_en_dos.cpp rename to autograder/Tareas_Entregadas/busqueda_dos_en_dos.cpp diff --git a/autograder/contar_vocales.cpp b/autograder/Tareas_Entregadas/contar_vocales.cpp similarity index 100% rename from autograder/contar_vocales.cpp rename to autograder/Tareas_Entregadas/contar_vocales.cpp diff --git a/autograder/numero_primo.cpp b/autograder/Tareas_Entregadas/numero_primo.cpp similarity index 100% rename from autograder/numero_primo.cpp rename to autograder/Tareas_Entregadas/numero_primo.cpp diff --git a/autograder/suma_digitos.cpp b/autograder/Tareas_Entregadas/suma_digitos.cpp similarity index 100% rename from autograder/suma_digitos.cpp rename to autograder/Tareas_Entregadas/suma_digitos.cpp From b5b9c2dd7c86cd13163196ac08458a33980debcb Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Thu, 23 Apr 2026 05:56:31 -0600 Subject: [PATCH 10/28] Guardando avances del examen antes de sincronizar --- examenes/parcial2/.gitignore | 1 + .../parcial2/examen_parcial2_estudiante.ipynb | 54 ++++++++++++++----- 2 files changed, 42 insertions(+), 13 deletions(-) create mode 100644 examenes/parcial2/.gitignore diff --git a/examenes/parcial2/.gitignore b/examenes/parcial2/.gitignore new file mode 100644 index 0000000..4c49bd7 --- /dev/null +++ b/examenes/parcial2/.gitignore @@ -0,0 +1 @@ +.env diff --git a/examenes/parcial2/examen_parcial2_estudiante.ipynb b/examenes/parcial2/examen_parcial2_estudiante.ipynb index 44e2450..8c48f7d 100644 --- a/examenes/parcial2/examen_parcial2_estudiante.ipynb +++ b/examenes/parcial2/examen_parcial2_estudiante.ipynb @@ -8,9 +8,9 @@ "# Examen Parcial 2 — Programación 1 (F12)\n", "## Tormentas Geomagnéticas con la API de NASA / DONKI\n", "\n", - "**Nombre:** ___________________________ \n", - "**Carnet:** ___________________________ \n", - "**Fecha:** ___________________________ \n", + "**Nombre:** __Alicia Tomás Laroj________________ \n", + "**Carnet:** ___201016313________________________ \n", + "**Fecha:** ____22/04/2026_______________________ \n", "**Punteo total:** 100 puntos\n", "\n", "---\n", @@ -181,15 +181,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "setup-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ API key cargada: FbD3************************************\n", + "\n", + "Librerías importadas correctamente ✓\n" + ] + } + ], "source": [ "import os\n", "import requests\n", "import json\n", "import time\n", + "from dotenv import load_dotenv\n", + "load_dotenv()\n", "\n", "# Cargar la API key desde la variable de entorno.\n", "# Si no está configurada, usa DEMO_KEY como respaldo (30 req/hora).\n", @@ -206,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "datos-respaldo", "metadata": {}, "outputs": [ @@ -244,15 +256,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "ident-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estudiante : Alicia Tomás Laroj\n", + "Carnet : 201016313\n" + ] + } + ], "source": [ "# Completa tus datos\n", - "NOMBRE = \"Nombre Apellido\" # TU NOMBRE AQUI\n", - "CARNET = \"202300000\" # TU CARNET AQUI\n", - "\n", + "NOMBRE = \"Alicia Tomás Laroj\" # Tu nombre\n", + "CARNET = \"201016313\" # tu carnet \n", "if NOMBRE == \"Nombre Apellido\" or CARNET == \"202300000\":\n", " raise ValueError(\"Completa tu nombre y carnet antes de continuar.\")\n", "\n", @@ -644,11 +664,19 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7025ad02", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -662,7 +690,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.11.7" } }, "nbformat": 4, From a7357890d50275847535fe52da012e6ad055fb83 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 24 Apr 2026 09:44:28 -0600 Subject: [PATCH 11/28] =?UTF-8?q?Terminado=20el=20an=C3=A1lisis=20de=20la?= =?UTF-8?q?=20NASA=20y=20correci=C3=B3n=20de=20f=5Fstring?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ejemplos/python/XML_JSON_APIs.ipynb | 2080 +---------------- .../parcial2/examen_parcial2_estudiante.ipynb | 299 ++- 2 files changed, 316 insertions(+), 2063 deletions(-) diff --git a/ejemplos/python/XML_JSON_APIs.ipynb b/ejemplos/python/XML_JSON_APIs.ipynb index 88a2405..1b283a0 100644 --- a/ejemplos/python/XML_JSON_APIs.ipynb +++ b/ejemplos/python/XML_JSON_APIs.ipynb @@ -98,22 +98,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "xml-parse", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Experimento: Caida libre\n", - "Lugar : Laboratorio F12\n", - "Descripción: Medicion de posicion de un objeto en caida libre\n", - "\n", - "g = 9.8 m/s² h0 = 20.0 m\n" - ] - } - ], + "outputs": [], "source": [ "import xml.etree.ElementTree as ET\n", "\n", @@ -155,30 +143,10 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "xml-loop", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Datos experimentales:\n", - " t (s) y (m) vy (m/s)\n", - "--------------------------------\n", - " 0.0 20.00 0.00\n", - " 0.5 18.78 4.90\n", - " 1.0 15.10 9.80\n", - " 1.5 8.98 14.70\n", - " 2.0 0.40 19.60\n", - "\n", - "Listas reconstruidas:\n", - "t : [0.0, 0.5, 1.0, 1.5, 2.0]\n", - "y : [20.0, 18.78, 15.1, 8.98, 0.4]\n" - ] - } - ], + "outputs": [], "source": [ "# Iterar sobre las mediciones\n", "print(\"\\nDatos experimentales:\")\n", @@ -272,45 +240,10 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "json-example", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "── Texto JSON ──\n", - "{\n", - " \"nombre\": \"Péndulo simple\",\n", - " \"fecha\": \"2025-04-12\",\n", - " \"longitud_m\": 1.0,\n", - " \"mediciones\": [\n", - " {\n", - " \"t\": 0.0,\n", - " \"theta\": 15.0\n", - " },\n", - " {\n", - " \"t\": 0.25,\n", - " \"theta\": 8.3\n", - " },\n", - " {\n", - " \"t\": 0.5,\n", - " \"theta\": -0.1\n", - " },\n", - " {\n", - " \"t\": 0.75,\n", - " \"theta\": -8.4\n", - " },\n", - " {\n", - " \"t\": 1.0,\n", - " \"theta\": -14.9\n", - " }\n", - " ]\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", "\n", @@ -339,27 +272,10 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "json-loads", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tipo del resultado: \n", - "Nombre : Péndulo simple\n", - "Longitud (m) : 1.0\n", - "\n", - "Mediciones del péndulo:\n", - " t = 0.00 s θ = +15.0°\n", - " t = 0.25 s θ = +8.3°\n", - " t = 0.50 s θ = -0.1°\n", - " t = 0.75 s θ = -8.4°\n", - " t = 1.00 s θ = -14.9°\n" - ] - } - ], + "outputs": [], "source": [ "# Leer el JSON de vuelta a un diccionario de Python\n", "datos = json.loads(texto_json)\n", @@ -464,18 +380,10 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "http-codes-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ 200 OK — datos recibidos: ['date', 'explanation', 'hdurl', 'media_type', 'service_version', 'title', 'url']\n" - ] - } - ], + "outputs": [], "source": [ "import requests\n", "\n", @@ -492,18 +400,10 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "http-codes-code2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Solicitud exitosa\n" - ] - } - ], + "outputs": [], "source": [ "# ── Ejemplo 2: usar respuesta.ok (True si 200-399) ──────────────────────────\n", "respuesta = requests.get(url, timeout=15)\n", @@ -517,18 +417,10 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "http-codes-code3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✗ 429 Too Many Requests — espera unos minutos y vuelve a intentar\n" - ] - } - ], + "outputs": [], "source": [ "# ── Ejemplo 3: manejar cada código con mensajes útiles ──────────────────────\n", "respuesta = requests.get(url, timeout=10)\n", @@ -584,18 +476,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "requests-import", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Librería requests lista ✓\n" - ] - } - ], + "outputs": [], "source": [ "import requests # pip install requests\n", "import json\n", @@ -620,28 +504,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "iss-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Código de estado HTTP: 200\n", - "\n", - "── Respuesta completa ──\n", - "{\n", - " \"timestamp\": 1776182634,\n", - " \"iss_position\": {\n", - " \"longitude\": \"-78.0268\",\n", - " \"latitude\": \"49.6159\"\n", - " },\n", - " \"message\": \"success\"\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "# Hacer la solicitud al endpoint\n", "url_iss = \"http://api.open-notify.org/iss-now.json\"\n", @@ -659,25 +525,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "iss-explore", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tipo de datos_iss: \n", - "Claves del diccionario: ['iss_position', 'message', 'timestamp']\n", - "\n", - "Posición actual de la ISS:\n", - " Latitud : +33.1915°\n", - " Longitud : -148.8347°\n", - " Hora UTC : 2026-04-12 23:56:34 UTC\n", - " Velocidad orbital aprox.: 7.66 km/s (~27,600 km/h)\n" - ] - } - ], + "outputs": [], "source": [ "# Explorar el diccionario\n", "print(\"Tipo de datos_iss:\", type(datos_iss))\n", @@ -807,22 +658,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "dotenv-demo-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n", - "✓ Archivo .env creado. Edítalo con tu NASA API key.\n", - "Variables cargadas desde .env: archivo vacío o sin variables activas\n", - "\n", - " (ninguna variable activa en .env — descomenta NASA_API_KEY para activarla)\n" - ] - } - ], + "outputs": [], "source": [ "# Instalar python-dotenv (solo la primera vez; --quiet suprime la salida larga)\n", "%pip install python-dotenv --quiet\n", @@ -864,18 +703,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "env-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ API key cargada: 4VAQ************************************\n" - ] - } - ], + "outputs": [], "source": [ "import os, requests, time\n", "from dotenv import load_dotenv\n", @@ -913,28 +744,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "apod-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intento 1/3... HTTP 200\n", - "\n", - "── Claves del JSON ──\n", - " copyright : Haythem Hamdi\n", - " date : 2026-04-14\n", - " explanation : Why does Comet R3 (PanSTARRS) have a wispy tail? The newest bright member of...\n", - " hdurl : https://apod.nasa.gov/apod/image/2604/CometR3_Hamdi_2710.jpg\n", - " media_type : image\n", - " service_version : v1\n", - " title : The Long Wispy Tail of Comet R3 (PanSTARRS)\n", - " url : https://apod.nasa.gov/apod/image/2604/CometR3_Hamdi_960.jpg\n" - ] - } - ], + "outputs": [], "source": [ "# Algunos endpoints de NASA devuelven 503 de forma intermitente.\n", "# Este código reintenta hasta 3 veces antes de rendirse.\n", @@ -980,41 +793,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "apod-explore", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================================================\n", - "IMAGEN ASTRONÓMICA DEL DÍA\n", - "============================================================\n", - "Título : The Long Wispy Tail of Comet R3 (PanSTARRS)\n", - "Fecha : 2026-04-14\n", - "Tipo : image\n", - "Autor : Haythem Hamdi\n", - "\n", - "Descripción:\n", - " Why does Comet R3 (PanSTARRS) have a wispy tail? The newest bright member of the\n", - " inner Solar System, Comet C/2025 R3 (PanSTARRS) is already extending an\n", - " impressive stream of glowing gas. This tail starts from an unseen central\n", - " nucleus of dirty ice that is likely a few kilometers across. The nucleus is\n", - " warmed by the Sun and emits a cloud of neutral gas into a coma that glows light\n", - " green. Nuclear gas ionized by energetic sunlight is pushed away from the Sun by\n", - " the solar wind into an ion tail that glows light blue. The wispy nature of the\n", - " ion tail is caused by the constantly changing structure of the solar wind.\n", - " Pictured from Rhode Island, USA two days ago, Comet R3 (PanSTARRS) shows off a\n", - " many-degree ion tail. Comet R3 (PanSTARRS) is best seen before dawn from\n", - " northern skies for another 10 days, after which it will be best visible from\n", - " southern skies. Growing Gallery: Comet R3 in 2026\n", - "\n", - "URL de la imagen:\n", - " https://apod.nasa.gov/apod/image/2604/CometR3_Hamdi_960.jpg\n" - ] - } - ], + "outputs": [], "source": [ "# Explorar los datos con más detalle\n", "print(\"=\" * 60)\n", @@ -1047,30 +829,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "apod-image", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mostrando: The Long Wispy Tail of Comet R3 (PanSTARRS)\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from IPython.display import Image, IFrame, display\n", "\n", @@ -1105,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "0c1bbbe9-d04b-4209-9778-7b636c9b78d8", "metadata": {}, "outputs": [], @@ -1120,18 +882,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "neo-request", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Código HTTP: 200\n" - ] - } - ], + "outputs": [], "source": [ "\n", "respuesta = requests.get(url_neo)\n", @@ -1147,1533 +901,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "2665383f-0b5e-4d9c-a8a8-4acc61153248", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'links': {'next': 'http://api.nasa.gov/neo/rest/v1/feed?start_date=2025-01-03&end_date=2025-01-05&detailed=false&api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh',\n", - " 'previous': 'http://api.nasa.gov/neo/rest/v1/feed?start_date=2024-12-30&end_date=2025-01-01&detailed=false&api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh',\n", - " 'self': 'http://api.nasa.gov/neo/rest/v1/feed?start_date=2025-01-01&end_date=2025-01-03&detailed=false&api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'element_count': 55,\n", - " 'near_earth_objects': {'2025-01-03': [{'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3553148?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3553148',\n", - " 'neo_reference_id': '3553148',\n", - " 'name': '(2010 XB11)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3553148',\n", - " 'absolute_magnitude_h': 19.69,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.3065878759,\n", - " 'estimated_diameter_max': 0.6855513317},\n", - " 'meters': {'estimated_diameter_min': 306.5878759288,\n", - " 'estimated_diameter_max': 685.5513316542},\n", - " 'miles': {'estimated_diameter_min': 0.1905048151,\n", - " 'estimated_diameter_max': 0.4259817165},\n", - " 'feet': {'estimated_diameter_min': 1005.8657668624,\n", - " 'estimated_diameter_max': 2249.1842309443}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-03',\n", - " 'close_approach_date_full': '2025-Jan-03 04:13',\n", - " 'epoch_date_close_approach': 1735877580000,\n", - " 'relative_velocity': {'kilometers_per_second': '18.9395018272',\n", - " 'kilometers_per_hour': '68182.206577871',\n", - " 'miles_per_hour': '42365.7716372333'},\n", - " 'miss_distance': {'astronomical': '0.1271184775',\n", - " 'lunar': '49.4490877475',\n", - " 'kilometers': '19016653.471642925',\n", - " 'miles': '11816400.533505365'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3645041?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3645041',\n", - " 'neo_reference_id': '3645041',\n", - " 'name': '(2013 NC15)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3645041',\n", - " 'absolute_magnitude_h': 22.14,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0992098919,\n", - " 'estimated_diameter_max': 0.2218400624},\n", - " 'meters': {'estimated_diameter_min': 99.2098919421,\n", - " 'estimated_diameter_max': 221.8400624229},\n", - " 'miles': {'estimated_diameter_min': 0.0616461498,\n", - " 'estimated_diameter_max': 0.1378449814},\n", - " 'feet': {'estimated_diameter_min': 325.4917818793,\n", - " 'estimated_diameter_max': 727.8217503997}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-03',\n", - " 'close_approach_date_full': '2025-Jan-03 11:13',\n", - " 'epoch_date_close_approach': 1735902780000,\n", - " 'relative_velocity': {'kilometers_per_second': '12.0604970908',\n", - " 'kilometers_per_hour': '43417.7895268897',\n", - " 'miles_per_hour': '26978.1259424166'},\n", - " 'miss_distance': {'astronomical': '0.1712353484',\n", - " 'lunar': '66.6105505276',\n", - " 'kilometers': '25616443.389347908',\n", - " 'miles': '15917319.8262128104'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3647191?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3647191',\n", - " 'neo_reference_id': '3647191',\n", - " 'name': '(2013 RT9)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3647191',\n", - " 'absolute_magnitude_h': 26.5,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0133215567,\n", - " 'estimated_diameter_max': 0.0297879063},\n", - " 'meters': {'estimated_diameter_min': 13.3215566698,\n", - " 'estimated_diameter_max': 29.7879062798},\n", - " 'miles': {'estimated_diameter_min': 0.008277629,\n", - " 'estimated_diameter_max': 0.0185093411},\n", - " 'feet': {'estimated_diameter_min': 43.7058959846,\n", - " 'estimated_diameter_max': 97.7293544391}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-03',\n", - " 'close_approach_date_full': '2025-Jan-03 17:43',\n", - " 'epoch_date_close_approach': 1735926180000,\n", - " 'relative_velocity': {'kilometers_per_second': '4.5094400585',\n", - " 'kilometers_per_hour': '16233.9842106775',\n", - " 'miles_per_hour': '10087.1664669068'},\n", - " 'miss_distance': {'astronomical': '0.2441933616',\n", - " 'lunar': '94.9912176624',\n", - " 'kilometers': '36530806.763499792',\n", - 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" 'close_approach_date_full': '2025-Jan-03 23:18',\n", - " 'epoch_date_close_approach': 1735946280000,\n", - " 'relative_velocity': {'kilometers_per_second': '11.8634390203',\n", - " 'kilometers_per_hour': '42708.3804730289',\n", - " 'miles_per_hour': '26537.3267444773'},\n", - " 'miss_distance': {'astronomical': '0.3233704888',\n", - " 'lunar': '125.7911201432',\n", - " 'kilometers': '48375536.345338856',\n", - " 'miles': '30059164.4230179728'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3843735?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3843735',\n", - " 'neo_reference_id': '3843735',\n", - " 'name': '(2019 QL7)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3843735',\n", - " 'absolute_magnitude_h': 26.65,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0124324001,\n", - " 'estimated_diameter_max': 0.0277996916},\n", - 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" 'absolute_magnitude_h': 26.44,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0136947774,\n", - " 'estimated_diameter_max': 0.0306224531},\n", - " 'meters': {'estimated_diameter_min': 13.694777373,\n", - " 'estimated_diameter_max': 30.6224531427},\n", - " 'miles': {'estimated_diameter_min': 0.0085095375,\n", - " 'estimated_diameter_max': 0.0190279043},\n", - " 'feet': {'estimated_diameter_min': 44.9303733964,\n", - " 'estimated_diameter_max': 100.4673691687}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 21:38',\n", - " 'epoch_date_close_approach': 1735767480000,\n", - " 'relative_velocity': {'kilometers_per_second': '4.0085267342',\n", - " 'kilometers_per_hour': '14430.6962430265',\n", - " 'miles_per_hour': '8966.6734516739'},\n", - " 'miss_distance': {'astronomical': '0.011204063',\n", - " 'lunar': '4.358380507',\n", - " 'kilometers': '1676103.96014581',\n", - " 'miles': '1041482.706639778'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54511956?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54511956',\n", - " 'neo_reference_id': '54511956',\n", - " 'name': '(2024 YZ8)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54511956',\n", - " 'absolute_magnitude_h': 23.68,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0488151892,\n", - " 'estimated_diameter_max': 0.1091540813},\n", - " 'meters': {'estimated_diameter_min': 48.8151891662,\n", - " 'estimated_diameter_max': 109.1540813101},\n", - " 'miles': {'estimated_diameter_min': 0.0303323429,\n", - " 'estimated_diameter_max': 0.0678251807},\n", - " 'feet': {'estimated_diameter_min': 160.154825224,\n", - " 'estimated_diameter_max': 358.1170761255}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 02:32',\n", - " 'epoch_date_close_approach': 1735698720000,\n", - " 'relative_velocity': {'kilometers_per_second': '21.4762910009',\n", - " 'kilometers_per_hour': '77314.64760315',\n", - " 'miles_per_hour': '48040.3153398573'},\n", - " 'miss_distance': {'astronomical': '0.0725309008',\n", - " 'lunar': '28.2145204112',\n", - " 'kilometers': '10850468.268861296',\n", - " 'miles': '6742168.3437700448'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54511954?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54511954',\n", - " 'neo_reference_id': '54511954',\n", - " 'name': '(2024 YY8)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54511954',\n", - " 'absolute_magnitude_h': 28.73,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.004770402,\n", - " 'estimated_diameter_max': 0.0106669431},\n", - " 'meters': {'estimated_diameter_min': 4.7704019801,\n", - " 'estimated_diameter_max': 10.6669431075},\n", - " 'miles': {'estimated_diameter_min': 0.0029641894,\n", - " 'estimated_diameter_max': 0.0066281291},\n", - " 'feet': {'estimated_diameter_min': 15.6509256325,\n", - " 'estimated_diameter_max': 34.996533625}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 07:14',\n", - " 'epoch_date_close_approach': 1735715640000,\n", - " 'relative_velocity': {'kilometers_per_second': '4.7580939652',\n", - " 'kilometers_per_hour': '17129.1382745626',\n", - " 'miles_per_hour': '10643.3803906581'},\n", - " 'miss_distance': {'astronomical': '0.0086258496',\n", - " 'lunar': '3.3554554944',\n", - " 'kilometers': '1290408.727100352',\n", - " 'miles': '801822.8020027776'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54513028?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54513028',\n", - " 'neo_reference_id': '54513028',\n", - " 'name': '(2024 YV10)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54513028',\n", - " 'absolute_magnitude_h': 26.18,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0154367182,\n", - " 'estimated_diameter_max': 0.0345175513},\n", - " 'meters': {'estimated_diameter_min': 15.4367182177,\n", - " 'estimated_diameter_max': 34.5175512843},\n", - " 'miles': {'estimated_diameter_min': 0.009591929,\n", - " 'estimated_diameter_max': 0.0214482054},\n", - " 'feet': {'estimated_diameter_min': 50.6454025974,\n", - " 'estimated_diameter_max': 113.2465629557}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 14:52',\n", - " 'epoch_date_close_approach': 1735743120000,\n", - " 'relative_velocity': {'kilometers_per_second': '5.8480710628',\n", - " 'kilometers_per_hour': '21053.0558259305',\n", - " 'miles_per_hour': '13081.5501602844'},\n", - " 'miss_distance': {'astronomical': '0.0568220725',\n", - " 'lunar': '22.1037862025',\n", - " 'kilometers': '8500461.014985575',\n", - " 'miles': '5281941.547827935'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54511964?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54511964',\n", - " 'neo_reference_id': '54511964',\n", - " 'name': '(2024 YJ9)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54511964',\n", - " 'absolute_magnitude_h': 27.91,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0069591304,\n", - " 'estimated_diameter_max': 0.0155610887},\n", - " 'meters': {'estimated_diameter_min': 6.9591304358,\n", - " 'estimated_diameter_max': 15.5610887188},\n", - " 'miles': {'estimated_diameter_min': 0.0043242018,\n", - " 'estimated_diameter_max': 0.0096692093},\n", - " 'feet': {'estimated_diameter_min': 22.8317934991,\n", - " 'estimated_diameter_max': 51.0534423123}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 07:39',\n", - " 'epoch_date_close_approach': 1735717140000,\n", - " 'relative_velocity': {'kilometers_per_second': '14.1845754203',\n", - " 'kilometers_per_hour': '51064.4715130282',\n", - " 'miles_per_hour': '31729.4767576369'},\n", - " 'miss_distance': {'astronomical': '0.0084552349',\n", - " 'lunar': '3.2890863761',\n", - " 'kilometers': '1264885.131389663',\n", - " 'miles': '785963.1750488294'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54514249?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54514249',\n", - " 'neo_reference_id': '54514249',\n", - " 'name': '(2025 AG1)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54514249',\n", - " 'absolute_magnitude_h': 29.49,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0033616692,\n", - " 'estimated_diameter_max': 0.0075169209},\n", - " 'meters': {'estimated_diameter_min': 3.3616692116,\n", - " 'estimated_diameter_max': 7.516920875},\n", - " 'miles': {'estimated_diameter_min': 0.0020888438,\n", - " 'estimated_diameter_max': 0.0046707966},\n", - " 'feet': {'estimated_diameter_min': 11.0290988161,\n", - " 'estimated_diameter_max': 24.6618146834}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 05:24',\n", - " 'epoch_date_close_approach': 1735709040000,\n", - " 'relative_velocity': {'kilometers_per_second': '2.645140072',\n", - " 'kilometers_per_hour': '9522.5042591951',\n", - " 'miles_per_hour': '5916.9138270538'},\n", - " 'miss_distance': {'astronomical': '0.0044854773',\n", - " 'lunar': '1.7448506697',\n", - " 'kilometers': '671017.850013351',\n", - " 'miles': '416951.1577162038'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54516067?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54516067',\n", - " 'neo_reference_id': '54516067',\n", - " 'name': '(2025 AC4)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54516067',\n", - " 'absolute_magnitude_h': 24.57,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0324007435,\n", - " 'estimated_diameter_max': 0.0724502651},\n", - " 'meters': {'estimated_diameter_min': 32.4007435394,\n", - " 'estimated_diameter_max': 72.4502650757},\n", - " 'miles': {'estimated_diameter_min': 0.0201328824,\n", - " 'estimated_diameter_max': 0.0450184937},\n", - " 'feet': {'estimated_diameter_min': 106.3016554339,\n", - " 'estimated_diameter_max': 237.6977276709}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 10:18',\n", - " 'epoch_date_close_approach': 1735726680000,\n", - " 'relative_velocity': {'kilometers_per_second': '15.9737833452',\n", - " 'kilometers_per_hour': '57505.6200425926',\n", - " 'miles_per_hour': '35731.7559647987'},\n", - " 'miss_distance': {'astronomical': '0.0429341231',\n", - " 'lunar': '16.7013738859',\n", - " 'kilometers': '6422853.366077797',\n", - " 'miles': '3990976.0176637186'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False}]}}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "datos_neo" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "cb333883-8f2e-428d-8544-059fdac76069", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Claves en la respuesta: ['links', 'element_count', 'near_earth_objects']\n", - "Total de asteroides en el período: 55\n", - "Fechas disponibles: ['2025-01-03', '2025-01-02', '2025-01-01']\n" - ] - } - ], + "outputs": [], "source": [ "print(\"\\nClaves en la respuesta:\", list(datos_neo.keys()))\n", "print(\"Total de asteroides en el período:\", datos_neo[\"element_count\"])\n", @@ -2683,73 +924,20 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "5e2cc865-de35-4f3f-95f7-dc54b3c82b98", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3837864?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3837864',\n", - " 'neo_reference_id': '3837864',\n", - " 'name': '(2019 AS11)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3837864',\n", - " 'absolute_magnitude_h': 26.8,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0116025908,\n", - " 'estimated_diameter_max': 0.0259441818},\n", - " 'meters': {'estimated_diameter_min': 11.6025908209,\n", - " 'estimated_diameter_max': 25.9441817907},\n", - " 'miles': {'estimated_diameter_min': 0.0072095135,\n", - " 'estimated_diameter_max': 0.0161209622},\n", - " 'feet': {'estimated_diameter_min': 38.066244069,\n", - " 'estimated_diameter_max': 85.1187093863}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-03',\n", - " 'close_approach_date_full': '2025-Jan-03 23:18',\n", - " 'epoch_date_close_approach': 1735946280000,\n", - " 'relative_velocity': {'kilometers_per_second': '11.8634390203',\n", - " 'kilometers_per_hour': '42708.3804730289',\n", - " 'miles_per_hour': '26537.3267444773'},\n", - " 'miss_distance': {'astronomical': '0.3233704888',\n", - " 'lunar': '125.7911201432',\n", - " 'kilometers': '48375536.345338856',\n", - " 'miles': '30059164.4230179728'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False}" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "datos_neo[\"near_earth_objects\"]['2025-01-03'][3]" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "neo-explore", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "── Primer asteroide del 2025-01-03 ──\n", - "Nombre : (2010 XB11)\n", - "ID : 3553148\n", - "Mag. absoluta (H): 19.69\n", - "¿Potencialmente peligroso?: False\n", - "Diámetro estimado: 306.6 – 685.6 m\n", - "Fecha acercamiento : 2025-01-03\n", - "Distancia (km) : 19,016,653 km\n", - "Velocidad (km/h) : 68,182 km/h\n" - ] - } - ], + "outputs": [], "source": [ "# Explorar la estructura de UN asteroide para entender el JSON\n", "primera_fecha = fechas[0]\n", @@ -2774,76 +962,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "neo-loop", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nombre Diám. máx (m) Dist. (km) Peligroso\n", - "---------------------------------------------------------------------------\n", - "(2011 GE3) 39.6 49,668,157 no\n", - "(2021 NT5) 22.4 70,334,863 no\n", - "(2022 EQ6) 34.0 70,563,039 no\n", - "(2024 YV1) 33.9 14,250,724 no\n", - "(2024 YD2) 63.1 9,147,402 no\n", - "(2024 YY4) 30.6 1,676,104 no\n", - "(2024 YZ8) 109.2 10,850,468 no\n", - "(2024 YY8) 10.7 1,290,409 no\n", - "(2024 YV10) 34.5 8,500,461 no\n", - "(2024 YJ9) 15.6 1,264,885 no\n", - "(2025 AG1) 7.5 671,018 no\n", - "(2025 AC4) 72.5 6,422,853 no\n", - "459462 (2013 AY52) 622.4 15,830,337 no\n", - "(2013 TK4) 95.1 53,240,802 no\n", - "(2014 BM25) 14.3 42,733,614 no\n", - "(2015 BF) 28.4 72,149,352 no\n", - "(2017 AZ13) 239.9 24,373,476 no\n", - "(2019 AR8) 44.9 66,096,247 no\n", - "(2019 LS1) 189.7 18,639,596 no\n", - "(2019 QS6) 35.8 40,941,596 no\n", - "(2019 YA5) 37.5 41,802,119 no\n", - "(2022 UF4) 73.5 57,230,310 no\n", - "(2022 WF2) 32.2 49,729,030 no\n", - "(2024 GE2) 15.1 25,623,356 no\n", - "(2024 XP10) 72.5 7,568,818 no\n", - "(2024 XB20) 37.5 13,658,066 no\n", - "(2024 YR3) 48.3 9,453,012 no\n", - "(2024 YF7) 41.9 1,938,314 no\n", - "(2024 YR8) 71.5 8,089,872 no\n", - "(2024 YR9) 42.3 3,348,567 no\n", - "(2024 YS9) 16.2 1,582,436 no\n", - "(2025 AC) 9.3 140,221 no\n", - "(2025 AA2) 36.0 1,245,829 no\n", - "(2025 AE2) 68.9 7,292,137 no\n", - "(2025 AO1) 15.3 527,693 no\n", - "(2010 XB11) 685.6 19,016,653 no\n", - "(2013 NC15) 221.8 25,616,443 no\n", - "(2013 RT9) 29.8 36,530,807 no\n", - "(2019 AS11) 25.9 48,375,536 no\n", - "(2019 QL7) 27.8 58,599,409 no\n", - "(2020 JH1) 62.2 62,796,482 no\n", - "(2020 RL) 56.8 40,923,342 no\n", - "(2022 KA) 55.5 37,624,381 no\n", - "(2022 YS5) 64.0 16,708,864 no\n", - "(2024 YL1) 20.4 2,356,964 no\n", - "(2024 YE2) 53.5 22,520,907 no\n", - "(2024 YC9) 24.1 1,306,415 no\n", - "(2025 AB) 22.3 153,127 no\n", - "(2025 AE) 42.3 6,288,670 no\n", - "(2025 AX1) 25.4 1,091,513 no\n", - "(2025 AO) 10.6 459,327 no\n", - "(2025 AN2) 23.2 706,413 no\n", - "(2025 AC3) 43.1 7,830,455 no\n", - "(2025 BE1) 30.2 7,947,528 no\n", - "(2025 OC17) 141.3 20,430,381 no\n", - "---------------------------------------------------------------------------\n", - "Total: 55 asteroides | Potencialmente peligrosos: 0\n" - ] - } - ], + "outputs": [], "source": [ "# ── Exploración con ciclos ──────────────────────────────────────────────────\n", "# Recorrer TODOS los asteroides de TODAS las fechas y hacer un resumen\n", @@ -2877,33 +999,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "neo-stats", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "── Estadísticas del período ──\n", - "Asteroides analizados : 55\n", - "\n", - "Diámetro máximo estimado:\n", - " Promedio : 73.2 m\n", - " Máximo : 685.6 m\n", - " Mínimo : 7.5 m\n", - "\n", - "Distancia de acercamiento (miss distance):\n", - " Promedio : 22,275,615 km\n", - " Más cerca: 140,221 km\n", - " Más lejos: 72,149,352 km\n", - "\n", - "Velocidad relativa:\n", - " Promedio : 39,817 km/h\n", - " Máxima : 92,434 km/h\n" - ] - } - ], + "outputs": [], "source": [ "# ── Estadísticas básicas con ciclos ────────────────────────────────────────\n", "diametros = []\n", @@ -3003,22 +1102,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "eonet-fetch", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Código HTTP: 200\n", - "Título : EONET Events\n", - "Eventos : 20\n", - "\n", - "Claves de cada evento: ['id', 'title', 'description', 'link', 'closed', 'categories', 'sources', 'geometry']\n" - ] - } - ], + "outputs": [], "source": [ "# EONET no requiere API key\n", "url_eonet = \"https://eonet.gsfc.nasa.gov/api/v3/events?limit=20&days=30&status=all\"\n", @@ -3038,28 +1125,10 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "eonet-explore", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=======================================================\n", - "ID : EONET_19513\n", - "Título : Wildfire in Russian Federation 1028298\n", - "Categoría: Wildfires\n", - "Estado : Cerrado: 2026-04-11T00:00:00Z\n", - "Lecturas : 1 puntos de trayectoria\n", - "\n", - "Últimas posiciones registradas:\n", - " Fecha Lon Lat Magnitud\n", - " ----------------------------------------------------------\n", - " 2026-04-10T19:00:00Z 133.34 48.83 6003.0 hectare\n" - ] - } - ], + "outputs": [], "source": [ "# Explorar la estructura de un evento\n", "primer_evento = datos_eonet['events'][0]\n", @@ -3086,43 +1155,10 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "eonet-loop", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Categoría Estado Título\n", - "----------------------------------------------------------------------\n", - " Wildfires Cerrado Wildfire in Russian Federation 1028298\n", - " Wildfires Cerrado Wildfire in Mongolia 1028274\n", - " Wildfires Cerrado Wildfire in Australia 1028290\n", - " Severe Storms Activo Super Typhoon Sinlaku\n", - " Wildfires Activo rx-Northern Scupp EWO Prescribed Fire, W\n", - " Wildfires Activo HARRISON Wildfire, Osage, Oklahoma\n", - " Wildfires Activo JOY Wildfire, Osage, Oklahoma\n", - " Wildfires Activo Rx Cherokee 3464 Prescribed Fire, Cherok\n", - " Wildfires Activo Rosindale Road Wildfire, Bladen, North C\n", - " Wildfires Activo RX Blanchard 2 Prescribed Fire, Stone, A\n", - " Wildfires Activo HUGHEY Wildfire, Osage, Oklahoma\n", - " Wildfires Activo Chickasawhay CPT 378 RX Prescribed Fire,\n", - " Wildfires Activo RX CALE BU 363 Prescribed Fire, Rapides,\n", - " Wildfires Activo Compartment 180 RX Prescribed Fire, Gree\n", - " Wildfires Activo HC 1 Wildfire, San Miguel, New Mexico\n", - " Wildfires Activo MA Mainline Non-Statistical/Other, Polk,\n", - " Wildfires Cerrado Wildfire in Australia 1028234\n", - " Wildfires Cerrado Wildfire in Australia 1028235\n", - " Wildfires Cerrado Wildfire in Australia 1028275\n", - " Wildfires Cerrado Wildfire in Australia 1028299\n", - "\n", - "── Resumen por categoría ──\n", - " Wildfires : 19 eventos\n", - " Severe Storms : 1 evento\n" - ] - } - ], + "outputs": [], "source": [ "# Recorrer todos los eventos y agrupar por categoría\n", "print(f\"{'Categoría':<22} {'Estado':<10} {'Título'}\")\n", @@ -3212,20 +1248,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "save-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Datos guardados en 'asteroides_nasa.json'\n", - "Asteroides cargados desde archivo: 55\n", - "✓ Los datos guardados son idénticos a los descargados\n" - ] - } - ], + "outputs": [], "source": [ "# Guardar los datos de asteroides en un archivo JSON local\n", "nombre_archivo = \"asteroides_nasa.json\"\n", diff --git a/examenes/parcial2/examen_parcial2_estudiante.ipynb b/examenes/parcial2/examen_parcial2_estudiante.ipynb index 8c48f7d..512c59c 100644 --- a/examenes/parcial2/examen_parcial2_estudiante.ipynb +++ b/examenes/parcial2/examen_parcial2_estudiante.ipynb @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "setup-code", "metadata": {}, "outputs": [ @@ -309,10 +309,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "ej1-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ estructura de datos: ['gstID', 'startTime', 'allKpIndex', 'link', 'linkedEvents', 'submissionTime', 'versionId', 'sentNotifications']\n", + "Solicitud exitosa ✓ — 5 tormentas recibidas\n" + ] + } + ], "source": [ "URL_BASE = \"https://api.nasa.gov/DONKI/GST\"\n", "\n", @@ -321,12 +330,19 @@ "# endDate = \"2024-05-31\"\n", "# Hint: los parámetros van separados por \"&\" → ?param1=val1¶m2=val2\n", "\n", - "url = f\"\" # TU CÓDIGO AQUÍ\n", + "url = f\"https://api.nasa.gov/DONKI/GST?startDate=2024-05-01&endDate=2024-05-31&api_key={API_KEY}\" # TU CÓDIGO AQUÍ # TU CÓDIGO AQUÍ\n", + "\n", "\n", "# Completa la llamada — usa timeout=15\n", "try:\n", " respuesta = requests.get(url, timeout=15) # TU CÓDIGO AQUÍ — ya está la estructura\n", "\n", + " if respuesta.status_code == 200:\n", + " tormenta = respuesta.json()\n", + " print(\"✓ estructura de datos:\", list(tormenta[0].keys()))\n", + " else:\n", + " print(f\"✗ Error {respuesta.status_code}\")\n", + "\n", " # Verifica que la respuesta fue exitosa antes de parsear el JSON\n", " # Hint: usa respuesta.ok o compara respuesta.status_code con 200\n", " if not respuesta.ok: # TU CÓDIGO AQUÍ — condición de error\n", @@ -358,7 +374,15 @@ "execution_count": null, "id": "ej1-url-check", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "URL: https://api.nasa.gov/DONKI/GST?startDate=2024-05-01&endDate=2024-05-31&api_key=FbD3kK2WJc88EDGYSLbStERkYjX6CCahJpTM8XCE\n" + ] + } + ], "source": [ "# Descomenta la siguiente línea para verificar que la URL se formó correctamente.\n", "# ⚠ Vuelve a comentarla antes de entregar — el output guarda tu API key en el archivo.\n", @@ -378,15 +402,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "ej1b-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durante el período hubo un total de 5 tormentas reales.\n", + "\n", + "ID: 2024-05-02T15:00:00-GST-001 | Comenzó: 2024-05-02T15:00Z\n", + "ID: 2024-05-10T15:00:00-GST-001 | Comenzó: 2024-05-10T15:00Z\n", + "ID: 2024-05-12T21:00:00-GST-001 | Comenzó: 2024-05-12T21:00Z\n", + "ID: 2024-05-16T06:00:00-GST-001 | Comenzó: 2024-05-16T06:00Z\n", + "ID: 2024-05-17T18:00:00-GST-001 | Comenzó: 2024-05-17T18:00Z\n" + ] + } + ], "source": [ "# Hint: len(tormentas) da el total de eventos\n", "# Hint: cada elemento de tormentas es un dict — accede con tormenta[\"gstID\"], etc.\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "# Total de eventos reales\n", + "print(f\"Durante el período hubo un total de {len(tormentas)} tormentas reales.\\n\")\n", + "\n", + "# Ciclo FOR para recorrer cada objeto\n", + "for evento in tormentas:\n", + " if isinstance(evento, dict):\n", + " id_nasa = evento[\"gstID\"]\n", + " fecha_inicio = evento[\"startTime\"]\n", + " print(f\"ID: {id_nasa} | Comenzó: {fecha_inicio}\")\n", + " else:\n", + " print(f\"⚠️ Aviso: Se encontró texto en lugar de datos: {evento}\")" ] }, { @@ -406,10 +455,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "ej2-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inicio: 2024-05-02T15:00Z | Kp máxino destacado = 6.67\n", + " Inicio: 2024-05-10T15:00Z | Kp máxino destacado = 9.00\n", + " Inicio: 2024-05-12T21:00Z | Kp máxino destacado = 6.33\n", + " Inicio: 2024-05-16T06:00Z | Kp máxino destacado = 6.00\n", + " Inicio: 2024-05-17T18:00Z | Kp máxino destacado = 6.00\n", + "ID: 2024-05-02T15:00:00-GST-001 | Máximo Evento: 6.67\n", + "ID: 2024-05-10T15:00:00-GST-001 | Máximo Evento: 9.0\n", + "ID: 2024-05-12T21:00:00-GST-001 | Máximo Evento: 6.33\n", + "ID: 2024-05-16T06:00:00-GST-001 | Máximo Evento: 6.0\n", + "ID: 2024-05-17T18:00:00-GST-001 | Máximo Evento: 6.0\n", + "------------------------------\n", + "La más intensa: 2024-05-10T15:00:00-GST-001\n", + "Kp máximo global: 9.0\n" + ] + } + ], "source": [ "# ── Parte a) ──────────────────────────────────────────────────────────────────\n", "# Hint: allKpIndex es una lista de dicts, cada uno con la clave \"kpIndex\"\n", @@ -418,9 +487,12 @@ "\n", "for tormenta in tormentas:\n", " # TU CÓDIGO AQUÍ — obtener el kp máximo de esta tormenta\n", - " kp_max = ...\n", + " if isinstance(tormenta, dict):\n", + " valores_kp = [medicion[\"kpIndex\"] for medicion in tormenta[\"allKpIndex\"]]\n", + " Kp_max = max(valores_kp)\n", + " inicio = tormenta[\"startTime\"]\n", "\n", - " print(f\" {tormenta['startTime']} Kp máx = {kp_max:.2f}\")\n", + " print(f\" Inicio: {inicio} | Kp máxino destacado = {Kp_max:.2f}\")\n", "\n", "# ── Parte b) ──────────────────────────────────────────────────────────────────\n", "# Hint: necesitas dos variables auxiliares:\n", @@ -428,7 +500,24 @@ "# kp_global_max = 0\n", "# Recorre todas las tormentas y actualiza estas variables cuando encuentres un Kp mayor\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "tormenta_mas_intensa = None\n", + "kp_global_max = 0\n", + "\n", + "for evento in tormentas:\n", + " if isinstance(evento, dict):\n", + " valores_kp =[m[\"kpIndex\"] for m in evento[\"allKpIndex\"]]\n", + " kp_max_evento = max(valores_kp)\n", + " print(f\"ID: {evento['gstID']} | Máximo Evento: {kp_max_evento}\")\n", + " \n", + " if kp_max_evento > kp_global_max:\n", + " kp_global_max = kp_max_evento\n", + " tormenta_mas_intensa = evento\n", + "\n", + "if tormenta_mas_intensa:\n", + " print(\"-\" * 30)\n", + " print(f\"La más intensa: {tormenta_mas_intensa['gstID']}\")\n", + " print(f\"Kp máximo global: {kp_global_max}\")\n" ] }, { @@ -458,7 +547,29 @@ "execution_count": null, "id": "ej3-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " kp=2.00 → Quieto\n", + " kp=4.00 → Activo\n", + " kp=5.33 → G5 - Estrema\n", + " kp=6.67 → G5 - Estrema\n", + " kp=7.00 → G3 - Fuerte\n", + " kp=8.67 → G5 - Estrema\n", + " kp=9.00 → G5 - Estrema\n", + "\n", + "Fecha inicio Kp máx Categoría\n", + "--------------------------------------------------\n", + " 2024-05-02T15:00Z 6.670 G5 - Estrema \n", + " 2024-05-10T15:00Z 9.000 G5 - Estrema \n", + " 2024-05-12T21:00Z 6.330 G5 - Estrema \n", + " 2024-05-16T06:00Z 6.000 G2 - Moderada \n", + " 2024-05-17T18:00Z 6.000 G2 - Moderada \n" + ] + } + ], "source": [ "# ── Parte a) ──────────────────────────────────────────────────────────────────\n", "# Hint: usa if / elif / else\n", @@ -467,9 +578,23 @@ "def clasificar_kp(kp):\n", " \"\"\"Clasifica la intensidad de una tormenta según el índice Kp.\"\"\"\n", " # TU CÓDIGO AQUÍ\n", - " pass\n", - "\n", - "\n", + " if kp < 4:\n", + " return \"Quieto\"\n", + " elif kp == 4:\n", + " return \"Activo\"\n", + " elif kp == 5:\n", + " return \"G1 - Menor\"\n", + " elif kp == 6:\n", + " return \"G2 - Moderada\"\n", + " elif kp == 7:\n", + " return \"G3 - Fuerte\"\n", + " elif kp == 8:\n", + " return \"G4 - Severa\"\n", + " elif kp >= 9:\n", + " return \"G5 - Estrema\"\n", + " else:\n", + " return \"G5 - Estrema\"\n", + " \n", "# Prueba tu función con estos valores — verifica que los resultados son correctos:\n", "for kp_prueba in [2.0, 4.0, 5.33, 6.67, 7.0, 8.67, 9.0]:\n", " print(f\" kp={kp_prueba:.2f} → {clasificar_kp(kp_prueba)}\")\n", @@ -478,7 +603,14 @@ "print(f\"\\n{'Fecha inicio':<22} {'Kp máx':>7} {'Categoría'}\")\n", "print(\"-\" * 50)\n", "\n", - "# TU CÓDIGO AQUÍ — recorre tormentas, calcula kp_max, llama clasificar_kp()\n" + "# TU CÓDIGO AQUÍ — recorre tormentas, calcula kp_max, llama clasificar_kp()\n", + "for tormenta in tormentas:\n", + " if isinstance(tormenta, dict):\n", + " valor_kp = [m[\"kpIndex\"] for m in tormenta[\"allKpInde x\"]]\n", + " kp_max = max(valor_kp)\n", + " categoria = clasificar_kp(kp_max)\n", + " inicio = tormenta[\"startTime\"]\n", + " print(f\"{inicio:^22} {kp_max:> 7.3f} {categoria} \")\n" ] }, { @@ -507,15 +639,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "ej4-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lista de valores Kp de la NASA: [6.67, 6.67]\n", + "Valores ordenados Kp de la NASA: [6.67, 6.67]\n", + "ID del Evento: 2024-05-02T15:00:00-GST-001\n", + " > Inicio: 2024-05-02T15:00Z\n", + " > Kp Máximo: 6.67 (G5 - Estrema)\n", + " > Promedio: 6.67\n", + " > Eventos vinculados: 2\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [7.67, 8.67, 9.0, 9.0, 8.33, 8.67, 9.0, 8.67, 8.33, 7.33, 7.33, 6.67, 7.0]\n", + "Valores ordenados Kp de la NASA: [6.67, 7.0, 7.33, 7.33, 7.67, 8.33, 8.33, 8.67, 8.67, 8.67, 9.0, 9.0, 9.0]\n", + "ID del Evento: 2024-05-10T15:00:00-GST-001\n", + " > Inicio: 2024-05-10T15:00Z\n", + " > Kp Máximo: 9.0 (G5 - Estrema)\n", + " > Promedio: 8.13\n", + " > Eventos vinculados: 6\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [6.33, 5.67, 6.0]\n", + "Valores ordenados Kp de la NASA: [5.67, 6.0, 6.33]\n", + "ID del Evento: 2024-05-12T21:00:00-GST-001\n", + " > Inicio: 2024-05-12T21:00Z\n", + " > Kp Máximo: 6.33 (G5 - Estrema)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 2\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [6.0]\n", + "Valores ordenados Kp de la NASA: [6.0]\n", + "ID del Evento: 2024-05-16T06:00:00-GST-001\n", + " > Inicio: 2024-05-16T06:00Z\n", + " > Kp Máximo: 6.0 (G2 - Moderada)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 1\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [6.0]\n", + "Valores ordenados Kp de la NASA: [6.0]\n", + "ID del Evento: 2024-05-17T18:00:00-GST-001\n", + " > Inicio: 2024-05-17T18:00Z\n", + " > Kp Máximo: 6.0 (G2 - Moderada)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 2\n", + "----------------------------------------\n" + ] + } + ], "source": [ "def analizar_tormenta(tormenta):\n", " \"\"\"\n", " Analiza un evento de tormenta geomagnética.\n", - " \n", + " P\n", " Parámetro:\n", " tormenta (dict): un elemento de la lista retornada por el endpoint GST.\n", " \n", @@ -524,24 +703,33 @@ " num_mediciones, categoria, eventos_vinculados.\n", " \"\"\"\n", " # Hint: extrae la lista de mediciones primero\n", + "\n", " mediciones = tormenta[\"allKpIndex\"]\n", + " lista_kp = [m[\"kpIndex\"] for m in mediciones]\n", + " print(f\"lista de valores Kp de la NASA: {lista_kp}\")\n", + " print(f\"Valores ordenados Kp de la NASA: {sorted(lista_kp)}\")\n", + "\n", " \n", " # Hint: para kp_promedio, suma todos los kpIndex y divide entre len(mediciones)\n", " # usa round(..., 2) para redondear a 2 decimales\n", - " \n", + " kp_max = max( lista_kp)\n", + " kp_min = min( lista_kp)\n", + " kp_promedio = round(sum(lista_kp) / len(lista_kp), 2)\n", + "\n", " # Hint: linkedEvents puede ser None — usa (tormenta[\"linkedEvents\"] or [])\n", " # para tratar None como lista vacía y poder llamar len() sin error\n", - " \n", + " evento_v = len(tormenta.get(\"linkedEvents\") or [])\n", + "\n", " # TU CÓDIGO AQUÍ\n", " resultado = {\n", - " \"id\": ...,\n", - " \"inicio\": ...,\n", - " \"kp_max\": ...,\n", - " \"kp_min\": ...,\n", - " \"kp_promedio\": ...,\n", - " \"num_mediciones\": ...,\n", - " \"categoria\": ...,\n", - " \"eventos_vinculados\": ...,\n", + " \"id\": tormenta[\"gstID\"],\n", + " \"inicio\": tormenta[\"startTime\"],\n", + " \"kp_max\": kp_max,\n", + " \"kp_min\": kp_min,\n", + " \"kp_promedio\": kp_promedio,\n", + " \"num_mediciones\": len(mediciones),\n", + " \"categoria\": clasificar_kp(max(lista_kp)),\n", + " \"eventos_vinculados\": evento_v\n", " }\n", " return resultado\n", "\n", @@ -550,7 +738,13 @@ "for tormenta in tormentas:\n", " r = analizar_tormenta(tormenta)\n", " # Hint: accede a cada clave del dict retornado con r[\"clave\"]\n", - " # TU CÓDIGO AQUÍ — imprime los campos de r de forma legible\n" + " # TU CÓDIGO AQUÍ — imprime los campos de r de forma legible\n", + " print(f\"ID del Evento: {r['id']}\")\n", + " print(f\" > Inicio: {r['inicio']}\")\n", + " print(f\" > Kp Máximo: {r['kp_max']} ({r['categoria']})\")\n", + " print(f\" > Promedio: {r['kp_promedio']}\")\n", + " print(f\" > Eventos vinculados: {r['eventos_vinculados']}\")\n", + " print(\"-\" * 40)" ] }, { @@ -579,6 +773,20 @@ "*(escribe aqui tu respuesta en texto libre)*" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd7aa793", + "metadata": {}, + "outputs": [], + "source": [ + "# PREGUNTA FINAL \n", + "# RESPUESTA 1: La tormenta mas intensa de mayo 2024 fue 2024-05-10T15:00Z de acuerdo a la escala Kp fue de: 9.000 G5 - Estrema\n", + "# RESPUESTA 2: Patrones observados en los datos: Se observa que en general los datos terminan en .0, .33 .67, deacuedo a las documentación de la NASA la escala Kp mide tecios, \n", + "# a esto debe esos decimales, luego ID del Evento: 2024-05-10T15:00:00-GST-001 la mayor intensidad fue 9.00 G5 estremo. \n", + "# RESPUESTA 3: impactó profundamente en sistemas tecnológicos y ambientales de la Tierra, dejando también un legado científico que ha abierto nuevas vías para el estudio del clima espacial.(Por Ambientum Portal Ambiental -24 enero, 2025)" + ] + }, { "cell_type": "markdown", "id": "sello-md", @@ -599,10 +807,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "sello-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consultando posicion de la ISS...\n", + "\n", + "=======================================================\n", + " SELLO DE ENTREGA -- PARCIAL 2\n", + "=======================================================\n", + " Estudiante : Alicia Tomás Laroj\n", + " Carnet : 201016313\n", + " Fecha/Hora : 2026-04-24 15:12:55 UTC\n", + " ISS Latitud : -51.4136 deg\n", + " ISS Longitud : +27.4089 deg\n", + " ISS Hora UTC : 2026-04-24 15:12:54 UTC\n", + "=======================================================\n" + ] + } + ], "source": [ "from datetime import datetime, timezone\n", "\n", @@ -630,8 +857,8 @@ "print(\"=\" * 55)\n", "print(\" SELLO DE ENTREGA -- PARCIAL 2\")\n", "print(\"=\" * 55)\n", - "print(f\" Estudiante : {NOMBRE}\")\n", - "print(f\" Carnet : {CARNET}\")\n", + "print(f\" Estudiante : {'Alicia Tomás Laroj'}\")\n", + "print(f\" Carnet : {'201016313'}\")\n", "print(f\" Fecha/Hora : {ts_entrega}\")\n", "print(f\" ISS Latitud : {iss_lat:+.4f} deg\")\n", "print(f\" ISS Longitud : {iss_lon:+.4f} deg\")\n", From c0ec3d569ca1eca2bb1ad0acd25888ea60c8ba05 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 24 Apr 2026 09:50:31 -0600 Subject: [PATCH 12/28] =?UTF-8?q?Terminado=20el=20an=C3=A1lisis=20de=20la?= =?UTF-8?q?=20NASA=20y=20correci=C3=B3n=20de=20f=5Fstring?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../parcial2/examen_parcial2_estudiante.ipynb | 48 +++++++++---------- 1 file changed, 23 insertions(+), 25 deletions(-) diff --git a/examenes/parcial2/examen_parcial2_estudiante.ipynb b/examenes/parcial2/examen_parcial2_estudiante.ipynb index 512c59c..a956523 100644 --- a/examenes/parcial2/examen_parcial2_estudiante.ipynb +++ b/examenes/parcial2/examen_parcial2_estudiante.ipynb @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "id": "setup-code", "metadata": {}, "outputs": [ @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "id": "datos-respaldo", "metadata": {}, "outputs": [ @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "ident-code", "metadata": {}, "outputs": [ @@ -309,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "ej1-code", "metadata": {}, "outputs": [ @@ -371,18 +371,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "ej1-url-check", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "URL: https://api.nasa.gov/DONKI/GST?startDate=2024-05-01&endDate=2024-05-31&api_key=FbD3kK2WJc88EDGYSLbStERkYjX6CCahJpTM8XCE\n" - ] - } - ], + "outputs": [], "source": [ "# Descomenta la siguiente línea para verificar que la URL se formó correctamente.\n", "# ⚠ Vuelve a comentarla antes de entregar — el output guarda tu API key en el archivo.\n", @@ -402,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "id": "ej1b-code", "metadata": {}, "outputs": [ @@ -455,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 15, "id": "ej2-code", "metadata": {}, "outputs": [ @@ -544,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "ej3-code", "metadata": {}, "outputs": [ @@ -561,12 +553,18 @@ " kp=9.00 → G5 - Estrema\n", "\n", "Fecha inicio Kp máx Categoría\n", - "--------------------------------------------------\n", - " 2024-05-02T15:00Z 6.670 G5 - Estrema \n", - " 2024-05-10T15:00Z 9.000 G5 - Estrema \n", - " 2024-05-12T21:00Z 6.330 G5 - Estrema \n", - " 2024-05-16T06:00Z 6.000 G2 - Moderada \n", - " 2024-05-17T18:00Z 6.000 G2 - Moderada \n" + "--------------------------------------------------\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'allKpInde x'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 36\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tormenta \u001b[38;5;129;01min\u001b[39;00m tormentas:\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tormenta, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m---> 36\u001b[0m valor_kp \u001b[38;5;241m=\u001b[39m [m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkpIndex\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m tormenta[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallKpInde x\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m 37\u001b[0m kp_max \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(valor_kp)\n\u001b[1;32m 38\u001b[0m categoria \u001b[38;5;241m=\u001b[39m clasificar_kp(kp_max)\n", + "\u001b[0;31mKeyError\u001b[0m: 'allKpInde x'" ] } ], @@ -639,7 +637,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "id": "ej4-code", "metadata": {}, "outputs": [ @@ -807,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "sello-code", "metadata": {}, "outputs": [ From 7d7730f0991f4646446fa4a37a549471241a164e Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 24 Apr 2026 10:21:14 -0600 Subject: [PATCH 13/28] =?UTF-8?q?=20Actualizaci=C3=B3n=20de=20=20examen=5F?= =?UTF-8?q?parcial2=5Festudiante?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../parcial2/examen_parcial2_estudiante.ipynb | 167 +++++++++--------- 1 file changed, 81 insertions(+), 86 deletions(-) diff --git a/examenes/parcial2/examen_parcial2_estudiante.ipynb b/examenes/parcial2/examen_parcial2_estudiante.ipynb index a956523..4b4c2ca 100644 --- a/examenes/parcial2/examen_parcial2_estudiante.ipynb +++ b/examenes/parcial2/examen_parcial2_estudiante.ipynb @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "setup-code", "metadata": {}, "outputs": [ @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "datos-respaldo", "metadata": {}, "outputs": [ @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "ident-code", "metadata": {}, "outputs": [ @@ -309,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "ej1-code", "metadata": {}, "outputs": [ @@ -317,8 +317,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "✓ estructura de datos: ['gstID', 'startTime', 'allKpIndex', 'link', 'linkedEvents', 'submissionTime', 'versionId', 'sentNotifications']\n", - "Solicitud exitosa ✓ — 5 tormentas recibidas\n" + "⚠ API no disponible (HTTPSConnectionPool(host='api.nasa.gov', port=443): Read timed out. (read timeout=15))\n", + " Usando datos de respaldo de mayo 2024.\n", + " Datos cargados ✓ — 5 tormentas\n" ] } ], @@ -371,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "ej1-url-check", "metadata": {}, "outputs": [], @@ -394,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "ej1b-code", "metadata": {}, "outputs": [ @@ -404,11 +405,11 @@ "text": [ "Durante el período hubo un total de 5 tormentas reales.