From b549b60eacf395e3254152c994c4cb3e92f36913 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= <104874484+Boyito@users.noreply.github.com> Date: Wed, 4 Feb 2026 22:09:30 -0600 Subject: [PATCH 01/12] Create tarea 1 creao la tarea 1 --- tareas/tarea 1 | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 tareas/tarea 1 diff --git a/tareas/tarea 1 b/tareas/tarea 1 new file mode 100644 index 0000000..26f06e9 --- /dev/null +++ b/tareas/tarea 1 @@ -0,0 +1,26 @@ +\documentclass{article} +\usepackage{graphicx} + + +\begin{document} +\title{tarea 1} +\author{Ruben Samayoa} +\maketitle +\section{Ensayo} +mi campo en la fisica seria stronomia, amo los astros y estudiarlos, en la astrofisica se estudian estos a travez de instrumentos como radiotelescopios y demas, en estos los resultados no necesariamente es una imagen, si no datos; y poder aprender a visualizarlos, interpretarlos etc. + +en mi caso la programacion seria importante o hasta necesario por varias razones: + +\subsection{Limpiar datos observacionales (ruido, errores instrumentales).} + +\subsection{Ajustar modelos físicos a observaciones reales.} + +\subsection{Calcular incertidumbres y estadísticas.} + +\subsection{Visualizar resultados (gráficas, mapas celestes).} + + \centering + \includegraphics[width=0.5\linewidth]{logo_ecfm (1).jpg} + \label{fig:placeholder} + +\end{document} From e20d9ae9c4b16970c190ba0fefcd56a89b6328d0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= <104874484+Boyito@users.noreply.github.com> Date: Wed, 4 Feb 2026 22:13:59 -0600 Subject: [PATCH 02/12] creo doc de latex --- tareas/tarea1.tex | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 tareas/tarea1.tex diff --git a/tareas/tarea1.tex b/tareas/tarea1.tex new file mode 100644 index 0000000..26f06e9 --- /dev/null +++ b/tareas/tarea1.tex @@ -0,0 +1,26 @@ +\documentclass{article} +\usepackage{graphicx} + + +\begin{document} +\title{tarea 1} +\author{Ruben Samayoa} +\maketitle +\section{Ensayo} +mi campo en la fisica seria stronomia, amo los astros y estudiarlos, en la astrofisica se estudian estos a travez de instrumentos como radiotelescopios y demas, en estos los resultados no necesariamente es una imagen, si no datos; y poder aprender a visualizarlos, interpretarlos etc. + +en mi caso la programacion seria importante o hasta necesario por varias razones: + +\subsection{Limpiar datos observacionales (ruido, errores instrumentales).} + +\subsection{Ajustar modelos físicos a observaciones reales.} + +\subsection{Calcular incertidumbres y estadísticas.} + +\subsection{Visualizar resultados (gráficas, mapas celestes).} + + \centering + \includegraphics[width=0.5\linewidth]{logo_ecfm (1).jpg} + \label{fig:placeholder} + +\end{document} From d6352c0f40b00c331fc76ae4e4781dbebbf79508 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Sun, 26 Apr 2026 00:59:01 -0600 Subject: [PATCH 03/12] subimos pandas --- Pandas.ipynb | 1096 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1096 insertions(+) create mode 100644 Pandas.ipynb diff --git a/Pandas.ipynb b/Pandas.ipynb new file mode 100644 index 0000000..015290a --- /dev/null +++ b/Pandas.ipynb @@ -0,0 +1,1096 @@ +{ + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Resumen estadístico automático\n", + "print(\"Estadísticas descriptivas:\")\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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))" + ] + }, + { + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TU CÓDIGO AQUÍ\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": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TU CÓDIGO AQUÍ\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": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TU CÓDIGO AQUÍ\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": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TU CÓDIGO AQUÍ\n" + ] + }, + { + "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": "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": 4 +} From bcb742fe44c5f104ae8f51f85c77fdc1a0cb5c04 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Sun, 26 Apr 2026 01:28:03 -0600 Subject: [PATCH 04/12] pandtias --- Pandas.ipynb | 1220 ++++++++++++++++++++++++++++++++++++++++++----- estudiantes.csv | 6 + resultado.csv | 6 + 3 files changed, 1108 insertions(+), 124 deletions(-) create mode 100644 estudiantes.csv create mode 100644 resultado.csv diff --git a/Pandas.ipynb b/Pandas.ipynb index 015290a..c9f58c9 100644 --- a/Pandas.ipynb +++ b/Pandas.ipynb @@ -41,9 +41,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 418, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pandas versión: 3.0.2\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np # Pandas y NumPy se usan frecuentemente juntos\n", @@ -61,9 +69,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 419, + "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", @@ -74,9 +98,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 420, + "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", @@ -92,9 +133,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 421, + "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", @@ -111,9 +166,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 422, + "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", @@ -139,9 +216,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 423, + "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", @@ -157,9 +247,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 424, + "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 str\n", + "Edad int64\n", + "Carrera str\n", + "Promedio float64\n", + "dtype: object\n" + ] + } + ], "source": [ "# Información básica del DataFrame\n", "print(\"Forma (filas x columnas):\", df.shape)\n", @@ -170,9 +277,103 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 425, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas descriptivas:\n" + ] + }, + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "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": 425, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Resumen estadístico automático\n", "print(\"Estadísticas descriptivas:\")\n", @@ -181,9 +382,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 426, + "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", @@ -210,9 +431,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 427, + "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: str\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", @@ -224,9 +467,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 428, + "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", @@ -238,9 +500,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 429, + "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", @@ -259,9 +536,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 430, + "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", @@ -270,9 +559,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 431, + "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", @@ -281,9 +582,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 432, + "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", @@ -294,9 +611,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 433, + "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", @@ -321,9 +653,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 434, + "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", @@ -340,9 +689,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 435, + "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", @@ -360,9 +721,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 436, + "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", @@ -379,9 +754,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 437, + "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", @@ -401,9 +789,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 438, + "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", @@ -415,9 +816,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 439, + "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", @@ -431,9 +855,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 440, + "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", @@ -457,9 +901,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 441, + "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", @@ -479,9 +936,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 442, + "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", @@ -499,9 +977,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 443, + "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", @@ -514,9 +1006,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 444, + "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", @@ -533,9 +1035,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 445, + "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", @@ -547,9 +1070,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 446, + "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", @@ -562,9 +1099,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 447, + "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", @@ -584,9 +1130,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 448, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/tmp/estudiantes.csv'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[448]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m 6\u001b[39m Eduardo,\u001b[32m23\u001b[39m,Computación,\u001b[32m68\u001b[39m\n\u001b[32m 7\u001b[39m Fátima,\u001b[32m20\u001b[39m,Matemática,\u001b[32m79\u001b[39m\n\u001b[32m 8\u001b[39m \"\"\"\n\u001b[32m 9\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m open(\u001b[33m\"/tmp/estudiantes.csv\"\u001b[39m, \u001b[33m\"w\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m 11\u001b[39m f.write(csv_contenido)\n\u001b[32m 12\u001b[39m \n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# Leer el CSV\u001b[39;00m\n", + "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/tmp/estudiantes.csv'" + ] + } + ], "source": [ "# Crear un CSV de ejemplo para practicar\n", "csv_contenido = \"\"\"nombre,edad,carrera,nota\n", @@ -612,7 +1170,15 @@ "cell_type": "code", "execution_count": null, "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", @@ -628,7 +1194,21 @@ "cell_type": "code", "execution_count": null, "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", @@ -662,7 +1242,22 @@ "cell_type": "code", "execution_count": null, "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", @@ -678,7 +1273,26 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Categorías únicas:\n", + "\n", + "['nublado', 'parcialmente nublado', 'mayormente despejado', 'despejado']\n", + "Length: 4, dtype: str\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", @@ -692,7 +1306,15 @@ "cell_type": "code", "execution_count": null, "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", @@ -713,7 +1335,30 @@ "cell_type": "code", "execution_count": null, "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", @@ -728,7 +1373,22 @@ "cell_type": "code", "execution_count": null, "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", @@ -750,7 +1410,22 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tipo original: str\n", + "Tipo convertido: datetime64[us]\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[us]\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", @@ -767,7 +1442,23 @@ "cell_type": "code", "execution_count": null, "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", @@ -779,7 +1470,22 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\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", @@ -815,7 +1521,19 @@ "cell_type": "code", "execution_count": null, "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", @@ -826,7 +1544,20 @@ "cell_type": "code", "execution_count": null, "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", @@ -840,7 +1571,18 @@ "cell_type": "code", "execution_count": null, "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", @@ -864,7 +1606,25 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib is building the font cache; this may take a moment.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -889,7 +1649,18 @@ "cell_type": "code", "execution_count": null, "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", @@ -902,7 +1673,18 @@ "cell_type": "code", "execution_count": null, "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", @@ -926,7 +1708,27 @@ "cell_type": "code", "execution_count": null, "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", @@ -938,7 +1740,19 @@ "cell_type": "code", "execution_count": null, "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", @@ -949,7 +1763,21 @@ "cell_type": "code", "execution_count": null, "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", @@ -982,9 +1810,53 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precipitación Mensual (mm):\n", + "Ene 2\n", + "Feb 3\n", + "Mar 13\n", + "Abr 31\n", + "May 152\n", + "Jun 274\n", + "Jul 203\n", + "Ago 198\n", + "Sep 231\n", + "Oct 172\n", + "Nov 23\n", + "Dic 7\n", + "dtype: int64\n", + "------------------------------\n", + "Media: 109.08 mm\n", + "Mediana: 91.50 mm\n", + "Máximo: 274 mm\n", + "Mes con mayor precipitación: Jun (posición 5)\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "import pandas as pd\n", + "\n", + "# Datos aproximados de precipitación mensual (mm) sisisisisis\n", + "precipitacion = pd.Series(\n", + " [2, 3, 13, 31, 152, 274, 203, 198, 231, 172, 23, 7],\n", + " index=[\"Ene\", \"Feb\", \"Mar\", \"Abr\", \"May\", \"Jun\", \"Jul\", \"Ago\", \"Sep\", \"Oct\", \"Nov\", \"Dic\"]\n", + ")\n", + "\n", + "print(\"Precipitación Mensual (mm):\")\n", + "print(precipitacion)\n", + "print(\"-\" * 30)\n", + "print(f\"Media: {precipitacion.mean():.2f} mm\")\n", + "print(f\"Mediana: {precipitacion.median():.2f} mm\")\n", + "print(f\"Máximo: {precipitacion.max()} mm\")\n", + "\n", + "# argmax() para que me devuelva el valor maximo U-U\n", + "idx_max = precipitacion.argmax()\n", + "mes_max = precipitacion.index[idx_max]\n", + "print(f\"Mes con mayor precipitación: {mes_max} (posición {idx_max})\")\n" ] }, { @@ -1000,9 +1872,32 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meses con temperatura entre 5°C y 15°C:\n", + " temp_C rain_mm\n", + "apr 7.4 100\n", + "may 12.0 143\n", + "jun 15.0 153\n", + "sep 13.1 135\n", + "oct 9.1 89\n", + "\n", + "Lluvia total en este subconjunto: 620 mm\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "\n", + "filtro_clima = df_clima[(df_clima['temp_C'] >= 5) & (df_clima['temp_C'] <= 15)]\n", + "\n", + "print(\"Meses con temperatura entre 5°C y 15°C:\")\n", + "print(filtro_clima)\n", + "\n", + "lluvia_total = filtro_clima['rain_mm'].sum()\n", + "print(f\"\\nLluvia total en este subconjunto: {lluvia_total} mm\")\n" ] }, { @@ -1018,9 +1913,42 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [ - "# TU CÓDIGO AQUÍ\n" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame con Amplitud Térmica:\n", + " temp_min_C temp_max_C amplitud_termica\n", + "jan -1.9 2.5 4.4\n", + "feb -2.5 3.3 5.8\n", + "mar 0.6 7.3 6.7\n", + "apr 3.5 11.5 8.0\n", + "may 7.8 16.3 8.5\n", + "jun 11.0 19.2 8.2\n", + "jul 13.1 21.6 8.5\n", + "aug 13.0 20.9 7.9\n", + "sep 9.7 16.8 7.1\n", + "oct 6.2 12.3 6.1\n", + "nov 1.0 6.5 5.5\n", + "dec -3.0 3.5 6.5\n", + "\n", + "El mes con mayor amplitud térmica es jul con 8.500000000000002°C\n" + ] + } + ], + "source": [ + "# Agrego la columna calculada 🤓\n", + "df_completo['amplitud_termica'] = df_completo['temp_max_C'] - df_completo['temp_min_C']\n", + "\n", + "print(\"DataFrame con Amplitud Térmica:\")\n", + "print(df_completo[['temp_min_C', 'temp_max_C', 'amplitud_termica']])\n", + "\n", + "# Encontrar el índice con el valor máximo de amplitud 🤓\n", + "mes_mayor_amplitud = df_completo['amplitud_termica'].idxmax()\n", + "valor_amplitud = df_completo['amplitud_termica'].max()\n", + "\n", + "print(f\"\\nEl mes con mayor amplitud térmica es {mes_mayor_amplitud} con {valor_amplitud}°C\")\n" ] }, { @@ -1038,9 +1966,53 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [ - "# TU CÓDIGO AQUÍ\n" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NaN por columna:\n", + "Nombre 0\n", + "Nota1 1\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n", + "\n", + "Resultado Final:\n", + " Nombre Nota1 Nota2 Nota3 Promedio_Final\n", + "0 Rubén 85.0 78.0 92.0 85.0\n", + "1 María 83.2 65.0 70.0 72.8\n", + "2 José 90.0 79.0 85.0 84.7\n", + "3 Lucía 70.0 82.0 80.8 77.6\n", + "4 Pedro 88.0 91.0 76.0 85.0\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# a) Crear DataFrame con 3 NaNs repartidos🤓\n", + "df_estudiantes = pd.DataFrame({\n", + " \"Nombre\": [\"Rubén\", \"María\", \"José\", \"Lucía\", \"Pedro\"],\n", + " \"Nota1\": [85, np.nan, 90, 70, 88],\n", + " \"Nota2\": [78, 65, np.nan, 82, 91],\n", + " \"Nota3\": [92, 70, 85, np.nan, 76]\n", + "})\n", + "\n", + "print(\"NaN por columna:\")\n", + "print(df_estudiantes.isna().sum())\n", + "\n", + "# b) Rellenar con la media de cada columna numérica 🤓\n", + "# Usamos numeric_only=True, si no:☠️\n", + "df_estudiantes_lleno = df_estudiantes.fillna(df_estudiantes.mean(numeric_only=True))\n", + "\n", + "# c) Calcular nota promedio final por estudiante 🤓\n", + "df_estudiantes_lleno[\"Promedio_Final\"] = df_estudiantes_lleno[[\"Nota1\", \"Nota2\", \"Nota3\"]].mean(axis=1)\n", + "\n", + "print(\"\\nResultado Final:\")\n", + "print(df_estudiantes_lleno.round(1))\n", + "\n", + "#ayuda tengo el impulso de terminar mis comentarios con \"🤓\"" ] }, { @@ -1074,7 +2046,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1088,7 +2060,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.14.4" } }, "nbformat": 4, diff --git a/estudiantes.csv b/estudiantes.csv new file mode 100644 index 0000000..9007158 --- /dev/null +++ b/estudiantes.csv @@ -0,0 +1,6 @@ +nombre,edad,carrera,nota +Ana,20,Física,85 +Carlos,22,Matemática,72 +Diana,21,Física,91 +Eduardo,23,Computación,68 +Fátima,20,Matemática,79 diff --git a/resultado.csv b/resultado.csv new file mode 100644 index 0000000..9007158 --- /dev/null +++ b/resultado.csv @@ -0,0 +1,6 @@ +nombre,edad,carrera,nota +Ana,20,Física,85 +Carlos,22,Matemática,72 +Diana,21,Física,91 +Eduardo,23,Computación,68 +Fátima,20,Matemática,79 From f6bc4cf9074f631b8d59c8ca16db9e487e07bb39 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Sun, 26 Apr 2026 01:46:31 -0600 Subject: [PATCH 05/12] pandita --- Pandas.ipynb | 1157 ++++++-------------------------------------------- 1 file changed, 124 insertions(+), 1033 deletions(-) diff --git a/Pandas.ipynb b/Pandas.ipynb index c9f58c9..4983b0f 100644 --- a/Pandas.ipynb +++ b/Pandas.ipynb @@ -41,17 +41,9 @@ }, { "cell_type": "code", - "execution_count": 418, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pandas versión: 3.0.2\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np # Pandas y NumPy se usan frecuentemente juntos\n", @@ -69,25 +61,9 @@ }, { "cell_type": "code", - "execution_count": 419, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Crear una Serie desde una lista\n", "temperaturas = pd.Series([25.1, 27.3, 24.8, 28.5, 26.0])\n", @@ -98,26 +74,9 @@ }, { "cell_type": "code", - "execution_count": 420, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Serie con índices personalizados (etiquetas)\n", "dias = pd.Series(\n", @@ -133,23 +92,9 @@ }, { "cell_type": "code", - "execution_count": 421, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "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", @@ -166,31 +111,9 @@ }, { "cell_type": "code", - "execution_count": 422, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Índice con etiquetas — set_axis() (§8.1)\n", "s2 = s.set_axis(['jan', 'feb', 'mar', 'apr', 'may', 'jun',\n", @@ -216,22 +139,9 @@ }, { "cell_type": "code", - "execution_count": 423, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Crear DataFrame desde un diccionario\n", "datos = {\n", @@ -247,26 +157,9 @@ }, { "cell_type": "code", - "execution_count": 424, - "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 str\n", - "Edad int64\n", - "Carrera str\n", - "Promedio float64\n", - "dtype: object\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Información básica del DataFrame\n", "print(\"Forma (filas x columnas):\", df.shape)\n", @@ -277,103 +170,9 @@ }, { "cell_type": "code", - "execution_count": 425, - "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": 425, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Resumen estadístico automático\n", "print(\"Estadísticas descriptivas:\")\n", @@ -382,29 +181,9 @@ }, { "cell_type": "code", - "execution_count": 426, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# head(), tail() y atributos del índice (§8.2.1)\n", "df_clima = pd.DataFrame({\n", @@ -431,31 +210,9 @@ }, { "cell_type": "code", - "execution_count": 427, - "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: str\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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Acceder a una columna\n", "print(\"Columna 'Nombre':\")\n", @@ -467,28 +224,9 @@ }, { "cell_type": "code", - "execution_count": 428, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Acceder a filas con .iloc (por posición numérica)\n", "print(\"Primera fila (iloc[0]):\")\n", @@ -500,24 +238,9 @@ }, { "cell_type": "code", - "execution_count": 429, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Acceder a filas con .loc (por etiqueta/índice)\n", "print(\"Fila con índice 2:\")\n", @@ -536,21 +259,9 @@ }, { "cell_type": "code", - "execution_count": 430, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# loc con lista de etiquetas (§8.2.2)\n", "print(\"Marzo, abril y junio:\")\n", @@ -559,21 +270,9 @@ }, { "cell_type": "code", - "execution_count": 431, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# loc con slice de etiquetas — el extremo final SÍ se incluye (§8.2.2)\n", "print(\"De marzo a mayo:\")\n", @@ -582,25 +281,9 @@ }, { "cell_type": "code", - "execution_count": 432, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# loc combinando filas Y columnas: df.loc[, ] (§8.2.2)\n", "print(\"Lluvia de mayo a septiembre:\")\n", @@ -611,24 +294,9 @@ }, { "cell_type": "code", - "execution_count": 433, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Máscara booleana con loc (§8.2.2)\n", "# Temperatura en meses con lluvia < 100 mm\n", @@ -653,26 +321,9 @@ }, { "cell_type": "code", - "execution_count": 434, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Estudiantes con promedio mayor a 75\n", "buenos = df[df[\"Promedio\"] > 75]\n", @@ -689,21 +340,9 @@ }, { "cell_type": "code", - "execution_count": 435, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Condiciones múltiples\n", "# & = AND, | = OR\n", @@ -721,23 +360,9 @@ }, { "cell_type": "code", - "execution_count": 436, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Ordenar por promedio (descendente)\n", "ranking = df.sort_values(\"Promedio\", ascending=False)\n", @@ -754,22 +379,9 @@ }, { "cell_type": "code", - "execution_count": 437, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Agregar columna calculada\n", "df[\"Estado\"] = df[\"Promedio\"].apply(lambda p: \"Aprobado\" if p >= 70 else \"Reprobado\")\n", @@ -789,22 +401,9 @@ }, { "cell_type": "code", - "execution_count": 438, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# assign() — agregar columna con función (§8.2.3)\n", "def celsius_a_f(df):\n", @@ -816,32 +415,9 @@ }, { "cell_type": "code", - "execution_count": 439, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# pd.concat() — combinar filas (§8.2.3)\n", "df_anual = pd.DataFrame({\n", @@ -855,29 +431,9 @@ }, { "cell_type": "code", - "execution_count": 440, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# pd.concat() con axis=1 — combinar columnas (§8.2.3)\n", "df_minmax = pd.DataFrame({\n", @@ -901,22 +457,9 @@ }, { "cell_type": "code", - "execution_count": 441, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# groupby: agrupar por una columna y calcular estadísticas\n", "por_carrera = df.groupby(\"Carrera\")[\"Promedio\"].agg([\"mean\", \"min\", \"max\", \"count\"])\n", @@ -936,30 +479,9 @@ }, { "cell_type": "code", - "execution_count": 442, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Crear DataFrame con datos faltantes (NaN = Not a Number)\n", "datos_incompletos = pd.DataFrame({\n", @@ -977,23 +499,9 @@ }, { "cell_type": "code", - "execution_count": 443, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Opción 1: Rellenar con la media de la columna\n", "df_rellenado = datos_incompletos.copy()\n", @@ -1006,19 +514,9 @@ }, { "cell_type": "code", - "execution_count": 444, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Opción 2: Eliminar filas con datos faltantes\n", "df_limpio = datos_incompletos.dropna()\n", @@ -1035,30 +533,9 @@ }, { "cell_type": "code", - "execution_count": 445, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# isna() / notna() — mapa de valores faltantes (§8.2.4)\n", "print(\"Mapa de NaN:\")\n", @@ -1070,23 +547,9 @@ }, { "cell_type": "code", - "execution_count": 446, - "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" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# fillna con la media de cada columna (§8.2.4)\n", "df_relleno2 = datos_incompletos.copy()\n", @@ -1099,18 +562,9 @@ }, { "cell_type": "code", - "execution_count": 447, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original: [ 1. nan 3. nan 5.]\n", - "Interpolado: [1. 2. 3. 4. 5.]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# interpolate() — interpolación lineal (§8.2.4)\n", "s_nan = pd.Series([1.0, np.nan, 3.0, np.nan, 5.0])\n", @@ -1119,32 +573,22 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "---\n", + "\n", "## Nivel 6 — Leer Datos desde Archivos\n", "\n", - "En la práctica, los datos vienen de archivos externos. Pandas puede leer muchos formatos:" + "#En la práctica, los datos vienen de archivos externos. Pandas puede leer muchos formatos:" ] }, { "cell_type": "code", - "execution_count": 448, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '/tmp/estudiantes.csv'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[448]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m 6\u001b[39m Eduardo,\u001b[32m23\u001b[39m,Computación,\u001b[32m68\u001b[39m\n\u001b[32m 7\u001b[39m Fátima,\u001b[32m20\u001b[39m,Matemática,\u001b[32m79\u001b[39m\n\u001b[32m 8\u001b[39m \"\"\"\n\u001b[32m 9\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m open(\u001b[33m\"/tmp/estudiantes.csv\"\u001b[39m, \u001b[33m\"w\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m 11\u001b[39m f.write(csv_contenido)\n\u001b[32m 12\u001b[39m \n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# Leer el CSV\u001b[39;00m\n", - "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/tmp/estudiantes.csv'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Crear un CSV de ejemplo para practicar\n", "csv_contenido = \"\"\"nombre,edad,carrera,nota\n", @@ -1170,15 +614,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Archivo guardado como /tmp/resultado.csv\n" - ] - } - ], + "outputs": [], "source": [ "# Guardar un DataFrame como CSV\n", "df_csv.to_csv(\"/tmp/resultado.csv\", index=False)\n", @@ -1194,21 +630,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# read_csv con opciones avanzadas (§8.6)\n", "import io\n", @@ -1242,22 +664,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# DataFrame con datos categóricos\n", "df_cat = pd.DataFrame({\n", @@ -1273,26 +680,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Categorías únicas:\n", - "\n", - "['nublado', 'parcialmente nublado', 'mayormente despejado', 'despejado']\n", - "Length: 4, dtype: str\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" - ] - } - ], + "outputs": [], "source": [ "# unique() — valores únicos | value_counts() — frecuencia (§8.3.1)\n", "print(\"Categorías únicas:\")\n", @@ -1306,15 +694,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "UV promedio en días parcialmente nublados: 1.0\n" - ] - } - ], + "outputs": [], "source": [ "# Filtrar por categoría y aplicar método (§8.3.1)\n", "uv_parcial = df_cat.loc[df_cat['nubes'] == 'parcialmente nublado', 'uv'].mean()\n", @@ -1335,30 +715,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# str.contains() — filtrar filas que contienen una subcadena (§8.3.2)\n", "mascara = df_cat['nubes'].str.contains('nublado', regex=False)\n", @@ -1373,22 +730,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# str.replace() — reemplazar texto (§8.3.2)\n", "df_cat2 = df_cat.copy()\n", @@ -1410,22 +752,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tipo original: str\n", - "Tipo convertido: datetime64[us]\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[us]\n" - ] - } - ], + "outputs": [], "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", @@ -1442,23 +769,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# Acceder a componentes con el accessor .dt (§8.3.3)\n", "print(\"Horas:\")\n", @@ -1470,22 +781,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\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" - ] - } - ], + "outputs": [], "source": [ "# Diferencias de tiempo — timedelta (§8.3.3)\n", "delta = fechas - fechas.iloc[0]\n", @@ -1521,19 +817,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# agg() con lista de métodos (§8.4)\n", "print(\"Estadísticas de temperatura y lluvia:\")\n", @@ -1544,20 +828,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# agg() con diccionario — distintas métricas por columna (§8.4)\n", "resultado = df_clima[['temp_C', 'rain_mm']].agg({\n", @@ -1571,18 +842,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# apply() — función personalizada por columna (§8.4)\n", "def rango(col):\n", @@ -1606,25 +866,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Matplotlib is building the font cache; this may take a moment.\n" - ] - }, - { - "data": { - "image/png": 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/NjDILXZWx1KQO3Xq5Or2WJrzJ598ovvvvz91Wl5utMl+x768bZqZPZdlytjSujYtMH22jhVQt6DFY4895ubs2zaWkWLPYWeTunTp4lLB7Qt9xIgRrp0rVqzI0d97KO0IBmu3nRWzuk9W7Nr+Vguw/PTTTy5LyWosHUpGkmW1WfDQgl02MLLaDzbwWbt2bZplmq0wuxXmtroQNn3QBp4WGEv/f1m7Hn30Ubd882mnnebaZdM2D1Yr6WAseONvp2UJWbq7nW21ZZAPxpaBfvfdd10Ay5ZitimFFtSxrCRLnfcXZj9SNpC0fc7OdtrrYFMULLhn/4/1qRVWv+uuu9y+b9vZ/mP7pQ3MbRsbSOakn5555hn3t9gCBbYv79q1yy1RbbUjbGlrAAAOlY2HbDyX3m233Zbp9jZN36aN2eIi9l1kNZ/sZEvt2rW1devWDNvbd519V1kNpLp166bWdcxKcnKyy7B6+umnXcDmqKOOcidf7PsyFHL6nX4obAxp9Tpt5oDV8fz111/didwLL7zQPbexsZfVnmrTpo0bs9jJQxvTWrZWYHH3cI9HgZiSw1X6gJh18803Z1gi1r+8fKNGjXwFCxb0FS1a1Fe3bl3fPffc41u1alXqNrbk7IUXXpjhd+357HkD+ZfqfeaZZ9IsW1u4cGHfsmXLfM2bN/cVKlTIV65cObds7YEDB3K1Teajjz7y1atXz5eUlOSrUqWK76mnnvKNGDHCtcva57dmzRr3HPZ/2GOBy+DOnTvX17hxY1+BAgV8lStX9g0cODB1OeTA58iNdmTG32fpWRszWyY4s3Zs27bN17t3b99xxx3n/o7SpUv7TjvtNN+AAQNSlz3O7PXys/vtNTIbNmxwr3XNmjVdu4oVK+b6Z+zYsWl+x17Pe++91/1f9jrbksNLly517bO/yW/37t2+O++801ehQgX3Op9++um+WbNmZViOOLOlnzPjf22mT5/uu+GGG3wlSpTwFSlSxHf11Vf7Nm7ceNC+8rN99PLLL/cVL17cvW6nnHKKb8KECZkuHz1u3LgcLZftX555/fr1ae7/3//+52vatKnrT7tY31ofL168OM12Q4cO9VWtWtWXmJjoO+mkk9zy1jntpylTpri+tT5OTk72tW7d2vfLL79k25cAAKTn/47L6rJy5cosv4vefvttX7Vq1dxYpEGDBr7PPvvMjQns+zi9lJQUX6VKldzzPPbYYxkez+z/+Ouvv3xt2rRx3902PmnXrp0bMwaOY7ISyu/0rMZw6fti+PDhvjPPPNNXqlQp991/7LHH+u6++27fli1b0vze559/7qtTp47r1xo1arh+9rfvcMaj6ccWAA5NnP0T7sAYgMxZpo5l59jZGSAYbEqbZV3Z8sk27Q0AAAAAQoWaUgAAAAAAAAg5glIAAAAAAAAIOYJSAAAAAAAACDlqSgEAAAAAACDkyJQCAAAAAABAyBGUAgAAAAAAQMglhP6/jHwpKSlatWqVihYtqri4uHA3BwAARACfz6dt27apYsWKio/nvJ4f4yYAAHC44yaCUpmwgFSlSpWy7DQAABC7Vq5cqaOPPjrczYgYjJsAAMDhjpsISmXCMqT8nZecnKxgnFFcv369ypQpw5lW+jWisa/Sr9GE/ZV+DbatW7e6k1b+cQJCM24CAAB5d9xEUCoT/il7NrAKVlBq9+7d7rlJ/6dfIxn7Kv0aTdhf6ddQYWp/aMdNAAAg746bKIgAAAAAAACAkCMoBQAAAAAAgNgKSn311Vdq3bq1q8ZuKV3jx49P87jdl9nlmWeeyfI5H3nkkQzb16xZMwR/DQAAQPSNpapUqZLh8SeffJKXEgAA5O2g1I4dO1S/fn0NGTIk08dXr16d5jJixAg3UGrbtm22z1u7du00vzdjxowg/QUAAADRP5Z69NFH02x3yy23hOgvAAAAsSyshc5btmzpLlkpX758mtsffvihzjnnHFWrVi3b501ISMjwuwAAAHlNbo2lbGUcxk4AACDUomb1vbVr1+qTTz7RG2+8cdBtlyxZ4tLYk5KS1KRJE/Xv31+VK1fOcvs9e/a4S+DShf6VnOyS2+w5fT5fUJ47ltGv9Gm0YF+lX6MJ+2vavohm2Y2lbLpev3793Hjpqquu0h133OFO8gEAAART1Iw2bABlZ/Euu+yybLdr3LixRo4cqRo1arj08759++qMM87QwoUL3e9nxoJWtl1669ev1+7duxWMQe2WLVtcYCo+nlrz9GvkYl8NSqcq4Y9FSlm7WpvLVdD+qjUlPgdyqWv5bA0G+vU/27ZtUzTLaix166236sQTT1TJkiU1c+ZM9e7d242hBg4ceEgn8wAAAPJsUMpqIFx99dUu+yk7gSns9erVc0GqY445RmPHjlWXLl0y/R0bfPXq1SvN4KpSpUoqU6aMkpOTFYwBvtVzsOcnKEW/RjL21Vw27xvFjRmuuE0bUu/ylSgt35XdpRNPz+3/Leawv9KvwXawMUi0jqUCx0A2dipQoIC6d+/uTtolJibm+GQeAABAngxKff3111q8eLHGjBlzyL9bvHhxVa9eXUuXLs1yGxtwZTbosoBRsIJGFpQK5vPHKvqVPo1Yc2dIwx7PcLcFqOLs/h59pEZNw9K0vITPAPo1mKL5O/tQxlJ2Qm///v1avny5yzzP6ck8AACAQxUVo6vXXntNjRo1cqvLHKrt27dr2bJlqlChQlDaBgAHlXJAGj0s+21GD/e2A4Awj6Xmz5/vAnBly5bN9HE7kWeZ5IEXAACAqMuUsoBRYAbTH3/84QZCVtPAX5jczr6NGzdOzz77bKbP0axZM7Vp00Y9e/Z0t++66y61bt3aTdlbtWqVHn74YeXLl08dOnQI0V8FAOn8tlAKmLKXqU3rpV/nS7Ub0X0AQjaWmjVrlr799lu3Ip/Vm7LbVuT8mmuuUYkSJXglAABA3g1KzZkzxw2C/Pyp4J06dXLFys3o0aNdQfCsgkqWBbVhw38He3/99ZfbduPGja5mU9OmTTV79mx3HQDCYss/OdvuuQek0uWkckdJ5Y7+9+e/10uVkeLzBbulAKLMkY6lLOvJHn/kkUdc8fKqVau6oFTg9DwAAIBgifPZKAVp2BnFYsWKuRXyglXofN26dS4tPprrU0Qa+pU+jViLfpQG3Htkz5GQXypbIWOwyn4mF7diSop1fAbQr9E+PohW9AuAw9Xz1RlB6bzBXanTCUTL+CAqCp0DQFQrWNgqJFvUJOttSpSRHnheWr9KWvu3tOZvae1f3vV1q6T9+6RVK7xLhucvlHl2VbmK3v99uKzGlU09tEyvYiWl6nXI1gIAAACQawhKAUAwzflaGjEg+4CUad9dKl7SuxxfJ2Nw6J/1XoDKBaz+DVbZZeNaaddOafkS75JesRKZZ1eVKS/lL5D9aoFWnD2wFlaJ0lL7G1klEAAAAECuICgFAMFgQaiP3pYmjPJun3CidOq50gcj0wV6yngBqUbZpJlbLanS5b1L+kLo+/ZK61dnzK6yy9ZN0pZ/L7/9lPb34uKlUmX/C1aVDwhcLf9NGvZExnZYu196TOrRh8AUAAAAgCNGUAoActvundKrz0jzZ3m3z28jXd5VypdPOvUcpSz+SVtXLFdy5SqKr1H3yKbEWbZTxWO8S3o7d0jrMsmusou1ccMa7/Lz3EP7P0cPlxo2YSofAAAAgCNCUAoAcpPVfxrcV1r1p1ec/NpbpdPP/+9xC0DVqKfdJcoruWxZr9ZUsBQqLFWp7l0C2foWlkUVGKRyl7+8bCubLpidTeu9WlM16wev7QAAAADyPIJSAJBbfv3Bm/a2Y5tXGPzmh6RqNSOvf22lPmufK15eN+1js6d6WV4HY8XPAQAAAOAIEJQCgCNlmUdTP5TGvuzVkqpawwtIFS8VfX1bvHTOtrOAFgAAAAAcAYJSAHAkrND4O4OlGZ97t5s0kzrelv3KdpGseh1vlb3AYuzpWXF22w4AAAAAjkAQi5kAQB5nU9gG3OsFpGw1u3bdpOvvit6AlL/mVfsbs9+mQWOKnAMAAAA4YgSlAOBwLP9NeuxWadmvUsHC0m2PSi3aevWaol2jplKPPl7GVCD7O803k6VVK8LSNAAAAAB5B9P3AOBQfTtNGvmcN3WvfCWp58NS+aPzVj9aYKphE2+VPcsIsxpSx50gPf+g9Ot86ZUnpfsHRXdWGAAAAICwIlMKAHIq5YD03mvSK095Aal6p3iBmbwWkAqcylezvtT4HO9nQn6py91S0WLSyt+lca+Gu4UAAAAAohhBKQDIiZ3bpRcekSaN8263vMLLkCr075S2WGErCna+07v+xUfS/NnhbhEAAACAKMX0PQA4mDV/SYMf8X7adLXr7vCyh2KVZYid30aa/IE0cqD08NCM9acAAADykJ6vzgjK8w7u2jQozwtECzKlACA7C+dIj9/mBaQs8HLvs7EdkPK7rLNU+Thp+1bp1ae9qY0AAAAAcAgISgFAZnw+b6re8w9Ju3Z4Rb77vChVOZ7+MpYxdsN9UmKStHiBNHEM/QIAAADgkBCUAoD09u6RXnvGK2ruS5GatpDufFIqVoK+CmQF3q/u6V3/6G1pyc/0DwAAAIAcIygFAIE2bZCevkua/YUUHy9ddZPU6XYvMwgZNWnmTWdMSfFWJdyxjV4CAAAAkCMEpQDAb9mv0mO3SMuXSIWLSnc8IZ17sRQXRx9lxfrm2lukMhWkf9ZJbz7vTX0EAAAAgIMgKAUA5pvPpWfukbZsko6qIvV5QarVgL7JiaRCUvfeUr580twZ0lef0m8AAAAADoqgFIDYduCANHqY9PpAaf8+qeFpUu/nvMwf5FyV6t6KfMb68+/l9B4AAACAbBGUAhC7tm+TBvWRpoz3bre+WurRR0oqGO6WRafzL5NqN5L27ZVeftIrGA8AAAAAWSAoBSA2WSbP47dKv/4gFUj0glGXXOsVN8fhsb67/i4puYTXv2NfoScBAAAAZImjLwCxZ/4s6Yk7pPWrpdLlvOl6jZqGu1V5Q7ESUpe7vOtfTvBqTAEAAABAJghKAYgdtirchHelIY9Ke3ZJNepJD7wgVaoW7pblLTaF74J23vU3Bkkb14W7RQAAAAAiEEEpALFhz25p+BPS+De84NQ5raU7npCKFgt3y/KmSzt6xc93bpdefcorKA8AAAAAAQhKAcj7Nq6VnrxTmvO1lC+fdO2t0tU3SwkJ4W5Z3pWQX7rhPimpkLTkZ2nCqHC3CAAAAECEISgFIG/77SfpsVullcu8rKg7n5LOahXuVsWGshWla2/xrtu0ycULwt0iAAAAABGEoBSA6JdyQFr0o/TtNO+n3TbTP5GevU/atkWqfKzU50Wpep1wtza2ND5HOu18yZcivfq0tH1ruFsEAAAAIEKENSj11VdfqXXr1qpYsaLi4uI0fvz4NI9fd9117v7AywUXXHDQ5x0yZIiqVKmipKQkNW7cWN99910Q/woAYWWru93bSRpwr/TKU97PezpJzz0gvfWiV8vo5LOke5+VSpXlxQqHq26Syh0lbdogjXzOq+kFAAAAIOaFNSi1Y8cO1a9f3wWRsmJBqNWrV6de3n333Wyfc8yYMerVq5cefvhhzZs3zz1/ixYttG4dqz8BeTIg9dJjXrAj0OYN0s9zveuXXefVNkpMCksTISmpoNS9t1dnav4sadrHdAsAAAAAhbXKb8uWLd0lO4mJiSpfvnyOn3PgwIHq1q2bOnfu7G4PGzZMn3zyiUaMGKH77rvviNsMIELYFL3Rw7LfpkiydEE7KS4uVK1CViofJ13exXvNxr4iHV9HqlSN/gIAIJf1fHVGrvfp4K5Nc/05AcBE/NJTX375pcqWLasSJUro3HPP1WOPPaZSpUpluu3evXs1d+5c9e7dO/W++Ph4nXfeeZo1a1aW/8eePXvcxW/rVq/mSUpKirvkNntOn88XlOeOZfRrjPXp4p8Unz5DKr3tW5Wy+CepRj1Fkoju12A6p7Xifp6ruJ++l+/l/vLd/3yuZrDFbL8GGf2ati8ijZVCeOaZZ9z4xzLKP/jgA1166aVpSiG88cYbaX7HMsgnTZqUevuff/7RLbfcoo8//tiNm9q2bavnn39eRYoUCenfAgAAYk9EB6Vs6t5ll12mqlWratmyZbr//vtdZpUFmPLZsu7pbNiwQQcOHFC5cuXS3G+3Fy1alOX/079/f/Xt2zfD/evXr9fu3bsVjEHtli1b3MGTDf5Av0aqSN5Xk1YsV/EcbLd1xXLtLpHzbMtY79dgi7voWpVevkT5Vq/Urjee19ZLvazW3BDL/RpM9Ot/tm3bpkjjL4Vw/fXXuzFTVuOp119/PU0WeqCrr77aBbQmT56sffv2uWzzG264QaNGjQp6+wEAQGyL6KBU+/btU6/XrVtX9erV07HHHuuyp5o1a5Zr/49lVlkdqsBMqUqVKqlMmTJKTk5WMAb4VrTdnp8DJ/o1kkX0vlq5So42S65cRcllI6vAeUT3a9CVlbrdI99zD6jQnOlKOvE06aQzcuWZY7tfg4d+/Y8toBJpjrQUwq+//uqypr7//nuddNJJ7r4XX3xRrVq10oABA9xiNAAAADEZlEqvWrVqKl26tJYuXZppUMoeswyqtWvXprnfbmdXl8oGa+nPGho7qAnWgY0dOAXz+WMV/RpDfVqjrlSspLTln6y3KVFG8bZdpLU9kvs1FE44UWp5pTRxtOLfekGqVkMqnTvZbDHdr0FEv3qidb/KrhSCZZ8XL148NSBlrOyB/a3ffvut2rRpk+OyBwAAAIcqqkZXf/31lzZu3KgKFSpk+niBAgXUqFEjTZ06Nc0ZXrvdpEmTELYUQNDt2inFZ5zGm0b77gffBuFx8TVStZrSrh3Sy09K+/fzSgBBYFP33nzzTTcWeuqppzR9+nSXWWXlDsyaNWtcwCpQQkKCSpYs6R7LquxBsWLFUi+WXQ4AABB1Qant27dr/vz57mL++OMPd33FihXusbvvvluzZ8/W8uXL3WDqkksu0XHHHecKdPpZxtTgwYNTb9s0vFdeecUV9bSU9B49erh6C/7V+ADkAXv3SIP7SpvWS4WKSMVKpH28RBmpRx+pESvFRKyEBOmG+6SChaXfF0kfvR3uFgF5kpVCuPjii10ZBCuAPmHCBDdVz7KnjqTsgdVv819WrlyZq20GAACxI6zT9+bMmaNzzjkn9ba/rlOnTp300ksvacGCBS64tHnzZlfToHnz5urXr1+aqXZWAN0KnPtdeeWVrkD5Qw895M7wNWjQwNVKSF/8HECUSjkgvfKUtGShF9C45xmpYmXpt4XeVD6b0le9DhlS0cCm7HW6TRr2hPTpGKlWA+8CIGSlEKy8wbp169Jss3//frciX1alD7IqewAAABBVQamzzz7brZKUlc8+++ygz2FZVOn17NnTXQDkMfZ58c5Q6YeZUkJ+qefD0tFVvcdq1g9363A4TjpTOmOe9PUk6dWnpUeGSkVzsq4igNwohWDlDezk39y5c10JBPPFF1+48geNGzemkwEAQFBFVU0pADHuk3el6Z9Y1WWp6z1SjXrhbhFyQ/sbpQqVvUy31wd6wUcAISmFUKtWLVd3qlu3bvruu+/0zTffuBN7Nu2PlfcAAECwEZQCEB2++lQa/6Z3vUMP6aQzwt0i5JbEJK++lGW/LfhOmvohfQscQimEhg0buou/FIJdtzIGtiKxlUKwmlLVq1dXly5dXDbU119/nWb63TvvvKOaNWu66XytWrVS06ZN9fLLL/MaAACAvD19DwByZP4s6a0Xveut2kvnXkzH5TWVqklXdJNGDZXee82rC1b5uHC3Coh4uVEKwVbaGzVqVC63DAAA4ODIlAIQ2Zb+Ig3vL/lSpNPOl9p0CneLECzntJYaNJH27/Ne89276GsAAAAgDyMoBSByrVohvfiwtG+vVPdkqeNtXj0p5E322l53h1SitLT2by9rCgAAAECeRVAKQGTatEEa1EfasU2qVlO68QEpgRnHeV6RZK+IfVy8NHOy9O20cLcIAAAAQJAQlAIQeXZu9wJS/6yTyh0l3dLXK4aN2GCrKl7UwbtutcTWrQp3iwAAAAAEAUEpAJHFpuoN7iv9vVwqVkK643GpaLFwtwqhdtFV0vG1pd07pZef9OpMAQAAAMhTmAsDIHKkHJBefVr67SepYCHp9sel0uXD3SqEQ758Utd7pb43Sct/k8a/KV3ehdcCABByPV+dEZTnHdy1aVCeFwCiCZlSACKDLWn+7jBp7gwpIb9088NSpWrhbhXCqVRZr/C5mTRO+nkurwcAAACQhxCUAhAZJo6Rpn3srcDW5W6pZv1wtwiR4MTTpbMv8q6/NkDasincLQIAAACQSwhKAQi/GZ9LH4z0rl/ZXTr5zHC3CJHkim7SUVWkrZukEQOklJRwtwgAAABALiAoBSC8FnwrvTnIu97yCum8S3lFkFaBROmG+6T8BbwpfJM/oIcAAACAPICgFIDw+X2RNOwJL/OlyXnSZZ15NZA5y5Rq3927/v7rXvFzAAAAAFGNoBSA8FizUnrhIWnvHqnOSVKn2716UkBWzmwlNWoqHdgvDe8v7d5JXwEAAABRjKAUgNDbvFF67gFp+1apSnXpxgekhAReCWTPgpYdb5NKlpXWr5beHkyPAQAAAFGMoBSA0Nq5Q3r+QWnjOqlsRem2R6WkgrwKyJnCRaVu90rx8dLsL6SZU+g5AAAAIEoRlAIQOvv2SkP6Sit