\n", "\n", - "ID: 2024-05-02T15:00:00-GST-001 | Comenzó: 2024-05-02T15:00Z\n", - "ID: 2024-05-10T15:00:00-GST-001 | Comenzó: 2024-05-10T15:00Z\n", - "ID: 2024-05-12T21:00:00-GST-001 | Comenzó: 2024-05-12T21:00Z\n", - "ID: 2024-05-16T06:00:00-GST-001 | Comenzó: 2024-05-16T06:00Z\n", - "ID: 2024-05-17T18:00:00-GST-001 | Comenzó: 2024-05-17T18:00Z\n" + "ID: 2024-05-03T12:00:00-GST-001 | Comenzó: 2024-05-03T12:00Z\n", + "ID: 2024-05-05T06:00:00-GST-001 | Comenzó: 2024-05-05T06:00Z\n", + "ID: 2024-05-10T17:00:00-GST-001 | Comenzó: 2024-05-10T17:00Z\n", + "ID: 2024-05-12T03:00:00-GST-001 | Comenzó: 2024-05-12T03:00Z\n", + "ID: 2024-05-20T09:00:00-GST-001 | Comenzó: 2024-05-20T09:00Z\n" ] } ], @@ -447,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "id": "ej2-code", "metadata": {}, "outputs": [ @@ -455,18 +456,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " Inicio: 2024-05-02T15:00Z | Kp máxino destacado = 6.67\n", - " Inicio: 2024-05-10T15:00Z | Kp máxino destacado = 9.00\n", - " Inicio: 2024-05-12T21:00Z | Kp máxino destacado = 6.33\n", - " Inicio: 2024-05-16T06:00Z | Kp máxino destacado = 6.00\n", - " Inicio: 2024-05-17T18:00Z | Kp máxino destacado = 6.00\n", - "ID: 2024-05-02T15:00:00-GST-001 | Máximo Evento: 6.67\n", - "ID: 2024-05-10T15:00:00-GST-001 | Máximo Evento: 9.0\n", - "ID: 2024-05-12T21:00:00-GST-001 | Máximo Evento: 6.33\n", - "ID: 2024-05-16T06:00:00-GST-001 | Máximo Evento: 6.0\n", - "ID: 2024-05-17T18:00:00-GST-001 | Máximo Evento: 6.0\n", + " Inicio: 2024-05-03T12:00Z | Kp máxino destacado = 5.33\n", + " Inicio: 2024-05-05T06:00Z | Kp máxino destacado = 6.00\n", + " Inicio: 2024-05-10T17:00Z | Kp máxino destacado = 9.00\n", + " Inicio: 2024-05-12T03:00Z | Kp máxino destacado = 7.67\n", + " Inicio: 2024-05-20T09:00Z | Kp máxino destacado = 5.00\n", + "ID: 2024-05-03T12:00:00-GST-001 | Máximo Evento: 5.33\n", + "ID: 2024-05-05T06:00:00-GST-001 | Máximo Evento: 6.0\n", + "ID: 2024-05-10T17:00:00-GST-001 | Máximo Evento: 9.0\n", + "ID: 2024-05-12T03:00:00-GST-001 | Máximo Evento: 7.67\n", + "ID: 2024-05-20T09:00:00-GST-001 | Máximo Evento: 5.0\n", "------------------------------\n", - "La más intensa: 2024-05-10T15:00:00-GST-001\n", + "La más intensa: 2024-05-10T17:00:00-GST-001\n", "Kp máximo global: 9.0\n" ] } @@ -536,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "ej3-code", "metadata": {}, "outputs": [ @@ -546,25 +547,19 @@ "text": [ " kp=2.00 → Quieto\n", " kp=4.00 → Activo\n", - " kp=5.33 → G5 - Estrema\n", - " kp=6.67 → G5 - Estrema\n", + " kp=5.33 → G5 - Extrema\n", + " kp=6.67 → G5 - Extrema\n", " kp=7.00 → G3 - Fuerte\n", - " kp=8.67 → G5 - Estrema\n", - " kp=9.00 → G5 - Estrema\n", + " kp=8.67 → G5 - Extrema\n", + " kp=9.00 → G5 - Extrema\n", "\n", "Fecha inicio Kp máx Categoría\n", - "--------------------------------------------------\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'allKpInde x'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[16], line 36\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tormenta \u001b[38;5;129;01min\u001b[39;00m tormentas:\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tormenta, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m---> 36\u001b[0m valor_kp \u001b[38;5;241m=\u001b[39m [m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkpIndex\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m tormenta[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallKpInde x\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m 37\u001b[0m kp_max \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(valor_kp)\n\u001b[1;32m 38\u001b[0m categoria \u001b[38;5;241m=\u001b[39m clasificar_kp(kp_max)\n", - "\u001b[0;31mKeyError\u001b[0m: 'allKpInde x'" + "--------------------------------------------------\n", + " 2024-05-03T12:00Z 5.330 G5 - Extrema \n", + " 2024-05-05T06:00Z 6.000 G2 - Moderada \n", + " 2024-05-10T17:00Z 9.000 G5 - Extrema \n", + " 2024-05-12T03:00Z 7.670 G5 - Extrema \n", + " 2024-05-20T09:00Z 5.000 G1 - Menor \n" ] } ], @@ -589,9 +584,9 @@ " elif kp == 8:\n", " return \"G4 - Severa\"\n", " elif kp >= 9:\n", - " return \"G5 - Estrema\"\n", + " return \"G5 - Extrema\"\n", " else:\n", - " return \"G5 - Estrema\"\n", + " return \"G5 - Extrema\"\n", " \n", "# Prueba tu función con estos valores — verifica que los resultados son correctos:\n", "for kp_prueba in [2.0, 4.0, 5.33, 6.67, 7.0, 8.67, 9.0]:\n", @@ -604,7 +599,7 @@ "# TU CÓDIGO AQUÍ — recorre tormentas, calcula kp_max, llama clasificar_kp()\n", "for tormenta in tormentas:\n", " if isinstance(tormenta, dict):\n", - " valor_kp = [m[\"kpIndex\"] for m in tormenta[\"allKpInde x\"]]\n", + " valor_kp = [m[\"kpIndex\"] for m in tormenta[\"allKpIndex\"]]\n", " kp_max = max(valor_kp)\n", " categoria = clasificar_kp(kp_max)\n", " inicio = tormenta[\"startTime\"]\n", @@ -637,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "ej4-code", "metadata": {}, "outputs": [ @@ -645,45 +640,45 @@ "name": "stdout", "output_type": "stream", "text": [ - "lista de valores Kp de la NASA: [6.67, 6.67]\n", - "Valores ordenados Kp de la NASA: [6.67, 6.67]\n", - "ID del Evento: 2024-05-02T15:00:00-GST-001\n", - " > Inicio: 2024-05-02T15:00Z\n", - " > Kp Máximo: 6.67 (G5 - Estrema)\n", - " > Promedio: 6.67\n", - " > Eventos vinculados: 2\n", - "----------------------------------------\n", - "lista de valores Kp de la NASA: [7.67, 8.67, 9.0, 9.0, 8.33, 8.67, 9.0, 8.67, 8.33, 7.33, 7.33, 6.67, 7.0]\n", - "Valores ordenados Kp de la NASA: [6.67, 7.0, 7.33, 7.33, 7.67, 8.33, 8.33, 8.67, 8.67, 8.67, 9.0, 9.0, 9.0]\n", - "ID del Evento: 2024-05-10T15:00:00-GST-001\n", - " > Inicio: 2024-05-10T15:00Z\n", - " > Kp Máximo: 9.0 (G5 - Estrema)\n", - " > Promedio: 8.13\n", - " > Eventos vinculados: 6\n", - "----------------------------------------\n", - "lista de valores Kp de la NASA: [6.33, 5.67, 6.0]\n", - "Valores ordenados Kp de la NASA: [5.67, 6.0, 6.33]\n", - "ID del Evento: 2024-05-12T21:00:00-GST-001\n", - " > Inicio: 2024-05-12T21:00Z\n", - " > Kp Máximo: 6.33 (G5 - Estrema)\n", - " > Promedio: 6.0\n", - " > Eventos vinculados: 2\n", + "lista de valores Kp de la NASA: [5.0, 5.33, 4.67, 3.33]\n", + "Valores ordenados Kp de la NASA: [3.33, 4.67, 5.0, 5.33]\n", + "ID del Evento: 2024-05-03T12:00:00-GST-001\n", + " > Inicio: 2024-05-03T12:00Z\n", + " > Kp Máximo: 5.33 (G5 - Extrema)\n", + " > Promedio: 4.58\n", + " > Eventos vinculados: 1\n", "----------------------------------------\n", - "lista de valores Kp de la NASA: [6.0]\n", - "Valores ordenados Kp de la NASA: [6.0]\n", - "ID del Evento: 2024-05-16T06:00:00-GST-001\n", - " > Inicio: 2024-05-16T06:00Z\n", + "lista de valores Kp de la NASA: [5.67, 6.0, 5.33, 4.0, 3.67]\n", + "Valores ordenados Kp de la NASA: [3.67, 4.0, 5.33, 5.67, 6.0]\n", + "ID del Evento: 2024-05-05T06:00:00-GST-001\n", + " > Inicio: 2024-05-05T06:00Z\n", " > Kp Máximo: 6.0 (G2 - Moderada)\n", - " > Promedio: 6.0\n", + " > Promedio: 4.93\n", " > Eventos vinculados: 1\n", "----------------------------------------\n", - "lista de valores Kp de la NASA: [6.0]\n", - "Valores ordenados Kp de la NASA: [6.0]\n", - "ID del Evento: 2024-05-17T18:00:00-GST-001\n", - " > Inicio: 2024-05-17T18:00Z\n", - " > Kp Máximo: 6.0 (G2 - Moderada)\n", - " > Promedio: 6.0\n", + "lista de valores Kp de la NASA: [7.67, 8.33, 9.0, 8.67, 7.33, 6.67, 6.0, 5.33]\n", + "Valores ordenados Kp de la NASA: [5.33, 6.0, 6.67, 7.33, 7.67, 8.33, 8.67, 9.0]\n", + "ID del Evento: 2024-05-10T17:00:00-GST-001\n", + " > Inicio: 2024-05-10T17:00Z\n", + " > Kp Máximo: 9.0 (G5 - Extrema)\n", + " > Promedio: 7.38\n", + " > Eventos vinculados: 3\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [7.0, 7.67, 6.33, 5.67, 4.33]\n", + "Valores ordenados Kp de la NASA: [4.33, 5.67, 6.33, 7.0, 7.67]\n", + "ID del Evento: 2024-05-12T03:00:00-GST-001\n", + " > Inicio: 2024-05-12T03:00Z\n", + " > Kp Máximo: 7.67 (G5 - Extrema)\n", + " > Promedio: 6.2\n", " > Eventos vinculados: 2\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [4.33, 5.0, 4.67, 3.33]\n", + "Valores ordenados Kp de la NASA: [3.33, 4.33, 4.67, 5.0]\n", + "ID del Evento: 2024-05-20T09:00:00-GST-001\n", + " > Inicio: 2024-05-20T09:00Z\n", + " > Kp Máximo: 5.0 (G1 - Menor)\n", + " > Promedio: 4.33\n", + " > Eventos vinculados: 0\n", "----------------------------------------\n" ] } @@ -773,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "bd7aa793", "metadata": {}, "outputs": [], @@ -805,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "sello-code", "metadata": {}, "outputs": [ @@ -820,10 +815,10 @@ "=======================================================\n", " Estudiante : Alicia Tomás Laroj\n", " Carnet : 201016313\n", - " Fecha/Hora : 2026-04-24 15:12:55 UTC\n", - " ISS Latitud : -51.4136 deg\n", - " ISS Longitud : +27.4089 deg\n", - " ISS Hora UTC : 2026-04-24 15:12:54 UTC\n", + " Fecha/Hora : 2026-04-24 16:17:49 UTC\n", + " ISS Latitud : +17.4850 deg\n", + " ISS Longitud : -86.7104 deg\n", + " ISS Hora UTC : 2026-04-24 16:17:49 UTC\n", "=======================================================\n" ] } From a40df16bbc0cef99c92f4a0bfe88a4be39e99561 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 24 Apr 2026 10:42:27 -0600 Subject: [PATCH 14/28] Asegurando guardado de cambios --- .../parcial2/examen_parcial2_estudiante.ipynb | 123 +++++++++--------- 1 file changed, 61 insertions(+), 62 deletions(-) diff --git a/examenes/parcial2/examen_parcial2_estudiante.ipynb b/examenes/parcial2/examen_parcial2_estudiante.ipynb index 4b4c2ca..c3762bb 100644 --- a/examenes/parcial2/examen_parcial2_estudiante.ipynb +++ b/examenes/parcial2/examen_parcial2_estudiante.ipynb @@ -317,9 +317,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "⚠ API no disponible (HTTPSConnectionPool(host='api.nasa.gov', port=443): Read timed out. (read timeout=15))\n", - " Usando datos de respaldo de mayo 2024.\n", - " Datos cargados ✓ — 5 tormentas\n" + "✓ estructura de datos: ['gstID', 'startTime', 'allKpIndex', 'link', 'linkedEvents', 'submissionTime', 'versionId', 'sentNotifications']\n", + "Solicitud exitosa ✓ — 5 tormentas recibidas\n" ] } ], @@ -405,11 +404,11 @@ "text": [ "Durante el período hubo un total de 5 tormentas reales.\n", "\n", - "ID: 2024-05-03T12:00:00-GST-001 | Comenzó: 2024-05-03T12:00Z\n", - "ID: 2024-05-05T06:00:00-GST-001 | Comenzó: 2024-05-05T06:00Z\n", - "ID: 2024-05-10T17:00:00-GST-001 | Comenzó: 2024-05-10T17:00Z\n", - "ID: 2024-05-12T03:00:00-GST-001 | Comenzó: 2024-05-12T03:00Z\n", - "ID: 2024-05-20T09:00:00-GST-001 | Comenzó: 2024-05-20T09:00Z\n" + "ID: 2024-05-02T15:00:00-GST-001 | Comenzó: 2024-05-02T15:00Z\n", + "ID: 2024-05-10T15:00:00-GST-001 | Comenzó: 2024-05-10T15:00Z\n", + "ID: 2024-05-12T21:00:00-GST-001 | Comenzó: 2024-05-12T21:00Z\n", + "ID: 2024-05-16T06:00:00-GST-001 | Comenzó: 2024-05-16T06:00Z\n", + "ID: 2024-05-17T18:00:00-GST-001 | Comenzó: 2024-05-17T18:00Z\n" ] } ], @@ -456,18 +455,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " Inicio: 2024-05-03T12:00Z | Kp máxino destacado = 5.33\n", - " Inicio: 2024-05-05T06:00Z | Kp máxino destacado = 6.00\n", - " Inicio: 2024-05-10T17:00Z | Kp máxino destacado = 9.00\n", - " Inicio: 2024-05-12T03:00Z | Kp máxino destacado = 7.67\n", - " Inicio: 2024-05-20T09:00Z | Kp máxino destacado = 5.00\n", - "ID: 2024-05-03T12:00:00-GST-001 | Máximo Evento: 5.33\n", - "ID: 2024-05-05T06:00:00-GST-001 | Máximo Evento: 6.0\n", - "ID: 2024-05-10T17:00:00-GST-001 | Máximo Evento: 9.0\n", - "ID: 2024-05-12T03:00:00-GST-001 | Máximo Evento: 7.67\n", - "ID: 2024-05-20T09:00:00-GST-001 | Máximo Evento: 5.0\n", + " Inicio: 2024-05-02T15:00Z | Kp máxino destacado = 6.67\n", + " Inicio: 2024-05-10T15:00Z | Kp máxino destacado = 9.00\n", + " Inicio: 2024-05-12T21:00Z | Kp máxino destacado = 6.33\n", + " Inicio: 2024-05-16T06:00Z | Kp máxino destacado = 6.00\n", + " Inicio: 2024-05-17T18:00Z | Kp máxino destacado = 6.00\n", + "ID: 2024-05-02T15:00:00-GST-001 | Máximo Evento: 6.67\n", + "ID: 2024-05-10T15:00:00-GST-001 | Máximo Evento: 9.0\n", + "ID: 2024-05-12T21:00:00-GST-001 | Máximo Evento: 6.33\n", + "ID: 2024-05-16T06:00:00-GST-001 | Máximo Evento: 6.0\n", + "ID: 2024-05-17T18:00:00-GST-001 | Máximo Evento: 6.0\n", "------------------------------\n", - "La más intensa: 2024-05-10T17:00:00-GST-001\n", + "La más intensa: 2024-05-10T15:00:00-GST-001\n", "Kp máximo global: 9.0\n" ] } @@ -555,11 +554,11 @@ "\n", "Fecha inicio Kp máx Categoría\n", "--------------------------------------------------\n", - " 2024-05-03T12:00Z 5.330 G5 - Extrema \n", - " 2024-05-05T06:00Z 6.000 G2 - Moderada \n", - " 2024-05-10T17:00Z 9.000 G5 - Extrema \n", - " 2024-05-12T03:00Z 7.670 G5 - Extrema \n", - " 2024-05-20T09:00Z 5.000 G1 - Menor \n" + " 2024-05-02T15:00Z 6.670 G5 - Extrema \n", + " 2024-05-10T15:00Z 9.000 G5 - Extrema \n", + " 2024-05-12T21:00Z 6.330 G5 - Extrema \n", + " 2024-05-16T06:00Z 6.000 G2 - Moderada \n", + " 2024-05-17T18:00Z 6.000 G2 - Moderada \n" ] } ], @@ -640,45 +639,45 @@ "name": "stdout", "output_type": "stream", "text": [ - "lista de valores Kp de la NASA: [5.0, 5.33, 4.67, 3.33]\n", - "Valores ordenados Kp de la NASA: [3.33, 4.67, 5.0, 5.33]\n", - "ID del Evento: 2024-05-03T12:00:00-GST-001\n", - " > Inicio: 2024-05-03T12:00Z\n", - " > Kp Máximo: 5.33 (G5 - Extrema)\n", - " > Promedio: 4.58\n", - " > Eventos vinculados: 1\n", - "----------------------------------------\n", - "lista de valores Kp de la NASA: [5.67, 6.0, 5.33, 4.0, 3.67]\n", - "Valores ordenados Kp de la NASA: [3.67, 4.0, 5.33, 5.67, 6.0]\n", - "ID del Evento: 2024-05-05T06:00:00-GST-001\n", - " > Inicio: 2024-05-05T06:00Z\n", - " > Kp Máximo: 6.0 (G2 - Moderada)\n", - " > Promedio: 4.93\n", - " > Eventos vinculados: 1\n", + "lista de valores Kp de la NASA: [6.67, 6.67]\n", + "Valores ordenados Kp de la NASA: [6.67, 6.67]\n", + "ID del Evento: 2024-05-02T15:00:00-GST-001\n", + " > Inicio: 2024-05-02T15:00Z\n", + " > Kp Máximo: 6.67 (G5 - Extrema)\n", + " > Promedio: 6.67\n", + " > Eventos vinculados: 2\n", "----------------------------------------\n", - "lista de valores Kp de la NASA: [7.67, 8.33, 9.0, 8.67, 7.33, 6.67, 6.0, 5.33]\n", - "Valores ordenados Kp de la NASA: [5.33, 6.0, 6.67, 7.33, 7.67, 8.33, 8.67, 9.0]\n", - "ID del Evento: 2024-05-10T17:00:00-GST-001\n", - " > Inicio: 2024-05-10T17:00Z\n", + "lista de valores Kp de la NASA: [7.67, 8.67, 9.0, 9.0, 8.33, 8.67, 9.0, 8.67, 8.33, 7.33, 7.33, 6.67, 7.0]\n", + "Valores ordenados Kp de la NASA: [6.67, 7.0, 7.33, 7.33, 7.67, 8.33, 8.33, 8.67, 8.67, 8.67, 9.0, 9.0, 9.0]\n", + "ID del Evento: 2024-05-10T15:00:00-GST-001\n", + " > Inicio: 2024-05-10T15:00Z\n", " > Kp Máximo: 9.0 (G5 - Extrema)\n", - " > Promedio: 7.38\n", - " > Eventos vinculados: 3\n", + " > Promedio: 8.13\n", + " > Eventos vinculados: 6\n", "----------------------------------------\n", - "lista de valores Kp de la NASA: [7.0, 7.67, 6.33, 5.67, 4.33]\n", - "Valores ordenados Kp de la NASA: [4.33, 5.67, 6.33, 7.0, 7.67]\n", - "ID del Evento: 2024-05-12T03:00:00-GST-001\n", - " > Inicio: 2024-05-12T03:00Z\n", - " > Kp Máximo: 7.67 (G5 - Extrema)\n", - " > Promedio: 6.2\n", + "lista de valores Kp de la NASA: [6.33, 5.67, 6.0]\n", + "Valores ordenados Kp de la NASA: [5.67, 6.0, 6.33]\n", + "ID del Evento: 2024-05-12T21:00:00-GST-001\n", + " > Inicio: 2024-05-12T21:00Z\n", + " > Kp Máximo: 6.33 (G5 - Extrema)\n", + " > Promedio: 6.0\n", " > Eventos vinculados: 2\n", "----------------------------------------\n", - "lista de valores Kp de la NASA: [4.33, 5.0, 4.67, 3.33]\n", - "Valores ordenados Kp de la NASA: [3.33, 4.33, 4.67, 5.0]\n", - "ID del Evento: 2024-05-20T09:00:00-GST-001\n", - " > Inicio: 2024-05-20T09:00Z\n", - " > Kp Máximo: 5.0 (G1 - Menor)\n", - " > Promedio: 4.33\n", - " > Eventos vinculados: 0\n", + "lista de valores Kp de la NASA: [6.0]\n", + "Valores ordenados Kp de la NASA: [6.0]\n", + "ID del Evento: 2024-05-16T06:00:00-GST-001\n", + " > Inicio: 2024-05-16T06:00Z\n", + " > Kp Máximo: 6.0 (G2 - Moderada)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 1\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [6.0]\n", + "Valores ordenados Kp de la NASA: [6.0]\n", + "ID del Evento: 2024-05-17T18:00:00-GST-001\n", + " > Inicio: 2024-05-17T18:00Z\n", + " > Kp Máximo: 6.0 (G2 - Moderada)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 2\n", "----------------------------------------\n" ] } @@ -815,10 +814,10 @@ "=======================================================\n", " Estudiante : Alicia Tomás Laroj\n", " Carnet : 201016313\n", - " Fecha/Hora : 2026-04-24 16:17:49 UTC\n", - " ISS Latitud : +17.4850 deg\n", - " ISS Longitud : -86.7104 deg\n", - " ISS Hora UTC : 2026-04-24 16:17:49 UTC\n", + " Fecha/Hora : 2026-04-24 16:41:28 UTC\n", + " ISS Latitud : -47.0214 deg\n", + " ISS Longitud : -20.0337 deg\n", + " ISS Hora UTC : 2026-04-24 16:41:28 UTC\n", "=======================================================\n" ] } From 71f9876488f61cc2e01eb228803d984b354ea2a1 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 24 Apr 2026 10:42:27 -0600 Subject: [PATCH 15/28] Asegurando guardado de cambios --- ejemplos/python/Numpy.ipynb | 31 ++++- ejemplos/python/Pandas.ipynb | 116 +++++++++++++---- .../parcial2/examen_parcial2_estudiante.ipynb | 123 +++++++++--------- 3 files changed, 174 insertions(+), 96 deletions(-) diff --git a/ejemplos/python/Numpy.ipynb b/ejemplos/python/Numpy.ipynb index f51f56f..84dd64e 100644 --- a/ejemplos/python/Numpy.ipynb +++ b/ejemplos/python/Numpy.ipynb @@ -41,14 +41,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "NumPy versión: 2.4.2\n" + "NumPy versión: 1.26.4\n" ] } ], @@ -1221,7 +1221,26 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## Tarea 4 — NumPy en Práctica\n\nLa programación se aprende haciendo, no leyendo. Esta tarea tiene dos partes:\n\n**Parte 1 — Descarga y ejecuta** (obligatorio)\nDescarga este notebook y ejecútalo celda por celda en tu computadora. \nAsegúrate de que todas las celdas corran sin errores antes de continuar.\n\n**Parte 2 — Ejercicios** (obligatorio) \nCompleta cada celda que dice `# TU CÓDIGO AQUÍ` con tu solución. \nEjecuta la celda para verificar que el resultado es correcto.\n\n**Entrega: 1 de mayo** \nSube el notebook a tu repositorio de GitHub y envía el **link directo al archivo `.ipynb`**.\nEl notebook debe tener todas las celdas ya ejecutadas (con outputs visibles).\n\nEjemplo de link válido: \n`https://github.com/tu-usuario/tu-repo/blob/main/Numpy.ipynb`" + "source": [ + "## Tarea 4 — NumPy en Práctica\n", + "\n", + "La programación se aprende haciendo, no leyendo. Esta tarea tiene dos partes:\n", + "\n", + "**Parte 1 — Descarga y ejecuta** (obligatorio)\n", + "Descarga este notebook y ejecútalo celda por celda en tu computadora. \n", + "Asegúrate de que todas las celdas corran sin errores antes de continuar.\n", + "\n", + "**Parte 2 — Ejercicios** (obligatorio) \n", + "Completa cada celda que dice `# TU CÓDIGO AQUÍ` con tu solución. \n", + "Ejecuta la celda para verificar que el resultado es correcto.\n", + "\n", + "**Entrega: 1 de mayo** \n", + "Sube el notebook a tu repositorio de GitHub y envía el **link directo al archivo `.ipynb`**.\n", + "El notebook debe tener todas las celdas ya ejecutadas (con outputs visibles).\n", + "\n", + "Ejemplo de link válido: \n", + "`https://github.com/tu-usuario/tu-repo/blob/main/Numpy.ipynb`" + ] }, { "cell_type": "markdown", @@ -1415,7 +1434,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1429,9 +1448,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/ejemplos/python/Pandas.ipynb b/ejemplos/python/Pandas.ipynb index 88152dd..276d1cb 100644 --- a/ejemplos/python/Pandas.ipynb +++ b/ejemplos/python/Pandas.ipynb @@ -41,9 +41,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pandas versión: 2.1.4\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np # Pandas y NumPy se usan frecuentemente juntos\n", @@ -964,65 +972,117 @@ { "cell_type": "markdown", "metadata": {}, - "source": "---\n## Ejercicios" + "source": [ + "---\n", + "## Ejercicios" + ] }, { "cell_type": "markdown", - "source": "### Ejercicio 1\nCrea una Serie con los valores de precipitación mensual de tu ciudad.\nCalcula: media, mediana, máximo y el mes con mayor precipitación (`argmax()`).", - "metadata": {} + "metadata": {}, + "source": [ + "### Ejercicio 1\n", + "Crea una Serie con los valores de precipitación mensual de tu ciudad.\n", + "Calcula: media, mediana, máximo y el mes con mayor precipitación (`argmax()`)." + ] }, { "cell_type": "code", - "source": "# TU CÓDIGO AQUÍ\n", - "metadata": {}, "execution_count": null, - "outputs": [] + "metadata": {}, + "outputs": [], + "source": [ + "# TU CÓDIGO AQUÍ\n" + ] }, { "cell_type": "markdown", - "source": "### Ejercicio 2\nDado `df_clima`, filtra los meses con temperatura entre 5°C y 15°C.\nCalcula la lluvia total en ese subconjunto.", - "metadata": {} + "metadata": {}, + "source": [ + "### Ejercicio 2\n", + "Dado `df_clima`, filtra los meses con temperatura entre 5°C y 15°C.\n", + "Calcula la lluvia total en ese subconjunto." + ] }, { "cell_type": "code", - "source": "# TU CÓDIGO AQUÍ\n", - "metadata": {}, "execution_count": null, - "outputs": [] + "metadata": {}, + "outputs": [], + "source": [ + "# TU CÓDIGO AQUÍ\n" + ] }, { "cell_type": "markdown", - "source": "### Ejercicio 3\nAgrega al DataFrame `df_completo` (con `temp_min_C` y `temp_max_C`) una columna\n`amplitud_termica = temp_max_C - temp_min_C`. Encuentra el mes con mayor amplitud.", - "metadata": {} + "metadata": {}, + "source": [ + "### Ejercicio 3\n", + "Agrega al DataFrame `df_completo` (con `temp_min_C` y `temp_max_C`) una columna\n", + "`amplitud_termica = temp_max_C - temp_min_C`. Encuentra el mes con mayor amplitud." + ] }, { "cell_type": "code", - "source": "# TU CÓDIGO AQUÍ\n", - "metadata": {}, "execution_count": null, - "outputs": [] + "metadata": {}, + "outputs": [], + "source": [ + "# TU CÓDIGO AQUÍ\n" + ] }, { "cell_type": "markdown", - "source": "### Ejercicio 4\nCrea un DataFrame con 5 estudiantes (nombre, nota1, nota2, nota3) con 3 valores NaN. \na) Identifica cuántos NaN hay por columna. \nb) Rellena con la media de cada columna. \nc) Calcula la nota promedio final de cada estudiante.", - "metadata": {} + "metadata": {}, + "source": [ + "### Ejercicio 4\n", + "Crea un DataFrame con 5 estudiantes (nombre, nota1, nota2, nota3) con 3 valores NaN. \n", + "a) Identifica cuántos NaN hay por columna. \n", + "b) Rellena con la media de cada columna. \n", + "c) Calcula la nota promedio final de cada estudiante." + ] }, { "cell_type": "code", - "source": "# TU CÓDIGO AQUÍ\n", - "metadata": {}, "execution_count": null, - "outputs": [] + "metadata": {}, + "outputs": [], + "source": [ + "# TU CÓDIGO AQUÍ\n" + ] }, { "cell_type": "markdown", "metadata": {}, - "source": "## Tarea 5 (opcional) — Pandas en Práctica\n\nLa programación se aprende haciendo. Esta tarea tiene dos partes:\n\n**Parte 1 — Descarga y ejecuta** (obligatorio)\nDescarga este notebook y ejecútalo celda por celda en tu computadora. \nVerifica que todas las celdas corran sin errores antes de continuar.\n\n**Parte 2 — Ejercicios** (obligatorio) \nResuelve los ejercicios de la sección anterior (Ejercicios 1 al 4).\n\n**Parte 3 — Dataset real** (opcional, puntos extra) \nDescarga cualquier dataset CSV que te interese y aplica al menos 3 herramientas vistas:\nlectura, limpieza, `groupby`, visualización. \nAgrega una celda Markdown con tus conclusiones.\n\n**Entrega: 1 de mayo** \nSube el notebook a tu repositorio de GitHub y envía el **link directo al archivo `.ipynb`**.\nEl notebook debe tener todas las celdas ya ejecutadas (con outputs visibles).\n\nEjemplo de link válido: \n`https://github.com/tu-usuario/tu-repo/blob/main/Pandas.ipynb`" + "source": [ + "## Tarea 5 (opcional) — Pandas en Práctica\n", + "\n", + "La programación se aprende haciendo. Esta tarea tiene dos partes:\n", + "\n", + "**Parte 1 — Descarga y ejecuta** (obligatorio)\n", + "Descarga este notebook y ejecútalo celda por celda en tu computadora. \n", + "Verifica que todas las celdas corran sin errores antes de continuar.\n", + "\n", + "**Parte 2 — Ejercicios** (obligatorio) \n", + "Resuelve los ejercicios de la sección anterior (Ejercicios 1 al 4).\n", + "\n", + "**Parte 3 — Dataset real** (opcional, puntos extra) \n", + "Descarga cualquier dataset CSV que te interese y aplica al menos 3 herramientas vistas:\n", + "lectura, limpieza, `groupby`, visualización. \n", + "Agrega una celda Markdown con tus conclusiones.\n", + "\n", + "**Entrega: 1 de mayo** \n", + "Sube el notebook a tu repositorio de GitHub y envía el **link directo al archivo `.ipynb`**.\n", + "El notebook debe tener todas las celdas ya ejecutadas (con outputs visibles).\n", + "\n", + "Ejemplo de link válido: \n", + "`https://github.com/tu-usuario/tu-repo/blob/main/Pandas.ipynb`" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1036,9 +1096,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/examenes/parcial2/examen_parcial2_estudiante.ipynb b/examenes/parcial2/examen_parcial2_estudiante.ipynb index 4b4c2ca..c3762bb 100644 --- a/examenes/parcial2/examen_parcial2_estudiante.ipynb +++ b/examenes/parcial2/examen_parcial2_estudiante.ipynb @@ -317,9 +317,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "⚠ API no disponible (HTTPSConnectionPool(host='api.nasa.gov', port=443): Read timed out. (read timeout=15))\n", - " Usando datos de respaldo de mayo 2024.\n", - " Datos cargados ✓ — 5 tormentas\n" + "✓ estructura de datos: ['gstID', 'startTime', 'allKpIndex', 'link', 'linkedEvents', 'submissionTime', 'versionId', 'sentNotifications']\n", + "Solicitud exitosa ✓ — 5 tormentas recibidas\n" ] } ], @@ -405,11 +404,11 @@ "text": [ "Durante el período hubo un total de 5 tormentas reales.\n", "\n", - "ID: 2024-05-03T12:00:00-GST-001 | Comenzó: 2024-05-03T12:00Z\n", - "ID: 2024-05-05T06:00:00-GST-001 | Comenzó: 2024-05-05T06:00Z\n", - "ID: 2024-05-10T17:00:00-GST-001 | Comenzó: 2024-05-10T17:00Z\n", - "ID: 2024-05-12T03:00:00-GST-001 | Comenzó: 2024-05-12T03:00Z\n", - "ID: 2024-05-20T09:00:00-GST-001 | Comenzó: 2024-05-20T09:00Z\n" + "ID: 2024-05-02T15:00:00-GST-001 | Comenzó: 2024-05-02T15:00Z\n", + "ID: 2024-05-10T15:00:00-GST-001 | Comenzó: 2024-05-10T15:00Z\n", + "ID: 2024-05-12T21:00:00-GST-001 | Comenzó: 2024-05-12T21:00Z\n", + "ID: 2024-05-16T06:00:00-GST-001 | Comenzó: 2024-05-16T06:00Z\n", + "ID: 2024-05-17T18:00:00-GST-001 | Comenzó: 2024-05-17T18:00Z\n" ] } ], @@ -456,18 +455,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " Inicio: 2024-05-03T12:00Z | Kp máxino destacado = 5.33\n", - " Inicio: 2024-05-05T06:00Z | Kp máxino destacado = 6.00\n", - " Inicio: 2024-05-10T17:00Z | Kp máxino destacado = 9.00\n", - " Inicio: 2024-05-12T03:00Z | Kp máxino destacado = 7.67\n", - " Inicio: 2024-05-20T09:00Z | Kp máxino destacado = 5.00\n", - "ID: 2024-05-03T12:00:00-GST-001 | Máximo Evento: 5.33\n", - "ID: 2024-05-05T06:00:00-GST-001 | Máximo Evento: 6.0\n", - "ID: 2024-05-10T17:00:00-GST-001 | Máximo Evento: 9.0\n", - "ID: 2024-05-12T03:00:00-GST-001 | Máximo Evento: 7.67\n", - "ID: 2024-05-20T09:00:00-GST-001 | Máximo Evento: 5.0\n", + " Inicio: 2024-05-02T15:00Z | Kp máxino destacado = 6.67\n", + " Inicio: 2024-05-10T15:00Z | Kp máxino destacado = 9.00\n", + " Inicio: 2024-05-12T21:00Z | Kp máxino destacado = 6.33\n", + " Inicio: 2024-05-16T06:00Z | Kp máxino destacado = 6.00\n", + " Inicio: 2024-05-17T18:00Z | Kp máxino destacado = 6.00\n", + "ID: 2024-05-02T15:00:00-GST-001 | Máximo Evento: 6.67\n", + "ID: 2024-05-10T15:00:00-GST-001 | Máximo Evento: 9.0\n", + "ID: 2024-05-12T21:00:00-GST-001 | Máximo Evento: 6.33\n", + "ID: 2024-05-16T06:00:00-GST-001 | Máximo Evento: 6.0\n", + "ID: 2024-05-17T18:00:00-GST-001 | Máximo Evento: 6.0\n", "------------------------------\n", - "La más intensa: 2024-05-10T17:00:00-GST-001\n", + "La más intensa: 2024-05-10T15:00:00-GST-001\n", "Kp máximo global: 9.0\n" ] } @@ -555,11 +554,11 @@ "\n", "Fecha inicio Kp máx Categoría\n", "--------------------------------------------------\n", - " 2024-05-03T12:00Z 5.330 G5 - Extrema \n", - " 2024-05-05T06:00Z 6.000 G2 - Moderada \n", - " 2024-05-10T17:00Z 9.000 G5 - Extrema \n", - " 2024-05-12T03:00Z 7.670 G5 - Extrema \n", - " 2024-05-20T09:00Z 5.000 G1 - Menor \n" + " 2024-05-02T15:00Z 6.670 G5 - Extrema \n", + " 2024-05-10T15:00Z 9.000 G5 - Extrema \n", + " 2024-05-12T21:00Z 6.330 G5 - Extrema \n", + " 2024-05-16T06:00Z 6.000 G2 - Moderada \n", + " 2024-05-17T18:00Z 6.000 G2 - Moderada \n" ] } ], @@ -640,45 +639,45 @@ "name": "stdout", "output_type": "stream", "text": [ - "lista de valores Kp de la NASA: [5.0, 5.33, 4.67, 3.33]\n", - "Valores ordenados Kp de la NASA: [3.33, 4.67, 5.0, 5.33]\n", - "ID del Evento: 2024-05-03T12:00:00-GST-001\n", - " > Inicio: 2024-05-03T12:00Z\n", - " > Kp Máximo: 5.33 (G5 - Extrema)\n", - " > Promedio: 4.58\n", - " > Eventos vinculados: 1\n", - "----------------------------------------\n", - "lista de valores Kp de la NASA: [5.67, 6.0, 5.33, 4.0, 3.67]\n", - "Valores ordenados Kp de la NASA: [3.67, 4.0, 5.33, 5.67, 6.0]\n", - "ID del Evento: 2024-05-05T06:00:00-GST-001\n", - " > Inicio: 2024-05-05T06:00Z\n", - " > Kp Máximo: 6.0 (G2 - Moderada)\n", - " > Promedio: 4.93\n", - " > Eventos vinculados: 1\n", + "lista de valores Kp de la NASA: [6.67, 6.67]\n", + "Valores ordenados Kp de la NASA: [6.67, 6.67]\n", + "ID del Evento: 2024-05-02T15:00:00-GST-001\n", + " > Inicio: 2024-05-02T15:00Z\n", + " > Kp Máximo: 6.67 (G5 - Extrema)\n", + " > Promedio: 6.67\n", + " > Eventos vinculados: 2\n", "----------------------------------------\n", - "lista de valores Kp de la NASA: [7.67, 8.33, 9.0, 8.67, 7.33, 6.67, 6.0, 5.33]\n", - "Valores ordenados Kp de la NASA: [5.33, 6.0, 6.67, 7.33, 7.67, 8.33, 8.67, 9.0]\n", - "ID del Evento: 2024-05-10T17:00:00-GST-001\n", - " > Inicio: 2024-05-10T17:00Z\n", + "lista de valores Kp de la NASA: [7.67, 8.67, 9.0, 9.0, 8.33, 8.67, 9.0, 8.67, 8.33, 7.33, 7.33, 6.67, 7.0]\n", + "Valores ordenados Kp de la NASA: [6.67, 7.0, 7.33, 7.33, 7.67, 8.33, 8.33, 8.67, 8.67, 8.67, 9.0, 9.0, 9.0]\n", + "ID del Evento: 2024-05-10T15:00:00-GST-001\n", + " > Inicio: 2024-05-10T15:00Z\n", " > Kp Máximo: 9.0 (G5 - Extrema)\n", - " > Promedio: 7.38\n", - " > Eventos vinculados: 3\n", + " > Promedio: 8.13\n", + " > Eventos vinculados: 6\n", "----------------------------------------\n", - "lista de valores Kp de la NASA: [7.0, 7.67, 6.33, 5.67, 4.33]\n", - "Valores ordenados Kp de la NASA: [4.33, 5.67, 6.33, 7.0, 7.67]\n", - "ID del Evento: 2024-05-12T03:00:00-GST-001\n", - " > Inicio: 2024-05-12T03:00Z\n", - " > Kp Máximo: 7.67 (G5 - Extrema)\n", - " > Promedio: 6.2\n", + "lista de valores Kp de la NASA: [6.33, 5.67, 6.0]\n", + "Valores ordenados Kp de la NASA: [5.67, 6.0, 6.33]\n", + "ID del Evento: 2024-05-12T21:00:00-GST-001\n", + " > Inicio: 2024-05-12T21:00Z\n", + " > Kp Máximo: 6.33 (G5 - Extrema)\n", + " > Promedio: 6.0\n", " > Eventos vinculados: 2\n", "----------------------------------------\n", - "lista de valores Kp de la NASA: [4.33, 5.0, 4.67, 3.33]\n", - "Valores ordenados Kp de la NASA: [3.33, 4.33, 4.67, 5.0]\n", - "ID del Evento: 2024-05-20T09:00:00-GST-001\n", - " > Inicio: 2024-05-20T09:00Z\n", - " > Kp Máximo: 5.0 (G1 - Menor)\n", - " > Promedio: 4.33\n", - " > Eventos vinculados: 0\n", + "lista de valores Kp de la NASA: [6.0]\n", + "Valores ordenados Kp de la NASA: [6.0]\n", + "ID del Evento: 2024-05-16T06:00:00-GST-001\n", + " > Inicio: 2024-05-16T06:00Z\n", + " > Kp Máximo: 6.0 (G2 - Moderada)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 1\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [6.0]\n", + "Valores ordenados Kp de la NASA: [6.0]\n", + "ID del Evento: 2024-05-17T18:00:00-GST-001\n", + " > Inicio: 2024-05-17T18:00Z\n", + " > Kp Máximo: 6.0 (G2 - Moderada)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 2\n", "----------------------------------------\n" ] } @@ -815,10 +814,10 @@ "=======================================================\n", " Estudiante : Alicia Tomás Laroj\n", " Carnet : 201016313\n", - " Fecha/Hora : 2026-04-24 16:17:49 UTC\n", - " ISS Latitud : +17.4850 deg\n", - " ISS Longitud : -86.7104 deg\n", - " ISS Hora UTC : 2026-04-24 16:17:49 UTC\n", + " Fecha/Hora : 2026-04-24 16:41:28 UTC\n", + " ISS Latitud : -47.0214 deg\n", + " ISS Longitud : -20.0337 deg\n", + " ISS Hora UTC : 2026-04-24 16:41:28 UTC\n", "=======================================================\n" ] } From 8445f211d5d734666aa064d61b8d62f05a5b389e Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 1 May 2026 11:54:52 -0600 Subject: [PATCH 16/28] =?UTF-8?q?Parte=203=20completada:=20An=C3=A1lisis?= =?UTF-8?q?=20y=20visualizaci=C3=B3n=20de=20la=20Canasta=20B=C3=A1sica=20I?= =?UTF-8?q?NE=202020-2023?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ejemplos/python/Numpy.ipynb | 792 +++++++++++++++++-- ejemplos/python/Pandas.ipynb | 1433 +++++++++++++++++++++++++++++++--- 2 files changed, 2042 insertions(+), 183 deletions(-) diff --git a/ejemplos/python/Numpy.ipynb b/ejemplos/python/Numpy.ipynb index 84dd64e..b3e9d69 100644 --- a/ejemplos/python/Numpy.ipynb +++ b/ejemplos/python/Numpy.ipynb @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -101,12 +101,12 @@ "# Array de NumPy\n", "arreglo = np.array([1, 2, 3, 4, 5])\n", "print(\"Array NumPy:\", arreglo)\n", - "print(\"Tipo:\", type(arreglo))" + "print(\"Tipo:\", type(arreglo))\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -142,16 +142,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Lista Python : 1.046 segundos\n", - "Array NumPy : 0.017 segundos\n", - "NumPy es 61× más rápido\n" + "Lista Python : 5.741 segundos\n", + "Array NumPy : 1.110 segundos\n", + "NumPy es 5 × más rápido\n" ] } ], @@ -176,7 +176,7 @@ "\n", "print(f\"Lista Python : {t_lista:.3f} segundos\")\n", "print(f\"Array NumPy : {t_numpy:.3f} segundos\")\n", - "print(f\"NumPy es {t_lista / t_numpy:.0f}× más rápido\")" + "print(f\"NumPy es {t_lista / t_numpy:.0f} × más rápido\")" ] }, { @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -197,7 +197,8 @@ "text": [ "Ceros: [0. 0. 0. 0. 0.]\n", "Unos : [1. 1. 1. 1. 1.]\n", - "Vacío: [1. 1. 1. 1. 1.]\n", + "Vacío: [1.26207700e-315 0.00000000e+000 1.27198647e-315 1.27148252e-315\n", + " 0.00000000e+000]\n", "\n", "Arange (0 a 10, paso 2): [0 2 4 6 8]\n", "Linspace (6 puntos entre 0 y 1): [0. 0.2 0.4 0.6 0.8 1. ]\n", @@ -237,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -263,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -309,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -373,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -400,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -448,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -487,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -538,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -592,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -622,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -671,9 +672,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "np.pi = 3.141592653589793\n", + "np.e = 2.718281828459045\n", + "np.inf = inf\n", + "np.nan = nan\n", + "\n", + "sqrt de [-4. -1. 0. 1. 4.] → [nan nan 0. 1. 2.]\n", + "\n", + "Notas con nan: [85. nan 72. nan 91.]\n", + "Media normal : nan\n", + "Media sin nan : 82.66666666666667\n", + "Suma sin nan : 248.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1283/3405442486.py:8: RuntimeWarning: invalid value encountered in sqrt\n", + " print(\"\\nsqrt de\", x, \"→\", np.sqrt(x)) # sqrt de negativos → nan\n" + ] + } + ], "source": [ "print(\"np.pi =\", np.pi)\n", "print(\"np.e =\", np.e)\n", @@ -704,9 +731,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperaturas: [18.5 22. 35.1 15.3 28.7 12. 30. ]\n", + "temps > 25 : [False False True False True False True]\n", + "\n", + "Días calurosos (> 25°C): [35.1 28.7 30. ]\n", + "Días moderados (20-30°C): [22. 28.7 30. ]\n", + "\n", + "¿Cuántos días > 25?: 3\n", + "¿Algún día < 10? : False\n", + "¿Todos > 10? : True\n" + ] + } + ], "source": [ "temps = np.array([18.5, 22.0, 35.1, 15.3, 28.7, 12.0, 30.0])\n", "\n", @@ -739,9 +782,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nota 55 → Reprobado\n", + " Nota 72 → Aprobado\n", + " Nota 88 → Aprobado\n", + " Nota 45 → Reprobado\n", + " Nota 91 → Aprobado\n", + " Nota 63 → Aprobado\n", + " Nota 78 → Aprobado\n", + "\n", + " Notas penalizadas: [ 0 72 88 0 91 63 78]\n" + ] + } + ], "source": [ "notas = np.array([55, 72, 88, 45, 91, 63, 78])\n", "\n", @@ -754,7 +813,7 @@ "# También funciona con números\n", "# Reemplazar notas menores a 61 con 0 (para penalización)\n", "penalizadas = np.where(notas >= 61, notas, 0)\n", - "print(\"\\nNotas penalizadas:\", penalizadas)" + "print(\"\\n Notas penalizadas:\", penalizadas)" ] }, { @@ -776,9 +835,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Alias modifica 'original': [999 2 3 4 5]\n", + "Slice (vista) modifica 'original2': [ 1 777 3 4 5]\n", + "Copia NO modifica 'original3': [1 2 3 4 5]\n" + ] + } + ], "source": [ "original = np.array([1, 2, 3, 4, 5])\n", "\n", @@ -816,9 +885,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "concatenate: [1 2 3 4 5 6]\n", + "hstack : [1 2 3 4 5 6]\n", + "\n", + "vstack (apilar filas, axis=0):\n", + "[[1 2]\n", + " [3 4]\n", + " [5 6]\n", + " [7 8]]\n", + "\n", + "hstack (unir columnas, axis=1):\n", + "[[1 2 5 6]\n", + " [3 4 7 8]]\n", + "\n", + "stack — crea NUEVO eje (shape resultante: 2×2×2):\n", + "[[[1 2]\n", + " [3 4]]\n", + "\n", + " [[5 6]\n", + " [7 8]]]\n", + "Shape: (2, 2, 2)\n" + ] + } + ], "source": [ "a = np.array([1, 2, 3])\n", "b = np.array([4, 5, 6])\n", @@ -855,9 +951,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notas originales: [78 45 92 61 88 34 73]\n", + "Ordenadas (asc) : [34 45 61 73 78 88 92]\n", + "Ordenadas (desc): [92 88 78 73 61 45 34]\n", + "\n", + "Índices (argsort): [5 1 3 6 0 4 2]\n", + "Aplicando índices : [34 45 61 73 78 88 92]\n", + "\n", + "Ranking:\n", + " 1. Diana: 92\n", + " 2. Fátima: 88\n", + " 3. Ana: 78\n", + " 4. Helena: 73\n", + " 5. Eduardo: 61\n", + " 6. Carlos: 45\n", + " 7. Gabriel: 34\n" + ] + } + ], "source": [ "notas = np.array([78, 45, 92, 61, 88, 34, 73])\n", "print(\"Notas originales:\", notas)\n", @@ -890,9 +1008,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Puntos por nivel : [100 250 180 320 210]\n", + "Puntos acumulados : [ 100 350 530 850 1060]\n", + "\n", + "Gastos diarios : [ 50 120 30 85 200 45]\n", + "Total acumulado : [ 50 170 200 285 485 530]\n", + "Total de la semana : 530\n" + ] + } + ], "source": [ "puntos_por_nivel = np.array([100, 250, 180, 320, 210])\n", "\n", @@ -923,9 +1054,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datos: [23 45 12 67 34 89 56 78 90 11]\n", + "\n", + "--- Estadísticas ---\n", + "Mínimo: 11\n", + "Máximo: 90\n", + "Suma: 505\n", + "Media (promedio): 50.50\n", + "Mediana: 50.50\n", + "Desviación estándar: 28.64\n", + "Varianza: 820.25\n", + "\n", + "Índice del máximo: 8\n", + "Índice del mínimo: 9\n" + ] + } + ], "source": [ "datos = np.array([23, 45, 12, 67, 34, 89, 56, 78, 90, 11])\n", "\n", @@ -956,9 +1107,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conteo por intervalo: [ 6 16 31 44 39 39 12 9 3 1]\n", + "Bordes de intervalos: [48.7 53.7 58.8 63.8 68.9 73.9 79. 84. 89. 94.1 99.1]\n", + "\n", + "Media simple : 2.86\n", + "Media ponderada : 2.995\n", + "\n", + "Coeficiente de correlación Pearson:\n", + "[[1. 0.9987]\n", + " [0.9987 1. ]]\n" + ] + } + ], "source": [ "from numpy.random import default_rng\n", "rng = default_rng(42) # semilla para reproducibilidad\n", @@ -994,9 +1161,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arreglo original: [ 1 2 3 4 5 6 7 8 9 10 11 12]\n", + "Forma: (12,)\n", + "\n", + "Reorganizado a 3x4:\n", + "[[ 1 2 3 4]\n", + " [ 5 6 7 8]\n", + " [ 9 10 11 12]]\n", + "\n", + "Reorganizado a 2x6:\n", + "[[ 1 2 3 4 5 6]\n", + " [ 7 8 9 10 11 12]]\n" + ] + } + ], "source": [ "# Crear un arreglo de 12 elementos y reorganizarlo\n", "v = np.arange(1, 13)\n", @@ -1030,9 +1215,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A * B (elemento a elemento):\n", + "[[ 5 12]\n", + " [21 32]]\n", + "\n", + "A @ B (multiplicación matricial):\n", + "[[19 22]\n", + " [43 50]]\n", + "\n", + "A.T (transpuesta):\n", + "[[1 3]\n", + " [2 4]]\n", + "\n", + "np.identity(3) — matriz identidad:\n", + "[[1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]]\n", + "\n", + "A.ravel() — aplanar a 1D: [1 2 3 4]\n" + ] + } + ], "source": [ "A = np.array([[1, 2], [3, 4]])\n", "B = np.array([[5, 6], [7, 8]])\n", @@ -1075,9 +1285,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matriz A:\n", + "[[4. 2. 0.]\n", + " [9. 3. 7.]\n", + " [1. 2. 1.]]\n", + "\n", + "Determinante: -48.0\n", + "\n", + "Inversa A⁻¹:\n", + "[[ 0.2292 0.0417 -0.2917]\n", + " [ 0.0417 -0.0833 0.5833]\n", + " [-0.3125 0.125 0.125 ]]\n", + "\n", + "A @ A⁻¹ (≈ identidad):\n", + "[[ 1. 0. -0.]\n", + " [ 0. 1. -0.]\n", + " [ 0. 0. 1.]]\n", + "\n", + "Norma del vector [3, 4]: 5.0\n" + ] + } + ], "source": [ "import numpy.linalg as la\n", "\n", @@ -1107,9 +1342,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solución x: [ 1. 2. -2.]\n", + "Verificación Ax - b ≈ 0: [ 0. 0. -0.]\n", + "\n", + "--- Eigenvalores ---\n", + "Eigenvalores: [-5. 3. 6.]\n", + "Primer eigenvector: [ 0.816 0.408 -0.408]\n" + ] + } + ], "source": [ "import numpy.linalg as la\n", "\n", @@ -1154,9 +1402,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coeficientes [a, b, c]: [ 0.051 -0. 0. ]\n", + "Modelo: d = 0.0510·v² + -0.0000·v + 0.0000\n", + "\n", + "Predicciones:\n", + " v=25 m/s → d≈31.87 m\n", + " v=35 m/s → d≈62.47 m\n", + " v=45 m/s → d≈103.27 m\n" + ] + } + ], "source": [ "# Datos: velocidad inicial vs distancia máxima (proyectil simplificado)\n", "# Queremos ajustar un polinomio de grado 2: d = a*v^2 + b*v + c\n", @@ -1186,9 +1448,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notas (Tarea1 | Tarea2 | Parcial | Final):\n", + "--------------------------------------------------\n", + "Ana [78 91 68 54]\n", + "Carlos [ 82 47 100 60]\n", + "Diana [78 97 58 62]\n", + "Eduardo [50 50 63 92]\n", + "Fátima [75 79 63 42]\n", + "Gabriel [61 92 41 63]\n", + "Helena [83 69 77 41]\n", + "Iván [99 60 72 51]\n", + "\n", + "--- Resultados Finales ---\n", + "Ana Nota: 67.3 → APROBADO\n", + "Carlos Nota: 73.3 → APROBADO\n", + "Diana Nota: 68.5 → APROBADO\n", + "Eduardo Nota: 70.7 → APROBADO\n", + "Fátima Nota: 58.8 → REPROBADO\n", + "Gabriel Nota: 60.5 → REPROBADO\n", + "Helena Nota: 62.3 → APROBADO\n", + "Iván Nota: 65.8 → APROBADO\n", + "\n", + "Promedio del curso: 65.91\n", + "Nota más alta: 73.35\n", + "Nota más baja: 58.80\n", + "Aprobados: 6 / 8\n" + ] + } + ], "source": [ "# Simulación de notas de un curso\n", "# Filas = estudiantes, Columnas = [Tarea1, Tarea2, Examen parcial, Examen final]\n", @@ -1218,6 +1512,26 @@ "print(f\"Aprobados: {np.sum(notas_finales >= 61)} / {len(notas_finales)}\")" ] }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumPy versión: 1.26.4\n" + ] + } + ], + "source": [ + "\n", + "import numpy as np\n", + "\n", + "print(\"NumPy versión:\", np.__version__)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1257,11 +1571,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista principal:, [ 1 2 3 4 5 6 7 8 9 10]\n", + "Multiplicación por 5: [ 5 10 15 20 25 30 35 40 45 50]\n", + "Números pares: [ 2 4 6 8 10]\n", + "Suma total: 55\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "\n", + "lista_principal = np.arange(1, 11)\n", + "Multiplicacion = lista_principal * 5\n", + "pares = lista_principal[lista_principal % 2 ==0]\n", + "suma = lista_principal.sum()\n", + "\n", + "print(f\"Lista principal:, {lista_principal}\")\n", + "print(f\"Multiplicación por 5: {Multiplicacion}\")\n", + "print(f\"Números pares: {pares}\")\n", + "print(f\"Suma total: {suma}\")\n", + "\n", + "\n", + "\n", + "\n" ] }, { @@ -1278,13 +1617,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grados : [ 0 30 45 60 90 180]\n", + "Radianes: [0. 0.52359878 0.78539816 1.04719755 1.57079633 3.14159265]\n", + "seno: [0. 0.5 0.7071 0.866 1. 0. ]\n" + ] + } + ], "source": [ "grados = np.array([0, 30, 45, 60, 90, 180])\n", + "radianes = grados * np.pi /180\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "print(f\"Grados : {grados}\")\n", + "print(f\"Radianes: {radianes}\")\n", + "print(\"seno: \" , np.sin(radianes).round(4))" ] }, { @@ -1302,10 +1655,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " DATOS ORIGINALES \n", + " \n", + "EN DESORDEN : [ 22.1 19.5 31.2 28.0 17.3 25.8 30.1 14.2 26.7 23.4 18.9 32.0 29.5 21.1 16.8 27.3\n", + " 24.6 13.5 20.0 28.9 33.1 15.7 22.8 19.0 31.5 26.2 18.1 29.8 12.4 25.0]\n", + " \n", + "EN ORDEN : [ 12.4 13.5 14.2 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n", + " \n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + " ESTADÍSTICA \n", + " \n", + "Mínimo: 12.4\n", + " \n", + "Máximo: 33.1\n", + " \n", + "Número de días que superaron 28°C: 8\n", + " \n", + "Temperatura de los días menores a 20°C: [ 19.5 17.3 14.2 18.9 16.8 13.5 15.7 19.0 18.1 12.4]\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + " TEMPERATURAS MÍNIMAS REGISTRADAS < 15°C, MODIFICADOS \n", + " \n", + "Temperaturas originales ordenas: [ 12.4 13.5 14.2 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n", + " \n", + "DATOS MODIFICADOS < 15°C : [ 15.0 15.0 15.0 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n" + ] + } + ], "source": [ + "np.set_printoptions(formatter={'float': '{: 0.1f}'.format}, linewidth=100)\n", "temperaturas = np.array([\n", " 22.1, 19.5, 31.2, 28.0, 17.3, 25.8, 30.1,\n", " 14.2, 26.7, 23.4, 18.9, 32.0, 29.5, 21.1,\n", @@ -1313,8 +1703,49 @@ " 15.7, 22.8, 19.0, 31.5, 26.2, 18.1, 29.8,\n", " 12.4, 25.0\n", "])\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "#--------------------------\n", + "\n", + "a = np.sort(temperaturas) # Temperaturas ordenadas\n", + "b =(temperaturas > 28).sum() # Temperaturas mayores a 28°C.\n", + "c = temperaturas[temperaturas < 20] # Temperaturas de los días menores a 20°C.\n", + "nuevas_temperaturas = np.where(temperaturas < 15, 15.0, temperaturas) # Remplazo de tempetaturas menores a 15°C\n", + "d = np.sort(nuevas_temperaturas) \n", + "\n", + "#--------------------------\n", + "\n", + "\n", + "# Lista de temperaturas diarias de un mes.\n", + "print(\"\\n\" + \" \"*20 + \" DATOS ORIGINALES \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"EN DESORDEN : {temperaturas}\")\n", + "print(\" \" * 40)\n", + "print(f\"EN ORDEN : {a}\")\n", + "print(\" \" * 40)\n", + "print(\"----\" * 40)\n", + "\n", + "#--------------------------\n", + "\n", + "print(\"\\n\" + \" \" * 21 + \" ESTADÍSTICA \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"Mínimo: {np.min(temperaturas)}\")\n", + "print(\" \" * 40)\n", + "print(f\"Máximo: {np.max(temperaturas)}\")\n", + "print(\" \" * 40)\n", + "print(f\"Número de días que superaron 28°C: {b}\")\n", + "print(\" \" * 40)\n", + "print(f\"Temperatura de los días menores a 20°C: {c}\")\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "print(\"----\" * 40)\n", + "\n", + "#--------------------------\n", + "\n", + "print(\"\\n\" + \" \" * 17 + \" TEMPERATURAS MÍNIMAS REGISTRADAS < 15°C, MODIFICADOS \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"Temperaturas originales ordenas: {a}\")\n", + "print(\" \" * 40)\n", + "print(f\"DATOS MODIFICADOS < 15°C : {d}\")\n" ] }, { @@ -1333,11 +1764,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " VALORES DEL 1 AL 16: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16]\n", + " Forma: (16,)\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " MATRIZ ORIGINAL \n", + "Forma: (4, 4)\n", + "MATRIZ 4X4: \n", + " [[ 1 2 3 4]\n", + " [ 5 6 7 8]\n", + " [ 9 10 11 12]\n", + " [13 14 15 16]]\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " OPERACIONES CON LA MATRIZ \n", + " \n", + "PRIMERA FILA: \n", + " [1 2 3 4]\n", + " \n", + "ÚLTIMA COLUMNA DE LA MATRIZ: \n", + " [ 4 8 12 16]\n", + " \n", + "SUBMATRIZ DE LAS 2 PRIMERAS FILAS Y LAS 2 ÚLTIMAS COLUMNAS:\n", + " [[3 4]\n", + " [7 8]]\n", + " \n", + "SUMAS DE CADA FILA: \n", + " [10 26 42 58]\n", + " \n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "\n", + "a = np.arange(1, 17) # Matrix fila 1 al 16 o arreglo original\n", + "b = a.reshape(4, 4) # Matriz 4x4\n", + "\n", + "#--------------------------\n", + "print(f\"\\n VALORES DEL 1 AL 16: {a}\")\n", + "print(\" Forma:\", a.shape)\n", + "print(\"----\" * 40)\n", + "#--------------------------\n", + "print(\" \"*30 + \"MATRIZ ORIGINAL\" + \" \"*20)\n", + "print(\"Forma:\", b.shape)\n", + "print(f\"MATRIZ 4X4: \\n {b}\")\n", + "print(\"----\" * 40)\n", + "print(\" \"*27 + \" OPERACIONES CON LA MATRIZ\" + \" \"*20)\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"PRIMERA FILA: \\n {b[0, :]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"ÚLTIMA COLUMNA DE LA MATRIZ: \\n {b[:, -1]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"SUBMATRIZ DE LAS 2 PRIMERAS FILAS Y LAS 2 ÚLTIMAS COLUMNAS:\\n {b[0:2, 2:4]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"SUMAS DE CADA FILA: \\n {np.sum(b, axis=1)}\") # Suma de la matriz utilizando .sum() y axis=1 para sumar la filas\n", + "print(\" \" * 40)\n", + "\n" ] }, { @@ -1354,13 +1847,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------\n", + "\n", + " NOTA DE LAS 8 TAREAS DEL CURSO:\n", + " [45 38 52 29 61 47 55 33]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA SUMA ACUMULADA DE TODA LAS NOTAS: \n", + " [ 45 83 135 164 225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA SUMA CORRELATIVA QUE SUPERÓ LOS 100 PUNTOS, ESTÁ EN LA NOTA CON ÍNDICE: 2\n", + "\n", + " LA SUMA ACUMULADA QUE SUPERÓ 100 pt.: 135\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 100 pt.: \n", + " [135 164 225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 200 pt.: \n", + " [225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 300 pt.s: \n", + " [327 360]\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ + "# Puntos de las OCho tareas del curso\n", + "print(\"---\" * 30)\n", "puntos_tareas = np.array([45, 38, 52, 29, 61, 47, 55, 33])\n", + "a = np.cumsum(puntos_tareas)\n", + "b= np.where(a > 100)[0][0]# Suma correlativa en que se superó lo 100 puntos\n", + "\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "print(f\"\\n NOTA DE LAS 8 TAREAS DEL CURSO:\\n {puntos_tareas}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n LA SUMA ACUMULADA DE TODA LAS NOTAS: \\n {a}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n LA SUMA CORRELATIVA QUE SUPERÓ LOS 100 PUNTOS, ESTÁ EN LA NOTA CON ÍNDICE: {b}\")\n", + "print(f\"\\n LA SUMA ACUMULADA QUE SUPERÓ 100 pt.: {a[2]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 100 pt.: \\n {a[a > 100]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 200 pt.: \\n {a[a > 200]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 300 pt.s: \\n {a[a > 300]}\")\n", + "print(\"---\" * 30)\n", + "\n" ] }, { @@ -1384,9 +1934,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " SOLUCIÓN DEL SISTEMA:\n", + " [ 31.2 37.5 56.2]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " VERIFICACIÓN DE LA SOLUCIÓN A @ X-b ; ≈ 0: \n", + " [ 0.0 0.0 0.0]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " Determinante: \n", + " 8.0000\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA NORMA DE X: \n", + " 74.4773\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "import numpy.linalg as la\n", "\n", @@ -1400,7 +1973,25 @@ " [0, 1, 2]], dtype=float)\n", "b = np.array([100, 200, 150], dtype=float)\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "x = la.solve(A, b) # la es el alias de np.linalg\n", + "y = A @ x - b # Verificación\n", + "n = la.norm (x)\n", + "\n", + "\n", + "print(f\"\\n SOLUCIÓN DEL SISTEMA:\\n {x}\")\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n VERIFICACIÓN DE LA SOLUCIÓN A @ X-b ; ≈ 0: \\n {y}\" )\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n Determinante: \\n {la.det(A):.4f}\")\n", + "\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n LA NORMA DE X: \\n {n:.4f}\" )\n", + "print(\"---\" * 30)\n", + "\n" ] }, { @@ -1421,14 +2012,71 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " COEFICIENTES [a, b, c] de [at^2 + bt + c)]:: \n", + " a=4.9000, \n", + " b=0.0000, \n", + " c=-0.0000\n", + "------------------------------------------------------------------------------------------\n", + " \n", + " MODELOR [y(t)= at^2 + bt + c)]: \n", + " \n", + " y(t)= 4.9000·t² + 0.0000·t + -0.0000\n", + " \n", + "------------------------------------------------------------------------------------------\n", + " \n", + " ESTIMACIÓN DE LA GRAVEDAD: \n", + " 9.80000 m/s^2\n", + "------------------------------------------------------------------------------------------\n", + " POSICIONES \n", + " \n", + " POSICIÓN t1 = 2.5 s: \n", + " y(t1) = 30.62500 m\n", + " \n", + " \n", + " POSICIÓN t2 = 6.0 s:\n", + " y(t2) = 176.40000 m\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "t = np.array([0, 1, 2, 3, 4, 5], dtype=float) # tiempo (s)\n", "pos = np.array([0.0, 4.9, 19.6, 44.1, 78.4, 122.5]) # posición (m)\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "\n", + "coeficientes = np.polyfit(t, pos, deg=2)\n", + "a= coeficientes[0]\n", + "g_estimada = a * 2\n", + "t1 = 2.5\n", + "t2 = 6 \n", + "posicion_t1 = np.polyval(coeficientes, t1) \n", + "posicion_t2 = np.polyval(coeficientes, t2)\n", + "\n", + "\n", + "print(f\"\\n COEFICIENTES [a, b, c] de [at^2 + bt + c)]:: \\n a={coeficientes[0]:.4f}, \\n b={coeficientes[1]:.4f}, \\n c={coeficientes[2]:.4f}\")\n", + "print(\"---\" * 30)\n", + "# Modelo para un movimiento vertical, buscamos las alturas (y)\n", + "print(f\" \\n MODELOR [y(t)= at^2 + bt + c)]: \\n \\n y(t)= {coeficientes[0]:.4f}·t² + {coeficientes[1]:.4f}·t + {coeficientes[2]:.4f}\")\n", + "print(\" \" * 30)\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\" \\n ESTIMACIÓN DE LA GRAVEDAD: \\n {g_estimada:.5f} m/s^2\")\n", + "print(\"---\" * 30)\n", + "\n", + "print( \" \" * 15 + \"POSICIONES\" + \" \"*20)\n", + "print(f\" \\n POSICIÓN t1 = 2.5 s: \\n y(t1) = {posicion_t1:.5f} m\")\n", + "print(\" \" * 30)\n", + "print(f\" \\n POSICIÓN t2 = 6.0 s:\\n y(t2) = {posicion_t2:.5f} m\")\n", + "print(\"---\" * 30)\n" ] } ], diff --git a/ejemplos/python/Pandas.ipynb b/ejemplos/python/Pandas.ipynb index 276d1cb..36aeedb 100644 --- a/ejemplos/python/Pandas.ipynb +++ b/ejemplos/python/Pandas.ipynb @@ -69,9 +69,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serie de temperaturas:\n", + "0 25.1\n", + "1 27.3\n", + "2 24.8\n", + "3 28.5\n", + "4 26.0\n", + "dtype: float64\n", + "\n", + "Tipo: \n" + ] + } + ], "source": [ "# Crear una Serie desde una lista\n", "temperaturas = pd.Series([25.1, 27.3, 24.8, 28.5, 26.0])\n", @@ -82,9 +98,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperatura por día:\n", + "Lunes 25.1\n", + "Martes 27.3\n", + "Miércoles 24.8\n", + "Jueves 28.5\n", + "Viernes 26.0\n", + "dtype: float64\n", + "\n", + "Temperatura del Martes: 27.3\n", + "Promedio de la semana: 26.339999999999996\n" + ] + } + ], "source": [ "# Serie con índices personalizados (etiquetas)\n", "dias = pd.Series(\n", @@ -100,9 +133,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean: 8.258333333333333\n", + "min: -0.3\n", + "standard deviation: 6.520242373260415\n", + "index of max element: 6\n", + "values as list: [-0.3 0.4 3.9 7.4 12. 15. 17.2 16.8 13.1 9.1 3.7 0.8]\n", + "indices of sorted Series: [ 0 1 11 10 2 3 9 4 8 5 7 6]\n", + "cumulative sum: [-0.3 0.1 4. 11.4 23.4 38.4 55.6 72.4 85.5 94.6 98.3 99.1]\n" + ] + } + ], "source": [ "# Métodos estadísticos de una Serie (§8.1)\n", "s = pd.Series([-0.3, 0.4, 3.9, 7.4, 12.0, 15.0, 17.2,\n", @@ -119,9 +166,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jan -0.3\n", + "feb 0.4\n", + "mar 3.9\n", + "apr 7.4\n", + "may 12.0\n", + "jun 15.0\n", + "jul 17.2\n", + "aug 16.8\n", + "sep 13.1\n", + "oct 9.1\n", + "nov 3.7\n", + "dec 0.8\n", + "Name: temp_C, dtype: float64\n", + "\n", + "Temperatura en octubre: 9.1\n" + ] + } + ], "source": [ "# Índice con etiquetas — set_axis() (§8.1)\n", "s2 = s.set_axis(['jan', 'feb', 'mar', 'apr', 'may', 'jun',\n", @@ -147,9 +216,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "1 Carlos 22 Matemática 72.0\n", + "2 Diana 21 Física 91.3\n", + "3 Eduardo 23 Computación 68.7\n", + "4 Fátima 20 Matemática 79.2\n" + ] + } + ], "source": [ "# Crear DataFrame desde un diccionario\n", "datos = {\n", @@ -165,9 +247,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forma (filas x columnas): (5, 4)\n", + "\n", + "Nombres de columnas: ['Nombre', 'Edad', 'Carrera', 'Promedio']\n", + "\n", + "Tipos de datos:\n", + "Nombre object\n", + "Edad int64\n", + "Carrera object\n", + "Promedio float64\n", + "dtype: object\n" + ] + } + ], "source": [ "# Información básica del DataFrame\n", "print(\"Forma (filas x columnas):\", df.shape)\n", @@ -178,9 +277,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas descriptivas:\n" + ] + }, + { + "data": { + "text/html": [ + "
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EdadPromedio
count5.000005.000000
mean21.2000079.340000
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" + ], + "text/plain": [ + " Edad Promedio\n", + "count 5.00000 5.000000\n", + "mean 21.20000 79.340000\n", + "std 1.30384 9.328612\n", + "min 20.00000 68.700000\n", + "25% 20.00000 72.000000\n", + "50% 21.00000 79.200000\n", + "75% 22.00000 85.500000\n", + "max 23.00000 91.300000" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Resumen estadístico automático\n", "print(\"Estadísticas descriptivas:\")\n", @@ -189,9 +382,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Primeras 3 filas:\n", + " temp_C rain_mm\n", + "jan -0.3 59\n", + "feb 0.4 57\n", + "mar 3.9 84\n", + "\n", + "Últimas 2 filas:\n", + " temp_C rain_mm\n", + "nov 3.7 88\n", + "dec 0.8 80\n", + "\n", + "Columnas: ['temp_C', 'rain_mm']\n", + "Índice (list): ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']\n" + ] + } + ], "source": [ "# head(), tail() y atributos del índice (§8.2.1)\n", "df_clima = pd.DataFrame({\n", @@ -218,9 +431,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columna 'Nombre':\n", + "0 Ana\n", + "1 Carlos\n", + "2 Diana\n", + "3 Eduardo\n", + "4 Fátima\n", + "Name: Nombre, dtype: object\n", + "\n", + "Columnas 'Nombre' y 'Promedio':\n", + " Nombre Promedio\n", + "0 Ana 85.5\n", + "1 Carlos 72.0\n", + "2 Diana 91.3\n", + "3 Eduardo 68.7\n", + "4 Fátima 79.2\n" + ] + } + ], "source": [ "# Acceder a una columna\n", "print(\"Columna 'Nombre':\")\n", @@ -232,9 +467,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Primera fila (iloc[0]):\n", + "Nombre Ana\n", + "Edad 20\n", + "Carrera Física\n", + "Promedio 85.5\n", + "Name: 0, dtype: object\n", + "\n", + "Filas 1 a 3 (iloc[1:4]):\n", + " Nombre Edad Carrera Promedio\n", + "1 Carlos 22 Matemática 72.0\n", + "2 Diana 21 Física 91.3\n", + "3 Eduardo 23 Computación 68.7\n" + ] + } + ], "source": [ "# Acceder a filas con .iloc (por posición numérica)\n", "print(\"Primera fila (iloc[0]):\")\n", @@ -246,9 +500,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fila con índice 2:\n", + "Nombre Diana\n", + "Edad 21\n", + "Carrera Física\n", + "Promedio 91.3\n", + "Name: 2, dtype: object\n", + "\n", + "Valor en fila 0, columna 'Nombre': Ana\n" + ] + } + ], "source": [ "# Acceder a filas con .loc (por etiqueta/índice)\n", "print(\"Fila con índice 2:\")\n", @@ -267,9 +536,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Marzo, abril y junio:\n", + " temp_C rain_mm\n", + "mar 3.9 84\n", + "apr 7.4 100\n", + "jun 15.0 153\n" + ] + } + ], "source": [ "# loc con lista de etiquetas (§8.2.2)\n", "print(\"Marzo, abril y junio:\")\n", @@ -278,9 +559,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "De marzo a mayo:\n", + " temp_C rain_mm\n", + "mar 3.9 84\n", + "apr 7.4 100\n", + "may 12.0 143\n" + ] + } + ], "source": [ "# loc con slice de etiquetas — el extremo final SÍ se incluye (§8.2.2)\n", "print(\"De marzo a mayo:\")\n", @@ -289,9 +582,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lluvia de mayo a septiembre:\n", + "may 143\n", + "jun 153\n", + "jul 172\n", + "aug 164\n", + "sep 135\n", + "Name: rain_mm, dtype: int64\n", + "\n", + "Total de lluvia en verano: 767\n" + ] + } + ], "source": [ "# loc combinando filas Y columnas: df.loc[, ] (§8.2.2)\n", "print(\"Lluvia de mayo a septiembre:\")\n", @@ -302,9 +611,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temp en meses con lluvia < 100 mm:\n", + "jan -0.3\n", + "feb 0.4\n", + "mar 3.9\n", + "oct 9.1\n", + "nov 3.7\n", + "dec 0.8\n", + "Name: temp_C, dtype: float64\n" + ] + } + ], "source": [ "# Máscara booleana con loc (§8.2.2)\n", "# Temperatura en meses con lluvia < 100 mm\n", @@ -329,9 +653,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estudiantes con promedio > 75:\n", + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "2 Diana 21 Física 91.3\n", + "4 Fátima 20 Matemática 79.2\n", + "\n", + "Estudiantes de Física:\n", + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "2 Diana 21 Física 91.3\n" + ] + } + ], "source": [ "# Estudiantes con promedio mayor a 75\n", "buenos = df[df[\"Promedio\"] > 75]\n", @@ -348,9 +689,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Promedio > 70 Y Edad <= 21:\n", + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "2 Diana 21 Física 91.3\n", + "4 Fátima 20 Matemática 79.2\n" + ] + } + ], "source": [ "# Condiciones múltiples\n", "# & = AND, | = OR\n", @@ -368,9 +721,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ranking por promedio:\n", + " Nombre Promedio\n", + "2 Diana 91.3\n", + "0 Ana 85.5\n", + "4 Fátima 79.2\n", + "1 Carlos 72.0\n", + "3 Eduardo 68.7\n" + ] + } + ], "source": [ "# Ordenar por promedio (descendente)\n", "ranking = df.sort_values(\"Promedio\", ascending=False)\n", @@ -387,9 +754,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nombre Edad Carrera Promedio Estado Puntos_extra\n", + "0 Ana 20 Física 85.5 Aprobado 15.5\n", + "1 Carlos 22 Matemática 72.0 Aprobado 2.0\n", + "2 Diana 21 Física 91.3 Aprobado 21.3\n", + "3 Eduardo 23 Computación 68.7 Reprobado 0.0\n", + "4 Fátima 20 Matemática 79.2 Aprobado 9.2\n" + ] + } + ], "source": [ "# Agregar columna calculada\n", "df[\"Estado\"] = df[\"Promedio\"].apply(lambda p: \"Aprobado\" if p >= 70 else \"Reprobado\")\n", @@ -409,9 +789,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm temp_F\n", + "jan -0.3 59 31.46\n", + "feb 0.4 57 32.72\n", + "mar 3.9 84 39.02\n", + "apr 7.4 100 45.32\n", + "may 12.0 143 53.60\n" + ] + } + ], "source": [ "# assign() — agregar columna con función (§8.2.3)\n", "def celsius_a_f(df):\n", @@ -423,9 +816,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm\n", + "jan -0.3 59.0\n", + "feb 0.4 57.0\n", + "mar 3.9 84.0\n", + "apr 7.4 100.0\n", + "may 12.0 143.0\n", + "jun 15.0 153.0\n", + "jul 17.2 172.0\n", + "aug 16.8 164.0\n", + "sep 13.1 135.0\n", + "oct 9.1 89.0\n", + "nov 3.7 88.0\n", + "dec 0.8 80.0\n", + "2020 9.7 165.8\n", + "2019 9.5 146.1\n", + "2018 9.9 139.2\n" + ] + } + ], "source": [ "# pd.concat() — combinar filas (§8.2.3)\n", "df_anual = pd.DataFrame({\n", @@ -439,9 +855,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm temp_min_C temp_max_C\n", + "jan -0.3 59 -1.9 2.5\n", + "feb 0.4 57 -2.5 3.3\n", + "mar 3.9 84 0.6 7.3\n", + "apr 7.4 100 3.5 11.5\n", + "may 12.0 143 7.8 16.3\n", + "jun 15.0 153 11.0 19.2\n", + "jul 17.2 172 13.1 21.6\n", + "aug 16.8 164 13.0 20.9\n", + "sep 13.1 135 9.7 16.8\n", + "oct 9.1 89 6.2 12.3\n", + "nov 3.7 88 1.0 6.5\n", + "dec 0.8 80 -3.0 3.5\n" + ] + } + ], "source": [ "# pd.concat() con axis=1 — combinar columnas (§8.2.3)\n", "df_minmax = pd.DataFrame({\n", @@ -465,9 +901,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas por carrera:\n", + " Promedio_medio Mínimo Máximo Estudiantes\n", + "Carrera \n", + "Computación 68.7 68.7 68.7 1\n", + "Física 88.4 85.5 91.3 2\n", + "Matemática 75.6 72.0 79.2 2\n" + ] + } + ], "source": [ "# groupby: agrupar por una columna y calcular estadísticas\n", "por_carrera = df.groupby(\"Carrera\")[\"Promedio\"].agg([\"mean\", \"min\", \"max\", \"count\"])\n", @@ -487,9 +936,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame con valores faltantes:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n", + "1 Carlos NaN 65.0 71.0\n", + "2 Diana 90.0 NaN 85.0\n", + "3 Eduardo 72.0 80.0 NaN\n", + "4 Fátima NaN 88.0 76.0\n", + "\n", + "Cantidad de nulos por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n" + ] + } + ], "source": [ "# Crear DataFrame con datos faltantes (NaN = Not a Number)\n", "datos_incompletos = pd.DataFrame({\n", @@ -507,9 +977,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Después de rellenar con la media:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n", + "1 Carlos 82.3 65.0 71.0\n", + "2 Diana 90.0 77.8 85.0\n", + "3 Eduardo 72.0 80.0 81.0\n", + "4 Fátima 82.3 88.0 76.0\n" + ] + } + ], "source": [ "# Opción 1: Rellenar con la media de la columna\n", "df_rellenado = datos_incompletos.copy()\n", @@ -522,9 +1006,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Después de eliminar filas con nulos:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n" + ] + } + ], "source": [ "# Opción 2: Eliminar filas con datos faltantes\n", "df_limpio = datos_incompletos.dropna()\n", @@ -541,9 +1035,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mapa de NaN:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 False False False False\n", + "1 False True False False\n", + "2 False False True False\n", + "3 False False False True\n", + "4 False True False False\n", + "\n", + "Total de NaN por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n" + ] + } + ], "source": [ "# isna() / notna() — mapa de valores faltantes (§8.2.4)\n", "print(\"Mapa de NaN:\")\n", @@ -555,9 +1070,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rellenado con la media de cada columna:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n", + "1 Carlos 82.3 65.0 71.0\n", + "2 Diana 90.0 77.8 85.0\n", + "3 Eduardo 72.0 80.0 81.0\n", + "4 Fátima 82.3 88.0 76.0\n" + ] + } + ], "source": [ "# fillna con la media de cada columna (§8.2.4)\n", "df_relleno2 = datos_incompletos.copy()\n", @@ -570,9 +1099,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original: [ 1. nan 3. nan 5.]\n", + "Interpolado: [1. 2. 3. 4. 5.]\n" + ] + } + ], "source": [ "# interpolate() — interpolación lineal (§8.2.4)\n", "s_nan = pd.Series([1.0, np.nan, 3.0, np.nan, 5.0])\n", @@ -592,9 +1130,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datos leídos desde CSV:\n", + " nombre edad carrera nota\n", + "0 Ana 20 Física 85\n", + "1 Carlos 22 Matemática 72\n", + "2 Diana 21 Física 91\n", + "3 Eduardo 23 Computación 68\n", + "4 Fátima 20 Matemática 79\n", + "\n", + "Primeras 3 filas (head):\n", + " nombre edad carrera nota\n", + "0 Ana 20 Física 85\n", + "1 Carlos 22 Matemática 72\n", + "2 Diana 21 Física 91\n" + ] + } + ], "source": [ "# Crear un CSV de ejemplo para practicar\n", "csv_contenido = \"\"\"nombre,edad,carrera,nota\n", @@ -618,9 +1176,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Archivo guardado como /tmp/resultado.csv\n" + ] + } + ], "source": [ "# Guardar un DataFrame como CSV\n", "df_csv.to_csv(\"/tmp/resultado.csv\", index=False)\n", @@ -634,9 +1200,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C uv nubes\n", + "dia \n", + "lunes 12.3 5 clear\n", + "martes 13.5 4 clear\n", + "miercoles 9.2 1 mostly cloudy\n", + "jueves 8.2 2 partly cloudy\n", + "viernes 10.2 3 partly cloudy\n" + ] + } + ], "source": [ "# read_csv con opciones avanzadas (§8.6)\n", "import io\n", @@ -668,9 +1248,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " nubes uv\n", + "lun nublado 0\n", + "mar nublado 0\n", + "mié parcialmente nublado 1\n", + "jue mayormente despejado 3\n", + "vie despejado 5\n", + "sáb despejado 5\n", + "dom parcialmente nublado 1\n" + ] + } + ], "source": [ "# DataFrame con datos categóricos\n", "df_cat = pd.DataFrame({\n", @@ -684,9 +1279,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Categorías únicas:\n", + "['nublado' 'parcialmente nublado' 'mayormente despejado' 'despejado']\n", + "\n", + "Frecuencia de cada categoría:\n", + "nubes\n", + "nublado 2\n", + "parcialmente nublado 2\n", + "despejado 2\n", + "mayormente despejado 1\n", + "Name: count, dtype: int64\n" + ] + } + ], "source": [ "# unique() — valores únicos | value_counts() — frecuencia (§8.3.1)\n", "print(\"Categorías únicas:\")\n", @@ -698,9 +1310,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UV promedio en días parcialmente nublados: 1.0\n" + ] + } + ], "source": [ "# Filtrar por categoría y aplicar método (§8.3.1)\n", "uv_parcial = df_cat.loc[df_cat['nubes'] == 'parcialmente nublado', 'uv'].mean()\n", @@ -719,9 +1339,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "¿Contiene 'nublado'?\n", + "lun True\n", + "mar True\n", + "mié True\n", + "jue False\n", + "vie False\n", + "sáb False\n", + "dom True\n", + "Name: nubes, dtype: bool\n", + "\n", + "Filas con nubosidad:\n", + " nubes uv\n", + "lun nublado 0\n", + "mar nublado 0\n", + "mié parcialmente nublado 1\n", + "dom parcialmente nublado 1\n" + ] + } + ], "source": [ "# str.contains() — filtrar filas que contienen una subcadena (§8.3.2)\n", "mascara = df_cat['nubes'].str.contains('nublado', regex=False)\n", @@ -734,9 +1377,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " nubes uv\n", + "lun soleado 0\n", + "mar soleado 0\n", + "mié parcialmente soleado 1\n", + "jue mayormente despejado 3\n", + "vie despejado 5\n", + "sáb despejado 5\n", + "dom parcialmente soleado 1\n" + ] + } + ], "source": [ "# str.replace() — reemplazar texto (§8.3.2)\n", "df_cat2 = df_cat.copy()\n", @@ -756,9 +1414,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tipo original: object\n", + "Tipo convertido: datetime64[ns]\n", + "0 2020-01-01 12:34:00\n", + "1 2020-03-01 08:47:00\n", + "2 2020-06-01 14:23:00\n", + "3 2020-09-01 22:56:00\n", + "4 2020-12-01 13:45:00\n", + "dtype: datetime64[ns]\n" + ] + } + ], "source": [ "# pd.to_datetime() — convertir strings a fechas (§8.3.3)\n", "fechas = pd.Series(['2020-01-01 12:34', '2020-03-01 08:47',\n", @@ -773,9 +1446,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Horas:\n", + "0 12:34:00\n", + "1 08:47:00\n", + "2 14:23:00\n", + "3 22:56:00\n", + "4 13:45:00\n", + "dtype: object\n", + "\n", + "Meses: [ 1 3 6 9 12]\n" + ] + } + ], "source": [ "# Acceder a componentes con el accessor .dt (§8.3.3)\n", "print(\"Horas:\")\n", @@ -785,9 +1474,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diferencia respecto al primer registro:\n", + "0 0 days 00:00:00\n", + "1 59 days 20:13:00\n", + "2 152 days 01:49:00\n", + "3 244 days 10:22:00\n", + "4 335 days 01:11:00\n", + "dtype: timedelta64[ns]\n", + "\n", + "En segundos (float):\n", + "0 0.0\n", + "1 5170380.0\n", + "2 13139340.0\n", + "3 21118920.0\n", + "4 28948260.0\n", + "dtype: float64\n" + ] + } + ], "source": [ "# Diferencias de tiempo — timedelta (§8.3.3)\n", "delta = fechas - fechas.iloc[0]\n", @@ -795,7 +1506,8 @@ "print(delta)\n", "\n", "print(\"\\nEn segundos (float):\")\n", - "print(delta.astype('timedelta64[s]').astype(float))" + "#print(delta.astype('timedelta64[s]').astype(float))\n", + "print( (fechas - fechas.iloc[0]).dt.total_seconds() )" ] }, { @@ -821,9 +1533,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas de temperatura y lluvia:\n", + " temp_C rain_mm\n", + "min -0.30 57.00\n", + "max 17.20 172.00\n", + "mean 8.26 110.33\n" + ] + } + ], "source": [ "# agg() con lista de métodos (§8.4)\n", "print(\"Estadísticas de temperatura y lluvia:\")\n", @@ -832,9 +1556,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm\n", + "min -0.30 NaN\n", + "max 17.20 NaN\n", + "mean 8.26 NaN\n", + "sum NaN 1324.0\n", + "median NaN 94.5\n" + ] + } + ], "source": [ "# agg() con diccionario — distintas métricas por columna (§8.4)\n", "resultado = df_clima[['temp_C', 'rain_mm']].agg({\n", @@ -846,9 +1583,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rango (max - min) por columna numérica:\n", + "temp_C 17.5\n", + "rain_mm 115.0\n", + "dtype: float64\n" + ] + } + ], "source": [ "# apply() — función personalizada por columna (§8.4)\n", "def rango(col):\n", @@ -870,9 +1618,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -895,9 +1654,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# df.hist() — histogramas de todas las columnas numéricas (§8.5)\n", "df_clima.hist(figsize=(8, 4))\n", @@ -908,9 +1678,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# value_counts().plot() — histograma de datos categóricos (§8.5)\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", @@ -932,9 +1713,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reporte climático mensual:\n", + " jan: -0.3°C, 59 mm de lluvia\n", + " feb: 0.4°C, 57 mm de lluvia\n", + " mar: 3.9°C, 84 mm de lluvia\n", + " apr: 7.4°C, 100 mm de lluvia\n", + " may: 12.0°C, 143 mm de lluvia\n", + " jun: 15.0°C, 153 mm de lluvia\n", + " jul: 17.2°C, 172 mm de lluvia\n", + " aug: 16.8°C, 164 mm de lluvia\n", + " sep: 13.1°C, 135 mm de lluvia\n", + " oct: 9.1°C, 89 mm de lluvia\n", + " nov: 3.7°C, 88 mm de lluvia\n", + " dec: 0.8°C, 80 mm de lluvia\n" + ] + } + ], "source": [ "# iterrows() — iterar sobre filas (§8.7)\n", "print(\"Reporte climático mensual:\")\n", @@ -944,9 +1745,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperatura media según si llueve ≥ 100 mm:\n", + "rain_mm\n", + "False 2.93\n", + "True 13.58\n", + "Name: temp_C, dtype: float64\n" + ] + } + ], "source": [ "# groupby() con condición booleana (§8.7)\n", "print(\"Temperatura media según si llueve ≥ 100 mm:\")\n", @@ -955,9 +1768,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meses cálidos (> 10°C):\n", + " temp_C temp_F rain_mm\n", + "jul 17.2 62.96 172\n", + "aug 16.8 62.24 164\n", + "jun 15.0 59.00 153\n", + "sep 13.1 55.58 135\n", + "may 12.0 53.60 143\n" + ] + } + ], "source": [ "# Encadenar métodos — flujo de trabajo legible\n", "resumen = (df_clima\n", @@ -988,11 +1815,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " VALORES DE PRECIPITACIÓN ANUAL\n", + " ESTACIÓN ANTIGUA GUATEMALA\n", + "\n", + " VALORES: \n", + " [[ 2.1 1.5 12.4]\n", + " [ 35.8 150.2 250.4]\n", + " [180.1 195.3 280.9]\n", + " [160.4 25.2 5.1]] \n", + "\n", + " FECHAS: \n", + " [['Enero' 'Febrero' 'Marzo']\n", + " ['Abril' 'Mayo' 'Junio']\n", + " ['Julio' 'Agosto' 'Septiembre']\n", + " ['Octubre' 'Noviembre' 'Diciembre']]\n", + "------------------------------------------------------------\n", + " ESTADÍSTICAS\n", + "MEDIA: 108.3 mm\n", + "MÍNIMO: 1.5 mm\n", + "ÍNDICE DEL VALOR MÁXIMO: 8\n", + "DÍA CON MAYOR PRECIPITACIÓN: 280.9 mm\n", + "------------------------------------------------------------\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "# Valores de la Estación Antigua Guatemala. lluvias en mm\n", + "lluvias = [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "meses = ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre']\n", + "\n", + "# Arreglo de matriz\n", + "a = np.array(lluvias[:12])\n", + "b = np.array(meses[:12])\n", + "\n", + "d = a.reshape(4, 3)\n", + "F = b.reshape(4, 3)\n", + "\n", + "print(\"--\" * 30)\n", + "print(\" \" * 15 + \"VALORES DE PRECIPITACIÓN ANUAL\")\n", + "print(\" \" * 17 + \"ESTACIÓN ANTIGUA GUATEMALA\")\n", + "\n", + "print(f\"\\n VALORES: \\n {d} \\n\\n FECHAS: \\n {F}\")\n", + "print(\"--\" * 30)\n", + "\n", + "print(\" \" * 20 + \"ESTADÍSTICAS\")\n", + "\n", + "print(f'MEDIA: {a.mean():.1f} mm')\n", + "print(f'MÍNIMO: {a.min():.1f} mm')\n", + "print('ÍNDICE DEL VALOR MÁXIMO:', a.argmax())\n", + "# Usamos max(a) o a.max()\n", + "print(f'DÍA CON MAYOR PRECIPITACIÓN: {a.max()} mm')\n", + "print(\"--\" * 30)\n", + "\n" ] }, { @@ -1006,11 +1895,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " DATOS REPRESENTATIVOS DE TEMPERATURA (°C) PARA ANTIGUA GUATEMALA\n", + " Mes Temp_Min Temp_Max Lluvia_mm\n", + "0 Enero 10.5 23.8 2.1\n", + "1 Febrero 11.2 25.1 1.5\n", + "2 Marzo 12.8 26.7 12.4\n", + "3 Abril 14.5 27.5 35.8\n", + "4 Mayo 15.8 26.8 150.2\n", + "5 Junio 16.2 25.4 250.4\n", + "6 Julio 15.9 25.1 180.1\n", + "7 Agosto 15.7 25.3 195.3\n", + "8 Septiembre 15.8 24.8 280.9\n", + "9 Octubre 14.9 24.2 160.4\n", + "10 Noviembre 13.1 23.9 25.2\n", + "11 Diciembre 11.4 23.5 5.1\n", + "------------------------------------------------------------\n", + " MESES CON TEMPERATURA ENTRE 5°C Y 15°C: \n", + " Mes Temp_Min\n", + "0 Enero 10.5\n", + "1 Febrero 11.2\n", + "2 Marzo 12.8\n", + "3 Abril 14.5\n", + "9 Octubre 14.9\n", + "10 Noviembre 13.1\n", + "11 Diciembre 11.4\n", + "------------------------------------------------------------\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "# Datos representativos de temperatura (°C) para Antigua Guatemala\n", + "datos = {\n", + " 'Mes': ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'],\n", + " 'Temp_Min': [10.5, 11.2, 12.8, 14.5, 15.8, 16.2, 15.9, 15.7, 15.8, 14.9, 13.1, 11.4],\n", + " 'Temp_Max': [23.8, 25.1, 26.7, 27.5, 26.8, 25.4, 25.1, 25.3, 24.8, 24.2, 23.9, 23.5],\n", + " 'Lluvia_mm': [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "}\n", + "\n", + "df_clima = pd.DataFrame(datos)\n", + "filtro = (df_clima [ 'Temp_Min'] >=5) & (df_clima[ 'Temp_Min'] <= 15)\n", + "subconjunto = df_clima[filtro]\n", + "Luvia_total = subconjunto [ 'Lluvia_mm'].sum()\n", + "print(\"--\" * 30)\n", + "\n", + "print(\" \" * 1 + \"DATOS REPRESENTATIVOS DE TEMPERATURA (°C) PARA ANTIGUA GUATEMALA\")\n", + "print(df_clima)\n", + "print(\"--\" * 30)\n", + "print (f\" MESES CON TEMPERATURA ENTRE 5°C Y 15°C: \\n {subconjunto[['Mes','Temp_Min' ]]}\")\n", + "print(\"--\" * 30)\n" ] }, { @@ -1024,11 +1966,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " EL MES CON MAYOR AMPLITUD TÉRMINCA \n", + " MES: Febrero\n", + "------------------------------------------------------------\n", + " LA MAYOR AMPLITUD TÉRMINCA \n", + " AMPLITUD: 13.9 °C\n", + "------------------------------------------------------------\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "# Datos representativos de temperatura (°C) para Antigua Guatemala\n", + "datos = {\n", + " 'Mes': ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'],\n", + " 'Temp_Min': [10.5, 11.2, 12.8, 14.5, 15.8, 16.2, 15.9, 15.7, 15.8, 14.9, 13.1, 11.4],\n", + " 'Temp_Max': [23.8, 25.1, 26.7, 27.5, 26.8, 25.4, 25.1, 25.3, 24.8, 24.2, 23.9, 23.5],\n", + " 'Lluvia_mm': [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "}\n", + "\n", + "df_clima = pd.DataFrame(datos)\n", + "\n", + "df_clima['amplitud_termica'] = df_clima['Temp_Max'] - df_clima['Temp_Min']\n", + "\n", + "indice_max = df_clima['amplitud_termica'].idxmax()\n", + "\n", + "mes_max = df_clima.loc[indice_max]\n", + "print(\"--\" * 30)\n", + "print(\" \" * 2 +\" EL MES CON MAYOR AMPLITUD TÉRMINCA \")\n", + "print(f\" MES: {mes_max['Mes']}\")\n", + "print(\"--\" * 30)\n", + "print(\" \" * 2 +\" LA MAYOR AMPLITUD TÉRMINCA \")\n", + "print(f\" AMPLITUD: {mes_max['amplitud_termica']:.1f} °C\")\n", + "print(\"--\" * 30)\n" ] }, { @@ -1044,11 +2023,243 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + "DataFrame con valores faltantes:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Herlinda 90.0 NaN NaN\n", + "1 Rafael 80.0 65.0 71.0\n", + "2 Irinea NaN 70.0 NaN\n", + "3 Magaly 88.0 80.0 90.0\n", + "4 Adán NaN NaN 76.0\n", + "------------------------------------------------------------\n", + "Cantidad de nulos por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n", + "------------------------------------------------------------\n", + " RELLENAR CON LA MEDIA EN CADA COLUMNA:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Herlinda 90.0 71.7 79.0\n", + "1 Rafael 80.0 65.0 71.0\n", + "2 Irinea 86.0 70.0 79.0\n", + "3 Magaly 88.0 80.0 90.0\n", + "4 Adán 86.0 71.7 76.0\n", + "------------------------------------------------------------\n", + " NOTA PROMEDIO FINAL DE CADA ESTUDIANTE:\n", + " Nombre Promedio\n", + "0 Herlinda 90.0\n", + "1 Rafael 72.0\n", + "2 Irinea 70.0\n", + "3 Magaly 86.0\n", + "4 Adán 76.0\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "# Crear DataFrame con datos faltantes (NaN = Not a Number)\n", + "Informacion = pd.DataFrame({\n", + " \"Nombre\": [\"Herlinda\", \"Rafael\", \"Irinea\", \"Magaly\", \"Adán\"],\n", + " \"Nota1\": [90, 80, np.nan, 88, np.nan],\n", + " \"Nota2\": [np.nan, 65, 70, 80, np.nan],\n", + " \"Nota3\": [np.nan, 71, np.nan, 90, 76]\n", + "})\n", + "\n", + "NOTA1 = Informacion[\"Nota1\"]\n", + "NOTA2 = Informacion[\"Nota2\"]\n", + "NOTA3 = Informacion[\"Nota3\"]\n", + "print(\"--\" * 30)\n", + "print(\"DataFrame con valores faltantes:\")\n", + "print(Informacion)\n", + "print(\"--\" * 30)\n", + "print(\"Cantidad de nulos por columna:\")\n", + "print(datos_incompletos.isnull().sum())\n", + "print(\"--\" * 30)\n", + "\n", + "df_rellenado = Informacion .copy()\n", + "for col in [\"Nota1\", \"Nota2\", \"Nota3\"]:\n", + " df_rellenado[col] = df_rellenado[col].fillna(df_rellenado[col].mean())\n", + "\n", + "print(\" RELLENAR CON LA MEDIA EN CADA COLUMNA:\")\n", + "print(df_rellenado.round(1))\n", + "\n", + "print(\"--\" * 30)\n", + "print(\" NOTA PROMEDIO FINAL DE CADA ESTUDIANTE:\")\n", + "Informacion[\"Promedio\"] = Informacion[[\"Nota1\", \"Nota2\", \"Nota3\"]].mean(axis=1)\n", + "Informacion[\"Promedio\"] = Informacion[\"Promedio\"].round(3)\n", + "print(Informacion[[\"Nombre\", \"Promedio\"]])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pandas versión: 2.1.4\n", + " Año Mes Costo_Diario_Q Costo_Mensual_Q\n", + "0 2020 Diciembre 99.65 2989.38\n", + "1 2021 Enero 99.49 2984.73\n", + "2 2021 Febrero 99.58 2987.39\n", + "3 2021 Marzo 99.27 2978.10\n", + "4 2021 Abril 99.72 2991.70\n", + "5 2021 Mayo 99.77 2993.03\n", + "6 2021 Junio 99.99 2999.67\n", + "7 2021 Julio 100.11 3003.32\n", + "8 2021 Agosto 100.42 3012.61\n", + "9 2021 Septiembre 100.85 3025.55\n", + "10 2021 Octubre 101.84 3055.09\n", + "11 2021 Noviembre 102.74 3082.30\n", + "12 2021 Diciembre 103.24 3097.23\n", + "13 2022 Enero 103.67 3110.18\n", + "14 2022 Febrero 104.48 3134.40\n", + "15 2022 Marzo 106.05 3181.53\n", + "16 2022 Abril 107.27 3218.03\n", + "17 2022 Mayo 107.82 3234.62\n", + "18 2022 Junio 110.40 3311.95\n", + "19 2022 Julio 112.32 3369.69\n", + "20 2022 Agosto 115.17 3454.98\n", + "21 2022 Septiembre 117.96 3538.94\n", + "22 2022 Octubre 121.13 3633.85\n", + "23 2022 Noviembre 120.62 3618.58\n", + "24 2022 Diciembre 121.14 3634.18\n", + "25 2023 Enero 121.27 3638.16\n", + "26 2023 Febrero 123.20 3695.91\n", + "27 2023 Marzo 124.28 3728.43\n", + "28 2023 Abril 124.20 3726.11\n", + "29 2023 Mayo 124.39 3731.75\n", + "30 2023 Junio 124.52 3735.73\n", + "31 2023 Julio 125.50 3764.93\n", + "32 2023 Agosto 126.99 3809.73\n", + "33 2023 Septiembre 127.50 3825.00\n", + "34 2023 Octubre 131.24 3937.17\n", + "35 2023 Noviembre 129.99 3899.67\n", + "36 2023 Diciembre 130.17 3904.98\n", + "------------------------------------------------------------\n", + "COSTO PROMEDIO MENDUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\n", + "Año\n", + "2020 2989.380000\n", + "2021 3017.560000\n", + "2022 3370.077500\n", + "2023 3783.130833\n", + "Name: Costo_Mensual_Q, dtype: float64\n", + "------------------------------------------------------------\n", + "COSTO PROMEDIO MENSUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\n", + "Mes\n", + "Abril 3311.946667\n", + "Agosto 3425.773333\n", + "Diciembre 3406.442500\n", + "Enero 3244.356667\n", + "Febrero 3272.566667\n", + "Julio 3379.313333\n", + "Junio 3349.116667\n", + "Marzo 3296.020000\n", + "Mayo 3319.800000\n", + "Noviembre 3533.516667\n", + "Octubre 3542.036667\n", + "Septiembre 3463.163333\n", + "Name: Costo_Mensual_Q, dtype: float64\n", + "------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TAREA 5(OPCIONAL) - PANDAS EN PRÁCTICA \n", + "# Dataset real (opcional, puntos estra)\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np \n", + "print(\"Pandas versión:\", pd.__version__)\n", + "\n", + "# (LECTURA) Diccionario de la Canasta Básica Alimentaria (2020-2023)Obtenida del INE, Guatemala\n", + "df_Canasta_Basica = {\n", + " 'Año': [\n", + " 2020, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021,\n", + " 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022,\n", + " 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023\n", + " ],\n", + " 'Mes': [\n", + " 'Diciembre', 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre',\n", + " 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre',\n", + " 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'\n", + " ],\n", + " 'Costo_Diario_Q': [\n", + " 99.65, 99.49, 99.58, 99.27, 99.72, 99.77, 99.99, 100.11, 100.42, 100.85, 101.84, 102.74, 103.24,\n", + " 103.67, 104.48, 106.05, 107.27, 107.82, 110.40, 112.32, 115.17, 117.96, 121.13, 120.62, 121.14,\n", + " 121.27, 123.20, 124.28, 124.20, 124.39, 124.52, 125.50, 126.99, 127.50, 131.24, 129.99, 130.17\n", + " ],\n", + " 'Costo_Mensual_Q': [\n", + " 2989.38, 2984.73, 2987.39, 2978.10, 2991.70, 2993.03, 2999.67, 3003.32, 3012.61, 3025.55, 3055.09, 3082.30, 3097.23,\n", + " 3110.18, 3134.40, 3181.53, 3218.03, 3234.62, 3311.95, 3369.69, 3454.98, 3538.94, 3633.85, 3618.58, 3634.18,\n", + " 3638.16, 3695.91, 3728.43, 3726.11, 3731.75, 3735.73, 3764.93, 3809.73, 3825.00, 3937.17, 3899.67, 3904.98\n", + " ]\n", + "}\n", + "\n", + "# (LIMPIEZA) UTILIZANDO .groupby\n", + "df_Canasta_Basica = pd.DataFrame(df_Canasta_Basica )\n", + "print(df_Canasta_Basica) \n", + "print(\"--\" * 30)\n", + "promedio_anual = df_Canasta_Basica.groupby('Año')['Costo_Mensual_Q'].mean()\n", + "print(\"COSTO PROMEDIO MENDUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\")\n", + "print(promedio_anual)\n", + "print(\"--\" * 30)\n", + "promedio_mensual = df_Canasta_Basica.groupby('Mes')['Costo_Mensual_Q'].mean()\n", + "print(\"COSTO PROMEDIO MENSUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\")\n", + "print(promedio_mensual)\n", + "print(\"--\" * 30)\n", + "\n", + "\n", + "# (VISUALIZACIÓN) \n", + "# df.plot() — gráfica de línea (§8.5)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n", + "promedio_anual.plot(kind='line', ax=ax, marker='o', color='blue')\n", + "\n", + "ax.set_title('PROMEDIO ANUAL DE LA CANASTA BÁSICA EN GUATEMALA 2020-2023')\n", + "ax.set_ylabel('Quetzales')\n", + "ax.set_xlabel('Año')\n", + "ax.set_xticks([2020, 2021, 2022, 2023])\n", + "\n", + "ax.set_ylim(2800, 4000)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# TU CÓDIGO AQUÍ\n" + "CONCLUSIONES: \n", + "Sobre las Canasta básica en Guatemala\n", + "El promedio en la cansta básica en Guatemala, relacionando los promedios anuales obtenidos con la gráfica, la canasta básica ha ido aumentando Q 200.00 desde el año 2021.\n", + "En julio de 2021 un año y dos meses después de la pandemia de COVID-19, la canasta básica aumentó a Q100.11, en Guatemala." ] }, { From 253a701f8f90f874d0804742674e2a882ce87553 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 1 May 2026 12:29:06 -0600 Subject: [PATCH 17/28] =?UTF-8?q?actulizaci=C3=B3n=20de=20=20hoja=20de=20t?= =?UTF-8?q?rabajo=204=20y=205?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ejemplos/python/Numpy.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/ejemplos/python/Numpy.ipynb b/ejemplos/python/Numpy.ipynb index b3e9d69..93bfdaa 100644 --- a/ejemplos/python/Numpy.ipynb +++ b/ejemplos/python/Numpy.ipynb @@ -2012,7 +2012,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2076,7 +2076,8 @@ "print(f\" \\n POSICIÓN t1 = 2.5 s: \\n y(t1) = {posicion_t1:.5f} m\")\n", "print(\" \" * 30)\n", "print(f\" \\n POSICIÓN t2 = 6.0 s:\\n y(t2) = {posicion_t2:.5f} m\")\n", - "print(\"---\" * 30)\n" + "print(\"---\" * 30)\n", + " " ] } ], From 124d068bf8ef4e56fdbdbf293306156ee5ff5cbc Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 1 May 2026 13:13:40 -0600 Subject: [PATCH 18/28] Agregando soluciones de arrays y ejercicios faltantes --- ejemplos/python/Numpy.ipynb | 4 ++-- ejemplos/python/Pandas.ipynb | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ejemplos/python/Numpy.ipynb b/ejemplos/python/Numpy.ipynb index 93bfdaa..dc2642e 100644 --- a/ejemplos/python/Numpy.ipynb +++ b/ejemplos/python/Numpy.ipynb @@ -1847,7 +1847,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1909,7 +1909,7 @@ "print(\"---\" * 30)\n", "\n", "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 300 pt.s: \\n {a[a > 300]}\")\n", - "print(\"---\" * 30)\n", + "print(\"---\" * 30) \n", "\n" ] }, diff --git a/ejemplos/python/Pandas.ipynb b/ejemplos/python/Pandas.ipynb index 36aeedb..016286c 100644 --- a/ejemplos/python/Pandas.ipynb +++ b/ejemplos/python/Pandas.ipynb @@ -2101,7 +2101,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2247,7 +2247,7 @@ "\n", "plt.tight_layout()\n", "plt.show()\n", - "\n", + " \n", "\n", "\n" ] From 38267766ae945f807d95cc824000ec1452e885a5 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 1 May 2026 13:21:02 -0600 Subject: [PATCH 19/28] Solucion final verificada 1 de mayo --- tareas/Numpy.ipynb | 2105 ++++++++++++++++++++++++++++++++++++++ tareas/Pandas.ipynb | 2315 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 4420 insertions(+) create mode 100644 tareas/Numpy.ipynb create mode 100644 tareas/Pandas.ipynb diff --git a/ tareas/Numpy.ipynb b/ tareas/Numpy.ipynb new file mode 100644 index 0000000..dc2642e --- /dev/null +++ b/ tareas/Numpy.ipynb @@ -0,0 +1,2105 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NumPy — Computación Numérica en Python\n", + "\n", + "## ¿Qué es NumPy?\n", + "\n", + "**NumPy** (Numerical Python) es la librería fundamental para computación científica en Python. Proporciona:\n", + "\n", + "- Un objeto de arreglo (array) multidimensional de alto rendimiento\n", + "- Herramientas para trabajar con estos arreglos\n", + "- Funciones matemáticas, lógicas, de ordenamiento, estadísticas, y más\n", + "\n", + "## ¿Para qué sirve?\n", + "\n", + "| Tarea | Sin NumPy | Con NumPy |\n", + "|---|---|---|\n", + "| Sumar dos listas de 1 millón de elementos | ~0.5 segundos | ~0.005 segundos |\n", + "| Multiplicar matrices | Código complejo con loops | Una línea |\n", + "| Operaciones matemáticas | Loop por cada elemento | Operaciones vectorizadas |\n", + "\n", + "NumPy es la base de casi todas las librerías de ciencia de datos: Pandas, Matplotlib, Scikit-learn, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Instalación e Importación\n", + "\n", + "Para instalar NumPy (si no está instalado):\n", + "```bash\n", + "pip install numpy\n", + "```\n", + "\n", + "Por convención, se importa con el alias `np`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumPy versión: 1.26.4\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(\"NumPy versión:\", np.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 1 — Conceptos Básicos: Arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ¿Qué diferencia hay entre una lista de Python y un array de NumPy?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista Python: [1, 2, 3, 4, 5]\n", + "Tipo: \n", + "\n", + "Array NumPy: [1 2 3 4 5]\n", + "Tipo: \n" + ] + } + ], + "source": [ + "# Lista de Python\n", + "lista = [1, 2, 3, 4, 5]\n", + "print(\"Lista Python:\", lista)\n", + "print(\"Tipo:\", type(lista))\n", + "\n", + "print()\n", + "\n", + "# Array de NumPy\n", + "arreglo = np.array([1, 2, 3, 4, 5])\n", + "print(\"Array NumPy:\", arreglo)\n", + "print(\"Tipo:\", type(arreglo))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista * 3: [1, 2, 3, 1, 2, 3, 1, 2, 3]\n", + "Array * 3: [3 6 9]\n" + ] + } + ], + "source": [ + "# La diferencia clave: operaciones elemento a elemento\n", + "lista_python = [1, 2, 3]\n", + "\n", + "# Con listas, el operador * repite la lista\n", + "print(\"Lista * 3:\", lista_python * 3) # [1, 2, 3, 1, 2, 3, 1, 2, 3]\n", + "\n", + "arreglo_np = np.array([1, 2, 3])\n", + "\n", + "# Con NumPy, * multiplica cada elemento\n", + "print(\"Array * 3:\", arreglo_np * 3) # [3, 6, 9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ¿Por qué NumPy es tan rápido? — Comparación de rendimiento\n", + "\n", + "NumPy implementa las operaciones sobre arrays en **código C precompilado**. La misma operación que requiere un ciclo `for` en Python se ejecuta internamente sin ciclos explícitos. Esto se conoce como *código vectorizado* (Stewart & Mommert, §4.1)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista Python : 5.741 segundos\n", + "Array NumPy : 1.110 segundos\n", + "NumPy es 5 × más rápido\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "N = 10_000_000 # 10 millones de elementos\n", + "\n", + "# ── Enfoque con lista Python ──────────────────────────────────────────────────\n", + "a_lista = list(range(N))\n", + "t0 = time.time()\n", + "b_lista = []\n", + "for i in range(len(a_lista)):\n", + " b_lista.append(a_lista[i] + 1)\n", + "t_lista = time.time() - t0\n", + "\n", + "# ── Enfoque con array NumPy ───────────────────────────────────────────────────\n", + "a_np = np.array(a_lista)\n", + "t0 = time.time()\n", + "b_np = a_np + 1 # ← una sola línea, sin ciclo explícito\n", + "t_numpy = time.time() - t0\n", + "\n", + "print(f\"Lista Python : {t_lista:.3f} segundos\")\n", + "print(f\"Array NumPy : {t_numpy:.3f} segundos\")\n", + "print(f\"NumPy es {t_lista / t_numpy:.0f} × más rápido\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Crear arrays de diferentes formas" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ceros: [0. 0. 0. 0. 0.]\n", + "Unos : [1. 1. 1. 1. 1.]\n", + "Vacío: [1.26207700e-315 0.00000000e+000 1.27198647e-315 1.27148252e-315\n", + " 0.00000000e+000]\n", + "\n", + "Arange (0 a 10, paso 2): [0 2 4 6 8]\n", + "Linspace (6 puntos entre 0 y 1): [0. 0.2 0.4 0.6 0.8 1. ]\n", + "Logspace (10^0 a 10^3): [ 1. 10. 100. 1000.]\n", + "\n", + "zeros_like(base): [0 0 0 0 0]\n", + "ones_like(base) : [1 1 1 1 1]\n" + ] + } + ], + "source": [ + "# Array de ceros y unos\n", + "ceros = np.zeros(5)\n", + "unos = np.ones(5)\n", + "vacio = np.empty(5) # sin inicializar — muy rápido, valores arbitrarios\n", + "print(\"Ceros:\", ceros)\n", + "print(\"Unos :\", unos)\n", + "print(\"Vacío:\", vacio)\n", + "\n", + "# Array con rango (como range(), pero retorna array)\n", + "rango = np.arange(0, 10, 2) # inicio, fin (exclusivo), paso\n", + "print(\"\\nArange (0 a 10, paso 2):\", rango)\n", + "\n", + "# N valores igualmente espaciados — escala lineal\n", + "espacio = np.linspace(0, 1, 6) # inicio, fin (inclusivo), N puntos\n", + "print(\"Linspace (6 puntos entre 0 y 1):\", espacio)\n", + "\n", + "# N valores en escala logarítmica (base 10 por defecto)\n", + "log_esp = np.logspace(0, 3, 4) # 10^0 a 10^3, 4 valores\n", + "print(\"Logspace (10^0 a 10^3):\", log_esp)\n", + "\n", + "# Constructores \"look-alike\": misma forma que otro array\n", + "base = np.array([1, 2, 3, 4, 5])\n", + "print(\"\\nzeros_like(base):\", np.zeros_like(base))\n", + "print(\"ones_like(base) :\", np.ones_like(base))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5,)\n", + "1\n" + ] + } + ], + "source": [ + "print(base.shape)\n", + "print(base.ndim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Arrays 2D (matrices)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matriz:\n", + "[[1 2 3]\n", + " [4 5 6]\n", + " [7 8 9]]\n", + "\n", + "Forma (filas x columnas): (3, 3)\n", + "Número de dimensiones: 2\n", + "Total de elementos: 9\n", + "Tipo de dato: int64\n" + ] + } + ], + "source": [ + "# Crear una matriz 2D\n", + "matriz = np.array([\n", + " [1, 2, 3],\n", + " [4, 5, 6],\n", + " [7, 8, 9]\n", + "])\n", + "\n", + "print(\"Matriz:\")\n", + "print(matriz)\n", + "print(\"\\nForma (filas x columnas):\", matriz.shape)\n", + "print(\"Número de dimensiones:\", matriz.ndim)\n", + "print(\"Total de elementos:\", matriz.size)\n", + "print(\"Tipo de dato:\", matriz.dtype)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tipos de datos (`dtype`) y atributos del array\n", + "\n", + "Todos los elementos de un array NumPy tienen el **mismo tipo de dato** (`dtype`). NumPy infiere el tipo al crear el array, pero puedes especificarlo. Tipos comunes: `int64`, `float64`, `complex128`, `bool`. Puedes convertir con `.astype()` (Stewart & Mommert, §4.1.1)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dtype inferido: float64\n", + "dtype=int : [1 2 3] → int64\n", + "dtype=complex: [1.+0.j 2.+0.j 3.+0.j] → complex128\n", + "\n", + "x_int.astype(float): [1. 2. 3.] → float64\n", + "\n", + "Array m:\n", + "[[1 2 3]\n", + " [4 5 6]]\n", + " ndim : 2\n", + " shape: (2, 3)\n", + " size : 6\n", + " dtype: int64\n" + ] + } + ], + "source": [ + "x = np.array([1, 2, 3.268999]) # Python convierte todo a float64\n", + "print(\"dtype inferido:\", x.dtype)\n", + "\n", + "# Especificar dtype manualmente\n", + "x_int = np.array([1.5566666666, 2, 3], dtype=int)\n", + "x_cpx = np.array([1, 2, 3], dtype=complex)\n", + "print(\"dtype=int :\", x_int, \"→\", x_int.dtype)\n", + "print(\"dtype=complex:\", x_cpx, \"→\", x_cpx.dtype)\n", + "\n", + "# Convertir dtype con astype()\n", + "x_float = x_int.astype(float)\n", + "print(\"\\nx_int.astype(float):\", x_float, \"→\", x_float.dtype)\n", + "\n", + "# Atributos importantes de un array\n", + "m = np.array([[1, 2, 3], [4, 5, 6]])\n", + "print(\"\\nArray m:\")\n", + "print(m)\n", + "print(\" ndim :\", m.ndim) # número de dimensiones\n", + "print(\" shape:\", m.shape) # (filas, columnas)\n", + "print(\" size :\", m.size) # total de elementos\n", + "print(\" dtype:\", m.dtype) # tipo de dato" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 2 — Indexación y Operaciones" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Acceder a elementos (indexación)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arreglo: [10 20 30 40 50]\n", + "Primer elemento (índice 0): 10\n", + "Último elemento: 50\n", + "Elementos del índice 1 al 3: [20 30 40]\n", + "Cada dos elementos: [10 30 50]\n" + ] + } + ], + "source": [ + "v = np.array([10, 20, 30, 40, 50])\n", + "\n", + "print(\"Arreglo:\", v)\n", + "print(\"Primer elemento (índice 0):\", v[0])\n", + "print(\"Último elemento:\", v[-1])\n", + "print(\"Elementos del índice 1 al 3:\", v[1:4])\n", + "print(\"Cada dos elementos:\", v[::2])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matriz:\n", + "[[1 2 3]\n", + " [4 5 6]\n", + " [7 8 9]]\n", + "\n", + "Elemento fila 0, columna 1: 2\n", + "Toda la primera fila: [1 2 3]\n", + "Toda la segunda columna: [2 5 8]\n", + "\n", + "Submatriz (filas 0-1, columnas 1-2):\n", + "[[2 3]\n", + " [5 6]]\n" + ] + } + ], + "source": [ + "# Indexación en matrices 2D\n", + "m = np.array([\n", + " [1, 2, 3],\n", + " [4, 5, 6],\n", + " [7, 8, 9]\n", + "])\n", + "\n", + "print(\"Matriz:\")\n", + "print(m)\n", + "print(\"\\nElemento fila 0, columna 1:\", m[0, 1]) # 2\n", + "print(\"Toda la primera fila:\", m[0, :]) # [1, 2, 3]\n", + "print(\"Toda la segunda columna:\", m[:, 1]) # [2, 5, 8]\n", + "print(\"\\nSubmatriz (filas 0-1, columnas 1-2):\")\n", + "print(m[0:2, 1:3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indexación booleana (filtrado)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[72 88 91 78]\n", + "Todas las notas: [55 72 88 45 91 63 78]\n", + "Notas > 70: [72 88 91 78]\n", + "Cantidad de aprobados: 4\n", + "Notas en zona (61-70): [63]\n" + ] + } + ], + "source": [ + "notas = np.array([55, 72, 88, 45, 91, 63, 78])\n", + "\n", + "print(notas[notas > 70])\n", + "# ¿Cuáles notas son mayores de 70?\n", + "aprobados = notas[notas > 70]\n", + "print(\"Todas las notas:\", notas)\n", + "print(\"Notas > 70:\", aprobados)\n", + "print(\"Cantidad de aprobados:\", aprobados.shape[0])\n", + "\n", + "# Condición compuesta\n", + "zona = notas[(notas >= 61) & (notas <= 70)]\n", + "print(\"Notas en zona (61-70):\", zona)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operaciones matemáticas" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a + b = [11 22 33 44]\n", + "a * b = [ 10 40 90 160]\n", + "b / a = [10. 10. 10. 10.]\n", + "a ** 2 = [ 1 4 9 16]\n", + "\n", + "Raíz cuadrada de b: [3.16227766 4.47213595 5.47722558 6.32455532]\n", + "e^a: [ 2.71828183 7.3890561 20.08553692 54.59815003]\n" + ] + } + ], + "source": [ + "a = np.array([1, 2, 3, 4])\n", + "b = np.array([10, 20, 30, 40])\n", + "\n", + "print(\"a + b =\", a + b)\n", + "print(\"a * b =\", a * b)\n", + "print(\"b / a =\", b / a)\n", + "print(\"a ** 2 =\", a ** 2) # Elevar al cuadrado\n", + "print(\"\\nRaíz cuadrada de b:\", np.sqrt(b))\n", + "print(\"e^a:\", np.exp(a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Broadcasting — reglas para operar arrays de distinta forma\n", + "\n", + "Cuando aplicas una operación entre un array y un escalar (o dos arrays de forma distinta), NumPy ajusta los tamaños automáticamente siguiendo **dos reglas** (Stewart & Mommert, §4.1.4):\n", + "\n", + "1. **Regla 1** — Si los arrays tienen distinto número de dimensiones, la forma del más pequeño se *extiende* a la izquierda con \"1\" hasta igualar.\n", + "2. **Regla 2** — Un eje de tamaño 1 se comporta como si tuviera el tamaño del eje más grande en esa dimensión (se \"replica\").\n", + "\n", + "```\n", + "array → [1, 2, 3, 4, 5] forma: (5,)\n", + "escalar → 10 forma: () → (1,) → (5,) [reglas 1 y 2]\n", + "resultado→ [11, 12, 13, 14, 15]\n", + "```\n", + "\n", + "Si dos ejes no son 1 y son distintos entre sí → **ValueError** (no se puede hacer broadcast)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Array original: [1 2 3 4 5]\n", + "v + 10 : [11 12 13 14 15]\n", + "v * 3 : [ 3 6 9 12 15]\n", + "v ** 2 : [ 1 4 9 16 25]\n", + "v / 2 : [0.5 1. 1.5 2. 2.5]\n", + "v - 1 : [0 1 2 3 4]\n", + "\n", + "Matriz + 100:\n", + "[[101 102 103]\n", + " [104 105 106]]\n", + "\n", + "Matriz * 2:\n", + "[[ 2 4 6]\n", + " [ 8 10 12]]\n" + ] + } + ], + "source": [ + "v = np.array([1, 2, 3, 4, 5])\n", + "\n", + "print(\"Array original:\", v)\n", + "print(\"v + 10 :\", v + 10) # suma 10 a cada elemento\n", + "print(\"v * 3 :\", v * 3) # multiplica cada elemento por 3\n", + "print(\"v ** 2 :\", v ** 2) # eleva al cuadrado cada elemento\n", + "print(\"v / 2 :\", v / 2) # divide cada elemento entre 2\n", + "print(\"v - 1 :\", v - 1)\n", + "\n", + "# También funciona con matrices\n", + "m = np.array([[1, 2, 3],\n", + " [4, 5, 6]])\n", + "print(\"\\nMatriz + 100:\")\n", + "print(m + 100)\n", + "print(\"\\nMatriz * 2:\")\n", + "print(m * 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Funciones matemáticas (ufuncs)\n", + "\n", + "NumPy tiene versiones de las funciones matemáticas de `math` que funcionan directamente sobre **arrays completos** sin necesidad de un ciclo `for`. Estas se llaman *ufuncs* (universal functions)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x : [0 1 2 3 4 5]\n", + "np.sqrt(x) : [0. 1. 1.41421356 1.73205081 2. 2.23606798]\n", + "np.exp(x) : [ 1. 2.71828183 7.3890561 20.08553692 54.59815003\n", + " 148.4131591 ]\n", + "np.log(x+1): [0. 0.69314718 1.09861229 1.38629436 1.60943791 1.79175947]\n", + "np.abs(x-3): [3 2 1 0 1 2]\n", + "np.sign(x-3): [-1 -1 -1 0 1 1]\n" + ] + } + ], + "source": [ + "x = np.array([0, 1, 2, 3, 4, 5])\n", + "\n", + "print(\"x :\", x)\n", + "print(\"np.sqrt(x) :\", np.sqrt(x)) # raíz cuadrada de cada elemento\n", + "print(\"np.exp(x) :\", np.exp(x)) # e^x\n", + "print(\"np.log(x+1):\", np.log(x + 1)) # logaritmo natural (evitamos log(0))\n", + "print(\"np.abs(x-3):\", np.abs(x - 3)) # valor absoluto\n", + "print(\"np.sign(x-3):\", np.sign(x - 3)) # -1, 0 o 1 según el signo" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ángulos (rad): [0. 0.5236 0.7854 1.0472 1.5708]\n", + "sin : [0. 0.5 0.7071 0.866 1. ]\n", + "cos : [1. 0.866 0.7071 0.5 0. ]\n", + "\n", + "Velocidad vertical (v0=10): [ 0. 5. 7.071 8.66 10. ]\n" + ] + } + ], + "source": [ + "# Funciones trigonométricas (el argumento está en radianes)\n", + "angulos = np.array([0, np.pi/6, np.pi/4, np.pi/3, np.pi/2])\n", + "\n", + "print(\"Ángulos (rad):\", angulos.round(4))\n", + "print(\"sin :\", np.sin(angulos).round(4))\n", + "print(\"cos :\", np.cos(angulos).round(4))\n", + "\n", + "# Caso práctico: calcular la componente vertical de lanzamientos\n", + "# con distintos ángulos y velocidad inicial v0 = 10 m/s\n", + "v0 = 10\n", + "v_vertical = v0 * np.sin(angulos)\n", + "print(\"\\nVelocidad vertical (v0=10):\", v_vertical.round(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3b — Valores especiales y operadores lógicos\n", + "\n", + "### Valores especiales: `nan` e `inf`\n", + "\n", + "Algunas operaciones producen resultados especiales:\n", + "- `np.nan` (*Not a Number*): resultado indefinido, como `log(-1)` o `0/0`\n", + "- `np.inf` (*infinito*): resultado de dividir un número entre cero\n", + "\n", + "NumPy tiene versiones de las funciones estadísticas que **ignoran** los `nan`:\n", + "`np.nanmean`, `np.nansum`, `np.nanmax`, `np.nanmin`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "np.pi = 3.141592653589793\n", + "np.e = 2.718281828459045\n", + "np.inf = inf\n", + "np.nan = nan\n", + "\n", + "sqrt de [-4. -1. 0. 1. 4.] → [nan nan 0. 1. 2.]\n", + "\n", + "Notas con nan: [85. nan 72. nan 91.]\n", + "Media normal : nan\n", + "Media sin nan : 82.66666666666667\n", + "Suma sin nan : 248.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1283/3405442486.py:8: RuntimeWarning: invalid value encountered in sqrt\n", + " print(\"\\nsqrt de\", x, \"→\", np.sqrt(x)) # sqrt de negativos → nan\n" + ] + } + ], + "source": [ + "print(\"np.pi =\", np.pi)\n", + "print(\"np.e =\", np.e)\n", + "print(\"np.inf =\", np.inf)\n", + "print(\"np.nan =\", np.nan)\n", + "\n", + "# nan aparece en operaciones indefinidas\n", + "x = np.array([-4.0, -1.0, 0.0, 1.0, 4.0])\n", + "print(\"\\nsqrt de\", x, \"→\", np.sqrt(x)) # sqrt de negativos → nan\n", + "\n", + "# Funciones que ignoran nan\n", + "notas = np.array([85.0, np.nan, 72.0, np.nan, 91.0])\n", + "print(\"\\nNotas con nan:\", notas)\n", + "print(\"Media normal :\", np.mean(notas)) # ← devuelve nan\n", + "print(\"Media sin nan :\", np.nanmean(notas)) # ← ignora nan\n", + "print(\"Suma sin nan :\", np.nansum(notas))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operadores lógicos sobre arrays\n", + "\n", + "Puedes comparar un array con un valor y obtienes un array de booleanos (`True`/`False`).\n", + "Luego ese array booleano puede usarse como filtro para seleccionar elementos." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperaturas: [18.5 22. 35.1 15.3 28.7 12. 30. ]\n", + "temps > 25 : [False False True False True False True]\n", + "\n", + "Días calurosos (> 25°C): [35.1 28.7 30. ]\n", + "Días moderados (20-30°C): [22. 28.7 30. ]\n", + "\n", + "¿Cuántos días > 25?: 3\n", + "¿Algún día < 10? : False\n", + "¿Todos > 10? : True\n" + ] + } + ], + "source": [ + "temps = np.array([18.5, 22.0, 35.1, 15.3, 28.7, 12.0, 30.0])\n", + "\n", + "# Comparación → array de True/False\n", + "print(\"Temperaturas:\", temps)\n", + "print(\"temps > 25 :\", temps > 25)\n", + "\n", + "# Usar el array booleano para filtrar\n", + "calurosos = temps[temps > 25]\n", + "print(\"\\nDías calurosos (> 25°C):\", calurosos)\n", + "\n", + "# Condición compuesta: & (y) , | (o)\n", + "moderados = temps[(temps >= 20) & (temps <= 30)]\n", + "print(\"Días moderados (20-30°C):\", moderados)\n", + "\n", + "# Contar cuántos cumplen la condición\n", + "print(\"\\n¿Cuántos días > 25?:\", np.sum(temps > 25))\n", + "print(\"¿Algún día < 10? :\", np.any(temps < 10))\n", + "print(\"¿Todos > 10? :\", np.all(temps > 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `np.where` — asignar valores según condición\n", + "\n", + "`np.where(condición, valor_si_True, valor_si_False)` crea un array nuevo aplicando una regla a cada elemento. Es el equivalente vectorizado del operador ternario." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nota 55 → Reprobado\n", + " Nota 72 → Aprobado\n", + " Nota 88 → Aprobado\n", + " Nota 45 → Reprobado\n", + " Nota 91 → Aprobado\n", + " Nota 63 → Aprobado\n", + " Nota 78 → Aprobado\n", + "\n", + " Notas penalizadas: [ 0 72 88 0 91 63 78]\n" + ] + } + ], + "source": [ + "notas = np.array([55, 72, 88, 45, 91, 63, 78])\n", + "\n", + "# Etiquetar cada nota como \"Aprobado\" o \"Reprobado\"\n", + "estados = np.where(notas >= 61, \"Aprobado\", \"Reprobado\")\n", + "\n", + "for n, e in zip(notas, estados):\n", + " print(f\" Nota {n:3d} → {e}\")\n", + "\n", + "# También funciona con números\n", + "# Reemplazar notas menores a 61 con 0 (para penalización)\n", + "penalizadas = np.where(notas >= 61, notas, 0)\n", + "print(\"\\n Notas penalizadas:\", penalizadas)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Copias vs Vistas (*views*) — diferencia crítica con listas\n", + "\n", + "En NumPy existen **tres comportamientos** distintos al \"asignar\" un array:\n", + "\n", + "| Operación | Resultado | Modifica original |\n", + "|---|---|---|\n", + "| `b = a` | **alias** — misma memoria | Sí |\n", + "| `b = a[1:4]` | **vista** (*view*) — misma memoria | **Sí** ⚠️ |\n", + "| `b = a.copy()` | **copia** — memoria nueva | No |\n", + "\n", + "> **¡Diferencia clave con listas!** En Python, `l[1:4]` es siempre una copia. En NumPy, `a[1:4]` es una **vista** del array original — modificarla cambia el original. Esto es fuente frecuente de errores (Stewart & Mommert, §4.1.2)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Alias modifica 'original': [999 2 3 4 5]\n", + "Slice (vista) modifica 'original2': [ 1 777 3 4 5]\n", + "Copia NO modifica 'original3': [1 2 3 4 5]\n" + ] + } + ], + "source": [ + "original = np.array([1, 2, 3, 4, 5])\n", + "\n", + "# ⚠ ALIAS — dos nombres, un solo array\n", + "alias = original\n", + "alias[0] = 999\n", + "print(\"Alias modifica 'original':\", original)\n", + "\n", + "# ⚠ VISTA (slice) — ¡diferente a listas Python!\n", + "original2 = np.array([1, 2, 3, 4, 5])\n", + "vista = original2[1:4] # NO es copia — es vista\n", + "vista[0] = 777\n", + "print(\"Slice (vista) modifica 'original2':\", original2)\n", + "\n", + "# ✅ COPIA independiente — usa .copy()\n", + "original3 = np.array([1, 2, 3, 4, 5])\n", + "copia = original3.copy()\n", + "copia[0] = 999\n", + "print(\"Copia NO modifica 'original3':\", original3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unir arrays — concatenate, hstack, vstack, stack\n", + "\n", + "NumPy ofrece varias formas de unir arrays (Stewart & Mommert, §4.1.5):\n", + "\n", + "- `np.concatenate()`: función **general** — une a lo largo de un eje existente.\n", + "- `np.hstack()`: atajo para unir **horizontalmente** (eje 1 en 2D, eje 0 en 1D).\n", + "- `np.vstack()`: atajo para unir **verticalmente** (eje 0).\n", + "- `np.stack()`: apila creando un **nuevo eje**." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "concatenate: [1 2 3 4 5 6]\n", + "hstack : [1 2 3 4 5 6]\n", + "\n", + "vstack (apilar filas, axis=0):\n", + "[[1 2]\n", + " [3 4]\n", + " [5 6]\n", + " [7 8]]\n", + "\n", + "hstack (unir columnas, axis=1):\n", + "[[1 2 5 6]\n", + " [3 4 7 8]]\n", + "\n", + "stack — crea NUEVO eje (shape resultante: 2×2×2):\n", + "[[[1 2]\n", + " [3 4]]\n", + "\n", + " [[5 6]\n", + " [7 8]]]\n", + "Shape: (2, 2, 2)\n" + ] + } + ], + "source": [ + "a = np.array([1, 2, 3])\n", + "b = np.array([4, 5, 6])\n", + "\n", + "# Función general: np.concatenate\n", + "print(\"concatenate:\", np.concatenate((a, b)))\n", + "print(\"hstack :\", np.hstack((a, b))) # equivalente para 1D\n", + "\n", + "# Para matrices\n", + "m1 = np.array([[1, 2], [3, 4]])\n", + "m2 = np.array([[5, 6], [7, 8]])\n", + "\n", + "print(\"\\nvstack (apilar filas, axis=0):\")\n", + "print(np.vstack((m1, m2)))\n", + "\n", + "print(\"\\nhstack (unir columnas, axis=1):\")\n", + "print(np.hstack((m1, m2)))\n", + "\n", + "print(\"\\nstack — crea NUEVO eje (shape resultante: 2×2×2):\")\n", + "apilado = np.stack((m1, m2), axis=0)\n", + "print(apilado)\n", + "print(\"Shape:\", apilado.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ordenar y buscar — `np.sort` y `np.argsort`\n", + "\n", + "- `np.sort(x)` — retorna una **copia ordenada** (no modifica `x`).\n", + "- `np.argsort(x)` — retorna los **índices** que ordenarían `x`. Muy útil para ordenar arrays relacionados (e.g., ordenar nombres por nota) (Stewart & Mommert, §4.2.5)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notas originales: [78 45 92 61 88 34 73]\n", + "Ordenadas (asc) : [34 45 61 73 78 88 92]\n", + "Ordenadas (desc): [92 88 78 73 61 45 34]\n", + "\n", + "Índices (argsort): [5 1 3 6 0 4 2]\n", + "Aplicando índices : [34 45 61 73 78 88 92]\n", + "\n", + "Ranking:\n", + " 1. Diana: 92\n", + " 2. Fátima: 88\n", + " 3. Ana: 78\n", + " 4. Helena: 73\n", + " 5. Eduardo: 61\n", + " 6. Carlos: 45\n", + " 7. Gabriel: 34\n" + ] + } + ], + "source": [ + "notas = np.array([78, 45, 92, 61, 88, 34, 73])\n", + "print(\"Notas originales:\", notas)\n", + "\n", + "# np.sort — retorna una copia ordenada (no modifica el original)\n", + "print(\"Ordenadas (asc) :\", np.sort(notas))\n", + "print(\"Ordenadas (desc):\", np.sort(notas)[::-1])\n", + "\n", + "# np.argsort — retorna los ÍNDICES que ordenarían el array\n", + "indices = np.argsort(notas)\n", + "print(\"\\nÍndices (argsort):\", indices)\n", + "print(\"Aplicando índices :\", notas[indices]) # equivale a sort\n", + "\n", + "# Caso práctico: obtener ranking de estudiantes\n", + "nombres = np.array([\"Ana\", \"Carlos\", \"Diana\", \"Eduardo\", \"Fátima\", \"Gabriel\", \"Helena\"])\n", + "ranking = np.argsort(notas)[::-1] # mayor a menor\n", + "print(\"\\nRanking:\")\n", + "for pos, i in enumerate(ranking, 1):\n", + " print(f\" {pos}. {nombres[i]}: {notas[i]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Suma acumulativa — `np.cumsum`\n", + "\n", + "`np.cumsum` calcula la suma acumulada: cada elemento del resultado es la suma de todos los anteriores más el actual. Útil para calcular totales progresivos (gastos, puntajes acumulados, etc.)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Puntos por nivel : [100 250 180 320 210]\n", + "Puntos acumulados : [ 100 350 530 850 1060]\n", + "\n", + "Gastos diarios : [ 50 120 30 85 200 45]\n", + "Total acumulado : [ 50 170 200 285 485 530]\n", + "Total de la semana : 530\n" + ] + } + ], + "source": [ + "puntos_por_nivel = np.array([100, 250, 180, 320, 210])\n", + "\n", + "print(\"Puntos por nivel :\", puntos_por_nivel)\n", + "print(\"Puntos acumulados :\", np.cumsum(puntos_por_nivel))\n", + "\n", + "# Ejemplo: gastos diarios y total acumulado\n", + "gastos = np.array([50, 120, 30, 85, 200, 45])\n", + "print(\"\\nGastos diarios :\", gastos)\n", + "print(\"Total acumulado :\", np.cumsum(gastos))\n", + "print(\"Total de la semana :\", np.sum(gastos))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3 — Estadística y Álgebra Lineal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Funciones estadísticas" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datos: [23 45 12 67 34 89 56 78 90 11]\n", + "\n", + "--- Estadísticas ---\n", + "Mínimo: 11\n", + "Máximo: 90\n", + "Suma: 505\n", + "Media (promedio): 50.50\n", + "Mediana: 50.50\n", + "Desviación estándar: 28.64\n", + "Varianza: 820.25\n", + "\n", + "Índice del máximo: 8\n", + "Índice del mínimo: 9\n" + ] + } + ], + "source": [ + "datos = np.array([23, 45, 12, 67, 34, 89, 56, 78, 90, 11])\n", + "\n", + "print(\"Datos:\", datos)\n", + "print(\"\\n--- Estadísticas ---\")\n", + "print(f\"Mínimo: {np.min(datos)}\")\n", + "print(f\"Máximo: {np.max(datos)}\")\n", + "print(f\"Suma: {np.sum(datos)}\")\n", + "print(f\"Media (promedio): {np.mean(datos):.2f}\")\n", + "print(f\"Mediana: {np.median(datos):.2f}\")\n", + "print(f\"Desviación estándar: {np.std(datos):.2f}\")\n", + "print(f\"Varianza: {np.var(datos):.2f}\")\n", + "print(f\"\\nÍndice del máximo: {np.argmax(datos)}\")\n", + "print(f\"Índice del mínimo: {np.argmin(datos)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estadísticas avanzadas — histograma, media ponderada, correlación\n", + "\n", + "- `np.histogram(x, bins)` — distribución de frecuencias (retorna conteo y bordes).\n", + "- `np.average(x, weights=w)` — **media ponderada** (cada dato tiene un peso diferente).\n", + "- `np.corrcoef(x, y)` — **coeficiente de correlación** de Pearson entre dos arrays.\n", + "- **API moderna de números aleatorios**: usar `default_rng(semilla)` en lugar de `np.random.seed()` (Stewart & Mommert, §4.4)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conteo por intervalo: [ 6 16 31 44 39 39 12 9 3 1]\n", + "Bordes de intervalos: [48.7 53.7 58.8 63.8 68.9 73.9 79. 84. 89. 94.1 99.1]\n", + "\n", + "Media simple : 2.86\n", + "Media ponderada : 2.995\n", + "\n", + "Coeficiente de correlación Pearson:\n", + "[[1. 0.9987]\n", + " [0.9987 1. ]]\n" + ] + } + ], + "source": [ + "from numpy.random import default_rng\n", + "rng = default_rng(42) # semilla para reproducibilidad\n", + "\n", + "# Generar datos con distribución normal (media=70, desv=10)\n", + "calificaciones = rng.normal(loc=70, scale=10, size=200).clip(0, 100)\n", + "\n", + "# np.histogram — distribución de frecuencias\n", + "conteo, bordes = np.histogram(calificaciones, bins=10)\n", + "print(\"Conteo por intervalo:\", conteo)\n", + "print(\"Bordes de intervalos:\", bordes.round(1))\n", + "\n", + "# Media ponderada: notas con distintos pesos\n", + "mediciones = np.array([3.1, 2.7, 2.5, 3.1, 2.9])\n", + "incertidumbres = np.array([0.05, 0.15, 0.60, 0.10, 0.05])\n", + "print(\"\\nMedia simple :\", np.mean(mediciones).round(4))\n", + "print(\"Media ponderada :\", np.average(mediciones, weights=1/incertidumbres**2).round(4))\n", + "# Pesos = 1/σ² → mayor peso a mediciones más precisas\n", + "\n", + "# Correlación entre dos arrays\n", + "x = np.array([1, 2, 3, 4, 5])\n", + "y = np.array([2.1, 3.9, 6.2, 7.8, 10.1])\n", + "print(\"\\nCoeficiente de correlación Pearson:\")\n", + "print(np.corrcoef(x, y).round(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reshape — cambiar la forma de un array" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arreglo original: [ 1 2 3 4 5 6 7 8 9 10 11 12]\n", + "Forma: (12,)\n", + "\n", + "Reorganizado a 3x4:\n", + "[[ 1 2 3 4]\n", + " [ 5 6 7 8]\n", + " [ 9 10 11 12]]\n", + "\n", + "Reorganizado a 2x6:\n", + "[[ 1 2 3 4 5 6]\n", + " [ 7 8 9 10 11 12]]\n" + ] + } + ], + "source": [ + "# Crear un arreglo de 12 elementos y reorganizarlo\n", + "v = np.arange(1, 13)\n", + "print(\"Arreglo original:\", v)\n", + "print(\"Forma:\", v.shape)\n", + "\n", + "# Convertir a matriz 3x4\n", + "m = v.reshape(3, 4)\n", + "print(\"\\nReorganizado a 3x4:\")\n", + "print(m)\n", + "\n", + "# Convertir a matriz 2x6\n", + "print(\"\\nReorganizado a 2x6:\")\n", + "print(v.reshape(2, 6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multiplicación de matrices y operaciones esenciales\n", + "\n", + "| Operación | Sintaxis | Descripción |\n", + "|---|---|---|\n", + "| Elemento a elemento | `A * B` | Hadamard product |\n", + "| Matricial | `A @ B` o `np.matmul(A, B)` | Producto matricial |\n", + "| Transpuesta | `A.T` o `np.transpose(A)` | Intercambia filas↔columnas |\n", + "| Identidad | `np.identity(n)` | Matriz identidad n×n |\n", + "| Aplanar | `A.ravel()` o `np.ravel(A)` | Convierte a vector 1D |" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A * B (elemento a elemento):\n", + "[[ 5 12]\n", + " [21 32]]\n", + "\n", + "A @ B (multiplicación matricial):\n", + "[[19 22]\n", + " [43 50]]\n", + "\n", + "A.T (transpuesta):\n", + "[[1 3]\n", + " [2 4]]\n", + "\n", + "np.identity(3) — matriz identidad:\n", + "[[1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]]\n", + "\n", + "A.ravel() — aplanar a 1D: [1 2 3 4]\n" + ] + } + ], + "source": [ + "A = np.array([[1, 2], [3, 4]])\n", + "B = np.array([[5, 6], [7, 8]])\n", + "\n", + "print(\"A * B (elemento a elemento):\")\n", + "print(A * B)\n", + "\n", + "print(\"\\nA @ B (multiplicación matricial):\")\n", + "print(A @ B)\n", + "\n", + "print(\"\\nA.T (transpuesta):\")\n", + "print(A.T)\n", + "\n", + "print(\"\\nnp.identity(3) — matriz identidad:\")\n", + "print(np.identity(3))\n", + "\n", + "# Aplanar una matriz a vector 1D\n", + "print(\"\\nA.ravel() — aplanar a 1D:\", A.ravel())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3d — Álgebra Lineal (`numpy.linalg`)\n", + "\n", + "NumPy incluye el módulo `numpy.linalg` (importado como `la`) para operaciones matriciales avanzadas (Stewart & Mommert, §4.7):\n", + "\n", + "| Función | Descripción |\n", + "|---|---|\n", + "| `la.det(A)` | Determinante de A |\n", + "| `la.inv(A)` | Inversa de A (si det ≠ 0) |\n", + "| `la.norm(v)` | Norma euclidiana de un vector |\n", + "| `la.solve(A, b)` | Resuelve el sistema **Ax = b** |\n", + "| `la.eig(A)` | Eigenvalores y eigenvectores de A |\n", + "\n", + "> `la.solve(A, b)` es numéricamente más estable y eficiente que calcular `la.inv(A) @ b`." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matriz A:\n", + "[[4. 2. 0.]\n", + " [9. 3. 7.]\n", + " [1. 2. 1.]]\n", + "\n", + "Determinante: -48.0\n", + "\n", + "Inversa A⁻¹:\n", + "[[ 0.2292 0.0417 -0.2917]\n", + " [ 0.0417 -0.0833 0.5833]\n", + " [-0.3125 0.125 0.125 ]]\n", + "\n", + "A @ A⁻¹ (≈ identidad):\n", + "[[ 1. 0. -0.]\n", + " [ 0. 1. -0.]\n", + " [ 0. 0. 1.]]\n", + "\n", + "Norma del vector [3, 4]: 5.0\n" + ] + } + ], + "source": [ + "import numpy.linalg as la\n", + "\n", + "A = np.array([[4, 2, 0],\n", + " [9, 3, 7],\n", + " [1, 2, 1]], dtype=float)\n", + "\n", + "print(\"Matriz A:\")\n", + "print(A)\n", + "\n", + "# Determinante\n", + "print(\"\\nDeterminante:\", la.det(A).round(4))\n", + "\n", + "# Inversa (si det ≠ 0)\n", + "A_inv = la.inv(A)\n", + "print(\"\\nInversa A⁻¹:\")\n", + "print(A_inv.round(4))\n", + "\n", + "# Verificación: A @ A⁻¹ debe ser la identidad\n", + "print(\"\\nA @ A⁻¹ (≈ identidad):\")\n", + "print((A @ A_inv).round(10))\n", + "\n", + "# Norma de un vector\n", + "v = np.array([3.0, 4.0])\n", + "print(\"\\nNorma del vector [3, 4]:\", la.norm(v)) # = 5.0" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solución x: [ 1. 2. -2.]\n", + "Verificación Ax - b ≈ 0: [ 0. 0. -0.]\n", + "\n", + "--- Eigenvalores ---\n", + "Eigenvalores: [-5. 3. 6.]\n", + "Primer eigenvector: [ 0.816 0.408 -0.408]\n" + ] + } + ], + "source": [ + "import numpy.linalg as la\n", + "\n", + "# ─── Resolución de sistemas de ecuaciones: Ax = b ───────────────────────────\n", + "# Ejemplo: sistema de 3 ecuaciones\n", + "# 3x + 2y + z = 5\n", + "# 5x + 5y + 5z = 5\n", + "# x + 4y + 6z = -3\n", + "A = np.array([[3, 2, 1],\n", + " [5, 5, 5],\n", + " [1, 4, 6]], dtype=float)\n", + "b = np.array([5, 5, -3], dtype=float)\n", + "\n", + "x = la.solve(A, b)\n", + "print(\"Solución x:\", x.round(4))\n", + "print(\"Verificación Ax - b ≈ 0:\", (A @ x - b).round(10))\n", + "\n", + "# ─── Eigenvalores y eigenvectores ───────────────────────────────────────────\n", + "print(\"\\n--- Eigenvalores ---\")\n", + "M = np.array([[-2, -4, 2],\n", + " [-2, 1, 2],\n", + " [ 4, 2, 5]], dtype=float)\n", + "eigenvalores, eigenvectores = la.eig(M)\n", + "print(\"Eigenvalores:\", eigenvalores.round(3))\n", + "print(\"Primer eigenvector:\", eigenvectores[:, 0].round(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3e — Polinomios\n", + "\n", + "NumPy permite representar polinomios por sus coeficientes y ofrece funciones para ajustar, evaluar y manipularlos (Stewart & Mommert, §4.6):\n", + "\n", + "- `np.polyfit(x, y, deg)` — ajusta un polinomio de grado `deg` a datos *(x, y)* por mínimos cuadrados. Retorna los coeficientes `[a_n, ..., a_1, a_0]`.