/l5JLSHc8LhUtziuAQ3N8benia7zr7wyW1vxFDwIAAABRiKAUgNCwYuavPSMtXiAlFZJu7yeVqUDv4/C0ulKqUU/as1t6+Ukv4AkAAAAgqhCUAhB8Pp80epg052spX4J084NS5ePoeRy++HxS13u86Xwrlkr/e90FPJN+nO0FPlMO0LsAAABAhKOyMIDgmzRO+uIj73qXu6RaDel1HLkSpaXOvaTBfaUpHyh+ygcqHvhY+xu91foAAAAARCQypQAE1zeTpf+N8K5f2V065Wx6HLnnQBYZUZs2SC89Js2dQW8DAAAAEYpMKQDB89P30hvPeddbXC6d34beRu6xKXo2LTQ7o4dLDZt40/0AAABiRM9Xc//E3OCuZKAj95EpBSA4fl/sZapYgfNTz5XaXk9PI3f9ttDLiMrOpvXedgAAAAAiDkEpALlv7d/SCw9Je/dItU+UrrtDiufjBrlsyz+5ux0AAACAkOIoEUDusgDAcw9I27dIxxwv9egjJeSnl5H7ipXM3e0AAAAAhBRBKQC5Z9cOadCD0oY1UpkK0m39pKRC9DCCo3odb5W97JQo420HAAAAIOIQlAKQO/btlYb2k1Yuk4oWl+54XEouTu8ieKx4efsbs9+m+WUUOQcAAAAiFEEpAEfOipm//qz063wpsaCXIVW2Ij2L4GvU1Jsimj5jKn8B7+fsL6T9+3klAAAAgAgU1qDUV199pdatW6tixYqKi4vT+PHjUx/bt2+f7r33XtWtW1eFCxd223Ts2FGrVq3K9jkfeeQR91yBl5o1a4bgrwFilM8njX1F+m66lC+fdNODUpXjw90qxFpg6qk3lHLnk9rc7kb3U4+/JhUqIv25RJo0NtwtBAAAABBpQakdO3aofv36GjJkSIbHdu7cqXnz5unBBx90P99//30tXrxYF1988UGft3bt2lq9enXqZcaMGUH6CwDo8/9JUz7wOqLznd5qe0A4pvLVqKfd9U91P1WyjNShh/fYx6Oklb/zmgAAAAARJiGc/3nLli3dJTPFihXT5MmT09w3ePBgnXLKKVqxYoUqV66c5fMmJCSofPnyud5eAOnMmiqNe9W73q6bdOq5dBEih+2P876RfpgpjRggPfA8K0ECAAAAESSsQalDtWXLFjcdr3jx7IsnL1myxE33S0pKUpMmTdS/f/9sg1h79uxxF7+tW7e6nykpKe6S2+w5fT5fUJ47ltGvIe7Tn+cq7vWBirMZfOe3cRdXWwpH1q/I3f316psV99tCxa38Xb4J78p38TX0cG70a4yiDwAAAGI0KLV7925XY6pDhw5KTk7OcrvGjRtr5MiRqlGjhpu617dvX51xxhlauHChihYtmunvWNDKtktv/fr17v8NxqDWAmw2yI+Pp9Y8/Rq5stpXE/7+QyVfe1JxKQe0q96p2nJGa2ndurC2NZrwGRDafk266BoVHzNUmjha/1Sqrv1HVQlSC/Im9tf/bNu2LYyvBAAAQN4TFUEpK3p+xRVXuAONl156KdttA6cD1qtXzwWpjjnmGI0dO1ZdunTJ9Hd69+6tXr16pcmUqlSpksqUKZNtAOxIBviW8WXPT1CKfo1kme6r61Yp7u1Bitu7R75aDZR4Y2+VTcgf7qZGFT4DQtyvzS6Sb9lPipvztUp9+Lp8D7wg5WefPeJ+jUGWgR1pbNGYZ555RnPnznUn4z744ANdeumlqeOnPn36aOLEifr9999daYTzzjtPTz75pMso96tSpYr+/PPPDCfs7rvvvpD/PQAAILYkREtAygZLX3zxxSEHiWyqX/Xq1bV06dIst0lMTHSX9GzwHawBuA3wg/n8sYp+DXKfbtkkPf+gtG2LVPk4xd38kOIKZHzv4BD7FcHZXwNd3VNa/JPiVv2puAnvSG2vp9dzo19jTCT+/f5FY66//npddtllWS4aY9ts2rRJt912m1s0Zs6cOWm2ffTRR9WtW7fU21lllwMAAMRMUMofkLIaUdOmTVOpUqUO+Tm2b9+uZcuW6dprrw1KG4E8K+WAO4hPWrFcqlxFOuZYLyC1frVUpoJ0Wz8pqVC4WwnkTNFi0rW3SEP7SZPekxo0kY6tRe8h6uXWojEWhGKRGAAAEGphPeVnAaP58+e7i/njjz/cdRsoWUDq8ssvd2fy3nnnHR04cEBr1qxxl71796Y+R7NmzdwAy++uu+7S9OnTtXz5cs2cOVNt2rRRvnz5XC0qADk0d4Z0byfFP3ufio8b5n6q11XSiqXewf3tj0nFStCdiC4nni41PkfypUivPyvt/W+BCyBWZLVojE3ps5N/DRs2dNMB9+/fH7Y2AgCA2BHWTCkLOJ1zzjmpt/11nTp16qRHHnlEH330kbvdoEGDNL9nWVNnn322u25ZUBs2bEh97K+//nIBqI0bN7r6F02bNtXs2bPddQA5DEi99FjG+/fv8342byuVO4quRHS66iZp0Y/Smr+kD96Qrrwh3C0Cwr5ozK233qoTTzxRJUuWdCf0rNam1acaOHDgIa1aDAAAEFVBKQssWfHyrGT3mJ9lRAUaPXp0rrQNiNkpe6OHZb/NFx9LLdpK8flC1Sog9xQuKnW6TXrhYWnKB1LD06Tqdehh5HnZLRoTuNiLLRJToEABde/e3RU7z6zmZlarFgMAAByqyKvYCSB8flsobfov8zBTm9Z72wHRql5j6fTmdubDm8a3Z3e4WwSEbNEYqzF1sEVjbOVim76X/sSfn2VS2TRA/2XlypVBajkAAMjrCEoB+M+Wf3J3OyBSXdldKlHaK9z/vxHhbg0QkkVjpkyZkqNFY6y+p600WLZs2Uwft+wpC2wFXgAAAPLc6nsAQqxYydzdDohUhQpL190hPfeA9MVHXhH0mvXD3SrgsBaNWbp0aept/6IxVh+qQoUKbtGYefPmacKECamLxhh73KbpzZo1S99++62r8Wkr8NntO+64Q9dcc41KlGBBCwAAEFxkSgH4j9XWseyR7JQoQw0e5A21G0lntfKujxwo7d4Z7hYBh7VojK2YZxd/fSi7/tBDD+nvv/92i8bYIjC2aIwFqfwXK2juz3qyepxnnXWWateurccff9wFpV5++WVeDQAAEHRkSgH4jxUvt2lNwx7Pulfad6fIOfKOdl2ln+dKG9ZK416Vrr013C0CQrpojK26Z6sUAwAAhAOZUgDS2vffMt8ZMqR69JEaNaXHkHckFZKu+3flsekTvQAVAAAAgJAgUwrAf2z60nuvedcvu04pVWtq64rlSq5cRfE16pIhhbzJakmde7FXW2rkc1LfYVKhIuFuFQAAAJDnkSkF4D8T3pW2bJLKVpTOv0yqUU+765/qfrqpfUBe1fZ6b7/ftEEaQy0dAAAAIBQISgHwrPlLmvzBf3Wj8hegZxA7EpOkzr2kuDjpm8+lH78Nd4sAAACAPI+gFADPmOHSgf1S3ZOleo3pFcSe4+tI57Xxrr/5vLR9W7hbBAAAAORpBKUASAu+lX76XsqX4K2+B8SqNp2k8pWkLf9I7w4Nd2sAAACAPI2gFBDr9u2VRg/3rp/fRip/dLhbBIRPgUTp+juluHjp22nSvG94NQAAAIAgISgFxLop46V1q6RiJaWLOoS7NUD4VaspXXC5d/2tF6Rtm8PdIgAAACBPIigFxLLNG6UJo7zrl3eRkgqFu0VAZLj4GqniMdK2LdI7Q8LdGgAAACBPIigFxLJxr0p7dkvH1pJOPTfcrQEih60+ef1dUny8NOdr6fuvwt0iAAAAIM8hKAXEqiU/ezVz4uKkDjd5PwH8p8rx0oX/Tml9Z7C0ZRO9AwAAAOQiglJALEo58N/KYmdc4B18A8jowvZSpWOl7Vu9+lI+H70EAAAA5BKCUkAs+vozacUyqWBhqU2ncLcGiFwJ+b3V+PIlSPNnSbO/CHeLAAAAgDyDoBQQa3Zskz4Y6V2/5FqpaPFwtwiIbJWqSa2v9q6/+5K0aUO4WwQAAADkCQSlgFjz4VveVCRbWezsi8LdGiA6tLzCm+a6c7v05vNM4wMAAAByAUEpIJb89Yf05QTveocbpYSEcLcIiA758nmr8dl0vp++l775PNwtAgAAAKIeQSkgVliB5tHDpJQUqVFTqVbDcLcIiC6WXXhJR+/6mOHSxnXhbhEAAAAQ1QhKAbFi7gxp0Y9S/gJSu67hbg0QnVpcJh1bS9q1U3pjENP4AAAAgCNAUAqIBXt2S2Nf8a5f0E4qXT7cLQKiU3w+qfOdXnD3l3nS9InhbhEAAAAQtQhKAbFg0jjpn3VSybJeUArA4St/tHRZZ+/6uFek9WvoTQAAAOAwEJQC8roNa7yglLmim5SYFO4WAdGv2SXS8XW8LMTXn/VqtQEAAAA4JASlgLzOpu3t2yvVbOAVOAdw5OLjpc69pAKJ0m8/SdM+plcBAACAQ0RQCsjLrObNvG+8A+gON0pxceFuEZB3lK3436IB/xshrf073C0CAAAAokpYg1JfffWVWrdurYoVKyouLk7jx49P87jP59NDDz2kChUqqGDBgjrvvPO0ZMmSgz7vkCFDVKVKFSUlJalx48b67rvvgvhXABFq/35p9DDv+jmtpaOqhLtFQN5z1oVSrQbS3j3SCJvGdyDcLQIAAACiRliDUjt27FD9+vVdECkzTz/9tF544QUNGzZM3377rQoXLqwWLVpo9+7dWT7nmDFj1KtXLz388MOaN2+ee377nXXr1gXxLwEi0JcTpFUrpCLFpIuvCXdrgLzJshCvu0NKKiQt+0WanPbkCgAAAIAIDUq1bNlSjz32mNq0aZPhMcuSGjRokPr06aNLLrlE9erV05tvvqlVq1ZlyKgKNHDgQHXr1k2dO3fWCSec4AJahQoV0ogRI4L81wARZOtm6cO3vOuXXScVLhruFgF5V6ly3iIC5oORXjAYAAAAQPTWlPrjjz+0Zs0aN2XPr1ixYm463qxZszL9nb1792ru3Llpfic+Pt7dzup3gDzJDox37ZAqHyc1bR7u1gB53xkXSHVOkvbv81bjO8A0PgAAAOBgEhShLCBlypUrl+Z+u+1/LL0NGzbowIEDmf7OokWLsvy/9uzZ4y5+W7dudT9TUlLcJbfZc1omWDCeO5bRr/9a/pviZnwmK2me0r67ZNcOc1+jT4ODfs2j/XrtrYp7pIfi/lislEljpZZXKi8Ie79GEPoAAAAgRoJSodS/f3/17ds3w/3r16/Ptn7VkQxqt2zZ4gb5lskF+jUXdy6VfGuwCvh82lW/ibYkl5GOoJ4a+2pw0K95t1+TWnZQ8fdfVdxHb2vjUcdpf/lKinaR0K+RYtu2beFuAgAAQJ4SsUGp8uXLu59r1651q+/52e0GDRpk+julS5dWvnz53DaB7Lb/+TLTu3dvVxw9MFOqUqVKKlOmjJKTkxWMAb6tNmjPH+sD/NxEv0qaNVXxK5fKl5ikxKtvUtnipejTCMS+mof7tUUb+Zb+pLgF36rUhyPl6/2clBCxX7XR068Rwlb1BQAAQO6J2JFy1apVXSBp6tSpqUEoCxbZKnw9evTI9HcKFCigRo0aud+59NJLUwfTdrtnz55Z/l+JiYnukp4NvoM1ALcBfjCfP1bFdL/u3im97xX0j7voKsWVLJMrTxvTfRpE9Gse7tdOt0kPdVfcymWKs2l8eWD1y4jo1wgQ638/AABAbgvr6Gr79u2aP3++u/iLm9v1FStWuAHw7bff7lbn++ijj/TTTz+pY8eOqlixYmrAyTRr1kyDBw9OvW0ZT6+88oreeOMN/frrry6AtWPHDrcaH5CnTXhX2rJJKltROu+/9wiAECtWUrrqJu/6J+9KK5byEgAAAACRFpSaM2eOGjZs6C7+gJJdf+ihh9zte+65R7fccotuuOEGnXzyyS6INWnSpDTp88uWLXMFzv2uvPJKDRgwwD2HZVhZkMt+J33xcyBPWfOXNPkD77oVN89fINwtAmLbKWdLjZp6q/C9NkDatzfcLUIe9dVXX6l169bupJ2d0Bs/fnyax60WmI2JrBRCwYIF3YrES5YsSbPNP//8o6uvvtqVLChevLi6dOnixlwAAAB5Oih19tlnu8FS+svIkSPd4za4evTRR91qe1ZwfMqUKapevXqa51i+fLkeeeSRNPfZVL0///zTrahn0/0aN24c0r8LCCmfTxo9XDqwX6p7slSP/R0Iu7g46eqeUpFi0t/LpQmjwt0i5FGWDV6/fn0NGTIk08effvppvfDCCxo2bJgbExUuXFgtWrRIs5CLBaR+/vlnTZ48WRMmTHCBLjshCAAAEGwURwCi3YLvpIXfS/kSpPY3hrs1APySi0vX3uJd/3Ss9Mdi+ga5rmXLlq7UQZs2bTI8Zif6Bg0apD59+uiSSy5RvXr19Oabb2rVqlWpGVVW6sAyyl999VV3Eq9p06Z68cUXNXr0aLcdAABAMBGUAqKZTQkaPcy7fv5lUrmjwt0iAIFsCp9N5UtJkUY8yzQ+hJTV6rRsc5uy51esWDEXfJo1a5a7bT9tyt5JJ52Uuo1tb0XdLbMqM5aJbovPBF4AAADy1Op7AHLA6kitX+0VVr6oPV0GRCIrer74R2n1Cmn8m1K7ruFuEWKEBaRM+rqadtv/mP0sW7ZsmscTEhJUsmTJ1G3S69+/v/r27Ru0diM29Hx1RlCed3DXpkF5XgBAcJApBUSrTRu8lb3M5V2kpELhbhGAzBRJlq691bv++f+k3xZKi36Uvp3m/Uw5QL8hqvTu3VtbtmxJvaxcuTLcTQIAAFGKTCkgWr33mrRnt3RsLenUc8PdGgDZadBEanKeNGuKNOAebzqfX4nSXj04m+oH5KLy5cu7n2vXrnWr7/nZbVuh2L/NunXr0vze/v373Yp8/t9PLzEx0V0AAACOFJlSQDRa8rOXZWErfHW4yfsJILKd4AUB0gSk/FmPLz0mzQ3OVBbErqpVq7rA0tSpU1Pvs/pPViuqSZMm7rb93Lx5s+bOnZu6zRdffKGUlBRWLwYAAEFHphQQbWyqz7tDvetnXCBVOT7cLQKQk/ft+yOz32b0cKlhEyk+H/2JHNu+fbuWLl2aprj5/PnzXU2oypUr6/bbb3er8x1//PEuSPXggw+qYsWKuvTSS932tWrV0gUXXKBu3bpp2LBh2rdvn3r27Kn27du77QAAAIKJoBQQbb7+TFqxTCpYWGrTKdytAZATVkfKMqKys2m9t13N+vQpcmzOnDk655xzUm/36tXL/ezUqZNGjhype+65Rzt27NANN9zgMqKaNm2qSZMmKSkpKfV33nnnHReIatasmVt1r23btnrhhRd4FQAAQNARlAKiyY5t0gf/Zltccq1UtHi4WwQgJ7b8k7vbAf86++yz5fP5suyPuLg4Pfroo+6SFcuqGjVqFH0KAABCjqAUEE0+fEvavlWqeIx09kXhbg2AnCpWMne3Q9TbvXu3XnzxRU2bNs0VGrcaToHmzZsXtrYBAACECkEpIFr89Yf05QTveocbpQTevkDUqF7HW2Uvuyl8Jcp42yEmdOnSRZ9//rkuv/xynXLKKS6jCQAAINZwVAtEA5ua8e5L3qpdtmx8rYbhbhGAQ2HFy9vf6K2yl5X23SlyHkMmTJigiRMn6vTTTw93UwAAAMImPnz/NYAcm/u1tHiBlL+AdEU3Og6IRhZQ7tHHy5hKr8l53uOIGUcddZSKFi0a7mYAAACEFZlSQKTbs1sa+4p3veUVUqly4W4RgMNlgaeGTbxV9qyoua2k+dl70sI53ns98b8V0ZC3Pfvss7r33ns1bNgwHXPMMeFuDgAAQORnSn3xxRc64YQTtHXr1gyPbdmyRbVr19bXX3+dm+0DMGmc9M96qVRZ6YJ29AeQF6by1awvNT5HanOdVLq8tG2z9OUn4W4ZQuikk05yxc6rVavmMqZsBbzACwAAQCw4pEypQYMGqVu3bkpOTs7wWLFixdS9e3cNHDhQZ5xxRm62EYhd69dIn471rl9xg1QgMdwtApCbbMGCizpII5+TC0CffSHZUjGiQ4cO+vvvv/XEE0+oXLlyFDoHAAAx6ZCCUj/++KOeeuqpLB9v3ry5BgwYkBvtAmDGvSLt3yfVbCCdSDFcIE86tZk04V1pwxovW6pF23C3CCEwc+ZMzZo1S/Xr16e/AQBAzDqk6Xtr165V/vz5s3w8ISFB69evz412AfhlnjTvGyk+Xupwo8Ry4UDezpYyli1ltaWQ59WsWVO7du0KdzMAAACiJyhlK8UsXLgwy8cXLFigChUq5Ea7gNi2f780eph3/ZzW0lFVwt0iAMHOlvLXlppObalY8OSTT+rOO+/Ul19+qY0bN7p6nYEXAACAWHBIQalWrVrpwQcfdIU507OzfQ8//LAuuuii3GwfEJu+nCCtWiEVKSZdfE24WwMgFNlSF7b3rn9KtlQsuOCCC9z0vXPPPVdly5ZViRIl3KV48eLuJwAAQCw4pJpSffr00fvvv6/q1aurZ8+eqlGjhrt/0aJFGjJkiA4cOKAHHnggWG0FYsPWzdKHb3nXL7tOKlw03C0CEApNzpM+sdpSa71sqebUlsrLpk2bFu4mAAAARFdQylaH+eabb3TTTTepd+/e8vl87v64uDi1aNHCBaZsGwBH4IOR0q4dUuXjpKbN6UogprKlOkhvDPKypc5iJb687KyzznKZ51b6YN26dUpJSQl3kwAACIuer84IyvMO7to0KM+LMAalTJUqVTRx4kRt2rRJS5cudYGp448/nlRzIDcs/02a8Zl3vUMPKT4f/QrEErKlYsakSZPUsWNHbdiwIcNjdrLPss8BAADyukMKStkZvUGDBmnz5s267bbbdPLJJwevZUCssbPko4ZKloF46rnS8bXD3SIAoUa2VMy45ZZb1K5dOz300ENkmQMAECXI6gpzofMuXbpoyZIlKlWqlM4777wgNAeIYbO/kH5fJCUWlC7vEu7WAAhntlTpcv+uxDeR1yGPWrt2rXr16kVACgAAxLT4Qy3KaQOou+++2wWnrAYCgFxgNaT+95p3/aKrpOKl6FYglrOlWv27Et8kVuLLqy6//HJ9+eWX4W4GAABA9Ezfs6Kczz//vFt9r3Llym4JYwC5YMK70pZNUrmjpPMuoUuBWHfaedLE0f+uxDdRan5ZuFuEXDZ48GA3fe/rr79W3bp1lT9//jSP33rrrfQ5AADI8w4pKPXaa6+5mlKWcj516tTgtQqIJWtWSlPGe9ev7C7lLxDuFgEIt4T8XrbUm8972VJntZISk8LdKuSid999V59//rmSkpJcxpQVN/ez6wSlAABALDikoFShQoV0//33B681QKyxouajh0sH9kv1TvEuAGDIlsrTHnjgAfXt21f33Xef4uMPqZoCAABAbAalwqFKlSr6888/M9x/0003aciQIRnuHzlypDp37pzmvsTERLdyIBBxFnwnLZwj5UvwsqQAwI9sqTxt7969uvLKKwlIIeJXhRrctWmuPycAAH4Rf2ru+++/1+rVq1MvkydPdvdbHYasJCcnp/mdzIJaQFikHJAW/Sh9O036ea707kve/edf5tWTAoD02VK2Et/WTazEl8d06tRJY8aMCXczAAAAwiriM6XKlCmT5vaTTz6pY4891hVdz4rVYihfvnwIWgccgrkzpNHDpE0b0t5fqIh00b8rbQFAVtlSn42Tzr5QKpBIH+UBBw4c0NNPP63PPvtM9erVy1DofODAgWFrGwAAQKhEfKZU+lT3t99+W9dff32agqDpbd++Xcccc4wqVaqkSy65RD///HNI2wlkGpB66bGMASmzc7v08zw6DUDW2VKlynordNpKfMgTfvrpJzVs2NBN31u4cKF++OGH1Mv8+fPD3TwAAICQiPhMqUDjx4/X5s2bdd1112W5TY0aNTRixAh31nHLli0aMGCATjvtNBeYOvroozP9nT179riL39atW93PlJQUd8lt9pw+ny8ozx3LIrZfUw4ozjKkLIsvk4d99s/oYfLVbyzF51Mkidg+jXL0K/16SOxzoVV7xb/1gnyfjpXvjAtCmi3F/pq2L3LLtGnTcu25AAAAolVUBaVee+01tWzZUhUrVsxymyZNmriLnwWkatWqpeHDh6tfv36Z/k7//v3dCjjprV+/PigF0m1QawEzO9hnxZ28368Ffv9VJTPLkPqXC1Rt2qBN383Q3mq1FEkitU+jHf1Kvx6yY+upTPFSyrd5o7ZNHKudp7VQqLC//mfbtm0h63cAAIBYEDVBKStWPmXKFL3//vuH9HtWo8HS45cuXZrlNr1791avXr3SZErZ1D+rZ2VF04MxwLfph/b8HOjHQL8u/yVHmxWP90llyyqSRGyfRjn6lX49LK2vlt56QUVnTFKRVleELFuK/fU/SUlJIelzAACAWBE1QanXX39dZcuW1YUXXnjIhUStbkOrVq2y3CYxMdFd0rOD8GAdiNuBfjCfP1ZFZL8WL5WjzeJtu0hqdyT3aR5Av9Kvh+z086WJoxW3cZ3ivp4knd9GocL+6uFzEAAARJOer87I9ecc3LVprj5fVBxl2llaC0rZ8skJCWnjaB07dnSZTn6PPvqoPv/8c/3++++aN2+errnmGpdl1bVr1zC0HJBUvY5UonT2XVGijLcdAGS3Et+FHbzrk8ZKe/+rhQgAAABEo6gIStm0vRUrVrhV99Kz+1evXp16e9OmTerWrZurI2XZUTYVb+bMmTrhhBNC3GogoEixZThkp333iCtyDiDCV+L76tNwtwYAAADI+9P3mjdv7gotZ+bLL79Mc/u5555zFyBibFwnffmJd71AkrR3d9oMKQtINcrdFEgAeThbqlV7V1tKn46RzmwZ0pX4AAAAgJjLlAKi1r690rDHpe1bpcrHSc+9K931lNTtXu/nUyMJSAE4NJZ5WZJsKeRMlSpVXE2w9Jebb77ZPX722WdneOzGG2+kewEAQEhERaYUELXGviL9sVgqVETq0UdKLCjVrB/uVgGI+tpSZEshZ77//nu36IvfwoULdf7556tdu3ap91nZA6vJ6VeoUCG6FwAAhASZUkCwfDtNmvaxd73r3VKZ8vQ1gNxBthRyqEyZMipfvnzqZcKECTr22GN11llnpQlCBW6TnJxM/wIAgJAgKAUEw6o/pTcGedcto6FeY/oZQO5nS5lPWYkPObN37169/fbbbuEYm6bn984776h06dKqU6eOW9F4586ddCkAAAgJpu8BuW33TmnoY95y7bUaSJdcSx8DCE621CejpX/WeSvxnXcpvYxsjR8/Xps3b9Z1112Xet9VV12lY445RhUrVtSCBQt07733avHixXr//fezfJ49e/a4i5+tdAwAAHA4CEoBuclWiXzjeWnNSqlEaanbfVJ8PvoYQJBrS41lJT4c1GuvvaaWLVu6AJTfDTfckHq9bt26qlChgpo1a6Zly5a5aX6Z6d+/v/r27UuPAwCAI8b0PSA3Tf1Q+n66lC+f1P1+Kbk4/QsgBLWl/vGypYAs/Pnnn5oyZYq6du2abR81buxNN1+6dGmW29gUvy1btqReVq5cSb8DAIDDQlAKyC3LfpHGveJdb9dNOu4E+hZACLKlrvSuU1sK2Xj99ddVtmxZXXjhhdn20/z5891Py5jKSmJioiuGHngBAAA4HASlgNywbbM07AnJlt0+6Uyp2SX0K4DQOL25VLIM2VLIUkpKigtKderUSQkJ/1VusCl6/fr109y5c7V8+XJ99NFH6tixo84880zVq1ePHgUAAEFHUAo4UikHpJefkjZtkMofLV13uxSwqhEAhHQlvn176XCkYdP2VqxY4VbdC1SgQAH3WPPmzVWzZk3deeedatu2rT7++GN6EAAAhASFzoEj9dHb0q8/SAUSpR59pKRC9CmA0GdLuZX41nu1pcjWRAALOvlsIY50KlWqpOnTp9NXAAAgbMiUAo7Egu+kCe961zvdLh1Vhf4EEN5sqYljyJYCAABAVCAoBRyuDWukV5/2rp9zkdT4HPoSQPhQWwoAAABRhqAUcDisZstLj0s7t0tVa0hX3EA/Agh/tlQrsqUAAAAQPQhKAYdj9DDpzyVSkWTpxgek/AXoRwDhd/r5rMQHAACAqEFQCjhUs6ZI0yd6K+x1vUcqVZY+BBAZLEDuz5ZiJT4AAABEOIJSwKH46w/prRe96xddJdU5if4DEJnZUps3Sl9PCndrAAAAgCwRlAJyatcO6aXHpL17pNonSq2vou8ARHa2FCvxAQAAIIIRlAJywueTXh8orf3by0Doeq8Un4++AxCZyJYCAABAFCAoBeTE5Peled9I+RKkG/tIRYvRbwAiF9lSAAAAiAIEpYCDWbJQeu817/qVN0jVatBnAKIjW6pEaWpLAQAAIGIRlAKys2WTNOwJKSVFOuVs6ZzW9BeA6EC2FAAAACIcQSkgKwcOSC/3l7b8I1WsLHW8TYqLo78ARI+mzcmWAgAAQMQiKAVkZfwb0uIFUmJBqceDUlJB+gpA9GZLfTpW2rc33C0CAAAAUhGUAjIzf5Z3AGeuu0OqUIl+AhDd2VKbNkhfTwp3awAAAIBUBKWA9Navll4b4F1vdol08pn0EYDoRbYUAAAAIhRBKSDQ3j3S0MekXTukY2tJ7brSPwCiH9lSAAAAiEAEpYBAo4ZKK5dJRYpJ3e+XEvLTPwDySLbUld51aksBAAAgQhCUAvxmfC7N+MxbYe+Ge6WSZegbAHlH0xbUlgIAAEBEieig1COPPKK4uLg0l5o1a2b7O+PGjXPbJCUlqW7dupo4cWLI2osotmKZ9M5g7/ol10onnBjuFgFA7iJbCgAAABEmooNSpnbt2lq9enXqZcaMGVluO3PmTHXo0EFdunTRDz/8oEsvvdRdFi5cGNI2I8rs3C699Ji3VHrdk/9bPh0A8nK2lGWGAgAAAGEU8UGphIQElS9fPvVSunTpLLd9/vnndcEFF+juu+9WrVq11K9fP5144okaPPjfDBggPZ9PGvGst+Je6XJSl3uk+Ih/WwDAkWdLTRzjBeMBAACAMElQhFuyZIkqVqzopuM1adJE/fv3V+XKlTPddtasWerVq1ea+1q0aKHx48eHqLWIOpPek+bP8gqa3/iAVKRouFsEAMHPlrKAlD9b6pzW9DhCquerWWe9H4nBXZsG5XkBAECMBqUaN26skSNHqkaNGm7qXt++fXXGGWe46XhFi2YMHqxZs0blypVLc5/dtvuzs2fPHnfx27p1q/uZkpLiLrnNntPn8wXluWPZIffr4gWKe/91xdnvXtldqnycPUmwmxlV2Ffp12jC/ppD+RKkllcoftRQ+T4ZLd9pzaX8Wa80Sr+m7QsAAADESFCqZcuWqdfr1avnglTHHHOMxo4d6+pG5RbLvrKAV3rr16/X7t27FYxB7ZYtW1wAJZ6pYmHp1/htm1Vq+BOK96VoV4PTtaVmI2ndutxrTB7Bvkq/RhP210NQvaHKJJdQvs0btXXSe9rVuBn9mgPbtm073N0TAAAA0RaUSq948eKqXr26li5dmunjVnNq7dq1ae6z23Z/dnr37p1m2p9lSlWqVEllypRRcnKygnHgZCsJ2vMTlApDv+7fr7g3nlHc9q3yHVVFiV3uVNnEpFxsSd7Bvkq/RhP210N0YXvp3ZeUPONTFb3g8iyzpejX/1gpAcQWphoCABBcURWU2r59u5YtW6Zrr70208et5tTUqVN1++23p943efJkd392EhMT3SU9C2wEK2hkwZNgPn+sylG/fviGtORnKamQ4nr0UVzBQqFsYtRhX6Vfown76yE4s6U0aZziNm1Q3MzJ0jkX0a8HwXc2AABA7oroiMhdd92l6dOna/ny5Zo5c6batGmjfPnyqUOHDu7xjh07uiwnv9tuu02TJk3Ss88+q0WLFumRRx7RnDlz1LNnzzD+FYgoc2dIn/3Pu965l1T+6HC3CADCtxJfyyu86xNHsxIfAAAAQi6ig1J//fWXC0BZofMrrrhCpUqV0uzZs930LLNixQpXAN3vtNNO06hRo/Tyyy+rfv36eu+999zKe3Xq1AnjX4GIsfZvaeRA73rztlIjVukBEOPOuEAqUfrflfg+D3drAAAAEGMievre6NGjs338yy+/zHBfu3bt3AVIY89uaWg/addO6fja0mWd6SAA8GdLjRrqZUs1tZX4CtAvAAAACImIzpQCcoXPJ70zWPp7uZRcQup+v5QQ0fFYAAhttlTxUmRLAQAAIOQISiHv+3qSNHOKFBcv3XCfd/AFAPBYZlSrK73rn46hthQAAABChqAU8rblS7xpKeay66Sa9cPdIgCI3Gypf9ZL30wOd2sAAAAQIwhKIe/avk0a9pi0f5/U4FTpAmqNAcBBs6VYiS9PsZWI4+Li0lxq1qyZ+vju3bt18803u8VkihQporZt22rt2rVhbTMAAIgdBKWQN6WkSCOekTaslcpUkK6/S4qLC3erACBykS2VZ9WuXdutVuy/zJgxI/WxO+64Qx9//LHGjRun6dOna9WqVbrsssvC2l4AABA7CEohb/p0rLTgOykhv9TjAalQkXC3CAAiG9lSeVZCQoLKly+feildurS7f8uWLXrttdc0cOBAnXvuuWrUqJFef/11zZw5U7Nnzw53swEAQAwgKIW8IeWAtHiBkn6cLX3+vvTBG9791/SUKh8X7tYBQHQgWypPWrJkiSpWrKhq1arp6quv1ooVK9z9c+fO1b59+3TeeeelbmtT+ypXrqxZs2Zl+Xx79uzR1q1b01wAAAAOR8Jh/RYQSebOkEYPU/ymDSoeeL8VNW/aInztAoBozJZqeYX07ktebanTz5fyMVSIZo0bN9bIkSNVo0YNN3Wvb9++OuOMM7Rw4UKtWbNGBQoUUPHiab49Va5cOfdYVvr37++eBwAA4EiRKYXoD0i99Ji0aUPGxxb96D0OAMi5M1tKxUp6K/HNZCW+aNeyZUu1a9dO9erVU4sWLTRx4kRt3rxZY8eOPezn7N27t5v657+sXLkyV9sMAABiB0EpRPeUvdHDst9m9HBvOwDAodeWmvCu9Ms8b2r04gV8nuYBlhVVvXp1LV261NWX2rt3rwtSBbLV9+yxrCQmJio5OTnNBQAA4HAQlEL0+m1h5hlSgTat97YDABxatpQtELFpg+IH9VHxccMU/+x90r2dyECNctu3b9eyZctUoUIFV9g8f/78mjp1aurjixcvdjWnmjRpEtZ2AgCA2EBQCtFryz+5ux0AwGOrl+7cnrE37ESATZlmanTUuOuuuzR9+nQtX77crarXpk0b5cuXTx06dFCxYsXUpUsX9erVS9OmTXOFzzt37uwCUqeeemq4mw4AAGIA1UsRvazmSW5uBwDI+dTohk2k+Hz0WIT766+/XABq48aNKlOmjJo2barZs2e76+a5555TfHy82rZt61bVs7pTQ4cODXezAQBAjCAoheh1fG2pQKK0d0/W25QoI1WvE8pWAUDsTI22VU4R0UaPHp3t40lJSRoyZIi7AAAAhBrT9xC9vpqUfUDKtO/OmXwAOBRMjQYAAECIEJRCdPp98X/TS5qcJ5UonTFDqkcfqVHTsDQPAKIWU6MBAAAQIkzfQ/TZvlUa9rh0YL904unS9XdKvhSlLP5JW1csV3LlKoqvUZcMKQA4HDbl2QL92U3hs8eZGg0AAIAjRKYUoktKivTq09I/66SyFaXreklxcV4AqkY97a5/qvtJ8V0AOEz2edr+xuy3qVRNimMIAQAAgCPDiBLR5ZN3pYVzvALnNj2vUOFwtwgA8h6b+myfsemnRhcu6v1c8J30SfYFtAEAAICDYfoeoocFoz5627t+zS3emXoAQPACUw2bZJwaPfUjacxwafwbUuEi0jmteQUAAABwWAhKITpsXOdN2/P5pDNbSqedF+4WAUDe558aXaK8ksuWleLjpfPbSDu2SRNGSaOGSoWKSI3PCXdLAQAAEIWYvofIt3+fNPwJr8B55eOkDj3C3SIAiG2XXOtlSNmJghEDpAXfhrtFAAAAiEIEpRD5xr4i/b7IOxtvNU7yFwh3iwAgttkCE3aCwDKkDhyQXnpc+m1huFsFAACAKENQCpHt22nSFx9517veLZUpH+4WAQCMTeXrfKdUr7G0b6/04kPSiqX0DQAAAHKMoBQi16o/pTcGedcvbO8d+AAAIkdCgnTj/VL1utKundJzD0hr/gp3qwAAABAlCEohMu3eKQ19TNq7R6rVwKtfAgCIPAUSpZ6PeDX/tm2RBt4v/bM+3K0CAABAFCAohchjhXPfeF5as1IqUVrqdp+3AhQAIDIVKizd8ZhU/mjpn3VeYGrb5nC3CgAAABGOoBQiz9QPpe+nS/nySd3vl5KLh7tFAICDKVpcuuMJqWQZ76TCoAelXTvoNwAAAGSJoBQiy7JfpHGveNfbdZOOOyHcLQIA5FSpslKvJ6QixaQ/l0gvPuJNwwYAAACiLSjVv39/nXzyySpatKjKli2rSy+9VIsXL872d0aOHKm4uLg0l6SkpJC1GUfApnoMe8JbXvykM6Vml9CdABBtylfypvIlFZJ++0ka/oS0f3+4WwUAAIAIFNFBqenTp+vmm2/W7NmzNXnyZO3bt0/NmzfXjh3ZTwdITk7W6tWrUy9//vlnyNqMw5RyQHr5KWnTBq8myXW3S3FxdCcARKNjjpdueUTKX0D68Vtp5EApJSXcrQIAAECESVAEmzRpUoYsKMuYmjt3rs4888wsf8+yo8qXLx+CFiLXfPS29OsP3ipOPfp4Z9gBANGrRj3pxgekIX2l2V9IhYpIHXpwwgEAAADREZRKb8uWLe5nyZIls91u+/btOuaYY5SSkqITTzxRTzzxhGrXrp3l9nv27HEXv61bt7qf9vt2yW32nD6fLyjPHZV++l7xE951V1OuvVWqUPmwzqjTr7mPPg0O+pV+jZn9te7JUuc7Ff/aM9IXH8lXqIh8F1+jaMX3NgAAQIwGpWwgePvtt+v0009XnTp1styuRo0aGjFihOrVq+eCWAMGDNBpp52mn3/+WUcffXSWtav69u2b4f7169dr9+7dCsbfYm2zQX58fETPoAy6fJvWq9SrT7vrOxqfq21Va0vr1h3Wc9GvuY8+DQ76lX6Nqf21am0VuugaJU94W3ETRmlbirTztOaKRtu2bQt3EwAAAPKUqAlKWW2phQsXasaMGdlu16RJE3fxs4BUrVq1NHz4cPXr1y/T3+ndu7d69eqVJlOqUqVKKlOmjKtPFYwBvk0xtOeP6aDUvr2Ke+Uxxe3aIV+V6irY8TYVzJ//sJ+Ofs199Glw0K/0a8ztrxdfpZR4Kf6jt5U8cZSKlKsgNWmmaMPCKQAAADEYlOrZs6cmTJigr776Kstsp6zkz59fDRs21NKlS7PcJjEx0V3Ss8F3sIJGNsAP5vNHhbEvS38ulYokK65HH8Vl8hocKvo199GnwUG/0q8xt7+2vlrauUOa8oHi33jOqzHV8L+TSNEgpr+zAQAAgiCiR1c2VcACUh988IG++OILVa1a9ZCf48CBA/rpp59UoUKFoLQRh2nWFGn6RK/gbdd7pFJl6UoAyMvs8/6KbtJp53l1A4c/IS36MdytAgAAQBjFR/qUvbffflujRo1S0aJFtWbNGnfZtWtX6jYdO3Z00+/8Hn30UX3++ef6/fffNW/ePF1zzTX6888/1bVr1zD9Fcjgrz+kt170rl90lVTnJDoJAGKBZRp1ukNq0ETav0968RFp+W/hbhUAAADCJKKDUi+99JIrrnr22We7TCf/ZcyYManbrFixQqtXr069vWnTJnXr1s3VkWrVqpWrDzVz5kydcMIJYforkMauHdJLj0l790i1T5RaX0UHAUAsyZdP6t5bqtlA2rNLGtRHWrUi3K0CAABAGCRE+vS9g/nyyy/T3H7uuefcBRHIXs/XB0pr/5ZKlpG63ivF5wt3qwAAoZa/gNTzIWnAfV6m1HP3S/c9K5Uqx2sBAAAQQyI6Uwp5zOT3pXnfSPkSpBv7SEWLhbtFAIBwSSok3f6YVLGytGmDNPB+acsmXg8AAIAYQlAKobFkofTea971K2+QqtWg5wEg1hVJlu54wlvswrJobSrfzu3hbhUAAABChKAUgs/OfA97wltt6ZSzpXNa0+sAAE+J0lKvJ6XkEtLKZdKLD0t7dtM7AAAAMYCgFILrwAHp5f7Sln+8KRodb/OWBQcAwK9cRemOx6WChaUlP0svPe6tzgcAAIA8jaAUgmv8G9LiBVJiQanHg1JSQXocAJBRpWrSrY9KBRKlhd9LIwZIKQfoKQAAgDyMoBSCZ/4s6dOx3vXr7pAqVKK3AQBZO762dNOD3oIY302X3hnqrdyKw9a/f3+dfPLJKlq0qMqWLatLL71UixcvTrPN2Wefrbi4uDSXG2+8kV4HAABBR1AKwbF+tfTaAO96s0ukk8+kpwEAB1fnJKnL3d5U7+mfSB+8Qa8dgenTp+vmm2/W7NmzNXnyZO3bt0/NmzfXjh070mzXrVs3rV69OvXy9NNP0+8AACDoEoL/XyDm7N0jDX1M2rVDOraW1K5ruFsEAIgmp5wl7douvfWiNHG0VLiI1OLycLcqKk2aNCnN7ZEjR7qMqblz5+rMM/87YVSoUCGVL18+DC0EAACxjEwp5L5RQ70VlIoUk7rfLyXkp5cBAIfmrAulyzp718e9Kn2dNriCw7Nlyxb3s2TJkmnuf+edd1S6dGnVqVNHvXv31s6dO7N8jj179mjr1q1pLgAAAIeDTCnkrhmfeRebdnHDvVLJMvQwAODwtLxC2rFN+uw96c0XpEJFpEZN6c3DlJKSottvv12nn366Cz75XXXVVTrmmGNUsWJFLViwQPfee6+rO/X+++9nWaeqb9++vA4AAOCIEZRC7lmxTHpniHf9kmulE06kdwEAh89OcFzexQtM2QmPV56SkgpJtfl+ORxWW2rhwoWaMWNGmvtvuOGG1Ot169ZVhQoV1KxZMy1btkzHHntshuexTKpevXql3rZMqUqVWMwEAAAcOqbvIXfs3C699Ji0b69U92SpVXt6FgCQO4Gpjrd6GVL790lDH5WW/UrPHqKePXtqwoQJmjZtmo4++uhst23cuLH7uXTp0kwfT0xMVHJycpoLAADA4SAohSNny3WPeNZbca9UWanLPVI8uxYAIJfE55O63uNl4O7ZLT3/oPT3cro3R1/RPheQ+uCDD/TFF1+oatWqB/2d+fPnu5+WMQUAABBMRA5w5Ca9J82f5RU079FHKlKUXgUA5K78BaSbHpSq1fSycwfe750MwUGn7L399tsaNWqUihYtqjVr1rjLrl273OM2Ra9fv35uNb7ly5fro48+UseOHd3KfPXq1aN3AQBAUBGUwpFZ9KP0/uve9Q43SlWq06MAgOBIKijd1k86qoq05R8vMLV5I72djZdeesmtuHf22We7zCf/ZcyYMe7xAgUKaMqUKWrevLlq1qypO++8U23bttXHH39MvwIAgKCj0DkOnx0IvPyk5EuRmjSTzmxFbwIAgqtwUemOx6Wn7vIypZ57QLr7GalQIem3hV6wqlhJqXodb9pfjLPpe9mxAuXTp08PWXsAAAACEZTC4dm/XxreX9q6yTtjfc0tXjFaAACCrXgpqdcT0pN3erWl+t/u1ZoKzJoqUVpqf6NXIB0AAAARiel7ODwfvC4tWegtzW11pBKT6EkAQOiUqeAFpgokSWv/zjiNb9MGb1XYuTN4VQAAACIUQSkcOhvgf/Y/73rnXlL57JeWBgAgKCpUkhITs99m9HAp5QAvAAAAQAQiKIVDY2ejRw70rp9/GdMiAADhYzWktm3JfptN673tAAAAEHEISiHnrF7H0H7Srp3S8bWlttfTewCA8LGi5rm5HQAAAEKKoBRyxlbveXuwV1A2uYTU/X4pgTr5AIAwslX2cnM7AAAAhBRRBWTNanD4l9devkSaNUWKi5duuM9b+QgAgHCqXsdbZc+KmmelRBlvOwAAAEQcglLIupj56GEZB/qnnCXVrE+vAQDCLz6f1P5Gb5W9rLTv7m0HAACAiMP0PWQekLIBfmZnnr+dxvLaAIDI0aip1KOPlzGVPkPK7rfHAQAAEJHIlELGKXuWIXWw5bUbNuHMMwAgMljgyb6X/FPOrYaUTdkjQwoAACCiEZRC2oDUV59lX5sjcHltpvEBACKFBaD4XgIAAIgqBKVifUW91SulRfOlX+dLixdIO7fn7HdZXhsAAAAAABwBglKxZuNaLwBll0U/ZgwuFUiU9u45+POwvDYAAAAAAMjrhc6HDBmiKlWqKCkpSY0bN9Z3332X7fbjxo1TzZo13fZ169bVxIkTFbO2bJK++1J683mpd2fp3k7SyOe8guUWkMpfQKrVQLrsOun+QdLz4zIWi02P5bUBAAAAAEBez5QaM2aMevXqpWHDhrmA1KBBg9SiRQstXrxYZcuWzbD9zJkz1aFDB/Xv318XXXSRRo0apUsvvVTz5s1TnTp1lOfZ9LvffvovE+rv5Wkfj4+XqtaUatWXajaQjq3lBaYCsbw2AAAAAACI9aDUwIED1a1bN3Xu3NndtuDUJ598ohEjRui+++7LsP3zzz+vCy64QHfffbe73a9fP02ePFmDBw92v5vn7NktLfvlvyDU8iWSLyXtNpWO9Yq/WkaUrUaUVChny2vbKnyBRc8tQ6p9d5bXBgAAAAAAeTsotXfvXs2dO1e9e/dOvS8+Pl7nnXeeZs2alenv2P2WWRXIMqvGjx+f5f+zZ88ed/HbunWr+5mSkuIuuc2e0+fzHd5z798vLV/sAlBxFoT6/VfF2X0BfOWOckEon2VCVa8rFS2WvgEH/38anibVbywt+fm/5bWPr+2tbhSEPgl7v4I+DSH2Vfo1mrC/pu0LAAAAxEhQasOGDTpw4IDKlSuX5n67vWjRokx/Z82aNZlub/dnxab69e3bN8P969ev1+7du5WrUlKU8Mcipaxdrc3lKmi/TaWzKXXZbb9mpQr8/osSf/9V+ZcvVny6QuQHkktq77G1tKfaCdpbrZZSAouQ79oj7Vp3+O0tUd67mA0bFekHC1u2bHGBKQtegj6NVOyr9Gs0YX/9z7Zt28L4SgAAAOQ9ER2UChXLxArMrrJMqUqVKqlMmTJKTk7Ovf9o3jeKGzNccQFT4nwlSst3ZXfpxNP/vcMnrf3730yo+dLiBYrbkXYQ7CucLNWsJ59NyavZQHFlKyoxLk6Jim124BQXF+deN4JS9GkkY1+lX6MJ++t/bAEVAAAAxEhQqnTp0sqXL5/Wrl2b5n67Xb78v9k76dj9h7K9SUxMdJf0LLCRa8GNuTOkYY9nuNsCVHF2/zmtpd07vbpQgXWcXAMLSjXq/lsXqqHijqrisqvicqdleYoFpXL1dQN9GiTsq/RrNGF/9fDdAgAAEENBqQIFCqhRo0aaOnWqW0HPf8bWbvfs2TPT32nSpIl7/Pbbb0+9zwqd2/1hk3LAKxqenWkf/3c9Ib903Ane6ni2St4x1aWEiH6pAAAAAAAADknERzpsWl2nTp100kkn6ZRTTtGgQYO0Y8eO1NX4OnbsqKOOOsrVhTK33XabzjrrLD377LO68MILNXr0aM2ZM0cvv/xy+P6I3xZmzH7KTONzpKbNpWNPkArE+mQ8AAAAAACQl0V8UOrKK690BccfeughV6y8QYMGmjRpUmox8xUrVqRJpz/ttNM0atQo9enTR/fff7+OP/54t/JenTp1wvdH2Op1OVHvFDc9DwAAAAAAIK+L+KCUsal6WU3X+/LLLzPc165dO3eJGIGr4eXGdgAAAAAAAFGOatChUL2OVKJ09tuUKONtBwAAAAAAEAMISoWkl/NJ7W/Mfpv23b3tAAAAAAAAYgBBqVBp1FTq0SdjxpRlSNn99jgAAAAAAECMiIqaUnmGBZ4aNlHK4p+0dcVyJVeuovgadcmQAgAAAAAAMYegVKjZFL0a9bS7RHklly0rBawcCAAAAAAAECuIiAAAAAAAACDkCEoBAAAAAAAg5AhKAQAAQEOGDFGVKlWUlJSkxo0b67vvvqNXAABAUBGUAgAAiHFjxoxRr1699PDDD2vevHmqX7++WrRooXXr1oW7aQAAIA8jKAUAABDjBg4cqG7duqlz58464YQTNGzYMBUqVEgjRowId9MAAEAeRlAKAAAghu3du1dz587Veeedl3pffHy8uz1r1qywtg0AAORtCeFuQCTy+Xzu59atW4Py/CkpKdq2bZur2WCDPtCvkYp9lX6NJuyv9Guw+ccF/nFCXrFhwwYdOHBA5cqVS3O/3V60aFGG7ffs2eMuflu2bDmkcdPeXTsUDMEYt0VTW4PV3mhqq2E/YD9gPwjee4zPg+j67Ar3fpDTcVOcL6+NrHLBX3/9pUqVKoW7GQAAIAKtXLlSRx99tPKKVatW6aijjtLMmTPVpEmT1PvvueceTZ8+Xd9++22a7R955BH17ds3DC0FAAB5bdxEplQmKlas6DquaNGiiouLy/UXxSKGFvSy/yM5OTnXnz9W0a/0abRgX6Vfown763/sPJ5lOts4IS8pXbq08uXLp7Vr16a5326XL18+w/a9e/d2RdEDsxT/+ecflSpVKlfHTdG070VTW6OtvbSVvmU/YB/g8yA6P2dzOm4iKJUJm1IXijOg9oJH+kAgGtGv9Gm0YF+lX6MJ+6unWLFiymsKFCigRo0aaerUqbr00ktTA012u2fPnhm2T0xMdJdAxYsXD1r7omnfi6a2Rlt7aSt9y37APsDnQfR9zuZk3ERQCgAAIMZZ5lOnTp100kkn6ZRTTtGgQYO0Y8cOtxofAABAsBCUAgAAiHFXXnml1q9fr4ceekhr1qxRgwYNNGnSpAzFzwEAAHITQakwsJT3hx9+OEPqO+jXSMO+Sr9GE/ZX+hVHxqbqZTZdL1yi6T0dTW2NtvbSVvqW/YB9gM+DvP05y+p7AAAAAAAACLn40P+XAAAAAAAAiHUEpQAAAAAAABByBKUAAAAAAAAQcgSlguC6667TpZdeGoynBpDHPw/i4uI0fvz4oLYJAAAgmixYsED79+8PdzMABAGr7wXB888/L5/PF4ynBhBl+DwAAAA4fI8++qgeeeQRTZ48WWeffbby5ctHdwJ5CJlSQVCsWDEVL148GE+NXLZ37176FEHF5wEAALlv9erVUXcSOBLbm75NkdjGhx56SM2bN3fZ59OmTdOBAwcUjfx9G4l9HI2iOXMuJSUl3E2IKASlgjxdZ9KkSWratKkLUpUqVUoXXXSRli1blrrt8uXL3XSd999/X+ecc44KFSqk+vXra9asWYp1dibklltu0e23364SJUqoXLlyeuWVV7Rjxw517txZRYsW1XHHHadPP/3UbW9fUF26dFHVqlVVsGBB1ahRw2WpZPbaPP7446pYsaLbJlbkdn9+9dVXyp8/v9asWZPm/7HnP+OMM0L+90XD50GVKlU0aNCgNI83aNDAnf1D9t577z3VrVvX7Yv2WXreeee5fde8+uqrqlWrlpKSklSzZk0NHTo0w2fs6NGjddppp7lt6tSpo+nTp8d8l9tnwq233qp77rlHJUuWVPny5dPsiytWrNAll1yiIkWKKDk5WVdccYXWrl3rHvvtt99cvy5atChNPz733HM69thjY75vEdxBe6Qe0Fm7IrVtOWXTx+2gP9L17NnTfX5t2rRJ0STS2mvvMfssDzxR678dKfbt25d6TGXf8Z06dYrawJT/+M76ONo/K8LJxiJbt25VQkKC2y++/vprRQtrq42v4uMjNwzj+3ffnD9/vpYsWRKS/zNyeyOPsIOmXr16ac6cOZo6darbAdu0aZNhoPXAAw/orrvuci9+9erV1aFDh6iO/uaWN954Q6VLl9Z3333nAio9evRQu3bt3MHlvHnz3FmTa6+9Vjt37nR9evTRR2vcuHH65Zdf3FmV+++/X2PHjk3znPY6LF682KUAT5gwQbEkN/vzzDPPVLVq1fTWW2+lGTi88847uv7668P4VyIvng23z0Tbr3799Vd9+eWXuuyyy9yXpu1vtm9aoNkee+KJJ/Tggw+6fT3Q3XffrTvvvFM//PCDmjRpotatW2vjxo2KddZPhQsX1rfffqunn37aTZGwz0Z7/1tA6p9//nEBPLvv999/15VXXul+z76nTjrpJNf/gez2VVddFaa/BnmJvb/9g/YXX3zRfWd1795d69ati7iDZv+Yzg6SrW0zZsxwwXH7XPrjjz9SA+iRbu7cuS74bGOkSPb333+7z6zbbrvNBdSjhZ0cad++fcTsD4HvsQEDBriTaPa5b8cikTKTwN5bdgLUz76L7CRUNAamrF8tUcF/4ixSA1P2mWXjqUjN5rGxW8eOHd2x85tvvqlWrVq5sUo02LZtmx5++GH1798/Yvddn8+XmjBz4YUXuhO/IelfH3Jdp06dfJdcckmmj61fv94+fXw//fSTu/3HH3+426+++mrqNj///LO779dff43pV+ess87yNW3aNPX2/v37fYULF/Zde+21qfetXr3a9dWsWbMyfY6bb77Z17Zt2zSvTbly5Xx79uzxxZpg9OdTTz3lq1WrVurt//3vf74iRYr4tm/fHrS/I5o/D4455hjfc889l+bx+vXr+x5++OHU29b/H3zwQcjbGcnmzp3r+mX58uUZHjv22GN9o0aNSnNfv379fE2aNEnzGfvkk0+mPr5v3z7f0Ucf7fbfWJb+M8GcfPLJvnvvvdf3+eef+/Lly+dbsWJFhu+m7777zt22fdn632/x4sV8dyFXHDhwIPX6Qw895CtevLjvyiuv9FWrVs19js6YMSNienro0KG+2rVrp44rxo0b50tKSvKdcsop7nOmQoUKvieeeMK3atUqXySzMefjjz/u69u3ry+SWV/ad+rVV1/t27lzpy+avPjii+4zc+PGjRn281AL/L/79+/vK1asmO/WW291Y5IyZcr43n777Ygay02YMME3c+bM1NvNmjXzVaxY0Td58mQ3no10Q4YM8d1yyy2+ggUL+uLj49OMBVNSUnyR4r777nOfsUWLFvWdeOKJvmeeecb3zz//+CLJ3r173fitevXqvvz58/uGDRvm7o+G/cA88MADbqzl//wK5+dAVqZOneorVKiQ77XXXvOtWbPGFwpkSgWZpbzZGX7LKLHpDzZ9x1jaXqB69eqlXq9QoYL7aWcDY11gv1hRQ5u2Y1N4/GwKWmBfDRkyRI0aNVKZMmXclJOXX345Q1/b7xcoUECxKLf7086qLV26VLNnz3a3R44c6c6yWuYFkFtsSnOzZs3cvmqZfTbt1KZA2Nlmmw5t00xt//RfHnvssTTTpI1lR/lZurdl+fjPBMaywM8E//ePvf+tbypVquQufieccIKbiu7vNzvjb9Mj/e9/y5I68cQT3fQK4Ej4szdsX/zzzz/1+eefuywTy+CpXbu2+xyIlOkaDRs21Pbt23X++ee7s+A21dgyu2yK+8qVK930+DFjxris4l27dikS2fvYMqdtir4/Cy0SMySsTTa+sGwZyzrxtzUSs00C+dtnUw4TExPVu3dvdzuc03f8/7e9vywz5uOPP3avv/WrZUdYdrFlSkRCVpe97+17fvjw4W7miZkyZUpqxpRlT0dq1onp06eP+vbtq1NPPVUvvPCCOy60jG7LToukjKm3337bZR5Zu6x/7fv8f//7n/r166ctW7YokjLn7PPWxoFWisXKCVj77LgmkvcD/2tss06s9IntEyYSp/F98skn7nvWZijYMWAovhMirxfyGJsiYilvdhBlqcZ2MenTYgNTUyN5QBBqgf3i75us+soGrDYF0r64bABrX6w2GEzf17EcMMnt/ixbtqzbx19//XU3v9vqUTF1L2v2xZN+4OGvlYCs2UDDDkJs/7LAiB3wWY2zhQsXusft89X2T//F7vcHSnDonwk5/e6xGlTnnnuuRo0a5W7bz6uvvpouR6547bXXdPzxx+vnn392J/X8AWUbLFsgyIKiNk0u3OxA0w7e//rrL1enbcOGDe5kjgUfjE3ha9mypTvg37x5syKRnTC1NlptyY8++shNj7Hvq0gbh1qbbBxi3wF2IGonIEykTedML7B9Ng3V2m7BoEgIQljdUAugWr1GPxvTXXDBBbrvvvv0wQcfuGBrOOuy2fe9veY2vfSll15KE5iyMYHtE1ZXKNL2V2PBB2ubTY+3qe1du3bVk08+6Uq7WOmBwYMHR0RgymrJrV+/Xvfee68uv/xynXLKKW5sZWP8L774wpU+iZTppnaywmowWwkWqys3c+ZMN5XPakwFBqYiIdBnbFxq7yF/u6ztd9xxhyulkj5xIhJYO7///vvU9vqPXQJPFgUDQakgsi91i+5bhNzO8ltEP9IKHOYl33zzjauNdNNNN7kBqxXtTp8tgdzvT/uCtbPAlkVlBY5PP/10ujkLdrbB6iP52ReonaHEwdmAzfYtO7NkdaEs29H2UTtLZrWObP8MvNhAO1BgkMrq9dng1j6TkTnrG8vysIuf1Zazg2o7CPCzIJS9/614q70OFigAcoMdDJ188sluQG+BHuM/6LTAlAV+rLbhjz/+GPYOt4wCex9YkNcO4Kwuo9mzZ4/7afVD7ASEBa8ilS14YAd4diBi163PIyUwZQsq2Ge2tc2yYe3kly0aYv3qD0xF0kFo4MIPVkPIsk38NVlskQ47cfLhhx+GvD3pX8trrrlGF198sTtWsQzYwJNkI0aMcIFKq91j37WhYIFd+673B/FsjORnWRu2EId911hgyuqgGjthZVn/NgaNxIwTC6RbANL/GWasXquNnS0L3N5zlj0VzuCqtc32Bau7mX5Malk9tjiSZVBFQp0je99YJp/trxY4syCvfVfY54Mdb1vwxwJTw4YNC9l+mx3L4LJ6vWeddZY70e8/BrCFz3766Sd99tlnEffZlS9fPve5ZWM6C6AHBk1tX7bMuWAcu0TeuzcPsTex/4PSpjjZQMUi4wgOO6NqZ0/sDW5vIkuNtUgvgtufLVq0cGexbWBoZ6uQNcsqsSkcNu3Evows7dw+/JE9yzC1Aua2P9pZJTuwszN6FjixIJUdmNigzvZT61c7yztw4MA0z2FTUe2Mrx3c3Hzzze4EAVl9WbMDJ5suaUEnG/zbGT07OLGBlU199LOC8zYItKk/toKsBQmBQ5VZ4MMycd99910X8OnWrZsbRwVmm1pGjx1E2WqakcCCZJb1YIsA2Flwe1/4s6XswMT+Hpv+GikWLFjg+teyDSzgbOwA2d7z9jlpWTL2ORvuwJS1w7477eSuZczYZ/6qVavcCTPLnrHblo0WaRlTFkBr3Lixyz6z6VAWYLWFJYoVK+Zu23WbNhlK/qCNBXX9q8BZhowdNFsg0ha1CJz+ZAWObWxnU6WCzb6X/cEZY9ct+yWwj9q2beteb5tqaEFJf2DKTlTZ93sk8U/VtUL8FjSxcUzgKmY2Nd4+22y/tv3B3ovh4l8AycZUNhUy/X5p3/u7d+8Oa2a/vbfts8qmPtqCK/6TinYiwKaaWh/b/mDBS1sAwT4f7Bg83Oz9bkFo23ftM9fKG1hQxz5TLWPOAtc2ro0L02eX//vUPust4OQ/oWKfCbaghAWn/Qtf2GeDja8t8y99ln1uNQZBLGxsBfisEHRiYqKvXr16vi+//DJNIWN/Ed4ffvgh9fc3bdrk7ps2bZov1ovw3nbbbWnuy6xQtL8/d+/e7bvuuutcsUYritqjRw9XsM+KNuakCH1eF4z+9HvwwQddUeRIL+QaDoH73JYtW1yx3uTkZF+lSpV8I0eOpNB5Dvzyyy++Fi1auOKr9llqxS2tYKzfO++842vQoIGvQIECvhIlSvjOPPNM3/vvv5/mM9aKoVvhYdvmhBNO8H3xxRe+WJfZZ4Ltq7bPmj///NN38cUXuwURrOhpu3btMi14ecUVV7g+HjFiRMjajrwjsMjrggULfPPmzfP99ddfaRaIadSokSsmvmTJkkwLA4eywK393/7/34r/T5w40Tdp0iT3WWOs/VaQ/aSTTvJ99tlnvm+++cYVtrXv0aVLl/oigS1KUr58eVfI2Pr13HPP9X388cepjw8YMMB9jtp7e8OGDWFrpxWNt2LxH330kfseuOeee3ynnnqq74YbbvCtXbvWbWNFjuPi4nyvv/66L1ItXLjQ7QN2HGB9fsYZZ/hq1qzpigmHutDxokWL3II/9jn//fffZygcPmXKlEzfT8F+j40fP94VsDa2aICNj0qVKuXr3bt3hkVOHnnkEfd+su8kew+Gqo059fLLL7vPgHXr1rnb7777rnu9bf+1/jdbt271tWnTxm1r7zMr3G/j7lAWPbf39ubNm93Y1NgiXLYwg+0Lts9aofsdO3a499xVV13lCydro30m2cIXgWzhGv/PV155xfXlOeec4/vxxx994WSLxNh+m37Rsqefftp3wQUX+MqWLetr3Lix24/tcy4c/PuaHfPZGNr22YYNG7rFbqwIu43p6tat675/W7Vq5S52TGjfccFAUCoI2rdv7z5cgFhx/fXX+1q3bh3uZkQkPg/CK7PAP4DwswFx4MG4ndywQbFdbBVXCzL4V32ygycL8thBfbhWJraDyPSBHTuAO+2009wBp/1866233GN2sF+nTh0XLLEDTwsABGsgf6gsIG8BflsNzB8MsKDz8ccf7xszZkzqdo8++qg7GRCuk012IuHZZ5/NsErqCy+84Pr2zTffdLft4MkOqvwHp+Fm/Wmr2VlQJTDoYyyA8t5777mDPNs3bJ8JZrsDA6iBxo4d6/rQxm6BbTzvvPPcCTNb6S5UgbL07bN+a9mypXu/2f5owVM7SPYHfY2dTLWTKva+isSVyyzwVKNGDbfCmn+lxZdeein1AN9O/thP/0neu+66y500C2VQzVYpPv/8893nrQWc/MkSFoyyfcCCJhY8vfzyy12gwr+6aLhWCrSTE9ZWe38Ze939bbGfge8jC6SFk3032MlTa68FcW666abUYKQ/YGUnLCwQZAFi/8mWUPK/byx5xk48Dhw40CXF3H333e7kr31O+R+395t9j1lAMJjfvwSlcpG9IewLx7JPbMlaIK+zMyxff/21W/7alpDHf/g8iAwEpYDIs3LlyjS3+/bt6wI8/u+Ra665xmWU2lllGyj7A1OVK1d2j4Vat27d3AG8/6Dx22+/9ZUsWTI1sGPZUgkJCe5Az2/OnDm+qlWrugP9SAmYWCaGHSDdcccdqa9DlSpV3AHHZZdd5g6iAjOm/AfUoWYBCcvascBN165dMzx+6aWXugPm9MLdz5YJY5ldFjCx9lnQ6ZNPPsmwnQXS3n77bZcpYTMognGwn/495s+G8bODTpvJYfv13LlzU++3A2XLkA2XoUOHun6xQIntB5ZlZJ8N1rf2vrNsKttXLbvE32eRFJjyt8myIi3wZ/3pfx9ZQNgO8C2bxzLA7P1oOnbs6GYm+AM/wdanTx+XhWYZ5RaAtMwiyzL37zN2LGttt36fOXNm6t/kz2QLh127drnPJ8s49PN/HttJR9tPQtV/2bH3c8GCBV0Q0mY8WR+XLl3aBfd+++03t42/P+2kiwXbQuXNN99M/c4y1l9dunRJ/T6w7FP7PrDvCH8b/Z+poQhGEpTKRfamsB3R0tv8Z/eAvMwGXrbP33777eFuSsTh8yAyEJQCIosNeO1srJ8dAFngxh8MsTPhdoBk2QQWlLDAlH8KmZ0ICfUUHTvYscyiwEynV1991WVz+D9jbCB/4403pj7uP7iz74FwnAXPjp3ptpNJFqSwbA1/0MemyNn0Zgu2hWs6SSDLJrCgjgX2AqdomWeeecZ39tlnuwPVSGEZXHZS2g7ijWUeWH9axoRlTvj5D+y3bdvmDvwtQzDY77Hnn3/e17NnzzTZRv6MKdu3r7322jSBqXAHeSxb6vTTT3flDiwwZfujZRlZ1pT1p005DeXBck6MHj069bq/Tfbet7balM3MpsHa54QFp2wKl2UohYJNy7fpeP6pozb12E4A2DTCwP3T3nPW3zbVzE4MhLKf/f+XTcGz9tnrb6UD7FjDphpbFmUgC6rY50H6bNZwuP/++10cIJB9D9jnaq9evVLvC/V+u337dvc926RJkzRTnW0KrGUk2lRTOxFgU6P97LvYThSF6vOAoBQAAABCIrB2jAWZ7LoFeeys7VdffeUGxv6acZZRYAdsNm0g8IAjlIEpC4rZ9Dx/2y3TwQ7gbPC+evVq31FHHeXr3r176sDdBvH2OxZ0iBRWj8n69vfff0+9z6aP2JRIO0g1s2fPdgctlpGybNmysLTTporYNKIPP/ww9aDdghE2fciyZOyEr/WrBSwsWyZS2L5p2S7+/dYCfDZtxwIOlnVkGR6WTefn31dser8FM21/zs2D1GitzxTYB3bg7A9M2etuWSYWjLAaYv6AVKTUkLL91AKQzZs3z/C3WEDC+teC2PZ54Wf7sQUPLTAZyvICixcvdhlQFuSx/dSmSltWjz+Dz/rX/zlhgTILtFogI9RZkxaIsn3WMs3s5IRNJbbP2QsvvNBldlk9TPvesEw/C6qFu4aU/zXv3Llz6n5g73N/9pZN7bYpkekzGENp1apV7n1uATx/ENJOSlhmogX/bX/0v7csiGWfT08++WTI3mcEpQAAABBU6Q+633jjDXcW3jJiAqfJ2aDef0Bt2R0WkLCD03BlRHz33XeuPoydobeDI5uOYRebtm4HTbfcckua7S1YZZknNqiPBBbksQPP4447ztUKsYNOO8iwaWV2MOdfVMcCFjaFyAKF4WALqViAz15v61urF2QHcLZ/WO0dq3tiGScWpLBgWrhr3KRnQRML5llgxw7wLDvJH1yxqZ0WmLAi4n6WqWJZSlbcP7fkhfpM6QNTTZs2da+5P1DifzxSAlJ+FvS1qZv2mRbIgjlWL8o+O+z9Fciyp0JVs80+sywgadPF7LPMgpEWOPUHpMz8+fN9bdu29U2fPj31Pgv22DRPf/A6FCwr1aa8WdDJApIWzLPPVAtE33rrra7tloFmASvr73AHpOw19texsn62z1kLsBv/e8o+h60fwzElOiUlJU0GnH0mWKDRMjjtfWWfp/bZmz7jywKSocz0JSgFAACAkNeOsYFxhw4d3Nl7Y6sr3XzzzanbWK0jO1AKLGgbDnYG2Q4qrb1+dnAUHx/vDj4skGMHmHbAb4EGy0wKN+srOwCygN7w4cPdwYXVO7W/wwpxz5o1yx2A2oG0TeexwFW4Du6smLllb1g2lLGMI2unHYRaUMoudnbfgmj+6XHhrnFjPv30UzdtKzDDyDIQrJ6UPyhpWUu2Hw8ePDhDICVwlclgiNb6TIHvc1sBzPozMDAVKey9b0EIf90wmxabPjBlU0wtm8feW4Gvfyg/yyzgbEEHm2LqD5zb+ytwiqftrzbtzNqefj8Nda0mW1HZVkm2Kcb+frLAlH1XWEDFX4vL2hzuKbwWbLLPWFsowjJ67TPBsrgsuzaw1q4F3W26dDjK+6T824cWmLbsY/seK1SokDtRYZ9X9hlm+60F+SyTyj4TbLphqBfnICgFAACAkLNMDsvSsIGyBXXswN0CPXbwYRkzdmAS7toxNqXFMgtsmoO1x6Y0GDszbgfKdlbcBvcW2LEzy5Gyyp4drFnb7Yx34IHQoEGDXB/bAaodQFnmlG0TuDpUKP39998uS8dfk8fO3ltNMau1ZJkcdoBkGUgWmLJMAwuy2O+Emx1kWvaWHYxaJpQF0izIY9k9FvixzBk7mLeViS0oEJjhE8rATzTWZ0rfFvsbLGBtQV8LSERCO/2ZfXYgb5l9VjDagusWmLIac3a/BSms3RYE8L/moc7wslU0LevIMj4DsyBtlXhb+c2yUa1WkwV9bSqhP9AbzuCkBU+PPfbY1OmO/v3TgpIWTLM6U5HA6rDZZ5T1sQWiLOhk3w9WT87qXOXPn999Xlm2n2VKhvO7Yfbs2S4Q9dprr7nPejtJYd+9drHAlGV62n5gn8WWieYvyh5KBKUAAAAQ9ik6dhBiWT0WpLL6If4DpHBP1fFPzbABvU3ls6kkflb/yP4G+xnOeiGBLDvHarBYEM3O2KfPgLKDJjuQfvjhh8OeGWPBM8s2sWLK33//vTug9099e/bZZ91BqNWQsWlH/hpTFkwJdpZRdvuuTX+zfdaytiwbbcCAAa6dVn/FVlizzCjLNLBgZbgCq9Fanymrv+Guu+5yfR4JK6xlldlnn1u26p7tH1b7qFmzZm5fCFegx/ZNqxNnKz0ae8/Y/mnTpK3Gka1kaicAbDVLC0z794Vwr2JpfWjBflslMJBNP7T3vwVYws3aaKutPvbYY6n3WY0u62/LNrLvA1uJzwKptr+EI8gTyL5X7bPITlT42eepfS7Y55R9BocbQSkAAACEdYqOPzBlBXgDD+DCfYAUyIoTW1stMGUHc5HIAjs2zc0KaFsNGztbb2fx0xe4til8dvY+lEuSZ8V/0G5tsoN5f0aHHexbANCmFPn3A/s7LPsg/UpyoTzQtwNMy5QJDOQEZqBZnS6b1mP1esIZ9InW+kyB/G207A0rGB+ummeHktnnD5gGTi8Nx+eYBSBt4YgHHnjA1Ymy197qW/lrCPnrSQXuJ5GyL1ggzYrH2/vMsnrWrl3r/o5KlSqFPVPSphVaH1rhcmtfIAtMWRDd9oNQFrA/mDfffNN9b9kqe4H7ptW0s6nbljFpGYkmXJmIBKUAAAAQMYEp/0FzuLN4MmN1TKytNtXFpmZFEjt7b1OGLLgTWFfI6oXYwVP6wFQ46ptktx9YkXvbB+ygzzKoLrrootSDfxPuTA7LJjn55JNd8KFevXoZpjxa0XA7kLaD50DhPNCPlvpMB/sbxo4d6+rLhVtOMvtsKlxg1mQ4pxtasXALmlmg2mqJ+Qtw2/Q9WzEyUlmf2TS+okWL+ipXruyyI+1zzKbMRQKbimdtskwjW6UwkNUYs+mb1seWZRsJ002XLFnismMteBpozpw5bgqfnWQJXHQkHBIEAAAAhFhcXJydHHU/O3furPj4eI0YMULDhw9X3759VaBAgYh7TQoXLqwrrrhCu3fv1siRI7Vq1SpVrFgx3M3S1q1b1b59ey1fvlw33HBD6v09evRQSkqK+vfvr3z58qlLly6qWrWqe6x48eKKBPb6G2v3mWeeqdNPP1179uxRUlKS2rZtm7pdQkJCmp+hNHr0aL3++uvq3bu3fv/9d7388st69dVX1bNnTx1zzDFum9tvv107duzQxIkTU/drY/0eye8xfzsjlbWvXbt2igS2T1500UXKnz+/pkyZotq1a6tTp07uMevLa665RuvXr0/zmRDO/rX3+/nnn+/eT8cff7y7zz4P1qxZo1NPPVWRyvrMPs+aNGmiRYsW6cCBA6pXr56OPvpoRYKGDRtq3Lhx7rV/4YUXdOutt7p9wbRq1cp9RtWoUUOFChVSJDjuuOP0yiuv6Prrr3d92a1bN/f5/+GHH6pKlSrub0hOTg5rG+MsMhXWFgAAACBmBR7A33333Zo9e7amTp0akUEpv507d2rfvn0qVqyYIsUPP/ygK6+8UmXLltWwYcNUp06d1Mfs9h133OGCKvfff39YAjs5MW/ePL3//vvuAKlXr16unfv37w9re6dPn66xY8eqcePG6tixo7tv6NChLtB39dVXu8CfPzAVuD8H7tfhFo3vsUjl70s7wF+yZIk++eQT148WOLOglL0H/cEfCwJGiu3bt2v+/Pl66qmn9Oeff7r3WqR+DkQL+8zt2rWrTjzxRPf5esIJJyiS99vRo0e74H+ZMmXcvrlp0yZNnjzZtT/c2BMBAAAQNoEH8EWKFHHZR7t27YroA+ZIOQOe/uz9e++9587ev/jii2nO3t94440uu8MykSL5QNQOjgIPkMIdkLKMEss2Wbt2rapXr556/0033eT22SeffDI1A61atWrusUgLSEXreyxS5TSzL5ICUvbaz5kzR88++6wLps+dO9e9ryxrJpyZfNHOPnMtY9I+X/v166eHH35YNWvWVKTutx06dHDZZwsWLHDvfwu0W6ZUJCBTCgAAABFx4GRBFTv4r1+/fribE7Wi6ex9NLADOMuCsWwoO6ivW7du6mMvvfSSbrnlFg0ePNgdmEY63mN5P7MvKxY4++WXX9xnqwXMIrWd0ej77793GYjvvvuuKlSoEO7mRCWCUgAAAEAeC0xZkMSydyL57H20+PHHH11NppNOOkm33XZbagaasaDEJZdcQsYJoibQE2lTC/MCqzNo2XI4PASlAAAAgDyGs/fByUBr1KiRK2qePgONqVAAcHgISgEAAAB5EGfvcz8w1b17dzeV7+mnn05dyRAAcPjI2wMAAADyIKaT5H5hY6sfVbRo0TQr7gEADh+ZUgAAAACQQ/6V7KjNAwBHjqAUAAAAABxGYAoAcGSYvgcAAAAAh4CAFADkDoJSAAAAAAAACDmCUgAAAAAAAAg5glIAAAAAAAAIOYJSAAAAAAAACDmCUgAAAAAAAAg5glIAAAAAAAAIOYJSAAAAAAAACDmCUgAAAAAAAAg5glIAAAAAAABQqP0fGftbTj34iuYAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -1649,18 +891,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# df.hist() — histogramas de todas las columnas numéricas (§8.5)\n", "df_clima.hist(figsize=(8, 4))\n", @@ -1673,18 +904,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# value_counts().plot() — histograma de datos categóricos (§8.5)\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", @@ -1708,27 +928,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# iterrows() — iterar sobre filas (§8.7)\n", "print(\"Reporte climático mensual:\")\n", @@ -1740,19 +940,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# groupby() con condición booleana (§8.7)\n", "print(\"Temperatura media según si llueve ≥ 100 mm:\")\n", @@ -1763,21 +951,7 @@ "cell_type": "code", "execution_count": null, "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" - ] - } - ], + "outputs": [], "source": [ "# Encadenar métodos — flujo de trabajo legible\n", "resumen = (df_clima\n", @@ -1810,33 +984,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Precipitación Mensual (mm):\n", - "Ene 2\n", - "Feb 3\n", - "Mar 13\n", - "Abr 31\n", - "May 152\n", - "Jun 274\n", - "Jul 203\n", - "Ago 198\n", - "Sep 231\n", - "Oct 172\n", - "Nov 23\n", - "Dic 7\n", - "dtype: int64\n", - "------------------------------\n", - "Media: 109.08 mm\n", - "Mediana: 91.50 mm\n", - "Máximo: 274 mm\n", - "Mes con mayor precipitación: Jun (posición 5)\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -1872,23 +1020,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meses con temperatura entre 5°C y 15°C:\n", - " temp_C rain_mm\n", - "apr 7.4 100\n", - "may 12.0 143\n", - "jun 15.0 153\n", - "sep 13.1 135\n", - "oct 9.1 89\n", - "\n", - "Lluvia total en este subconjunto: 620 mm\n" - ] - } - ], + "outputs": [], "source": [ "\n", "filtro_clima = df_clima[(df_clima['temp_C'] >= 5) & (df_clima['temp_C'] <= 15)]\n", @@ -1913,30 +1045,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DataFrame con Amplitud Térmica:\n", - " temp_min_C temp_max_C amplitud_termica\n", - "jan -1.9 2.5 4.4\n", - "feb -2.5 3.3 5.8\n", - "mar 0.6 7.3 6.7\n", - "apr 3.5 11.5 8.0\n", - "may 7.8 16.3 8.5\n", - "jun 11.0 19.2 8.2\n", - "jul 13.1 21.6 8.5\n", - "aug 13.0 20.9 7.9\n", - "sep 9.7 16.8 7.1\n", - "oct 6.2 12.3 6.1\n", - "nov 1.0 6.5 5.5\n", - "dec -3.0 3.5 6.5\n", - "\n", - "El mes con mayor amplitud térmica es jul con 8.500000000000002°C\n" - ] - } - ], + "outputs": [], "source": [ "# Agrego la columna calculada 🤓\n", "df_completo['amplitud_termica'] = df_completo['temp_max_C'] - df_completo['temp_min_C']\n", @@ -1966,28 +1075,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NaN por columna:\n", - "Nombre 0\n", - "Nota1 1\n", - "Nota2 1\n", - "Nota3 1\n", - "dtype: int64\n", - "\n", - "Resultado Final:\n", - " Nombre Nota1 Nota2 Nota3 Promedio_Final\n", - "0 Rubén 85.0 78.0 92.0 85.0\n", - "1 María 83.2 65.0 70.0 72.8\n", - "2 José 90.0 79.0 85.0 84.7\n", - "3 Lucía 70.0 82.0 80.8 77.6\n", - "4 Pedro 88.0 91.0 76.0 85.0\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -2011,7 +1099,10 @@ "\n", "print(\"\\nResultado Final:\")\n", "print(df_estudiantes_lleno.round(1))\n", + " \n", + "\n", "\n", + " \n", "#ayuda tengo el impulso de terminar mis comentarios con \"🤓\"" ] }, From 6c9604644cedf9bfc1d16d2fb0224dd8be164c28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Sun, 26 Apr 2026 21:30:34 -0600 Subject: [PATCH 06/12] cambios --- analisis_estrellas_estudiante.ipynb | 647 ++++++++++++++++++++++++++++ estudiantes.csv | 10 +- pandita.ipynb | 10 + 3 files changed, 662 insertions(+), 5 deletions(-) create mode 100644 analisis_estrellas_estudiante.ipynb create mode 100644 pandita.ipynb diff --git a/analisis_estrellas_estudiante.ipynb b/analisis_estrellas_estudiante.ipynb new file mode 100644 index 0000000..90aaa74 --- /dev/null +++ b/analisis_estrellas_estudiante.ipynb @@ -0,0 +1,647 @@ +{ + "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": 16, + "id": "code-01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "numpy 2.4.4\n", + "pandas 3.0.2\n", + "seaborn 0.13.2\n" + ] + } + ], + "source": [ + "# Importa las cuatro librerías con sus alias convencionales\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:s\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": 