\n", + "- `np.polyval(coefs, x)` — evalúa el polinomio con coeficientes `coefs` en los puntos `x`.\n", + "- `np.poly(roots)` — construye coeficientes desde las raíces.\n", + "- `np.roots(coefs)` — calcula las raíces a partir de los coeficientes." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coeficientes [a, b, c]: [ 0.051 -0. 0. ]\n", + "Modelo: d = 0.0510·v² + -0.0000·v + 0.0000\n", + "\n", + "Predicciones:\n", + " v=25 m/s → d≈31.87 m\n", + " v=35 m/s → d≈62.47 m\n", + " v=45 m/s → d≈103.27 m\n" + ] + } + ], + "source": [ + "# Datos: velocidad inicial vs distancia máxima (proyectil simplificado)\n", + "# Queremos ajustar un polinomio de grado 2: d = a*v^2 + b*v + c\n", + "v = np.array([10, 20, 30, 40, 50], dtype=float) # velocidad (m/s)\n", + "d = np.array([5.1, 20.4, 45.9, 81.6, 127.5]) # distancia (m)\n", + "\n", + "# np.polyfit: ajuste de mínimos cuadrados\n", + "coefs = np.polyfit(v, d, deg=2)\n", + "print(\"Coeficientes [a, b, c]:\", coefs.round(4))\n", + "print(f\"Modelo: d = {coefs[0]:.4f}·v² + {coefs[1]:.4f}·v + {coefs[2]:.4f}\")\n", + "\n", + "# np.polyval: evaluar el polinomio en nuevos valores\n", + "v_nuevo = np.array([25, 35, 45])\n", + "d_pred = np.polyval(coefs, v_nuevo)\n", + "print(\"\\nPredicciones:\")\n", + "for vi, di in zip(v_nuevo, d_pred):\n", + " print(f\" v={vi} m/s → d≈{di:.2f} m\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 4 — Ejemplo Aplicado: Cálculo de Notas" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notas (Tarea1 | Tarea2 | Parcial | Final):\n", + "--------------------------------------------------\n", + "Ana [78 91 68 54]\n", + "Carlos [ 82 47 100 60]\n", + "Diana [78 97 58 62]\n", + "Eduardo [50 50 63 92]\n", + "Fátima [75 79 63 42]\n", + "Gabriel [61 92 41 63]\n", + "Helena [83 69 77 41]\n", + "Iván [99 60 72 51]\n", + "\n", + "--- Resultados Finales ---\n", + "Ana Nota: 67.3 → APROBADO\n", + "Carlos Nota: 73.3 → APROBADO\n", + "Diana Nota: 68.5 → APROBADO\n", + "Eduardo Nota: 70.7 → APROBADO\n", + "Fátima Nota: 58.8 → REPROBADO\n", + "Gabriel Nota: 60.5 → REPROBADO\n", + "Helena Nota: 62.3 → APROBADO\n", + "Iván Nota: 65.8 → APROBADO\n", + "\n", + "Promedio del curso: 65.91\n", + "Nota más alta: 73.35\n", + "Nota más baja: 58.80\n", + "Aprobados: 6 / 8\n" + ] + } + ], + "source": [ + "# Simulación de notas de un curso\n", + "# Filas = estudiantes, Columnas = [Tarea1, Tarea2, Examen parcial, Examen final]\n", + "np.random.seed(42) # Para reproducibilidad\n", + "\n", + "notas = np.random.randint(40, 101, size=(8, 4))\n", + "pesos = np.array([0.15, 0.15, 0.30, 0.40]) # Pesos de cada evaluación\n", + "\n", + "nombres = [\"Ana\", \"Carlos\", \"Diana\", \"Eduardo\", \"Fátima\", \"Gabriel\", \"Helena\", \"Iván\"]\n", + "\n", + "print(\"Notas (Tarea1 | Tarea2 | Parcial | Final):\")\n", + "print(\"-\" * 50)\n", + "for nombre, fila in zip(nombres, notas):\n", + " print(f\"{nombre:<10} {fila}\")\n", + "\n", + "# Calcular nota final ponderada para cada estudiante\n", + "notas_finales = notas @ pesos\n", + "\n", + "print(\"\\n--- Resultados Finales ---\")\n", + "for nombre, nota in zip(nombres, notas_finales):\n", + " estado = \"APROBADO\" if nota >= 61 else \"REPROBADO\"\n", + " print(f\"{nombre:<10} Nota: {nota:.1f} → {estado}\")\n", + "\n", + "print(f\"\\nPromedio del curso: {np.mean(notas_finales):.2f}\")\n", + "print(f\"Nota más alta: {np.max(notas_finales):.2f}\")\n", + "print(f\"Nota más baja: {np.min(notas_finales):.2f}\")\n", + "print(f\"Aprobados: {np.sum(notas_finales >= 61)} / {len(notas_finales)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumPy versión: 1.26.4\n" + ] + } + ], + "source": [ + "\n", + "import numpy as np\n", + "\n", + "print(\"NumPy versión:\", np.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tarea 4 — NumPy en Práctica\n", + "\n", + "La programación se aprende haciendo, no leyendo. Esta tarea tiene dos partes:\n", + "\n", + "**Parte 1 — Descarga y ejecuta** (obligatorio)\n", + "Descarga este notebook y ejecútalo celda por celda en tu computadora. \n", + "Asegúrate de que todas las celdas corran sin errores antes de continuar.\n", + "\n", + "**Parte 2 — Ejercicios** (obligatorio) \n", + "Completa cada celda que dice `# TU CÓDIGO AQUÍ` con tu solución. \n", + "Ejecuta la celda para verificar que el resultado es correcto.\n", + "\n", + "**Entrega: 1 de mayo** \n", + "Sube el notebook a tu repositorio de GitHub y envía el **link directo al archivo `.ipynb`**.\n", + "El notebook debe tener todas las celdas ya ejecutadas (con outputs visibles).\n", + "\n", + "Ejemplo de link válido: \n", + "`https://github.com/tu-usuario/tu-repo/blob/main/Numpy.ipynb`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 1 — Crear y operar arrays\n", + "\n", + "Crea un array con los números del 1 al 10 usando `np.arange`. \n", + "Luego calcula e imprime:\n", + "- Cada número multiplicado por 5\n", + "- Solo los números pares (usa indexación booleana)\n", + "- La suma de todos los números" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista principal:, [ 1 2 3 4 5 6 7 8 9 10]\n", + "Multiplicación por 5: [ 5 10 15 20 25 30 35 40 45 50]\n", + "Números pares: [ 2 4 6 8 10]\n", + "Suma total: 55\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "\n", + "lista_principal = np.arange(1, 11)\n", + "Multiplicacion = lista_principal * 5\n", + "pares = lista_principal[lista_principal % 2 ==0]\n", + "suma = lista_principal.sum()\n", + "\n", + "print(f\"Lista principal:, {lista_principal}\")\n", + "print(f\"Multiplicación por 5: {Multiplicacion}\")\n", + "print(f\"Números pares: {pares}\")\n", + "print(f\"Suma total: {suma}\")\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 2 — Funciones matemáticas\n", + "\n", + "Dado el array de ángulos en grados, conviértelos a radianes y calcula su seno. \n", + "Fórmula: `radianes = grados * π / 180`\n", + "\n", + "Ángulos: 0°, 30°, 45°, 60°, 90°, 180°" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grados : [ 0 30 45 60 90 180]\n", + "Radianes: [0. 0.52359878 0.78539816 1.04719755 1.57079633 3.14159265]\n", + "seno: [0. 0.5 0.7071 0.866 1. 0. ]\n" + ] + } + ], + "source": [ + "grados = np.array([0, 30, 45, 60, 90, 180])\n", + "radianes = grados * np.pi /180\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "print(f\"Grados : {grados}\")\n", + "print(f\"Radianes: {radianes}\")\n", + "print(\"seno: \" , np.sin(radianes).round(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 3 — Filtrado y estadísticas\n", + "\n", + "Dada la lista de temperaturas diarias de un mes, calcula:\n", + "1. La temperatura promedio, mínima y máxima\n", + "2. Cuántos días superaron los 28°C\n", + "3. Las temperaturas de los días fríos (menores a 20°C)\n", + "4. Reemplaza con `np.where` las temperaturas menores a 15°C por exactamente 15.0 (temperatura mínima registrada)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " DATOS ORIGINALES \n", + " \n", + "EN DESORDEN : [ 22.1 19.5 31.2 28.0 17.3 25.8 30.1 14.2 26.7 23.4 18.9 32.0 29.5 21.1 16.8 27.3\n", + " 24.6 13.5 20.0 28.9 33.1 15.7 22.8 19.0 31.5 26.2 18.1 29.8 12.4 25.0]\n", + " \n", + "EN ORDEN : [ 12.4 13.5 14.2 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n", + " \n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + " ESTADÍSTICA \n", + " \n", + "Mínimo: 12.4\n", + " \n", + "Máximo: 33.1\n", + " \n", + "Número de días que superaron 28°C: 8\n", + " \n", + "Temperatura de los días menores a 20°C: [ 19.5 17.3 14.2 18.9 16.8 13.5 15.7 19.0 18.1 12.4]\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + " TEMPERATURAS MÍNIMAS REGISTRADAS < 15°C, MODIFICADOS \n", + " \n", + "Temperaturas originales ordenas: [ 12.4 13.5 14.2 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n", + " \n", + "DATOS MODIFICADOS < 15°C : [ 15.0 15.0 15.0 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n" + ] + } + ], + "source": [ + "np.set_printoptions(formatter={'float': '{: 0.1f}'.format}, linewidth=100)\n", + "temperaturas = np.array([\n", + " 22.1, 19.5, 31.2, 28.0, 17.3, 25.8, 30.1,\n", + " 14.2, 26.7, 23.4, 18.9, 32.0, 29.5, 21.1,\n", + " 16.8, 27.3, 24.6, 13.5, 20.0, 28.9, 33.1,\n", + " 15.7, 22.8, 19.0, 31.5, 26.2, 18.1, 29.8,\n", + " 12.4, 25.0\n", + "])\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "#--------------------------\n", + "\n", + "a = np.sort(temperaturas) # Temperaturas ordenadas\n", + "b =(temperaturas > 28).sum() # Temperaturas mayores a 28°C.\n", + "c = temperaturas[temperaturas < 20] # Temperaturas de los días menores a 20°C.\n", + "nuevas_temperaturas = np.where(temperaturas < 15, 15.0, temperaturas) # Remplazo de tempetaturas menores a 15°C\n", + "d = np.sort(nuevas_temperaturas) \n", + "\n", + "#--------------------------\n", + "\n", + "\n", + "# Lista de temperaturas diarias de un mes.\n", + "print(\"\\n\" + \" \"*20 + \" DATOS ORIGINALES \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"EN DESORDEN : {temperaturas}\")\n", + "print(\" \" * 40)\n", + "print(f\"EN ORDEN : {a}\")\n", + "print(\" \" * 40)\n", + "print(\"----\" * 40)\n", + "\n", + "#--------------------------\n", + "\n", + "print(\"\\n\" + \" \" * 21 + \" ESTADÍSTICA \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"Mínimo: {np.min(temperaturas)}\")\n", + "print(\" \" * 40)\n", + "print(f\"Máximo: {np.max(temperaturas)}\")\n", + "print(\" \" * 40)\n", + "print(f\"Número de días que superaron 28°C: {b}\")\n", + "print(\" \" * 40)\n", + "print(f\"Temperatura de los días menores a 20°C: {c}\")\n", + "\n", + "print(\"----\" * 40)\n", + "\n", + "#--------------------------\n", + "\n", + "print(\"\\n\" + \" \" * 17 + \" TEMPERATURAS MÍNIMAS REGISTRADAS < 15°C, MODIFICADOS \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"Temperaturas originales ordenas: {a}\")\n", + "print(\" \" * 40)\n", + "print(f\"DATOS MODIFICADOS < 15°C : {d}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 4 — Matrices\n", + "\n", + "Crea una matriz de 4×4 con valores del 1 al 16 usando `np.arange` y `reshape`. \n", + "Luego:\n", + "1. Imprime la primera fila\n", + "2. Imprime la última columna\n", + "3. Imprime la submatriz de las 2 primeras filas y las 2 últimas columnas\n", + "4. Calcula la suma de cada fila (investiga `np.sum` con el parámetro `axis`)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " VALORES DEL 1 AL 16: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16]\n", + " Forma: (16,)\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " MATRIZ ORIGINAL \n", + "Forma: (4, 4)\n", + "MATRIZ 4X4: \n", + " [[ 1 2 3 4]\n", + " [ 5 6 7 8]\n", + " [ 9 10 11 12]\n", + " [13 14 15 16]]\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " OPERACIONES CON LA MATRIZ \n", + " \n", + "PRIMERA FILA: \n", + " [1 2 3 4]\n", + " \n", + "ÚLTIMA COLUMNA DE LA MATRIZ: \n", + " [ 4 8 12 16]\n", + " \n", + "SUBMATRIZ DE LAS 2 PRIMERAS FILAS Y LAS 2 ÚLTIMAS COLUMNAS:\n", + " [[3 4]\n", + " [7 8]]\n", + " \n", + "SUMAS DE CADA FILA: \n", + " [10 26 42 58]\n", + " \n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "\n", + "a = np.arange(1, 17) # Matrix fila 1 al 16 o arreglo original\n", + "b = a.reshape(4, 4) # Matriz 4x4\n", + "\n", + "#--------------------------\n", + "print(f\"\\n VALORES DEL 1 AL 16: {a}\")\n", + "print(\" Forma:\", a.shape)\n", + "print(\"----\" * 40)\n", + "#--------------------------\n", + "print(\" \"*30 + \"MATRIZ ORIGINAL\" + \" \"*20)\n", + "print(\"Forma:\", b.shape)\n", + "print(f\"MATRIZ 4X4: \\n {b}\")\n", + "print(\"----\" * 40)\n", + "print(\" \"*27 + \" OPERACIONES CON LA MATRIZ\" + \" \"*20)\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"PRIMERA FILA: \\n {b[0, :]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"ÚLTIMA COLUMNA DE LA MATRIZ: \\n {b[:, -1]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"SUBMATRIZ DE LAS 2 PRIMERAS FILAS Y LAS 2 ÚLTIMAS COLUMNAS:\\n {b[0:2, 2:4]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"SUMAS DE CADA FILA: \\n {np.sum(b, axis=1)}\") # Suma de la matriz utilizando .sum() y axis=1 para sumar la filas\n", + "print(\" \" * 40)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 5 — Suma acumulativa\n", + "\n", + "Un estudiante anotó los puntos que obtuvo en cada una de las 8 tareas del curso. \n", + "Calcula e imprime los puntos acumulados después de cada tarea para saber en qué momento superó 100, 200 y 300 puntos.\n", + "\n", + "**Tip:** usa `np.cumsum` y filtra con indexación booleana." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------\n", + "\n", + " NOTA DE LAS 8 TAREAS DEL CURSO:\n", + " [45 38 52 29 61 47 55 33]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA SUMA ACUMULADA DE TODA LAS NOTAS: \n", + " [ 45 83 135 164 225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA SUMA CORRELATIVA QUE SUPERÓ LOS 100 PUNTOS, ESTÁ EN LA NOTA CON ÍNDICE: 2\n", + "\n", + " LA SUMA ACUMULADA QUE SUPERÓ 100 pt.: 135\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 100 pt.: \n", + " [135 164 225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 200 pt.: \n", + " [225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 300 pt.s: \n", + " [327 360]\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Puntos de las OCho tareas del curso\n", + "print(\"---\" * 30)\n", + "puntos_tareas = np.array([45, 38, 52, 29, 61, 47, 55, 33])\n", + "a = np.cumsum(puntos_tareas)\n", + "b= np.where(a > 100)[0][0]# Suma correlativa en que se superó lo 100 puntos\n", + "\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "print(f\"\\n NOTA DE LAS 8 TAREAS DEL CURSO:\\n {puntos_tareas}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n LA SUMA ACUMULADA DE TODA LAS NOTAS: \\n {a}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n LA SUMA CORRELATIVA QUE SUPERÓ LOS 100 PUNTOS, ESTÁ EN LA NOTA CON ÍNDICE: {b}\")\n", + "print(f\"\\n LA SUMA ACUMULADA QUE SUPERÓ 100 pt.: {a[2]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 100 pt.: \\n {a[a > 100]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 200 pt.: \\n {a[a > 200]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 300 pt.s: \\n {a[a > 300]}\")\n", + "print(\"---\" * 30) \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 6 — Álgebra Lineal\n", + "\n", + "El sistema de ecuaciones representa un balance de fuerzas en una estructura:\n", + "\n", + "$$2F_1 + F_2 = 100$$\n", + "$$F_1 + 3F_2 + F_3 = 200$$\n", + "$$F_2 + 2F_3 = 150$$\n", + "\n", + "Usando `numpy.linalg`:\n", + "1. Resuelve el sistema con `la.solve(A, b)` para encontrar $F_1$, $F_2$, $F_3$\n", + "2. Verifica la solución calculando `A @ x - b` (debe ser ≈ 0)\n", + "3. Calcula el determinante de A con `la.det(A)`\n", + "4. Calcula la norma de la solución `x` con `la.norm(x)`" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " SOLUCIÓN DEL SISTEMA:\n", + " [ 31.2 37.5 56.2]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " VERIFICACIÓN DE LA SOLUCIÓN A @ X-b ; ≈ 0: \n", + " [ 0.0 0.0 0.0]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " Determinante: \n", + " 8.0000\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA NORMA DE X: \n", + " 74.4773\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "import numpy.linalg as la\n", + "\n", + "# Sistema de ecuaciones (balance de fuerzas en un puente):\n", + "# 2F1 + F2 = 100\n", + "# F1 + 3F2 + F3 = 200\n", + "# F2 + 2F3 = 150\n", + "\n", + "A = np.array([[2, 1, 0],\n", + " [1, 3, 1],\n", + " [0, 1, 2]], dtype=float)\n", + "b = np.array([100, 200, 150], dtype=float)\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "x = la.solve(A, b) # la es el alias de np.linalg\n", + "y = A @ x - b # Verificación\n", + "n = la.norm (x)\n", + "\n", + "\n", + "print(f\"\\n SOLUCIÓN DEL SISTEMA:\\n {x}\")\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n VERIFICACIÓN DE LA SOLUCIÓN A @ X-b ; ≈ 0: \\n {y}\" )\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n Determinante: \\n {la.det(A):.4f}\")\n", + "\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n LA NORMA DE X: \\n {n:.4f}\" )\n", + "print(\"---\" * 30)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 7 — Polinomios (ajuste de curva)\n", + "\n", + "Un objeto en caída libre sigue la ecuación $x(t) = \\frac{1}{2}g t^2$.\n", + "\n", + "Con los datos de tiempo y posición dados, usa `np.polyfit` para ajustar un polinomio de grado 2 y:\n", + "1. Muestra los coeficientes obtenidos\n", + "2. Estima la aceleración de gravedad `g` a partir del coeficiente cuadrático\n", + "3. Usa `np.polyval` para predecir la posición en `t = 2.5` s y `t = 6` s\n", + "\n", + "**Pista:** el coeficiente de $t^2$ en el polinomio es $\\frac{g}{2}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " COEFICIENTES [a, b, c] de [at^2 + bt + c)]:: \n", + " a=4.9000, \n", + " b=0.0000, \n", + " c=-0.0000\n", + "------------------------------------------------------------------------------------------\n", + " \n", + " MODELOR [y(t)= at^2 + bt + c)]: \n", + " \n", + " y(t)= 4.9000·t² + 0.0000·t + -0.0000\n", + " \n", + "------------------------------------------------------------------------------------------\n", + " \n", + " ESTIMACIÓN DE LA GRAVEDAD: \n", + " 9.80000 m/s^2\n", + "------------------------------------------------------------------------------------------\n", + " POSICIONES \n", + " \n", + " POSICIÓN t1 = 2.5 s: \n", + " y(t1) = 30.62500 m\n", + " \n", + " \n", + " POSICIÓN t2 = 6.0 s:\n", + " y(t2) = 176.40000 m\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "t = np.array([0, 1, 2, 3, 4, 5], dtype=float) # tiempo (s)\n", + "pos = np.array([0.0, 4.9, 19.6, 44.1, 78.4, 122.5]) # posición (m)\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "coeficientes = np.polyfit(t, pos, deg=2)\n", + "a= coeficientes[0]\n", + "g_estimada = a * 2\n", + "t1 = 2.5\n", + "t2 = 6 \n", + "posicion_t1 = np.polyval(coeficientes, t1) \n", + "posicion_t2 = np.polyval(coeficientes, t2)\n", + "\n", + "\n", + "print(f\"\\n COEFICIENTES [a, b, c] de [at^2 + bt + c)]:: \\n a={coeficientes[0]:.4f}, \\n b={coeficientes[1]:.4f}, \\n c={coeficientes[2]:.4f}\")\n", + "print(\"---\" * 30)\n", + "# Modelo para un movimiento vertical, buscamos las alturas (y)\n", + "print(f\" \\n MODELOR [y(t)= at^2 + bt + c)]: \\n \\n y(t)= {coeficientes[0]:.4f}·t² + {coeficientes[1]:.4f}·t + {coeficientes[2]:.4f}\")\n", + "print(\" \" * 30)\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\" \\n ESTIMACIÓN DE LA GRAVEDAD: \\n {g_estimada:.5f} m/s^2\")\n", + "print(\"---\" * 30)\n", + "\n", + "print( \" \" * 15 + \"POSICIONES\" + \" \"*20)\n", + "print(f\" \\n POSICIÓN t1 = 2.5 s: \\n y(t1) = {posicion_t1:.5f} m\")\n", + "print(\" \" * 30)\n", + "print(f\" \\n POSICIÓN t2 = 6.0 s:\\n y(t2) = {posicion_t2:.5f} m\")\n", + "print(\"---\" * 30)\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ tareas/Pandas.ipynb b/ tareas/Pandas.ipynb new file mode 100644 index 0000000..016286c --- /dev/null +++ b/ tareas/Pandas.ipynb @@ -0,0 +1,2315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas — Análisis y Manipulación de Datos\n", + "\n", + "## ¿Qué es Pandas?\n", + "\n", + "**Pandas** es la librería más utilizada para análisis y manipulación de datos en Python. Su nombre viene de **Pan**el **Da**ta (datos de panel), un término de econometría.\n", + "\n", + "Pandas introduce dos estructuras de datos fundamentales:\n", + "\n", + "| Estructura | Descripción | Analogía |\n", + "|---|---|---|\n", + "| **Series** | Arreglo unidimensional con etiquetas | Una columna de Excel |\n", + "| **DataFrame** | Tabla bidimensional con filas y columnas | Una hoja de Excel |\n", + "\n", + "## ¿Para qué sirve?\n", + "\n", + "- Leer y escribir datos (CSV, Excel, SQL, JSON, etc.)\n", + "- Limpiar datos (manejar valores nulos, duplicados, errores)\n", + "- Filtrar, ordenar y agrupar datos\n", + "- Calcular estadísticas y resúmenes\n", + "- Preparar datos para visualización o machine learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Instalación e Importación\n", + "\n", + "```bash\n", + "pip install pandas\n", + "```\n", + "\n", + "Por convención, se importa con el alias `pd`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pandas versión: 2.1.4\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np # Pandas y NumPy se usan frecuentemente juntos\n", + "\n", + "print(\"Pandas versión:\", pd.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 1 — Series" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serie de temperaturas:\n", + "0 25.1\n", + "1 27.3\n", + "2 24.8\n", + "3 28.5\n", + "4 26.0\n", + "dtype: float64\n", + "\n", + "Tipo: \n" + ] + } + ], + "source": [ + "# Crear una Serie desde una lista\n", + "temperaturas = pd.Series([25.1, 27.3, 24.8, 28.5, 26.0])\n", + "print(\"Serie de temperaturas:\")\n", + "print(temperaturas)\n", + "print(\"\\nTipo:\", type(temperaturas))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperatura por día:\n", + "Lunes 25.1\n", + "Martes 27.3\n", + "Miércoles 24.8\n", + "Jueves 28.5\n", + "Viernes 26.0\n", + "dtype: float64\n", + "\n", + "Temperatura del Martes: 27.3\n", + "Promedio de la semana: 26.339999999999996\n" + ] + } + ], + "source": [ + "# Serie con índices personalizados (etiquetas)\n", + "dias = pd.Series(\n", + " [25.1, 27.3, 24.8, 28.5, 26.0],\n", + " index=[\"Lunes\", \"Martes\", \"Miércoles\", \"Jueves\", \"Viernes\"]\n", + ")\n", + "print(\"Temperatura por día:\")\n", + "print(dias)\n", + "\n", + "print(\"\\nTemperatura del Martes:\", dias[\"Martes\"])\n", + "print(\"Promedio de la semana:\", dias.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean: 8.258333333333333\n", + "min: -0.3\n", + "standard deviation: 6.520242373260415\n", + "index of max element: 6\n", + "values as list: [-0.3 0.4 3.9 7.4 12. 15. 17.2 16.8 13.1 9.1 3.7 0.8]\n", + "indices of sorted Series: [ 0 1 11 10 2 3 9 4 8 5 7 6]\n", + "cumulative sum: [-0.3 0.1 4. 11.4 23.4 38.4 55.6 72.4 85.5 94.6 98.3 99.1]\n" + ] + } + ], + "source": [ + "# Métodos estadísticos de una Serie (§8.1)\n", + "s = pd.Series([-0.3, 0.4, 3.9, 7.4, 12.0, 15.0, 17.2,\n", + " 16.8, 13.1, 9.1, 3.7, 0.8], name='temp_C')\n", + "\n", + "print('mean:', s.mean())\n", + "print('min:', s.min())\n", + "print('standard deviation:', s.std())\n", + "print('index of max element:', s.argmax())\n", + "print('values as list:', s.values)\n", + "print('indices of sorted Series:', s.argsort().values)\n", + "print('cumulative sum:', s.cumsum().values)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jan -0.3\n", + "feb 0.4\n", + "mar 3.9\n", + "apr 7.4\n", + "may 12.0\n", + "jun 15.0\n", + "jul 17.2\n", + "aug 16.8\n", + "sep 13.1\n", + "oct 9.1\n", + "nov 3.7\n", + "dec 0.8\n", + "Name: temp_C, dtype: float64\n", + "\n", + "Temperatura en octubre: 9.1\n" + ] + } + ], + "source": [ + "# Índice con etiquetas — set_axis() (§8.1)\n", + "s2 = s.set_axis(['jan', 'feb', 'mar', 'apr', 'may', 'jun',\n", + " 'jul', 'aug', 'sep', 'oct', 'nov', 'dec'])\n", + "print(s2)\n", + "print(\"\\nTemperatura en octubre:\", s2['oct'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 2 — DataFrames" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Crear un DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "1 Carlos 22 Matemática 72.0\n", + "2 Diana 21 Física 91.3\n", + "3 Eduardo 23 Computación 68.7\n", + "4 Fátima 20 Matemática 79.2\n" + ] + } + ], + "source": [ + "# Crear DataFrame desde un diccionario\n", + "datos = {\n", + " \"Nombre\": [\"Ana\", \"Carlos\", \"Diana\", \"Eduardo\", \"Fátima\"],\n", + " \"Edad\": [20, 22, 21, 23, 20],\n", + " \"Carrera\": [\"Física\", \"Matemática\", \"Física\", \"Computación\", \"Matemática\"],\n", + " \"Promedio\": [85.5, 72.0, 91.3, 68.7, 79.2]\n", + "}\n", + "\n", + "df = pd.DataFrame(datos)\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forma (filas x columnas): (5, 4)\n", + "\n", + "Nombres de columnas: ['Nombre', 'Edad', 'Carrera', 'Promedio']\n", + "\n", + "Tipos de datos:\n", + "Nombre object\n", + "Edad int64\n", + "Carrera object\n", + "Promedio float64\n", + "dtype: object\n" + ] + } + ], + "source": [ + "# Información básica del DataFrame\n", + "print(\"Forma (filas x columnas):\", df.shape)\n", + "print(\"\\nNombres de columnas:\", df.columns.tolist())\n", + "print(\"\\nTipos de datos:\")\n", + "print(df.dtypes)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas descriptivas:\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Edad Promedio\n", + "count 5.00000 5.000000\n", + "mean 21.20000 79.340000\n", + "std 1.30384 9.328612\n", + "min 20.00000 68.700000\n", + "25% 20.00000 72.000000\n", + "50% 21.00000 79.200000\n", + "75% 22.00000 85.500000\n", + "max 23.00000 91.300000" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Resumen estadístico automático\n", + "print(\"Estadísticas descriptivas:\")\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Primeras 3 filas:\n", + " temp_C rain_mm\n", + "jan -0.3 59\n", + "feb 0.4 57\n", + "mar 3.9 84\n", + "\n", + "Últimas 2 filas:\n", + " temp_C rain_mm\n", + "nov 3.7 88\n", + "dec 0.8 80\n", + "\n", + "Columnas: ['temp_C', 'rain_mm']\n", + "Índice (list): ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']\n" + ] + } + ], + "source": [ + "# head(), tail() y atributos del índice (§8.2.1)\n", + "df_clima = pd.DataFrame({\n", + " 'temp_C': [-0.3, 0.4, 3.9, 7.4, 12.0, 15.0,\n", + " 17.2, 16.8, 13.1, 9.1, 3.7, 0.8],\n", + " 'rain_mm': [59, 57, 84, 100, 143, 153, 172, 164, 135, 89, 88, 80]\n", + "}, index=['jan','feb','mar','apr','may','jun',\n", + " 'jul','aug','sep','oct','nov','dec'])\n", + "\n", + "print(\"Primeras 3 filas:\")\n", + "print(df_clima.head(3))\n", + "print(\"\\nÚltimas 2 filas:\")\n", + "print(df_clima.tail(2))\n", + "print(\"\\nColumnas:\", df_clima.columns.tolist())\n", + "print(\"Índice (list):\", list(df_clima.index))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Acceder a datos" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columna 'Nombre':\n", + "0 Ana\n", + "1 Carlos\n", + "2 Diana\n", + "3 Eduardo\n", + "4 Fátima\n", + "Name: Nombre, dtype: object\n", + "\n", + "Columnas 'Nombre' y 'Promedio':\n", + " Nombre Promedio\n", + "0 Ana 85.5\n", + "1 Carlos 72.0\n", + "2 Diana 91.3\n", + "3 Eduardo 68.7\n", + "4 Fátima 79.2\n" + ] + } + ], + "source": [ + "# Acceder a una columna\n", + "print(\"Columna 'Nombre':\")\n", + "print(df[\"Nombre\"])\n", + "\n", + "print(\"\\nColumnas 'Nombre' y 'Promedio':\")\n", + "print(df[[\"Nombre\", \"Promedio\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Primera fila (iloc[0]):\n", + "Nombre Ana\n", + "Edad 20\n", + "Carrera Física\n", + "Promedio 85.5\n", + "Name: 0, dtype: object\n", + "\n", + "Filas 1 a 3 (iloc[1:4]):\n", + " Nombre Edad Carrera Promedio\n", + "1 Carlos 22 Matemática 72.0\n", + "2 Diana 21 Física 91.3\n", + "3 Eduardo 23 Computación 68.7\n" + ] + } + ], + "source": [ + "# Acceder a filas con .iloc (por posición numérica)\n", + "print(\"Primera fila (iloc[0]):\")\n", + "print(df.iloc[0])\n", + "\n", + "print(\"\\nFilas 1 a 3 (iloc[1:4]):\")\n", + "print(df.iloc[1:4])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fila con índice 2:\n", + "Nombre Diana\n", + "Edad 21\n", + "Carrera Física\n", + "Promedio 91.3\n", + "Name: 2, dtype: object\n", + "\n", + "Valor en fila 0, columna 'Nombre': Ana\n" + ] + } + ], + "source": [ + "# Acceder a filas con .loc (por etiqueta/índice)\n", + "print(\"Fila con índice 2:\")\n", + "print(df.loc[2])\n", + "\n", + "# Acceder a un valor específico: fila 0, columna 'Nombre'\n", + "print(\"\\nValor en fila 0, columna 'Nombre':\", df.loc[0, \"Nombre\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `loc` avanzado — selección por etiqueta (§8.2.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Marzo, abril y junio:\n", + " temp_C rain_mm\n", + "mar 3.9 84\n", + "apr 7.4 100\n", + "jun 15.0 153\n" + ] + } + ], + "source": [ + "# loc con lista de etiquetas (§8.2.2)\n", + "print(\"Marzo, abril y junio:\")\n", + "print(df_clima.loc[['mar', 'apr', 'jun']])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "De marzo a mayo:\n", + " temp_C rain_mm\n", + "mar 3.9 84\n", + "apr 7.4 100\n", + "may 12.0 143\n" + ] + } + ], + "source": [ + "# loc con slice de etiquetas — el extremo final SÍ se incluye (§8.2.2)\n", + "print(\"De marzo a mayo:\")\n", + "print(df_clima.loc['mar':'may'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lluvia de mayo a septiembre:\n", + "may 143\n", + "jun 153\n", + "jul 172\n", + "aug 164\n", + "sep 135\n", + "Name: rain_mm, dtype: int64\n", + "\n", + "Total de lluvia en verano: 767\n" + ] + } + ], + "source": [ + "# loc combinando filas Y columnas: df.loc[, ] (§8.2.2)\n", + "print(\"Lluvia de mayo a septiembre:\")\n", + "print(df_clima.loc['may':'sep', 'rain_mm'])\n", + "\n", + "print(\"\\nTotal de lluvia en verano:\", df_clima.loc['may':'sep', 'rain_mm'].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temp en meses con lluvia < 100 mm:\n", + "jan -0.3\n", + "feb 0.4\n", + "mar 3.9\n", + "oct 9.1\n", + "nov 3.7\n", + "dec 0.8\n", + "Name: temp_C, dtype: float64\n" + ] + } + ], + "source": [ + "# Máscara booleana con loc (§8.2.2)\n", + "# Temperatura en meses con lluvia < 100 mm\n", + "print(\"Temp en meses con lluvia < 100 mm:\")\n", + "print(df_clima.loc[df_clima['rain_mm'] < 100, 'temp_C'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3 — Filtrado, Ordenamiento y Nuevas Columnas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filtrar filas con condiciones" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estudiantes con promedio > 75:\n", + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "2 Diana 21 Física 91.3\n", + "4 Fátima 20 Matemática 79.2\n", + "\n", + "Estudiantes de Física:\n", + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "2 Diana 21 Física 91.3\n" + ] + } + ], + "source": [ + "# Estudiantes con promedio mayor a 75\n", + "buenos = df[df[\"Promedio\"] > 75]\n", + "print(\"Estudiantes con promedio > 75:\")\n", + "print(buenos)\n", + "\n", + "print()\n", + "\n", + "# Estudiantes de Física\n", + "fisica = df[df[\"Carrera\"] == \"Física\"]\n", + "print(\"Estudiantes de Física:\")\n", + "print(fisica)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Promedio > 70 Y Edad <= 21:\n", + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "2 Diana 21 Física 91.3\n", + "4 Fátima 20 Matemática 79.2\n" + ] + } + ], + "source": [ + "# Condiciones múltiples\n", + "# & = AND, | = OR\n", + "filtro = df[(df[\"Promedio\"] > 70) & (df[\"Edad\"] <= 21)]\n", + "print(\"Promedio > 70 Y Edad <= 21:\")\n", + "print(filtro)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ordenar datos" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ranking por promedio:\n", + " Nombre Promedio\n", + "2 Diana 91.3\n", + "0 Ana 85.5\n", + "4 Fátima 79.2\n", + "1 Carlos 72.0\n", + "3 Eduardo 68.7\n" + ] + } + ], + "source": [ + "# Ordenar por promedio (descendente)\n", + "ranking = df.sort_values(\"Promedio\", ascending=False)\n", + "print(\"Ranking por promedio:\")\n", + "print(ranking[[\"Nombre\", \"Promedio\"]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Agregar nuevas columnas" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nombre Edad Carrera Promedio Estado Puntos_extra\n", + "0 Ana 20 Física 85.5 Aprobado 15.5\n", + "1 Carlos 22 Matemática 72.0 Aprobado 2.0\n", + "2 Diana 21 Física 91.3 Aprobado 21.3\n", + "3 Eduardo 23 Computación 68.7 Reprobado 0.0\n", + "4 Fátima 20 Matemática 79.2 Aprobado 9.2\n" + ] + } + ], + "source": [ + "# Agregar columna calculada\n", + "df[\"Estado\"] = df[\"Promedio\"].apply(lambda p: \"Aprobado\" if p >= 70 else \"Reprobado\")\n", + "df[\"Puntos_extra\"] = (df[\"Promedio\"] - 70).clip(lower=0).round(1)\n", + "\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modificar datos y combinar DataFrames (§8.2.3)\n", + "\n", + "`df.assign()` crea columnas nuevas de forma funcional sin modificar el original." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm temp_F\n", + "jan -0.3 59 31.46\n", + "feb 0.4 57 32.72\n", + "mar 3.9 84 39.02\n", + "apr 7.4 100 45.32\n", + "may 12.0 143 53.60\n" + ] + } + ], + "source": [ + "# assign() — agregar columna con función (§8.2.3)\n", + "def celsius_a_f(df):\n", + " return df['temp_C'] * 1.8 + 32\n", + "\n", + "df_clima_f = df_clima.assign(temp_F=celsius_a_f)\n", + "print(df_clima_f.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm\n", + "jan -0.3 59.0\n", + "feb 0.4 57.0\n", + "mar 3.9 84.0\n", + "apr 7.4 100.0\n", + "may 12.0 143.0\n", + "jun 15.0 153.0\n", + "jul 17.2 172.0\n", + "aug 16.8 164.0\n", + "sep 13.1 135.0\n", + "oct 9.1 89.0\n", + "nov 3.7 88.0\n", + "dec 0.8 80.0\n", + "2020 9.7 165.8\n", + "2019 9.5 146.1\n", + "2018 9.9 139.2\n" + ] + } + ], + "source": [ + "# pd.concat() — combinar filas (§8.2.3)\n", + "df_anual = pd.DataFrame({\n", + " 'temp_C': [9.7, 9.5, 9.9],\n", + " 'rain_mm': [165.8, 146.1, 139.2]\n", + "}, index=['2020', '2019', '2018'])\n", + "\n", + "df_ext = pd.concat([df_clima, df_anual])\n", + "print(df_ext)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm temp_min_C temp_max_C\n", + "jan -0.3 59 -1.9 2.5\n", + "feb 0.4 57 -2.5 3.3\n", + "mar 3.9 84 0.6 7.3\n", + "apr 7.4 100 3.5 11.5\n", + "may 12.0 143 7.8 16.3\n", + "jun 15.0 153 11.0 19.2\n", + "jul 17.2 172 13.1 21.6\n", + "aug 16.8 164 13.0 20.9\n", + "sep 13.1 135 9.7 16.8\n", + "oct 9.1 89 6.2 12.3\n", + "nov 3.7 88 1.0 6.5\n", + "dec 0.8 80 -3.0 3.5\n" + ] + } + ], + "source": [ + "# pd.concat() con axis=1 — combinar columnas (§8.2.3)\n", + "df_minmax = pd.DataFrame({\n", + " 'temp_min_C': [-1.9, -2.5, 0.6, 3.5, 7.8, 11.0,\n", + " 13.1, 13.0, 9.7, 6.2, 1.0, -3.0],\n", + " 'temp_max_C': [2.5, 3.3, 7.3, 11.5, 16.3, 19.2,\n", + " 21.6, 20.9, 16.8, 12.3, 6.5, 3.5]\n", + "}, index=df_clima.index)\n", + "\n", + "df_completo = pd.concat([df_clima, df_minmax], axis=1)\n", + "print(df_completo)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 4 — Agrupaciones y Agregaciones" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas por carrera:\n", + " Promedio_medio Mínimo Máximo Estudiantes\n", + "Carrera \n", + "Computación 68.7 68.7 68.7 1\n", + "Física 88.4 85.5 91.3 2\n", + "Matemática 75.6 72.0 79.2 2\n" + ] + } + ], + "source": [ + "# groupby: agrupar por una columna y calcular estadísticas\n", + "por_carrera = df.groupby(\"Carrera\")[\"Promedio\"].agg([\"mean\", \"min\", \"max\", \"count\"])\n", + "por_carrera.columns = [\"Promedio_medio\", \"Mínimo\", \"Máximo\", \"Estudiantes\"]\n", + "\n", + "print(\"Estadísticas por carrera:\")\n", + "print(por_carrera.round(2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 5 — Manejo de Datos Faltantes" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame con valores faltantes:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n", + "1 Carlos NaN 65.0 71.0\n", + "2 Diana 90.0 NaN 85.0\n", + "3 Eduardo 72.0 80.0 NaN\n", + "4 Fátima NaN 88.0 76.0\n", + "\n", + "Cantidad de nulos por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Crear DataFrame con datos faltantes (NaN = Not a Number)\n", + "datos_incompletos = pd.DataFrame({\n", + " \"Nombre\": [\"Ana\", \"Carlos\", \"Diana\", \"Eduardo\", \"Fátima\"],\n", + " \"Nota1\": [85, np.nan, 90, 72, np.nan],\n", + " \"Nota2\": [78, 65, np.nan, 80, 88],\n", + " \"Nota3\": [92, 71, 85, np.nan, 76]\n", + "})\n", + "\n", + "print(\"DataFrame con valores faltantes:\")\n", + "print(datos_incompletos)\n", + "print(\"\\nCantidad de nulos por columna:\")\n", + "print(datos_incompletos.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Después de rellenar con la media:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n", + "1 Carlos 82.3 65.0 71.0\n", + "2 Diana 90.0 77.8 85.0\n", + "3 Eduardo 72.0 80.0 81.0\n", + "4 Fátima 82.3 88.0 76.0\n" + ] + } + ], + "source": [ + "# Opción 1: Rellenar con la media de la columna\n", + "df_rellenado = datos_incompletos.copy()\n", + "for col in [\"Nota1\", \"Nota2\", \"Nota3\"]:\n", + " df_rellenado[col] = df_rellenado[col].fillna(df_rellenado[col].mean())\n", + "\n", + "print(\"Después de rellenar con la media:\")\n", + "print(df_rellenado.round(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Después de eliminar filas con nulos:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n" + ] + } + ], + "source": [ + "# Opción 2: Eliminar filas con datos faltantes\n", + "df_limpio = datos_incompletos.dropna()\n", + "print(\"Después de eliminar filas con nulos:\")\n", + "print(df_limpio)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `isna()`, `fillna()` e `interpolate()` (§8.2.4)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mapa de NaN:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 False False False False\n", + "1 False True False False\n", + "2 False False True False\n", + "3 False False False True\n", + "4 False True False False\n", + "\n", + "Total de NaN por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# isna() / notna() — mapa de valores faltantes (§8.2.4)\n", + "print(\"Mapa de NaN:\")\n", + "print(datos_incompletos.isna())\n", + "\n", + "print(\"\\nTotal de NaN por columna:\")\n", + "print(datos_incompletos.isna().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rellenado con la media de cada columna:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n", + "1 Carlos 82.3 65.0 71.0\n", + "2 Diana 90.0 77.8 85.0\n", + "3 Eduardo 72.0 80.0 81.0\n", + "4 Fátima 82.3 88.0 76.0\n" + ] + } + ], + "source": [ + "# fillna con la media de cada columna (§8.2.4)\n", + "df_relleno2 = datos_incompletos.copy()\n", + "df_relleno2 = df_relleno2.fillna(\n", + " df_relleno2.mean(axis=0, numeric_only=True)\n", + ")\n", + "print(\"Rellenado con la media de cada columna:\")\n", + "print(df_relleno2.round(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original: [ 1. nan 3. nan 5.]\n", + "Interpolado: [1. 2. 3. 4. 5.]\n" + ] + } + ], + "source": [ + "# interpolate() — interpolación lineal (§8.2.4)\n", + "s_nan = pd.Series([1.0, np.nan, 3.0, np.nan, 5.0])\n", + "print(\"Original:\", s_nan.values)\n", + "print(\"Interpolado:\", s_nan.interpolate().values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 6 — Leer Datos desde Archivos\n", + "\n", + "En la práctica, los datos vienen de archivos externos. Pandas puede leer muchos formatos:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datos leídos desde CSV:\n", + " nombre edad carrera nota\n", + "0 Ana 20 Física 85\n", + "1 Carlos 22 Matemática 72\n", + "2 Diana 21 Física 91\n", + "3 Eduardo 23 Computación 68\n", + "4 Fátima 20 Matemática 79\n", + "\n", + "Primeras 3 filas (head):\n", + " nombre edad carrera nota\n", + "0 Ana 20 Física 85\n", + "1 Carlos 22 Matemática 72\n", + "2 Diana 21 Física 91\n" + ] + } + ], + "source": [ + "# Crear un CSV de ejemplo para practicar\n", + "csv_contenido = \"\"\"nombre,edad,carrera,nota\n", + "Ana,20,Física,85\n", + "Carlos,22,Matemática,72\n", + "Diana,21,Física,91\n", + "Eduardo,23,Computación,68\n", + "Fátima,20,Matemática,79\n", + "\"\"\"\n", + "\n", + "with open(\"/tmp/estudiantes.csv\", \"w\") as f:\n", + " f.write(csv_contenido)\n", + "\n", + "# Leer el CSV\n", + "df_csv = pd.read_csv(\"/tmp/estudiantes.csv\")\n", + "print(\"Datos leídos desde CSV:\")\n", + "print(df_csv)\n", + "print(\"\\nPrimeras 3 filas (head):\")\n", + "print(df_csv.head(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Archivo guardado como /tmp/resultado.csv\n" + ] + } + ], + "source": [ + "# Guardar un DataFrame como CSV\n", + "df_csv.to_csv(\"/tmp/resultado.csv\", index=False)\n", + "print(\"Archivo guardado como /tmp/resultado.csv\")\n", + "\n", + "# Otros formatos comunes:\n", + "# df.to_excel(\"archivo.xlsx\", index=False)\n", + "# df = pd.read_excel(\"archivo.xlsx\")\n", + "# df = pd.read_json(\"archivo.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C uv nubes\n", + "dia \n", + "lunes 12.3 5 clear\n", + "martes 13.5 4 clear\n", + "miercoles 9.2 1 mostly cloudy\n", + "jueves 8.2 2 partly cloudy\n", + "viernes 10.2 3 partly cloudy\n" + ] + } + ], + "source": [ + "# read_csv con opciones avanzadas (§8.6)\n", + "import io\n", + "\n", + "csv_sin_header = \"\"\"lunes,12.3,5,clear\n", + "martes,13.5,4,clear\n", + "miercoles,9.2,1,mostly cloudy\n", + "jueves,8.2,2,partly cloudy\n", + "viernes,10.2,3,partly cloudy\n", + "\"\"\"\n", + "\n", + "df_weather = pd.read_csv(\n", + " io.StringIO(csv_sin_header),\n", + " names=['dia', 'temp_C', 'uv', 'nubes'],\n", + " index_col='dia'\n", + ")\n", + "print(df_weather)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 7 — Datos Categóricos (§8.3.1)\n", + "\n", + "Los datos categóricos toman valores de un conjunto discreto y finito de categorías." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " nubes uv\n", + "lun nublado 0\n", + "mar nublado 0\n", + "mié parcialmente nublado 1\n", + "jue mayormente despejado 3\n", + "vie despejado 5\n", + "sáb despejado 5\n", + "dom parcialmente nublado 1\n" + ] + } + ], + "source": [ + "# DataFrame con datos categóricos\n", + "df_cat = pd.DataFrame({\n", + " 'nubes': ['nublado', 'nublado', 'parcialmente nublado',\n", + " 'mayormente despejado', 'despejado', 'despejado', 'parcialmente nublado'],\n", + " 'uv': [0, 0, 1, 3, 5, 5, 1]\n", + "}, index=['lun', 'mar', 'mié', 'jue', 'vie', 'sáb', 'dom'])\n", + "\n", + "print(df_cat)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Categorías únicas:\n", + "['nublado' 'parcialmente nublado' 'mayormente despejado' 'despejado']\n", + "\n", + "Frecuencia de cada categoría:\n", + "nubes\n", + "nublado 2\n", + "parcialmente nublado 2\n", + "despejado 2\n", + "mayormente despejado 1\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# unique() — valores únicos | value_counts() — frecuencia (§8.3.1)\n", + "print(\"Categorías únicas:\")\n", + "print(df_cat['nubes'].unique())\n", + "\n", + "print(\"\\nFrecuencia de cada categoría:\")\n", + "print(df_cat['nubes'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UV promedio en días parcialmente nublados: 1.0\n" + ] + } + ], + "source": [ + "# Filtrar por categoría y aplicar método (§8.3.1)\n", + "uv_parcial = df_cat.loc[df_cat['nubes'] == 'parcialmente nublado', 'uv'].mean()\n", + "print(\"UV promedio en días parcialmente nublados:\", uv_parcial)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 8 — Datos de Texto: `pd.Series.str` (§8.3.2)\n", + "\n", + "Pandas provee funciones vectorizadas para strings a través del submódulo `str`." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "¿Contiene 'nublado'?\n", + "lun True\n", + "mar True\n", + "mié True\n", + "jue False\n", + "vie False\n", + "sáb False\n", + "dom True\n", + "Name: nubes, dtype: bool\n", + "\n", + "Filas con nubosidad:\n", + " nubes uv\n", + "lun nublado 0\n", + "mar nublado 0\n", + "mié parcialmente nublado 1\n", + "dom parcialmente nublado 1\n" + ] + } + ], + "source": [ + "# str.contains() — filtrar filas que contienen una subcadena (§8.3.2)\n", + "mascara = df_cat['nubes'].str.contains('nublado', regex=False)\n", + "print(\"¿Contiene 'nublado'?\")\n", + "print(mascara)\n", + "\n", + "print(\"\\nFilas con nubosidad:\")\n", + "print(df_cat.loc[mascara])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " nubes uv\n", + "lun soleado 0\n", + "mar soleado 0\n", + "mié parcialmente soleado 1\n", + "jue mayormente despejado 3\n", + "vie despejado 5\n", + "sáb despejado 5\n", + "dom parcialmente soleado 1\n" + ] + } + ], + "source": [ + "# str.replace() — reemplazar texto (§8.3.2)\n", + "df_cat2 = df_cat.copy()\n", + "df_cat2.loc[:, 'nubes'] = df_cat2['nubes'].str.replace('nublado', 'soleado')\n", + "print(df_cat2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 9 — Fechas y Tiempos (§8.3.3)\n", + "\n", + "`pd.to_datetime()` convierte strings a objetos `datetime64` para series de tiempo." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tipo original: object\n", + "Tipo convertido: datetime64[ns]\n", + "0 2020-01-01 12:34:00\n", + "1 2020-03-01 08:47:00\n", + "2 2020-06-01 14:23:00\n", + "3 2020-09-01 22:56:00\n", + "4 2020-12-01 13:45:00\n", + "dtype: datetime64[ns]\n" + ] + } + ], + "source": [ + "# pd.to_datetime() — convertir strings a fechas (§8.3.3)\n", + "fechas = pd.Series(['2020-01-01 12:34', '2020-03-01 08:47',\n", + " '2020-06-01 14:23', '2020-09-01 22:56',\n", + " '2020-12-01 13:45'])\n", + "\n", + "print(\"Tipo original:\", fechas.dtype)\n", + "fechas = pd.to_datetime(fechas)\n", + "print(\"Tipo convertido:\", fechas.dtype)\n", + "print(fechas)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Horas:\n", + "0 12:34:00\n", + "1 08:47:00\n", + "2 14:23:00\n", + "3 22:56:00\n", + "4 13:45:00\n", + "dtype: object\n", + "\n", + "Meses: [ 1 3 6 9 12]\n" + ] + } + ], + "source": [ + "# Acceder a componentes con el accessor .dt (§8.3.3)\n", + "print(\"Horas:\")\n", + "print(fechas.dt.time)\n", + "print(\"\\nMeses:\", fechas.dt.month.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diferencia respecto al primer registro:\n", + "0 0 days 00:00:00\n", + "1 59 days 20:13:00\n", + "2 152 days 01:49:00\n", + "3 244 days 10:22:00\n", + "4 335 days 01:11:00\n", + "dtype: timedelta64[ns]\n", + "\n", + "En segundos (float):\n", + "0 0.0\n", + "1 5170380.0\n", + "2 13139340.0\n", + "3 21118920.0\n", + "4 28948260.0\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# Diferencias de tiempo — timedelta (§8.3.3)\n", + "delta = fechas - fechas.iloc[0]\n", + "print(\"Diferencia respecto al primer registro:\")\n", + "print(delta)\n", + "\n", + "print(\"\\nEn segundos (float):\")\n", + "#print(delta.astype('timedelta64[s]').astype(float))\n", + "print( (fechas - fechas.iloc[0]).dt.total_seconds() )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 10 — Funciones de Agregación (§8.4)\n", + "\n", + "Pandas provee métodos **agregadores** (producen un escalar) y **no-agregadores** (misma longitud que la entrada).\n", + "\n", + "| Agregadores | | | |\n", + "|---|---|---|---|\n", + "| `count()` | `sum()` | `mean()` | `max()` |\n", + "| `min()` | `median()` | `std()` | `var()` |\n", + "| `prod()` | `quantile(x)` | `abs()` | |\n", + "\n", + "| No-agregadores | | |\n", + "|---|---|---|\n", + "| `cumsum()` | `cumprod()` | `cummax()` |\n", + "| `cummin()` | | |" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas de temperatura y lluvia:\n", + " temp_C rain_mm\n", + "min -0.30 57.00\n", + "max 17.20 172.00\n", + "mean 8.26 110.33\n" + ] + } + ], + "source": [ + "# agg() con lista de métodos (§8.4)\n", + "print(\"Estadísticas de temperatura y lluvia:\")\n", + "print(df_clima[['temp_C', 'rain_mm']].agg(['min', 'max', 'mean']).round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm\n", + "min -0.30 NaN\n", + "max 17.20 NaN\n", + "mean 8.26 NaN\n", + "sum NaN 1324.0\n", + "median NaN 94.5\n" + ] + } + ], + "source": [ + "# agg() con diccionario — distintas métricas por columna (§8.4)\n", + "resultado = df_clima[['temp_C', 'rain_mm']].agg({\n", + " 'temp_C': ['min', 'max', 'mean'],\n", + " 'rain_mm': ['sum', 'median']\n", + "})\n", + "print(resultado.round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rango (max - min) por columna numérica:\n", + "temp_C 17.5\n", + "rain_mm 115.0\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# apply() — función personalizada por columna (§8.4)\n", + "def rango(col):\n", + " return col.max() - col.min()\n", + "\n", + "print(\"Rango (max - min) por columna numérica:\")\n", + "print(df_clima[['temp_C', 'rain_mm']].apply(rango))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 11 — Visualización con Pandas (§8.5)\n", + "\n", + "Pandas ofrece métodos de graficación directa sobre DataFrames y Series que usan Matplotlib internamente." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# df.plot() — gráfica de línea (§8.5)\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "df_clima['temp_C'].plot(kind='line', ax=axes[0], marker='o', color='tomato')\n", + "axes[0].set_title('Temperatura mensual promedio')\n", + "axes[0].set_ylabel('°C')\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "df_clima['rain_mm'].plot(kind='bar', ax=axes[1], color='steelblue', alpha=0.8)\n", + "axes[1].set_title('Lluvia mensual')\n", + "axes[1].set_ylabel('mm')\n", + "axes[1].tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# df.hist() — histogramas de todas las columnas numéricas (§8.5)\n", + "df_clima.hist(figsize=(8, 4))\n", + "plt.suptitle('Distribución de variables climáticas', y=1.02)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# value_counts().plot() — histograma de datos categóricos (§8.5)\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "df_cat['nubes'].value_counts().plot(kind='bar', ax=ax, color='slategray')\n", + "ax.set_title('Frecuencia de tipos de nubosidad')\n", + "ax.set_ylabel('Días')\n", + "ax.tick_params(axis='x', rotation=30)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 12 — Consejos de Pandas (§8.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reporte climático mensual:\n", + " jan: -0.3°C, 59 mm de lluvia\n", + " feb: 0.4°C, 57 mm de lluvia\n", + " mar: 3.9°C, 84 mm de lluvia\n", + " apr: 7.4°C, 100 mm de lluvia\n", + " may: 12.0°C, 143 mm de lluvia\n", + " jun: 15.0°C, 153 mm de lluvia\n", + " jul: 17.2°C, 172 mm de lluvia\n", + " aug: 16.8°C, 164 mm de lluvia\n", + " sep: 13.1°C, 135 mm de lluvia\n", + " oct: 9.1°C, 89 mm de lluvia\n", + " nov: 3.7°C, 88 mm de lluvia\n", + " dec: 0.8°C, 80 mm de lluvia\n" + ] + } + ], + "source": [ + "# iterrows() — iterar sobre filas (§8.7)\n", + "print(\"Reporte climático mensual:\")\n", + "for mes, datos in df_clima.iterrows():\n", + " print(f\" {mes:>3}: {datos['temp_C']:5.1f}°C, {datos['rain_mm']:3.0f} mm de lluvia\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperatura media según si llueve ≥ 100 mm:\n", + "rain_mm\n", + "False 2.93\n", + "True 13.58\n", + "Name: temp_C, dtype: float64\n" + ] + } + ], + "source": [ + "# groupby() con condición booleana (§8.7)\n", + "print(\"Temperatura media según si llueve ≥ 100 mm:\")\n", + "print(df_clima.groupby(df_clima['rain_mm'] >= 100)['temp_C'].mean().round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meses cálidos (> 10°C):\n", + " temp_C temp_F rain_mm\n", + "jul 17.2 62.96 172\n", + "aug 16.8 62.24 164\n", + "jun 15.0 59.00 153\n", + "sep 13.1 55.58 135\n", + "may 12.0 53.60 143\n" + ] + } + ], + "source": [ + "# Encadenar métodos — flujo de trabajo legible\n", + "resumen = (df_clima\n", + " .assign(temp_F=lambda d: d['temp_C'] * 1.8 + 32)\n", + " .loc[df_clima['temp_C'] > 10, ['temp_C', 'temp_F', 'rain_mm']]\n", + " .sort_values('temp_C', ascending=False)\n", + ")\n", + "print(\"Meses cálidos (> 10°C):\")\n", + "print(resumen)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Ejercicios" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 1\n", + "Crea una Serie con los valores de precipitación mensual de tu ciudad.\n", + "Calcula: media, mediana, máximo y el mes con mayor precipitación (`argmax()`)." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " VALORES DE PRECIPITACIÓN ANUAL\n", + " ESTACIÓN ANTIGUA GUATEMALA\n", + "\n", + " VALORES: \n", + " [[ 2.1 1.5 12.4]\n", + " [ 35.8 150.2 250.4]\n", + " [180.1 195.3 280.9]\n", + " [160.4 25.2 5.1]] \n", + "\n", + " FECHAS: \n", + " [['Enero' 'Febrero' 'Marzo']\n", + " ['Abril' 'Mayo' 'Junio']\n", + " ['Julio' 'Agosto' 'Septiembre']\n", + " ['Octubre' 'Noviembre' 'Diciembre']]\n", + "------------------------------------------------------------\n", + " ESTADÍSTICAS\n", + "MEDIA: 108.3 mm\n", + "MÍNIMO: 1.5 mm\n", + "ÍNDICE DEL VALOR MÁXIMO: 8\n", + "DÍA CON MAYOR PRECIPITACIÓN: 280.9 mm\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "# Valores de la Estación Antigua Guatemala. lluvias en mm\n", + "lluvias = [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "meses = ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre']\n", + "\n", + "# Arreglo de matriz\n", + "a = np.array(lluvias[:12])\n", + "b = np.array(meses[:12])\n", + "\n", + "d = a.reshape(4, 3)\n", + "F = b.reshape(4, 3)\n", + "\n", + "print(\"--\" * 30)\n", + "print(\" \" * 15 + \"VALORES DE PRECIPITACIÓN ANUAL\")\n", + "print(\" \" * 17 + \"ESTACIÓN ANTIGUA GUATEMALA\")\n", + "\n", + "print(f\"\\n VALORES: \\n {d} \\n\\n FECHAS: \\n {F}\")\n", + "print(\"--\" * 30)\n", + "\n", + "print(\" \" * 20 + \"ESTADÍSTICAS\")\n", + "\n", + "print(f'MEDIA: {a.mean():.1f} mm')\n", + "print(f'MÍNIMO: {a.min():.1f} mm')\n", + "print('ÍNDICE DEL VALOR MÁXIMO:', a.argmax())\n", + "# Usamos max(a) o a.max()\n", + "print(f'DÍA CON MAYOR PRECIPITACIÓN: {a.max()} mm')\n", + "print(\"--\" * 30)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 2\n", + "Dado `df_clima`, filtra los meses con temperatura entre 5°C y 15°C.\n", + "Calcula la lluvia total en ese subconjunto." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " DATOS REPRESENTATIVOS DE TEMPERATURA (°C) PARA ANTIGUA GUATEMALA\n", + " Mes Temp_Min Temp_Max Lluvia_mm\n", + "0 Enero 10.5 23.8 2.1\n", + "1 Febrero 11.2 25.1 1.5\n", + "2 Marzo 12.8 26.7 12.4\n", + "3 Abril 14.5 27.5 35.8\n", + "4 Mayo 15.8 26.8 150.2\n", + "5 Junio 16.2 25.4 250.4\n", + "6 Julio 15.9 25.1 180.1\n", + "7 Agosto 15.7 25.3 195.3\n", + "8 Septiembre 15.8 24.8 280.9\n", + "9 Octubre 14.9 24.2 160.4\n", + "10 Noviembre 13.1 23.9 25.2\n", + "11 Diciembre 11.4 23.5 5.1\n", + "------------------------------------------------------------\n", + " MESES CON TEMPERATURA ENTRE 5°C Y 15°C: \n", + " Mes Temp_Min\n", + "0 Enero 10.5\n", + "1 Febrero 11.2\n", + "2 Marzo 12.8\n", + "3 Abril 14.5\n", + "9 Octubre 14.9\n", + "10 Noviembre 13.1\n", + "11 Diciembre 11.4\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "# Datos representativos de temperatura (°C) para Antigua Guatemala\n", + "datos = {\n", + " 'Mes': ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'],\n", + " 'Temp_Min': [10.5, 11.2, 12.8, 14.5, 15.8, 16.2, 15.9, 15.7, 15.8, 14.9, 13.1, 11.4],\n", + " 'Temp_Max': [23.8, 25.1, 26.7, 27.5, 26.8, 25.4, 25.1, 25.3, 24.8, 24.2, 23.9, 23.5],\n", + " 'Lluvia_mm': [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "}\n", + "\n", + "df_clima = pd.DataFrame(datos)\n", + "filtro = (df_clima [ 'Temp_Min'] >=5) & (df_clima[ 'Temp_Min'] <= 15)\n", + "subconjunto = df_clima[filtro]\n", + "Luvia_total = subconjunto [ 'Lluvia_mm'].sum()\n", + "print(\"--\" * 30)\n", + "\n", + "print(\" \" * 1 + \"DATOS REPRESENTATIVOS DE TEMPERATURA (°C) PARA ANTIGUA GUATEMALA\")\n", + "print(df_clima)\n", + "print(\"--\" * 30)\n", + "print (f\" MESES CON TEMPERATURA ENTRE 5°C Y 15°C: \\n {subconjunto[['Mes','Temp_Min' ]]}\")\n", + "print(\"--\" * 30)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 3\n", + "Agrega al DataFrame `df_completo` (con `temp_min_C` y `temp_max_C`) una columna\n", + "`amplitud_termica = temp_max_C - temp_min_C`. Encuentra el mes con mayor amplitud." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " EL MES CON MAYOR AMPLITUD TÉRMINCA \n", + " MES: Febrero\n", + "------------------------------------------------------------\n", + " LA MAYOR AMPLITUD TÉRMINCA \n", + " AMPLITUD: 13.9 °C\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "# Datos representativos de temperatura (°C) para Antigua Guatemala\n", + "datos = {\n", + " 'Mes': ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'],\n", + " 'Temp_Min': [10.5, 11.2, 12.8, 14.5, 15.8, 16.2, 15.9, 15.7, 15.8, 14.9, 13.1, 11.4],\n", + " 'Temp_Max': [23.8, 25.1, 26.7, 27.5, 26.8, 25.4, 25.1, 25.3, 24.8, 24.2, 23.9, 23.5],\n", + " 'Lluvia_mm': [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "}\n", + "\n", + "df_clima = pd.DataFrame(datos)\n", + "\n", + "df_clima['amplitud_termica'] = df_clima['Temp_Max'] - df_clima['Temp_Min']\n", + "\n", + "indice_max = df_clima['amplitud_termica'].idxmax()\n", + "\n", + "mes_max = df_clima.loc[indice_max]\n", + "print(\"--\" * 30)\n", + "print(\" \" * 2 +\" EL MES CON MAYOR AMPLITUD TÉRMINCA \")\n", + "print(f\" MES: {mes_max['Mes']}\")\n", + "print(\"--\" * 30)\n", + "print(\" \" * 2 +\" LA MAYOR AMPLITUD TÉRMINCA \")\n", + "print(f\" AMPLITUD: {mes_max['amplitud_termica']:.1f} °C\")\n", + "print(\"--\" * 30)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 4\n", + "Crea un DataFrame con 5 estudiantes (nombre, nota1, nota2, nota3) con 3 valores NaN. \n", + "a) Identifica cuántos NaN hay por columna. \n", + "b) Rellena con la media de cada columna. \n", + "c) Calcula la nota promedio final de cada estudiante." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + "DataFrame con valores faltantes:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Herlinda 90.0 NaN NaN\n", + "1 Rafael 80.0 65.0 71.0\n", + "2 Irinea NaN 70.0 NaN\n", + "3 Magaly 88.0 80.0 90.0\n", + "4 Adán NaN NaN 76.0\n", + "------------------------------------------------------------\n", + "Cantidad de nulos por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n", + "------------------------------------------------------------\n", + " RELLENAR CON LA MEDIA EN CADA COLUMNA:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Herlinda 90.0 71.7 79.0\n", + "1 Rafael 80.0 65.0 71.0\n", + "2 Irinea 86.0 70.0 79.0\n", + "3 Magaly 88.0 80.0 90.0\n", + "4 Adán 86.0 71.7 76.0\n", + "------------------------------------------------------------\n", + " NOTA PROMEDIO FINAL DE CADA ESTUDIANTE:\n", + " Nombre Promedio\n", + "0 Herlinda 90.0\n", + "1 Rafael 72.0\n", + "2 Irinea 70.0\n", + "3 Magaly 86.0\n", + "4 Adán 76.0\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "# Crear DataFrame con datos faltantes (NaN = Not a Number)\n", + "Informacion = pd.DataFrame({\n", + " \"Nombre\": [\"Herlinda\", \"Rafael\", \"Irinea\", \"Magaly\", \"Adán\"],\n", + " \"Nota1\": [90, 80, np.nan, 88, np.nan],\n", + " \"Nota2\": [np.nan, 65, 70, 80, np.nan],\n", + " \"Nota3\": [np.nan, 71, np.nan, 90, 76]\n", + "})\n", + "\n", + "NOTA1 = Informacion[\"Nota1\"]\n", + "NOTA2 = Informacion[\"Nota2\"]\n", + "NOTA3 = Informacion[\"Nota3\"]\n", + "print(\"--\" * 30)\n", + "print(\"DataFrame con valores faltantes:\")\n", + "print(Informacion)\n", + "print(\"--\" * 30)\n", + "print(\"Cantidad de nulos por columna:\")\n", + "print(datos_incompletos.isnull().sum())\n", + "print(\"--\" * 30)\n", + "\n", + "df_rellenado = Informacion .copy()\n", + "for col in [\"Nota1\", \"Nota2\", \"Nota3\"]:\n", + " df_rellenado[col] = df_rellenado[col].fillna(df_rellenado[col].mean())\n", + "\n", + "print(\" RELLENAR CON LA MEDIA EN CADA COLUMNA:\")\n", + "print(df_rellenado.round(1))\n", + "\n", + "print(\"--\" * 30)\n", + "print(\" NOTA PROMEDIO FINAL DE CADA ESTUDIANTE:\")\n", + "Informacion[\"Promedio\"] = Informacion[[\"Nota1\", \"Nota2\", \"Nota3\"]].mean(axis=1)\n", + "Informacion[\"Promedio\"] = Informacion[\"Promedio\"].round(3)\n", + "print(Informacion[[\"Nombre\", \"Promedio\"]])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pandas versión: 2.1.4\n", + " Año Mes Costo_Diario_Q Costo_Mensual_Q\n", + "0 2020 Diciembre 99.65 2989.38\n", + "1 2021 Enero 99.49 2984.73\n", + "2 2021 Febrero 99.58 2987.39\n", + "3 2021 Marzo 99.27 2978.10\n", + "4 2021 Abril 99.72 2991.70\n", + "5 2021 Mayo 99.77 2993.03\n", + "6 2021 Junio 99.99 2999.67\n", + "7 2021 Julio 100.11 3003.32\n", + "8 2021 Agosto 100.42 3012.61\n", + "9 2021 Septiembre 100.85 3025.55\n", + "10 2021 Octubre 101.84 3055.09\n", + "11 2021 Noviembre 102.74 3082.30\n", + "12 2021 Diciembre 103.24 3097.23\n", + "13 2022 Enero 103.67 3110.18\n", + "14 2022 Febrero 104.48 3134.40\n", + "15 2022 Marzo 106.05 3181.53\n", + "16 2022 Abril 107.27 3218.03\n", + "17 2022 Mayo 107.82 3234.62\n", + "18 2022 Junio 110.40 3311.95\n", + "19 2022 Julio 112.32 3369.69\n", + "20 2022 Agosto 115.17 3454.98\n", + "21 2022 Septiembre 117.96 3538.94\n", + "22 2022 Octubre 121.13 3633.85\n", + "23 2022 Noviembre 120.62 3618.58\n", + "24 2022 Diciembre 121.14 3634.18\n", + "25 2023 Enero 121.27 3638.16\n", + "26 2023 Febrero 123.20 3695.91\n", + "27 2023 Marzo 124.28 3728.43\n", + "28 2023 Abril 124.20 3726.11\n", + "29 2023 Mayo 124.39 3731.75\n", + "30 2023 Junio 124.52 3735.73\n", + "31 2023 Julio 125.50 3764.93\n", + "32 2023 Agosto 126.99 3809.73\n", + "33 2023 Septiembre 127.50 3825.00\n", + "34 2023 Octubre 131.24 3937.17\n", + "35 2023 Noviembre 129.99 3899.67\n", + "36 2023 Diciembre 130.17 3904.98\n", + "------------------------------------------------------------\n", + "COSTO PROMEDIO MENDUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\n", + "Año\n", + "2020 2989.380000\n", + "2021 3017.560000\n", + "2022 3370.077500\n", + "2023 3783.130833\n", + "Name: Costo_Mensual_Q, dtype: float64\n", + "------------------------------------------------------------\n", + "COSTO PROMEDIO MENSUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\n", + "Mes\n", + "Abril 3311.946667\n", + "Agosto 3425.773333\n", + "Diciembre 3406.442500\n", + "Enero 3244.356667\n", + "Febrero 3272.566667\n", + "Julio 3379.313333\n", + "Junio 3349.116667\n", + "Marzo 3296.020000\n", + "Mayo 3319.800000\n", + "Noviembre 3533.516667\n", + "Octubre 3542.036667\n", + "Septiembre 3463.163333\n", + "Name: Costo_Mensual_Q, dtype: float64\n", + "------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TAREA 5(OPCIONAL) - PANDAS EN PRÁCTICA \n", + "# Dataset real (opcional, puntos estra)\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np \n", + "print(\"Pandas versión:\", pd.__version__)\n", + "\n", + "# (LECTURA) Diccionario de la Canasta Básica Alimentaria (2020-2023)Obtenida del INE, Guatemala\n", + "df_Canasta_Basica = {\n", + " 'Año': [\n", + " 2020, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021,\n", + " 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022,\n", + " 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023\n", + " ],\n", + " 'Mes': [\n", + " 'Diciembre', 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre',\n", + " 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre',\n", + " 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'\n", + " ],\n", + " 'Costo_Diario_Q': [\n", + " 99.65, 99.49, 99.58, 99.27, 99.72, 99.77, 99.99, 100.11, 100.42, 100.85, 101.84, 102.74, 103.24,\n", + " 103.67, 104.48, 106.05, 107.27, 107.82, 110.40, 112.32, 115.17, 117.96, 121.13, 120.62, 121.14,\n", + " 121.27, 123.20, 124.28, 124.20, 124.39, 124.52, 125.50, 126.99, 127.50, 131.24, 129.99, 130.17\n", + " ],\n", + " 'Costo_Mensual_Q': [\n", + " 2989.38, 2984.73, 2987.39, 2978.10, 2991.70, 2993.03, 2999.67, 3003.32, 3012.61, 3025.55, 3055.09, 3082.30, 3097.23,\n", + " 3110.18, 3134.40, 3181.53, 3218.03, 3234.62, 3311.95, 3369.69, 3454.98, 3538.94, 3633.85, 3618.58, 3634.18,\n", + " 3638.16, 3695.91, 3728.43, 3726.11, 3731.75, 3735.73, 3764.93, 3809.73, 3825.00, 3937.17, 3899.67, 3904.98\n", + " ]\n", + "}\n", + "\n", + "# (LIMPIEZA) UTILIZANDO .groupby\n", + "df_Canasta_Basica = pd.DataFrame(df_Canasta_Basica )\n", + "print(df_Canasta_Basica) \n", + "print(\"--\" * 30)\n", + "promedio_anual = df_Canasta_Basica.groupby('Año')['Costo_Mensual_Q'].mean()\n", + "print(\"COSTO PROMEDIO MENDUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\")\n", + "print(promedio_anual)\n", + "print(\"--\" * 30)\n", + "promedio_mensual = df_Canasta_Basica.groupby('Mes')['Costo_Mensual_Q'].mean()\n", + "print(\"COSTO PROMEDIO MENSUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\")\n", + "print(promedio_mensual)\n", + "print(\"--\" * 30)\n", + "\n", + "\n", + "# (VISUALIZACIÓN) \n", + "# df.plot() — gráfica de línea (§8.5)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n", + "promedio_anual.plot(kind='line', ax=ax, marker='o', color='blue')\n", + "\n", + "ax.set_title('PROMEDIO ANUAL DE LA CANASTA BÁSICA EN GUATEMALA 2020-2023')\n", + "ax.set_ylabel('Quetzales')\n", + "ax.set_xlabel('Año')\n", + "ax.set_xticks([2020, 2021, 2022, 2023])\n", + "\n", + "ax.set_ylim(2800, 4000)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CONCLUSIONES: \n", + "Sobre las Canasta básica en Guatemala\n", + "El promedio en la cansta básica en Guatemala, relacionando los promedios anuales obtenidos con la gráfica, la canasta básica ha ido aumentando Q 200.00 desde el año 2021.\n", + "En julio de 2021 un año y dos meses después de la pandemia de COVID-19, la canasta básica aumentó a Q100.11, en Guatemala." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tarea 5 (opcional) — Pandas en Práctica\n", + "\n", + "La programación se aprende haciendo. Esta tarea tiene dos partes:\n", + "\n", + "**Parte 1 — Descarga y ejecuta** (obligatorio)\n", + "Descarga este notebook y ejecútalo celda por celda en tu computadora. \n", + "Verifica que todas las celdas corran sin errores antes de continuar.\n", + "\n", + "**Parte 2 — Ejercicios** (obligatorio) \n", + "Resuelve los ejercicios de la sección anterior (Ejercicios 1 al 4).\n", + "\n", + "**Parte 3 — Dataset real** (opcional, puntos extra) \n", + "Descarga cualquier dataset CSV que te interese y aplica al menos 3 herramientas vistas:\n", + "lectura, limpieza, `groupby`, visualización. \n", + "Agrega una celda Markdown con tus conclusiones.\n", + "\n", + "**Entrega: 1 de mayo** \n", + "Sube el notebook a tu repositorio de GitHub y envía el **link directo al archivo `.ipynb`**.\n", + "El notebook debe tener todas las celdas ya ejecutadas (con outputs visibles).\n", + "\n", + "Ejemplo de link válido: \n", + "`https://github.com/tu-usuario/tu-repo/blob/main/Pandas.ipynb`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From cfad6e852bc43c870e5bc1b41251ac6a7b5611ba Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 1 May 2026 13:28:55 -0600 Subject: [PATCH 20/28] git push origin ejemplospython --- tareas/{Numpy.ipynb => numpy.ipynb} | 0 tareas/{Pandas.ipynb => pandas.ipynb} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename tareas/{Numpy.ipynb => numpy.ipynb} (100%) rename tareas/{Pandas.ipynb => pandas.ipynb} (100%) diff --git a/ tareas/Numpy.ipynb b/ tareas/numpy.ipynb similarity index 100% rename from tareas/Numpy.ipynb rename to tareas/numpy.ipynb diff --git a/ tareas/Pandas.ipynb b/ tareas/pandas.ipynb similarity index 100% rename from tareas/Pandas.ipynb rename to tareas/pandas.ipynb From 87b2b90233a0e0942f581120838c0dfce1f32fdd Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Wed, 6 May 2026 10:24:54 -0600 Subject: [PATCH 21/28] Actulizar repositorio --- tareas/practica_dos.ipynb | 0 ...strellas_estudiante-Copy1-checkpoint.ipynb | 574 ++++++++++++++++++ ...isis_estrellas_estudiante-checkpoint.ipynb | 566 +++++++++++++++++ .../analisis_estrellas_estudiante-Copy1.ipynb | 574 ++++++++++++++++++ .../analisis_estrellas_estudiante.ipynb | 16 +- ejemplos/python/MatplotlibSeaborn.ipynb | 40 +- 6 files changed, 1760 insertions(+), 10 deletions(-) create mode 100644 tareas/practica_dos.ipynb create mode 100644 ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-Copy1-checkpoint.ipynb create mode 100644 ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-checkpoint.ipynb create mode 100644 ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante-Copy1.ipynb diff --git a/ tareas/practica_dos.ipynb b/ tareas/practica_dos.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-Copy1-checkpoint.ipynb b/ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-Copy1-checkpoint.ipynb new file mode 100644 index 0000000..79caa4e --- /dev/null +++ b/ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-Copy1-checkpoint.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "md-00", + "metadata": {}, + "source": [ + "# Práctica 2 — Análisis Exploratorio de Datos\n", + "\n", + "En este notebook realizarás un **análisis exploratorio de datos (EDA)** sobre un\n", + "dataset de 240 estrellas clasificadas en 6 tipos.\n", + "\n", + "**Dataset:** [Stars Dataset — Kaggle](https://www.kaggle.com/datasets/waqi786/stars-dataset)\n", + "\n", + "## Instrucciones generales\n", + "- Cada sección tiene celdas marcadas con `# tu código aquí` — ahí debes escribir tu solución.\n", + "- Lee las instrucciones en cada celda de markdown **antes** de escribir el código.\n", + "- Consulta los enlaces a la documentación oficial para entender los parámetros de cada función.\n", + "- Ejecuta el notebook completo **sin errores** antes de hacer commit (`Kernel → Restart & Run All`).\n", + "\n", + "## Contenido\n", + "1. [Importar librerías](#1.-Importar-librerías)\n", + "2. [Cargar los datos](#2.-Cargar-los-datos)\n", + "3. [Exploración inicial](#3.-Exploración-inicial)\n", + "4. [Distribución por tipo de estrella](#4.-Distribución-por-tipo-de-estrella)\n", + "5. [Temperatura por tipo](#5.-Temperatura-por-tipo-de-estrella)\n", + "6. [Luminosidad vs Temperatura](#6.-Luminosidad-vs-Temperatura)\n", + "7. [Estadísticas con NumPy](#7.-Estadísticas-con-NumPy)\n", + "8. [Diagrama Hertzsprung-Russell](#8.-Diagrama-Hertzsprung-Russell)" + ] + }, + { + "cell_type": "markdown", + "id": "md-setup", + "metadata": {}, + "source": [ + "---\n", + "## Configuración del ambiente\n", + "\n", + "Este proyecto usa **Poetry** para gestionar las dependencias. Antes de abrir el notebook\n", + "ejecuta estos comandos **desde la carpeta `practica2_analisis_datos/`**:\n", + "\n", + "```bash\n", + "poetry install\n", + "poetry run jupyter notebook\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "md-01-inst", + "metadata": {}, + "source": [ + "---\n", + "## 1. Importar librerías\n", + "\n", + "Importa las cuatro librerías con sus **alias convencionales**.\n", + "Estos alias son estándares en la comunidad — siempre se usan así:\n", + "\n", + "| Librería | Alias | ¿Para qué sirve? | Documentación |\n", + "|---|---|---|---|\n", + "| `numpy` | `np` | Operaciones matemáticas vectorizadas sobre arrays | [numpy.org/doc/stable](https://numpy.org/doc/stable/user/whatisnumpy.html) |\n", + "| `pandas` | `pd` | Análisis y manipulación de datos tabulares (DataFrames) | [pandas.pydata.org/docs](https://pandas.pydata.org/docs/getting_started/index.html) |\n", + "| `matplotlib.pyplot` | `plt` | Visualización de datos (gráficas de bajo nivel) | [matplotlib.org/tutorials](https://matplotlib.org/stable/tutorials/index.html) |\n", + "| `seaborn` | `sns` | Visualización estadística de alto nivel (sobre matplotlib) | [seaborn.pydata.org/tutorial](https://seaborn.pydata.org/tutorial.html) |\n", + "\n", + "**Sintaxis:** `import librería as alias`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "code-01", + "metadata": {}, + "outputs": [], + "source": [ + "# Importa las cuatro librerías con sus alias convencionales\n", + "# tu código aquí\n", + "\n", + "\n", + "\n", + "\n", + "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", + "# print(f'numpy {np.__version__}')\n", + "# print(f'pandas {pd.__version__}')\n", + "# print(f'seaborn {sns.__version__}')" + ] + }, + { + "cell_type": "markdown", + "id": "md-02-inst", + "metadata": {}, + "source": [ + "---\n", + "## 2. Cargar los datos\n", + "\n", + "Usa [`pd.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)\n", + "para leer el archivo CSV y cargarlo en un **DataFrame** de pandas.\n", + "\n", + "- Revisa la documentación: ¿qué parámetro recibe `read_csv`? ¿qué devuelve?\n", + "- El archivo se encuentra en `'../data/star_dataset.csv'` (relativo a la carpeta `notebooks/`)\n", + "- Guarda el resultado en una variable llamada `stars`\n", + "- Después usa [`.head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)\n", + " para mostrar las primeras 5 filas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-02", + "metadata": {}, + "outputs": [], + "source": [ + "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", + "# tu código aquí\n", + "\n", + "\n", + "# Muestra las primeras 5 filas del DataFrame\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-03-inst", + "metadata": {}, + "source": [ + "---\n", + "## 3. Exploración inicial\n", + "\n", + "Antes de analizar datos siempre hay que entender qué tenemos.\n", + "\n", + "**Celda 3a** — Imprime la información básica del DataFrame:\n", + "1. **Dimensiones** con [`.shape`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shape.html)\n", + " — devuelve una tupla `(filas, columnas)`\n", + "2. **Nombres de columnas** con [`.columns.tolist()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html)\n", + "3. **Tipos de datos** con [`.dtypes`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html)\n", + " — indica si cada columna es `int64`, `float64` o `object` (texto)\n", + "\n", + "**Celda 3b** — Obtén estadísticas descriptivas con\n", + "[`.describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html).\n", + "Fíjate en la media, desviación estándar, mínimo y máximo de cada columna numérica.\n", + "\n", + "**Celda 3c** — Verifica si hay valores nulos con\n", + "[`.isnull()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html)\n", + "seguido de `.sum()`. En un dataset limpio todos los valores deben ser 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03a", + "metadata": {}, + "outputs": [], + "source": [ + "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03b", + "metadata": {}, + "outputs": [], + "source": [ + "# Obtén el resumen estadístico de las columnas numéricas\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03c", + "metadata": {}, + "outputs": [], + "source": [ + "# Cuenta los valores nulos por columna\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-04-inst", + "metadata": {}, + "source": [ + "---\n", + "## 4. Distribución por tipo de estrella\n", + "\n", + "**Celda 4a** — Usa\n", + "[`.value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html)\n", + "sobre la columna `'Spectral Class'` para contar cuántas estrellas hay de cada tipo.\n", + "Guarda el resultado en una variable llamada `conteo`.\n", + "\n", + "> Pista: accede a una columna del DataFrame con `df['nombre_columna']`, que devuelve una **Serie**.\n", + "> `.value_counts()` es un método de Series.\n", + "\n", + "**Celda 4b** — Crea una **gráfica de barras** de `conteo`:\n", + "1. La línea `plt.figure(figsize=(8, 4))` ya está incluida — no la borres\n", + "2. Llama [`.plot(kind='bar')`](https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.html)\n", + " sobre `conteo`, usando `color='steelblue'` y `edgecolor='black'`\n", + "3. Agrega título con `plt.title(...)`, etiquetas con `plt.xlabel(...)` y `plt.ylabel(...)`\n", + "4. Rota las etiquetas del eje X: `plt.xticks(rotation=30, ha='right')`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-04a", + "metadata": {}, + "outputs": [], + "source": [ + "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", + "# tu código aquí\n", + "\n", + "\n", + "print(conteo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-04b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "\n", + "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.xticks(rotation=30, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-05-inst", + "metadata": {}, + "source": [ + "---\n", + "## 5. Temperatura por tipo de estrella\n", + "\n", + "En esta sección calcularás la temperatura media de dos formas distintas para comparar.\n", + "\n", + "**Celda 5a — Ciclo `for` (enfoque manual):**\n", + "\n", + "Primero filtra el DataFrame para obtener solo las estrellas de tipo `'A7V'`:\n", + "```python\n", + "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", + "```\n", + "Luego recorre la columna `'Temperature (K)'` del DataFrame filtrado con un `for`,\n", + "acumula la `suma` y el `conteo` (`n`), y calcula la media como `suma / n`.\n", + "\n", + "Consulta [cómo filtrar un DataFrame por valor](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html)\n", + "si necesitas orientación sobre la sintaxis de filtrado.\n", + "\n", + "**Celda 5b — Pandas `.groupby()` (enfoque vectorizado):**\n", + "\n", + "Usa [`.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)\n", + "para calcular la media de **todas las clases a la vez** en una sola línea:\n", + "```python\n", + "df.groupby('columna_categorica')['columna_numerica'].mean()\n", + "```\n", + "Ordena de mayor a menor con `.sort_values(ascending=False)`. Guarda en `temp_por_tipo`.\n", + "Compara el resultado de `'A7V'` con el valor que obtuviste con el `for`.\n", + "\n", + "**Celda 5c — Boxplot:**\n", + "\n", + "Usa [`sns.boxplot()`](https://seaborn.pydata.org/generated/seaborn.boxplot.html) con\n", + "`data=stars`, `x='Spectral Class'`, `y='Temperature (K)'`, `order=orden`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-05a", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", + "tipo_objetivo = 'A7V'\n", + "\n", + "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", + "# filtrado = ...\n", + "# tu código aquí\n", + "\n", + "\n", + "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", + "# acumula la suma y el conteo (n)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Calcula la media y guárdala en media_manual\n", + "# media_manual = suma / n\n", + "# tu código aquí\n", + "\n", + "\n", + "print(f'Temperatura media (for loop) : {media_manual:,.1f} K')" + ] + }, + { + "cell_type": "markdown", + "id": "5dto65riasc", + "metadata": {}, + "source": [ + "### Comparación: `for` vs. pandas\n", + "\n", + "Con el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\n", + "Ahora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n", + "\n", + "Cuando termines la celda 5b, verifica que el resultado de `A7V`\n", + "coincida con el valor que obtuviste con el `for`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4j2wkkt78ju", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", + "# Calcula la temperatura promedio por tipo con groupby\n", + "# Ordena de mayor a menor y guarda en temp_por_tipo\n", + "# tu código aquí\n", + "\n", + "\n", + "print('Temperatura promedio por clase espectral (K):')\n", + "print(temp_por_tipo)\n", + "print()\n", + "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", + "# tu código aquí" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-05b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(9, 5))\n", + "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", + "\n", + "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.xticks(rotation=20, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-06-inst", + "metadata": {}, + "source": [ + "---\n", + "## 6. Luminosidad vs Temperatura\n", + "\n", + "La luminosidad varía en muchos órdenes de magnitud (de 0.00008 a 849 420 L/Lo),\n", + "por eso necesitamos **escala logarítmica** en el eje Y.\n", + "\n", + "Usa [`sns.scatterplot()`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html)\n", + "con los siguientes parámetros (revisa la documentación para entender cada uno):\n", + "- `data=stars` — el DataFrame\n", + "- `x='Temperature (K)'` — temperatura en el eje X\n", + "- `y='Luminosity (L/Lo)'` — luminosidad en el eje Y\n", + "- `hue='Spectral Class'` — colorea los puntos según la clase espectral\n", + "- `style='Spectral Class'` — usa un marcador diferente por clase espectral\n", + "- `s=60` — tamaño de los puntos\n", + "\n", + "Después de crear el plot, aplica escala logarítmica al eje Y con\n", + "[`plt.yscale('log')`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yscale.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-06", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(9, 6))\n", + "\n", + "# Crea el scatter plot con sns.scatterplot(...)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Aplica escala logarítmica al eje Y\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega título, etiquetas de ejes y leyenda\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-07-inst", + "metadata": {}, + "source": [ + "---\n", + "## 7. Estadísticas con NumPy\n", + "\n", + "NumPy opera sobre **arrays completos** sin ciclos `for`. Por ejemplo,\n", + "`np.mean(arr)` calcula la media de todos los elementos de `arr` de una sola vez.\n", + "\n", + "**Celda 7a** — Extrae los arrays con `.values` y calcula estadísticas:\n", + "- Extrae: `temperaturas = stars['Temperature (K)'].values`\n", + "- Extrae: `radios = stars['Radius (R/Ro)'].values`\n", + "- Verifica el tipo con `type(temperaturas)`\n", + "- Calcula usando estas funciones de [`numpy.statistics`](https://numpy.org/doc/stable/reference/routines.statistics.html):\n", + " - [`np.mean(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html) — media\n", + " - [`np.median(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.median.html) — mediana\n", + " - [`np.std(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.std.html) — desviación estándar\n", + " - [`np.min(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amin.html) y [`np.max(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amax.html)\n", + "\n", + "**Celda 7b** — Percentiles y conversión vectorizada:\n", + "- Usa [`np.percentile(arr, q)`](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html)\n", + " con `q=[25, 50, 75, 90]` para calcular los 4 percentiles de `radios` de una vez\n", + "- Convierte `temperaturas` de Kelvin a Celsius **sin usar ciclo `for`**:\n", + " `celsius = temperaturas - 273.15` (operación vectorizada)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-07a", + "metadata": {}, + "outputs": [], + "source": [ + "# Extrae los arrays NumPy con .values\n", + "# tu código aquí\n", + "\n", + "\n", + "# Imprime el tipo del array\n", + "# tu código aquí\n", + "\n", + "\n", + "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-07b", + "metadata": {}, + "outputs": [], + "source": [ + "niveles = [25, 50, 75, 90]\n", + "\n", + "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", + "# celsius = ...\n", + "# tu código aquí\n", + "\n", + "\n", + "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", + "# (usa np.round para redondear a 1 decimal)\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-08-inst", + "metadata": {}, + "source": [ + "---\n", + "## 8. Diagrama Hertzsprung-Russell\n", + "\n", + "El diagrama H-R es el gráfico más importante en astronomía estelar.\n", + "Relaciona temperatura con luminosidad y revela la estructura evolutiva de las estrellas.\n", + "\n", + "Este diagrama tiene **dos particularidades** que debes implementar:\n", + "1. **Ambos ejes logarítmicos**: `plt.xscale('log')` y `plt.yscale('log')`\n", + "2. **Eje X invertido** (las más calientes a la izquierda): `plt.gca().invert_xaxis()`\n", + "\n", + "**Estructura del código** (el inicio ya está dado, completa las partes marcadas):\n", + "\n", + "```python\n", + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos)) # paleta de colores\n", + "mapa = dict(zip(tipos, colores)) # tipo -> color\n", + "\n", + "for tipo, grupo in stars.groupby('Spectral Class'):\n", + " plt.scatter(grupo['Temperature (K)'],\n", + " grupo['Luminosity (L/Lo)'],\n", + " label=tipo, color=mapa[tipo], s=40, alpha=0.8)\n", + "```\n", + "\n", + "Parámetros de [`plt.scatter()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)\n", + "que debes entender: `x`, `y`, `label`, `color`, `s` (tamaño), `alpha` (transparencia)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-08", + "metadata": {}, + "outputs": [], + "source": [ + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos))\n", + "mapa = dict(zip(tipos, colores))\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "\n", + "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", + "# tu código aquí\n", + "\n", + "\n", + "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Invierte el eje X con plt.gca().invert_xaxis()\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega: título, etiquetas de ejes, leyenda y grid\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-checkpoint.ipynb b/ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-checkpoint.ipynb new file mode 100644 index 0000000..3e12a46 --- /dev/null +++ b/ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-checkpoint.ipynb @@ -0,0 +1,566 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "md-00", + "metadata": {}, + "source": [ + "# Práctica 2 — Análisis Exploratorio de Datos\n", + "\n", + "En este notebook realizarás un **análisis exploratorio de datos (EDA)** sobre un\n", + "dataset de 240 estrellas clasificadas en 6 tipos.\n", + "\n", + "**Dataset:** [Stars Dataset — Kaggle](https://www.kaggle.com/datasets/waqi786/stars-dataset)\n", + "\n", + "## Instrucciones generales\n", + "- Cada sección tiene celdas marcadas con `# tu código aquí` — ahí debes escribir tu solución.\n", + "- Lee las instrucciones en cada celda de markdown **antes** de escribir el código.\n", + "- Consulta los enlaces a la documentación oficial para entender los parámetros de cada función.\n", + "- Ejecuta el notebook completo **sin errores** antes de hacer commit (`Kernel → Restart & Run All`).\n", + "\n", + "## Contenido\n", + "1. [Importar librerías](#1.-Importar-librerías)\n", + "2. [Cargar los datos](#2.-Cargar-los-datos)\n", + "3. [Exploración inicial](#3.-Exploración-inicial)\n", + "4. [Distribución por tipo de estrella](#4.-Distribución-por-tipo-de-estrella)\n", + "5. [Temperatura por tipo](#5.-Temperatura-por-tipo-de-estrella)\n", + "6. [Luminosidad vs Temperatura](#6.-Luminosidad-vs-Temperatura)\n", + "7. [Estadísticas con NumPy](#7.-Estadísticas-con-NumPy)\n", + "8. [Diagrama Hertzsprung-Russell](#8.-Diagrama-Hertzsprung-Russell)" + ] + }, + { + "cell_type": "markdown", + "id": "md-setup", + "metadata": {}, + "source": [ + "---\n", + "## Configuración del ambiente\n", + "\n", + "Este proyecto usa **Poetry** para gestionar las dependencias. Antes de abrir el notebook\n", + "ejecuta estos comandos **desde la carpeta `practica2_analisis_datos/`**:\n", + "\n", + "```bash\n", + "poetry install\n", + "poetry run jupyter notebook\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "md-01-inst", + "metadata": {}, + "source": [ + "---\n", + "## 1. Importar librerías\n", + "\n", + "Importa las cuatro librerías con sus **alias convencionales**.\n", + "Estos alias son estándares en la comunidad — siempre se usan así:\n", + "\n", + "| Librería | Alias | ¿Para qué sirve? | Documentación |\n", + "|---|---|---|---|\n", + "| `numpy` | `np` | Operaciones matemáticas vectorizadas sobre arrays | [numpy.org/doc/stable](https://numpy.org/doc/stable/user/whatisnumpy.html) |\n", + "| `pandas` | `pd` | Análisis y manipulación de datos tabulares (DataFrames) | [pandas.pydata.org/docs](https://pandas.pydata.org/docs/getting_started/index.html) |\n", + "| `matplotlib.pyplot` | `plt` | Visualización de datos (gráficas de bajo nivel) | [matplotlib.org/tutorials](https://matplotlib.org/stable/tutorials/index.html) |\n", + "| `seaborn` | `sns` | Visualización estadística de alto nivel (sobre matplotlib) | [seaborn.pydata.org/tutorial](https://seaborn.pydata.org/tutorial.html) |\n", + "\n", + "**Sintaxis:** `import librería as alias`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-01", + "metadata": {}, + "outputs": [], + "source": [ + "# Importa las cuatro librerías con sus alias convencionales\n", + "# tu código aquí\n", + "\n", + "\n", + "\n", + "\n", + "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", + "# print(f'numpy {np.__version__}')\n", + "# print(f'pandas {pd.__version__}')\n", + "# print(f'seaborn {sns.__version__}')" + ] + }, + { + "cell_type": "markdown", + "id": "md-02-inst", + "metadata": {}, + "source": [ + "---\n", + "## 2. Cargar los datos\n", + "\n", + "Usa [`pd.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)\n", + "para leer el archivo CSV y cargarlo en un **DataFrame** de pandas.\n", + "\n", + "- Revisa la documentación: ¿qué parámetro recibe `read_csv`? ¿qué devuelve?\n", + "- El archivo se encuentra en `'../data/star_dataset.csv'` (relativo a la carpeta `notebooks/`)\n", + "- Guarda el resultado en una variable llamada `stars`\n", + "- Después usa [`.head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)\n", + " para mostrar las primeras 5 filas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-02", + "metadata": {}, + "outputs": [], + "source": [ + "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", + "# tu código aquí\n", + "\n", + "\n", + "# Muestra las primeras 5 filas del DataFrame\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-03-inst", + "metadata": {}, + "source": [ + "---\n", + "## 3. Exploración inicial\n", + "\n", + "Antes de analizar datos siempre hay que entender qué tenemos.\n", + "\n", + "**Celda 3a** — Imprime la información básica del DataFrame:\n", + "1. **Dimensiones** con [`.shape`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shape.html)\n", + " — devuelve una tupla `(filas, columnas)`\n", + "2. **Nombres de columnas** con [`.columns.tolist()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html)\n", + "3. **Tipos de datos** con [`.dtypes`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html)\n", + " — indica si cada columna es `int64`, `float64` o `object` (texto)\n", + "\n", + "**Celda 3b** — Obtén estadísticas descriptivas con\n", + "[`.describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html).\n", + "Fíjate en la media, desviación estándar, mínimo y máximo de cada columna numérica.\n", + "\n", + "**Celda 3c** — Verifica si hay valores nulos con\n", + "[`.isnull()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html)\n", + "seguido de `.sum()`. En un dataset limpio todos los valores deben ser 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03a", + "metadata": {}, + "outputs": [], + "source": [ + "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03b", + "metadata": {}, + "outputs": [], + "source": [ + "# Obtén el resumen estadístico de las columnas numéricas\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03c", + "metadata": {}, + "outputs": [], + "source": [ + "# Cuenta los valores nulos por columna\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-04-inst", + "metadata": {}, + "source": [ + "---\n", + "## 4. Distribución por tipo de estrella\n", + "\n", + "**Celda 4a** — Usa\n", + "[`.value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html)\n", + "sobre la columna `'Spectral Class'` para contar cuántas estrellas hay de cada tipo.\n", + "Guarda el resultado en una variable llamada `conteo`.\n", + "\n", + "> Pista: accede a una columna del DataFrame con `df['nombre_columna']`, que devuelve una **Serie**.\n", + "> `.value_counts()` es un método de Series.\n", + "\n", + "**Celda 4b** — Crea una **gráfica de barras** de `conteo`:\n", + "1. La línea `plt.figure(figsize=(8, 4))` ya está incluida — no la borres\n", + "2. Llama [`.plot(kind='bar')`](https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.html)\n", + " sobre `conteo`, usando `color='steelblue'` y `edgecolor='black'`\n", + "3. Agrega título con `plt.title(...)`, etiquetas con `plt.xlabel(...)` y `plt.ylabel(...)`\n", + "4. Rota las etiquetas del eje X: `plt.xticks(rotation=30, ha='right')`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-04a", + "metadata": {}, + "outputs": [], + "source": [ + "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", + "# tu código aquí\n", + "\n", + "\n", + "print(conteo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-04b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "\n", + "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.xticks(rotation=30, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-05-inst", + "metadata": {}, + "source": [ + "---\n", + "## 5. Temperatura por tipo de estrella\n", + "\n", + "En esta sección calcularás la temperatura media de dos formas distintas para comparar.\n", + "\n", + "**Celda 5a — Ciclo `for` (enfoque manual):**\n", + "\n", + "Primero filtra el DataFrame para obtener solo las estrellas de tipo `'A7V'`:\n", + "```python\n", + "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", + "```\n", + "Luego recorre la columna `'Temperature (K)'` del DataFrame filtrado con un `for`,\n", + "acumula la `suma` y el `conteo` (`n`), y calcula la media como `suma / n`.\n", + "\n", + "Consulta [cómo filtrar un DataFrame por valor](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html)\n", + "si necesitas orientación sobre la sintaxis de filtrado.\n", + "\n", + "**Celda 5b — Pandas `.groupby()` (enfoque vectorizado):**\n", + "\n", + "Usa [`.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)\n", + "para calcular la media de **todas las clases a la vez** en una sola línea:\n", + "```python\n", + "df.groupby('columna_categorica')['columna_numerica'].mean()\n", + "```\n", + "Ordena de mayor a menor con `.sort_values(ascending=False)`. Guarda en `temp_por_tipo`.\n", + "Compara el resultado de `'A7V'` con el valor que obtuviste con el `for`.\n", + "\n", + "**Celda 5c — Boxplot:**\n", + "\n", + "Usa [`sns.boxplot()`](https://seaborn.pydata.org/generated/seaborn.boxplot.html) con\n", + "`data=stars`, `x='Spectral Class'`, `y='Temperature (K)'`, `order=orden`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-05a", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", + "tipo_objetivo = 'A7V'\n", + "\n", + "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", + "# filtrado = ...\n", + "# tu código aquí\n", + "\n", + "\n", + "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", + "# acumula la suma y el conteo (n)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Calcula la media y guárdala en media_manual\n", + "# media_manual = suma / n\n", + "# tu código aquí\n", + "\n", + "\n", + "print(f'Temperatura media (for loop) : {media_manual:,.1f} K')" + ] + }, + { + "cell_type": "markdown", + "id": "5dto65riasc", + "metadata": {}, + "source": "### Comparación: `for` vs. pandas\n\nCon el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\nAhora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n\nCuando termines la celda 5b, verifica que el resultado de `A7V`\ncoincida con el valor que obtuviste con el `for`." + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4j2wkkt78ju", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", + "# Calcula la temperatura promedio por tipo con groupby\n", + "# Ordena de mayor a menor y guarda en temp_por_tipo\n", + "# tu código aquí\n", + "\n", + "\n", + "print('Temperatura promedio por clase espectral (K):')\n", + "print(temp_por_tipo)\n", + "print()\n", + "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", + "# tu código aquí" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-05b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(9, 5))\n", + "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", + "\n", + "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.xticks(rotation=20, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-06-inst", + "metadata": {}, + "source": [ + "---\n", + "## 6. Luminosidad vs Temperatura\n", + "\n", + "La luminosidad varía en muchos órdenes de magnitud (de 0.00008 a 849 420 L/Lo),\n", + "por eso necesitamos **escala logarítmica** en el eje Y.\n", + "\n", + "Usa [`sns.scatterplot()`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html)\n", + "con los siguientes parámetros (revisa la documentación para entender cada uno):\n", + "- `data=stars` — el DataFrame\n", + "- `x='Temperature (K)'` — temperatura en el eje X\n", + "- `y='Luminosity (L/Lo)'` — luminosidad en el eje Y\n", + "- `hue='Spectral Class'` — colorea los puntos según la clase espectral\n", + "- `style='Spectral Class'` — usa un marcador diferente por clase espectral\n", + "- `s=60` — tamaño de los puntos\n", + "\n", + "Después de crear el plot, aplica escala logarítmica al eje Y con\n", + "[`plt.yscale('log')`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yscale.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-06", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(9, 6))\n", + "\n", + "# Crea el scatter plot con sns.scatterplot(...)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Aplica escala logarítmica al eje Y\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega título, etiquetas de ejes y leyenda\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-07-inst", + "metadata": {}, + "source": [ + "---\n", + "## 7. Estadísticas con NumPy\n", + "\n", + "NumPy opera sobre **arrays completos** sin ciclos `for`. Por ejemplo,\n", + "`np.mean(arr)` calcula la media de todos los elementos de `arr` de una sola vez.\n", + "\n", + "**Celda 7a** — Extrae los arrays con `.values` y calcula estadísticas:\n", + "- Extrae: `temperaturas = stars['Temperature (K)'].values`\n", + "- Extrae: `radios = stars['Radius (R/Ro)'].values`\n", + "- Verifica el tipo con `type(temperaturas)`\n", + "- Calcula usando estas funciones de [`numpy.statistics`](https://numpy.org/doc/stable/reference/routines.statistics.html):\n", + " - [`np.mean(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html) — media\n", + " - [`np.median(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.median.html) — mediana\n", + " - [`np.std(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.std.html) — desviación estándar\n", + " - [`np.min(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amin.html) y [`np.max(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amax.html)\n", + "\n", + "**Celda 7b** — Percentiles y conversión vectorizada:\n", + "- Usa [`np.percentile(arr, q)`](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html)\n", + " con `q=[25, 50, 75, 90]` para calcular los 4 percentiles de `radios` de una vez\n", + "- Convierte `temperaturas` de Kelvin a Celsius **sin usar ciclo `for`**:\n", + " `celsius = temperaturas - 273.15` (operación vectorizada)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-07a", + "metadata": {}, + "outputs": [], + "source": [ + "# Extrae los arrays NumPy con .values\n", + "# tu código aquí\n", + "\n", + "\n", + "# Imprime el tipo del array\n", + "# tu código aquí\n", + "\n", + "\n", + "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-07b", + "metadata": {}, + "outputs": [], + "source": [ + "niveles = [25, 50, 75, 90]\n", + "\n", + "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", + "# celsius = ...\n", + "# tu código aquí\n", + "\n", + "\n", + "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", + "# (usa np.round para redondear a 1 decimal)\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-08-inst", + "metadata": {}, + "source": [ + "---\n", + "## 8. Diagrama Hertzsprung-Russell\n", + "\n", + "El diagrama H-R es el gráfico más importante en astronomía estelar.\n", + "Relaciona temperatura con luminosidad y revela la estructura evolutiva de las estrellas.\n", + "\n", + "Este diagrama tiene **dos particularidades** que debes implementar:\n", + "1. **Ambos ejes logarítmicos**: `plt.xscale('log')` y `plt.yscale('log')`\n", + "2. **Eje X invertido** (las más calientes a la izquierda): `plt.gca().invert_xaxis()`\n", + "\n", + "**Estructura del código** (el inicio ya está dado, completa las partes marcadas):\n", + "\n", + "```python\n", + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos)) # paleta de colores\n", + "mapa = dict(zip(tipos, colores)) # tipo -> color\n", + "\n", + "for tipo, grupo in stars.groupby('Spectral Class'):\n", + " plt.scatter(grupo['Temperature (K)'],\n", + " grupo['Luminosity (L/Lo)'],\n", + " label=tipo, color=mapa[tipo], s=40, alpha=0.8)\n", + "```\n", + "\n", + "Parámetros de [`plt.scatter()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)\n", + "que debes entender: `x`, `y`, `label`, `color`, `s` (tamaño), `alpha` (transparencia)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-08", + "metadata": {}, + "outputs": [], + "source": [ + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos))\n", + "mapa = dict(zip(tipos, colores))\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "\n", + "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", + "# tu código aquí\n", + "\n", + "\n", + "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Invierte el eje X con plt.gca().invert_xaxis()\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega: título, etiquetas de ejes, leyenda y grid\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante-Copy1.ipynb b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante-Copy1.ipynb new file mode 100644 index 0000000..79caa4e --- /dev/null +++ b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante-Copy1.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "md-00", + "metadata": {}, + "source": [ + "# Práctica 2 — Análisis Exploratorio de Datos\n", + "\n", + "En este notebook realizarás un **análisis exploratorio de datos (EDA)** sobre un\n", + "dataset de 240 estrellas clasificadas en 6 tipos.\n", + "\n", + "**Dataset:** [Stars Dataset — Kaggle](https://www.kaggle.com/datasets/waqi786/stars-dataset)\n", + "\n", + "## Instrucciones generales\n", + "- Cada sección tiene celdas marcadas con `# tu código aquí` — ahí debes escribir tu solución.\n", + "- Lee las instrucciones en cada celda de markdown **antes** de escribir el código.\n", + "- Consulta los enlaces a la documentación oficial para entender los parámetros de cada función.\n", + "- Ejecuta el notebook completo **sin errores** antes de hacer commit (`Kernel → Restart & Run All`).\n", + "\n", + "## Contenido\n", + "1. [Importar librerías](#1.-Importar-librerías)\n", + "2. [Cargar los datos](#2.-Cargar-los-datos)\n", + "3. [Exploración inicial](#3.-Exploración-inicial)\n", + "4. [Distribución por tipo de estrella](#4.-Distribución-por-tipo-de-estrella)\n", + "5. [Temperatura por tipo](#5.-Temperatura-por-tipo-de-estrella)\n", + "6. [Luminosidad vs Temperatura](#6.-Luminosidad-vs-Temperatura)\n", + "7. [Estadísticas con NumPy](#7.-Estadísticas-con-NumPy)\n", + "8. [Diagrama Hertzsprung-Russell](#8.-Diagrama-Hertzsprung-Russell)" + ] + }, + { + "cell_type": "markdown", + "id": "md-setup", + "metadata": {}, + "source": [ + "---\n", + "## Configuración del ambiente\n", + "\n", + "Este proyecto usa **Poetry** para gestionar las dependencias. Antes de abrir el notebook\n", + "ejecuta estos comandos **desde la carpeta `practica2_analisis_datos/`**:\n", + "\n", + "```bash\n", + "poetry install\n", + "poetry run jupyter notebook\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "md-01-inst", + "metadata": {}, + "source": [ + "---\n", + "## 1. Importar librerías\n", + "\n", + "Importa las cuatro librerías con sus **alias convencionales**.\n", + "Estos alias son estándares en la comunidad — siempre se usan así:\n", + "\n", + "| Librería | Alias | ¿Para qué sirve? | Documentación |\n", + "|---|---|---|---|\n", + "| `numpy` | `np` | Operaciones matemáticas vectorizadas sobre arrays | [numpy.org/doc/stable](https://numpy.org/doc/stable/user/whatisnumpy.html) |\n", + "| `pandas` | `pd` | Análisis y manipulación de datos tabulares (DataFrames) | [pandas.pydata.org/docs](https://pandas.pydata.org/docs/getting_started/index.html) |\n", + "| `matplotlib.pyplot` | `plt` | Visualización de datos (gráficas de bajo nivel) | [matplotlib.org/tutorials](https://matplotlib.org/stable/tutorials/index.html) |\n", + "| `seaborn` | `sns` | Visualización estadística de alto nivel (sobre matplotlib) | [seaborn.pydata.org/tutorial](https://seaborn.pydata.org/tutorial.html) |\n", + "\n", + "**Sintaxis:** `import librería as alias`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "code-01", + "metadata": {}, + "outputs": [], + "source": [ + "# Importa las cuatro librerías con sus alias convencionales\n", + "# tu código aquí\n", + "\n", + "\n", + "\n", + "\n", + "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", + "# print(f'numpy {np.__version__}')\n", + "# print(f'pandas {pd.__version__}')\n", + "# print(f'seaborn {sns.__version__}')" + ] + }, + { + "cell_type": "markdown", + "id": "md-02-inst", + "metadata": {}, + "source": [ + "---\n", + "## 2. Cargar los datos\n", + "\n", + "Usa [`pd.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)\n", + "para leer el archivo CSV y cargarlo en un **DataFrame** de pandas.\n", + "\n", + "- Revisa la documentación: ¿qué parámetro recibe `read_csv`? ¿qué devuelve?\n", + "- El archivo se encuentra en `'../data/star_dataset.csv'` (relativo a la carpeta `notebooks/`)\n", + "- Guarda el resultado en una variable llamada `stars`\n", + "- Después usa [`.head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)\n", + " para mostrar las primeras 5 filas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-02", + "metadata": {}, + "outputs": [], + "source": [ + "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", + "# tu código aquí\n", + "\n", + "\n", + "# Muestra las primeras 5 filas del DataFrame\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-03-inst", + "metadata": {}, + "source": [ + "---\n", + "## 3. Exploración inicial\n", + "\n", + "Antes de analizar datos siempre hay que entender qué tenemos.\n", + "\n", + "**Celda 3a** — Imprime la información básica del DataFrame:\n", + "1. **Dimensiones** con [`.shape`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shape.html)\n", + " — devuelve una tupla `(filas, columnas)`\n", + "2. **Nombres de columnas** con [`.columns.tolist()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html)\n", + "3. **Tipos de datos** con [`.dtypes`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html)\n", + " — indica si cada columna es `int64`, `float64` o `object` (texto)\n", + "\n", + "**Celda 3b** — Obtén estadísticas descriptivas con\n", + "[`.describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html).\n", + "Fíjate en la media, desviación estándar, mínimo y máximo de cada columna numérica.\n", + "\n", + "**Celda 3c** — Verifica si hay valores nulos con\n", + "[`.isnull()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html)\n", + "seguido de `.sum()`. En un dataset limpio todos los valores deben ser 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03a", + "metadata": {}, + "outputs": [], + "source": [ + "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03b", + "metadata": {}, + "outputs": [], + "source": [ + "# Obtén el resumen estadístico de las columnas numéricas\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03c", + "metadata": {}, + "outputs": [], + "source": [ + "# Cuenta los valores nulos por columna\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-04-inst", + "metadata": {}, + "source": [ + "---\n", + "## 4. Distribución por tipo de estrella\n", + "\n", + "**Celda 4a** — Usa\n", + "[`.value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html)\n", + "sobre la columna `'Spectral Class'` para contar cuántas estrellas hay de cada tipo.\n", + "Guarda el resultado en una variable llamada `conteo`.\n", + "\n", + "> Pista: accede a una columna del DataFrame con `df['nombre_columna']`, que devuelve una **Serie**.\n", + "> `.value_counts()` es un método de Series.\n", + "\n", + "**Celda 4b** — Crea una **gráfica de barras** de `conteo`:\n", + "1. La línea `plt.figure(figsize=(8, 4))` ya está incluida — no la borres\n", + "2. Llama [`.plot(kind='bar')`](https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.html)\n", + " sobre `conteo`, usando `color='steelblue'` y `edgecolor='black'`\n", + "3. Agrega título con `plt.title(...)`, etiquetas con `plt.xlabel(...)` y `plt.ylabel(...)`\n", + "4. Rota las etiquetas del eje X: `plt.xticks(rotation=30, ha='right')`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-04a", + "metadata": {}, + "outputs": [], + "source": [ + "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", + "# tu código aquí\n", + "\n", + "\n", + "print(conteo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-04b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "\n", + "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.xticks(rotation=30, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-05-inst", + "metadata": {}, + "source": [ + "---\n", + "## 5. Temperatura por tipo de estrella\n", + "\n", + "En esta sección calcularás la temperatura media de dos formas distintas para comparar.\n", + "\n", + "**Celda 5a — Ciclo `for` (enfoque manual):**\n", + "\n", + "Primero filtra el DataFrame para obtener solo las estrellas de tipo `'A7V'`:\n", + "```python\n", + "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", + "```\n", + "Luego recorre la columna `'Temperature (K)'` del DataFrame filtrado con un `for`,\n", + "acumula la `suma` y el `conteo` (`n`), y calcula la media como `suma / n`.\n", + "\n", + "Consulta [cómo filtrar un DataFrame por valor](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html)\n", + "si necesitas orientación sobre la sintaxis de filtrado.\n", + "\n", + "**Celda 5b — Pandas `.groupby()` (enfoque vectorizado):**\n", + "\n", + "Usa [`.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)\n", + "para calcular la media de **todas las clases a la vez** en una sola línea:\n", + "```python\n", + "df.groupby('columna_categorica')['columna_numerica'].mean()\n", + "```\n", + "Ordena de mayor a menor con `.sort_values(ascending=False)`. Guarda en `temp_por_tipo`.\n", + "Compara el resultado de `'A7V'` con el valor que obtuviste con el `for`.\n", + "\n", + "**Celda 5c — Boxplot:**\n", + "\n", + "Usa [`sns.boxplot()`](https://seaborn.pydata.org/generated/seaborn.boxplot.html) con\n", + "`data=stars`, `x='Spectral Class'`, `y='Temperature (K)'`, `order=orden`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-05a", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", + "tipo_objetivo = 'A7V'\n", + "\n", + "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", + "# filtrado = ...\n", + "# tu código aquí\n", + "\n", + "\n", + "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", + "# acumula la suma y el conteo (n)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Calcula la media y guárdala en media_manual\n", + "# media_manual = suma / n\n", + "# tu código aquí\n", + "\n", + "\n", + "print(f'Temperatura media (for loop) : {media_manual:,.1f} K')" + ] + }, + { + "cell_type": "markdown", + "id": "5dto65riasc", + "metadata": {}, + "source": [ + "### Comparación: `for` vs. pandas\n", + "\n", + "Con el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\n", + "Ahora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n", + "\n", + "Cuando termines la celda 5b, verifica que el resultado de `A7V`\n", + "coincida con el valor que obtuviste con el `for`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4j2wkkt78ju", + "metadata": {}, + "outputs": [], + "source": [ + "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", + "# Calcula la temperatura promedio por tipo con groupby\n", + "# Ordena de mayor a menor y guarda en temp_por_tipo\n", + "# tu código aquí\n", + "\n", + "\n", + "print('Temperatura promedio por clase espectral (K):')\n", + "print(temp_por_tipo)\n", + "print()\n", + "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", + "# tu código aquí" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-05b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(9, 5))\n", + "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", + "\n", + "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.xticks(rotation=20, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-06-inst", + "metadata": {}, + "source": [ + "---\n", + "## 6. Luminosidad vs Temperatura\n", + "\n", + "La luminosidad varía en muchos órdenes de magnitud (de 0.00008 a 849 420 L/Lo),\n", + "por eso necesitamos **escala logarítmica** en el eje Y.\n", + "\n", + "Usa [`sns.scatterplot()`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html)\n", + "con los siguientes parámetros (revisa la documentación para entender cada uno):\n", + "- `data=stars` — el DataFrame\n", + "- `x='Temperature (K)'` — temperatura en el eje X\n", + "- `y='Luminosity (L/Lo)'` — luminosidad en el eje Y\n", + "- `hue='Spectral Class'` — colorea los puntos según la clase espectral\n", + "- `style='Spectral Class'` — usa un marcador diferente por clase espectral\n", + "- `s=60` — tamaño de los puntos\n", + "\n", + "Después de crear el plot, aplica escala logarítmica al eje Y con\n", + "[`plt.yscale('log')`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yscale.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-06", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(9, 6))\n", + "\n", + "# Crea el scatter plot con sns.scatterplot(...)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Aplica escala logarítmica al eje Y\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega título, etiquetas de ejes y leyenda\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-07-inst", + "metadata": {}, + "source": [ + "---\n", + "## 7. Estadísticas con NumPy\n", + "\n", + "NumPy opera sobre **arrays completos** sin ciclos `for`. Por ejemplo,\n", + "`np.mean(arr)` calcula la media de todos los elementos de `arr` de una sola vez.\n", + "\n", + "**Celda 7a** — Extrae los arrays con `.values` y calcula estadísticas:\n", + "- Extrae: `temperaturas = stars['Temperature (K)'].values`\n", + "- Extrae: `radios = stars['Radius (R/Ro)'].values`\n", + "- Verifica el tipo con `type(temperaturas)`\n", + "- Calcula usando estas funciones de [`numpy.statistics`](https://numpy.org/doc/stable/reference/routines.statistics.html):\n", + " - [`np.mean(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html) — media\n", + " - [`np.median(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.median.html) — mediana\n", + " - [`np.std(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.std.html) — desviación estándar\n", + " - [`np.min(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amin.html) y [`np.max(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amax.html)\n", + "\n", + "**Celda 7b** — Percentiles y conversión vectorizada:\n", + "- Usa [`np.percentile(arr, q)`](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html)\n", + " con `q=[25, 50, 75, 90]` para calcular los 4 percentiles de `radios` de una vez\n", + "- Convierte `temperaturas` de Kelvin a Celsius **sin usar ciclo `for`**:\n", + " `celsius = temperaturas - 273.15` (operación vectorizada)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-07a", + "metadata": {}, + "outputs": [], + "source": [ + "# Extrae los arrays NumPy con .values\n", + "# tu código aquí\n", + "\n", + "\n", + "# Imprime el tipo del array\n", + "# tu código aquí\n", + "\n", + "\n", + "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-07b", + "metadata": {}, + "outputs": [], + "source": [ + "niveles = [25, 50, 75, 90]\n", + "\n", + "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", + "# celsius = ...\n", + "# tu código aquí\n", + "\n", + "\n", + "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", + "# (usa np.round para redondear a 1 decimal)\n", + "# tu código aquí\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-08-inst", + "metadata": {}, + "source": [ + "---\n", + "## 8. Diagrama Hertzsprung-Russell\n", + "\n", + "El diagrama H-R es el gráfico más importante en astronomía estelar.\n", + "Relaciona temperatura con luminosidad y revela la estructura evolutiva de las estrellas.\n", + "\n", + "Este diagrama tiene **dos particularidades** que debes implementar:\n", + "1. **Ambos ejes logarítmicos**: `plt.xscale('log')` y `plt.yscale('log')`\n", + "2. **Eje X invertido** (las más calientes a la izquierda): `plt.gca().invert_xaxis()`\n", + "\n", + "**Estructura del código** (el inicio ya está dado, completa las partes marcadas):\n", + "\n", + "```python\n", + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos)) # paleta de colores\n", + "mapa = dict(zip(tipos, colores)) # tipo -> color\n", + "\n", + "for tipo, grupo in stars.groupby('Spectral Class'):\n", + " plt.scatter(grupo['Temperature (K)'],\n", + " grupo['Luminosity (L/Lo)'],\n", + " label=tipo, color=mapa[tipo], s=40, alpha=0.8)\n", + "```\n", + "\n", + "Parámetros de [`plt.scatter()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)\n", + "que debes entender: `x`, `y`, `label`, `color`, `s` (tamaño), `alpha` (transparencia)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-08", + "metadata": {}, + "outputs": [], + "source": [ + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos))\n", + "mapa = dict(zip(tipos, colores))\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "\n", + "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", + "# tu código aquí\n", + "\n", + "\n", + "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", + "# tu código aquí\n", + "\n", + "\n", + "# Invierte el eje X con plt.gca().invert_xaxis()\n", + "# tu código aquí\n", + "\n", + "\n", + "# Agrega: título, etiquetas de ejes, leyenda y grid\n", + "# tu código aquí\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb index 3e12a46..79caa4e 100644 --- a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb +++ b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "code-01", "metadata": {}, "outputs": [], @@ -308,7 +308,15 @@ "cell_type": "markdown", "id": "5dto65riasc", "metadata": {}, - "source": "### Comparación: `for` vs. pandas\n\nCon el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\nAhora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n\nCuando termines la celda 5b, verifica que el resultado de `A7V`\ncoincida con el valor que obtuviste con el `for`." + "source": [ + "### Comparación: `for` vs. pandas\n", + "\n", + "Con el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\n", + "Ahora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n", + "\n", + "Cuando termines la celda 5b, verifica que el resultado de `A7V`\n", + "coincida con el valor que obtuviste con el `for`." + ] }, { "cell_type": "code", @@ -558,9 +566,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/ejemplos/python/MatplotlibSeaborn.ipynb b/ejemplos/python/MatplotlibSeaborn.ipynb index a50aabb..d2015c3 100644 --- a/ejemplos/python/MatplotlibSeaborn.ipynb +++ b/ejemplos/python/MatplotlibSeaborn.ipynb @@ -43,9 +43,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matplotlib versión: 3.8.0\n", + "Seaborn versión: 0.12.2\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", @@ -159,9 +168,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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[Importar librerías](#1.-Importar-librerías)\n", - "2. [Cargar los datos](#2.-Cargar-los-datos)\n", - "3. [Exploración inicial](#3.-Exploración-inicial)\n", - "4. [Distribución por tipo de estrella](#4.-Distribución-por-tipo-de-estrella)\n", - "5. [Temperatura por tipo](#5.-Temperatura-por-tipo-de-estrella)\n", - "6. [Luminosidad vs Temperatura](#6.-Luminosidad-vs-Temperatura)\n", - "7. [Estadísticas con NumPy](#7.-Estadísticas-con-NumPy)\n", - "8. [Diagrama Hertzsprung-Russell](#8.-Diagrama-Hertzsprung-Russell)" - ] - }, - { - "cell_type": "markdown", - "id": "md-setup", - "metadata": {}, - "source": [ - "---\n", - "## Configuración del ambiente\n", - "\n", - "Este proyecto usa **Poetry** para gestionar las dependencias. Antes de abrir el notebook\n", - "ejecuta estos comandos **desde la carpeta `practica2_analisis_datos/`**:\n", - "\n", - "```bash\n", - "poetry install\n", - "poetry run jupyter notebook\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "md-01-inst", - "metadata": {}, - "source": [ - "---\n", - "## 1. Importar librerías\n", - "\n", - "Importa las cuatro librerías con sus **alias convencionales**.\n", - "Estos alias son estándares en la comunidad — siempre se usan así:\n", - "\n", - "| Librería | Alias | ¿Para qué sirve? | Documentación |\n", - "|---|---|---|---|\n", - "| `numpy` | `np` | Operaciones matemáticas vectorizadas sobre arrays | [numpy.org/doc/stable](https://numpy.org/doc/stable/user/whatisnumpy.html) |\n", - "| `pandas` | `pd` | Análisis y manipulación de datos tabulares (DataFrames) | [pandas.pydata.org/docs](https://pandas.pydata.org/docs/getting_started/index.html) |\n", - "| `matplotlib.pyplot` | `plt` | Visualización de datos (gráficas de bajo nivel) | [matplotlib.org/tutorials](https://matplotlib.org/stable/tutorials/index.html) |\n", - "| `seaborn` | `sns` | Visualización estadística de alto nivel (sobre matplotlib) | [seaborn.pydata.org/tutorial](https://seaborn.pydata.org/tutorial.html) |\n", - "\n", - "**Sintaxis:** `import librería as alias`" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "code-01", - "metadata": {}, - "outputs": [], - "source": [ - "# Importa las cuatro librerías con sus alias convencionales\n", - "# tu código aquí\n", - "\n", - "\n", - "\n", - "\n", - "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", - "# print(f'numpy {np.__version__}')\n", - "# print(f'pandas {pd.__version__}')\n", - "# print(f'seaborn {sns.__version__}')" - ] - }, - { - "cell_type": "markdown", - "id": "md-02-inst", - "metadata": {}, - "source": [ - "---\n", - "## 2. Cargar los datos\n", - "\n", - "Usa [`pd.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)\n", - "para leer el archivo CSV y cargarlo en un **DataFrame** de pandas.\n", - "\n", - "- Revisa la documentación: ¿qué parámetro recibe `read_csv`? ¿qué devuelve?\n", - "- El archivo se encuentra en `'../data/star_dataset.csv'` (relativo a la carpeta `notebooks/`)\n", - "- Guarda el resultado en una variable llamada `stars`\n", - "- Después usa [`.head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)\n", - " para mostrar las primeras 5 filas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-02", - "metadata": {}, - "outputs": [], - "source": [ - "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", - "# tu código aquí\n", - "\n", - "\n", - "# Muestra las primeras 5 filas del DataFrame\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "markdown", - "id": "md-03-inst", - "metadata": {}, - "source": [ - "---\n", - "## 3. Exploración inicial\n", - "\n", - "Antes de analizar datos siempre hay que entender qué tenemos.\n", - "\n", - "**Celda 3a** — Imprime la información básica del DataFrame:\n", - "1. **Dimensiones** con [`.shape`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shape.html)\n", - " — devuelve una tupla `(filas, columnas)`\n", - "2. **Nombres de columnas** con [`.columns.tolist()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html)\n", - "3. **Tipos de datos** con [`.dtypes`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html)\n", - " — indica si cada columna es `int64`, `float64` o `object` (texto)\n", - "\n", - "**Celda 3b** — Obtén estadísticas descriptivas con\n", - "[`.describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html).\n", - "Fíjate en la media, desviación estándar, mínimo y máximo de cada columna numérica.\n", - "\n", - "**Celda 3c** — Verifica si hay valores nulos con\n", - "[`.isnull()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html)\n", - "seguido de `.sum()`. En un dataset limpio todos los valores deben ser 0." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-03a", - "metadata": {}, - "outputs": [], - "source": [ - "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-03b", - "metadata": {}, - "outputs": [], - "source": [ - "# Obtén el resumen estadístico de las columnas numéricas\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-03c", - "metadata": {}, - "outputs": [], - "source": [ - "# Cuenta los valores nulos por columna\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "markdown", - "id": "md-04-inst", - "metadata": {}, - "source": [ - "---\n", - "## 4. Distribución por tipo de estrella\n", - "\n", - "**Celda 4a** — Usa\n", - "[`.value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html)\n", - "sobre la columna `'Spectral Class'` para contar cuántas estrellas hay de cada tipo.\n", - "Guarda el resultado en una variable llamada `conteo`.\n", - "\n", - "> Pista: accede a una columna del DataFrame con `df['nombre_columna']`, que devuelve una **Serie**.\n", - "> `.value_counts()` es un método de Series.\n", - "\n", - "**Celda 4b** — Crea una **gráfica de barras** de `conteo`:\n", - "1. La línea `plt.figure(figsize=(8, 4))` ya está incluida — no la borres\n", - "2. Llama [`.plot(kind='bar')`](https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.html)\n", - " sobre `conteo`, usando `color='steelblue'` y `edgecolor='black'`\n", - "3. Agrega título con `plt.title(...)`, etiquetas con `plt.xlabel(...)` y `plt.ylabel(...)`\n", - "4. Rota las etiquetas del eje X: `plt.xticks(rotation=30, ha='right')`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-04a", - "metadata": {}, - "outputs": [], - "source": [ - "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", - "# tu código aquí\n", - "\n", - "\n", - "print(conteo)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-04b", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(8, 4))\n", - "\n", - "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega título y etiquetas de ejes\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.xticks(rotation=30, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "md-05-inst", - "metadata": {}, - "source": [ - "---\n", - "## 5. Temperatura por tipo de estrella\n", - "\n", - "En esta sección calcularás la temperatura media de dos formas distintas para comparar.\n", - "\n", - "**Celda 5a — Ciclo `for` (enfoque manual):**\n", - "\n", - "Primero filtra el DataFrame para obtener solo las estrellas de tipo `'A7V'`:\n", - "```python\n", - "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", - "```\n", - "Luego recorre la columna `'Temperature (K)'` del DataFrame filtrado con un `for`,\n", - "acumula la `suma` y el `conteo` (`n`), y calcula la media como `suma / n`.\n", - "\n", - "Consulta [cómo filtrar un DataFrame por valor](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html)\n", - "si necesitas orientación sobre la sintaxis de filtrado.\n", - "\n", - "**Celda 5b — Pandas `.groupby()` (enfoque vectorizado):**\n", - "\n", - "Usa [`.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)\n", - "para calcular la media de **todas las clases a la vez** en una sola línea:\n", - "```python\n", - "df.groupby('columna_categorica')['columna_numerica'].mean()\n", - "```\n", - "Ordena de mayor a menor con `.sort_values(ascending=False)`. Guarda en `temp_por_tipo`.\n", - "Compara el resultado de `'A7V'` con el valor que obtuviste con el `for`.\n", - "\n", - "**Celda 5c — Boxplot:**\n", - "\n", - "Usa [`sns.boxplot()`](https://seaborn.pydata.org/generated/seaborn.boxplot.html) con\n", - "`data=stars`, `x='Spectral Class'`, `y='Temperature (K)'`, `order=orden`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-05a", - "metadata": {}, - "outputs": [], - "source": [ - "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", - "tipo_objetivo = 'A7V'\n", - "\n", - "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", - "# filtrado = ...\n", - "# tu código aquí\n", - "\n", - "\n", - "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", - "# acumula la suma y el conteo (n)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Calcula la media y guárdala en media_manual\n", - "# media_manual = suma / n\n", - "# tu código aquí\n", - "\n", - "\n", - "print(f'Temperatura media (for loop) : {media_manual:,.1f} K')" - ] - }, - { - "cell_type": "markdown", - "id": "5dto65riasc", - "metadata": {}, - "source": [ - "### Comparación: `for` vs. pandas\n", - "\n", - "Con el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\n", - "Ahora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n", - "\n", - "Cuando termines la celda 5b, verifica que el resultado de `A7V`\n", - "coincida con el valor que obtuviste con el `for`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4j2wkkt78ju", - "metadata": {}, - "outputs": [], - "source": [ - "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", - "# Calcula la temperatura promedio por tipo con groupby\n", - "# Ordena de mayor a menor y guarda en temp_por_tipo\n", - "# tu código aquí\n", - "\n", - "\n", - "print('Temperatura promedio por clase espectral (K):')\n", - "print(temp_por_tipo)\n", - "print()\n", - "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", - "# tu código aquí" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-05b", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(9, 5))\n", - "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", - "\n", - "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega título y etiquetas de ejes\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.xticks(rotation=20, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "md-06-inst", - "metadata": {}, - "source": [ - "---\n", - "## 6. Luminosidad vs Temperatura\n", - "\n", - "La luminosidad varía en muchos órdenes de magnitud (de 0.00008 a 849 420 L/Lo),\n", - "por eso necesitamos **escala logarítmica** en el eje Y.\n", - "\n", - "Usa [`sns.scatterplot()`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html)\n", - "con los siguientes parámetros (revisa la documentación para entender cada uno):\n", - "- `data=stars` — el DataFrame\n", - "- `x='Temperature (K)'` — temperatura en el eje X\n", - "- `y='Luminosity (L/Lo)'` — luminosidad en el eje Y\n", - "- `hue='Spectral Class'` — colorea los puntos según la clase espectral\n", - "- `style='Spectral Class'` — usa un marcador diferente por clase espectral\n", - "- `s=60` — tamaño de los puntos\n", - "\n", - "Después de crear el plot, aplica escala logarítmica al eje Y con\n", - "[`plt.yscale('log')`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yscale.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-06", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(9, 6))\n", - "\n", - "# Crea el scatter plot con sns.scatterplot(...)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Aplica escala logarítmica al eje Y\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega título, etiquetas de ejes y leyenda\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "md-07-inst", - "metadata": {}, - "source": [ - "---\n", - "## 7. Estadísticas con NumPy\n", - "\n", - "NumPy opera sobre **arrays completos** sin ciclos `for`. Por ejemplo,\n", - "`np.mean(arr)` calcula la media de todos los elementos de `arr` de una sola vez.\n", - "\n", - "**Celda 7a** — Extrae los arrays con `.values` y calcula estadísticas:\n", - "- Extrae: `temperaturas = stars['Temperature (K)'].values`\n", - "- Extrae: `radios = stars['Radius (R/Ro)'].values`\n", - "- Verifica el tipo con `type(temperaturas)`\n", - "- Calcula usando estas funciones de [`numpy.statistics`](https://numpy.org/doc/stable/reference/routines.statistics.html):\n", - " - [`np.mean(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html) — media\n", - " - [`np.median(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.median.html) — mediana\n", - " - [`np.std(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.std.html) — desviación estándar\n", - " - [`np.min(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amin.html) y [`np.max(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amax.html)\n", - "\n", - "**Celda 7b** — Percentiles y conversión vectorizada:\n", - "- Usa [`np.percentile(arr, q)`](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html)\n", - " con `q=[25, 50, 75, 90]` para calcular los 4 percentiles de `radios` de una vez\n", - "- Convierte `temperaturas` de Kelvin a Celsius **sin usar ciclo `for`**:\n", - " `celsius = temperaturas - 273.15` (operación vectorizada)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-07a", - "metadata": {}, - "outputs": [], - "source": [ - "# Extrae los arrays NumPy con .values\n", - "# tu código aquí\n", - "\n", - "\n", - "# Imprime el tipo del array\n", - "# tu código aquí\n", - "\n", - "\n", - "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-07b", - "metadata": {}, - "outputs": [], - "source": [ - "niveles = [25, 50, 75, 90]\n", - "\n", - "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", - "# celsius = ...\n", - "# tu código aquí\n", - "\n", - "\n", - "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", - "# (usa np.round para redondear a 1 decimal)\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "markdown", - "id": "md-08-inst", - "metadata": {}, - "source": [ - "---\n", - "## 8. Diagrama Hertzsprung-Russell\n", - "\n", - "El diagrama H-R es el gráfico más importante en astronomía estelar.\n", - "Relaciona temperatura con luminosidad y revela la estructura evolutiva de las estrellas.\n", - "\n", - "Este diagrama tiene **dos particularidades** que debes implementar:\n", - "1. **Ambos ejes logarítmicos**: `plt.xscale('log')` y `plt.yscale('log')`\n", - "2. **Eje X invertido** (las más calientes a la izquierda): `plt.gca().invert_xaxis()`\n", - "\n", - "**Estructura del código** (el inicio ya está dado, completa las partes marcadas):\n", - "\n", - "```python\n", - "tipos = stars['Spectral Class'].unique()\n", - "colores = sns.color_palette('tab10', len(tipos)) # paleta de colores\n", - "mapa = dict(zip(tipos, colores)) # tipo -> color\n", - "\n", - "for tipo, grupo in stars.groupby('Spectral Class'):\n", - " plt.scatter(grupo['Temperature (K)'],\n", - " grupo['Luminosity (L/Lo)'],\n", - " label=tipo, color=mapa[tipo], s=40, alpha=0.8)\n", - "```\n", - "\n", - "Parámetros de [`plt.scatter()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)\n", - "que debes entender: `x`, `y`, `label`, `color`, `s` (tamaño), `alpha` (transparencia)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-08", - "metadata": {}, - "outputs": [], - "source": [ - "tipos = stars['Spectral Class'].unique()\n", - "colores = sns.color_palette('tab10', len(tipos))\n", - "mapa = dict(zip(tipos, colores))\n", - "\n", - "plt.figure(figsize=(10, 7))\n", - "\n", - "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", - "# tu código aquí\n", - "\n", - "\n", - "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Invierte el eje X con plt.gca().invert_xaxis()\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega: título, etiquetas de ejes, leyenda y grid\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-checkpoint.ipynb b/ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-checkpoint.ipynb deleted file mode 100644 index 3e12a46..0000000 --- a/ejemplos/practica2_analisis_datos/notebooks/.ipynb_checkpoints/analisis_estrellas_estudiante-checkpoint.ipynb +++ /dev/null @@ -1,566 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "md-00", - "metadata": {}, - "source": [ - "# Práctica 2 — Análisis Exploratorio de Datos\n", - "\n", - "En este notebook realizarás un **análisis exploratorio de datos (EDA)** sobre un\n", - "dataset de 240 estrellas clasificadas en 6 tipos.\n", - "\n", - "**Dataset:** [Stars Dataset — Kaggle](https://www.kaggle.com/datasets/waqi786/stars-dataset)\n", - "\n", - "## Instrucciones generales\n", - "- Cada sección tiene celdas marcadas con `# tu código aquí` — ahí debes escribir tu solución.\n", - "- Lee las instrucciones en cada celda de markdown **antes** de escribir el código.\n", - "- Consulta los enlaces a la documentación oficial para entender los parámetros de cada función.\n", - "- Ejecuta el notebook completo **sin errores** antes de hacer commit (`Kernel → Restart & Run All`).\n", - "\n", - "## Contenido\n", - "1. [Importar librerías](#1.-Importar-librerías)\n", - "2. [Cargar los datos](#2.-Cargar-los-datos)\n", - "3. [Exploración inicial](#3.-Exploración-inicial)\n", - "4. [Distribución por tipo de estrella](#4.-Distribución-por-tipo-de-estrella)\n", - "5. [Temperatura por tipo](#5.-Temperatura-por-tipo-de-estrella)\n", - "6. [Luminosidad vs Temperatura](#6.-Luminosidad-vs-Temperatura)\n", - "7. [Estadísticas con NumPy](#7.-Estadísticas-con-NumPy)\n", - "8. [Diagrama Hertzsprung-Russell](#8.-Diagrama-Hertzsprung-Russell)" - ] - }, - { - "cell_type": "markdown", - "id": "md-setup", - "metadata": {}, - "source": [ - "---\n", - "## Configuración del ambiente\n", - "\n", - "Este proyecto usa **Poetry** para gestionar las dependencias. Antes de abrir el notebook\n", - "ejecuta estos comandos **desde la carpeta `practica2_analisis_datos/`**:\n", - "\n", - "```bash\n", - "poetry install\n", - "poetry run jupyter notebook\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "md-01-inst", - "metadata": {}, - "source": [ - "---\n", - "## 1. Importar librerías\n", - "\n", - "Importa las cuatro librerías con sus **alias convencionales**.\n", - "Estos alias son estándares en la comunidad — siempre se usan así:\n", - "\n", - "| Librería | Alias | ¿Para qué sirve? | Documentación |\n", - "|---|---|---|---|\n", - "| `numpy` | `np` | Operaciones matemáticas vectorizadas sobre arrays | [numpy.org/doc/stable](https://numpy.org/doc/stable/user/whatisnumpy.html) |\n", - "| `pandas` | `pd` | Análisis y manipulación de datos tabulares (DataFrames) | [pandas.pydata.org/docs](https://pandas.pydata.org/docs/getting_started/index.html) |\n", - "| `matplotlib.pyplot` | `plt` | Visualización de datos (gráficas de bajo nivel) | [matplotlib.org/tutorials](https://matplotlib.org/stable/tutorials/index.html) |\n", - "| `seaborn` | `sns` | Visualización estadística de alto nivel (sobre matplotlib) | [seaborn.pydata.org/tutorial](https://seaborn.pydata.org/tutorial.html) |\n", - "\n", - "**Sintaxis:** `import librería as alias`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-01", - "metadata": {}, - "outputs": [], - "source": [ - "# Importa las cuatro librerías con sus alias convencionales\n", - "# tu código aquí\n", - "\n", - "\n", - "\n", - "\n", - "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", - "# print(f'numpy {np.__version__}')\n", - "# print(f'pandas {pd.__version__}')\n", - "# print(f'seaborn {sns.__version__}')" - ] - }, - { - "cell_type": "markdown", - "id": "md-02-inst", - "metadata": {}, - "source": [ - "---\n", - "## 2. Cargar los datos\n", - "\n", - "Usa [`pd.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)\n", - "para leer el archivo CSV y cargarlo en un **DataFrame** de pandas.\n", - "\n", - "- Revisa la documentación: ¿qué parámetro recibe `read_csv`? ¿qué devuelve?\n", - "- El archivo se encuentra en `'../data/star_dataset.csv'` (relativo a la carpeta `notebooks/`)\n", - "- Guarda el resultado en una variable llamada `stars`\n", - "- Después usa [`.head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)\n", - " para mostrar las primeras 5 filas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-02", - "metadata": {}, - "outputs": [], - "source": [ - "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", - "# tu código aquí\n", - "\n", - "\n", - "# Muestra las primeras 5 filas del DataFrame\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "markdown", - "id": "md-03-inst", - "metadata": {}, - "source": [ - "---\n", - "## 3. Exploración inicial\n", - "\n", - "Antes de analizar datos siempre hay que entender qué tenemos.\n", - "\n", - "**Celda 3a** — Imprime la información básica del DataFrame:\n", - "1. **Dimensiones** con [`.shape`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shape.html)\n", - " — devuelve una tupla `(filas, columnas)`\n", - "2. **Nombres de columnas** con [`.columns.tolist()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html)\n", - "3. **Tipos de datos** con [`.dtypes`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html)\n", - " — indica si cada columna es `int64`, `float64` o `object` (texto)\n", - "\n", - "**Celda 3b** — Obtén estadísticas descriptivas con\n", - "[`.describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html).\n", - "Fíjate en la media, desviación estándar, mínimo y máximo de cada columna numérica.\n", - "\n", - "**Celda 3c** — Verifica si hay valores nulos con\n", - "[`.isnull()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html)\n", - "seguido de `.sum()`. En un dataset limpio todos los valores deben ser 0." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-03a", - "metadata": {}, - "outputs": [], - "source": [ - "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-03b", - "metadata": {}, - "outputs": [], - "source": [ - "# Obtén el resumen estadístico de las columnas numéricas\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-03c", - "metadata": {}, - "outputs": [], - "source": [ - "# Cuenta los valores nulos por columna\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "markdown", - "id": "md-04-inst", - "metadata": {}, - "source": [ - "---\n", - "## 4. Distribución por tipo de estrella\n", - "\n", - "**Celda 4a** — Usa\n", - "[`.value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html)\n", - "sobre la columna `'Spectral Class'` para contar cuántas estrellas hay de cada tipo.\n", - "Guarda el resultado en una variable llamada `conteo`.\n", - "\n", - "> Pista: accede a una columna del DataFrame con `df['nombre_columna']`, que devuelve una **Serie**.\n", - "> `.value_counts()` es un método de Series.\n", - "\n", - "**Celda 4b** — Crea una **gráfica de barras** de `conteo`:\n", - "1. La línea `plt.figure(figsize=(8, 4))` ya está incluida — no la borres\n", - "2. Llama [`.plot(kind='bar')`](https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.html)\n", - " sobre `conteo`, usando `color='steelblue'` y `edgecolor='black'`\n", - "3. Agrega título con `plt.title(...)`, etiquetas con `plt.xlabel(...)` y `plt.ylabel(...)`\n", - "4. Rota las etiquetas del eje X: `plt.xticks(rotation=30, ha='right')`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-04a", - "metadata": {}, - "outputs": [], - "source": [ - "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", - "# tu código aquí\n", - "\n", - "\n", - "print(conteo)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-04b", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(8, 4))\n", - "\n", - "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega título y etiquetas de ejes\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.xticks(rotation=30, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "md-05-inst", - "metadata": {}, - "source": [ - "---\n", - "## 5. Temperatura por tipo de estrella\n", - "\n", - "En esta sección calcularás la temperatura media de dos formas distintas para comparar.\n", - "\n", - "**Celda 5a — Ciclo `for` (enfoque manual):**\n", - "\n", - "Primero filtra el DataFrame para obtener solo las estrellas de tipo `'A7V'`:\n", - "```python\n", - "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", - "```\n", - "Luego recorre la columna `'Temperature (K)'` del DataFrame filtrado con un `for`,\n", - "acumula la `suma` y el `conteo` (`n`), y calcula la media como `suma / n`.\n", - "\n", - "Consulta [cómo filtrar un DataFrame por valor](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html)\n", - "si necesitas orientación sobre la sintaxis de filtrado.\n", - "\n", - "**Celda 5b — Pandas `.groupby()` (enfoque vectorizado):**\n", - "\n", - "Usa [`.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)\n", - "para calcular la media de **todas las clases a la vez** en una sola línea:\n", - "```python\n", - "df.groupby('columna_categorica')['columna_numerica'].mean()\n", - "```\n", - "Ordena de mayor a menor con `.sort_values(ascending=False)`. Guarda en `temp_por_tipo`.\n", - "Compara el resultado de `'A7V'` con el valor que obtuviste con el `for`.\n", - "\n", - "**Celda 5c — Boxplot:**\n", - "\n", - "Usa [`sns.boxplot()`](https://seaborn.pydata.org/generated/seaborn.boxplot.html) con\n", - "`data=stars`, `x='Spectral Class'`, `y='Temperature (K)'`, `order=orden`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-05a", - "metadata": {}, - "outputs": [], - "source": [ - "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", - "tipo_objetivo = 'A7V'\n", - "\n", - "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", - "# filtrado = ...\n", - "# tu código aquí\n", - "\n", - "\n", - "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", - "# acumula la suma y el conteo (n)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Calcula la media y guárdala en media_manual\n", - "# media_manual = suma / n\n", - "# tu código aquí\n", - "\n", - "\n", - "print(f'Temperatura media (for loop) : {media_manual:,.1f} K')" - ] - }, - { - "cell_type": "markdown", - "id": "5dto65riasc", - "metadata": {}, - "source": "### Comparación: `for` vs. pandas\n\nCon el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\nAhora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n\nCuando termines la celda 5b, verifica que el resultado de `A7V`\ncoincida con el valor que obtuviste con el `for`." - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4j2wkkt78ju", - "metadata": {}, - "outputs": [], - "source": [ - "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", - "# Calcula la temperatura promedio por tipo con groupby\n", - "# Ordena de mayor a menor y guarda en temp_por_tipo\n", - "# tu código aquí\n", - "\n", - "\n", - "print('Temperatura promedio por clase espectral (K):')\n", - "print(temp_por_tipo)\n", - "print()\n", - "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", - "# tu código aquí" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-05b", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(9, 5))\n", - "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", - "\n", - "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega título y etiquetas de ejes\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.xticks(rotation=20, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "md-06-inst", - "metadata": {}, - "source": [ - "---\n", - "## 6. Luminosidad vs Temperatura\n", - "\n", - "La luminosidad varía en muchos órdenes de magnitud (de 0.00008 a 849 420 L/Lo),\n", - "por eso necesitamos **escala logarítmica** en el eje Y.\n", - "\n", - "Usa [`sns.scatterplot()`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html)\n", - "con los siguientes parámetros (revisa la documentación para entender cada uno):\n", - "- `data=stars` — el DataFrame\n", - "- `x='Temperature (K)'` — temperatura en el eje X\n", - "- `y='Luminosity (L/Lo)'` — luminosidad en el eje Y\n", - "- `hue='Spectral Class'` — colorea los puntos según la clase espectral\n", - "- `style='Spectral Class'` — usa un marcador diferente por clase espectral\n", - "- `s=60` — tamaño de los puntos\n", - "\n", - "Después de crear el plot, aplica escala logarítmica al eje Y con\n", - "[`plt.yscale('log')`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yscale.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-06", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(9, 6))\n", - "\n", - "# Crea el scatter plot con sns.scatterplot(...)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Aplica escala logarítmica al eje Y\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega título, etiquetas de ejes y leyenda\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "md-07-inst", - "metadata": {}, - "source": [ - "---\n", - "## 7. Estadísticas con NumPy\n", - "\n", - "NumPy opera sobre **arrays completos** sin ciclos `for`. Por ejemplo,\n", - "`np.mean(arr)` calcula la media de todos los elementos de `arr` de una sola vez.\n", - "\n", - "**Celda 7a** — Extrae los arrays con `.values` y calcula estadísticas:\n", - "- Extrae: `temperaturas = stars['Temperature (K)'].values`\n", - "- Extrae: `radios = stars['Radius (R/Ro)'].values`\n", - "- Verifica el tipo con `type(temperaturas)`\n", - "- Calcula usando estas funciones de [`numpy.statistics`](https://numpy.org/doc/stable/reference/routines.statistics.html):\n", - " - [`np.mean(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html) — media\n", - " - [`np.median(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.median.html) — mediana\n", - " - [`np.std(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.std.html) — desviación estándar\n", - " - [`np.min(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amin.html) y [`np.max(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amax.html)\n", - "\n", - "**Celda 7b** — Percentiles y conversión vectorizada:\n", - "- Usa [`np.percentile(arr, q)`](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html)\n", - " con `q=[25, 50, 75, 90]` para calcular los 4 percentiles de `radios` de una vez\n", - "- Convierte `temperaturas` de Kelvin a Celsius **sin usar ciclo `for`**:\n", - " `celsius = temperaturas - 273.15` (operación vectorizada)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-07a", - "metadata": {}, - "outputs": [], - "source": [ - "# Extrae los arrays NumPy con .values\n", - "# tu código aquí\n", - "\n", - "\n", - "# Imprime el tipo del array\n", - "# tu código aquí\n", - "\n", - "\n", - "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-07b", - "metadata": {}, - "outputs": [], - "source": [ - "niveles = [25, 50, 75, 90]\n", - "\n", - "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", - "# celsius = ...\n", - "# tu código aquí\n", - "\n", - "\n", - "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", - "# (usa np.round para redondear a 1 decimal)\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "markdown", - "id": "md-08-inst", - "metadata": {}, - "source": [ - "---\n", - "## 8. Diagrama Hertzsprung-Russell\n", - "\n", - "El diagrama H-R es el gráfico más importante en astronomía estelar.\n", - "Relaciona temperatura con luminosidad y revela la estructura evolutiva de las estrellas.\n", - "\n", - "Este diagrama tiene **dos particularidades** que debes implementar:\n", - "1. **Ambos ejes logarítmicos**: `plt.xscale('log')` y `plt.yscale('log')`\n", - "2. **Eje X invertido** (las más calientes a la izquierda): `plt.gca().invert_xaxis()`\n", - "\n", - "**Estructura del código** (el inicio ya está dado, completa las partes marcadas):\n", - "\n", - "```python\n", - "tipos = stars['Spectral Class'].unique()\n", - "colores = sns.color_palette('tab10', len(tipos)) # paleta de colores\n", - "mapa = dict(zip(tipos, colores)) # tipo -> color\n", - "\n", - "for tipo, grupo in stars.groupby('Spectral Class'):\n", - " plt.scatter(grupo['Temperature (K)'],\n", - " grupo['Luminosity (L/Lo)'],\n", - " label=tipo, color=mapa[tipo], s=40, alpha=0.8)\n", - "```\n", - "\n", - "Parámetros de [`plt.scatter()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)\n", - "que debes entender: `x`, `y`, `label`, `color`, `s` (tamaño), `alpha` (transparencia)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-08", - "metadata": {}, - "outputs": [], - "source": [ - "tipos = stars['Spectral Class'].unique()\n", - "colores = sns.color_palette('tab10', len(tipos))\n", - "mapa = dict(zip(tipos, colores))\n", - "\n", - "plt.figure(figsize=(10, 7))\n", - "\n", - "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", - "# tu código aquí\n", - "\n", - "\n", - "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Invierte el eje X con plt.gca().invert_xaxis()\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega: título, etiquetas de ejes, leyenda y grid\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante-Copy1.ipynb b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante-Copy1.ipynb deleted file mode 100644 index 79caa4e..0000000 --- a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante-Copy1.ipynb +++ /dev/null @@ -1,574 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "md-00", - "metadata": {}, - "source": [ - "# Práctica 2 — Análisis Exploratorio de Datos\n", - "\n", - "En este notebook realizarás un **análisis exploratorio de datos (EDA)** sobre un\n", - "dataset de 240 estrellas clasificadas en 6 tipos.\n", - "\n", - "**Dataset:** [Stars Dataset — Kaggle](https://www.kaggle.com/datasets/waqi786/stars-dataset)\n", - "\n", - "## Instrucciones generales\n", - "- Cada sección tiene celdas marcadas con `# tu código aquí` — ahí debes escribir tu solución.\n", - "- Lee las instrucciones en cada celda de markdown **antes** de escribir el código.\n", - "- Consulta los enlaces a la documentación oficial para entender los parámetros de cada función.\n", - "- Ejecuta el notebook completo **sin errores** antes de hacer commit (`Kernel → Restart & Run All`).\n", - "\n", - "## Contenido\n", - "1. [Importar librerías](#1.-Importar-librerías)\n", - "2. [Cargar los datos](#2.-Cargar-los-datos)\n", - "3. [Exploración inicial](#3.-Exploración-inicial)\n", - "4. [Distribución por tipo de estrella](#4.-Distribución-por-tipo-de-estrella)\n", - "5. [Temperatura por tipo](#5.-Temperatura-por-tipo-de-estrella)\n", - "6. [Luminosidad vs Temperatura](#6.-Luminosidad-vs-Temperatura)\n", - "7. [Estadísticas con NumPy](#7.-Estadísticas-con-NumPy)\n", - "8. [Diagrama Hertzsprung-Russell](#8.-Diagrama-Hertzsprung-Russell)" - ] - }, - { - "cell_type": "markdown", - "id": "md-setup", - "metadata": {}, - "source": [ - "---\n", - "## Configuración del ambiente\n", - "\n", - "Este proyecto usa **Poetry** para gestionar las dependencias. Antes de abrir el notebook\n", - "ejecuta estos comandos **desde la carpeta `practica2_analisis_datos/`**:\n", - "\n", - "```bash\n", - "poetry install\n", - "poetry run jupyter notebook\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "md-01-inst", - "metadata": {}, - "source": [ - "---\n", - "## 1. Importar librerías\n", - "\n", - "Importa las cuatro librerías con sus **alias convencionales**.\n", - "Estos alias son estándares en la comunidad — siempre se usan así:\n", - "\n", - "| Librería | Alias | ¿Para qué sirve? | Documentación |\n", - "|---|---|---|---|\n", - "| `numpy` | `np` | Operaciones matemáticas vectorizadas sobre arrays | [numpy.org/doc/stable](https://numpy.org/doc/stable/user/whatisnumpy.html) |\n", - "| `pandas` | `pd` | Análisis y manipulación de datos tabulares (DataFrames) | [pandas.pydata.org/docs](https://pandas.pydata.org/docs/getting_started/index.html) |\n", - "| `matplotlib.pyplot` | `plt` | Visualización de datos (gráficas de bajo nivel) | [matplotlib.org/tutorials](https://matplotlib.org/stable/tutorials/index.html) |\n", - "| `seaborn` | `sns` | Visualización estadística de alto nivel (sobre matplotlib) | [seaborn.pydata.org/tutorial](https://seaborn.pydata.org/tutorial.html) |\n", - "\n", - "**Sintaxis:** `import librería as alias`" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "code-01", - "metadata": {}, - "outputs": [], - "source": [ - "# Importa las cuatro librerías con sus alias convencionales\n", - "# tu código aquí\n", - "\n", - "\n", - "\n", - "\n", - "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", - "# print(f'numpy {np.__version__}')\n", - "# print(f'pandas {pd.__version__}')\n", - "# print(f'seaborn {sns.__version__}')" - ] - }, - { - "cell_type": "markdown", - "id": "md-02-inst", - "metadata": {}, - "source": [ - "---\n", - "## 2. Cargar los datos\n", - "\n", - "Usa [`pd.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)\n", - "para leer el archivo CSV y cargarlo en un **DataFrame** de pandas.\n", - "\n", - "- Revisa la documentación: ¿qué parámetro recibe `read_csv`? ¿qué devuelve?\n", - "- El archivo se encuentra en `'../data/star_dataset.csv'` (relativo a la carpeta `notebooks/`)\n", - "- Guarda el resultado en una variable llamada `stars`\n", - "- Después usa [`.head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)\n", - " para mostrar las primeras 5 filas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-02", - "metadata": {}, - "outputs": [], - "source": [ - "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", - "# tu código aquí\n", - "\n", - "\n", - "# Muestra las primeras 5 filas del DataFrame\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "markdown", - "id": "md-03-inst", - "metadata": {}, - "source": [ - "---\n", - "## 3. Exploración inicial\n", - "\n", - "Antes de analizar datos siempre hay que entender qué tenemos.\n", - "\n", - "**Celda 3a** — Imprime la información básica del DataFrame:\n", - "1. **Dimensiones** con [`.shape`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shape.html)\n", - " — devuelve una tupla `(filas, columnas)`\n", - "2. **Nombres de columnas** con [`.columns.tolist()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html)\n", - "3. **Tipos de datos** con [`.dtypes`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html)\n", - " — indica si cada columna es `int64`, `float64` o `object` (texto)\n", - "\n", - "**Celda 3b** — Obtén estadísticas descriptivas con\n", - "[`.describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html).\n", - "Fíjate en la media, desviación estándar, mínimo y máximo de cada columna numérica.\n", - "\n", - "**Celda 3c** — Verifica si hay valores nulos con\n", - "[`.isnull()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html)\n", - "seguido de `.sum()`. En un dataset limpio todos los valores deben ser 0." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-03a", - "metadata": {}, - "outputs": [], - "source": [ - "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-03b", - "metadata": {}, - "outputs": [], - "source": [ - "# Obtén el resumen estadístico de las columnas numéricas\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-03c", - "metadata": {}, - "outputs": [], - "source": [ - "# Cuenta los valores nulos por columna\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "markdown", - "id": "md-04-inst", - "metadata": {}, - "source": [ - "---\n", - "## 4. Distribución por tipo de estrella\n", - "\n", - "**Celda 4a** — Usa\n", - "[`.value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html)\n", - "sobre la columna `'Spectral Class'` para contar cuántas estrellas hay de cada tipo.\n", - "Guarda el resultado en una variable llamada `conteo`.\n", - "\n", - "> Pista: accede a una columna del DataFrame con `df['nombre_columna']`, que devuelve una **Serie**.\n", - "> `.value_counts()` es un método de Series.\n", - "\n", - "**Celda 4b** — Crea una **gráfica de barras** de `conteo`:\n", - "1. La línea `plt.figure(figsize=(8, 4))` ya está incluida — no la borres\n", - "2. Llama [`.plot(kind='bar')`](https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.html)\n", - " sobre `conteo`, usando `color='steelblue'` y `edgecolor='black'`\n", - "3. Agrega título con `plt.title(...)`, etiquetas con `plt.xlabel(...)` y `plt.ylabel(...)`\n", - "4. Rota las etiquetas del eje X: `plt.xticks(rotation=30, ha='right')`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-04a", - "metadata": {}, - "outputs": [], - "source": [ - "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", - "# tu código aquí\n", - "\n", - "\n", - "print(conteo)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-04b", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(8, 4))\n", - "\n", - "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega título y etiquetas de ejes\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.xticks(rotation=30, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "md-05-inst", - "metadata": {}, - "source": [ - "---\n", - "## 5. Temperatura por tipo de estrella\n", - "\n", - "En esta sección calcularás la temperatura media de dos formas distintas para comparar.\n", - "\n", - "**Celda 5a — Ciclo `for` (enfoque manual):**\n", - "\n", - "Primero filtra el DataFrame para obtener solo las estrellas de tipo `'A7V'`:\n", - "```python\n", - "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", - "```\n", - "Luego recorre la columna `'Temperature (K)'` del DataFrame filtrado con un `for`,\n", - "acumula la `suma` y el `conteo` (`n`), y calcula la media como `suma / n`.\n", - "\n", - "Consulta [cómo filtrar un DataFrame por valor](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html)\n", - "si necesitas orientación sobre la sintaxis de filtrado.\n", - "\n", - "**Celda 5b — Pandas `.groupby()` (enfoque vectorizado):**\n", - "\n", - "Usa [`.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)\n", - "para calcular la media de **todas las clases a la vez** en una sola línea:\n", - "```python\n", - "df.groupby('columna_categorica')['columna_numerica'].mean()\n", - "```\n", - "Ordena de mayor a menor con `.sort_values(ascending=False)`. Guarda en `temp_por_tipo`.\n", - "Compara el resultado de `'A7V'` con el valor que obtuviste con el `for`.\n", - "\n", - "**Celda 5c — Boxplot:**\n", - "\n", - "Usa [`sns.boxplot()`](https://seaborn.pydata.org/generated/seaborn.boxplot.html) con\n", - "`data=stars`, `x='Spectral Class'`, `y='Temperature (K)'`, `order=orden`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-05a", - "metadata": {}, - "outputs": [], - "source": [ - "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", - "tipo_objetivo = 'A7V'\n", - "\n", - "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", - "# filtrado = ...\n", - "# tu código aquí\n", - "\n", - "\n", - "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", - "# acumula la suma y el conteo (n)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Calcula la media y guárdala en media_manual\n", - "# media_manual = suma / n\n", - "# tu código aquí\n", - "\n", - "\n", - "print(f'Temperatura media (for loop) : {media_manual:,.1f} K')" - ] - }, - { - "cell_type": "markdown", - "id": "5dto65riasc", - "metadata": {}, - "source": [ - "### Comparación: `for` vs. pandas\n", - "\n", - "Con el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\n", - "Ahora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n", - "\n", - "Cuando termines la celda 5b, verifica que el resultado de `A7V`\n", - "coincida con el valor que obtuviste con el `for`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4j2wkkt78ju", - "metadata": {}, - "outputs": [], - "source": [ - "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", - "# Calcula la temperatura promedio por tipo con groupby\n", - "# Ordena de mayor a menor y guarda en temp_por_tipo\n", - "# tu código aquí\n", - "\n", - "\n", - "print('Temperatura promedio por clase espectral (K):')\n", - "print(temp_por_tipo)\n", - "print()\n", - "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", - "# tu código aquí" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-05b", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(9, 5))\n", - "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", - "\n", - "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega título y etiquetas de ejes\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.xticks(rotation=20, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "md-06-inst", - "metadata": {}, - "source": [ - "---\n", - "## 6. Luminosidad vs Temperatura\n", - "\n", - "La luminosidad varía en muchos órdenes de magnitud (de 0.00008 a 849 420 L/Lo),\n", - "por eso necesitamos **escala logarítmica** en el eje Y.\n", - "\n", - "Usa [`sns.scatterplot()`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html)\n", - "con los siguientes parámetros (revisa la documentación para entender cada uno):\n", - "- `data=stars` — el DataFrame\n", - "- `x='Temperature (K)'` — temperatura en el eje X\n", - "- `y='Luminosity (L/Lo)'` — luminosidad en el eje Y\n", - "- `hue='Spectral Class'` — colorea los puntos según la clase espectral\n", - "- `style='Spectral Class'` — usa un marcador diferente por clase espectral\n", - "- `s=60` — tamaño de los puntos\n", - "\n", - "Después de crear el plot, aplica escala logarítmica al eje Y con\n", - "[`plt.yscale('log')`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yscale.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-06", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(9, 6))\n", - "\n", - "# Crea el scatter plot con sns.scatterplot(...)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Aplica escala logarítmica al eje Y\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega título, etiquetas de ejes y leyenda\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "md-07-inst", - "metadata": {}, - "source": [ - "---\n", - "## 7. Estadísticas con NumPy\n", - "\n", - "NumPy opera sobre **arrays completos** sin ciclos `for`. Por ejemplo,\n", - "`np.mean(arr)` calcula la media de todos los elementos de `arr` de una sola vez.\n", - "\n", - "**Celda 7a** — Extrae los arrays con `.values` y calcula estadísticas:\n", - "- Extrae: `temperaturas = stars['Temperature (K)'].values`\n", - "- Extrae: `radios = stars['Radius (R/Ro)'].values`\n", - "- Verifica el tipo con `type(temperaturas)`\n", - "- Calcula usando estas funciones de [`numpy.statistics`](https://numpy.org/doc/stable/reference/routines.statistics.html):\n", - " - [`np.mean(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html) — media\n", - " - [`np.median(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.median.html) — mediana\n", - " - [`np.std(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.std.html) — desviación estándar\n", - " - [`np.min(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amin.html) y [`np.max(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amax.html)\n", - "\n", - "**Celda 7b** — Percentiles y conversión vectorizada:\n", - "- Usa [`np.percentile(arr, q)`](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html)\n", - " con `q=[25, 50, 75, 90]` para calcular los 4 percentiles de `radios` de una vez\n", - "- Convierte `temperaturas` de Kelvin a Celsius **sin usar ciclo `for`**:\n", - " `celsius = temperaturas - 273.15` (operación vectorizada)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-07a", - "metadata": {}, - "outputs": [], - "source": [ - "# Extrae los arrays NumPy con .values\n", - "# tu código aquí\n", - "\n", - "\n", - "# Imprime el tipo del array\n", - "# tu código aquí\n", - "\n", - "\n", - "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-07b", - "metadata": {}, - "outputs": [], - "source": [ - "niveles = [25, 50, 75, 90]\n", - "\n", - "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", - "# celsius = ...\n", - "# tu código aquí\n", - "\n", - "\n", - "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", - "# (usa np.round para redondear a 1 decimal)\n", - "# tu código aquí\n" - ] - }, - { - "cell_type": "markdown", - "id": "md-08-inst", - "metadata": {}, - "source": [ - "---\n", - "## 8. Diagrama Hertzsprung-Russell\n", - "\n", - "El diagrama H-R es el gráfico más importante en astronomía estelar.\n", - "Relaciona temperatura con luminosidad y revela la estructura evolutiva de las estrellas.\n", - "\n", - "Este diagrama tiene **dos particularidades** que debes implementar:\n", - "1. **Ambos ejes logarítmicos**: `plt.xscale('log')` y `plt.yscale('log')`\n", - "2. **Eje X invertido** (las más calientes a la izquierda): `plt.gca().invert_xaxis()`\n", - "\n", - "**Estructura del código** (el inicio ya está dado, completa las partes marcadas):\n", - "\n", - "```python\n", - "tipos = stars['Spectral Class'].unique()\n", - "colores = sns.color_palette('tab10', len(tipos)) # paleta de colores\n", - "mapa = dict(zip(tipos, colores)) # tipo -> color\n", - "\n", - "for tipo, grupo in stars.groupby('Spectral Class'):\n", - " plt.scatter(grupo['Temperature (K)'],\n", - " grupo['Luminosity (L/Lo)'],\n", - " label=tipo, color=mapa[tipo], s=40, alpha=0.8)\n", - "```\n", - "\n", - "Parámetros de [`plt.scatter()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)\n", - "que debes entender: `x`, `y`, `label`, `color`, `s` (tamaño), `alpha` (transparencia)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code-08", - "metadata": {}, - "outputs": [], - "source": [ - "tipos = stars['Spectral Class'].unique()\n", - "colores = sns.color_palette('tab10', len(tipos))\n", - "mapa = dict(zip(tipos, colores))\n", - "\n", - "plt.figure(figsize=(10, 7))\n", - "\n", - "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", - "# tu código aquí\n", - "\n", - "\n", - "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", - "# tu código aquí\n", - "\n", - "\n", - "# Invierte el eje X con plt.gca().invert_xaxis()\n", - "# tu código aquí\n", - "\n", - "\n", - "# Agrega: título, etiquetas de ejes, leyenda y grid\n", - "# tu código aquí\n", - "\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb index 79caa4e..0613f80 100644 --- a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb +++ b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "code-02", "metadata": {}, "outputs": [], @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "code-03a", "metadata": {}, "outputs": [], @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "code-03b", "metadata": {}, "outputs": [], @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "code-03c", "metadata": {}, "outputs": [], @@ -204,10 +204,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "code-04a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'conteo' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# tu código aquí\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(conteo)\n", + "\u001b[0;31mNameError\u001b[0m: name 'conteo' is not defined" + ] + } + ], "source": [ "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", "# tu código aquí\n", @@ -552,7 +564,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -566,7 +578,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.7" } }, "nbformat": 4, From 21f73699955f4aaec5235a2374e503c915f4bd5f Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Wed, 6 May 2026 10:57:16 -0600 Subject: [PATCH 23/28] Limpieza: aplicando gitignore para eliminar archivos temporales --- .gitignore | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/.gitignore b/.gitignore index f709485..50eda54 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,10 @@ *.log .code_config/ grader/ +# Jupyter Notebooks +.ipynb_checkpoints/ + +# Python +__pycache__/ +*.py[cod] +*$py.class \ No newline at end of file From eb0fc1a43068c7361872f6f84905e8acb769e045 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 8 May 2026 08:46:07 -0600 Subject: [PATCH 24/28] actulizar analisis_estrellas_estudiante --- .../analisis_estrellas_estudiante.ipynb | 923 +++++++++++++-- .../notebooks/star_dataset.csv | 1001 +++++++++++++++++ 2 files changed, 1859 insertions(+), 65 deletions(-) create mode 100644 ejemplos/practica2_analisis_datos/notebooks/star_dataset.csv diff --git a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb index 0613f80..e93b81b 100644 --- a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb +++ b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb @@ -72,18 +72,31 @@ "execution_count": 1, "id": "code-01", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "numpy 1.26.4\n", + "pandas 2.1.4\n", + "seaborn 0.12.2\n", + "Matplotlib versión: 3.8.0\n" + ] + } + ], "source": [ "# Importa las cuatro librerías con sus alias convencionales\n", "# tu código aquí\n", - "\n", - "\n", - "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", - "# print(f'numpy {np.__version__}')\n", - "# print(f'pandas {pd.__version__}')\n", - "# print(f'seaborn {sns.__version__}')" + "print(f'numpy {np.__version__}')\n", + "print(f'pandas {pd.__version__}')\n", + "print(f'seaborn {sns.__version__}')\n", + "print(\"Matplotlib versión:\", __import__('matplotlib').__version__)\n" ] }, { @@ -106,17 +119,170 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 34, "id": "code-02", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------------------------------------\n", + " CONTENIDO INICIAL DEL DATASET:\n" + ] + }, + { + "data": { + "text/html": [ + "
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NameDistance (ly)Luminosity (L/Lo)Radius (R/Ro)Temperature (K)Spectral Class
0Altair16.5941719.9791921.6326507509.294247A7V
1Deneb2600.490723196002.627856202.9705268503.284796A2Ia
2Barnard's Star6.0526164.8937160.2227113165.959639M4Ve
3Polaris322.6010022196.24193437.5468136048.326915F7Ib
4Barnard's Star5.902392-1.4964860.1923593130.602069M4Ve
\n", + "
" + ], + "text/plain": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "0 Altair 16.594171 9.979192 1.632650 \n", + "1 Deneb 2600.490723 196002.627856 202.970526 \n", + "2 Barnard's Star 6.052616 4.893716 0.222711 \n", + "3 Polaris 322.601002 2196.241934 37.546813 \n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "\n", + " Temperature (K) Spectral Class \n", + "0 7509.294247 A7V \n", + "1 8503.284796 A2Ia \n", + "2 3165.959639 M4Ve \n", + "3 6048.326915 F7Ib \n", + "4 3130.602069 M4Ve " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------------------------------------\n", + " INFORMACIÓN GENERAL DE CADA ESTRELLA:\n", + " \n", + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "0 Altair 16.594171 9.979192 1.632650 \n", + "1 Deneb 2600.490723 196002.627856 202.970526 \n", + "2 Barnard's Star 6.052616 4.893716 0.222711 \n", + "3 Polaris 322.601002 2196.241934 37.546813 \n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "\n", + " Temperature (K) Spectral Class \n", + "0 7509.294247 A7V \n", + "1 8503.284796 A2Ia \n", + "2 3165.959639 M4Ve \n", + "3 6048.326915 F7Ib \n", + "4 3130.602069 M4Ve \n", + "------------------------------------------------------------------------------------------------------------------------\n", + " DIMENSIONES (FILAS , COLUMNAS): (1000, 6)\n", + " TIPO DE DATOS:\n", + "Name object\n", + "Distance (ly) float64\n", + "Luminosity (L/Lo) float64\n", + "Radius (R/Ro) float64\n", + "Temperature (K) float64\n", + "Spectral Class object\n", + "dtype: object\n", + "------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", "\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"CONTENIDO INICIAL DEL DATASET:\" )\n", + "display(stars.head())\n", "\n", "# Muestra las primeras 5 filas del DataFrame\n", - "# tu código aquí\n" + "# tu código aquí\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"INFORMACIÓN GENERAL DE CADA ESTRELLA:\" )\n", + "print( \" \" * 40)\n", + "print(stars.head())\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"DIMENSIONES (FILAS , COLUMNAS):\", stars.shape)\n", + "print(\" \" * 1 +\"TIPO DE DATOS:\")\n", + "print(stars.dtypes)\n", + "print( \"---\" * 40)" ] }, { @@ -147,35 +313,337 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 50, "id": "code-03a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................................................................................................................\n", + " DIMENSIONES (COLUMNAS, FILAS):\n", + "\n", + "(1000, 6)\n", + "........................................................................................................................\n", + " \n", + " NOMBRE DE LAS COLUMNAS:\n", + "\n", + "['Name', 'Distance (ly)', 'Luminosity (L/Lo)', 'Radius (R/Ro)', 'Temperature (K)', 'Spectral Class']\n", + " \n", + "........................................................................................................................\n", + "\n", + " TIPO DE DATOS:\n", + "\n", + "Name object\n", + "Distance (ly) float64\n", + "Luminosity (L/Lo) float64\n", + "Radius (R/Ro) float64\n", + "Temperature (K) float64\n", + "Spectral Class object\n", + "dtype: object\n", + "........................................................................................................................\n" + ] + } + ], "source": [ "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", - "# tu código aquí\n" + "# tu código aquí\n", + "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "print( \"...\" * 40)\n", + "print( \" \" * 15 +\"DIMENSIONES (COLUMNAS, FILAS):\\n\" )\n", + "print(stars.shape)\n", + "print( \"...\" * 40)\n", + "print( \" \" * 40)\n", + "print( \" \" * 15 +\"NOMBRE DE LAS COLUMNAS:\\n\")\n", + "print(stars.columns.tolist())\n", + "print( \" \" * 40)\n", + "print( \"...\" * 40)\n", + "print(\"\\n \" + \" \" * 15 +\" TIPO DE DATOS:\\n\")\n", + "print(stars.dtypes)\n", + "print(\"...\" * 40)\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 63, "id": "code-03b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " ESTADÍSTICA DESCRIPTIVA DE LA DATASET: ESTRELLAS \n", + " \n", + " Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + " NÚMERO TOTAL DE REGISTROS 1000.000000 1000.000000 1000.000000 \n", + "PROMEDIO 295.505327 19644.909442 86.960696 \n", + "DESVIACIÓN ESTÁNDAR 541.478403 42223.595017 213.850005 \n", + "VALOR MÍNIMO 3.877798 -4.993141 0.068087 \n", + "CUARTIL 1 (25%) 11.716853 10.441039 1.664479 \n", + "MEDIANA (50%) 52.031435 171.097809 5.845444 \n", + "CUARTIL 3 (75%) 322.865874 10500.577117 33.719778 \n", + "VALOR MÁXIMO 2600.490723 196004.854081 887.097936 \n", + "\n", + " Temperature (K) \n", + " NÚMERO TOTAL DE REGISTROS 1000.000000 \n", + "PROMEDIO 9983.486779 \n", + "DESVIACIÓN ESTÁNDAR 7906.973529 \n", + "VALOR MÍNIMO 2750.183163 \n", + "CUARTIL 1 (25%) 3940.020856 \n", + "MEDIANA (50%) 7379.007975 \n", + "CUARTIL 3 (75%) 12055.975095 \n", + "VALOR MÁXIMO 28044.279272 \n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " LISTA DE DATOS EXTRAÑOS \n", + " \n", + " Total de Datos Extraños por ser Negativos: 88 \n" + ] + }, + { + "data": { + "text/html": [ + "
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NameDistance (ly)Luminosity (L/Lo)Radius (R/Ro)Temperature (K)Spectral Class
4Barnard's Star5.902392-1.4964860.1923593130.602069M4Ve
28Alpha Centauri B3.925939-2.5124240.8971565292.710092K1V
29Ross 1549.584936-0.2622130.0981502788.607876M3.5V
38Barnard's Star6.274309-4.6125040.2714203164.713490M4Ve
49Barnard's Star5.563557-3.4589550.1495363115.066681M4Ve
.....................
941Wolf 3597.509381-4.9350530.2081092841.890593M6V
944Rigil Kentaurus4.770200-1.1625151.2118415768.601304G2V
994Barnard's Star5.938952-3.1722540.2666883121.980225M4Ve
995Wolf 3597.455715-4.4351010.0680872774.148300M6V
998Alpha Centauri B4.044364-4.5490880.9391915286.304304K1V
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88 rows × 6 columns

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" + ], + "text/plain": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "28 Alpha Centauri B 3.925939 -2.512424 0.897156 \n", + "29 Ross 154 9.584936 -0.262213 0.098150 \n", + "38 Barnard's Star 6.274309 -4.612504 0.271420 \n", + "49 Barnard's Star 5.563557 -3.458955 0.149536 \n", + ".. ... ... ... ... \n", + "941 Wolf 359 7.509381 -4.935053 0.208109 \n", + "944 Rigil Kentaurus 4.770200 -1.162515 1.211841 \n", + "994 Barnard's Star 5.938952 -3.172254 0.266688 \n", + "995 Wolf 359 7.455715 -4.435101 0.068087 \n", + "998 Alpha Centauri B 4.044364 -4.549088 0.939191 \n", + "\n", + " Temperature (K) Spectral Class \n", + "4 3130.602069 M4Ve \n", + "28 5292.710092 K1V \n", + "29 2788.607876 M3.5V \n", + "38 3164.713490 M4Ve \n", + "49 3115.066681 M4Ve \n", + ".. ... ... \n", + "941 2841.890593 M6V \n", + "944 5768.601304 G2V \n", + "994 3121.980225 M4Ve \n", + "995 2774.148300 M6V \n", + "998 5286.304304 K1V \n", + "\n", + "[88 rows x 6 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "# Obtén el resumen estadístico de las columnas numéricas\n", - "# tu código aquí\n" + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "resumen = stars.describe()\n", + "resumen = resumen.rename(index={\n", + " \"count\" : \" NÚMERO TOTAL DE REGISTROS\",\n", + " \"mean\" : \"PROMEDIO\", \n", + " \"std\" : \"DESVIACIÓN ESTÁNDAR\",\n", + " \"min\" : \"VALOR MÍNIMO\",\n", + " \"max\" : \"VALOR MÁXIMO\", \n", + " \"25%\" : \"CUARTIL 1 (25%)\", \n", + " \"50%\" : \"MEDIANA (50%)\",\n", + " \"75%\" : \"CUARTIL 3 (75%)\",\n", + " \"mod\" : \"MODA\", \n", + "})\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"ESTADÍSTICA DESCRIPTIVA DE LA DATASET: ESTRELLAS \\n \")\n", + "print(resumen)\n", + "\n", + "\n", + "print( \"----\" * 40)\n", + "a = stars[stars['Luminosity (L/Lo)'] < 0] # explica por que en el valor mínimo se obtuvo un valor negativo\n", + "\n", + "print(\" \" * 15 + \"LISTA DE DATOS EXTRAÑOS \\n \")\n", + "print(f\" Total de Datos Extraños por ser Negativos: {len(a)} \")\n", + "display(a)\n", + "print( \"----\" * 40)\n" + ] + }, + { + "cell_type": "markdown", + "id": "217f2f7b", + "metadata": {}, + "source": [ + "En términos físicos, la luminosidad mide la cantidad de energía que emite una estrella por segundo. Al ser una medida de energía emitida, no puede ser negativa. Por eso se adjunta " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "code-03c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name 0\n", + "Distance (ly) 0\n", + "Luminosity (L/Lo) 0\n", + "Radius (R/Ro) 0\n", + "Temperature (K) 0\n", + "Spectral Class 0\n", + "dtype: int64\n" + ] + } + ], "source": [ "# Cuenta los valores nulos por columna\n", - "# tu código aquí\n" + "# tu código aquí\n", + "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "a = stars.isnull().sum()\n", + "print(a)" ] }, { @@ -204,47 +672,89 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 70, "id": "code-04a", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'conteo' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# tu código aquí\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(conteo)\n", - "\u001b[0;31mNameError\u001b[0m: name 'conteo' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectral Class\n", + "A7V 74\n", + "A1V 73\n", + "A9II 48\n", + "M3.5V 45\n", + "B1III 45\n", + "M2Iab 44\n", + "G8III 39\n", + "M4Ve 38\n", + "A0V 38\n", + "K1.5III 38\n", + "B2III 37\n", + "M7IIIe 37\n", + "B0Ia 36\n", + "G2V 36\n", + "F7Ib 35\n", + "B1III-IV 32\n", + "A3V 31\n", + "F5IV-V 30\n", + "B0.5IV 30\n", + "B6Vep 29\n", + "M2.1V 27\n", + "M1.5Iab 26\n", + "B7V 26\n", + "A2Ia 25\n", + "K1V 24\n", + "M6V 22\n", + "K5III 18\n", + "B8Ia 17\n", + "Name: count, dtype: int64\n" ] } ], "source": [ "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", "# tu código aquí\n", - "\n", - "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "conteo = stars[\"Spectral Class\"].value_counts() \n", "print(conteo)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "id": "code-04b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(8, 4))\n", "\n", "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "conteo = stars[\"Spectral Class\"].value_counts() \n", + "conteo.plot(kind='bar', color='steelblue', edgecolor='black')\n", + "\n", "\n", "\n", "# Agrega título y etiquetas de ejes\n", "# tu código aquí\n", "\n", - "\n", + "plt.title(\"DISTRIBUCIÓN DE ESTRELLAS POR CLASE ESPECTRAL\")\n", + "plt.xlabel(\"CLASE ESPECTRAL\")\n", + "plt.ylabel(\"No. DE ESTRELLAS\")\n", "plt.xticks(rotation=30, ha='right')\n", "plt.tight_layout()\n", "plt.show()" @@ -290,36 +800,93 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "id": "code-05a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) Temperature (K) \\\n", + "0 Altair 16.594171 9.979192 1.632650 7509.294247 \n", + "11 Altair 16.324632 10.457079 1.638568 7554.538238 \n", + "32 Altair 16.977835 8.978259 1.681768 7546.776074 \n", + "41 Altair 16.481724 10.654505 1.697977 7557.224465 \n", + "82 Altair 17.004753 15.509654 1.574136 7589.018862 \n", + ".. ... ... ... ... ... \n", + "974 Altair 16.502834 15.008535 1.537144 7531.390715 \n", + "984 Altair 17.154742 6.975772 1.548312 7537.641975 \n", + "989 Altair 17.111919 10.391013 1.609587 7504.094813 \n", + "990 Altair 16.874372 5.998471 1.559794 7561.789883 \n", + "993 Altair 17.121544 11.776187 1.635310 7555.418080 \n", + "\n", + " Spectral Class \n", + "0 A7V \n", + "11 A7V \n", + "32 A7V \n", + "41 A7V \n", + "82 A7V \n", + ".. ... \n", + "974 A7V \n", + "984 A7V \n", + "989 A7V \n", + "990 A7V \n", + "993 A7V \n", + "\n", + "[74 rows x 6 columns]\n", + "MEDIA DE TEMPERATURAS: A7V : 7550.178312906775 K\n", + "TEMPERATURA MEDIA (for loop) : 7,550.2 K\n" + ] + } + ], "source": [ "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", "tipo_objetivo = 'A7V'\n", - "\n", + "stars = pd.read_csv('star_dataset.csv')\n", "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", "# filtrado = ...\n", "# tu código aquí\n", - "\n", + "filtrado = stars[stars[\"Spectral Class\"] == tipo_objetivo ]\n", + "print(filtrado)\n", "\n", "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", "# acumula la suma y el conteo (n)\n", "# tu código aquí\n", - "\n", + "suma_temperaturas = 0\n", + "conteo_n = 0\n", + "for temperaturas in filtrado['Temperature (K)']:\n", + " suma_temperaturas += temperaturas\n", + " conteo_n += 1\n", "\n", "# Calcula la media y guárdala en media_manual\n", "# media_manual = suma / n\n", "# tu código aquí\n", + "if conteo_n > 0:\n", + " media_manual = suma_temperaturas / conteo_n\n", + " print(f\"MEDIA DE TEMPERATURAS: {tipo_objetivo} : { media_manual } K\")\n", + "else:\n", + " print(\"DATOS NO EXISTENTES\")\n", "\n", "\n", - "print(f'Temperatura media (for loop) : {media_manual:,.1f} K')" + "print(f'TEMPERATURA MEDIA (for loop) : {media_manual:,.1f} K')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 3, "id": "5dto65riasc", "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid decimal literal (1428141596.py, line 6)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[3], line 6\u001b[0;36m\u001b[0m\n\u001b[0;31m Cuando termines la celda 5b, verifica que el resultado de `A7V`\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid decimal literal\n" + ] + } + ], "source": [ "### Comparación: `for` vs. pandas\n", "\n", @@ -332,43 +899,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "4j2wkkt78ju", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Temperatura promedio por clase espectral (K):\n", + "Spectral Class\n", + "B0.5IV 28001.166630\n", + "B0Ia 27502.303666\n", + "B1III-IV 25403.170510\n", + "B1III 25001.131122\n", + "B2III 22600.139741\n", + "B6Vep 15003.610593\n", + "B7V 12462.119029\n", + "B8Ia 12092.293145\n", + "A1V 10136.022204\n", + "A0V 9607.458129\n", + "A3V 8584.693288\n", + "A2Ia 8516.840653\n", + "A7V 7550.178313\n", + "A9II 7349.223744\n", + "F5IV-V 6520.419327\n", + "F7Ib 6020.393400\n", + "G2V 5797.996506\n", + "K1V 5261.645715\n", + "G8III 4939.733287\n", + "K1.5III 4280.090548\n", + "K5III 3923.614977\n", + "M2Iab 3502.196868\n", + "M1.5Iab 3499.130207\n", + "M2.1V 3408.914115\n", + "M4Ve 3136.076002\n", + "M7IIIe 2914.515688\n", + "M3.5V 2802.627176\n", + "M6V 2795.196060\n", + "Name: Temperature (K), dtype: float64\n", + "\n", + "RESULTADO PANDAS ('A7V'): 7,550.178313 K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " COMPARACIÓN DE LA MEDIA DE LOS VALORES 'A7V' (PANDAS, CICLO FOR) \n", + " \n", + "RESULTADO PANDAS ('A7V'): 7,550.2 K\n", + "RESULTADO MANUAL('A7V'): 7,550.2 K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", "# Calcula la temperatura promedio por tipo con groupby\n", "# Ordena de mayor a menor y guarda en temp_por_tipo\n", "# tu código aquí\n", - "\n", - "\n", + "print( \"----\" * 40)\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temp_por_tipo = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False)\n", "print('Temperatura promedio por clase espectral (K):')\n", "print(temp_por_tipo)\n", "print()\n", + "valor_pd = temp_por_tipo['A7V']\n", + "print(f\"RESULTADO PANDAS ('A7V'): {valor_pd:,.6f} K\")\n", + "\n", + "\n", "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", - "# tu código aquí" + "# tu código aquí\n", + "tipo_objetivo = 'A7V'\n", + "filtrado = stars[stars[\"Spectral Class\"] == tipo_objetivo ]\n", + "suma_temperaturas = 0\n", + "conteo_n = 0\n", + "for temperaturas in filtrado['Temperature (K)']:\n", + " suma_temperaturas += temperaturas\n", + " conteo_n += 1\n", + "\n", + "# Calcula la media y guárdala en media_manual\n", + "# media_manual = suma / n\n", + "# tu código aquí\n", + "if conteo_n > 0:\n", + " media_manual = suma_temperaturas / conteo_n\n", + " \n", + "else:\n", + " print(\"DATOS NO EXISTENTES\")\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"COMPARACIÓN DE LA MEDIA DE LOS VALORES 'A7V' (PANDAS, CICLO FOR) \\n \")\n", + "valor_pd = temp_por_tipo['A7V']\n", + "print(f\"RESULTADO PANDAS ('A7V'): {valor_pd:,.1f} K\")\n", + "print(f\"RESULTADO MANUAL('A7V'): {media_manual:,.1f} K\")\n", + "print( \"----\" * 40)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "code-05b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", "plt.figure(figsize=(9, 5))\n", "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", "\n", "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", "# tu código aquí\n", + "sns.boxplot(data=stars, x='Spectral Class', y='Temperature (K)', order=orden)\n", "\n", "\n", "# Agrega título y etiquetas de ejes\n", "# tu código aquí\n", + "plt.title(\"ORDENAMIENTO DE MAYOR A MENOR (CLASE EXPECTRAL, TEMPERATURA)\")\n", + "plt.xlabel(\"CLASE ESPECTRAL\")\n", + "plt.ylabel('Temperature (K)')\n", "\n", - "\n", - "plt.xticks(rotation=20, ha='right')\n", + "plt.xticks(rotation=30, ha='right')\n", "plt.tight_layout()\n", "plt.show()" ] @@ -399,25 +1055,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "code-06", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", "plt.figure(figsize=(9, 6))\n", "\n", "# Crea el scatter plot con sns.scatterplot(...)\n", "# tu código aquí\n", + "sns.scatterplot(data=stars, \n", + " x='Temperature (K)', \n", + " y='Luminosity (L/Lo)', \n", + " hue='Spectral Class', \n", + " style='Spectral Class', \n", + " s=60)\n", "\n", "\n", "# Aplica escala logarítmica al eje Y\n", "# tu código aquí\n", - "\n", + "plt.yscale('log')\n", "\n", "# Agrega título, etiquetas de ejes y leyenda\n", "# tu código aquí\n", "\n", + "plt.legend(title='Spectral Class', bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0.)\n", + "\n", + "plt.title('LUMINOSIDAD VS. TEMPERATURA POR ESCALA ESPECTRAL ')\n", + "plt.xlabel('Temperatura (K)')\n", + "plt.ylabel('Luminosidad (L/Lo)')\n", "\n", + "plt.xticks(rotation=45, ha='right') \n", "plt.tight_layout()\n", "plt.show()" ] @@ -452,48 +1132,136 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "code-07a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "TIPO DE VARIABLE\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " ESTADÍSTICA BÁSICA\n", + "\n", + " a) Media de Temperaturas: 9983.49K\n", + "\n", + " b) Mediana de las Temperaturas: 7379.01K\n", + "\n", + " c) Desviación Estándar de las Temperaturas: 7903.02K\n", + "\n", + " d) Mínimo de Temperaturas: 2750.18K\n", + "\n", + " e) Máximo de Temperaturas: 28044.28K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "# Extrae los arrays NumPy con .values\n", "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temperaturas = stars['Temperature (K)'].values\n", + "radios = stars['Radius (R/Ro)'].values\n", + "\n", "\n", "\n", "# Imprime el tipo del array\n", "# tu código aquí\n", - "\n", + "print( \"----\" * 40)\n", + "print(\"TIPO DE VARIABLE\")\n", + "print(type(temperaturas))\n", "\n", "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", - "# tu código aquí\n" + "# tu código aquí\n", + "\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"ESTADÍSTICA BÁSICA\")\n", + "print(f\"\\n a) Media de Temperaturas: {np.mean(temperaturas):.2f}K\")\n", + "print(f\"\\n b) Mediana de las Temperaturas: {np.median(temperaturas):.2f}K\")\n", + "print(f\"\\n c) Desviación Estándar de las Temperaturas: {np.std(temperaturas):.2f}K\")\n", + "print(f\"\\n d) Mínimo de Temperaturas: {np.min(temperaturas):.2f}K\")\n", + "print(f\"\\n e) Máximo de Temperaturas: {np.max(temperaturas):.2f}K\")\n", + "print( \"----\" * 40)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "id": "code-07b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + " PERCENTILES\n", + " \n", + " Percentil 25: 1.66\n", + " Percentil 50: 5.85\n", + " Percentil 75: 33.72\n", + " Percentil 90: 369.93\n", + " \n", + "--------------------------------------------------------------------------------\n", + " \n", + " COMPARACIÓN DE LAS TEMPERATUARA DE LAS PRIMERAS 5 ESTRELLAS (K = °C)\n", + "\n", + " 1° Temperatura: 7509.294 K = 7236.144 °C\n", + "\n", + " 2° temperatura: 8503.285 K = 8230.135 °C\n", + "\n", + " 3° temperatura: 3165.960 K = 2892.810 °C\n", + "\n", + " 4° temperatura: 6048.327 K = 5775.177 °C\n", + "\n", + " 5° temperatura: 3130.602 K = 2857.452 °C\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "niveles = [25, 50, 75, 90]\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temperaturas = stars['Temperature (K)'].values\n", + "radios = stars['Radius (R/Ro)'].values\n", "\n", "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", "# tu código aquí\n", - "\n", + "p = np.percentile(radios, niveles)\n", "\n", "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", "# tu código aquí\n", - "\n", - "\n", + "print( \"--\" * 40)\n", + "print(\" \" * 15 + \"PERCENTILES\")\n", + "print( \" \" * 40)\n", + "for nivel, valor_p in zip(niveles, p):\n", + " print(f\" Percentil {nivel}: {valor_p:.2f}\")\n", + " \n", + "print( \" \" * 40)\n", "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", "# celsius = ...\n", "# tu código aquí\n", - "\n", + "print( \"--\" * 40)\n", + "celcius = temperaturas - 273.15\n", "\n", "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", "# (usa np.round para redondear a 1 decimal)\n", - "# tu código aquí\n" + "# tu código aquí\n", + "print( \" \" * 40)\n", + "print(\" \" * 2 + \"COMPARACIÓN DE LAS TEMPERATUARA DE LAS PRIMERAS 5 ESTRELLAS (K = °C)\")\n", + "print(f\"\\n 1° Temperatura: {temperaturas[0]:.3f} K = {celcius[0]:.3f} °C\")\n", + "print(f\"\\n 2° temperatura: {temperaturas[1]:.3f} K = {celcius[1]:.3f} °C\")\n", + "print(f\"\\n 3° temperatura: {temperaturas[2]:.3f} K = {celcius[2]:.3f} °C\")\n", + "print(f\"\\n 4° temperatura: {temperaturas[3]:.3f} K = {celcius[3]:.3f} °C\")\n", + "print(f\"\\n 5° temperatura: {temperaturas[4]:.3f} K = {celcius[4]:.3f} °C\")\n", + "\n", + "print( \"--\" * 40)\n", + "\n", + "\n", + "\n" ] }, { @@ -533,30 +1301,55 @@ "execution_count": null, "id": "code-08", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", "tipos = stars['Spectral Class'].unique()\n", "colores = sns.color_palette('tab10', len(tipos))\n", "mapa = dict(zip(tipos, colores))\n", "\n", "plt.figure(figsize=(10, 7))\n", + "sns.set_style(\"whitegrid\")\n", + "\n", "\n", "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", "# tu código aquí\n", + "for tipo, grupo in stars.groupby('Spectral Class'):\n", + " plt.scatter(grupo['Temperature (K)'], grupo['Luminosity (L/Lo)'], label= tipo , color=mapa[tipo], s=50, alpha=1.0)\n", + "\n", "\n", + "orden_clases = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False).index\n", "\n", "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", "# tu código aquí\n", "\n", + "plt.yscale(\"log\")\n", "\n", "# Invierte el eje X con plt.gca().invert_xaxis()\n", "# tu código aquí\n", - "\n", + "plt.gca().invert_xaxis()\n", "\n", "# Agrega: título, etiquetas de ejes, leyenda y grid\n", "# tu código aquí\n", "\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "\n", + "plt.title(\"DIAGRAMAS H-R, RELACIÓN TEMPERATURA-LUMINOSIDAD\")\n", + "plt.xlabel(\"Temperatura (K)\")\n", + "plt.ylabel(\"Luminosidad (L/Lo)\")\n", "\n", + "plt.xticks(rotation=45, ha='right') \n", "plt.tight_layout()\n", "plt.show()" ] diff --git a/ejemplos/practica2_analisis_datos/notebooks/star_dataset.csv b/ejemplos/practica2_analisis_datos/notebooks/star_dataset.csv new file mode 100644 index 0000000..a6ba6ab --- /dev/null +++ b/ejemplos/practica2_analisis_datos/notebooks/star_dataset.csv @@ -0,0 +1,1001 @@ +Name,Distance (ly),Luminosity (L/Lo),Radius (R/Ro),Temperature (K),Spectral Class +Altair,16.59417123707501,9.979192445462427,1.6326504020392336,7509.2942473638805,A7V +Deneb,2600.4907228269035,196002.62785575088,202.970526216013,8503.284796430231,A2Ia +Barnard's Star,6.052616358474774,4.893715684187678,0.22271101197217258,3165.9596392449016,M4Ve +Polaris,322.6010015236985,2196.241933606857,37.546813013740554,6048.326914763769,F7Ib +Barnard's Star,5.902391663373468,-1.4964862523273919,0.1923594077740656,3130.6020692351385,M4Ve +Sirius,8.370045208990435,25.62536254052454,1.7376163136938274,9903.971703749557,A1V +Fomalhaut,25.42303189349805,19.314803198342823,1.7634732970155798,8541.195355142636,A3V +Capella,42.56957816453575,77.46654686278187,12.015077077611398,4979.492462093153,G8III +Capella,43.260858866113374,76.75397466518646,11.926446347832353,4907.779548870081,G8III +Bellatrix,239.86410710431278,6399.344000391582,5.751378540226974,22573.286177203267,B2III +Castor,51.57021005818902,59.93364175572123,2.317294377415115,10334.140338882178,A1V +Altair,16.324631801826886,10.45707878952248,1.6385681357721884,7554.538238404283,A7V +Bellatrix,240.396008875072,6396.460553762988,5.702796835694758,22559.156170298847,B2III +Hadar,349.6510239295208,49995.691816097984,9.029686061904163,24977.138850418167,B1III +Betelgeuse,642.4036425044302,125997.20910038544,886.9436673356928,3472.6066444708454,M2Iab +Regulus,79.14834001968553,290.3679076360734,3.105720387017622,12478.930088621566,B7V +Regulus,78.8696975851003,292.77713927994625,3.2154445654455555,12493.540087369223,B7V +Mira,418.3541533597022,8700.111885115977,369.94647200932155,2929.214904446191,M7IIIe +Acrux,320.95215250850816,24995.853352250557,8.476536987947346,28016.58055121632,B0.5IV +Vega,25.40728672741662,45.02639994513558,2.3758185019255906,9589.720331802691,A0V +Betelgeuse,642.4501483192605,125997.82193788076,887.023713575872,3502.8036495770207,M2Iab +Lalande 21185,8.78389229917336,0.9306154071042486,0.36997218014157396,3420.0741335824846,M2.1V +Hadar,350.3393282903268,50002.885000950024,9.030531083873981,24972.4633828412,B1III +Regulus,79.23841041664882,283.3313068914609,3.1941978242994673,12471.609230270555,B7V +Regulus,78.51275351482992,289.1046797165257,3.093785398680339,12469.726650284285,B7V +Bellatrix,239.75802627864445,6402.532053023128,5.807903991855439,22628.845403711555,B2III +Sirius,8.997561546118135,27.612290603452433,1.7178118641359001,9965.640231971809,A1V +Arcturus,36.39116253215857,169.47569492442693,25.34766748214963,4306.771078623507,K1.5III +Alpha Centauri B,3.9259386007692,-2.5124238815560895,0.8971557531469336,5292.710091689776,K1V +Ross 154,9.584936005981074,-0.2622128873565414,0.09815009889642284,2788.6078755716367,M3.5V +Betelgeuse,642.1986705590544,125995.18267169305,887.0568849865539,3502.1107945651283,M2Iab +Sirius,8.790939416886154,29.685053444507503,1.778145234597857,9959.636695100216,A1V +Altair,16.97783483438638,8.978258642897934,1.6817677393535218,7546.77607407146,A7V +Regulus,79.37549284128201,287.2472405244145,3.0775092724640816,12466.383842658091,B7V +Lalande 21185,7.922999488356227,2.681120395944034,0.46518759639849927,3394.5169092653987,M2.1V +Canopus,309.65595488089645,10500.304364175136,70.9940908831198,7322.086648344391,A9II +Procyon,11.361710078531987,11.09891870112007,2.1252624415131467,6522.987352867349,F5IV-V +Capella,43.239838363858276,78.47758735453107,12.04168320043013,4903.922634597786,G8III +Barnard's Star,6.274309005192113,-4.612503609841812,0.27142011745919525,3164.7134904158015,M4Ve +Arcturus,36.23014602060893,167.67967953475542,25.372980607290035,4285.208656712724,K1.5III +Canopus,309.89434307337245,10497.613821111012,70.98540952072986,7395.822565906416,A9II +Altair,16.481723812762002,10.654505105436737,1.6979772339329253,7557.224465410894,A7V +Polaris,323.4890826975579,2197.4846549730764,37.5928890041696,6003.417411946257,F7Ib +Barnard's Star,6.210116619871699,3.922475110995184,0.20022109890664536,3104.152562146686,M4Ve +Castor,52.3208563941243,52.46460236565418,2.3580848977347673,10286.21254520186,A1V +Castor,51.687127344898485,57.564078324527344,2.4793370398018366,10314.20142645494,A1V +Bellatrix,240.3505524329565,6403.480526616087,5.803192364925976,22632.467298684776,B2III +Polaris,323.4772161689265,2202.925953103468,37.430399789571695,5986.795558342898,F7Ib +Castor,52.31788545862922,54.098680128048926,2.3832137598247782,10264.67768772168,A1V +Barnard's Star,5.5635565189978085,-3.4589548280686215,0.14953611720828194,3115.0666806206127,M4Ve +Vega,25.02265608280089,43.21333423901169,2.4233371866214184,9631.823941413108,A0V +Wolf 359,8.040322484690764,3.518164733709185,0.2159582033041309,2786.809004459221,M6V +Regulus,79.457885087804,284.3075843561486,3.1848771847975916,12429.346874442883,B7V +Polaris,322.7090337593032,2201.1895342191797,37.558280857740726,5986.9294428280555,F7Ib +Antares,549.8094446057289,10004.888893429817,679.9451981136416,3503.22033874066,M1.5Iab +Regulus,78.84980314077531,283.1518425898611,3.1746164987628713,12414.98856094767,B7V +Acrux,320.53588050006994,25000.424960003416,8.30013131527051,27969.950394242773,B0.5IV 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21185,8.539132551448054,-1.0184094871245468,0.3484419457491451,3396.2390187861406,M2.1V +Polaris,322.7829657396212,2197.4579935613187,37.56516587074775,6063.924563018583,F7Ib +Deneb,2599.541671531891,195998.9041368802,202.92157091325018,8503.08100456294,A2Ia +Vega,24.959311232910533,38.236535117812586,2.2955242719863977,9650.454657524815,A0V +Acrux,321.3978818285302,25002.451304950046,8.40073431108804,28000.481701856206,B0.5IV +Spica,250.3496408249325,22004.59479708713,7.48705009677652,25392.535590103464,B1III-IV +Capella,42.98764050809396,80.53611367039014,11.928552734356183,4936.351352278644,G8III +Altair,16.790538913857493,7.001586190285418,1.6190056898410539,7500.178549124861,A7V +Antares,550.3341533847138,9997.61131020143,680.029117220661,3516.839353089165,M1.5Iab +Capella,42.80414644083066,77.7419793077256,12.028719516305973,4937.014499217616,G8III +Wolf 359,7.987098773049044,-4.9033579226853,0.25746878785429467,2800.610804893218,M6V 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Star,5.77531813296572,-1.9476137463014966,0.18516435027980302,3100.1967912356345,M4Ve +Polaris,322.69367242714384,2203.214692898426,37.58057570411819,5973.267626537678,F7Ib +Spica,249.71633706178963,22000.712538786633,7.4530069804035035,25382.54424773179,B1III-IV +Spica,249.7559118255533,21997.467782210406,7.403967800257108,25413.23854342704,B1III-IV +Procyon,11.548017997505934,11.179783704362094,2.0768331643377036,6532.552150833123,F5IV-V +Acrux,320.68121543300316,24999.998219227884,8.331568002897049,27976.048166346933,B0.5IV +Antares,550.0925105716918,9997.105346601093,679.9637801969628,3462.614891178801,M1.5Iab +Rigil Kentaurus,3.9786325410020655,-2.119236183448141,1.154587336979579,5743.193545774129,G2V +Vega,25.47379567716754,41.94883657141961,2.2836267337545935,9636.2663929921,A0V +Barnard's Star,5.503354374007524,-1.4261367429859866,0.19373771952839025,3138.143529653492,M4Ve +Aldebaran,65.27837547917696,516.7874159559605,44.19667580773142,3891.6958488395844,K5III +Alpha Centauri B,3.9962177138878325,-2.5988815711687474,0.883037871335775,5231.122023964701,K1V +Canopus,310.2691827891006,10496.17932647521,71.09609603499091,7379.760016569846,A9II +Castor,51.78765008596086,52.55239172717802,2.377917302214843,10339.144211828336,A1V +Rigil Kentaurus,4.088694603367449,0.30507316365590653,1.159697069308526,5815.131546667408,G2V +Aldebaran,64.5902948982921,519.3372320256896,44.26426716265437,3916.6136134918343,K5III +Altair,16.342681874475897,14.591181440400177,1.6839372242618915,7534.755587192668,A7V +Arcturus,36.246122455758346,165.8219515674998,25.435121407803933,4263.974299475472,K1.5III +Fomalhaut,24.739227835351564,12.761776780006507,1.8660325221773046,8587.326514897997,A3V +Antares,549.6020841306806,10003.583240374617,680.0178309194273,3515.0718020662684,M1.5Iab +Hadar,349.85840398022134,49998.71828587879,8.997233568702034,24979.30083017181,B1III +Deneb,2600.3849852172243,195997.22402582658,203.01037378760626,8495.072256076804,A2Ia 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154,9.457219736836048,3.5558108313769368,0.16738653049763086,2757.8244007742705,M3.5V +Fomalhaut,24.94774315643359,19.078458000505698,1.7827045212416661,8578.83278985885,A3V +Mira,418.4800274472216,8699.413953474097,369.91940220002334,2882.169873058536,M7IIIe +Alnilam,1999.9722500667908,53695.46342027454,32.318443183674674,27519.496419622923,B0Ia +Betelgeuse,642.4314171166554,126001.74686159294,886.9717142807095,3460.211471007395,M2Iab +Procyon,11.232801031989052,11.483008311297528,2.0354606049471995,6531.580696599702,F5IV-V +Mira,417.96038900748766,8697.873320471392,369.9027391626386,2922.151960877924,M7IIIe +Sirius,8.97905760209878,24.461618038155763,1.6208975551577192,9949.850807441388,A1V +Hadar,350.1211769454686,49995.72669531548,9.02310133825602,25047.364915414353,B1III +Procyon,11.734879069041147,11.03453099609397,1.991069283038106,6531.624256212896,F5IV-V +Altair,16.650909255277465,13.984383744732718,1.5937402895793085,7547.85329895543,A7V 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+Mira,417.5482494592996,8697.641481122131,370.0433728646599,2881.4840403317608,M7IIIe +Betelgeuse,642.896853479847,126004.68469642337,886.9879566164066,3511.823072543017,M2Iab +Betelgeuse,642.1716997467348,126001.69509064495,886.9888377279874,3479.277355062287,M2Iab +Lalande 21185,8.731732298435377,4.786238308759169,0.3347032306768086,3382.689040227199,M2.1V +Altair,17.15474181167015,6.975771781193093,1.5483116113750846,7537.641975099961,A7V +Vega,24.776635908276518,35.27153115019188,2.29544479238952,9616.074160949815,A0V +Mira,417.695788448091,8704.185431731014,370.0625415276317,2955.0490698875665,M7IIIe +Rigil Kentaurus,3.996321706725146,1.626551918000496,1.3046574245238987,5794.091339181414,G2V +Mira,418.47024286572923,8702.898481160859,370.0818555771332,2943.151168595254,M7IIIe +Altair,17.111919168917005,10.39101273787362,1.6095873161618377,7504.094812609862,A7V +Altair,16.874372018837036,5.998470578062234,1.5597935011539061,7561.789883494114,A7V +Deneb,2600.0030216501546,195995.4474828539,203.02756825887886,8518.964904807914,A2Ia +Rigel,860.4364002835275,120000.52031072113,78.80303528052436,12131.669640550605,B8Ia +Altair,17.12154390276971,11.776186568379096,1.635310418129086,7555.4180801050825,A7V +Barnard's Star,5.938951989691774,-3.172253923292721,0.2666876758071142,3121.980224966519,M4Ve +Wolf 359,7.455714961927049,-4.43510093589817,0.06808667694271407,2774.1483001662027,M6V +Hadar,350.3016438778805,49997.50659083093,9.070882395506349,25010.502655808556,B1III +Bellatrix,239.7632940871353,6397.02015894308,5.706311488104068,22603.548765755073,B2III +Alpha Centauri B,4.044363778891645,-4.549088213039762,0.9391905375045383,5286.304303993784,K1V +Wolf 359,8.139786203659344,3.484204727275103,0.23632401553344878,2813.6003663283495,M6V From 1399c6ec16db6a6b6c2f5850636ce34d7bbd2452 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 8 May 2026 08:58:57 -0600 Subject: [PATCH 25/28] =?UTF-8?q?actulizar=20an=C3=A1lis=20de=20estrellas?= =?UTF-8?q?=20estudiantes?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../analisis_estrellas_estudiante.ipynb | 53 +++++++------------ 1 file changed, 20 insertions(+), 33 deletions(-) diff --git a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb index e93b81b..7fb4755 100644 --- a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb +++ b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 2, "id": "code-02", "metadata": {}, "outputs": [ @@ -313,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 3, "id": "code-03a", "metadata": {}, "outputs": [ @@ -368,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 4, "id": "code-03b", "metadata": {}, "outputs": [ @@ -619,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "code-03c", "metadata": {}, "outputs": [ @@ -672,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 6, "id": "code-04a", "metadata": {}, "outputs": [ @@ -723,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 7, "id": "code-04b", "metadata": {}, "outputs": [ @@ -800,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 8, "id": "code-05a", "metadata": {}, "outputs": [ @@ -873,33 +873,20 @@ ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "5dto65riasc", + "cell_type": "markdown", + "id": "03533f36", "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid decimal literal (1428141596.py, line 6)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[3], line 6\u001b[0;36m\u001b[0m\n\u001b[0;31m Cuando termines la celda 5b, verifica que el resultado de `A7V`\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid decimal literal\n" - ] - } - ], "source": [ - "### Comparación: `for` vs. pandas\n", + "Comparación: for vs. pandas:\n", "\n", - "Con el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\n", - "Ahora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n", + "Con el ciclo for calculaste la media de una sola clase espectral en varias líneas. Ahora verás cómo pandas obtiene la media de todas las clases a la vez en una sola línea.\n", "\n", - "Cuando termines la celda 5b, verifica que el resultado de `A7V`\n", - "coincida con el valor que obtuviste con el `for`." + "Cuando termines la celda 5b, verifica que el resultado de A7V coincida con el valor que obtuviste con el for." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "id": "4j2wkkt78ju", "metadata": {}, "outputs": [ @@ -993,7 +980,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "id": "code-05b", "metadata": {}, "outputs": [ @@ -1055,13 +1042,13 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 11, "id": "code-06", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1132,7 +1119,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 12, "id": "code-07a", "metadata": {}, "outputs": [ @@ -1189,7 +1176,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 13, "id": "code-07b", "metadata": {}, "outputs": [ @@ -1298,13 +1285,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "code-08", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From 63085c186c2429fbb1736683714171d6e7dd28ea Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 8 May 2026 09:45:33 -0600 Subject: [PATCH 26/28] actulizar repositorio --- .../analisis_estrellas_estudiante.ipynb | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb index 7fb4755..7843a3c 100644 --- a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb +++ b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "id": "code-01", "metadata": {}, "outputs": [ @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "id": "code-02", "metadata": {}, "outputs": [ @@ -313,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 31, "id": "code-03a", "metadata": {}, "outputs": [ @@ -368,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 32, "id": "code-03b", "metadata": {}, "outputs": [ @@ -619,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 33, "id": "code-03c", "metadata": {}, "outputs": [ @@ -672,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 34, "id": "code-04a", "metadata": {}, "outputs": [ @@ -723,13 +723,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 35, "id": "code-04b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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fffcdvvvuO+XvvHnz4ueff0atWrX0XraxHT9+HPfu3cOkSZOUiok+ffpg5syZWLNmjXKdAJL2/9WrV3Hq1CkcO3ZMOQ4PHz6MtWvX4tSpU1rHfd26dbF48eIUaer7UPGIESN0tqJWqlRJ+X9Gr7PJ94G1tTVmzpyJoUOHaqVhaN6Nwc7OTuvv+vXrpxi9Tx0YOjg4oEmTJgCSAls/Pz/s3LkTS5cu1ftheSJjYmBBpKe3b9/i5cuXqFq1arrz5s2bF02bNkXTpk0BJPVR7tSpE3777Tfs27cvQ4UVNXXhTkTw8OFDzJ49G3PmzEG1atWUwld6nJyclP66ANC4cWO8evUKCxcuRL9+/ZQbt4mJSardHdRdEtRBTunSpQH8r7k+M0qXLo27d+/i7du3qdbMqodwTa37QdmyZTF06FAsXboUo0aNynRekktrv1evXh1FixbN1HItLCy09kVaKleujMqVKwNIKlAsWbIEo0aNwtSpU7F9+/ZMpb9hwwZUrlwZkZGR2LZtG1asWIFu3bph3759BuXVGNTdOTp27AgRUZ4h6tSpE9auXYs1a9Zgzpw5Wr+xtbVFnz590KdPHwDAiRMn0Lx5c4wYMUKvwEJdmM2TJw9sbGzg6OioVTDVPM41C7iaUjtGO3fujLFjxyIuLg5//fUXJk6ciK5du+LSpUtaAZIu+pwbmaGu1W7fvr2yfa2trVG3bl2lYGpjY6PMnydPHtSvXx/169cHkHRe9OvXD9u2bcOaNWu0CujW1tYGHS+lSpXS6/cZuc6q94FKpYKVlRXKlSuns6U3vbwXLVoUlpaWBl3v0qOusAgLC8PKlSuxc+dOfPrpp1i+fLkyz5EjRxAUFIRRo0YhIiJCmd65c2ccPXoUP/zwQ4YqnYiMhV2hiPS0Z88eJCQkpDsWuy5FihTByJEjAQDXr1/PVPrqwp2Hhwc6deqEw4cPw9bWFiNHjsSbN28ytUwAqFatGkREq9uIra0tHj9+rHN+9XT1Q9bu7u4oXLgwdu/enemuXk2aNEFCQgJ+/fVXnd+LCH755RcULlw4zVreKVOmwNLSEpMmTcpUPnQxZL9nBZVKhc8//xw2NjaZPpaApGDF3d0dDRo0wPLly9G/f3/s378/3fdQZAd1P/8ff/wRhQoVUj7qYUHXr1+fbj//+vXrw9/fHy9evND5LpLk1IXZmjVrwsnJKUWrg7pWeNeuXakuY9euXTA1NU1xrBQrVgzu7u7w8vLCwIEDsWvXLrx9+xaff/55uvlq2rRpmudGZoSHh2Pnzp0Akmr+NbfxyZMnER0djS1btqS5jPz582PixIkAMn9NM7a0rrPqfVCrVi1UqFBB7+6jyZmYmKBRo0a4ePEi/v33X0OzrFP16tXh7u4Of39/7NixA02aNMHKlStx/vx5ZR51YLho0SKt/TdkyBCt74myGwMLIj0EBwdjzJgxsLa2TrMWKC4uLtWHR//++28AgL29vVHyVKRIEcydOxfPnj3DN998k+nlXLlyBQBQvHhxZVrjxo1x9OjRFP34RQQ7duxA2bJlUb58eQBJtYbjx4/HrVu3MGvWLJ1pPH/+HKdPn041D/3790fx4sUxceJEnYXA+fPn49atWxg3blyaI1gVKVIE48ePx48//og///wz1fn0pe9+zypPnz7VOf3JkyeIiIgw2rEEJG3jQoUKYdq0aVn6UHZ6EhISsH79epQrVw5Hjx5N8Rk9ejSePn2qtKw8e/ZMZ34TEhJw9+5dWFpaatW8Z1b79u1RpUoVzJ07F3fu3Enx/bZt23DgwAH0798/RVeW5OrVq4devXphz549Ol/Cp6lfv36ws7PDuHHjUg32f/rpJ/1XBEnPJ0RFRWHWrFk6t3HRokWV94cAqR+Hxr6m6Ss7r7O6TJw4ESKCAQMGpHhBqTp/xgoEVSoVvv32W5iYmGDKlCkAkh48//nnn+Hj46Nz//Xo0QPnz59/bwI++m9hVyiiZK5fv648JP38+XOcPHkSa9euhYmJCX7++ec0+5WHh4ejbNmy+Oijj9C4cWM4ODjgzZs3OHbsGL766itUrlwZHTp0MFpee/XqhUWLFuHLL7/EsGHD0u1Te/fuXfzxxx9KXg8dOoTVq1fD3d0d9erVU+abNm0afv31V3h6emLChAlwdnZGSEgIvv/+e5w/fz5F95uxY8fi77//xvTp0/Hnn3+ie/fuygvyTpw4gZUrV2LGjBnw8fHRmS8bGxv89NNPaNWqFWrVqqW86CwiIgLbtm3D5s2b0aVLF4wdOzbdbTJy5Eh8++23Orv0pCUz+/3ixYs6X5BXpUqVdPdFVFSUsi+SUz//MHDgQLx+/RodO3aEq6srTExMcOvWLSxevBh58uTReibAUIUKFcLEiRMxbtw4bNmyRauPuz55VUttPl9fX2Ub/vPPPzpbRqpUqYL79+/jyZMnmDdvns5WIldXVyxduhSrV69Gq1atsHHjRqxYsQLdu3eHh4cHrK2t8e+//2LVqlW4ceMGpk2bBjMzM303Q6pMTEywc+dONGnSBF5eXhg9ejS8vLwQExODX3/9FStXroSvry8WLlyo1/JmzZqFbdu2YerUqTh06FCq81lbW2P37t1o1aoV3NzctF6Qd/fuXWzatAlXr15NcV359ddfYWVllWJ5nTp1wurVq1GoUCGMGTNG5zNB6uvK1atXUb16dbi4uKBRo0Zo3rw5ypUrh+joaJw7dw4LFy6Era2t8vCw2uvXr3UeB+bm5srLBtMSHBys8/fFihVDuXLlsvQ6q0/evby8sGzZMgwdOhS1atXCkCFD4OLigri4OFy+fBkrV66Eq6ur0UbycnZ2xsCBA/Hdd9/h1KlTuHLlCqKjo/HZZ5/pPEeKFCmCzZs3Y/Xq1VrPi/z11186zzsPDw+UKVPGKHkl4gvyiP6f+sVh6o+ZmZkUL15cfH19JTAwUJ4/f57iN8lfaBQTEyNffvmlNG/eXEqXLi3m5uZiYWEhlStXlnHjxsnLly91pp2ZF+Sp7dmzR+vldGm9IE/zkz9/fqlSpYpMnz5dwsPDUyz37t278vHHH0uJEiXE1NRUbGxsxN/fXw4fPpzqNty9e7e0bNlSihUrJqamplKoUCFp0KCBLF++XGJiYpT5kMpLooKDg2XYsGHi5OQkZmZmYm1tLfXr15dNmzaleBlcWi99WrlypbKe+r4gLzP7PbXPwYMH00zT19c3zd/HxcWJiMjvv/8uffv2VV7MZmpqKiVKlJAOHTrI2bNnU11+Zl6QJyISFRUlpUuXFmdnZ4mPj89QXnUdY5of9QsV05pn+vTp0q5dOzEzM9O53dW6du0qpqamEhISIjdv3pTRo0eLu7u71nHn6+srGzduTHM/iGT85WGhoaEyYcIEqVSpklhYWEiBAgWkdu3asnTpUomNjU0xf2rHuojI2LFjBYAcP3483XRDQkJk/Pjx4uLiIpaWlmJubi7ly5eXQYMGyV9//aXMl96xefXqVQEgI0eOTDWtW7duCQD59NNPRURkxYoV0qFDB3FychJLS0sxMzOTcuXKyeDBg1O8UK9MmTKppl2yZMk011G9L1L79OjRQ0Qyfp1Nax8YkvcrV65I7969pXTp0mJmZib58+cXNzc3mTZtWqrHb1p5Sevlhs+ePZMCBQpIgwYNpEaNGlK8eHGta2pyderUkaJFi0pMTEy623Xt2rXpbhsifalE+CYVIiIiIiIyDJ+xICIiIiIigzGwICIiIiIigzGwICIiIiIigzGwICIiIiIigzGwICIiIiIigzGwICIiIiIig33wL8hLTEzEkydPYGVlBZVKldPZISIiIiLKNUQEkZGRsLe3R548abdJfPCBxZMnT+Dg4JDT2SAiIiIiyrUePXqEUqVKpTnPBx9YWFlZAUjaGAULFszh3BARERER5R4RERFwcHBQytRp+eADC3X3p4IFCzKwICIiIiLKBH0eKeDD20REREREZDAGFkREREREZDAGFkREREREZDAGFkREREREZDAGFkREREREZDAGFkREREREZDAGFkREREREZDAGFkREREREZDAGFkREREREZDAGFkREREREZDDTnM5ATgoODkZoaGiGflO0aFGULl06i3JERERERJQ7/WcDi+DgYFSsVBnRUe8y9DuLfJa4fetvBhdERERERBr+s4FFaGgooqPeoWrH0chfzEGv37x98Qh/7VyI0NBQBhZERERERBr+s4GFWv5iDihoXz5L02CXKyIiIiL60P3nA4usxi5XRERERPRfwMAii7HLFRERERH9FzCwyCbZ0eWKiIiIiCin8D0WRERERERkMAYWRERERERkMAYWRERERERkMAYWRERERERksBwNLMqWLQuVSpXiM2zYMACAiCAgIAD29vbIly8f/Pz8cOPGjZzMMhERERER6ZCjgcX58+fx9OlT5XPw4EEAwEcffQQAmD9/PhYtWoSlS5fi/PnzsLOzQ5MmTRAZGZmT2SYiIiIiomRyNLAoVqwY7OzslM9vv/2GcuXKwdfXFyKCJUuWYPLkyejQoQNcXV2xfv16vHv3Dlu2bMnJbBMRERERUTLvzTMWsbGx2LRpE/r27QuVSoWgoCCEhITA399fmcfc3By+vr44c+ZMqsuJiYlBRESE1oeIiIiIiLLWexNY7Nq1C69fv8Ynn3wCAAgJCQEA2Nraas1na2urfKfLnDlzYG1trXwcHPR72zUREREREWXeexNYrF69Gs2bN4e9vb3WdJVKpfW3iKSYpmnixIkIDw9XPo8ePcqS/BIRERER0f+Y5nQGAODhw4c4dOgQfvrpJ2WanZ0dgKSWixIlSijTnz9/nqIVQ5O5uTnMzc2zLrNERERERJTCe9FisXbtWhQvXhwtW7ZUpjk6OsLOzk4ZKQpIeg7j+PHj8Pb2zolsEhERERFRKnK8xSIxMRFr165F7969YWr6v+yoVCqMHDkSgYGBcHZ2hrOzMwIDA2FpaYnu3bvnYI6JiIiIiCi5HA8sDh06hODgYPTt2zfFd+PGjUNUVBSGDh2KV69ewdPTEwcOHICVlVUO5JSIiIiIiFKT44GFv78/RETndyqVCgEBAQgICMjeTBERERERUYa8F89YEBERERFR7sbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDJbjgcXjx4/x8ccfo0iRIrC0tESNGjVw8eJF5XsRQUBAAOzt7ZEvXz74+fnhxo0bOZhjIiIiIiJKLkcDi1evXsHHxwd58+bFvn37cPPmTSxcuBA2NjbKPPPnz8eiRYuwdOlSnD9/HnZ2dmjSpAkiIyNzLuNERERERKTFNCcTnzdvHhwcHLB27VplWtmyZZX/iwiWLFmCyZMno0OHDgCA9evXw9bWFlu2bMGgQYOyO8tERERERKRDjrZY/PLLL3B3d8dHH32E4sWLw83NDd9//73yfVBQEEJCQuDv769MMzc3h6+vL86cOaNzmTExMYiIiND6EBERERFR1srRwOL+/ftYtmwZnJ2d8fvvv2Pw4MH47LPPsGHDBgBASEgIAMDW1lbrd7a2tsp3yc2ZMwfW1tbKx8HBIWtXgoiIiIiIcjawSExMRM2aNREYGAg3NzcMGjQIAwYMwLJly7TmU6lUWn+LSIppahMnTkR4eLjyefToUZbln4iIiIiIkuRoYFGiRAlUqVJFa1rlypURHBwMALCzswOAFK0Tz58/T9GKoWZubo6CBQtqfYiIiIiIKGvlaGDh4+OD27dva027c+cOypQpAwBwdHSEnZ0dDh48qHwfGxuL48ePw9vbO1vzSkREREREqcvRUaE+//xzeHt7IzAwEJ07d8aff/6JlStXYuXKlQCSukCNHDkSgYGBcHZ2hrOzMwIDA2FpaYnu3bvnZNaJiIiIiEhDjgYWHh4e+PnnnzFx4kTMnDkTjo6OWLJkCXr06KHMM27cOERFRWHo0KF49eoVPD09ceDAAVhZWeVgzomIiIiISFOOBhYA0KpVK7Rq1SrV71UqFQICAhAQEJB9mSIiIiIiogzJ0WcsiIiIiIjow8DAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDGZwYPHw4UPcvHkTiYmJxsgPERERERHlQnoHFuvXr8eSJUu0pg0cOBBOTk6oWrUqXF1d8ejRI2Pnj4iIiIiIcgG9A4vly5fD2tpa+Xv//v1Yu3YtNmzYgPPnz8PGxgYzZszIkkwSEREREdH7Te83b9+5cwfu7u7K37t370abNm3Qo0cPAEBgYCD69Olj/BwSEREREdF7T+8Wi6ioKBQsWFD5+8yZM6hfv77yt5OTE0JCQoybOyIiIiIiyhX0DizKlCmDixcvAgBCQ0Nx48YN1K1bV/k+JCREq6sUERERERH9d+jdFapXr14YNmwYbty4gSNHjqBSpUqoVauW8v2ZM2fg6uqaJZkkIiIiIqL3m96Bxfjx4/Hu3Tv89NNPsLOzw44dO7S+P336NLp27Wr0DBIRERER0ftP78AiT548mDVrFmbNmqXz+x07diA+Pt5oGSMiIiIiotzDKG/evnnzJkaPHo2SJUsaY3FERERERJTLZDqwePPmDVatWgUvLy9Uq1YN586dw4QJE4yZNyIiIiIiyiX07gqldurUKaxatQo7d+6Eo6Mjbt68iePHj8PHxycr8kdERERERLmA3i0W8+fPR6VKldC1a1cUK1YMp06dwrVr16BSqVCoUKGszCMREREREb3n9G6xmDRpEsaPH4+ZM2fCxMQkK/NERERERES5jN4tFjNnzsSOHTvg6OiI8ePH4/r161mZLyIiIiIiykX0DiwmTZqEO3fuYOPGjQgJCUGdOnVQvXp1iAhevXqVlXkkIiIiIqL3XIZHhfL19cX69evx9OlTDBkyBLVq1YKvry+8vb2xaNGirMgjERERERG95zI93KyVlRUGDx6Mc+fO4fLly6hduzbmzp1rzLwREREREVEukeHhZnWpWrUqvvjiC7Rq1coYiyMiIiIiolzGKG/eBoB79+6hadOmGfpNQEAAVCqV1sfOzk75XkQQEBAAe3t75MuXD35+frhx44axskxEREREREZitMAis1xcXPD06VPl89dffynfzZ8/H4sWLcLSpUtx/vx52NnZoUmTJoiMjMzBHBMRERERUXI5HliYmprCzs5O+RQrVgxAUmvFkiVLMHnyZHTo0AGurq5Yv3493r17hy1btuRwromIiIiISFOOBxZ3796Fvb09HB0d0bVrV9y/fx8AEBQUhJCQEPj7+yvzmpubw9fXF2fOnEl1eTExMYiIiND6EBERERFR1tL74e1ffvklze+DgoIynLinpyc2bNiAChUq4NmzZ5g9eza8vb1x48YNhISEAABsbW21fmNra4uHDx+musw5c+ZgxowZGc4LERERERFlnt6BRbt27YyeePPmzZX/V61aFV5eXihXrhzWr1+POnXqAABUKpXWb0QkxTRNEydOxKhRo5S/IyIi4ODgYOScExERERGRJr27QiUmJur1MUT+/PlRtWpV3L17VxkdSt1yofb8+fMUrRiazM3NUbBgQa0PERERERFlLaM9Y/Hs2TPMnDnToGXExMTg77//RokSJeDo6Ag7OzscPHhQ+T42NhbHjx+Ht7e3odklIiIiIiIjMlpgERISkuFnG8aMGYPjx48jKCgI586dQ6dOnRAREYHevXtDpVJh5MiRCAwMxM8//4zr16/jk08+gaWlJbp3726sbBMRERERkREY5c3bmfXvv/+iW7duCA0NRbFixVCnTh388ccfKFOmDABg3LhxiIqKwtChQ/Hq1St4enriwIEDsLKyyslsExERERFRMjkaWGzdujXN71UqFQICAhAQEJA9GSIiIiIiokzJ0cCCjCs4OBihoaEZ+k3RokVRunTpLMoREREREf1X6B1YaA7hqsuLFy8MzgxlXnBwMCpWqozoqHcZ+p1FPkvcvvU3gwsiIiIiMojegcXly5fTnad+/foGZYYyLzQ0FNFR71C142jkL6bfezvevniEv3YuRGhoKAMLIiIiIjKI3oHF0aNHszIfZCT5izmgoH35nM4GEREREf3HGG24WSIiIiIi+u/SO7CoUqUKwsLClL8HDhyo9VzF8+fPYWlpadzcERERERFRrqB3YHHr1i3Ex8crf2/duhWRkZHK3yKC6Oho4+aOiIiIiIhyhUx3hRKRFNNUKpVBmSEiIiIiotyJz1gQEREREZHB9A4sVCpVihYJtlAQERERERGQgeFmRQSNGjWCqWnST6KiotC6dWuYmZkBgNbzF/Rhy643fPNN4kRERES5h96BxfTp07X+btu2bYp5OnbsaHiO6L2WXW/45pvEiYiIiHKXTAcW9N+UXW/45pvEiYiIiHIXvQOL9ERHR2Pp0qUYM2aMsRZJ77HsesM33yRORERElDtkKLAIDQ3FuXPnkDdvXjRq1AgmJiaIi4vDd999hzlz5iA+Pp6BBeU6GX2Wg89xEBEREaWkd2Bx5swZtGzZEuHh4VCpVHB3d8fatWvRrl07JCYmYsqUKejbt29W5pXI6DLzLAef4yAiIiJKSe/AYurUqWjatCmmTJmCNWvWYMmSJWjVqhUCAgLQs2dPDj1LuVJGn+XgcxxEREREuukdWFy9ehXHjx+Hi4sLZs+eja+++grz5s3DRx99lJX5I8oWfJaDiIiIyDB6vyAvLCwMxYoVAwBYWlrC0tISbm5uWZYxIiIiIiLKPfRusVCpVIiMjISFhQVEBCqVCu/evUNERITWfAULFjR6JomIiIiI6P2WoTdvV6hQQetvzRYLdbCRkJBg3BwSEREREdF7T+/A4ujRo1mZDyIiIiIiysX0DiwePnyILl26wNzcPCvzQ0REREREuZDeD2/36dMH4eHhWZkXIiIiIiLKpfQOLEQkK/NBRERERES5mN6BBYAsfQnenDlzoFKpMHLkSGWaiCAgIAD29vbIly8f/Pz8cOPGjSzLAxERERERZY7ez1gAwCeffJLuMxY//fRThjNx/vx5rFy5EtWqVdOaPn/+fCxatAjr1q1DhQoVMHv2bDRp0gS3b9+GlZVVhtMhIiIiIqKskaEWCysrK1hbW6f5yag3b96gR48e+P7771GoUCFluohgyZIlmDx5Mjp06ABXV1esX78e7969w5YtWzKcDhERERERZZ0MtVh8/fXXKF68uFEzMGzYMLRs2RKNGzfG7NmzlelBQUEICQmBv7+/Ms3c3By+vr44c+YMBg0apHN5MTExiImJUf5O/gI/IiIiIiIyvgy9edvYtm7dikuXLuH8+fMpvgsJCQEA2Nraak23tbXFw4cPU13mnDlzMGPGDONmlIiIiIiI0pRjo0I9evQII0aMwKZNm2BhYZHqfMkDGvUbvlMzceJEhIeHK59Hjx4ZLc9ERERERKRbht68XbhwYaMlfPHiRTx//hy1atVSpiUkJODEiRNYunQpbt++DSCp5aJEiRLKPM+fP0/RiqHJ3NycL/EjIiIiIspmegcWFhYWOHjwIJo3b65M27BhA6ZPn463b9+iXbt2+Oabb/Qu1Ddq1Ah//fWX1rQ+ffqgUqVKGD9+PJycnGBnZ4eDBw/Czc0NABAbG4vjx49j3rx5+mab6L0QHByM0NDQDP2maNGiKF26dBbliIiIiMi49A4sAgIC4OfnpwQWf/31F/r164dPPvkElStXxoIFC2Bvb4+AgAC9lmdlZQVXV1etafnz50eRIkWU6SNHjkRgYCCcnZ3h7OyMwMBAWFpaonv37vpmmyjHBQcHo2KlyoiOepeh31nks8TtW38zuCAiIqJcQe/A4sqVK5g1a5by99atW+Hp6Ynvv/8eAODg4IDp06frHVjoY9y4cYiKisLQoUPx6tUreHp64sCBA3yHBeUqoaGhiI56h6odRyN/MQe9fvP2xSP8tXMhQkNDGVgQERFRrqB3YPHq1SutZxuOHz+OZs2aKX97eHgY/KD0sWPHtP5WqVQICAgwarBClFPyF3NAQfvyOZ0NIiIioiyhd2Bha2uLoKAgODg4IDY2FpcuXdIa1jUyMhJ58+bNkkwSkX6y61mODy0dIiIiMpzegUWzZs0wYcIEzJs3D7t27YKlpSXq1aunfH/t2jWUK1cuSzJJROnLrmc5PrR0iIiIyDj0Dixmz56NDh06wNfXFwUKFMD69ethZmamfL9mzRqtt2QTUfbKrmc5PrR0iIiIyDj0DiyKFSuGkydPIjw8HAUKFICJiYnW9zt27ECBAgWMnkEiypjsepbjQ0rnfe3axW5dRESUm+gdWKhZW1vrnF64cGE8f/4cxYsXNzhTRETZ5X3u2sVuXURElJvoHVhYWlri4cOHKFasGICkZy7Wrl2rvBX72bNnsLe3R0JCQtbklIgoC7yvXbvYrYuIiHIbvQOL6OhoiIjy9+nTpxEVFaU1j+b3RES5yYfUtYuIiCgnZLgrVFpUKpUxF0dERBn0vj4vktl0iIgo9zBqYEFERDnnfX5eJDPpEBFR7qJ3YKFSqbRaJJL/TUREOet9fV4ks+kQEVHuondgISKoUKGCEky8efMGbm5uyJMnj/I9ERHlvA/peRF2uSIiyj30DizWrl2blfkgIiLSwi5XRES5i96BRe/evbMyH0RERFrY5YqIKHfhw9tERPRe4xC9RES5Q56czgAREREREeV+DCyIiIiIiMhgDCyIiIiIiMhgDCyIiIiIiMhgegcWVapUQVhYmPL3wIED8eLFC+Xv58+fw9LS0ri5IyIiIiKiXEHvwOLWrVuIj49X/t66dSsiIyOVv0UE0dHRxs0dERERERHlCpnuCqXrTdvqt3ITEREREdF/C99jQURE/3nBwcEIDQ3N0G+KFi3Kl/AREWnQO7BQqVQpWiTYQkFERLldcHAwKlaqjOiodxn6nUU+S9y+9TeDCyKi/6d3YCEiaNSoEUxNk34SFRWF1q1bw8zMDAC0nr8gIiLKLUJDQxEd9Q5VO45G/mIOev3m7YtH+GvnQoSGhjKwICL6f3oHFtOnT9f6u23btinm6dixo+E5IiIiygH5izmgoH35nM4GEVGulenAwhiWLVuGZcuW4cGDBwAAFxcXTJs2Dc2bNweQ1EoyY8YMrFy5Eq9evYKnpye+/fZbuLi4GD0vREREWY3PchDRhyxTD2+HhobiwYMHUKlUKFu2LIoUKZKpxEuVKoW5c+eifPmkGqL169ejbdu2uHz5MlxcXDB//nwsWrQI69atQ4UKFTB79mw0adIEt2/fhpWVVabSJCIiygl8loOIPnQZCixu3LiBIUOG4PTp01rTfX19sWzZMlSsWDFDibdu3Vrr7y+++ALLli3DH3/8gSpVqmDJkiWYPHkyOnToACAp8LC1tcWWLVswaNCgDKVFRESUk/gsBxF96PQOLEJCQuDr64tixYph0aJFqFSpEkQEN2/exPfff4969erh+vXrKF68eKYykpCQgB07duDt27fw8vJCUFAQQkJC4O/vr8xjbm4OX19fnDlzhoEFERHlSnyWg4g+VHoHFosXL0aZMmVw+vRpWFhYKNObNWuGIUOGoG7duli8eDHmzJmToQz89ddf8PLyQnR0NAoUKICff/4ZVapUwZkzZwAAtra2WvPb2tri4cOHqS4vJiYGMTExyt8REREZyg8REVFull3PcfB5ESJKTu/A4uDBg5gwYYJWUKGWL18+jB07FvPnz89wYFGxYkVcuXIFr1+/xs6dO9G7d28cP35c+T75uzJEJM33Z8yZMwczZszIUB6IiIg+BNn1HAefFyEiXfQOLO7fv4+aNWum+r27uzvu37+f4QyYmZkpD2+7u7vj/Pnz+OqrrzB+/HgASV2wSpQoocz//PnzFK0YmiZOnIhRo0Ypf0dERMDBQb++rERERLlZdj3HwedFiEgXvQOLyMhIFCxYMNXvrays8ObNG4MzJCKIiYmBo6Mj7OzscPDgQbi5uQEAYmNjcfz4ccybNy/V35ubm8Pc3NzgfBAREeVW2fUcR3akwy5XRLlHhkaFioyM1NkVCkhqGRCRDCU+adIkNG/eHA4ODoiMjMTWrVtx7Ngx7N+/HyqVCiNHjkRgYCCcnZ3h7OyMwMBAWFpaonv37hlKh4iIiHIfdrkiyl30DixEBBUqVEjz+7SefdDl2bNn6NmzJ54+fQpra2tUq1YN+/fvR5MmTQAA48aNQ1RUFIYOHaq8IO/AgQN8hwUREdF/ALtcEeUuegcWR48eNXriq1evTvN7lUqFgIAABAQEGD1tIiIiyh04RC9R7qB3YOHr65uV+SAiIiIiolwsT05ngIiIiIiIcj8GFkREREREZLAMjQpFRERE9CHisLZEhmNgQURERP9pHNaWyDgYWBAREdF/Goe1JTIOowUW3333HUJDQzFt2jRjLZKIiIgo23BYWyLDGC2w2LlzJ4KCghhYEBEREaUiu57lyGg6fF6EjMFogcXhw4eNtSgiIiKiD052PcuRmXT4vAgZg0GBhYgASHpDNhERERGlLrue5choOnxehIwlU4HFhg0bsGDBAty9excAUKFCBYwdOxY9e/Y0auaIiIiIPjTZ9SwHnxmh7JbhwGLRokWYOnUqhg8fDh8fH4gITp8+jcGDByM0NBSff/55VuSTiIiIiN4jfPcHJZfhwOKbb77BsmXL0KtXL2Va27Zt4eLigoCAAAYWRERERB84vvuDdMlwYPH06VN4e3unmO7t7Y2nT58aJVNERERE9P7iuz9IlwwHFuXLl8f27dsxadIkrenbtm2Ds7Oz0TJGRERERO+37HiOg12uco8MBxYzZsxAly5dcOLECfj4+EClUuHUqVM4fPgwtm/fnhV5JCIiIqL/IHa5yl0yHFh07NgR586dw+LFi7Fr1y6ICKpUqYI///wTbm5uWZFHIiIiIvoPYper3CVTw83WqlULmzZtMnZeiIiIiIhS4NC5uUOenM4AERERERHlfnq3WOTJkyfdN2yrVCrEx8cbnCkiIiIiIspd9A4sfv7551S/O3PmDL755huIiFEyRUREREREuYvegUXbtm1TTLt16xYmTpyIX3/9FT169MCsWbOMmjkiIiIiouzAYW0Nl6mHt588eYLp06dj/fr1aNq0Ka5cuQJXV1dj542IiIiIKMtxWFvjyFBgER4ejsDAQHzzzTeoUaMGDh8+jHr16mVV3oiIiIiIshyHtTUOvQOL+fPnY968ebCzs8MPP/ygs2sUEREREVFuxWFtDaP3cLMTJkxAdHQ0ypcvj/Xr16NDhw46PxkxZ84ceHh4wMrKCsWLF0e7du1w+/ZtrXlEBAEBAbC3t0e+fPng5+eHGzduZCgdIiIiIiLKWnq3WPTq1Svd4WYz6vjx4xg2bBg8PDwQHx+PyZMnw9/fHzdv3kT+/PkBJLWULFq0COvWrUOFChUwe/ZsNGnSBLdv34aVlZVR80NERERERJmjd2Cxbt06oye+f/9+rb/Xrl2L4sWL4+LFi6hfvz5EBEuWLMHkyZOV1pD169fD1tYWW7ZswaBBg4yeJyIiIiIiyrj36s3b4eHhAIDChQsDAIKCghASEgJ/f39lHnNzc/j6+uLMmTM5kkciIiIiIkopU8PNZgURwahRo1C3bl1l6NqQkBAAgK2trda8tra2ePjwoc7lxMTEICYmRvk7IiIii3JMRERERERq702LxfDhw3Ht2jX88MMPKb5L/myHiKT6vMecOXNgbW2tfBwc9BsyjIiIiIiIMu+9CCw+/fRT/PLLLzh69ChKlSqlTLezswPwv5YLtefPn6doxVCbOHEiwsPDlc+jR4+yLuNERERERAQghwMLEcHw4cPx008/4ciRI3B0dNT63tHREXZ2djh48KAyLTY2FsePH4e3t7fOZZqbm6NgwYJaHyIiIiIiylo5+ozFsGHDsGXLFuzevRtWVlZKy4S1tTXy5csHlUqFkSNHIjAwEM7OznB2dkZgYCAsLS3RvXv3nMw6ERERERFpyNHAYtmyZQAAPz8/relr167FJ598AgAYN24coqKiMHToULx69Qqenp44cOAA32FBRERERPQeydHAQkTSnUelUiEgIAABAQFZnyEiIiIiIsqU92a4WSIiIiKiD11wcDBCQ0Mz9JuiRYuidOnSWZQj42FgQURERESUDYKDg1GxUmVER73L0O8s8lni9q2/3/vggoEFEREREVE2CA0NRXTUO1TtOBr5i+n3rrW3Lx7hr50LERoaysCCiIiIiIj+J38xBxS0L5+laeRElysGFkREREREH5Cc6nLFwIKIiIiI6AOSU12uGFgQEREREX2AsqPLlaY82ZYSERERERF9sBhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwRhYEBERERGRwXI0sDhx4gRat24Ne3t7qFQq7Nq1S+t7EUFAQADs7e2RL18++Pn54caNGzmTWSIiIiIiSlWOBhZv375F9erVsXTpUp3fz58/H4sWLcLSpUtx/vx52NnZoUmTJoiMjMzmnBIRERERUVpMczLx5s2bo3nz5jq/ExEsWbIEkydPRocOHQAA69evh62tLbZs2YJBgwZlZ1aJiIiIiCgN7+0zFkFBQQgJCYG/v78yzdzcHL6+vjhz5kwO5oyIiIiIiJLL0RaLtISEhAAAbG1ttabb2tri4cOHqf4uJiYGMTExyt8RERFZk0EiIiIiIlK8ty0WaiqVSutvEUkxTdOcOXNgbW2tfBwcHLI6i0RERERE/3nvbWBhZ2cH4H8tF2rPnz9P0YqhaeLEiQgPD1c+jx49ytJ8EhERERHRexxYODo6ws7ODgcPHlSmxcbG4vjx4/D29k71d+bm5ihYsKDWh4iIiIiIslaOPmPx5s0b3Lt3T/k7KCgIV65cQeHChVG6dGmMHDkSgYGBcHZ2hrOzMwIDA2FpaYnu3bvnYK6JiIiIiCi5HA0sLly4gAYNGih/jxo1CgDQu3dvrFu3DuPGjUNUVBSGDh2KV69ewdPTEwcOHICVlVVOZZmIiIiIiHTI0cDCz88PIpLq9yqVCgEBAQgICMi+TBERERERUYa9t89YEBERERFR7sHAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDMbAgoiIiIiIDJYrAovvvvsOjo6OsLCwQK1atXDy5MmczhIREREREWl47wOLbdu2YeTIkZg8eTIuX76MevXqoXnz5ggODs7prBERERER0f977wOLRYsWoV+/fujfvz8qV66MJUuWwMHBAcuWLcvprBERERER0f97rwOL2NhYXLx4Ef7+/lrT/f39cebMmRzKFRERERERJWea0xlIS2hoKBISEmBra6s13dbWFiEhITp/ExMTg5iYGOXv8PBwAEBERITWfG/evEma/uQe4mOj9crPu9B/ld8mX15qmM6Hlc6HtC5M5/1O50NaF6aTfel8SOvCdLIvnQ9pXZiO8dNR/19E0l+IvMceP34sAOTMmTNa02fPni0VK1bU+Zvp06cLAH744Ycffvjhhx9++OHHSJ9Hjx6lW3Z/r1ssihYtChMTkxStE8+fP0/RiqE2ceJEjBo1Svk7MTERYWFhKFKkCFQqlV7pRkREwMHBAY8ePULBggUzvwL/oXQ+pHVhOu93Oh/SujCd9zcNpsN0sisNpvN+p/MhrUtm0xERREZGwt7ePt153+vAwszMDLVq1cLBgwfRvn17ZfrBgwfRtm1bnb8xNzeHubm51jQbG5tMpV+wYMEs3bkfYjof0rownfc7nQ9pXZjO+5sG02E62ZUG03m/0/mQ1iUz6VhbW+s133sdWADAqFGj0LNnT7i7u8PLywsrV65EcHAwBg8enNNZIyIiIiKi//feBxZdunTBy5cvMXPmTDx9+hSurq7Yu3cvypQpk9NZIyIiIiKi//feBxYAMHToUAwdOjTb0jM3N8f06dNTdKliOjmbBtNhOtmVBtN5v9P5kNaF6bzf6XxI68J03t80PqR0VCL6jB1FRERERESUuvf6BXlERERERJQ7MLAgIiIiIiKDMbAgIiIiIiKDMbAgIiIiIiKD/ecCiytXrmDChAl49epVrk8nJCQER48eBZD0VsSs9uLFiyxPAwASEhKyJZ3skJiYiOjo6JzOhlFk17kDZM/xDHxYx1p2OnToUE5nwWAigjdv3mDq1Kk4ffp0lqaVnedOdkpMTMzpLBhFdt3bssv169c/mPsOALx9+zbL09i+fXu2Hc8f+phJ/7nAYuvWrdi8eTMKFSqUpTs3q9NJSEjAwoUL0ahRI8TExEClUhk9DU3dunXDuHHjAADx8fFZkkZ0dDTq1KmDb7/9NkuWr5ZdJ/WTJ09QvHhx/Prrrx/EhSS7zp1bt24hODgYQNbtq+w61j5EK1euRPfu3XHnzp2czopBVCoV7t69iy+++AKWlpYAsu54y65z58mTJxg4cCCuXLmSZWn8/fffcHNzw4sXL5AnT+4vQmTHvS077d27F82aNcMff/yR01kxilGjRqFfv34Asq4i6NKlS+jatSt+//13AFlzHbhz5w6GDx+O+/fvQ6VSfRBlgtTk/quCntQ70d/fH2/evMGdO3eyZOdmdTrqQqqJiQkGDx4MR0dHTJkyBUDW1B6p812oUCHcvHkTAGBqatzXn6hrViwsLODm5oZFixYhJCTEaMu/ePEiACA2NhYAUgRhxj4GXr9+jcTERNjb26Ny5cpYunQpnj59atQ01JJfaLPiYpVd5w4APH/+HAMGDMBHH30EIOW+MlRWH2uasqs1RESy9SZlY2ODxMREFC1a1GjLjIqKUv4fFxdntOWmx9nZGU5OTjh27FiWLD87zx0A+PPPP7Fp0yaYmZllyfIB4MaNG3j27BkePnyYZWlkx7mT1fc29XEcHx+freeno6MjQkJCULhwYQDGLReISI608j548EAp92SFPHnywNnZWbkOGfO+o97+Fy5cwLFjx7Bhwwajp6GW3feC1PxnAgvNnVi0aFHcv38/xfT3PZ1Hjx6hbdu2GD9+PACgTJkyGDduHJYsWYL79+8btfZIfXCq812oUCHkzZsXz549M1oaQFLtiouLC54/fw4AmDt3LmJiYrBkyRKjLP/777+Hh4cHnj9/rtxsDxw4gFWrVuHPP/8EYNxjICAgAE2bNsW///4LANi0aRNOnjyJXbt2GbU2TH1xNzExQXx8PM6ePYuXL18qaRjzZpId586ePXsAAEWKFMFnn32GBw8eYNeuXQCMty5ZfaxpSkxMVG6CT58+VYJaY4uPj4dKpYJKpcq2ZnwfHx/ExMTgzJkzAAwPZrdv3442bdooFQB58+YFkNSdIzIyUpnP0HQ0C0TqZUVHR8Pe3h4vXrxAYmJirrof6NKoUSOYmpri1q1bAIxfqAQAd3d3vHz5EjExMUZPQ52O5rljbFl5bxMRREZGwsfHBz///DPi4+NhamoKlUqFR48e4eXLlwankV769vb2qFChAvbu3QsARisXJCQkQKVSwcTEBK9evcKTJ0+y5ZqTL18+xMfHIzw83KiFZs2816hRA9HR0bh9+zYAwwNbdT7XrFmDL774AgDQuXNnNGnSBIcPH1Zak4y5PjlxL0jNBxlYhIWFwdfXF3PnzkVoaCiA/9UeeHt74+nTp8oNy5AdkF3pqNnb22Px4sX47rvv8OTJE5iamqJDhw7w8PDAiBEjABh+oKpv7uqLrrqg6uHhgcuXLxvtTY3q7VSzZk08f/4cK1asAABYW1tj5syZ+Prrr/HXX38ZnE6LFi1QtWpVJRhr3bo1OnfujK+++gr16tXDxIkTlZuXIdvuzZs3AID27dvj/PnzOHLkCKKjo1GmTBkMHz4c8+bNw927dw1enx07dmgVWr/++muUKlUKw4cPh5+fHyZOnAgg8zeT7Dym1dt7z549aN26Nfbs2QMTExN4e3ujZcuWmDBhgkHropZdx5qmPHny4Pbt22jcuDFat24NDw8PbNy4USlYGOuGoq5h/eKLLzBhwgRs3LhRCZwM3T/bt2/H8ePHlbyql/fu3TuUL19eqbHOTCFZc/0rVKiAa9eu4dChQxAR7NixA4ULF0bHjh3h4+ODbdu2ZToddVrTp09HixYt8ODBA2VZIoKiRYuiYMGC+Pvvv5EnT55cdT/QbNlRb8/IyEhUqVIFV69eBWD4uaP57IHmNqtSpQoOHDhglDTU1IU5lUqFK1euwMvLC35+fmjbti327dtn8PKz496mUqlgZWUFEcGSJUvw6tUrREREoG3btvDw8EDdunUxd+5co3T1vHjxIo4fP64cB+qCPwDkz59fCfyMRX3PGTVqFKpXr44WLVqge/fuuHbtmlGWHxQUpJwfmi0j/v7+uHz5MmJjYw1u7fv5558xZ84cxMfHK8dtYmIiEhMT4ePjg3PnzgGAwS0jKpUK0dHROHDgAPbt24eLFy8q5TUzMzOsWbNGmc9YsvJekGHyAQkNDZUjR45IQkKCjB8/XooVKyYdOnSQyMhIZZ7o6Ghp2LChDBgwwOD0Xr9+LZMnT86ydFavXi0bNmyQGzduKNOePn0qNWrUkI8++khERBISEmTfvn2SJ08eOXDggIiIJCYmZiq9Xbt2iZWVlXTo0EEePXqk9d3Vq1eldOnSsmvXrkyuTZK4uDjx9/eXadOmKdO++uorKVCggNy6dUuZ5u7uLu3bt5f4+HiD0hMR2bZtm6hUKlm9erUMHDhQnj17Jq9evZJt27ZJiRIlJDAwUGJiYjK17LCwMLG3t5dVq1Ypy+jbt684OztrrY+NjY2MGTNG3r59m+n1uHjxoqhUKvntt99ERGTjxo1SqVIl2bhxo0RGRsqePXvEzMxMvvjii0ynERYWJhMnTszycycoKEiuX7+u/L9Hjx5So0YN5fujR4+Kg4ODzJkzR0SSjvOMyq5jLT4+XoKCgpT/i4icOHFCypUrJ3379pVz587Jl19+KbVr15bPPvssU2moxcXFaf39888/S8mSJaVmzZrSs2dPKV++vPTq1UtevXolIhm/FqjnT0hIkEqVKkm5cuUkMDAwxXxVqlSRcePGiYhkers9evRIIiIiRERk1KhR4u7uLhs3bpQmTZrIihUr5I8//pBu3bpJlSpVZNGiRUq+MmLnzp0iIrJnzx6xtbUVb29vOXTokNY88+fPl4oVK8rr168ztR5qr169ypZzJy4uTnr06CEdOnSQK1euiIj2PvDw8JARI0aISObOGxGR2NhY8fT0lIYNG8qFCxe0vouIiJD69evL6NGjM7381Lx9+1bu3LkjXbp0kc8++0y+//576dSpk+TLl0/27NmT6fSy494m8r/98OjRI8mTJ4+sXLlSPv/8c/noo4/k8OHDMm3aNKlUqZJ07txZ3r17l6k0oqOj5e3bt9KhQwexsrLSur6pt0/Hjh3F399fRDJfHvjll1+0fvv3339L3bp1xcfHR/bs2SPHjh2TJk2aSIcOHZTrX2bT2rt3r5QsWVLatGkj0dHRWt+dO3dOKlSoIDt27MjUskVEue/27dtXihQpIp07d5aQkBCteXr27CmtWrWSuLi4TK/Hw4cP5dNPP5WLFy+KiMjhw4elYcOGMnToUGWemTNniqenp/z4448ikvlzNKvvBYb4YAKLKVOmiEqlksKFCys7au3atVKlShXx8fGR06dPi0jSxm3durX06dMnxY7Rx9mzZ5XlqK1bt86o6Rw4cEBKly4tbm5u4uHhIZUqVZKxY8cq3//000+iUqnk5MmTIiISGRkp3bt3l8qVK2d4faKjo2XdunXKco4cOSK2trbi7++vrKuIyM2bN6Vs2bLyww8/ZDiN5KZMmSJFihSR+/fvi0jSCeLq6irdu3dX9t2xY8dEpVLJnj17MrRsXSdpeHi4fPTRR6JSqWT48OFa333++edSv359OXfuXCbXRqRt27ZSo0YNuXv3roiIREVFiaWlpUydOlUpOC1fvlwsLS2VfZYZly9flgoVKsjOnTslPj5efH19Zd68eSIi8vjxY2nXrp3ky5dPlixZkqHlrly5UlasWCFXr15Vpq1Zs0YqV65s1HNH7dWrV9K6dWspV66cMu33338XOzs7Wbx4sYgkBTjTpk2TokWLysuXL5X09aWeNyuPNRGRly9fSqNGjaRfv37y5s0bZXpAQIB07NhR+XvdunXK8RceHp7hdNRB0vTp05VpDx48kAYNGshXX32lTJs3b54ULVpUAgICRCRj2+zt27fy8uVLZbs8fvxYZsyYISqVSmbPni0PHz5U5h0+fLh4e3vrvezk5+Uvv/wirq6usnr1ahFJOkcrVKggFStWlP79+yvzvX79WiZNmiRlypSRZ8+e6Z2eSNL5olKpZM2aNSIicvLkSenatatYWVnJjh07JCoqSkREFi9eLFWrVpUHDx5kaPkiIqtWrZKlS5fK8ePHlW29Zs0ao993kvvxxx+lcuXK4urqqnXeioiMGzdOqlWrlqnl/vvvv7Jw4UKJiYmRXbt2ib+/v9jZ2cnRo0e1gpePP/5YfH19RSTzBZbkx8SrV6+kefPmkj9//hSBfqtWrcTX11fu3LmTqbSy+t6muQ3U+R4xYoTkz59fPD095ebNm8r3a9askTp16sjChQtT/DY906ZNkxo1aihB68SJE6Vw4cIyZMgQ5fxMSEiQL7/8UmrUqCHPnz/P1PoEBweLSqVSKg9EkgKwwYMHK2nfvXtXnJycxM7OTiZOnJipdNTevn0rP//8s9jY2Ejbtm3l2LFjynePHz8WW1tb2bJli4hkrCD+4sUL6dmzp4wZM0bevXsnsbGxcvLkSSlUqJA0b95cTp06pcz7/fffi42NjcTGxmZ6Pfbs2SNVq1aVzz//XJk2ZcoUqVOnjhK43r59W9q2bSvt27fPVGVGdtwLDJXrA4tNmzZJ4cKFpVq1atKrVy/x9PSUv//+W/n+7t274ubmJi4uLkrt1ezZs8XR0TFD6Rw4cECcnJwkf/78yoVcs5bbWOlERkZK69atZfLkySIi8uzZM9m+fbuoVCpZsWKFxMTESHR0tLRt21bc3NyU312+fFmsra1l6dKlIqLfQbRw4UIxMTGRevXqydu3b5UT9uDBg9KiRQspUqSI7N27V7lQVq5cWQlwMhpla94koqKipHz58tKvXz8ln3v37pU8efLI4cOHlfm6dOkiNWrUUArnaUlMTNRKQ7OQl5iYKKdPnxYbGxvlJFPvu8ePH0vhwoX1rj0ICwuTAwcOaLUivXr1SszMzGT27NnKRXfhwoVSsGBBrRuYi4uLdOjQQSko6yN5fhwcHJQa5BYtWsiOHTtk8uTJYmVlJV26dJF//vlHa/60joPTp09L6dKlxcXFRSpXriwODg4yatQo5XtjHdNq6poTkaQLsL29vaxcuVJEklobx40bJ7a2tsq+u3Tpkri5ucknn3yic1voknzbvnv3zujHmkjShVx9DI0bN07q1q0rP/30k4gkHVve3t6yd+9euXDhglSqVEmcnJxk48aNei07OfV6Jw+SREQ2b94s7969kxcvXkivXr2kQIEC4u3tLRUqVFCuU/pst6lTp0r16tXF29tbunTpohVEfPXVV+Ls7Cy+vr7KjXD8+PFSt25drX2qS/Lj76+//hKRpP3doEED6dWrl3LMqoOvQYMGaf3mjz/+EHd3d5k/f36666EpJiZGRo4cKSVLllS2QXx8vHTr1k0qVqyoVDLcvHlTVCqVUvjT59q5c+dOKVGihNSsWVNq164tpUqVUm70iYmJRj93tm3bpqyD+t8//vhDWrVqpXUeiYjMnTtXPDw8MhUobd26VczMzGTz5s0ikhTwNWrUSFxcXGT27NnKfN98841UqlRJHj9+nOE0EhISdLZyxcXFyfr166V48eLKvlGfY/fv3xdLS0vZvn27iKS/jzZu3KhVyFKnZ+x7W/L7TvLfOzg4iK2trQQHByvTXrx4IX379pXWrVvrXbDcuHGjFClSRAoXLix58uRRWt2ioqLkl19+keLFi0vTpk2VY3jZsmVStmzZDAfjavHx8bJkyRLJnz+/so/fvXsnjx8/lpiYGBk0aJAUKVJERowYIX369JEaNWrImTNnlG2Snr1798qGDRtSFOKPHTsmjRo1kmLFisnJkyeVVh1vb2+lwkHfAvLq1avF2tpaWrZsKfv27ZPQ0FDltwcOHJBWrVpJsWLF5MiRIxIfHy9Hjx6V8uXLy4kTJ/TbSP9PHfCoTZo0Sby8vGTfvn0iknTNa9mypXTu3Fm5t61YsUI8PT0zXAmolpX3AmPItYHF69evpXHjxmJubi7ffvutiPyvhkp9U1QftNevX5fBgweLhYWFLFmyRDZu3ChVq1aVa9eu6ZXW7t27xdPTUzp16iQNGjSQ7t27a32v3lmGpKM+4E+dOiUmJibKBSIhIUGePHki9vb2UrVqVaWg+ueff0q+fPlk1apVIpJ0AVa32mg2wety8uRJKVOmjJQpU0a2bdumc56YmBhp3bq1VKlSRSZNmiQiIv369ZO2bdvq3W1o586d4u7urtSaaF6Ad+7cKSYmJkqNnkhSrVSdOnWUZsugoCCxsrJKUaBIfnJoXmiCg4OlZ8+e0qxZMxk7dqyy7d++fSsjRozQKriqf+fk5CQzZ85Md32mTZsmNjY2UqtWLTEzM5OFCxcqBdmZM2dKkSJFtLoNlCtXTrp3766s/6lTp0SlUsmGDRvSvDj+9NNPEhgYqLW9EhISJCEhQbp27SodOnQQEZHatWtLnjx5pHbt2nL8+HFl3hcvXsicOXPSra3q16+fdO7cWUSSaiq3bNkiKpVKvv32W2UbXbt2zaBzR0Rk3759YmdnJ6tWrVJqa8PCwuSzzz6TUqVKKdPOnz8vLi4uSsHy3bt3snLlSrGxsZHz588r20GXH374Qby8vMTPz0/69eunVRNlyLGmy+bNm6Vq1arKufPs2TPx8/OTXr16Kdeezp07S968eaVYsWIyadIkpQARExMjBw8e1OqKlRp19wI1dZA0YMAArePn2bNnUr9+fWnZsqXcv39fzp07JyVLltSq+U/NtWvXxNvbW6pXry67du2SuXPniru7u1IbrXbo0CFxdnYWHx8fOXv2rJw8eVLMzc21AnhNiYmJWvtqy5Yt4uzsLD169FAKC5s2bRI3Nzetm6uvr680btxYq3IoOjpa3NzclO5QGXHnzh2xs7NTrmEiSV15li9fLqampjJ48GD59ddfxc/PT77++mu9lrl48WJxdHSUb775RhISEuTff/+VCRMmiLu7u0RGRhrlfqBJ3QVS3aKmeV1ITEyUHj16iIODg1IZdezYMcmbN6+EhYXptXzNAFskqQW2WbNmSj5DQkKUlqvJkydLWFiY7Nq1SxwdHTMUvCTv4nLr1i357LPPZMmSJUoNdVhYmHTs2FErAFNfH/z8/NI9pm/duiWtWrUSlUolderUUVqINbdZZu9tad13IiIi5Ouvv5Z9+/ZpBREbNmxQ9p1mHr744gupXr16ugW9q1evSpUqVaRIkSKyevVquX79ulSsWFEJVtW//+2338TPz0/Kli0rJ0+elEePHomZmZlyP0qvML5z507Zv3+/VjexJ0+eiJubm9LtWr2MZcuWiY+Pj3KN3bdvn1haWkr//v3TXZ+nT59Kt27dRKVSiZubm/z5558p8vf06VNp27atVKxYUWbMmCEiIp988ol06tQp3bKNWlRUlDRt2jTNgvvr16+lVatW4uzsLHPnzpX79++LjY2N3gGSulK7TJkyEh4eruzfq1evSrNmzaRHjx7K8fT111+Lp6enUlYNCwuTvn37ir+/v3KtSyu9U6dOaVUaRkdHG/1eYEy5NrB4+vSp/Pjjj8qBlpiYKA8ePJBSpUopzevJd9SIESPE09NT7OzspHLlylo3L100bxCjRo2SoKAgWbp0qVSsWFF2794tIrr7F2ckHc0mUhGRCxcuiIuLi/z666/KtIcPH0rz5s3F0tJSqVGOiYmRMWPGSIkSJZQLdlBQkAQEBKR5cVQHZAULFlSmhYaGSlBQUIraxxcvXsiiRYtEpVLJ3Llzxd/fXzp27CgxMTFpXkBev36t1D6bmppK7969RSTl/mjSpIn4+fkpXRL+/vtvMTMzk++//16ZZ+TIkeLj45PipiQiKWrnly5dKoUKFZJu3brJ9OnTpVWrVuLo6KgUZO7cuSMlSpSQwYMHy7///isiSbX2ZcuW1Sp06jJu3DipUaOGHD9+XEJDQ2XKlClSqlQprRrykiVLSr9+/SQ0NFRERPbv3y8qlUp+/fVX5ebYsGFDGTlyZJr90nv37i1FixZNtR9os2bNRCSpgKtSqZRaPLWvvvpK2rZtq9QO6/Ls2TMpXry4bNq0SWv6hAkTxN7eXqulRSTj545IUpDXqFEjsbS0lPHjx6fY/+fOnRNnZ2el2fjNmzfy9ddfi5WVlVKo+eeff6Rp06ZSsWJFnWm8ePFCevToISVKlJBFixbJvHnzpGHDhuLi4qJ1jBp6rImI0tUtKChI6tatKwMHDpQnT56ISFJ3spo1a8p3330nIkl9um1sbGTZsmVayzh69Kh06NAhza53W7dulfLly4uLi4s0btxYK/jfsWNHiiBp1apVUrFiReWYVh/n+fLlS7d7x9y5c6VFixby4sULZdrBgwelZMmScvv2ba1579y5Iy1atBBbW1uZOnWqlCtXTvbv35/m8i9fvixubm5StGhRcXJykrp162p937lzZ2nRooWyPX7//Xext7fXqh2PiIiQChUqpNiWammdS4mJifLVV1+JmZlZir71W7dulXr16knp0qXF3t5eVqxYkea6aFb+rFu3Tivd0aNHa3VN0JSZc0eTugukulU1eVequLg4+e6778TCwkL69u0rJ06ckCpVqsjWrVvTXO6+ffukdOnS4uHhIaGhocr6XLx4UUqWLClffvml1jNh3377rVSuXFmaNGkiV69elbx58yr7LbX7gXqbLVmyRIYNG6ZMnzFjhlhaWkr79u2lefPmUqlSJZk7d66IiBw/flysra21gvyIiAipWbOm8sxVar7++mtp3bq10t1o1KhRyjqoK2dEMn9vU9OcZ9GiRVKoUCGpXr26VKhQQZycnLQqmKpVqyb+/v5a96vp06eLh4dHqoG5SNL1cNiwYdK3b1+tAnWJEiWU7aBZ4x8ZGSkNGzaUSpUqyYABA6R+/frpHtM//fSTlCxZUtzc3KRkyZLi4eEh69evF5Gk82rXrl2iUqmUICImJkY6d+4srVq1UpaxcOFCKV++vFStWjVFkKoWFRUlDx8+lM2bN4u/v79s3LhRypUrJ5MnT1a6hmpu06ioKJk8ebKYm5vL4sWLpXPnztKgQQMRSb0A/u+//yoVOJs3b5YyZcqISFLlSe/evWXgwIHyzTffaNXyh4WFyZdffikqlUqmTJkiefPmVSoYUkvn9u3bUr16dSlSpIhWq5imRYsWiaenp1LxGxISIj169BB/f38l/Z07d0qlSpWULnG6bNu2TcqWLSsVK1aUokWLaj2n+eOPPxr1XmBMuSqwSF4IV9PsE1yhQgUlKkz+/Zs3b2Tfvn1SoEABUalUqd7cdaWjLqz/888/0rVrV/Hz81MOPM0HHvVNJyIiQlxcXMTc3Fy2bt2qXCD++ecf6dKli1SuXFl27twpmzdvliJFisiMGTNk9uzZUqxYMWUZ9+/fF2tr6ww/ELh//36xsLCQAwcOyOzZs8XJyUnc3NykTJkysnz58hQX1uXLl4uvr6+YmJiIiYlJut1Frly5Ih999JGsXr1ali9fLlZWVkotgOZN8cqVK2JqaqpVuB03bpzY2dkpJ4au/shv3ryR1q1bS9euXZWC/b1792TQoEFaN9MVK1aISqVSaqUSExPlm2++kTx58kiFChXk888/l/z580vXrl3TfKj65cuXUqdOHa2b2v3796VKlSpagcWPP/4opqamsn//fmUbNmvWTGrWrKncVHT133z16pVMnTpVDh48KCJJrSvqfqDNmjXTqn1ftWqVWFtbK8tp2rSp1KhRQzp27CjLli2TevXqSYkSJVIUKjSPafXxWq5cOaUApxmMlixZUoYPH651k9X33FFTP7fQtm3bVJvj3759KwsWLBBra2tl+9y6dUsaNWqkBE+JiYmya9cu5VmS5Bf7rVu3Sv369bX6mZ88eVKqV6+uDGYgkvljTeR/3RO7du2qtAJ98803UqtWLa0uKO3bt5eWLVvKrVu35N27d9KvXz+xtraWBQsWyIEDB2TKlClStGhRGT58eKoFCvWN/rvvvpMdO3ZIv379xNTUVHbs2KEEPI0aNZIGDRooQdJXX30lrq6uSgvHN998I71795aZM2cqNYJq7969k507d8qRI0fk4cOHcvr0aaXJXm3v3r0pulGoj4NHjx7JhAkTRKVSiUql0nrgVfO6ER0dLR06dBATExNlfXfu3CmlS5fWKuAfP35catasKVOmTFG2f8+ePcXKykp69OghGzZskEaNGknZsmVTXJejoqLE09Mzxc09efeUly9fKudI8nxevnxZmjdvLiqVSqZOnapznyRPV7OAKpJU+5wvXz6pVauW9OnTR+lKoV6fjJ47yfMoIloDGaiNHj1amjRpohSmVq9eLXXr1hVra2txcHBQnp1L7smTJ9KoUSPJnz+/0sqRPN1hw4ZJrVq1UjwTdvToUalYsaI4OzuLSqXSu5XHy8tLPv74YxERuXHjhtSsWVO53okktVQ5ODjIpUuX5O3btzJmzBgxMzOTCRMmyNGjR2XcuHFSuHBhrZZZXe7du6cUbmfMmCF16tRRavd1yci97fbt29KuXTulRSI2NlY2btwotWrVUrpBioiUL19eunbtqlTsnDlzRlQqlfj6+sratWtl8eLFYmlpmWrr6KVLl5TKKc3jOD4+XqKioqRTp05KS7PmdyJJlTmLFy9Wzk911+jkEhISZPXq1VKpUiX56quv5N27d3LhwgUZOHCg+Pn5KZWMkZGR0r59e6XbdUJCgvTt21eaNGkiu3fvlv3794u/v7+sXbs21YD54sWLUrhwYdm7d69cu3ZNaXkLDAyUSpUqpaic0Dz2N2zYIBUrVhQ7OztRqVQ6W8gSEhJkxowZkj9/fuUc2blzp9SpU0e2bdsmFSpUkH79+kmfPn2ULp3qa6fa6tWrxcXFRVQqlYwePVpnZUVUVJRcvXpVaXFRU3cR07ym//vvv0qlifr+smPHDqlfv77W8yia9/bkVq9eLWXKlJE1a9bIvXv3ZM2aNaJSqbSeM2rSpEmm7wVZKVcEFskL4eqdrnkAqgscPj4+0qtXrxTfa7pw4YJWH/n00lH/q05j69atUrVq1XRHKkktHRGR58+fS8+ePaVv377i6ekp48ePVw6Oq1evykcffSTVqlWTYsWKKTU5Fy5ckLJly2o1nW3atElpPdHXu3fv5OOPPxaVSiX+/v7y448/yu+//y4DBw6UypUrK+lpFrTOnz8vderUkTZt2sjbt29TFPAuXbqkVeupbtoOCQmRtm3bSp06dXTmZejQoeLk5KT8NiwsTKytreXnn38WkdSbn7/44gupU6eObNiwQUSS9pG6gHTlyhWpU6eOlClTRnr16iVmZmZKV5qnT59K3bp1pUyZMnLq1CmdNyrNwtfTp08lLCxMChcuLAsWLJDY2FhJTEyU5s2bS4MGDWTmzJly+/Zt5Rjx9vaWhg0bKheTe/fuSYECBZQa+OTbLTY2Vvr06SMqlUo+/fRTrT63afUDPXLkiLJ9169fL61atZLmzZvLp59+qhUkpHZMh4eHy/Dhw6VevXrKTURdcF25cqUULFhQaenRzHNax7SmJ0+eaNXYb968WYYMGSKjR4+WvXv3KoHcjRs3pF69ekoNWHx8vOzcuVNUKpVSw6LrWFPfeE+dOiWbN2+WhIQEZb47d+6ItbW1MjKHenp6x1pyQUFBSkFDfbypa/NiY2OlVatW0qFDB6UAsX//fqlRo4bMmDFDSXPYsGHi4eEhHh4eKQpTmtT7ZdiwYdK0aVOt73r27Clubm5Kv+rLly+LiYmJ8szG/v37xcPDQypUqCB169aV4sWLawVVanPnzhVHR0dp27atNG3aVPbt26fzodMtW7ZI1apV0xy1Zu7cuTJmzBiJjo7WGexdv35dOnfurNXla926dVKyZEml5UdtxIgRUq9ePWW0swcPHkjJkiWlYsWKMnDgQPnss8+0HnjXLBQMHjxYSpcuLU+fPhUR7evFo0ePlNae3bt3i0qlUs53zYJDRESEzu2V3n0nMTFRJk+eLNWrV5eVK1fKypUr5eOPPxYrKyvlGMvIubNz504JDAzUuu6qg5hu3bpJmzZtRCSp20uxYsWkdu3aWv3BExIS5NGjR+Lp6an10Lqm4OBg8fDwkMKFC2tViiQfUODly5dSrlw5GT16dIrnlv79919p3ry5mJiYyN69e1NdH5H/bedZs2aJk5OTiCQVdtq2bSsiSc9a1ahRQxwdHbVG/bl06ZLUqVNHChQoIDNmzBBPT88Urajp0eyiqL4e67pX67q36SpY7t+/X1xdXZWR3RISEuTw4cPKsXP9+nVp06aN5MuXT4oWLaoU2EVEBg4cqAze0LRpU521x7t27RIXFxepWLGiODo6pjoiXvfu3aV58+YSHx+fatnj22+/lTZt2qRo1Vd7+/atzJo1S6ZNmyYxMTFa3Zzc3Ny09rm62/Xy5ctFJKnrXMuWLcXe3l4KFy6cbivSggULpH79+immx8fHS/Xq1aVv377Kcxy6Wgn2798vPj4+0rBhQ53PJ75+/VpcXFykSJEi0qFDB7lz54789ttvUq1aNWnbtq1MmzZN2Z+HDx+WqlWryoQJE0REe9uePHlS5s6dq/O6d/HiRSlatKjs2bNHfvrpJylevLj8+uuvysA97u7u4uTkJNu3b1fOpU2bNomXl5fSzTouLk4++eQT8fDwSHW/qPMUHR0t7du3lyFDhijTY2JipGLFilpdKa9cuZKpe0FWyxWBha5CuK7uCgkJCTJq1CjlYWRjp6M+OF+8eCGffvqpVK9eXbmhZXQkgXfv3kn16tXl2LFjcvr0aWnQoIE0bdpUqzYveR/refPmib29vcHDIookHZCTJk3S6u4QHh4uo0ePlho1augciSd5lC+ifTF0cnJKMdRpYmKi/Pbbb1K0aFFZu3atiGhvq+fPn0vx4sW1Rp949eqV1gkfEREh9+7d07r5vX37Vpo2bSqdO3fWiuBDQkLEy8tLhg4dKs+ePZO3b9+Kg4OD9OzZU9mX+/bt03nTFdEufDVr1kx++eUXEUl6IKtcuXLStGlTyZs3r9SrV0/mzp0rrq6u4uPjo9QOXr9+XfLkySNLly5Vtld6z6S0aNFCvL29xdfXN8UNJzw8XO9+oLouiGkFsOrati+//FJE/nd8P336VLmIGuLrr78WlUol3t7eUr58eRkyZIi4uLhIhQoVlD6fsbGxsnnzZrGxsZHff/9dRJK6/gUEBMilS5e0lqd5rJUtW1bJt6aEhAS5fPmyODk5aTV5q7eFrmMtuefPn0u3bt2kVKlSSo3tu3fvlONNXVjetWuXuLm5aR3zQ4YMEV9fXyWAUG/T5HlRSx6Qe3t7Kw+uqo/X0NBQcXV1leHDhysB1ZAhQ8TR0VHCw8MlMTFRLl26JJMmTZIJEyakKCTev39fGjVqJOXLl5etW7fKs2fPUnSxE/lfRcKgQYNSfWA+eWWL5ve7du2SkiVLSqFChbRaOzSPKxMTEyUo1mylVZ+z6m3x2WefpQgoRJIKok5OTsryX79+LXZ2dlqj5okk1eYXKFBAGRL1zZs30rFjR3F3d9eaL61+zWndDzRbyTXPu9jYWClWrFi6XVA0qa/5Q4cOlcKFC6faBbJly5YiklThsHr16hQtbOo8PXz4MM3nOCZNmiSNGjWSU6dOyU8//SS+vr5Sv359adSokfz555/KOqq7uGhWXKn35ZMnT3Q+w6FZ46+5bdXdqJ49eyZffvmlODg4SNeuXcXGxkamTJmi7Oc3b97I48ePJSEhQZYuXSrW1tZa3Yo0KxDSos7n6tWrtboo6tpeIv+7tyVvdduwYYOynWNiYmTOnDlSsWJFpaZZvQ1+/vlnKVeunPTs2VOioqKkW7duUrlyZeU6/ezZM7G0tNS5X4KCgsTHx0cKFiwoCxYskDNnzsj06dPFxMRE6WqpmbfVq1eLjY2NzqAirW2TvOXtypUryr1J/bvff/9dKlSooLUfY2NjZcyYMWJnZ6fMHxMTI3/88YfOclZISIgSyCUmJoq/v3+KZxjV5/7WrVvFwcFB58AWycsAuqj38+DBg6V8+fLStm1bmTJlioiIVKhQIUVX4aioKBk/fry0bt1aybs+x9OCBQukXr16IpIUdPfr109UKpW4u7vL2rVrZfv27dK9e3epWrWq0gsgKipKhg4dKg0bNlSC4mvXrqX6XFLye0H58uVl2LBhyvk4duxYqVq1qnz77bdy7do1Zd0HDRqk970gu+SKwEJXIbx58+ZaI5eoD45JkyZJtWrV5N27dxkeXkufdNQH++HDh8Xb21s+//xz5YExffvOqg+IAQMGSL9+/UQk6SbWunVr6dChQ4px1kWSbsrt27dXRjVKzatXr+TEiRNy7969NOeLjo7WujGot9WiRYvE2dlZuTCkRp+LoXo9X758KcOGDZPSpUtr1fapzZ49W1xcXHR2D5k6daqULVtWatSoIU5OTnLw4EFludu3bxc3NzetAubSpUulUqVKysl7+fJlKVGihM5nETTpU/gKCwuTPn36SJ8+fZRpERER0qJFCxk5cqRSwOjcubP06tVLZ0Ch2Q80NjZWwsLCpEWLFnLjxg1p3LixdOvWTdl36ptdZvqBqqUWwD5//lyio6Olf//+4u7uroyHL5LUPcXOzk5p5cms2NhYadKkiXTq1Ek5JuLi4mT16tVibm6u1B4/evRIOnfuLLa2tjqXk9axpq7p0ixwrFq1Snx8fLS2j2ZLV2rHmkjSaEempqZKra9mt5UdO3aIm5ubLFiwQJnWp08fadSokVJYvnnzptSpU0d69eqV5kU9eUA+a9YsEUk63h0cHLS2oUjSA8NlypRR9pOuICk1X3zxhfj4+KS4Jty7d08CAgK0hkQVSRohR7PLWPJnLZJT7x91t6+05qtZs6bSIqqZ5qJFi6RatWpKN1ZdLXsiSYWWAgUKaBVUVq5cKRYWFkqhbdCgQVKrVi1ln6ipH4L+5ptv0lwftYzcDzRHaipZsmSa1xpNe/bskUqVKinddU6cOKFXF8jMUOfx77//lqZNm4qNjY2UL19eFi5cKIGBgdKoUSNxdHTUCiTq1KkjPXv2VALjtK43J06cEDMzM/nmm2+UY19dKD98+LBYWlrKo0eP5Pr161KyZEnlYXe1qKgomT9/vjIi1b1796RRo0bSokULEUm6Dt69e1dZdlp50fyuQ4cO0rp1a6VlMbVWI83faI6YqFmWuHLlirRs2VJpPVL/rnPnzjJs2DDlmh8QECCWlpYyYsQI5T6iq2IuLCxMqlatKqVKldIqpP/zzz/i5OSUonJFJOm+5+TkpAQt6Umt27Wu9R4xYoTSmqQZuKq7XQ8cODDNtEJDQ7VGcLpz547ky5dPOZZ17bNmzZpJixYtlMAneYWqpqCgIJk5c6ZW1+rExERZsmSJDB06VAYNGiReXl5y7do12bdvX4ohc0VEunXrphxTqUkvODp+/LhMmDBBqwtuXFycDB48WOrVq6eUPw4fPiyurq6pdrMUSf1esHHjRsmXL5+0bdtWSpYsKWXLlpVPP/1UGelJ/Szxixcv9L4XZJf3PrBIrxCuvnmoL5qnTp2SvHnzal38jZmO+sSIi4uTsWPHikqlElNTU/Hx8clQdJiYmCjz5s1Tum2JiHz33XeiUqmkVKlSsnv3bomPj5dTp07JmDFjpHTp0lK7du00A4YpU6ZIsWLFxN3dXfLlyydff/213qMoqLffp59+Ko0aNUqzlj0zF8M//vhDypcvL+PHjxeRpOhcs6Uh+Sgy9+7dk+bNmyt95Q8dOiTdu3eX0qVLa0X1H3/8sfj7+ysXmg0bNoilpaXcvXtX3rx5IzNnzpSFCxfK8uXLUzy8qSmtwtf06dOVC2OdOnWUVgz1MePr6yvt2rVLUYjVpNkPVLNglZCQIO7u7vLixQvZtGmT1KxZUzZv3iwxMTEp9oE+/UA1pXVMt23bVi5cuCA3b96UZs2aSaVKleTIkSMSFBQkn376qdSrV0+vYXHTC2zu3LmTohvY06dPxcvLSzkWRJJqymbNmiWJiYlay8zMsda8eXOtYXM1A8TUug5s2bJFihQpIq6urnL06FGJioqSWrVqpahpUx9v6uPh0qVL4uHhIWPGjFFq1SZPniyjR4/WGbykFSSFhITIyZMnxdHRUQkcNY8Ba2tr5WYiknTMurq6pvkAqLogrn5GRd19omfPnpI/f37Jnz+/jB49WgmKz58/L/b29vL06VN58OCBtG3bNkW/Xk2p7R9Nml1Jq1SpovTrj4+PV/b1mzdvpGXLlkqhVvM6q+9LDlu3bi0iSZUrus6N2NhY+frrr/V6xkHf+4Gm8PBw+eyzz6R169Z6j8j08uVLadiwofTs2VMpUO3bt88oQ2GmdW6uXLlSBg8erLykUs3T01M+/vhjpWXs119/1epqocuxY8eUY3Dw4MFSoUIF6dy5s1YB9smTJ1KiRAnZtGmTxMfHy9ChQ8Xa2lpu374tz549k4iICJk2bZpUrFhRa0hZdbfIjh07pqhgSo/mELMeHh4yfPhw6dSpk+TPnz/Vrij6jJi4YsUKqVSpkhIA3blzRxwcHJSBIBISEmTgwIHi7++fbgWDSFLFmp+fn9YxNXHiRClXrpwsWbJETp06pbUtr127JpaWlkrFT3rXYH16fKi7f3l7e+t8biY2NlbvbtezZs0SHx8f+eOPP2TTpk1Svnx5nc+vqffPhQsXpEyZMjJt2jTp16+flClTRunGqiksLEwqVqwoKpVKSpcuLRcuXFCuW3PnzpUOHTpIcHCw1KtXTzlnO3fuLJUrV5bly5dLVFSU3L17Vxo1apRmi2J6wZFIUo8JdW8Vkf/dV1auXCklS5bUuuek9u6qtO4F6qDm/v37EhgYKPXr19fqqdKiRQvp0aOHct7pcy/ITu99YCGiXyFcfeL99ttvUrhw4RQPJBo7neXLl4u5ubnUrl07wy9XU18IVq5cKXXr1pVnz55J8+bNxdzcXObMmSMDBgyQ2rVry5gxY+Svv/6Sxo0baxUokrt165b4+flJjRo15NChQ/L48WOZPHmylChRQq9xrN++fSvh4eHy5ZdfiqOjY6oXVE0ZvRi+e/dOvvzyS7GxsZHu3buLSqVS+mxq+vfff+Xt27eyc+dOqV+/vlZ/7Li4OMmfP79Wn8Fz585JrVq1ZPz48RITEyNv376V6tWrS6lSpaR48eLi6OiY7kNL+hS+xowZI2/evBFHR0etri9Xr14VLy8vpe99ajT7gXbs2FEpEJ8/f17rZVbqFx2m9uBdWv1AdUnrmC5Tpozs3r1bbt++Lc2aNRMnJycpUaKEVKpUKcXbdjWpxyDP6NvK1cf948ePpVChQlo3sLRujhk51kJCQsTBwUFOnDghCQkJMmnSpDRffqe+gAcEBGgNTRgZGalV06Revubxpq6BnD59utSoUUNrNBVd0gqS1A8nv3jxQoYMGSKVK1fWeoAzNjZWateurVUrpU+L7NWrV8XS0lKr/3p0dLS0a9dOrl27JgEBAeLl5aXUmK9du1aqVasmkyZNEnNzc2ndurVWdwxddO2f7777Tvr3768cI+obb//+/VO8WE+9vVKrjU8+Znt8fHyqLznUHE3PUOndD3755Rd58eKFnDlzRmbNmiWlS5eWmjVrpnhhnab169eLi4uLViFB17C7hgyFmVaNr/p3kZGRWoGZeh8sXbpUbG1ttZa/ceNGncf0mTNnxM3NTRloRCTpGr17927lLdfqQCgoKEirdTk0NFSaNGkiJUqUkNq1a0v58uXF2dk5xTNvZ8+elfLly0uFChVSVDDp+z6gd+/eSZUqVUSlUkmDBg1SHQTm1atXeo2YeP/+fendu7fWcNVNmjSRcuXKydChQ6V27drSrFmzdN/xoplu06ZNZeDAgXLgwAGpXr262NvbS//+/ZXnTDQf9I6KipISJUooNdv6rL8+PT5ev34tTk5OSsASHBwsI0eOTHWUvLTWp1mzZtKnTx/p0KFDiu7Rut5s7eHhISqVSqpUqZJm8PzFF18o71Xp1q2bMjDLvXv3pESJEhIaGipLly6VmjVryi+//CKhoaEyYsQIUalUUq9ePWWwlvQK4PoGR2rq69DYsWPF09Mz3QFu0qsw07z/Nm/eXBk5Sj3vyJEjpVy5csq+yc6X3+njvQ8s9CmEe3p6Kk104eHhmeobrm86w4cPl3/++Ue8vb1THf5QX8HBwWJiYqKMnqMubL5580Y2bdokpqamaRbw1IXwc+fOydSpU7VuKPfv3xdnZ+cU3XmS19iePXtWxo0bp/RbVz9AmZ6MXgyjo6OVFh5PT085evRoinzNmDFD8uXLJ0uXLpXIyEitkTZEkrrM6Lr5jB8/Xry9vZW8P3v2THbt2qXUKKUnvcLX9OnTpU6dOvLbb7/J+vXrRaVSSdOmTZVRf3r16pXmMz3J+4G2b99exowZIyJJ3Uzat28vIknNpra2tmJhYSGtW7fWeng6MxeO9I7pfv36iYeHh4wfP14iIiLk8ePHaQ67m9oY5BmlHgc9+UO8qdHnWFMHhZcvXxYXFxeZP3++lClTRipXrqxzCET18WZubp5irHN1Abdbt27i5+cnItoX7vHjx4uPj49Se/f06VNp1qyZXrWoaQVJixcvlosXL8rBgwfFx8dHmjdvrhTM7927J87OzinOm/TcunVLTExM5Ouvv9a6MWp2U2zSpIl06dJFXr16JX379hWVSqWzK1FqNPfPwYMHpXr16lK6dGmt80lt0qRJ4uPjo/MZD01Z/ULN9OhzP6hbt660bdtWFi5cKL6+vmlW/qiH3S1UqJByvGlei9UjyGhe7zPaBTKtYYr1Wd+4uDgZMGCAeHt7y9u3b9MsSB06dEhKly4tI0aMkBcvXqRooTl06JA0btxYSpQooVS8NW7cWCtIe/v2rTJ0r3ooXXVegoODlQqmevXqaV0r4uPjU1Qwiehujbx48aJYWFiIo6NjukMjiyS1FukzYuKPP/4obm5uyhDDwcHBMnnyZGnYsGGK5330sW3bNnFychJTU1MJDAzU2p5+fn7i6empdPt88uSJdO7cWa9rQUZa3n755Rdxc3OT169fy+TJk8XMzEz8/f11DtaSnh9++EFcXV1FpVKJra2tNGnSRBYsWCB///23VgWCurWnUKFCKYY+1yUsLEzat28vXbp0kZ9++kkcHBxk5syZsm/fPunWrZsysEebNm2kQ4cOyjb7448/ZOfOnXp379UnOFKLjo6W2NhY2bBhgzg7O2sNYZ6W9CrM1OWcNm3aKGUEkaRgt1mzZso7Pt5H731goZZeIdzMzCzdoeiMkY6pqanOZrrM+Pvvv6VmzZo6H0IVSTlSh5pmIVw9MpVmN593795Jq1atxM/PT2bPni337t3TujDExsYqJ9jr169l8eLFad4UU6PPxfDJkycSEREhvXr1EktLS1m1apXOi5S6Rr9o0aLSvn17uXz5stb6iiTdnIsUKaL0X1RP//fff6V+/fry0UcfaTVP6iu9wpe6dq1r164SEREha9askVGjRknv3r11FsT16Qdat25dOXHihBw6dEiKFSsmjRs3lvz588usWbNk8ODB0qhRo3RHXNGXPueOehQvXfQZgzz5PtXs/pKYmCh//vmnbN26VZo2bSqFCxfO8PGW3rFWu3ZtefXqlTLUYvHixdMcCjO1FiTNZ4BmzZolHh4eyjGlXifN401940qrEKYprSCpdu3aUqhQIQkMDJSzZ89KsWLFpEqVKtK7d28pWbKkNGvWTF68eJHhm7z6XFSfN+rfq9dn+PDh4uLiIkFBQbJr164Ub5LVR/L9k7w2Wb1dv//++1Rf4ql+oaa6lTW9F2q2bt1a6tSpo9Q+BgUFScGCBZUg0xjSO3csLCyUrpG6JCQkSGBgoKhUKhkzZkyK9VbvC81hd5O3DujTBTKtYYqTP6CbnPrY3bVrl1SrVk0ZZCMtvXv3lhEjRih///XXXylq6B88eCAtWrSQcuXKyZw5c2TBggXi7u6utPTpyk9MTEymK5hEku5tye/PixcvTnd91NIbMVFdwHz58qWMHz9eqlWrpvVsZUZbcTXz/dFHH0mDBg2Ua5u6smrKlCliY2OT6bdo69sTY9SoUZIvXz4pXry4ODk5pXlP0Gd9OnfuLLVr15bAwEDp37+/lCtXTiwtLaVs2bLSsWNHmTVrlly5ciXN57J0+eGHH8TT01P27Nkj58+fl44dO4qvr68ULFhQKaRv2bJF3NzctF6MmVH6BEdPnz6VUaNGSe3ataVw4cJ6nTtqad0LPD09pUCBAjJnzhzZsWOH5M2bV5o2bSqjR4+WcuXKibe3d5ojS+W0XBNYZLYQ/r6mo1auXDllHPb0+syrJR9eTX2zS0xMlGvXromZmZl4eXnJ9OnTpVq1auLj46P0kY2OjpapU6eKs7Oz8lBmZpvR9LkYqsf9Vz+QrqtWSb3eQ4YMUWr0NWt91N8vWbJE6UaR/HmG+fPny5AhQ/Rufk4uvcLXsGHDpHLlymk+pyGifz/Q+vXrS79+/eTMmTNSrVo16dKli3KDUrc2DRs2TOcDfxllyDGd0THIdUlISJAVK1aIj4+P9O3bN1PnkL433t9++y3V9VTT1YKkebypj9Fly5ZJqVKllC5Jmt8tWLAg08dbWkGSr6+v1K5dW6Kjo+Xy5cvyzTffSN++fVMdxUwfv/zyi5iYmMj06dOV55PU63H79m3p0KGD1jMMmaFr/+g61y9duqT18juR7HuhZmakd+6k1TqyceNGefLkiQwZMkScnJyU4279+vWybNkyrVp6kaSRsOrVq6ecU/oMhZneMMU1a9bUatVJfo+5ffu2jBw5Upo1ayaWlpapFsI18/Ly5UupX7++7N69W44cOSKurq7i4uIi9vb2MmHCBK2BP2JjY2XChAlia2srVlZW4uXlpXU+Jad5b9O3gkkzLfW9LXlrqD4vvVNLbcTEUaNGSY0aNZRz6OjRo+Li4qJ3l6T0nDt3Try8vLTOj/DwcGnZsqUMGTJE78oLTfp2u+7fv7907dpVihcvbtC1RtPZs2fFy8tLKd8kJCTInTt3ZOHChdKmTRtp27Ztpq6f6qClRYsWEh4eLo8fP1ZGadKsFPnkk0+U7qmZKeOkFRyVKVNGunTpIjNmzJC+fftmKHjVlNa9oH79+uLj4yPPnj2TrVu3yqBBg6RNmzYZCl5ySq4JLEQyVwh/n9MRSbr46xrjOTXpFYpev34tp06d0nooskmTJlrvN9i6dasMGTIkU7X7yaV3MdR1g9enRr9+/fopbrCdOnXSeqHTlStX0hxWMSP0KXyl9mbd5PTpB/rNN9+Ij4+PMoKKugZEvd/2799vlGGF1TJ7TGd2DPJnz57JgAEDlEDk+fPnSpCZWWkda4MGDUqRBzV9jzf1cLfqG/jt27fFwsJCGSows13SkksvSLK2tjZ4WyU3evRoMTU1lTZt2sjevXtl3759MnHiRClYsKDWiyYNod4/ybsNpCerX6hpqIyeO+phdwsXLixhYWFy5swZadSokdSrV088PT2levXqUqdOHVGpVNKhQwflodA7d+4ow+6qj4vU1icjwxR/+umnKfav5ns2BgwYICNHjtQZJF29elV69OghgwcPVlqCYmNjpWjRovLdd99Jt27dZMGCBXLx4kVZtmyZlChRQiZNmqTVh13z3TTVq1dPtWIhvXtbWhVMalu2bNG6tyUfPlafipr0RkxUvyAvPj5eGWnKGBITE2XEiBHSsGFDuXXrlhw8eFCcnJykZs2aBqeTXstbgQIFMl04To16ffz8/HQOsmGIc+fOiaenp3KtSUxMVAZeUe+vzLYeaUorOGrZsqV07do10y1JIjlzL8gOuSqwyGgh/H1PRyTpwc9FixalWmDJbCFc8/8eHh7StWvXLMl/Ri+GGa3RV9+gnj9/Ls7OznLs2DF59OiRMlqNujBoDMYqfOnbD7Rt27bSqVMn5UaV/I2+xqTvMW2sMchv3rwp7u7u0qlTJyPkXpT8ZPTGm9njTSSpIFu5cuUsqSHKitrJ9MybN08qV64sRYoUkerVq4u7u3umBrlIjXr/NGjQQBkWN7WCeFa9UDMr6HvuJB92V30ux8fHy/z586VSpUoyefJkefnypURGRsrly5elVq1aMnDgQKUSYeHChVKtWjWdA1uIGD5MsUhSwb1du3bKea6r8icyMlI6d+4sefPmlT59+kiPHj1EpVIpzxX26tVL8uTJIx4eHlqF8KlTp0qtWrV0VvgkH4EqowF/WhVM6RW8Z82aJRUrVtT5vFV6MjJioqGCg4PF29tbLCwsxMLCQmsEQUOk1/Km7+iRGfXo0SOpW7eu9OzZU2u6oRU0mtcaXa1axpKVwZFaevcCQ4aYzim5KrBIrxCe29IRSftEMKRQpHbq1CmpV69elr59MaMXQ31Hdqhdu7bSLHvixAmxtbWVQYMGibm5ubRq1cooLS7JGavwpW8/0Jo1a2rdJLOKPse0McYg13wRnDFr89Qyc+PNyPGmftGhmrm5eYb7AOsjK2sn0xIRESGvXr3S6+3pmZFaQUItq16omZX0OXfSG3b3yZMnsmPHjhSVE+r3qqhH6Ek+7K6aocMUa3Zv2bx5szg6Oiotjcnt3btXChQoIPXq1VMqPWJjY6Vv375KS8HevXvFzs5OWrVqpRWYPH/+XExMTNJ8DjEuLi7LKpiSD1v+/Plz6devn9SoUUMKFCgg3bp1y1DrXGZGTDTUkiVLZMKECUbpBqspO3tiaPryyy/lq6++Mnp5Kjg4OM1rjbFkVXCkuZycuBdkpVwVWGRVbW5OpaOPjBSK1Dfiq1evyrVr12T8+PFSuHBh+eyzz4zW5zg1GbkYZqRGv2PHjvLs2TNZvny5qFQq8fLyyvDIOBlljMJXZvqBZiV9j2ljjEGe1khmxpDRG29GW5DUhbzo6GiZPXt2msOHGiKraidzmq6CRHa9UDMr6Hvu6Brl5dtvv5X+/funGGJTfU5t3LhRGbde/X3yGkpjD1OcnsOHD0vlypVTDHfdqVMnWbhwobx69UrevHkjI0aMkPz582sNt3706FGpUqWK1lC2qcnKCqbHjx9LRESEPH36VD7//HM5ePCgHDp0SFQqlezcuTPVdw1pyuyIiYbKqgrN7OyJoSkrK2izKmjJ7nQ+tHtBrgos/osyWggPDQ2VTZs2iaurq3h6embohUqGyMxwdPrU6FevXl3mzp0rb968kQ0bNmRF1rNMdvUDNaasHIPcWDJzcX/fWpDUsqp2Micl3z9Z8ULN91FGht0VSQpcu3XrJoMHD0532cYcpji98ycxMVGGDh0qjRo1krCwMHn69Kk0bNhQGWY6f/78smjRIrl+/brUr19fqlSpIhMnTlSGK+3Ro4deXfmyooJJPWKipaWl0u1H830Nbdq0EXd3d6UlJjljjZj4PsrOnhjZJbvWJTvS+ZDuBQwscoGMFMJnz54tiYmJeo/XnFMyUqNvrFEqslt29QM1tqwagzwnZddIIhn1Id3k05JVL9R836Q37G5UVJScOnVKDh06JF5eXuLq6prmu2PUsnuY4nv37km9evXE1dVVChYsKH369JFbt27Jo0eP5LPPPhMrKyv57bffJDQ0VEaNGiV169aVypUry4QJEzK0vQypYEpv2HLNERPVx9aTJ0/ExMREFi1alKJSR3NUKUNHTHwfve/3nf+6D+lYY2CRC2SkUJQbhiJTy401+hmVXf1AjSkrxyDPSf+F4+19ZewXar6v0ht2NywsTJo2bSoODg7y2WefZWjZ2T1M8bx586REiRLKaEGa/fJNTU1lzpw5yt+RkZEpRoLSR2YrmDIzbLk6uBg/fryUKFFCq/+6enmbN2822oiJRP9VDCxyiQ+xUJRba/QzKrv6gRpTVo1BnpP+K8fb+yqzL9TMbVIbdld9nF24cCHdN4/rkt1DU75+/VqaNm0qvXv31mpNunLlijg6OsoPP/ygTNN8709Gr3OZvbdldMREze5ZxYoVkxEjRkhYWJjs27dP58h2RJQ5DCxyiQ+1UJQba/QzKjcFFGrZMcxeTvgvHG/vq8y8UDM3ysiwuxmV3cMUb9u2TWrXrq10dTx37pzUqVNHGjRooDx0b6jM3NsyO6qUOkDZvHmzmJqaKm8013zJIhEZJg8oV1CpVBg9ejTi4uKwaNEiZXqePLl7Fzo4OKBdu3Zwd3eHiOR0drKESqXK6SxkmEqlwpgxYxAfH4/FixdrfZeb99N/4Xh7X+XNmxdjxoxBdHQ0vvvuOwCApaUlIiIicPnyZXTr1g0FCxYEADRq1Cgns2oQ9bkTFxeHhQsXAgBMTEyMsmwPDw/Url0bR44cwe3bt3Ho0CG4ubnh6dOnGDp0KExNTY2Sjlr79u1Rvnx5rF+/Hu3atYOXlxfc3d1x5MgRlChRwihpZObeVqhQIfTq1QsNGzaElZUVFi5ciNmzZwMAOnXqhLNnz8LS0hKdO3fGX3/9hR9//BEAYGZmhkePHuHPP/9EQkICatasiadPn6J///5GWRciAox7FaIspS4U5c2bFyKSKwusuowaNeqDWZcPSalSpXQeb7l9X/F4yzmaBeNOnTrh0aNHGDRoEGxsbDB37lyYm5vndBaNIrVzx1DqQnjXrl1Ro0YNAEBAQADGjx9vlOUnlzdvXgwfPhwdO3aEq6sr7t69CycnJwBAQkKC0QKmzNzbhgwZggsXLqBo0aL46KOPMGLECFhYWMDDwwN+fn4IDg5G165dcfDgQfz2229o0KABSpcujVmzZmH37t04efIkfHx8jJJ/IvoflbDaLlf5kAIKev/xeCNje/ToEbp27YpLly4ByNqCcU7KynPnq6++QkhICKZPnw4LC4ssSUNNRBAUFKQVUOTJk8fo65aZ7bV161YsWbIE06ZNQ/HixTF37lyEhobi8uXL2LVrFxo0aIAffvgBX375JVq0aIFZs2YhJCQEdnZ2Rs07Ef0PAwsiIspW2Vkw/hDlVMBvzFYKY4iLi8PHH3+MN2/e4IcffsCbN28wbdo0rFmzBps3b0a3bt0AAH369IGfnx969+6dwzkm+vAxsCAiomzFljAylj///BOfffYZ2rRpg0mTJkFEcP/+fZQrV045zmJjY2FmZpbTWSX6T8jdT/4SEVGuw6CCjMXDwwN16tTBoUOHcOXKFahUKpQrVw6JiYnKccaggij7MLAgIiKiXOlDHTGRKLfiqFBERESUa32oIyYS5UZ8xoKIiIhyNQYURO8HthUSERFRrsagguj9wMCCiIiIiIgMxsCCiIiIiIgMxsCCiIiIiIgMxsCCiIiIiIgMxsCCiIiIiIgMxsCCiIiIiIgMxsCCiIiIiIgMxsCCiOg9ERISgk8//RROTk4wNzeHg4MDWrdujcOHDyvzlC1bFkuWLEl3WVu2bIGJiQkGDx6s8/sVK1agevXqyJ8/P2xsbODm5oZ58+Yp3wcEBEClUqX4VKpUKdU0161bp/M3FhYWyjzPnz/HoEGDULp0aZibm8POzg5NmzbF2bNntdZR/VtLS0u4urpixYoVGUonve157NgxncvQ/Kxbty7FfEWKFEHDhg1x+vRpndtg4MCBMDExwdatW1N8FxAQgBo1aqS6/YiIcjvTnM4AEREBDx48gI+PD2xsbDB//nxUq1YNcXFx+P333zFs2DDcunUrQ8tbs2YNxo0bh2XLlmHRokWwtLRUvlu9ejVGjRqFr7/+Gr6+voiJicG1a9dw8+ZNrWW4uLjg0KFDWtNMTdO+bRQsWBC3b9/Wmqb58rKOHTsiLi4O69evh5OTE549e4bDhw8jLCxM6zczZ87EgAED8ObNG6xbtw6DBw+GjY0NunTpolc66W3Pa9eu4enTp8r8I0aMQEREBNauXatMs7a2xrlz5wAAt2/fRsGCBfHixQvMnj0bLVu2xJ07d1C8eHFl/nfv3mHbtm0YO3YsVq9eja5du6a5rYiIPjQMLIiI3gNDhw6FSqXCn3/+ifz58yvTXVxc0Ldv3wwt68GDBzhz5gx27tyJo0eP4scff0SvXr2U73/99Vd07twZ/fr100onOVNTU9jZ2WUobZVKlepvXr9+jVOnTuHYsWPw9fUFAJQpUwa1a9dOMa+VlZWynNmzZ2P79u3YtWuXEliklQ6Q/vY0MzPT+n2+fPkQExOT6jKLFy8OGxsb2NnZYcqUKdi+fTvOnTuH1q1bK/Ps2LEDVapUwcSJE1GiRAk8ePAAZcuWTTWPREQfGnaFIiLKYWFhYdi/fz+GDRumVQhWs7GxydDy1qxZg5YtW8La2hoff/wxVq9erfW9nZ0d/vjjDzx8+NCQbGdYgQIFUKBAAezatQsxMTEZ+q2FhQXi4uL0mtfY21PTu3fvlFaNvHnzan23evVqfPzxx7C2tkaLFi20Wj+IiP4LGFgQEeWwe/fuQUTSfH5BX4mJiVi3bh0+/vhjAEDXrl1x9uxZ3Lt3T5ln+vTpsLGxQdmyZVGxYkV88skn2L59OxITE7WW9ddffynBgPrTv3//NNMPDw9P8Rt/f38ASS0g69atw/r162FjYwMfHx9MmjQJ165dS3V58fHxWLduHf766y80atRIr3SMuT3VSpUqpaSzePFi1KpVSys/d+/exR9//KG0qHz88cdYu3Ztim1KRPQhY1coIqIcJiIAtJ8RyKwDBw7g7du3aN68OQCgaNGi8Pf3x5o1axAYGAgAKFGiBM6ePYvr16/j+PHjOHPmDHr37o1Vq1Zh//79yJMnqc6pYsWK+OWXX7SWb2VllWb6VlZWuHTpkta0fPnyKf/v2LEjWrZsiZMnT+Ls2bPYv38/5s+fj1WrVuGTTz5R5hs/fjymTJmCmJgYmJmZYezYsRg0aJBe6Rhze6qdPHkS+fPnx+XLlzF+/HisW7dOq8Vi9erVaNq0KYoWLQoAaNGiBfr164dDhw4pAQ8R0YeOgQURUQ5zdnaGSqXC33//jXbt2hm0rDVr1iAsLEzrYe3ExERcvnwZs2bNgomJiTLd1dUVrq6uGDZsGE6dOoV69erh+PHjaNCgAQDAzMwM5cuXz1D6efLkSfc3FhYWaNKkCZo0aYJp06ahf//+mD59ulZgMXbsWHzyySewtLREiRIlUgQJaaVjzO2p5ujoCBsbG1SoUAHR0dFo3749rl+/DnNzcyQkJGDDhg0ICQnRerg9ISEBq1evZmBBRP8Z7ApFRJTDChcujP9r5/5dGgnCMI4/m8ZGC0mCGlQQrCJERFBiI4hgYUAUgpVNQMTKgJ2NILGRgIWYrTYWigg2WqiFCP4BomlEECxltVgUf6BgMFeIgWUvh7m94zz4fmCbZN6Z7LvVw2R2aGhIq6uren5+9nx/f3//pXkcx9Hu7q62trZUKBRc19PTkw4ODirWRqNRSfrp+n9bNBr1rBsKhdTe3q5IJFL1zsOf6mclExMTen9/Vy6XkyTt7+/r8fFRZ2dnrp5vb29rZ2dHjuP4Wg8A/hfsWADAN5DL5dTX16eenh4tLCwoFoupWCzq8PBQpmnq4uKiPPb6+lqFQsFV39raqvX1dQWDQSWTyfLfmT4lEglZlqVEIqHp6WlFIhENDAyoublZtm0rk8koHA4rHo+Xa4rFom5ublzzGIahhoaGivdRKpU8NdLHW5Xu7u6UTCaVSqUUi8VUV1enk5MTLS0taWRkpJp2/XKdQCBQVT+rFQgElE6nlclkNDU1JcuyNDw8rM7OTte4jo4OpdNpbWxsaGZmRpL08vLieXa1tbVV7wwBwHdEsACAb6CtrU2np6daXFzU7OysbNtWOBxWd3e3TNN0jc1ms8pms67P1tbWlM/nNTo66gkV0sfZhvHxcd3e3mpwcFD5fF6macpxHIVCIcXjcR0dHSkYDJZrzs/P1dTU5JqnpqZGr6+vFe/j4eHBUyNJtm2rvr5evb29Wl5e1tXVld7e3tTS0qLJyUnNzc19qU9fWaexsbGqfv6OVCql+fl5raysaG9vT5ubm54xhmFobGxMlmWVg8Xl5aW6urpc4/r7+3V8fOz7NwHAv2aUPk+5AQAAAMBv4owFAAAAAN8IFgAAAAB8I1gAAAAA8I1gAQAAAMA3ggUAAAAA3wgWAAAAAHwjWAAAAADwjWABAAAAwDeCBQAAAADfCBYAAAAAfCNYAAAAAPCNYAEAAADAtx+ZBD8eGTZNYAAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -800,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "id": "code-05a", "metadata": {}, "outputs": [ @@ -886,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 37, "id": "4j2wkkt78ju", "metadata": {}, "outputs": [ @@ -980,13 +980,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 38, "id": "code-05b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1042,13 +1042,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 39, "id": "code-06", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -1119,7 +1119,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 40, "id": "code-07a", "metadata": {}, "outputs": [ @@ -1176,7 +1176,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 41, "id": "code-07b", "metadata": {}, "outputs": [ @@ -1285,7 +1285,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "id": "code-08", "metadata": {}, "outputs": [ From c01a54f9668c7f600c49f7301aa56ed4b01c6f93 Mon Sep 17 00:00:00 2001 From: Alicia Tomas Date: Fri, 8 May 2026 09:55:51 -0600 Subject: [PATCH 27/28] actilizar tareas --- .../analisis_estrellas_estudiante.ipynb | 1366 +++++++++++++++++ tareas/practicados/pyproject.toml | 20 + tareas/practicados/star_dataset.csv | 1001 ++++++++++++ 3 files changed, 2387 insertions(+) create mode 100644 tareas/practicados/analisis_estrellas_estudiante.ipynb create mode 100644 tareas/practicados/pyproject.toml create mode 100644 tareas/practicados/star_dataset.csv diff --git a/ tareas/practicados/analisis_estrellas_estudiante.ipynb b/ tareas/practicados/analisis_estrellas_estudiante.ipynb new file mode 100644 index 0000000..7843a3c --- /dev/null +++ b/ tareas/practicados/analisis_estrellas_estudiante.ipynb @@ -0,0 +1,1366 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "md-00", + "metadata": {}, + "source": [ + "# Práctica 2 — Análisis Exploratorio de Datos\n", + "\n", + "En este notebook realizarás un **análisis exploratorio de datos (EDA)** sobre un\n", + "dataset de 240 estrellas clasificadas en 6 tipos.\n", + "\n", + "**Dataset:** [Stars Dataset — Kaggle](https://www.kaggle.com/datasets/waqi786/stars-dataset)\n", + "\n", + "## Instrucciones generales\n", + "- Cada sección tiene celdas marcadas con `# tu código aquí` — ahí debes escribir tu solución.\n", + "- Lee las instrucciones en cada celda de markdown **antes** de escribir el código.\n", + "- Consulta los enlaces a la documentación oficial para entender los parámetros de cada función.\n", + "- Ejecuta el notebook completo **sin errores** antes de hacer commit (`Kernel → Restart & Run All`).\n", + "\n", + "## Contenido\n", + "1. [Importar librerías](#1.-Importar-librerías)\n", + "2. [Cargar los datos](#2.-Cargar-los-datos)\n", + "3. [Exploración inicial](#3.-Exploración-inicial)\n", + "4. [Distribución por tipo de estrella](#4.-Distribución-por-tipo-de-estrella)\n", + "5. [Temperatura por tipo](#5.-Temperatura-por-tipo-de-estrella)\n", + "6. [Luminosidad vs Temperatura](#6.-Luminosidad-vs-Temperatura)\n", + "7. [Estadísticas con NumPy](#7.-Estadísticas-con-NumPy)\n", + "8. [Diagrama Hertzsprung-Russell](#8.-Diagrama-Hertzsprung-Russell)" + ] + }, + { + "cell_type": "markdown", + "id": "md-setup", + "metadata": {}, + "source": [ + "---\n", + "## Configuración del ambiente\n", + "\n", + "Este proyecto usa **Poetry** para gestionar las dependencias. Antes de abrir el notebook\n", + "ejecuta estos comandos **desde la carpeta `practica2_analisis_datos/`**:\n", + "\n", + "```bash\n", + "poetry install\n", + "poetry run jupyter notebook\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "md-01-inst", + "metadata": {}, + "source": [ + "---\n", + "## 1. Importar librerías\n", + "\n", + "Importa las cuatro librerías con sus **alias convencionales**.\n", + "Estos alias son estándares en la comunidad — siempre se usan así:\n", + "\n", + "| Librería | Alias | ¿Para qué sirve? | Documentación |\n", + "|---|---|---|---|\n", + "| `numpy` | `np` | Operaciones matemáticas vectorizadas sobre arrays | [numpy.org/doc/stable](https://numpy.org/doc/stable/user/whatisnumpy.html) |\n", + "| `pandas` | `pd` | Análisis y manipulación de datos tabulares (DataFrames) | [pandas.pydata.org/docs](https://pandas.pydata.org/docs/getting_started/index.html) |\n", + "| `matplotlib.pyplot` | `plt` | Visualización de datos (gráficas de bajo nivel) | [matplotlib.org/tutorials](https://matplotlib.org/stable/tutorials/index.html) |\n", + "| `seaborn` | `sns` | Visualización estadística de alto nivel (sobre matplotlib) | [seaborn.pydata.org/tutorial](https://seaborn.pydata.org/tutorial.html) |\n", + "\n", + "**Sintaxis:** `import librería as alias`" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "code-01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "numpy 1.26.4\n", + "pandas 2.1.4\n", + "seaborn 0.12.2\n", + "Matplotlib versión: 3.8.0\n" + ] + } + ], + "source": [ + "# Importa las cuatro librerías con sus alias convencionales\n", + "# tu código aquí\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", + "print(f'numpy {np.__version__}')\n", + "print(f'pandas {pd.__version__}')\n", + "print(f'seaborn {sns.__version__}')\n", + "print(\"Matplotlib versión:\", __import__('matplotlib').__version__)\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-02-inst", + "metadata": {}, + "source": [ + "---\n", + "## 2. Cargar los datos\n", + "\n", + "Usa [`pd.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)\n", + "para leer el archivo CSV y cargarlo en un **DataFrame** de pandas.\n", + "\n", + "- Revisa la documentación: ¿qué parámetro recibe `read_csv`? ¿qué devuelve?\n", + "- El archivo se encuentra en `'../data/star_dataset.csv'` (relativo a la carpeta `notebooks/`)\n", + "- Guarda el resultado en una variable llamada `stars`\n", + "- Después usa [`.head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)\n", + " para mostrar las primeras 5 filas" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "code-02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------------------------------------\n", + " CONTENIDO INICIAL DEL DATASET:\n" + ] + }, + { + "data": { + "text/html": [ + "
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NameDistance (ly)Luminosity (L/Lo)Radius (R/Ro)Temperature (K)Spectral Class
0Altair16.5941719.9791921.6326507509.294247A7V
1Deneb2600.490723196002.627856202.9705268503.284796A2Ia
2Barnard's Star6.0526164.8937160.2227113165.959639M4Ve
3Polaris322.6010022196.24193437.5468136048.326915F7Ib
4Barnard's Star5.902392-1.4964860.1923593130.602069M4Ve
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" + ], + "text/plain": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "0 Altair 16.594171 9.979192 1.632650 \n", + "1 Deneb 2600.490723 196002.627856 202.970526 \n", + "2 Barnard's Star 6.052616 4.893716 0.222711 \n", + "3 Polaris 322.601002 2196.241934 37.546813 \n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "\n", + " Temperature (K) Spectral Class \n", + "0 7509.294247 A7V \n", + "1 8503.284796 A2Ia \n", + "2 3165.959639 M4Ve \n", + "3 6048.326915 F7Ib \n", + "4 3130.602069 M4Ve " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------------------------------------\n", + " INFORMACIÓN GENERAL DE CADA ESTRELLA:\n", + " \n", + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "0 Altair 16.594171 9.979192 1.632650 \n", + "1 Deneb 2600.490723 196002.627856 202.970526 \n", + "2 Barnard's Star 6.052616 4.893716 0.222711 \n", + "3 Polaris 322.601002 2196.241934 37.546813 \n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "\n", + " Temperature (K) Spectral Class \n", + "0 7509.294247 A7V \n", + "1 8503.284796 A2Ia \n", + "2 3165.959639 M4Ve \n", + "3 6048.326915 F7Ib \n", + "4 3130.602069 M4Ve \n", + "------------------------------------------------------------------------------------------------------------------------\n", + " DIMENSIONES (FILAS , COLUMNAS): (1000, 6)\n", + " TIPO DE DATOS:\n", + "Name object\n", + "Distance (ly) float64\n", + "Luminosity (L/Lo) float64\n", + "Radius (R/Ro) float64\n", + "Temperature (K) float64\n", + "Spectral Class object\n", + "dtype: object\n", + "------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"CONTENIDO INICIAL DEL DATASET:\" )\n", + "display(stars.head())\n", + "\n", + "# Muestra las primeras 5 filas del DataFrame\n", + "# tu código aquí\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"INFORMACIÓN GENERAL DE CADA ESTRELLA:\" )\n", + "print( \" \" * 40)\n", + "print(stars.head())\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"DIMENSIONES (FILAS , COLUMNAS):\", stars.shape)\n", + "print(\" \" * 1 +\"TIPO DE DATOS:\")\n", + "print(stars.dtypes)\n", + "print( \"---\" * 40)" + ] + }, + { + "cell_type": "markdown", + "id": "md-03-inst", + "metadata": {}, + "source": [ + "---\n", + "## 3. Exploración inicial\n", + "\n", + "Antes de analizar datos siempre hay que entender qué tenemos.\n", + "\n", + "**Celda 3a** — Imprime la información básica del DataFrame:\n", + "1. **Dimensiones** con [`.shape`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shape.html)\n", + " — devuelve una tupla `(filas, columnas)`\n", + "2. **Nombres de columnas** con [`.columns.tolist()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html)\n", + "3. **Tipos de datos** con [`.dtypes`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html)\n", + " — indica si cada columna es `int64`, `float64` o `object` (texto)\n", + "\n", + "**Celda 3b** — Obtén estadísticas descriptivas con\n", + "[`.describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html).\n", + "Fíjate en la media, desviación estándar, mínimo y máximo de cada columna numérica.\n", + "\n", + "**Celda 3c** — Verifica si hay valores nulos con\n", + "[`.isnull()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html)\n", + "seguido de `.sum()`. En un dataset limpio todos los valores deben ser 0." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "code-03a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................................................................................................................\n", + " DIMENSIONES (COLUMNAS, FILAS):\n", + "\n", + "(1000, 6)\n", + "........................................................................................................................\n", + " \n", + " NOMBRE DE LAS COLUMNAS:\n", + "\n", + "['Name', 'Distance (ly)', 'Luminosity (L/Lo)', 'Radius (R/Ro)', 'Temperature (K)', 'Spectral Class']\n", + " \n", + "........................................................................................................................\n", + "\n", + " TIPO DE DATOS:\n", + "\n", + "Name object\n", + "Distance (ly) float64\n", + "Luminosity (L/Lo) float64\n", + "Radius (R/Ro) float64\n", + "Temperature (K) float64\n", + "Spectral Class object\n", + "dtype: object\n", + "........................................................................................................................\n" + ] + } + ], + "source": [ + "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", + "# tu código aquí\n", + "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "print( \"...\" * 40)\n", + "print( \" \" * 15 +\"DIMENSIONES (COLUMNAS, FILAS):\\n\" )\n", + "print(stars.shape)\n", + "print( \"...\" * 40)\n", + "print( \" \" * 40)\n", + "print( \" \" * 15 +\"NOMBRE DE LAS COLUMNAS:\\n\")\n", + "print(stars.columns.tolist())\n", + "print( \" \" * 40)\n", + "print( \"...\" * 40)\n", + "print(\"\\n \" + \" \" * 15 +\" TIPO DE DATOS:\\n\")\n", + "print(stars.dtypes)\n", + "print(\"...\" * 40)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "code-03b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " ESTADÍSTICA DESCRIPTIVA DE LA DATASET: ESTRELLAS \n", + " \n", + " Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + " NÚMERO TOTAL DE REGISTROS 1000.000000 1000.000000 1000.000000 \n", + "PROMEDIO 295.505327 19644.909442 86.960696 \n", + "DESVIACIÓN ESTÁNDAR 541.478403 42223.595017 213.850005 \n", + "VALOR MÍNIMO 3.877798 -4.993141 0.068087 \n", + "CUARTIL 1 (25%) 11.716853 10.441039 1.664479 \n", + "MEDIANA (50%) 52.031435 171.097809 5.845444 \n", + "CUARTIL 3 (75%) 322.865874 10500.577117 33.719778 \n", + "VALOR MÁXIMO 2600.490723 196004.854081 887.097936 \n", + "\n", + " Temperature (K) \n", + " NÚMERO TOTAL DE REGISTROS 1000.000000 \n", + "PROMEDIO 9983.486779 \n", + "DESVIACIÓN ESTÁNDAR 7906.973529 \n", + "VALOR MÍNIMO 2750.183163 \n", + "CUARTIL 1 (25%) 3940.020856 \n", + "MEDIANA (50%) 7379.007975 \n", + "CUARTIL 3 (75%) 12055.975095 \n", + "VALOR MÁXIMO 28044.279272 \n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " LISTA DE DATOS EXTRAÑOS \n", + " \n", + " Total de Datos Extraños por ser Negativos: 88 \n" + ] + }, + { + "data": { + "text/html": [ + "
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NameDistance (ly)Luminosity (L/Lo)Radius (R/Ro)Temperature (K)Spectral Class
4Barnard's Star5.902392-1.4964860.1923593130.602069M4Ve
28Alpha Centauri B3.925939-2.5124240.8971565292.710092K1V
29Ross 1549.584936-0.2622130.0981502788.607876M3.5V
38Barnard's Star6.274309-4.6125040.2714203164.713490M4Ve
49Barnard's Star5.563557-3.4589550.1495363115.066681M4Ve
.....................
941Wolf 3597.509381-4.9350530.2081092841.890593M6V
944Rigil Kentaurus4.770200-1.1625151.2118415768.601304G2V
994Barnard's Star5.938952-3.1722540.2666883121.980225M4Ve
995Wolf 3597.455715-4.4351010.0680872774.148300M6V
998Alpha Centauri B4.044364-4.5490880.9391915286.304304K1V
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" + ], + "text/plain": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "28 Alpha Centauri B 3.925939 -2.512424 0.897156 \n", + "29 Ross 154 9.584936 -0.262213 0.098150 \n", + "38 Barnard's Star 6.274309 -4.612504 0.271420 \n", + "49 Barnard's Star 5.563557 -3.458955 0.149536 \n", + ".. ... ... ... ... \n", + "941 Wolf 359 7.509381 -4.935053 0.208109 \n", + "944 Rigil Kentaurus 4.770200 -1.162515 1.211841 \n", + "994 Barnard's Star 5.938952 -3.172254 0.266688 \n", + "995 Wolf 359 7.455715 -4.435101 0.068087 \n", + "998 Alpha Centauri B 4.044364 -4.549088 0.939191 \n", + "\n", + " Temperature (K) Spectral Class \n", + "4 3130.602069 M4Ve \n", + "28 5292.710092 K1V \n", + "29 2788.607876 M3.5V \n", + "38 3164.713490 M4Ve \n", + "49 3115.066681 M4Ve \n", + ".. ... ... \n", + "941 2841.890593 M6V \n", + "944 5768.601304 G2V \n", + "994 3121.980225 M4Ve \n", + "995 2774.148300 M6V \n", + "998 5286.304304 K1V \n", + "\n", + "[88 rows x 6 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Obtén el resumen estadístico de las columnas numéricas\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "resumen = stars.describe()\n", + "resumen = resumen.rename(index={\n", + " \"count\" : \" NÚMERO TOTAL DE REGISTROS\",\n", + " \"mean\" : \"PROMEDIO\", \n", + " \"std\" : \"DESVIACIÓN ESTÁNDAR\",\n", + " \"min\" : \"VALOR MÍNIMO\",\n", + " \"max\" : \"VALOR MÁXIMO\", \n", + " \"25%\" : \"CUARTIL 1 (25%)\", \n", + " \"50%\" : \"MEDIANA (50%)\",\n", + " \"75%\" : \"CUARTIL 3 (75%)\",\n", + " \"mod\" : \"MODA\", \n", + "})\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"ESTADÍSTICA DESCRIPTIVA DE LA DATASET: ESTRELLAS \\n \")\n", + "print(resumen)\n", + "\n", + "\n", + "print( \"----\" * 40)\n", + "a = stars[stars['Luminosity (L/Lo)'] < 0] # explica por que en el valor mínimo se obtuvo un valor negativo\n", + "\n", + "print(\" \" * 15 + \"LISTA DE DATOS EXTRAÑOS \\n \")\n", + "print(f\" Total de Datos Extraños por ser Negativos: {len(a)} \")\n", + "display(a)\n", + "print( \"----\" * 40)\n" + ] + }, + { + "cell_type": "markdown", + "id": "217f2f7b", + "metadata": {}, + "source": [ + "En términos físicos, la luminosidad mide la cantidad de energía que emite una estrella por segundo. Al ser una medida de energía emitida, no puede ser negativa. Por eso se adjunta " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "code-03c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name 0\n", + "Distance (ly) 0\n", + "Luminosity (L/Lo) 0\n", + "Radius (R/Ro) 0\n", + "Temperature (K) 0\n", + "Spectral Class 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Cuenta los valores nulos por columna\n", + "# tu código aquí\n", + "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "a = stars.isnull().sum()\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "id": "md-04-inst", + "metadata": {}, + "source": [ + "---\n", + "## 4. Distribución por tipo de estrella\n", + "\n", + "**Celda 4a** — Usa\n", + "[`.value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html)\n", + "sobre la columna `'Spectral Class'` para contar cuántas estrellas hay de cada tipo.\n", + "Guarda el resultado en una variable llamada `conteo`.\n", + "\n", + "> Pista: accede a una columna del DataFrame con `df['nombre_columna']`, que devuelve una **Serie**.\n", + "> `.value_counts()` es un método de Series.\n", + "\n", + "**Celda 4b** — Crea una **gráfica de barras** de `conteo`:\n", + "1. La línea `plt.figure(figsize=(8, 4))` ya está incluida — no la borres\n", + "2. Llama [`.plot(kind='bar')`](https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.html)\n", + " sobre `conteo`, usando `color='steelblue'` y `edgecolor='black'`\n", + "3. Agrega título con `plt.title(...)`, etiquetas con `plt.xlabel(...)` y `plt.ylabel(...)`\n", + "4. Rota las etiquetas del eje X: `plt.xticks(rotation=30, ha='right')`" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "code-04a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectral Class\n", + "A7V 74\n", + "A1V 73\n", + "A9II 48\n", + "M3.5V 45\n", + "B1III 45\n", + "M2Iab 44\n", + "G8III 39\n", + "M4Ve 38\n", + "A0V 38\n", + "K1.5III 38\n", + "B2III 37\n", + "M7IIIe 37\n", + "B0Ia 36\n", + "G2V 36\n", + "F7Ib 35\n", + "B1III-IV 32\n", + "A3V 31\n", + "F5IV-V 30\n", + "B0.5IV 30\n", + "B6Vep 29\n", + "M2.1V 27\n", + "M1.5Iab 26\n", + "B7V 26\n", + "A2Ia 25\n", + "K1V 24\n", + "M6V 22\n", + "K5III 18\n", + "B8Ia 17\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "conteo = stars[\"Spectral Class\"].value_counts() \n", + "print(conteo)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "code-04b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "\n", + "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "conteo = stars[\"Spectral Class\"].value_counts() \n", + "conteo.plot(kind='bar', color='steelblue', edgecolor='black')\n", + "\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "\n", + "plt.title(\"DISTRIBUCIÓN DE ESTRELLAS POR CLASE ESPECTRAL\")\n", + "plt.xlabel(\"CLASE ESPECTRAL\")\n", + "plt.ylabel(\"No. DE ESTRELLAS\")\n", + "plt.xticks(rotation=30, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-05-inst", + "metadata": {}, + "source": [ + "---\n", + "## 5. Temperatura por tipo de estrella\n", + "\n", + "En esta sección calcularás la temperatura media de dos formas distintas para comparar.\n", + "\n", + "**Celda 5a — Ciclo `for` (enfoque manual):**\n", + "\n", + "Primero filtra el DataFrame para obtener solo las estrellas de tipo `'A7V'`:\n", + "```python\n", + "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", + "```\n", + "Luego recorre la columna `'Temperature (K)'` del DataFrame filtrado con un `for`,\n", + "acumula la `suma` y el `conteo` (`n`), y calcula la media como `suma / n`.\n", + "\n", + "Consulta [cómo filtrar un DataFrame por valor](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html)\n", + "si necesitas orientación sobre la sintaxis de filtrado.\n", + "\n", + "**Celda 5b — Pandas `.groupby()` (enfoque vectorizado):**\n", + "\n", + "Usa [`.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)\n", + "para calcular la media de **todas las clases a la vez** en una sola línea:\n", + "```python\n", + "df.groupby('columna_categorica')['columna_numerica'].mean()\n", + "```\n", + "Ordena de mayor a menor con `.sort_values(ascending=False)`. Guarda en `temp_por_tipo`.\n", + "Compara el resultado de `'A7V'` con el valor que obtuviste con el `for`.\n", + "\n", + "**Celda 5c — Boxplot:**\n", + "\n", + "Usa [`sns.boxplot()`](https://seaborn.pydata.org/generated/seaborn.boxplot.html) con\n", + "`data=stars`, `x='Spectral Class'`, `y='Temperature (K)'`, `order=orden`." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "code-05a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) Temperature (K) \\\n", + "0 Altair 16.594171 9.979192 1.632650 7509.294247 \n", + "11 Altair 16.324632 10.457079 1.638568 7554.538238 \n", + "32 Altair 16.977835 8.978259 1.681768 7546.776074 \n", + "41 Altair 16.481724 10.654505 1.697977 7557.224465 \n", + "82 Altair 17.004753 15.509654 1.574136 7589.018862 \n", + ".. ... ... ... ... ... \n", + "974 Altair 16.502834 15.008535 1.537144 7531.390715 \n", + "984 Altair 17.154742 6.975772 1.548312 7537.641975 \n", + "989 Altair 17.111919 10.391013 1.609587 7504.094813 \n", + "990 Altair 16.874372 5.998471 1.559794 7561.789883 \n", + "993 Altair 17.121544 11.776187 1.635310 7555.418080 \n", + "\n", + " Spectral Class \n", + "0 A7V \n", + "11 A7V \n", + "32 A7V \n", + "41 A7V \n", + "82 A7V \n", + ".. ... \n", + "974 A7V \n", + "984 A7V \n", + "989 A7V \n", + "990 A7V \n", + "993 A7V \n", + "\n", + "[74 rows x 6 columns]\n", + "MEDIA DE TEMPERATURAS: A7V : 7550.178312906775 K\n", + "TEMPERATURA MEDIA (for loop) : 7,550.2 K\n" + ] + } + ], + "source": [ + "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", + "tipo_objetivo = 'A7V'\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", + "# filtrado = ...\n", + "# tu código aquí\n", + "filtrado = stars[stars[\"Spectral Class\"] == tipo_objetivo ]\n", + "print(filtrado)\n", + "\n", + "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", + "# acumula la suma y el conteo (n)\n", + "# tu código aquí\n", + "suma_temperaturas = 0\n", + "conteo_n = 0\n", + "for temperaturas in filtrado['Temperature (K)']:\n", + " suma_temperaturas += temperaturas\n", + " conteo_n += 1\n", + "\n", + "# Calcula la media y guárdala en media_manual\n", + "# media_manual = suma / n\n", + "# tu código aquí\n", + "if conteo_n > 0:\n", + " media_manual = suma_temperaturas / conteo_n\n", + " print(f\"MEDIA DE TEMPERATURAS: {tipo_objetivo} : { media_manual } K\")\n", + "else:\n", + " print(\"DATOS NO EXISTENTES\")\n", + "\n", + "\n", + "print(f'TEMPERATURA MEDIA (for loop) : {media_manual:,.1f} K')" + ] + }, + { + "cell_type": "markdown", + "id": "03533f36", + "metadata": {}, + "source": [ + "Comparación: for vs. pandas:\n", + "\n", + "Con el ciclo for calculaste la media de una sola clase espectral en varias líneas. Ahora verás cómo pandas obtiene la media de todas las clases a la vez en una sola línea.\n", + "\n", + "Cuando termines la celda 5b, verifica que el resultado de A7V coincida con el valor que obtuviste con el for." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4j2wkkt78ju", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Temperatura promedio por clase espectral (K):\n", + "Spectral Class\n", + "B0.5IV 28001.166630\n", + "B0Ia 27502.303666\n", + "B1III-IV 25403.170510\n", + "B1III 25001.131122\n", + "B2III 22600.139741\n", + "B6Vep 15003.610593\n", + "B7V 12462.119029\n", + "B8Ia 12092.293145\n", + "A1V 10136.022204\n", + "A0V 9607.458129\n", + "A3V 8584.693288\n", + "A2Ia 8516.840653\n", + "A7V 7550.178313\n", + "A9II 7349.223744\n", + "F5IV-V 6520.419327\n", + "F7Ib 6020.393400\n", + "G2V 5797.996506\n", + "K1V 5261.645715\n", + "G8III 4939.733287\n", + "K1.5III 4280.090548\n", + "K5III 3923.614977\n", + "M2Iab 3502.196868\n", + "M1.5Iab 3499.130207\n", + "M2.1V 3408.914115\n", + "M4Ve 3136.076002\n", + "M7IIIe 2914.515688\n", + "M3.5V 2802.627176\n", + "M6V 2795.196060\n", + "Name: Temperature (K), dtype: float64\n", + "\n", + "RESULTADO PANDAS ('A7V'): 7,550.178313 K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " COMPARACIÓN DE LA MEDIA DE LOS VALORES 'A7V' (PANDAS, CICLO FOR) \n", + " \n", + "RESULTADO PANDAS ('A7V'): 7,550.2 K\n", + "RESULTADO MANUAL('A7V'): 7,550.2 K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", + "# Calcula la temperatura promedio por tipo con groupby\n", + "# Ordena de mayor a menor y guarda en temp_por_tipo\n", + "# tu código aquí\n", + "print( \"----\" * 40)\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temp_por_tipo = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False)\n", + "print('Temperatura promedio por clase espectral (K):')\n", + "print(temp_por_tipo)\n", + "print()\n", + "valor_pd = temp_por_tipo['A7V']\n", + "print(f\"RESULTADO PANDAS ('A7V'): {valor_pd:,.6f} K\")\n", + "\n", + "\n", + "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", + "# tu código aquí\n", + "tipo_objetivo = 'A7V'\n", + "filtrado = stars[stars[\"Spectral Class\"] == tipo_objetivo ]\n", + "suma_temperaturas = 0\n", + "conteo_n = 0\n", + "for temperaturas in filtrado['Temperature (K)']:\n", + " suma_temperaturas += temperaturas\n", + " conteo_n += 1\n", + "\n", + "# Calcula la media y guárdala en media_manual\n", + "# media_manual = suma / n\n", + "# tu código aquí\n", + "if conteo_n > 0:\n", + " media_manual = suma_temperaturas / conteo_n\n", + " \n", + "else:\n", + " print(\"DATOS NO EXISTENTES\")\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"COMPARACIÓN DE LA MEDIA DE LOS VALORES 'A7V' (PANDAS, CICLO FOR) \\n \")\n", + "valor_pd = temp_por_tipo['A7V']\n", + "print(f\"RESULTADO PANDAS ('A7V'): {valor_pd:,.1f} K\")\n", + "print(f\"RESULTADO MANUAL('A7V'): {media_manual:,.1f} K\")\n", + "print( \"----\" * 40)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "code-05b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", + "plt.figure(figsize=(9, 5))\n", + "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", + "\n", + "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", + "# tu código aquí\n", + "sns.boxplot(data=stars, x='Spectral Class', y='Temperature (K)', order=orden)\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "plt.title(\"ORDENAMIENTO DE MAYOR A MENOR (CLASE EXPECTRAL, TEMPERATURA)\")\n", + "plt.xlabel(\"CLASE ESPECTRAL\")\n", + "plt.ylabel('Temperature (K)')\n", + "\n", + "plt.xticks(rotation=30, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-06-inst", + "metadata": {}, + "source": [ + "---\n", + "## 6. Luminosidad vs Temperatura\n", + "\n", + "La luminosidad varía en muchos órdenes de magnitud (de 0.00008 a 849 420 L/Lo),\n", + "por eso necesitamos **escala logarítmica** en el eje Y.\n", + "\n", + "Usa [`sns.scatterplot()`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html)\n", + "con los siguientes parámetros (revisa la documentación para entender cada uno):\n", + "- `data=stars` — el DataFrame\n", + "- `x='Temperature (K)'` — temperatura en el eje X\n", + "- `y='Luminosity (L/Lo)'` — luminosidad en el eje Y\n", + "- `hue='Spectral Class'` — colorea los puntos según la clase espectral\n", + "- `style='Spectral Class'` — usa un marcador diferente por clase espectral\n", + "- `s=60` — tamaño de los puntos\n", + "\n", + "Después de crear el plot, aplica escala logarítmica al eje Y con\n", + "[`plt.yscale('log')`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yscale.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "code-06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", + "plt.figure(figsize=(9, 6))\n", + "\n", + "# Crea el scatter plot con sns.scatterplot(...)\n", + "# tu código aquí\n", + "sns.scatterplot(data=stars, \n", + " x='Temperature (K)', \n", + " y='Luminosity (L/Lo)', \n", + " hue='Spectral Class', \n", + " style='Spectral Class', \n", + " s=60)\n", + "\n", + "\n", + "# Aplica escala logarítmica al eje Y\n", + "# tu código aquí\n", + "plt.yscale('log')\n", + "\n", + "# Agrega título, etiquetas de ejes y leyenda\n", + "# tu código aquí\n", + "\n", + "plt.legend(title='Spectral Class', bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0.)\n", + "\n", + "plt.title('LUMINOSIDAD VS. TEMPERATURA POR ESCALA ESPECTRAL ')\n", + "plt.xlabel('Temperatura (K)')\n", + "plt.ylabel('Luminosidad (L/Lo)')\n", + "\n", + "plt.xticks(rotation=45, ha='right') \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-07-inst", + "metadata": {}, + "source": [ + "---\n", + "## 7. Estadísticas con NumPy\n", + "\n", + "NumPy opera sobre **arrays completos** sin ciclos `for`. Por ejemplo,\n", + "`np.mean(arr)` calcula la media de todos los elementos de `arr` de una sola vez.\n", + "\n", + "**Celda 7a** — Extrae los arrays con `.values` y calcula estadísticas:\n", + "- Extrae: `temperaturas = stars['Temperature (K)'].values`\n", + "- Extrae: `radios = stars['Radius (R/Ro)'].values`\n", + "- Verifica el tipo con `type(temperaturas)`\n", + "- Calcula usando estas funciones de [`numpy.statistics`](https://numpy.org/doc/stable/reference/routines.statistics.html):\n", + " - [`np.mean(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html) — media\n", + " - [`np.median(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.median.html) — mediana\n", + " - [`np.std(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.std.html) — desviación estándar\n", + " - [`np.min(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amin.html) y [`np.max(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amax.html)\n", + "\n", + "**Celda 7b** — Percentiles y conversión vectorizada:\n", + "- Usa [`np.percentile(arr, q)`](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html)\n", + " con `q=[25, 50, 75, 90]` para calcular los 4 percentiles de `radios` de una vez\n", + "- Convierte `temperaturas` de Kelvin a Celsius **sin usar ciclo `for`**:\n", + " `celsius = temperaturas - 273.15` (operación vectorizada)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "code-07a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "TIPO DE VARIABLE\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " ESTADÍSTICA BÁSICA\n", + "\n", + " a) Media de Temperaturas: 9983.49K\n", + "\n", + " b) Mediana de las Temperaturas: 7379.01K\n", + "\n", + " c) Desviación Estándar de las Temperaturas: 7903.02K\n", + "\n", + " d) Mínimo de Temperaturas: 2750.18K\n", + "\n", + " e) Máximo de Temperaturas: 28044.28K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Extrae los arrays NumPy con .values\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temperaturas = stars['Temperature (K)'].values\n", + "radios = stars['Radius (R/Ro)'].values\n", + "\n", + "\n", + "\n", + "# Imprime el tipo del array\n", + "# tu código aquí\n", + "print( \"----\" * 40)\n", + "print(\"TIPO DE VARIABLE\")\n", + "print(type(temperaturas))\n", + "\n", + "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", + "# tu código aquí\n", + "\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"ESTADÍSTICA BÁSICA\")\n", + "print(f\"\\n a) Media de Temperaturas: {np.mean(temperaturas):.2f}K\")\n", + "print(f\"\\n b) Mediana de las Temperaturas: {np.median(temperaturas):.2f}K\")\n", + "print(f\"\\n c) Desviación Estándar de las Temperaturas: {np.std(temperaturas):.2f}K\")\n", + "print(f\"\\n d) Mínimo de Temperaturas: {np.min(temperaturas):.2f}K\")\n", + "print(f\"\\n e) Máximo de Temperaturas: {np.max(temperaturas):.2f}K\")\n", + "print( \"----\" * 40)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "code-07b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + " PERCENTILES\n", + " \n", + " Percentil 25: 1.66\n", + " Percentil 50: 5.85\n", + " Percentil 75: 33.72\n", + " Percentil 90: 369.93\n", + " \n", + "--------------------------------------------------------------------------------\n", + " \n", + " COMPARACIÓN DE LAS TEMPERATUARA DE LAS PRIMERAS 5 ESTRELLAS (K = °C)\n", + "\n", + " 1° Temperatura: 7509.294 K = 7236.144 °C\n", + "\n", + " 2° temperatura: 8503.285 K = 8230.135 °C\n", + "\n", + " 3° temperatura: 3165.960 K = 2892.810 °C\n", + "\n", + " 4° temperatura: 6048.327 K = 5775.177 °C\n", + "\n", + " 5° temperatura: 3130.602 K = 2857.452 °C\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "niveles = [25, 50, 75, 90]\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temperaturas = stars['Temperature (K)'].values\n", + "radios = stars['Radius (R/Ro)'].values\n", + "\n", + "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", + "# tu código aquí\n", + "p = np.percentile(radios, niveles)\n", + "\n", + "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", + "# tu código aquí\n", + "print( \"--\" * 40)\n", + "print(\" \" * 15 + \"PERCENTILES\")\n", + "print( \" \" * 40)\n", + "for nivel, valor_p in zip(niveles, p):\n", + " print(f\" Percentil {nivel}: {valor_p:.2f}\")\n", + " \n", + "print( \" \" * 40)\n", + "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", + "# celsius = ...\n", + "# tu código aquí\n", + "print( \"--\" * 40)\n", + "celcius = temperaturas - 273.15\n", + "\n", + "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", + "# (usa np.round para redondear a 1 decimal)\n", + "# tu código aquí\n", + "print( \" \" * 40)\n", + "print(\" \" * 2 + \"COMPARACIÓN DE LAS TEMPERATUARA DE LAS PRIMERAS 5 ESTRELLAS (K = °C)\")\n", + "print(f\"\\n 1° Temperatura: {temperaturas[0]:.3f} K = {celcius[0]:.3f} °C\")\n", + "print(f\"\\n 2° temperatura: {temperaturas[1]:.3f} K = {celcius[1]:.3f} °C\")\n", + "print(f\"\\n 3° temperatura: {temperaturas[2]:.3f} K = {celcius[2]:.3f} °C\")\n", + "print(f\"\\n 4° temperatura: {temperaturas[3]:.3f} K = {celcius[3]:.3f} °C\")\n", + "print(f\"\\n 5° temperatura: {temperaturas[4]:.3f} K = {celcius[4]:.3f} °C\")\n", + "\n", + "print( \"--\" * 40)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-08-inst", + "metadata": {}, + "source": [ + "---\n", + "## 8. Diagrama Hertzsprung-Russell\n", + "\n", + "El diagrama H-R es el gráfico más importante en astronomía estelar.\n", + "Relaciona temperatura con luminosidad y revela la estructura evolutiva de las estrellas.\n", + "\n", + "Este diagrama tiene **dos particularidades** que debes implementar:\n", + "1. **Ambos ejes logarítmicos**: `plt.xscale('log')` y `plt.yscale('log')`\n", + "2. **Eje X invertido** (las más calientes a la izquierda): `plt.gca().invert_xaxis()`\n", + "\n", + "**Estructura del código** (el inicio ya está dado, completa las partes marcadas):\n", + "\n", + "```python\n", + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos)) # paleta de colores\n", + "mapa = dict(zip(tipos, colores)) # tipo -> color\n", + "\n", + "for tipo, grupo in stars.groupby('Spectral Class'):\n", + " plt.scatter(grupo['Temperature (K)'],\n", + " grupo['Luminosity (L/Lo)'],\n", + " label=tipo, color=mapa[tipo], s=40, alpha=0.8)\n", + "```\n", + "\n", + "Parámetros de [`plt.scatter()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)\n", + "que debes entender: `x`, `y`, `label`, `color`, `s` (tamaño), `alpha` (transparencia)." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "code-08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos))\n", + "mapa = dict(zip(tipos, colores))\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "\n", + "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", + "# tu código aquí\n", + "for tipo, grupo in stars.groupby('Spectral Class'):\n", + " plt.scatter(grupo['Temperature (K)'], grupo['Luminosity (L/Lo)'], label= tipo , color=mapa[tipo], s=50, alpha=1.0)\n", + "\n", + "\n", + "orden_clases = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False).index\n", + "\n", + "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", + "# tu código aquí\n", + "\n", + "plt.yscale(\"log\")\n", + "\n", + "# Invierte el eje X con plt.gca().invert_xaxis()\n", + "# tu código aquí\n", + "plt.gca().invert_xaxis()\n", + "\n", + "# Agrega: título, etiquetas de ejes, leyenda y grid\n", + "# tu código aquí\n", + "\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "\n", + "plt.title(\"DIAGRAMAS H-R, RELACIÓN TEMPERATURA-LUMINOSIDAD\")\n", + "plt.xlabel(\"Temperatura (K)\")\n", + "plt.ylabel(\"Luminosidad (L/Lo)\")\n", + "\n", + "plt.xticks(rotation=45, ha='right') \n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ tareas/practicados/pyproject.toml b/ tareas/practicados/pyproject.toml new file mode 100644 index 0000000..431f49e --- /dev/null +++ b/ tareas/practicados/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "analisis-datos" +version = "0.1.0" +description = "Análisis de datos con Python usando Poetry" +authors = [ + {name = "F12 Programacion"} +] +requires-python = ">=3.11" +dependencies = [ + "numpy (>=2.4.3,<3.0.0)", + "pandas (>=3.0.1,<4.0.0)", + "matplotlib (>=3.10.8,<4.0.0)", + "seaborn (>=0.13.2,<0.14.0)", + "jupyter (>=1.1.1,<2.0.0)" +] + + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/ tareas/practicados/star_dataset.csv b/ tareas/practicados/star_dataset.csv new file mode 100644 index 0000000..a6ba6ab --- /dev/null +++ b/ tareas/practicados/star_dataset.csv @@ -0,0 +1,1001 @@ +Name,Distance (ly),Luminosity (L/Lo),Radius (R/Ro),Temperature (K),Spectral Class +Altair,16.59417123707501,9.979192445462427,1.6326504020392336,7509.2942473638805,A7V +Deneb,2600.4907228269035,196002.62785575088,202.970526216013,8503.284796430231,A2Ia +Barnard's Star,6.052616358474774,4.893715684187678,0.22271101197217258,3165.9596392449016,M4Ve +Polaris,322.6010015236985,2196.241933606857,37.546813013740554,6048.326914763769,F7Ib +Barnard's Star,5.902391663373468,-1.4964862523273919,0.1923594077740656,3130.6020692351385,M4Ve +Sirius,8.370045208990435,25.62536254052454,1.7376163136938274,9903.971703749557,A1V +Fomalhaut,25.42303189349805,19.314803198342823,1.7634732970155798,8541.195355142636,A3V +Capella,42.56957816453575,77.46654686278187,12.015077077611398,4979.492462093153,G8III +Capella,43.260858866113374,76.75397466518646,11.926446347832353,4907.779548870081,G8III +Bellatrix,239.86410710431278,6399.344000391582,5.751378540226974,22573.286177203267,B2III +Castor,51.57021005818902,59.93364175572123,2.317294377415115,10334.140338882178,A1V +Altair,16.324631801826886,10.45707878952248,1.6385681357721884,7554.538238404283,A7V +Bellatrix,240.396008875072,6396.460553762988,5.702796835694758,22559.156170298847,B2III +Hadar,349.6510239295208,49995.691816097984,9.029686061904163,24977.138850418167,B1III +Betelgeuse,642.4036425044302,125997.20910038544,886.9436673356928,3472.6066444708454,M2Iab +Regulus,79.14834001968553,290.3679076360734,3.105720387017622,12478.930088621566,B7V +Regulus,78.8696975851003,292.77713927994625,3.2154445654455555,12493.540087369223,B7V +Mira,418.3541533597022,8700.111885115977,369.94647200932155,2929.214904446191,M7IIIe +Acrux,320.95215250850816,24995.853352250557,8.476536987947346,28016.58055121632,B0.5IV +Vega,25.40728672741662,45.02639994513558,2.3758185019255906,9589.720331802691,A0V +Betelgeuse,642.4501483192605,125997.82193788076,887.023713575872,3502.8036495770207,M2Iab +Lalande 21185,8.78389229917336,0.9306154071042486,0.36997218014157396,3420.0741335824846,M2.1V +Hadar,350.3393282903268,50002.885000950024,9.030531083873981,24972.4633828412,B1III +Regulus,79.23841041664882,283.3313068914609,3.1941978242994673,12471.609230270555,B7V +Regulus,78.51275351482992,289.1046797165257,3.093785398680339,12469.726650284285,B7V +Bellatrix,239.75802627864445,6402.532053023128,5.807903991855439,22628.845403711555,B2III +Sirius,8.997561546118135,27.612290603452433,1.7178118641359001,9965.640231971809,A1V +Arcturus,36.39116253215857,169.47569492442693,25.34766748214963,4306.771078623507,K1.5III +Alpha Centauri B,3.9259386007692,-2.5124238815560895,0.8971557531469336,5292.710091689776,K1V +Ross 154,9.584936005981074,-0.2622128873565414,0.09815009889642284,2788.6078755716367,M3.5V +Betelgeuse,642.1986705590544,125995.18267169305,887.0568849865539,3502.1107945651283,M2Iab +Sirius,8.790939416886154,29.685053444507503,1.778145234597857,9959.636695100216,A1V +Altair,16.97783483438638,8.978258642897934,1.6817677393535218,7546.77607407146,A7V +Regulus,79.37549284128201,287.2472405244145,3.0775092724640816,12466.383842658091,B7V +Lalande 21185,7.922999488356227,2.681120395944034,0.46518759639849927,3394.5169092653987,M2.1V +Canopus,309.65595488089645,10500.304364175136,70.9940908831198,7322.086648344391,A9II +Procyon,11.361710078531987,11.09891870112007,2.1252624415131467,6522.987352867349,F5IV-V +Capella,43.239838363858276,78.47758735453107,12.04168320043013,4903.922634597786,G8III +Barnard's Star,6.274309005192113,-4.612503609841812,0.27142011745919525,3164.7134904158015,M4Ve +Arcturus,36.23014602060893,167.67967953475542,25.372980607290035,4285.208656712724,K1.5III +Canopus,309.89434307337245,10497.613821111012,70.98540952072986,7395.822565906416,A9II +Altair,16.481723812762002,10.654505105436737,1.6979772339329253,7557.224465410894,A7V +Polaris,323.4890826975579,2197.4846549730764,37.5928890041696,6003.417411946257,F7Ib +Barnard's Star,6.210116619871699,3.922475110995184,0.20022109890664536,3104.152562146686,M4Ve +Castor,52.3208563941243,52.46460236565418,2.3580848977347673,10286.21254520186,A1V +Castor,51.687127344898485,57.564078324527344,2.4793370398018366,10314.20142645494,A1V +Bellatrix,240.3505524329565,6403.480526616087,5.803192364925976,22632.467298684776,B2III +Polaris,323.4772161689265,2202.925953103468,37.430399789571695,5986.795558342898,F7Ib +Castor,52.31788545862922,54.098680128048926,2.3832137598247782,10264.67768772168,A1V +Barnard's Star,5.5635565189978085,-3.4589548280686215,0.14953611720828194,3115.0666806206127,M4Ve +Vega,25.02265608280089,43.21333423901169,2.4233371866214184,9631.823941413108,A0V +Wolf 359,8.040322484690764,3.518164733709185,0.2159582033041309,2786.809004459221,M6V +Regulus,79.457885087804,284.3075843561486,3.1848771847975916,12429.346874442883,B7V +Polaris,322.7090337593032,2201.1895342191797,37.558280857740726,5986.9294428280555,F7Ib +Antares,549.8094446057289,10004.888893429817,679.9451981136416,3503.22033874066,M1.5Iab +Regulus,78.84980314077531,283.1518425898611,3.1746164987628713,12414.98856094767,B7V 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+Betelgeuse,642.1530702565499,126000.10150499652,886.9045632911253,3462.1435724115922,M2Iab +Alnilam,1999.880482110407,53695.72165124452,32.35326480955992,27490.40560120546,B0Ia +Bellatrix,240.30865829394963,6398.706399629343,5.727504552499588,22561.66109433901,B2III +Hadar,349.98072783140907,50000.69303998948,8.940299046830283,25043.812798420327,B1III +Barnard's Star,6.102187239521027,-0.6905551570802375,0.2265784314346753,3158.9781846800365,M4Ve +Barnard's Star,6.098594827311291,-3.3303098730228937,0.12291605693194557,3144.711153515979,M4Ve +Spica,250.4596817064251,21996.281907685272,7.481964414843237,25371.671565613717,B1III-IV +Deneb,2600.219144334045,195996.44277808978,203.09713635797436,8512.606175957664,A2Ia +Bellatrix,239.80471588472972,6400.473393470613,5.678484912182976,22628.27195885336,B2III +Fomalhaut,25.040609497955693,20.8052240111299,1.8933606922604622,8570.329145806458,A3V +Rigil Kentaurus,4.0542023166505174,5.3173565377787355,1.1417731281419847,5802.341326578711,G2V 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10:29:59 -0600 Subject: [PATCH 28/28] actualizar TAREASS --- { tareas => TAREASSS}/ensayo.tex | 0 { tareas => TAREASSS}/numpy.ipynb | 0 { tareas => TAREASSS}/pandas.ipynb | 0 { tareas => TAREASSS}/practica_dos.ipynb | 0 .../practicados/analisis_estrellas_estudiante.ipynb | 0 { tareas => TAREASSS}/practicados/pyproject.toml | 0 { tareas => TAREASSS}/practicados/star_dataset.csv | 0 7 files changed, 0 insertions(+), 0 deletions(-) rename { tareas => TAREASSS}/ensayo.tex (100%) rename { tareas => TAREASSS}/numpy.ipynb (100%) rename { tareas => TAREASSS}/pandas.ipynb (100%) rename { tareas => TAREASSS}/practica_dos.ipynb (100%) rename { tareas => TAREASSS}/practicados/analisis_estrellas_estudiante.ipynb (100%) rename { tareas => TAREASSS}/practicados/pyproject.toml (100%) rename { tareas => TAREASSS}/practicados/star_dataset.csv (100%) diff --git a/ tareas/ensayo.tex b/TAREASSS/ensayo.tex similarity index 100% rename from tareas/ensayo.tex rename to TAREASSS/ensayo.tex diff --git a/ 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b/TAREASSS/practicados/star_dataset.csv similarity index 100% rename from tareas/practicados/star_dataset.csv rename to TAREASSS/practicados/star_dataset.csv