17, + "id": "5fd8be11", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['.git', '.gitignore', 'analisis_estrellas_estudiante.ipynb', 'autograder', 'banner_ecfm.sh', 'banner_ecfm_small.sh', 'cpp', 'Dockerfile', 'ejemplos', 'estudiantes.csv', 'examenes', 'Makefile', 'ordenamiento', 'Pandas.ipynb', 'pandita.ipynb', 'parciales', 'plantilla_tareas', 'README.md', 'resultado.csv', 'tareas']\n" + ] + } + ], + "source": [ + "import os\n", + "print(os.listdir('.')) # Esto te dirá qué archivos hay en la carpeta donde estás parado" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "code-02", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '../data/star_dataset.csv'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m stars = pd.read_csv(\u001b[33m'../data/star_dataset.csv'\u001b[39m)\n\u001b[32m 3\u001b[39m \n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# Muestra las primeras 5 filas del DataFrame\u001b[39;00m\n\u001b[32m 5\u001b[39m stars.head()\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:873\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, skip_blank_lines, parse_dates, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 861\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 862\u001b[39m dialect,\n\u001b[32m 863\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 869\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 870\u001b[39m )\n\u001b[32m 871\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:300\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 297\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 299\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 302\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 303\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1645\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1642\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1644\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1645\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1904\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1902\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1903\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1904\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1905\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1906\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1907\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1908\u001b[39m \u001b[43m 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pd.read_csv('../data/star_dataset.csv')\n", + "\n", + "# Muestra las primeras 5 filas del DataFrame\n", + "stars.head()\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": [ + "# Celda 3a: dimensiones, nombres de columnas y tipos de datos\n", + "print(f\"Dimensiones: {stars.shape}\")\n", + "print(f\"Columnas: {stars.columns.tolist()}\")\n", + "print(\"\\nTipos de datos:\")\n", + "print(stars.dtypes)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03b", + "metadata": {}, + "outputs": [], + "source": [ + "# Celda 3b: resumen estadístico de las columnas numéricas\n", + "display(stars.describe())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-03c", + "metadata": {}, + "outputs": [], + "source": [ + "# Celda 3c: cuenta los valores nulos por columna\n", + "print(\"\\nValores nulos por columna:\")\n", + "print(stars.isnull().sum())\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": [ + "# Celda 4a: Cuenta las estrellas por tipo y guarda en 'conteo'\n", + "conteo = stars['Spectral Class'].value_counts()\n", + "print(conteo)\n", + "\n", + "\n", + "\n", + "print(conteo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-04b", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "\n", + "# Celda 4b: Gráfica de barras\n", + "plt.figure(figsize=(8, 4))\n", + "conteo.plot(kind='bar', color='steelblue', edgecolor='black')\n", + "\n", + "plt.title('Distribución de Estrellas por Clase Espectral')\n", + "plt.xlabel('Clase Espectral')\n", + "plt.ylabel('Cantidad de Estrellas')\n", + "\n", + "plt.xticks(rotation=30, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()\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", + "# Celda 5a: Enfoque manual con ciclo for\n", + "tipo_objetivo = 'A7V'\n", + "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", + "\n", + "suma = 0\n", + "n = 0\n", + "for temp in filtrado['Temperature (K)']:\n", + " suma += temp\n", + " n += 1\n", + "\n", + "media_manual = suma / n\n", + "print(f'Temperatura media (for loop) : {media_manual:,.1f} K')\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "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": [ + "# Celda 5b: Enfoque vectorizado con pandas\n", + "temp_por_tipo = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False)\n", + "\n", + "print('\\nTemperatura promedio por clase espectral (K):')\n", + "print(temp_por_tipo)\n", + "print(f\"\\nVerificación para 'A7V': {temp_por_tipo['A7V'] == media_manual}\")\n", + "\n", + "\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", + "# Celda 5c: Boxplot\n", + "plt.figure(figsize=(9, 5))\n", + "orden = temp_por_tipo.index\n", + "\n", + "sns.boxplot(data=stars, x='Spectral Class', y='Temperature (K)', order=orden)\n", + "\n", + "plt.title('Distribución de Temperatura por Clase Espectral')\n", + "plt.xlabel('Clase Espectral')\n", + "plt.ylabel('Temperatura (K)')\n", + "\n", + "plt.xticks(rotation=20, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print('Temperatura promedio por clase espectral (K):')\n", + "print(temp_por_tipo)\n", + "print()\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", + "plt.figure(figsize=(9, 6))\n", + "\n", + "# Crea el scatter plot\n", + "sns.scatterplot(data=stars, x='Temperature (K)', y='Luminosity (L/Lo)', \n", + " hue='Spectral Class', style='Spectral Class', s=60)\n", + "\n", + "# Aplica escala logarítmica al eje Y\n", + "plt.yscale('log')\n", + "\n", + "plt.title('Luminosidad vs Temperatura (Escala Semi-Log)')\n", + "plt.xlabel('Temperatura (K)')\n", + "plt.ylabel('Luminosidad (L/Lo)')\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\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": [ + "# Celda 7a: Extrae arrays y calcula estadísticas\n", + "temperaturas = stars['Temperature (K)'].values\n", + "radios = stars['Radius (R/Ro)'].values\n", + "\n", + "print(f\"Tipo de array: {type(temperaturas)}\")\n", + "print(f\"Media: {np.mean(temperaturas):.2f}\")\n", + "print(f\"Mediana: {np.median(temperaturas):.2f}\")\n", + "print(f\"Desv. Estándar: {np.std(temperaturas):.2f}\")\n", + "print(f\"Mínimo: {np.min(temperaturas)} | Máximo: {np.max(temperaturas)}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "code-07b", + "metadata": {}, + "outputs": [], + "source": [ + "niveles = [25, 50, 75, 90]\n", + "\n", + "# Celda 7b: Percentiles y conversión vectorizada\n", + "niveles = [25, 50, 75, 90]\n", + "p = np.percentile(radios, niveles)\n", + "\n", + "print(\"\\nPercentiles del Radio:\")\n", + "for n, val in zip(niveles, p):\n", + " print(f\"P{n}: {val:.3f} R/Ro\")\n", + "\n", + "celsius = temperaturas - 273.15\n", + "print(\"\\nComparación K vs C (primeros 5):\")\n", + "for k, c in zip(temperaturas[:5], celsius[:5]):\n", + " print(f\"{k} K -> {np.round(c, 1)} °C\")\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 y crea scatter plots individuales para la leyenda\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", + "# Escalas logarítmicas\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "# Invertir eje X (crucial para Diagrama H-R)\n", + "plt.gca().invert_xaxis()\n", + "\n", + "plt.title('Diagrama Hertzsprung-Russell')\n", + "plt.xlabel('Temperatura (K) - Eje Invertido')\n", + "plt.ylabel('Luminosidad (L/Lo)')\n", + "plt.legend(title='Clase Espectral')\n", + "plt.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.14.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/estudiantes.csv b/estudiantes.csv index 9007158..af5d88f 100644 --- a/estudiantes.csv +++ b/estudiantes.csv @@ -1,6 +1,6 @@ nombre,edad,carrera,nota -Ana,20,Física,85 -Carlos,22,Matemática,72 -Diana,21,Física,91 -Eduardo,23,Computación,68 -Fátima,20,Matemática,79 +Ana,20,Fsica,85 +Carlos,22,Matemtica,72 +Diana,21,Fsica,91 +Eduardo,23,Computacin,68 +Ftima,20,Matemtica,79 diff --git a/pandita.ipynb b/pandita.ipynb new file mode 100644 index 0000000..21c2679 --- /dev/null +++ b/pandita.ipynb @@ -0,0 +1,10 @@ +{ + "cells": [], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 44e305d6923ed0616703da177fa99a4ffb0d3d43 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Sun, 26 Apr 2026 21:52:58 -0600 Subject: [PATCH 07/12] pandas hecho --- Pandas.ipynb | 1264 ++++++++++++++++++++++++++++++++++++++++------- estudiantes.csv | 10 +- 2 files changed, 1101 insertions(+), 173 deletions(-) diff --git a/Pandas.ipynb b/Pandas.ipynb index 4983b0f..201feb1 100644 --- a/Pandas.ipynb +++ b/Pandas.ipynb @@ -41,9 +41,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pandas versión: 3.0.2\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np # Pandas y NumPy se usan frecuentemente juntos\n", @@ -61,9 +69,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 110, + "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", @@ -74,9 +98,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 111, + "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", @@ -92,9 +133,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 112, + "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", @@ -111,9 +166,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 113, + "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", @@ -139,9 +216,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 114, + "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", @@ -157,9 +247,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 115, + "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 str\n", + "Edad int64\n", + "Carrera str\n", + "Promedio float64\n", + "dtype: object\n" + ] + } + ], "source": [ "# Información básica del DataFrame\n", "print(\"Forma (filas x columnas):\", df.shape)\n", @@ -170,9 +277,103 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 116, + "metadata": {}, + "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": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Resumen estadístico automático\n", "print(\"Estadísticas descriptivas:\")\n", @@ -181,9 +382,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 117, + "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", @@ -210,9 +431,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 118, + "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: str\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", @@ -224,9 +467,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 119, + "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", @@ -238,9 +500,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 120, + "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", @@ -259,9 +536,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 121, + "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", @@ -270,9 +559,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 122, + "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", @@ -281,9 +582,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 123, + "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", @@ -294,9 +611,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 124, + "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", @@ -321,9 +653,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 125, + "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", @@ -340,9 +689,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 126, + "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", @@ -360,9 +721,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 127, + "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", @@ -379,9 +754,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 128, + "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", @@ -401,9 +789,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 129, + "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", @@ -415,9 +816,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 130, + "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", @@ -431,9 +855,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 131, + "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", @@ -457,9 +901,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 132, + "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", @@ -479,9 +936,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 133, + "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", @@ -499,9 +977,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 134, + "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", @@ -514,9 +1006,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 135, + "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", @@ -533,9 +1035,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 136, + "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", @@ -547,9 +1070,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 137, + "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", @@ -562,9 +1099,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 138, + "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", @@ -574,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ @@ -586,9 +1132,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 140, + "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", @@ -599,11 +1165,12 @@ "Fátima,20,Matemática,79\n", "\"\"\"\n", "\n", - "with open(\"/tmp/estudiantes.csv\", \"w\") as f:\n", + "\n", + "with open(\"estudiantes.csv\", \"w\", encoding=\"utf-8\") as f:\n", " f.write(csv_contenido)\n", "\n", - "# Leer el CSV\n", - "df_csv = pd.read_csv(\"/tmp/estudiantes.csv\")\n", + "\n", + "df_csv = pd.read_csv(\"estudiantes.csv\")\n", "print(\"Datos leídos desde CSV:\")\n", "print(df_csv)\n", "print(\"\\nPrimeras 3 filas (head):\")\n", @@ -612,13 +1179,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Archivo guardado como 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", + "df_csv.to_csv(\"resultado.csv\", index=False)\n", + "\n", + "print(\"Archivo guardado como resultado.csv\")\n", "\n", "# Otros formatos comunes:\n", "# df.to_excel(\"archivo.xlsx\", index=False)\n", @@ -628,9 +1205,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 142, + "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", @@ -662,9 +1253,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 143, + "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", @@ -678,9 +1284,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Categorías únicas:\n", + "\n", + "['nublado', 'parcialmente nublado', 'mayormente despejado', 'despejado']\n", + "Length: 4, dtype: str\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", @@ -692,9 +1317,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 145, + "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", @@ -713,9 +1346,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 146, + "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", @@ -728,9 +1384,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 147, + "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", @@ -750,9 +1421,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tipo original: str\n", + "Tipo convertido: datetime64[us]\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[us]\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", @@ -767,9 +1453,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 149, + "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", @@ -779,9 +1481,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 150, + "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[us]\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", @@ -789,7 +1513,8 @@ "print(delta)\n", "\n", "print(\"\\nEn segundos (float):\")\n", - "print(delta.astype('timedelta64[s]').astype(float))" + "# .dt permite acceder a propiedades de tiempo en Series de Pandas\n", + "print(delta.dt.total_seconds())" ] }, { @@ -815,9 +1540,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 151, + "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", @@ -826,9 +1563,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 152, + "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", @@ -840,9 +1590,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 153, + "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", @@ -864,9 +1625,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -889,9 +1661,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 155, + "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", @@ -902,9 +1685,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 156, + "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", @@ -926,9 +1720,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 157, + "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", @@ -938,9 +1752,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 158, + "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", @@ -949,9 +1775,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 159, + "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", @@ -982,9 +1822,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precipitación Mensual (mm):\n", + "Ene 2\n", + "Feb 3\n", + "Mar 13\n", + "Abr 31\n", + "May 152\n", + "Jun 274\n", + "Jul 203\n", + "Ago 198\n", + "Sep 231\n", + "Oct 172\n", + "Nov 23\n", + "Dic 7\n", + "dtype: int64\n", + "------------------------------\n", + "Media: 109.08 mm\n", + "Mediana: 91.50 mm\n", + "Máximo: 274 mm\n", + "Mes con mayor precipitación: Jun (posición 5)\n" + ] + } + ], "source": [ "import pandas as pd\n", "\n", @@ -1018,9 +1884,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meses con temperatura entre 5°C y 15°C:\n", + " temp_C rain_mm\n", + "apr 7.4 100\n", + "may 12.0 143\n", + "jun 15.0 153\n", + "sep 13.1 135\n", + "oct 9.1 89\n", + "\n", + "Lluvia total en este subconjunto: 620 mm\n" + ] + } + ], "source": [ "\n", "filtro_clima = df_clima[(df_clima['temp_C'] >= 5) & (df_clima['temp_C'] <= 15)]\n", @@ -1043,9 +1925,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame con Amplitud Térmica:\n", + " temp_min_C temp_max_C amplitud_termica\n", + "jan -1.9 2.5 4.4\n", + "feb -2.5 3.3 5.8\n", + "mar 0.6 7.3 6.7\n", + "apr 3.5 11.5 8.0\n", + "may 7.8 16.3 8.5\n", + "jun 11.0 19.2 8.2\n", + "jul 13.1 21.6 8.5\n", + "aug 13.0 20.9 7.9\n", + "sep 9.7 16.8 7.1\n", + "oct 6.2 12.3 6.1\n", + "nov 1.0 6.5 5.5\n", + "dec -3.0 3.5 6.5\n", + "\n", + "El mes con mayor amplitud térmica es jul con 8.500000000000002°C\n" + ] + } + ], "source": [ "# Agrego la columna calculada 🤓\n", "df_completo['amplitud_termica'] = df_completo['temp_max_C'] - df_completo['temp_min_C']\n", @@ -1075,7 +1980,28 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NaN por columna:\n", + "Nombre 0\n", + "Nota1 1\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n", + "\n", + "Resultado Final:\n", + " Nombre Nota1 Nota2 Nota3 Promedio_Final\n", + "0 Rubén 85.0 78.0 92.0 85.0\n", + "1 María 83.2 65.0 70.0 72.8\n", + "2 José 90.0 79.0 85.0 84.7\n", + "3 Lucía 70.0 82.0 80.8 77.6\n", + "4 Pedro 88.0 91.0 76.0 85.0\n" + ] + } + ], "source": [ "import numpy as np\n", "\n", @@ -1103,7 +2029,9 @@ "\n", "\n", " \n", - "#ayuda tengo el impulso de terminar mis comentarios con \"🤓\"" + "#ayuda tengo el impulso de terminar mis comentarios con \"🤓\"\n", + "# a si, corregi unas lineas en el codigo de los \"niveles\" sobre porque creo que la tenias estilo linux y yo estoy en visual/windows, no me daba\n", + "# pregunte a mi hermano porque (es programador) y me comento que era por eso y me ayudo a corregirlo :D" ] }, { diff --git a/estudiantes.csv b/estudiantes.csv index af5d88f..9007158 100644 --- a/estudiantes.csv +++ b/estudiantes.csv @@ -1,6 +1,6 @@ nombre,edad,carrera,nota -Ana,20,Fsica,85 -Carlos,22,Matemtica,72 -Diana,21,Fsica,91 -Eduardo,23,Computacin,68 -Ftima,20,Matemtica,79 +Ana,20,Física,85 +Carlos,22,Matemática,72 +Diana,21,Física,91 +Eduardo,23,Computación,68 +Fátima,20,Matemática,79 From 868e14c00189e0803f5f68c879d03bd4d579303b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Sun, 26 Apr 2026 22:12:50 -0600 Subject: [PATCH 08/12] borramos panditas --- pandita.ipynb | 10 ---------- 1 file changed, 10 deletions(-) delete mode 100644 pandita.ipynb diff --git a/pandita.ipynb b/pandita.ipynb deleted file mode 100644 index 21c2679..0000000 --- a/pandita.ipynb +++ /dev/null @@ -1,10 +0,0 @@ -{ - "cells": [], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 2b11de670128cc4759f18234930af21445ed1d1d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Mon, 27 Apr 2026 20:21:12 -0600 Subject: [PATCH 09/12] pokemon :D --- Pokemon.csv | 1216 ++++++++++++++++ analisis_estrellas_estudiante.ipynb | 16 +- puntos extra.ipynb | 2023 +++++++++++++++++++++++++++ 3 files changed, 3247 insertions(+), 8 deletions(-) create mode 100644 Pokemon.csv create mode 100644 puntos extra.ipynb diff --git a/Pokemon.csv b/Pokemon.csv new file mode 100644 index 0000000..1ee9bcf --- /dev/null +++ b/Pokemon.csv @@ -0,0 +1,1216 @@ +"ID","Name","Form","Type1","Type2","Total","HP","Attack","Defense","Sp. Atk","Sp. Def","Speed","Generation" +1,"Bulbasaur"," ","Grass","Poison",318,45,49,49,65,65,45,1 +2,"Ivysaur"," ","Grass","Poison",405,60,62,63,80,80,60,1 +3,"Venusaur"," ","Grass","Poison",525,80,82,83,100,100,80,1 +4,"Charmander"," ","Fire"," ",309,39,52,43,60,50,65,1 +5,"Charmeleon"," ","Fire"," ",405,58,64,58,80,65,80,1 +6,"Charizard"," ","Fire","Flying",534,78,84,78,109,85,100,1 +7,"Squirtle"," ","Water"," ",314,44,48,65,50,64,43,1 +8,"Wartortle"," ","Water"," ",405,59,63,80,65,80,58,1 +9,"Blastoise"," ","Water"," ",530,79,83,100,85,105,78,1 +10,"Caterpie"," ","Bug"," ",195,45,30,35,20,20,45,1 +11,"Metapod"," ","Bug"," ",205,50,20,55,25,25,30,1 +12,"Butterfree"," ","Bug","Flying",395,60,45,50,90,80,70,1 +13,"Weedle"," ","Bug","Poison",195,40,35,30,20,20,50,1 +14,"Kakuna"," ","Bug","Poison",205,45,25,50,25,25,35,1 +15,"Beedrill"," ","Bug","Poison",395,65,90,40,45,80,75,1 +16,"Pidgey"," ","Normal","Flying",251,40,45,40,35,35,56,1 +17,"Pidgeotto"," ","Normal","Flying",349,63,60,55,50,50,71,1 +18,"Pidgeot"," ","Normal","Flying",479,83,80,75,70,70,101,1 +19,"Rattata"," ","Normal"," ",253,30,56,35,25,35,72,1 +20,"Raticate"," ","Normal"," ",413,55,81,60,50,70,97,1 +21,"Spearow"," ","Normal","Flying",262,40,60,30,31,31,70,1 +22,"Fearow"," ","Normal","Flying",442,65,90,65,61,61,100,1 +23,"Ekans"," ","Poison"," ",288,35,60,44,40,54,55,1 +24,"Arbok"," ","Poison"," ",448,60,95,69,65,79,80,1 +25,"Pikachu"," ","Electric"," ",320,35,55,40,50,50,90,1 +26,"Raichu"," ","Electric"," ",485,60,90,55,90,80,110,1 +27,"Sandshrew"," ","Ground"," ",300,50,75,85,20,30,40,1 +28,"Sandslash"," ","Ground"," ",450,75,100,110,45,55,65,1 +29,"Nidoran","Female","Poison"," ",275,55,47,52,40,40,41,1 +30,"Nidorina"," ","Poison"," ",365,70,62,67,55,55,56,1 +31,"Nidoqueen"," ","Poison","Ground",505,90,92,87,75,85,76,1 +32,"Nidoran","Male","Poison"," ",273,46,57,40,40,40,50,1 +33,"Nidorino"," ","Poison"," ",365,61,72,57,55,55,65,1 +34,"Nidoking"," ","Poison","Ground",505,81,102,77,85,75,85,1 +35,"Clefairy"," ","Fairy"," ",323,70,45,48,60,65,35,1 +36,"Clefable"," ","Fairy"," ",483,95,70,73,95,90,60,1 +37,"Vulpix"," ","Fire"," ",299,38,41,40,50,65,65,1 +38,"Ninetales"," ","Fire"," ",505,73,76,75,81,100,100,1 +39,"Jigglypuff"," ","Normal","Fairy",270,115,45,20,45,25,20,1 +40,"Wigglytuff"," ","Normal","Fairy",435,140,70,45,85,50,45,1 +41,"Zubat"," ","Poison","Flying",245,40,45,35,30,40,55,1 +42,"Golbat"," ","Poison","Flying",455,75,80,70,65,75,90,1 +43,"Oddish"," ","Grass","Poison",320,45,50,55,75,65,30,1 +44,"Gloom"," ","Grass","Poison",395,60,65,70,85,75,40,1 +45,"Vileplume"," ","Grass","Poison",490,75,80,85,110,90,50,1 +46,"Paras"," ","Bug","Grass",285,35,70,55,45,55,25,1 +47,"Parasect"," ","Bug","Grass",405,60,95,80,60,80,30,1 +48,"Venonat"," ","Bug","Poison",305,60,55,50,40,55,45,1 +49,"Venomoth"," ","Bug","Poison",450,70,65,60,90,75,90,1 +50,"Diglett"," ","Ground"," ",265,10,55,25,35,45,95,1 +51,"Dugtrio"," ","Ground"," ",425,35,100,50,50,70,120,1 +52,"Meowth"," ","Normal"," ",290,40,45,35,40,40,90,1 +53,"Persian"," ","Normal"," ",440,65,70,60,65,65,115,1 +54,"Psyduck"," ","Water"," ",320,50,52,48,65,50,55,1 +55,"Golduck"," ","Water"," ",500,80,82,78,95,80,85,1 +56,"Mankey"," ","Fighting"," ",305,40,80,35,35,45,70,1 +57,"Primeape"," ","Fighting"," ",455,65,105,60,60,70,95,1 +58,"Growlithe"," ","Fire"," ",350,55,70,45,70,50,60,1 +59,"Arcanine"," ","Fire"," ",555,90,110,80,100,80,95,1 +60,"Poliwag"," ","Water"," ",300,40,50,40,40,40,90,1 +61,"Poliwhirl"," ","Water"," ",385,65,65,65,50,50,90,1 +62,"Poliwrath"," ","Water","Fighting",510,90,95,95,70,90,70,1 +63,"Abra"," ","Psychic"," ",310,25,20,15,105,55,90,1 +64,"Kadabra"," ","Psychic"," ",400,40,35,30,120,70,105,1 +65,"Alakazam"," ","Psychic"," ",500,55,50,45,135,95,120,1 +66,"Machop"," ","Fighting"," ",305,70,80,50,35,35,35,1 +67,"Machoke"," ","Fighting"," ",405,80,100,70,50,60,45,1 +68,"Machamp"," ","Fighting"," ",505,90,130,80,65,85,55,1 +69,"Bellsprout"," ","Grass","Poison",300,50,75,35,70,30,40,1 +70,"Weepinbell"," ","Grass","Poison",390,65,90,50,85,45,55,1 +71,"Victreebel"," ","Grass","Poison",490,80,105,65,100,70,70,1 +72,"Tentacool"," ","Water","Poison",335,40,40,35,50,100,70,1 +73,"Tentacruel"," ","Water","Poison",515,80,70,65,80,120,100,1 +74,"Geodude"," ","Rock","Ground",300,40,80,100,30,30,20,1 +75,"Graveler"," ","Rock","Ground",390,55,95,115,45,45,35,1 +76,"Golem"," ","Rock","Ground",495,80,120,130,55,65,45,1 +77,"Ponyta"," ","Fire"," ",410,50,85,55,65,65,90,1 +78,"Rapidash"," ","Fire"," ",500,65,100,70,80,80,105,1 +79,"Slowpoke"," ","Water","Psychic",315,90,65,65,40,40,15,1 +80,"Slowbro"," ","Water","Psychic",490,95,75,110,100,80,30,1 +81,"Magnemite"," ","Electric","Steel",325,25,35,70,95,55,45,1 +82,"Magneton"," ","Electric","Steel",465,50,60,95,120,70,70,1 +83,"Farfetch'd"," ","Normal","Flying",377,52,90,55,58,62,60,1 +84,"Doduo"," ","Normal","Flying",310,35,85,45,35,35,75,1 +85,"Dodrio"," ","Normal","Flying",470,60,110,70,60,60,110,1 +86,"Seel"," ","Water"," ",325,65,45,55,45,70,45,1 +87,"Dewgong"," ","Water","Ice",475,90,70,80,70,95,70,1 +88,"Grimer"," ","Poison"," ",325,80,80,50,40,50,25,1 +89,"Muk"," ","Poison"," ",500,105,105,75,65,100,50,1 +90,"Shellder"," ","Water"," ",305,30,65,100,45,25,40,1 +91,"Cloyster"," ","Water","Ice",525,50,95,180,85,45,70,1 +92,"Gastly"," ","Ghost","Poison",310,30,35,30,100,35,80,1 +93,"Haunter"," ","Ghost","Poison",405,45,50,45,115,55,95,1 +94,"Gengar"," ","Ghost","Poison",500,60,65,60,130,75,110,1 +95,"Onix"," ","Rock","Ground",385,35,45,160,30,45,70,1 +96,"Drowzee"," ","Psychic"," ",328,60,48,45,43,90,42,1 +97,"Hypno"," ","Psychic"," ",483,85,73,70,73,115,67,1 +98,"Krabby"," ","Water"," ",325,30,105,90,25,25,50,1 +99,"Kingler"," ","Water"," ",475,55,130,115,50,50,75,1 +100,"Voltorb"," ","Electric"," ",330,40,30,50,55,55,100,1 +101,"Electrode"," ","Electric"," ",490,60,50,70,80,80,150,1 +102,"Exeggcute"," ","Grass","Psychic",325,60,40,80,60,45,40,1 +103,"Exeggutor"," ","Grass","Psychic",530,95,95,85,125,75,55,1 +104,"Cubone"," ","Ground"," ",320,50,50,95,40,50,35,1 +105,"Marowak"," ","Ground"," ",425,60,80,110,50,80,45,1 +106,"Hitmonlee"," ","Fighting"," ",455,50,120,53,35,110,87,1 +107,"Hitmonchan"," ","Fighting"," ",455,50,105,79,35,110,76,1 +108,"Lickitung"," ","Normal"," ",385,90,55,75,60,75,30,1 +109,"Koffing"," ","Poison"," ",340,40,65,95,60,45,35,1 +110,"Weezing"," ","Poison"," ",490,65,90,120,85,70,60,1 +111,"Rhyhorn"," ","Ground","Rock",345,80,85,95,30,30,25,1 +112,"Rhydon"," ","Ground","Rock",485,105,130,120,45,45,40,1 +113,"Chansey"," ","Normal"," ",450,250,5,5,35,105,50,1 +114,"Tangela"," ","Grass"," ",435,65,55,115,100,40,60,1 +115,"Kangaskhan"," ","Normal"," ",490,105,95,80,40,80,90,1 +116,"Horsea"," ","Water"," ",295,30,40,70,70,25,60,1 +117,"Seadra"," ","Water"," ",440,55,65,95,95,45,85,1 +118,"Goldeen"," ","Water"," ",320,45,67,60,35,50,63,1 +119,"Seaking"," ","Water"," ",450,80,92,65,65,80,68,1 +120,"Staryu"," ","Water"," ",340,30,45,55,70,55,85,1 +121,"Starmie"," ","Water","Psychic",520,60,75,85,100,85,115,1 +122,"Mr. Mime"," ","Psychic","Fairy",460,40,45,65,100,120,90,1 +123,"Scyther"," ","Bug","Flying",500,70,110,80,55,80,105,1 +124,"Jynx"," ","Ice","Psychic",455,65,50,35,115,95,95,1 +125,"Electabuzz"," ","Electric"," ",490,65,83,57,95,85,105,1 +126,"Magmar"," ","Fire"," ",495,65,95,57,100,85,93,1 +127,"Pinsir"," ","Bug"," ",500,65,125,100,55,70,85,1 +128,"Tauros"," ","Normal"," ",490,75,100,95,40,70,110,1 +129,"Magikarp"," ","Water"," ",200,20,10,55,15,20,80,1 +130,"Gyarados"," ","Water","Flying",540,95,125,79,60,100,81,1 +131,"Lapras"," ","Water","Ice",535,130,85,80,85,95,60,1 +132,"Ditto"," ","Normal"," ",288,48,48,48,48,48,48,1 +133,"Eevee"," ","Normal"," ",325,55,55,50,45,65,55,1 +134,"Vaporeon"," ","Water"," ",525,130,65,60,110,95,65,1 +135,"Jolteon"," ","Electric"," ",525,65,65,60,110,95,130,1 +136,"Flareon"," ","Fire"," ",525,65,130,60,95,110,65,1 +137,"Porygon"," ","Normal"," ",395,65,60,70,85,75,40,1 +138,"Omanyte"," ","Rock","Water",355,35,40,100,90,55,35,1 +139,"Omastar"," ","Rock","Water",495,70,60,125,115,70,55,1 +140,"Kabuto"," ","Rock","Water",355,30,80,90,55,45,55,1 +141,"Kabutops"," ","Rock","Water",495,60,115,105,65,70,80,1 +142,"Aerodactyl"," ","Rock","Flying",515,80,105,65,60,75,130,1 +143,"Snorlax"," ","Normal"," ",540,160,110,65,65,110,30,1 +144,"Articuno"," ","Ice","Flying",580,90,85,100,95,125,85,1 +145,"Zapdos"," ","Electric","Flying",580,90,90,85,125,90,100,1 +146,"Moltres"," ","Fire","Flying",580,90,100,90,125,85,90,1 +147,"Dratini"," ","Dragon"," ",300,41,64,45,50,50,50,1 +148,"Dragonair"," ","Dragon"," ",420,61,84,65,70,70,70,1 +149,"Dragonite"," ","Dragon","Flying",600,91,134,95,100,100,80,1 +150,"Mewtwo"," ","Psychic"," ",680,106,110,90,154,90,130,1 +151,"Mew"," ","Psychic"," ",600,100,100,100,100,100,100,1 +152,"Chikorita"," ","Grass"," ",318,45,49,65,49,65,45,2 +153,"Bayleef"," ","Grass"," ",405,60,62,80,63,80,60,2 +154,"Meganium"," ","Grass"," ",525,80,82,100,83,100,80,2 +155,"Cyndaquil"," ","Fire"," ",309,39,52,43,60,50,65,2 +156,"Quilava"," ","Fire"," ",405,58,64,58,80,65,80,2 +157,"Typhlosion"," ","Fire"," ",534,78,84,78,109,85,100,2 +158,"Totodile"," ","Water"," ",314,50,65,64,44,48,43,2 +159,"Croconaw"," ","Water"," ",405,65,80,80,59,63,58,2 +160,"Feraligatr"," ","Water"," ",530,85,105,100,79,83,78,2 +161,"Sentret"," ","Normal"," ",215,35,46,34,35,45,20,2 +162,"Furret"," ","Normal"," ",415,85,76,64,45,55,90,2 +163,"Hoothoot"," ","Normal","Flying",262,60,30,30,36,56,50,2 +164,"Noctowl"," ","Normal","Flying",452,100,50,50,86,96,70,2 +165,"Ledyba"," ","Bug","Flying",265,40,20,30,40,80,55,2 +166,"Ledian"," ","Bug","Flying",390,55,35,50,55,110,85,2 +167,"Spinarak"," ","Bug","Poison",250,40,60,40,40,40,30,2 +168,"Ariados"," ","Bug","Poison",400,70,90,70,60,70,40,2 +169,"Crobat"," ","Poison","Flying",535,85,90,80,70,80,130,2 +170,"Chinchou"," ","Water","Electric",330,75,38,38,56,56,67,2 +171,"Lanturn"," ","Water","Electric",460,125,58,58,76,76,67,2 +172,"Pichu"," ","Electric"," ",205,20,40,15,35,35,60,2 +173,"Cleffa"," ","Fairy"," ",218,50,25,28,45,55,15,2 +174,"Igglybuff"," ","Normal","Fairy",210,90,30,15,40,20,15,2 +175,"Togepi"," ","Fairy"," ",245,35,20,65,40,65,20,2 +176,"Togetic"," ","Fairy","Flying",405,55,40,85,80,105,40,2 +177,"Natu"," ","Psychic","Flying",320,40,50,45,70,45,70,2 +178,"Xatu"," ","Psychic","Flying",470,65,75,70,95,70,95,2 +179,"Mareep"," ","Electric"," ",280,55,40,40,65,45,35,2 +180,"Flaaffy"," ","Electric"," ",365,70,55,55,80,60,45,2 +181,"Ampharos"," ","Electric"," ",510,90,75,85,115,90,55,2 +182,"Bellossom"," ","Grass"," ",490,75,80,95,90,100,50,2 +183,"Marill"," ","Water","Fairy",250,70,20,50,20,50,40,2 +184,"Azumarill"," ","Water","Fairy",420,100,50,80,60,80,50,2 +185,"Sudowoodo"," ","Rock"," ",410,70,100,115,30,65,30,2 +186,"Politoed"," ","Water"," ",500,90,75,75,90,100,70,2 +187,"Hoppip"," ","Grass","Flying",250,35,35,40,35,55,50,2 +188,"Skiploom"," ","Grass","Flying",340,55,45,50,45,65,80,2 +189,"Jumpluff"," ","Grass","Flying",460,75,55,70,55,95,110,2 +190,"Aipom"," ","Normal"," ",360,55,70,55,40,55,85,2 +191,"Sunkern"," ","Grass"," ",180,30,30,30,30,30,30,2 +192,"Sunflora"," ","Grass"," ",425,75,75,55,105,85,30,2 +193,"Yanma"," ","Bug","Flying",390,65,65,45,75,45,95,2 +194,"Wooper"," ","Water","Ground",210,55,45,45,25,25,15,2 +195,"Quagsire"," ","Water","Ground",430,95,85,85,65,65,35,2 +196,"Espeon"," ","Psychic"," ",525,65,65,60,130,95,110,2 +197,"Umbreon"," ","Dark"," ",525,95,65,110,60,130,65,2 +198,"Murkrow"," ","Dark","Flying",405,60,85,42,85,42,91,2 +199,"Slowking"," ","Water","Psychic",490,95,75,80,100,110,30,2 +200,"Misdreavus"," ","Ghost"," ",435,60,60,60,85,85,85,2 +201,"Unown"," ","Psychic"," ",336,48,72,48,72,48,48,2 +202,"Wobbuffet"," ","Psychic"," ",405,190,33,58,33,58,33,2 +203,"Girafarig"," ","Normal","Psychic",455,70,80,65,90,65,85,2 +204,"Pineco"," ","Bug"," ",290,50,65,90,35,35,15,2 +205,"Forretress"," ","Bug","Steel",465,75,90,140,60,60,40,2 +206,"Dunsparce"," ","Normal"," ",415,100,70,70,65,65,45,2 +207,"Gligar"," ","Ground","Flying",430,65,75,105,35,65,85,2 +208,"Steelix"," ","Steel","Ground",510,75,85,200,55,65,30,2 +209,"Snubbull"," ","Fairy"," ",300,60,80,50,40,40,30,2 +210,"Granbull"," ","Fairy"," ",450,90,120,75,60,60,45,2 +211,"Qwilfish"," ","Water","Poison",440,65,95,85,55,55,85,2 +212,"Scizor"," ","Bug","Steel",500,70,130,100,55,80,65,2 +213,"Shuckle"," ","Bug","Rock",505,20,10,230,10,230,5,2 +214,"Heracross"," ","Bug","Fighting",500,80,125,75,40,95,85,2 +215,"Sneasel"," ","Dark","Ice",430,55,95,55,35,75,115,2 +216,"Teddiursa"," ","Normal"," ",330,60,80,50,50,50,40,2 +217,"Ursaring"," ","Normal"," ",500,90,130,75,75,75,55,2 +218,"Slugma"," ","Fire"," ",250,40,40,40,70,40,20,2 +219,"Magcargo"," ","Fire","Rock",430,60,50,120,90,80,30,2 +220,"Swinub"," ","Ice","Ground",250,50,50,40,30,30,50,2 +221,"Piloswine"," ","Ice","Ground",450,100,100,80,60,60,50,2 +222,"Corsola"," ","Water","Rock",410,65,55,95,65,95,35,2 +223,"Remoraid"," ","Water"," ",300,35,65,35,65,35,65,2 +224,"Octillery"," ","Water"," ",480,75,105,75,105,75,45,2 +225,"Delibird"," ","Ice","Flying",330,45,55,45,65,45,75,2 +226,"Mantine"," ","Water","Flying",485,85,40,70,80,140,70,2 +227,"Skarmory"," ","Steel","Flying",465,65,80,140,40,70,70,2 +228,"Houndour"," ","Dark","Fire",330,45,60,30,80,50,65,2 +229,"Houndoom"," ","Dark","Fire",500,75,90,50,110,80,95,2 +230,"Kingdra"," ","Water","Dragon",540,75,95,95,95,95,85,2 +231,"Phanpy"," ","Ground"," ",330,90,60,60,40,40,40,2 +232,"Donphan"," ","Ground"," ",500,90,120,120,60,60,50,2 +233,"Porygon2"," ","Normal"," ",515,85,80,90,105,95,60,2 +234,"Stantler"," ","Normal"," ",465,73,95,62,85,65,85,2 +235,"Smeargle"," ","Normal"," ",250,55,20,35,20,45,75,2 +236,"Tyrogue"," ","Fighting"," ",210,35,35,35,35,35,35,2 +237,"Hitmontop"," ","Fighting"," ",455,50,95,95,35,110,70,2 +238,"Smoochum"," ","Ice","Psychic",305,45,30,15,85,65,65,2 +239,"Elekid"," ","Electric"," ",360,45,63,37,65,55,95,2 +240,"Magby"," ","Fire"," ",365,45,75,37,70,55,83,2 +241,"Miltank"," ","Normal"," ",490,95,80,105,40,70,100,2 +242,"Blissey"," ","Normal"," ",540,255,10,10,75,135,55,2 +243,"Raikou"," ","Electric"," ",580,90,85,75,115,100,115,2 +244,"Entei"," ","Fire"," ",580,115,115,85,90,75,100,2 +245,"Suicune"," ","Water"," ",580,100,75,115,90,115,85,2 +246,"Larvitar"," ","Rock","Ground",300,50,64,50,45,50,41,2 +247,"Pupitar"," ","Rock","Ground",410,70,84,70,65,70,51,2 +248,"Tyranitar"," ","Rock","Dark",600,100,134,110,95,100,61,2 +249,"Lugia"," ","Psychic","Flying",680,106,90,130,90,154,110,2 +250,"Ho-oh"," ","Fire","Flying",680,106,130,90,110,154,90,2 +251,"Celebi"," ","Psychic","Grass",600,100,100,100,100,100,100,2 +252,"Treecko"," ","Grass"," ",310,40,45,35,65,55,70,3 +253,"Grovyle"," ","Grass"," ",405,50,65,45,85,65,95,3 +254,"Sceptile"," ","Grass"," ",530,70,85,65,105,85,120,3 +255,"Torchic"," ","Fire"," ",310,45,60,40,70,50,45,3 +256,"Combusken"," ","Fire","Fighting",405,60,85,60,85,60,55,3 +257,"Blaziken"," ","Fire","Fighting",530,80,120,70,110,70,80,3 +258,"Mudkip"," ","Water"," ",310,50,70,50,50,50,40,3 +259,"Marshtomp"," ","Water","Ground",405,70,85,70,60,70,50,3 +260,"Swampert"," ","Water","Ground",535,100,110,90,85,90,60,3 +261,"Poochyena"," ","Dark"," ",220,35,55,35,30,30,35,3 +262,"Mightyena"," ","Dark"," ",420,70,90,70,60,60,70,3 +263,"Zigzagoon"," ","Normal"," ",240,38,30,41,30,41,60,3 +264,"Linoone"," ","Normal"," ",420,78,70,61,50,61,100,3 +265,"Wurmple"," ","Bug"," ",195,45,45,35,20,30,20,3 +266,"Silcoon"," ","Bug"," ",205,50,35,55,25,25,15,3 +267,"Beautifly"," ","Bug","Flying",395,60,70,50,100,50,65,3 +268,"Cascoon"," ","Bug"," ",205,50,35,55,25,25,15,3 +269,"Dustox"," ","Bug","Poison",385,60,50,70,50,90,65,3 +270,"Lotad"," ","Water","Grass",220,40,30,30,40,50,30,3 +271,"Lombre"," ","Water","Grass",340,60,50,50,60,70,50,3 +272,"Ludicolo"," ","Water","Grass",480,80,70,70,90,100,70,3 +273,"Seedot"," ","Grass"," ",220,40,40,50,30,30,30,3 +274,"Nuzleaf"," ","Grass","Dark",340,70,70,40,60,40,60,3 +275,"Shiftry"," ","Grass","Dark",480,90,100,60,90,60,80,3 +276,"Taillow"," ","Normal","Flying",270,40,55,30,30,30,85,3 +277,"Swellow"," ","Normal","Flying",455,60,85,60,75,50,125,3 +278,"Wingull"," ","Water","Flying",270,40,30,30,55,30,85,3 +279,"Pelipper"," ","Water","Flying",440,60,50,100,95,70,65,3 +280,"Ralts"," ","Psychic","Fairy",198,28,25,25,45,35,40,3 +281,"Kirlia"," ","Psychic","Fairy",278,38,35,35,65,55,50,3 +282,"Gardevoir"," ","Psychic","Fairy",518,68,65,65,125,115,80,3 +283,"Surskit"," ","Bug","Water",269,40,30,32,50,52,65,3 +284,"Masquerain"," ","Bug","Flying",454,70,60,62,100,82,80,3 +285,"Shroomish"," ","Grass"," ",295,60,40,60,40,60,35,3 +286,"Breloom"," ","Grass","Fighting",460,60,130,80,60,60,70,3 +287,"Slakoth"," ","Normal"," ",280,60,60,60,35,35,30,3 +288,"Vigoroth"," ","Normal"," ",440,80,80,80,55,55,90,3 +289,"Slaking"," ","Normal"," ",670,150,160,100,95,65,100,3 +290,"Nincada"," ","Bug","Ground",266,31,45,90,30,30,40,3 +291,"Ninjask"," ","Bug","Flying",456,61,90,45,50,50,160,3 +292,"Shedinja"," ","Bug","Ghost",236,1,90,45,30,30,40,3 +293,"Whismur"," ","Normal"," ",240,64,51,23,51,23,28,3 +294,"Loudred"," ","Normal"," ",360,84,71,43,71,43,48,3 +295,"Exploud"," ","Normal"," ",490,104,91,63,91,73,68,3 +296,"Makuhita"," ","Fighting"," ",237,72,60,30,20,30,25,3 +297,"Hariyama"," ","Fighting"," ",474,144,120,60,40,60,50,3 +298,"Azurill"," ","Normal","Fairy",190,50,20,40,20,40,20,3 +299,"Nosepass"," ","Rock"," ",375,30,45,135,45,90,30,3 +300,"Skitty"," ","Normal"," ",260,50,45,45,35,35,50,3 +301,"Delcatty"," ","Normal"," ",400,70,65,65,55,55,90,3 +302,"Sableye"," ","Dark","Ghost",380,50,75,75,65,65,50,3 +303,"Mawile"," ","Steel","Fairy",380,50,85,85,55,55,50,3 +304,"Aron"," ","Steel","Rock",330,50,70,100,40,40,30,3 +305,"Lairon"," ","Steel","Rock",430,60,90,140,50,50,40,3 +306,"Aggron"," ","Steel","Rock",530,70,110,180,60,60,50,3 +307,"Meditite"," ","Fighting","Psychic",280,30,40,55,40,55,60,3 +308,"Medicham"," ","Fighting","Psychic",410,60,60,75,60,75,80,3 +309,"Electrike"," ","Electric"," ",295,40,45,40,65,40,65,3 +310,"Manectric"," ","Electric"," ",475,70,75,60,105,60,105,3 +311,"Plusle"," ","Electric"," ",405,60,50,40,85,75,95,3 +312,"Minun"," ","Electric"," ",405,60,40,50,75,85,95,3 +313,"Volbeat"," ","Bug"," ",430,65,73,75,47,85,85,3 +314,"Illumise"," ","Bug"," ",430,65,47,75,73,85,85,3 +315,"Roselia"," ","Grass","Poison",400,50,60,45,100,80,65,3 +316,"Gulpin"," ","Poison"," ",302,70,43,53,43,53,40,3 +317,"Swalot"," ","Poison"," ",467,100,73,83,73,83,55,3 +318,"Carvanha"," ","Water","Dark",305,45,90,20,65,20,65,3 +319,"Sharpedo"," ","Water","Dark",460,70,120,40,95,40,95,3 +320,"Wailmer"," ","Water"," ",400,130,70,35,70,35,60,3 +321,"Wailord"," ","Water"," ",500,170,90,45,90,45,60,3 +322,"Numel"," ","Fire","Ground",305,60,60,40,65,45,35,3 +323,"Camerupt"," ","Fire","Ground",460,70,100,70,105,75,40,3 +324,"Torkoal"," ","Fire"," ",470,70,85,140,85,70,20,3 +325,"Spoink"," ","Psychic"," ",330,60,25,35,70,80,60,3 +326,"Grumpig"," ","Psychic"," ",470,80,45,65,90,110,80,3 +327,"Spinda"," ","Normal"," ",360,60,60,60,60,60,60,3 +328,"Trapinch"," ","Ground"," ",290,45,100,45,45,45,10,3 +329,"Vibrava"," ","Ground","Dragon",340,50,70,50,50,50,70,3 +330,"Flygon"," ","Ground","Dragon",520,80,100,80,80,80,100,3 +331,"Cacnea"," ","Grass"," ",335,50,85,40,85,40,35,3 +332,"Cacturne"," ","Grass","Dark",475,70,115,60,115,60,55,3 +333,"Swablu"," ","Normal","Flying",310,45,40,60,40,75,50,3 +334,"Altaria"," ","Dragon","Flying",490,75,70,90,70,105,80,3 +335,"Zangoose"," ","Normal"," ",458,73,115,60,60,60,90,3 +336,"Seviper"," ","Poison"," ",458,73,100,60,100,60,65,3 +337,"Lunatone"," ","Rock","Psychic",460,90,55,65,95,85,70,3 +338,"Solrock"," ","Rock","Psychic",460,90,95,85,55,65,70,3 +339,"Barboach"," ","Water","Ground",288,50,48,43,46,41,60,3 +340,"Whiscash"," ","Water","Ground",468,110,78,73,76,71,60,3 +341,"Corphish"," ","Water"," ",308,43,80,65,50,35,35,3 +342,"Crawdaunt"," ","Water","Dark",468,63,120,85,90,55,55,3 +343,"Baltoy"," ","Ground","Psychic",300,40,40,55,40,70,55,3 +344,"Claydol"," ","Ground","Psychic",500,60,70,105,70,120,75,3 +345,"Lileep"," ","Rock","Grass",355,66,41,77,61,87,23,3 +346,"Cradily"," ","Rock","Grass",495,86,81,97,81,107,43,3 +347,"Anorith"," ","Rock","Bug",355,45,95,50,40,50,75,3 +348,"Armaldo"," ","Rock","Bug",495,75,125,100,70,80,45,3 +349,"Feebas"," ","Water"," ",200,20,15,20,10,55,80,3 +350,"Milotic"," ","Water"," ",540,95,60,79,100,125,81,3 +351,"Castform"," ","Normal"," ",420,70,70,70,70,70,70,3 +351,"Castform","Sunny Form","Fire"," ",420,70,70,70,70,70,70,3 +351,"Castform","Rainy Form","Water"," ",420,70,70,70,70,70,70,3 +351,"Castform","Snowy Form","Ice"," ",420,70,70,70,70,70,70,3 +352,"Kecleon"," ","Normal"," ",440,60,90,70,60,120,40,3 +353,"Shuppet"," ","Ghost"," ",295,44,75,35,63,33,45,3 +354,"Banette"," ","Ghost"," ",455,64,115,65,83,63,65,3 +355,"Duskull"," ","Ghost"," ",295,20,40,90,30,90,25,3 +356,"Dusclops"," ","Ghost"," ",455,40,70,130,60,130,25,3 +357,"Tropius"," ","Grass","Flying",460,99,68,83,72,87,51,3 +358,"Chimecho"," ","Psychic"," ",455,75,50,80,95,90,65,3 +359,"Absol"," ","Dark"," ",465,65,130,60,75,60,75,3 +360,"Wynaut"," ","Psychic"," ",260,95,23,48,23,48,23,3 +361,"Snorunt"," ","Ice"," ",300,50,50,50,50,50,50,3 +362,"Glalie"," ","Ice"," ",480,80,80,80,80,80,80,3 +363,"Spheal"," ","Ice","Water",290,70,40,50,55,50,25,3 +364,"Sealeo"," ","Ice","Water",410,90,60,70,75,70,45,3 +365,"Walrein"," ","Ice","Water",530,110,80,90,95,90,65,3 +366,"Clamperl"," ","Water"," ",345,35,64,85,74,55,32,3 +367,"Huntail"," ","Water"," ",485,55,104,105,94,75,52,3 +368,"Gorebyss"," ","Water"," ",485,55,84,105,114,75,52,3 +369,"Relicanth"," ","Water","Rock",485,100,90,130,45,65,55,3 +370,"Luvdisc"," ","Water"," ",330,43,30,55,40,65,97,3 +371,"Bagon"," ","Dragon"," ",300,45,75,60,40,30,50,3 +372,"Shelgon"," ","Dragon"," ",420,65,95,100,60,50,50,3 +373,"Salamence"," ","Dragon","Flying",600,95,135,80,110,80,100,3 +374,"Beldum"," ","Steel","Psychic",300,40,55,80,35,60,30,3 +375,"Metang"," ","Steel","Psychic",420,60,75,100,55,80,50,3 +376,"Metagross"," ","Steel","Psychic",600,80,135,130,95,90,70,3 +377,"Regirock"," ","Rock"," ",580,80,100,200,50,100,50,3 +378,"Regice"," ","Ice"," ",580,80,50,100,100,200,50,3 +379,"Registeel"," ","Steel"," ",580,80,75,150,75,150,50,3 +380,"Latias"," ","Dragon","Psychic",600,80,80,90,110,130,110,3 +381,"Latios"," ","Dragon","Psychic",600,80,90,80,130,110,110,3 +382,"Kyogre"," ","Water"," ",670,100,100,90,150,140,90,3 +383,"Groudon"," ","Ground"," ",670,100,150,140,100,90,90,3 +384,"Rayquaza"," ","Dragon","Flying",680,105,150,90,150,90,95,3 +385,"Jirachi"," ","Steel","Psychic",600,100,100,100,100,100,100,3 +386,"Deoxys","Normal Forme","Psychic"," ",600,50,150,50,150,50,150,3 +386,"Deoxys","Attack Forme","Psychic"," ",600,50,180,20,180,20,150,3 +386,"Deoxys","Defense Forme","Psychic"," ",600,50,70,160,70,160,90,3 +386,"Deoxys","Speed Forme","Psychic"," ",600,50,95,90,95,90,180,3 +387,"Turtwig"," ","Grass"," ",318,55,68,64,45,55,31,4 +388,"Grotle"," ","Grass"," ",405,75,89,85,55,65,36,4 +389,"Torterra"," ","Grass","Ground",525,95,109,105,75,85,56,4 +390,"Chimchar"," ","Fire"," ",309,44,58,44,58,44,61,4 +391,"Monferno"," ","Fire","Fighting",405,64,78,52,78,52,81,4 +392,"Infernape"," ","Fire","Fighting",534,76,104,71,104,71,108,4 +393,"Piplup"," ","Water"," ",314,53,51,53,61,56,40,4 +394,"Prinplup"," ","Water"," ",405,64,66,68,81,76,50,4 +395,"Empoleon"," ","Water","Steel",530,84,86,88,111,101,60,4 +396,"Starly"," ","Normal","Flying",245,40,55,30,30,30,60,4 +397,"Staravia"," ","Normal","Flying",340,55,75,50,40,40,80,4 +398,"Staraptor"," ","Normal","Flying",485,85,120,70,50,60,100,4 +399,"Bidoof"," ","Normal"," ",250,59,45,40,35,40,31,4 +400,"Bibarel"," ","Normal","Water",410,79,85,60,55,60,71,4 +401,"Kricketot"," ","Bug"," ",194,37,25,41,25,41,25,4 +402,"Kricketune"," ","Bug"," ",384,77,85,51,55,51,65,4 +403,"Shinx"," ","Electric"," ",263,45,65,34,40,34,45,4 +404,"Luxio"," ","Electric"," ",363,60,85,49,60,49,60,4 +405,"Luxray"," ","Electric"," ",523,80,120,79,95,79,70,4 +406,"Budew"," ","Grass","Poison",280,40,30,35,50,70,55,4 +407,"Roserade"," ","Grass","Poison",515,60,70,65,125,105,90,4 +408,"Cranidos"," ","Rock"," ",350,67,125,40,30,30,58,4 +409,"Rampardos"," ","Rock"," ",495,97,165,60,65,50,58,4 +410,"Shieldon"," ","Rock","Steel",350,30,42,118,42,88,30,4 +411,"Bastiodon"," ","Rock","Steel",495,60,52,168,47,138,30,4 +412,"Burmy","Plant Cloak","Bug"," ",224,40,29,45,29,45,36,4 +412,"Burmy","Sandy Cloak","Bug"," ",224,40,29,45,29,45,36,4 +412,"Burmy","Trash Cloak","Bug"," ",224,40,29,45,29,45,36,4 +413,"Wormadam","Plant Cloak","Bug","Grass",424,60,59,85,79,105,36,4 +413,"Wormadam","Sandy Cloak","Bug","Ground",424,60,79,105,59,85,36,4 +413,"Wormadam","Trash Cloak","Bug","Steel",424,60,69,95,69,95,36,4 +414,"Mothim"," ","Bug","Flying",424,70,94,50,94,50,66,4 +415,"Combee"," ","Bug","Flying",244,30,30,42,30,42,70,4 +416,"Vespiquen"," ","Bug","Flying",474,70,80,102,80,102,40,4 +417,"Pachirisu"," ","Electric"," ",405,60,45,70,45,90,95,4 +418,"Buizel"," ","Water"," ",330,55,65,35,60,30,85,4 +419,"Floatzel"," ","Water"," ",495,85,105,55,85,50,115,4 +420,"Cherubi"," ","Grass"," ",275,45,35,45,62,53,35,4 +421,"Cherrim"," ","Grass"," ",450,70,60,70,87,78,85,4 +422,"Shellos"," ","Water"," ",325,76,48,48,57,62,34,4 +423,"Gastrodon"," ","Water","Ground",475,111,83,68,92,82,39,4 +424,"Ambipom"," ","Normal"," ",482,75,100,66,60,66,115,4 +425,"Drifloon"," ","Ghost","Flying",348,90,50,34,60,44,70,4 +426,"Drifblim"," ","Ghost","Flying",498,150,80,44,90,54,80,4 +427,"Buneary"," ","Normal"," ",350,55,66,44,44,56,85,4 +428,"Lopunny"," ","Normal"," ",480,65,76,84,54,96,105,4 +429,"Mismagius"," ","Ghost"," ",495,60,60,60,105,105,105,4 +430,"Honchkrow"," ","Dark","Flying",505,100,125,52,105,52,71,4 +431,"Glameow"," ","Normal"," ",310,49,55,42,42,37,85,4 +432,"Purugly"," ","Normal"," ",452,71,82,64,64,59,112,4 +433,"Chingling"," ","Psychic"," ",285,45,30,50,65,50,45,4 +434,"Stunky"," ","Poison","Dark",329,63,63,47,41,41,74,4 +435,"Skuntank"," ","Poison","Dark",479,103,93,67,71,61,84,4 +436,"Bronzor"," ","Steel","Psychic",300,57,24,86,24,86,23,4 +437,"Bronzong"," ","Steel","Psychic",500,67,89,116,79,116,33,4 +438,"Bonsly"," ","Rock"," ",290,50,80,95,10,45,10,4 +439,"Mime Jr."," ","Psychic","Fairy",310,20,25,45,70,90,60,4 +440,"Happiny"," ","Normal"," ",220,100,5,5,15,65,30,4 +441,"Chatot"," ","Normal","Flying",411,76,65,45,92,42,91,4 +442,"Spiritomb"," ","Ghost","Dark",485,50,92,108,92,108,35,4 +443,"Gible"," ","Dragon","Ground",300,58,70,45,40,45,42,4 +444,"Gabite"," ","Dragon","Ground",410,68,90,65,50,55,82,4 +445,"Garchomp"," ","Dragon","Ground",600,108,130,95,80,85,102,4 +446,"Munchlax"," ","Normal"," ",390,135,85,40,40,85,5,4 +447,"Riolu"," ","Fighting"," ",285,40,70,40,35,40,60,4 +448,"Lucario"," ","Fighting","Steel",525,70,110,70,115,70,90,4 +449,"Hippopotas"," ","Ground"," ",330,68,72,78,38,42,32,4 +450,"Hippowdon"," ","Ground"," ",525,108,112,118,68,72,47,4 +451,"Skorupi"," ","Poison","Bug",330,40,50,90,30,55,65,4 +452,"Drapion"," ","Poison","Dark",500,70,90,110,60,75,95,4 +453,"Croagunk"," ","Poison","Fighting",300,48,61,40,61,40,50,4 +454,"Toxicroak"," ","Poison","Fighting",490,83,106,65,86,65,85,4 +455,"Carnivine"," ","Grass"," ",454,74,100,72,90,72,46,4 +456,"Finneon"," ","Water"," ",330,49,49,56,49,61,66,4 +457,"Lumineon"," ","Water"," ",460,69,69,76,69,86,91,4 +458,"Mantyke"," ","Water","Flying",345,45,20,50,60,120,50,4 +459,"Snover"," ","Grass","Ice",334,60,62,50,62,60,40,4 +460,"Abomasnow"," ","Grass","Ice",494,90,92,75,92,85,60,4 +461,"Weavile"," ","Dark","Ice",510,70,120,65,45,85,125,4 +462,"Magnezone"," ","Electric","Steel",535,70,70,115,130,90,60,4 +463,"Lickilicky"," ","Normal"," ",515,110,85,95,80,95,50,4 +464,"Rhyperior"," ","Ground","Rock",535,115,140,130,55,55,40,4 +465,"Tangrowth"," ","Grass"," ",535,100,100,125,110,50,50,4 +466,"Electivire"," ","Electric"," ",540,75,123,67,95,85,95,4 +467,"Magmortar"," ","Fire"," ",540,75,95,67,125,95,83,4 +468,"Togekiss"," ","Fairy","Flying",545,85,50,95,120,115,80,4 +469,"Yanmega"," ","Bug","Flying",515,86,76,86,116,56,95,4 +470,"Leafeon"," ","Grass"," ",525,65,110,130,60,65,95,4 +471,"Glaceon"," ","Ice"," ",525,65,60,110,130,95,65,4 +472,"Gliscor"," ","Ground","Flying",510,75,95,125,45,75,95,4 +473,"Mamoswine"," ","Ice","Ground",530,110,130,80,70,60,80,4 +474,"Porygon-Z"," ","Normal"," ",535,85,80,70,135,75,90,4 +475,"Gallade"," ","Psychic","Fighting",518,68,125,65,65,115,80,4 +476,"Probopass"," ","Rock","Steel",525,60,55,145,75,150,40,4 +477,"Dusknoir"," ","Ghost"," ",525,45,100,135,65,135,45,4 +478,"Froslass"," ","Ice","Ghost",480,70,80,70,80,70,110,4 +479,"Rotom"," ","Electric","Ghost",440,50,50,77,95,77,91,4 +479,"Rotom","Heat Rotom","Electric","Fire",520,50,65,107,105,107,86,4 +479,"Rotom","Wash Rotom","Electric","Water",520,50,65,107,105,107,86,4 +479,"Rotom","Frost Rotom","Electric","Ice",520,50,65,107,105,107,86,4 +479,"Rotom","Fan Rotom","Electric","Flying",520,50,65,107,105,107,86,4 +479,"Rotom","Mow Rotom","Electric","Grass",520,50,65,107,105,107,86,4 +480,"Uxie"," ","Psychic"," ",580,75,75,130,75,130,95,4 +481,"Mesprit"," ","Psychic"," ",580,80,105,105,105,105,80,4 +482,"Azelf"," ","Psychic"," ",580,75,125,70,125,70,115,4 +483,"Dialga"," ","Steel","Dragon",680,100,120,120,150,100,90,4 +484,"Palkia"," ","Water","Dragon",680,90,120,100,150,120,100,4 +485,"Heatran"," ","Fire","Steel",600,91,90,106,130,106,77,4 +486,"Regigigas"," ","Normal"," ",670,110,160,110,80,110,100,4 +487,"Giratina","Altered Forme","Ghost","Dragon",680,150,100,120,100,120,90,4 +487,"Giratina","Origin Forme","Ghost","Dragon",680,150,120,100,120,100,90,4 +488,"Cresselia"," ","Psychic"," ",580,120,70,110,75,120,85,4 +489,"Phione"," ","Water"," ",480,80,80,80,80,80,80,4 +490,"Manaphy"," ","Water"," ",600,100,100,100,100,100,100,4 +491,"Darkrai"," ","Dark"," ",600,70,90,90,135,90,125,4 +492,"Shaymin","Land Forme","Grass"," ",600,100,100,100,100,100,100,4 +492,"Shaymin","Sky Forme","Grass","Flying",600,100,103,75,120,75,127,4 +493,"Arceus"," ","Normal"," ",720,120,120,120,120,120,120,4 +494,"Victini"," ","Psychic","Fire",600,100,100,100,100,100,100,5 +495,"Snivy"," ","Grass"," ",308,45,45,55,45,55,63,5 +496,"Servine"," ","Grass"," ",413,60,60,75,60,75,83,5 +497,"Serperior"," ","Grass"," ",528,75,75,95,75,95,113,5 +498,"Tepig"," ","Fire"," ",308,65,63,45,45,45,45,5 +499,"Pignite"," ","Fire","Fighting",418,90,93,55,70,55,55,5 +500,"Emboar"," ","Fire","Fighting",528,110,123,65,100,65,65,5 +501,"Oshawott"," ","Water"," ",308,55,55,45,63,45,45,5 +502,"Dewott"," ","Water"," ",413,75,75,60,83,60,60,5 +503,"Samurott"," ","Water"," ",528,95,100,85,108,70,70,5 +504,"Patrat"," ","Normal"," ",255,45,55,39,35,39,42,5 +505,"Watchog"," ","Normal"," ",420,60,85,69,60,69,77,5 +506,"Lillipup"," ","Normal"," ",275,45,60,45,25,45,55,5 +507,"Herdier"," ","Normal"," ",370,65,80,65,35,65,60,5 +508,"Stoutland"," ","Normal"," ",500,85,110,90,45,90,80,5 +509,"Purrloin"," ","Dark"," ",281,41,50,37,50,37,66,5 +510,"Liepard"," ","Dark"," ",446,64,88,50,88,50,106,5 +511,"Pansage"," ","Grass"," ",316,50,53,48,53,48,64,5 +512,"Simisage"," ","Grass"," ",498,75,98,63,98,63,101,5 +513,"Pansear"," ","Fire"," ",316,50,53,48,53,48,64,5 +514,"Simisear"," ","Fire"," ",498,75,98,63,98,63,101,5 +515,"Panpour"," ","Water"," ",316,50,53,48,53,48,64,5 +516,"Simipour"," ","Water"," ",498,75,98,63,98,63,101,5 +517,"Munna"," ","Psychic"," ",292,76,25,45,67,55,24,5 +518,"Musharna"," ","Psychic"," ",487,116,55,85,107,95,29,5 +519,"Pidove"," ","Normal","Flying",264,50,55,50,36,30,43,5 +520,"Tranquill"," ","Normal","Flying",358,62,77,62,50,42,65,5 +521,"Unfezant"," ","Normal","Flying",488,80,115,80,65,55,93,5 +522,"Blitzle"," ","Electric"," ",295,45,60,32,50,32,76,5 +523,"Zebstrika"," ","Electric"," ",497,75,100,63,80,63,116,5 +524,"Roggenrola"," ","Rock"," ",280,55,75,85,25,25,15,5 +525,"Boldore"," ","Rock"," ",390,70,105,105,50,40,20,5 +526,"Gigalith"," ","Rock"," ",515,85,135,130,60,80,25,5 +527,"Woobat"," ","Psychic","Flying",323,65,45,43,55,43,72,5 +528,"Swoobat"," ","Psychic","Flying",425,67,57,55,77,55,114,5 +529,"Drilbur"," ","Ground"," ",328,60,85,40,30,45,68,5 +530,"Excadrill"," ","Ground","Steel",508,110,135,60,50,65,88,5 +531,"Audino"," ","Normal"," ",445,103,60,86,60,86,50,5 +532,"Timburr"," ","Fighting"," ",305,75,80,55,25,35,35,5 +533,"Gurdurr"," ","Fighting"," ",405,85,105,85,40,50,40,5 +534,"Conkeldurr"," ","Fighting"," ",505,105,140,95,55,65,45,5 +535,"Tympole"," ","Water"," ",294,50,50,40,50,40,64,5 +536,"Palpitoad"," ","Water","Ground",384,75,65,55,65,55,69,5 +537,"Seismitoad"," ","Water","Ground",509,105,95,75,85,75,74,5 +538,"Throh"," ","Fighting"," ",465,120,100,85,30,85,45,5 +539,"Sawk"," ","Fighting"," ",465,75,125,75,30,75,85,5 +540,"Sewaddle"," ","Bug","Grass",310,45,53,70,40,60,42,5 +541,"Swadloon"," ","Bug","Grass",380,55,63,90,50,80,42,5 +542,"Leavanny"," ","Bug","Grass",500,75,103,80,70,80,92,5 +543,"Venipede"," ","Bug","Poison",260,30,45,59,30,39,57,5 +544,"Whirlipede"," ","Bug","Poison",360,40,55,99,40,79,47,5 +545,"Scolipede"," ","Bug","Poison",485,60,100,89,55,69,112,5 +546,"Cottonee"," ","Grass","Fairy",280,40,27,60,37,50,66,5 +547,"Whimsicott"," ","Grass","Fairy",480,60,67,85,77,75,116,5 +548,"Petilil"," ","Grass"," ",280,45,35,50,70,50,30,5 +549,"Lilligant"," ","Grass"," ",480,70,60,75,110,75,90,5 +550,"Basculin","Red-Striped Form","Water"," ",460,70,92,65,80,55,98,5 +550,"Basculin","Blue-Striped Form","Water"," ",460,70,92,65,80,55,98,5 +551,"Sandile"," ","Ground","Dark",292,50,72,35,35,35,65,5 +552,"Krokorok"," ","Ground","Dark",351,60,82,45,45,45,74,5 +553,"Krookodile"," ","Ground","Dark",519,95,117,80,65,70,92,5 +554,"Darumaka"," ","Fire"," ",315,70,90,45,15,45,50,5 +555,"Darmanitan","Standard Mode","Fire"," ",480,105,140,55,30,55,95,5 +555,"Darmanitan","Zen Mode","Fire","Psychic",540,105,30,105,140,105,55,5 +556,"Maractus"," ","Grass"," ",461,75,86,67,106,67,60,5 +557,"Dwebble"," ","Bug","Rock",325,50,65,85,35,35,55,5 +558,"Crustle"," ","Bug","Rock",485,70,105,125,65,75,45,5 +559,"Scraggy"," ","Dark","Fighting",348,50,75,70,35,70,48,5 +560,"Scrafty"," ","Dark","Fighting",488,65,90,115,45,115,58,5 +561,"Sigilyph"," ","Psychic","Flying",490,72,58,80,103,80,97,5 +562,"Yamask"," ","Ghost"," ",303,38,30,85,55,65,30,5 +563,"Cofagrigus"," ","Ghost"," ",483,58,50,145,95,105,30,5 +564,"Tirtouga"," ","Water","Rock",355,54,78,103,53,45,22,5 +565,"Carracosta"," ","Water","Rock",495,74,108,133,83,65,32,5 +566,"Archen"," ","Rock","Flying",401,55,112,45,74,45,70,5 +567,"Archeops"," ","Rock","Flying",567,75,140,65,112,65,110,5 +568,"Trubbish"," ","Poison"," ",329,50,50,62,40,62,65,5 +569,"Garbodor"," ","Poison"," ",474,80,95,82,60,82,75,5 +570,"Zorua"," ","Dark"," ",330,40,65,40,80,40,65,5 +571,"Zoroark"," ","Dark"," ",510,60,105,60,120,60,105,5 +572,"Minccino"," ","Normal"," ",300,55,50,40,40,40,75,5 +573,"Cinccino"," ","Normal"," ",470,75,95,60,65,60,115,5 +574,"Gothita"," ","Psychic"," ",290,45,30,50,55,65,45,5 +575,"Gothorita"," ","Psychic"," ",390,60,45,70,75,85,55,5 +576,"Gothitelle"," ","Psychic"," ",490,70,55,95,95,110,65,5 +577,"Solosis"," ","Psychic"," ",290,45,30,40,105,50,20,5 +578,"Duosion"," ","Psychic"," ",370,65,40,50,125,60,30,5 +579,"Reuniclus"," ","Psychic"," ",490,110,65,75,125,85,30,5 +580,"Ducklett"," ","Water","Flying",305,62,44,50,44,50,55,5 +581,"Swanna"," ","Water","Flying",473,75,87,63,87,63,98,5 +582,"Vanillite"," ","Ice"," ",305,36,50,50,65,60,44,5 +583,"Vanillish"," ","Ice"," ",395,51,65,65,80,75,59,5 +584,"Vanilluxe"," ","Ice"," ",535,71,95,85,110,95,79,5 +585,"Deerling"," ","Normal","Grass",335,60,60,50,40,50,75,5 +586,"Sawsbuck"," ","Normal","Grass",475,80,100,70,60,70,95,5 +587,"Emolga"," ","Electric","Flying",428,55,75,60,75,60,103,5 +588,"Karrablast"," ","Bug"," ",315,50,75,45,40,45,60,5 +589,"Escavalier"," ","Bug","Steel",495,70,135,105,60,105,20,5 +590,"Foongus"," ","Grass","Poison",294,69,55,45,55,55,15,5 +591,"Amoonguss"," ","Grass","Poison",464,114,85,70,85,80,30,5 +592,"Frillish"," ","Water","Ghost",335,55,40,50,65,85,40,5 +593,"Jellicent"," ","Water","Ghost",480,100,60,70,85,105,60,5 +594,"Alomomola"," ","Water"," ",470,165,75,80,40,45,65,5 +595,"Joltik"," ","Bug","Electric",319,50,47,50,57,50,65,5 +596,"Galvantula"," ","Bug","Electric",472,70,77,60,97,60,108,5 +597,"Ferroseed"," ","Grass","Steel",305,44,50,91,24,86,10,5 +598,"Ferrothorn"," ","Grass","Steel",489,74,94,131,54,116,20,5 +599,"Klink"," ","Steel"," ",300,40,55,70,45,60,30,5 +600,"Klang"," ","Steel"," ",440,60,80,95,70,85,50,5 +601,"Klinklang"," ","Steel"," ",520,60,100,115,70,85,90,5 +602,"Tynamo"," ","Electric"," ",275,35,55,40,45,40,60,5 +603,"Eelektrik"," ","Electric"," ",405,65,85,70,75,70,40,5 +604,"Eelektross"," ","Electric"," ",515,85,115,80,105,80,50,5 +605,"Elgyem"," ","Psychic"," ",335,55,55,55,85,55,30,5 +606,"Beheeyem"," ","Psychic"," ",485,75,75,75,125,95,40,5 +607,"Litwick"," ","Ghost","Fire",275,50,30,55,65,55,20,5 +608,"Lampent"," ","Ghost","Fire",370,60,40,60,95,60,55,5 +609,"Chandelure"," ","Ghost","Fire",520,60,55,90,145,90,80,5 +610,"Axew"," ","Dragon"," ",320,46,87,60,30,40,57,5 +611,"Fraxure"," ","Dragon"," ",410,66,117,70,40,50,67,5 +612,"Haxorus"," ","Dragon"," ",540,76,147,90,60,70,97,5 +613,"Cubchoo"," ","Ice"," ",305,55,70,40,60,40,40,5 +614,"Beartic"," ","Ice"," ",505,95,130,80,70,80,50,5 +615,"Cryogonal"," ","Ice"," ",515,80,50,50,95,135,105,5 +616,"Shelmet"," ","Bug"," ",305,50,40,85,40,65,25,5 +617,"Accelgor"," ","Bug"," ",495,80,70,40,100,60,145,5 +618,"Stunfisk"," ","Ground","Electric",471,109,66,84,81,99,32,5 +619,"Mienfoo"," ","Fighting"," ",350,45,85,50,55,50,65,5 +620,"Mienshao"," ","Fighting"," ",510,65,125,60,95,60,105,5 +621,"Druddigon"," ","Dragon"," ",485,77,120,90,60,90,48,5 +622,"Golett"," ","Ground","Ghost",303,59,74,50,35,50,35,5 +623,"Golurk"," ","Ground","Ghost",483,89,124,80,55,80,55,5 +624,"Pawniard"," ","Dark","Steel",340,45,85,70,40,40,60,5 +625,"Bisharp"," ","Dark","Steel",490,65,125,100,60,70,70,5 +626,"Bouffalant"," ","Normal"," ",490,95,110,95,40,95,55,5 +627,"Rufflet"," ","Normal","Flying",350,70,83,50,37,50,60,5 +628,"Braviary"," ","Normal","Flying",510,100,123,75,57,75,80,5 +629,"Vullaby"," ","Dark","Flying",370,70,55,75,45,65,60,5 +630,"Mandibuzz"," ","Dark","Flying",510,110,65,105,55,95,80,5 +631,"Heatmor"," ","Fire"," ",484,85,97,66,105,66,65,5 +632,"Durant"," ","Bug","Steel",484,58,109,112,48,48,109,5 +633,"Deino"," ","Dark","Dragon",300,52,65,50,45,50,38,5 +634,"Zweilous"," ","Dark","Dragon",420,72,85,70,65,70,58,5 +635,"Hydreigon"," ","Dark","Dragon",600,92,105,90,125,90,98,5 +636,"Larvesta"," ","Bug","Fire",360,55,85,55,50,55,60,5 +637,"Volcarona"," ","Bug","Fire",550,85,60,65,135,105,100,5 +638,"Cobalion"," ","Steel","Fighting",580,91,90,129,90,72,108,5 +639,"Terrakion"," ","Rock","Fighting",580,91,129,90,72,90,108,5 +640,"Virizion"," ","Grass","Fighting",580,91,90,72,90,129,108,5 +641,"Tornadus","Incarnate Forme","Flying"," ",580,79,115,70,125,80,111,5 +641,"Tornadus","Therian Forme","Flying"," ",580,79,100,80,110,90,121,5 +642,"Thundurus","Incarnate Forme","Electric","Flying",580,79,115,70,125,80,111,5 +642,"Thundurus","Therian Forme","Electric","Flying",580,79,105,70,145,80,101,5 +643,"Reshiram"," ","Dragon","Fire",680,100,120,100,150,120,90,5 +644,"Zekrom"," ","Dragon","Electric",680,100,150,120,120,100,90,5 +645,"Landorus","Incarnate Forme","Ground","Flying",600,89,125,90,115,80,101,5 +645,"Landorus","Therian Forme","Ground","Flying",600,89,145,90,105,80,91,5 +646,"Kyurem"," ","Dragon","Ice",660,125,130,90,130,90,95,5 +646,"Kyurem","White Kyurem","Dragon","Ice",700,125,120,90,170,100,95,5 +646,"Kyurem","Black Kyurem","Dragon","Ice",700,125,170,100,120,90,95,5 +647,"Keldeo","Ordinary Form","Water","Fighting",580,91,72,90,129,90,108,5 +647,"Keldeo","Resolute Form","Water","Fighting",580,91,72,90,129,90,108,5 +648,"Meloetta","Aria Forme","Normal","Psychic",600,100,77,77,128,128,90,5 +648,"Meloetta","Pirouette Forme","Normal","Fighting",600,100,128,90,77,77,128,5 +649,"Genesect"," ","Bug","Steel",600,71,120,95,120,95,99,5 +3,"Venusaur","Mega Venusaur","Grass","Poison",625,80,100,123,122,120,80,6 +6,"Charizard","Mega Charizard X","Fire","Dragon",634,78,130,111,130,85,100,6 +6,"Charizard","Mega Charizard Y","Fire","Flying",634,78,104,78,159,115,100,6 +9,"Blastoise","Mega Blastoise","Water"," ",630,79,103,120,135,115,78,6 +15,"Beedrill","Mega Beedrill","Bug","Poison",495,65,150,40,15,80,145,6 +18,"Pidgeot","Mega Pidgeot","Normal","Flying",579,83,80,80,135,80,121,6 +65,"Alakazam","Mega Alakazam","Psychic"," ",600,55,50,65,175,105,150,6 +80,"Slowbro","Mega Slowbro","Water","Psychic",590,95,75,180,130,80,30,6 +94,"Gengar","Mega Gengar","Ghost","Poison",600,60,65,80,170,95,130,6 +115,"Kangaskhan","Mega Kangaskhan","Normal"," ",590,105,125,100,60,100,100,6 +127,"Pinsir","Mega Pinsir","Bug","Flying",600,65,155,120,65,90,105,6 +130,"Gyarados","Mega Gyarados","Water","Dark",640,95,155,109,70,130,81,6 +142,"Aerodactyl","Mega Aerodactyl","Rock","Flying",615,80,135,85,70,95,150,6 +150,"Mewtwo","Mega Mewtwo X","Psychic","Fighting",780,106,190,100,154,100,130,6 +150,"Mewtwo","Mega Mewtwo Y","Psychic"," ",780,106,150,70,194,120,140,6 +181,"Ampharos","Mega Ampharos","Electric","Dragon",610,90,95,105,165,110,45,6 +208,"Steelix","Mega Steelix","Steel","Ground",610,75,125,230,55,95,30,6 +212,"Scizor","Mega Scizor","Bug","Steel",600,70,150,140,65,100,75,6 +214,"Heracross","Mega Heracross","Bug","Fighting",600,80,185,115,40,105,75,6 +229,"Houndoom","Mega Houndoom","Dark","Fire",600,75,90,90,140,90,115,6 +248,"Tyranitar","Mega Tyranitar","Rock","Dark",700,100,164,150,95,120,71,6 +254,"Sceptile","Mega Sceptile","Grass","Dragon",630,70,110,75,145,85,145,6 +257,"Blaziken","Mega Blaziken","Fire","Fighting",630,80,160,80,130,80,100,6 +260,"Swampert","Mega Swampert","Water","Ground",635,100,150,110,95,110,70,6 +282,"Gardevoir","Mega Gardevoir","Psychic","Fairy",618,68,85,65,165,135,100,6 +302,"Sableye","Mega Sableye","Dark","Ghost",480,50,85,125,85,115,20,6 +303,"Mawile","Mega Mawile","Steel","Fairy",480,50,105,125,55,95,50,6 +306,"Aggron","Mega Aggron","Steel"," ",630,70,140,230,60,80,50,6 +308,"Medicham","Mega Medicham","Fighting","Psychic",510,60,100,85,80,85,100,6 +310,"Manectric","Mega Manectric","Electric"," ",575,70,75,80,135,80,135,6 +319,"Sharpedo","Mega Sharpedo","Water","Dark",560,70,140,70,110,65,105,6 +323,"Camerupt","Mega Camerupt","Fire","Ground",560,70,120,100,145,105,20,6 +334,"Altaria","Mega Altaria","Dragon","Fairy",590,75,110,110,110,105,80,6 +354,"Banette","Mega Banette","Ghost"," ",555,64,165,75,93,83,75,6 +359,"Absol","Mega Absol","Dark"," ",565,65,150,60,115,60,115,6 +362,"Glalie","Mega Glalie","Ice"," ",580,80,120,80,120,80,100,6 +373,"Salamence","Mega Salamence","Dragon","Flying",700,95,145,130,120,90,120,6 +376,"Metagross","Mega Metagross","Steel","Psychic",700,80,145,150,105,110,110,6 +380,"Latias","Mega Latias","Dragon","Psychic",700,80,100,120,140,150,110,6 +381,"Latios","Mega Latios","Dragon","Psychic",700,80,130,100,160,120,110,6 +382,"Kyogre","Primal Kyogre","Water"," ",770,100,150,90,180,160,90,6 +383,"Groudon","Primal Groudon","Ground","Fire",770,100,180,160,150,90,90,6 +384,"Rayquaza","Mega Rayquaza","Dragon","Flying",780,105,180,100,180,100,115,6 +428,"Lopunny","Mega Lopunny","Normal","Fighting",580,65,136,94,54,96,135,6 +445,"Garchomp","Mega Garchomp","Dragon","Ground",700,108,170,115,120,95,92,6 +448,"Lucario","Mega Lucario","Fighting","Steel",625,70,145,88,140,70,112,6 +460,"Abomasnow","Mega Abomasnow","Grass","Ice",594,90,132,105,132,105,30,6 +475,"Gallade","Mega Gallade","Psychic","Fighting",618,68,165,95,65,115,110,6 +531,"Audino","Mega Audino","Normal","Fairy",545,103,60,126,80,126,50,6 +650,"Chespin"," ","Grass"," ",313,56,61,65,48,45,38,6 +651,"Quilladin"," ","Grass"," ",405,61,78,95,56,58,57,6 +652,"Chesnaught"," ","Grass","Fighting",530,88,107,122,74,75,64,6 +653,"Fennekin"," ","Fire"," ",307,40,45,40,62,60,60,6 +654,"Braixen"," ","Fire"," ",409,59,59,58,90,70,73,6 +655,"Delphox"," ","Fire","Psychic",534,75,69,72,114,100,104,6 +656,"Froakie"," ","Water"," ",314,41,56,40,62,44,71,6 +657,"Frogadier"," ","Water"," ",405,54,63,52,83,56,97,6 +658,"Greninja"," ","Water","Dark",530,72,95,67,103,71,122,6 +659,"Bunnelby"," ","Normal"," ",237,38,36,38,32,36,57,6 +660,"Diggersby"," ","Normal","Ground",423,85,56,77,50,77,78,6 +661,"Fletchling"," ","Normal","Flying",278,45,50,43,40,38,62,6 +662,"Fletchinder"," ","Fire","Flying",382,62,73,55,56,52,84,6 +663,"Talonflame"," ","Fire","Flying",499,78,81,71,74,69,126,6 +664,"Scatterbug"," ","Bug"," ",200,38,35,40,27,25,35,6 +665,"Spewpa"," ","Bug"," ",213,45,22,60,27,30,29,6 +666,"Vivillon"," ","Bug","Flying",411,80,52,50,90,50,89,6 +667,"Litleo"," ","Fire","Normal",369,62,50,58,73,54,72,6 +668,"Pyroar"," ","Fire","Normal",507,86,68,72,109,66,106,6 +669,"Flabébé"," ","Fairy"," ",303,44,38,39,61,79,42,6 +670,"Floette"," ","Fairy"," ",371,54,45,47,75,98,52,6 +671,"Florges"," ","Fairy"," ",552,78,65,68,112,154,75,6 +672,"Skiddo"," ","Grass"," ",350,66,65,48,62,57,52,6 +673,"Gogoat"," ","Grass"," ",531,123,100,62,97,81,68,6 +674,"Pancham"," ","Fighting"," ",348,67,82,62,46,48,43,6 +675,"Pangoro"," ","Fighting","Dark",495,95,124,78,69,71,58,6 +676,"Furfrou"," ","Normal"," ",472,75,80,60,65,90,102,6 +677,"Espurr"," ","Psychic"," ",355,62,48,54,63,60,68,6 +678,"Meowstic","Male","Psychic"," ",466,74,48,76,83,81,104,6 +678,"Meowstic","Female","Psychic"," ",466,74,48,76,83,81,104,6 +679,"Honedge"," ","Steel","Ghost",325,45,80,100,35,37,28,6 +680,"Doublade"," ","Steel","Ghost",448,59,110,150,45,49,35,6 +681,"Aegislash","Shield Forme","Steel","Ghost",500,60,50,140,50,140,60,6 +681,"Aegislash","Blade Forme","Steel","Ghost",500,60,140,50,140,50,60,6 +682,"Spritzee"," ","Fairy"," ",341,78,52,60,63,65,23,6 +683,"Aromatisse"," ","Fairy"," ",462,101,72,72,99,89,29,6 +684,"Swirlix"," ","Fairy"," ",341,62,48,66,59,57,49,6 +685,"Slurpuff"," ","Fairy"," ",480,82,80,86,85,75,72,6 +686,"Inkay"," ","Dark","Psychic",288,53,54,53,37,46,45,6 +687,"Malamar"," ","Dark","Psychic",482,86,92,88,68,75,73,6 +688,"Binacle"," ","Rock","Water",306,42,52,67,39,56,50,6 +689,"Barbaracle"," ","Rock","Water",500,72,105,115,54,86,68,6 +690,"Skrelp"," ","Poison","Water",320,50,60,60,60,60,30,6 +691,"Dragalge"," ","Poison","Dragon",494,65,75,90,97,123,44,6 +692,"Clauncher"," ","Water"," ",330,50,53,62,58,63,44,6 +693,"Clawitzer"," ","Water"," ",500,71,73,88,120,89,59,6 +694,"Helioptile"," ","Electric","Normal",289,44,38,33,61,43,70,6 +695,"Heliolisk"," ","Electric","Normal",481,62,55,52,109,94,109,6 +696,"Tyrunt"," ","Rock","Dragon",362,58,89,77,45,45,48,6 +697,"Tyrantrum"," ","Rock","Dragon",521,82,121,119,69,59,71,6 +698,"Amaura"," ","Rock","Ice",362,77,59,50,67,63,46,6 +699,"Aurorus"," ","Rock","Ice",521,123,77,72,99,92,58,6 +700,"Sylveon"," ","Fairy"," ",525,95,65,65,110,130,60,6 +701,"Hawlucha"," ","Fighting","Flying",500,78,92,75,74,63,118,6 +702,"Dedenne"," ","Electric","Fairy",431,67,58,57,81,67,101,6 +703,"Carbink"," ","Rock","Fairy",500,50,50,150,50,150,50,6 +704,"Goomy"," ","Dragon"," ",300,45,50,35,55,75,40,6 +705,"Sliggoo"," ","Dragon"," ",452,68,75,53,83,113,60,6 +706,"Goodra"," ","Dragon"," ",600,90,100,70,110,150,80,6 +707,"Klefki"," ","Steel","Fairy",470,57,80,91,80,87,75,6 +708,"Phantump"," ","Ghost","Grass",309,43,70,48,50,60,38,6 +709,"Trevenant"," ","Ghost","Grass",474,85,110,76,65,82,56,6 +710,"Pumpkaboo","Average Size","Ghost","Grass",335,49,66,70,44,55,51,6 +710,"Pumpkaboo","Small Size","Ghost","Grass",335,44,66,70,44,55,56,6 +710,"Pumpkaboo","Large Size","Ghost","Grass",335,54,66,70,44,55,46,6 +710,"Pumpkaboo","Super Size","Ghost","Grass",335,59,66,70,44,55,41,6 +711,"Gourgeist","Average Size","Ghost","Grass",494,65,90,122,58,75,84,6 +711,"Gourgeist","Small Size","Ghost","Grass",494,55,85,122,58,75,99,6 +711,"Gourgeist","Large Size","Ghost","Grass",494,75,95,122,58,75,69,6 +711,"Gourgeist","Super Size","Ghost","Grass",494,85,100,122,58,75,54,6 +712,"Bergmite"," ","Ice"," ",304,55,69,85,32,35,28,6 +713,"Avalugg"," ","Ice"," ",514,95,117,184,44,46,28,6 +714,"Noibat"," ","Flying","Dragon",245,40,30,35,45,40,55,6 +715,"Noivern"," ","Flying","Dragon",535,85,70,80,97,80,123,6 +716,"Xerneas"," ","Fairy"," ",680,126,131,95,131,98,99,6 +717,"Yveltal"," ","Dark","Flying",680,126,131,95,131,98,99,6 +718,"Zygarde50% Forme"," ","Dragon","Ground",600,108,100,121,81,95,95,6 +719,"Diancie"," ","Rock","Fairy",600,50,100,150,100,150,50,6 +719,"Diancie","Mega Diancie","Rock","Fairy",700,50,160,110,160,110,110,6 +720,"Hoopa","Hoopa Confined","Psychic","Ghost",600,80,110,60,150,130,70,6 +720,"Hoopa","Hoopa Unbound","Psychic","Dark",680,80,160,60,170,130,80,6 +721,"Volcanion"," ","Fire","Water",600,80,110,120,130,90,70,6 +19,"Rattata","Alolan Rattata","Dark","Normal",253,30,56,35,25,35,72,7 +20,"Raticate","Alolan Raticate","Dark","Normal",413,75,71,70,40,80,77,7 +25,"Pikachu","Partner Pikachu","Electric"," ",430,45,80,50,75,60,120,7 +26,"Raichu","Alolan Raichu","Electric","Psychic",485,60,85,50,95,85,110,7 +27,"Sandshrew","Alolan Sandshrew","Ice","Steel",300,50,75,90,10,35,40,7 +28,"Sandslash","Alolan Sandslash","Ice","Steel",450,75,100,120,25,65,65,7 +37,"Vulpix","Alolan Vulpix","Ice"," ",299,38,41,40,50,65,65,7 +38,"Ninetales","Alolan Ninetales","Ice","Fairy",505,73,67,75,81,100,109,7 +50,"Diglett","Alolan Diglett","Ground","Steel",265,10,55,30,35,45,90,7 +51,"Dugtrio","Alolan Dugtrio","Ground","Steel",425,35,100,60,50,70,110,7 +52,"Meowth","Alolan Meowth","Dark"," ",290,40,35,35,50,40,90,7 +53,"Persian","Alolan Persian","Dark"," ",440,65,60,60,75,65,115,7 +74,"Geodude","Alolan Geodude","Rock","Electric",300,40,80,100,30,30,20,7 +75,"Graveler","Alolan Graveler","Rock","Electric",390,55,95,115,45,45,35,7 +76,"Golem","Alolan Golem","Rock","Electric",495,80,120,130,55,65,45,7 +88,"Grimer","Alolan Grimer","Poison","Dark",325,80,80,50,40,50,25,7 +89,"Muk","Alolan Muk","Poison","Dark",500,105,105,75,65,100,50,7 +103,"Exeggutor","Alolan Exeggutor","Grass","Dragon",530,95,105,85,125,75,45,7 +105,"Marowak","Alolan Marowak","Fire","Ghost",425,60,80,110,50,80,45,7 +133,"Eevee","Partner Eevee","Normal"," ",435,65,75,70,65,85,75,7 +658,"Greninja","Ash-Greninja","Water","Dark",640,72,145,67,153,71,132,7 +718,"Zygarde10% Forme"," ","Dragon","Ground",486,54,100,71,61,85,115,7 +718,"Zygarde","Complete Forme","Dragon","Ground",708,216,100,121,91,95,85,7 +722,"Rowlet"," ","Grass","Flying",320,68,55,55,50,50,42,7 +723,"Dartrix"," ","Grass","Flying",420,78,75,75,70,70,52,7 +724,"Decidueye"," ","Grass","Ghost",530,78,107,75,100,100,70,7 +725,"Litten"," ","Fire"," ",320,45,65,40,60,40,70,7 +726,"Torracat"," ","Fire"," ",420,65,85,50,80,50,90,7 +727,"Incineroar"," ","Fire","Dark",530,95,115,90,80,90,60,7 +728,"Popplio"," ","Water"," ",320,50,54,54,66,56,40,7 +729,"Brionne"," ","Water"," ",420,60,69,69,91,81,50,7 +730,"Primarina"," ","Water","Fairy",530,80,74,74,126,116,60,7 +731,"Pikipek"," ","Normal","Flying",265,35,75,30,30,30,65,7 +732,"Trumbeak"," ","Normal","Flying",355,55,85,50,40,50,75,7 +733,"Toucannon"," ","Normal","Flying",485,80,120,75,75,75,60,7 +734,"Yungoos"," ","Normal"," ",253,48,70,30,30,30,45,7 +735,"Gumshoos"," ","Normal"," ",418,88,110,60,55,60,45,7 +736,"Grubbin"," ","Bug"," ",300,47,62,45,55,45,46,7 +737,"Charjabug"," ","Bug","Electric",400,57,82,95,55,75,36,7 +738,"Vikavolt"," ","Bug","Electric",500,77,70,90,145,75,43,7 +739,"Crabrawler"," ","Fighting"," ",338,47,82,57,42,47,63,7 +740,"Crabominable"," ","Fighting","Ice",478,97,132,77,62,67,43,7 +741,"Oricorio","Baile Style","Fire","Flying",476,75,70,70,98,70,93,7 +741,"Oricorio","Pom-Pom Style","Electric","Flying",476,75,70,70,98,70,93,7 +741,"Oricorio","Pa'u Style","Psychic","Flying",476,75,70,70,98,70,93,7 +741,"Oricorio","Sensu Style","Ghost","Flying",476,75,70,70,98,70,93,7 +742,"Cutiefly"," ","Bug","Fairy",304,40,45,40,55,40,84,7 +743,"Ribombee"," ","Bug","Fairy",464,60,55,60,95,70,124,7 +744,"Rockruff"," ","Rock"," ",280,45,65,40,30,40,60,7 +744,"Rockruff","Own Tempo Rockruff","Rock"," ",280,45,65,40,30,40,60,7 +745,"Lycanroc","Midday Form","Rock"," ",487,75,115,65,55,65,112,7 +745,"Lycanroc","Midnight Form","Rock"," ",487,85,115,75,55,75,82,7 +745,"Lycanroc","Dusk Form","Rock"," ",487,75,117,65,55,65,110,7 +746,"Wishiwashi","Solo Form","Water"," ",175,45,20,20,25,25,40,7 +746,"Wishiwashi","School Form","Water"," ",620,45,140,130,140,135,30,7 +747,"Mareanie"," ","Poison","Water",305,50,53,62,43,52,45,7 +748,"Toxapex"," ","Poison","Water",495,50,63,152,53,142,35,7 +749,"Mudbray"," ","Ground"," ",385,70,100,70,45,55,45,7 +750,"Mudsdale"," ","Ground"," ",500,100,125,100,55,85,35,7 +751,"Dewpider"," ","Water","Bug",269,38,40,52,40,72,27,7 +752,"Araquanid"," ","Water","Bug",454,68,70,92,50,132,42,7 +753,"Fomantis"," ","Grass"," ",250,40,55,35,50,35,35,7 +754,"Lurantis"," ","Grass"," ",480,70,105,90,80,90,45,7 +755,"Morelull"," ","Grass","Fairy",285,40,35,55,65,75,15,7 +756,"Shiinotic"," ","Grass","Fairy",405,60,45,80,90,100,30,7 +757,"Salandit"," ","Poison","Fire",320,48,44,40,71,40,77,7 +758,"Salazzle"," ","Poison","Fire",480,68,64,60,111,60,117,7 +759,"Stufful"," ","Normal","Fighting",340,70,75,50,45,50,50,7 +760,"Bewear"," ","Normal","Fighting",500,120,125,80,55,60,60,7 +761,"Bounsweet"," ","Grass"," ",210,42,30,38,30,38,32,7 +762,"Steenee"," ","Grass"," ",290,52,40,48,40,48,62,7 +763,"Tsareena"," ","Grass"," ",510,72,120,98,50,98,72,7 +764,"Comfey"," ","Fairy"," ",485,51,52,90,82,110,100,7 +765,"Oranguru"," ","Normal","Psychic",490,90,60,80,90,110,60,7 +766,"Passimian"," ","Fighting"," ",490,100,120,90,40,60,80,7 +767,"Wimpod"," ","Bug","Water",230,25,35,40,20,30,80,7 +768,"Golisopod"," ","Bug","Water",530,75,125,140,60,90,40,7 +769,"Sandygast"," ","Ghost","Ground",320,55,55,80,70,45,15,7 +770,"Palossand"," ","Ghost","Ground",480,85,75,110,100,75,35,7 +771,"Pyukumuku"," ","Water"," ",410,55,60,130,30,130,5,7 +772,"Type: Null"," ","Normal"," ",534,95,95,95,95,95,59,7 +773,"Silvally"," ","Normal"," ",570,95,95,95,95,95,95,7 +774,"Minior","Meteor Form","Rock","Flying",440,60,60,100,60,100,60,7 +774,"Minior","Core Form","Rock","Flying",500,60,100,60,100,60,120,7 +775,"Komala"," ","Normal"," ",480,65,115,65,75,95,65,7 +776,"Turtonator"," ","Fire","Dragon",485,60,78,135,91,85,36,7 +777,"Togedemaru"," ","Electric","Steel",435,65,98,63,40,73,96,7 +778,"Mimikyu"," ","Ghost","Fairy",476,55,90,80,50,105,96,7 +779,"Bruxish"," ","Water","Psychic",475,68,105,70,70,70,92,7 +780,"Drampa"," ","Normal","Dragon",485,78,60,85,135,91,36,7 +781,"Dhelmise"," ","Ghost","Grass",517,70,131,100,86,90,40,7 +782,"Jangmo-o"," ","Dragon"," ",300,45,55,65,45,45,45,7 +783,"Hakamo-o"," ","Dragon","Fighting",420,55,75,90,65,70,65,7 +784,"Kommo-o"," ","Dragon","Fighting",600,75,110,125,100,105,85,7 +785,"Tapu Koko"," ","Electric","Fairy",570,70,115,85,95,75,130,7 +786,"Tapu Lele"," ","Psychic","Fairy",570,70,85,75,130,115,95,7 +787,"Tapu Bulu"," ","Grass","Fairy",570,70,130,115,85,95,75,7 +788,"Tapu Fini"," ","Water","Fairy",570,70,75,115,95,130,85,7 +789,"Cosmog"," ","Psychic"," ",200,43,29,31,29,31,37,7 +790,"Cosmoem"," ","Psychic"," ",400,43,29,131,29,131,37,7 +791,"Solgaleo"," ","Psychic","Steel",680,137,137,107,113,89,97,7 +792,"Lunala"," ","Psychic","Ghost",680,137,113,89,137,107,97,7 +793,"Nihilego"," ","Rock","Poison",570,109,53,47,127,131,103,7 +794,"Buzzwole"," ","Bug","Fighting",570,107,139,139,53,53,79,7 +795,"Pheromosa"," ","Bug","Fighting",570,71,137,37,137,37,151,7 +796,"Xurkitree"," ","Electric"," ",570,83,89,71,173,71,83,7 +797,"Celesteela"," ","Steel","Flying",570,97,101,103,107,101,61,7 +798,"Kartana"," ","Grass","Steel",570,59,181,131,59,31,109,7 +799,"Guzzlord"," ","Dark","Dragon",570,223,101,53,97,53,43,7 +800,"Necrozma"," ","Psychic"," ",600,97,107,101,127,89,79,7 +800,"Necrozma","Dusk Mane Necrozma","Psychic","Steel",680,97,157,127,113,109,77,7 +800,"Necrozma","Dawn Wings Necrozma","Psychic","Ghost",680,97,113,109,157,127,77,7 +800,"Necrozma","Ultra Necrozma","Psychic","Dragon",754,97,167,97,167,97,129,7 +801,"Magearna"," ","Steel","Fairy",600,80,95,115,130,115,65,7 +802,"Marshadow"," ","Fighting","Ghost",600,90,125,80,90,90,125,7 +803,"Poipole"," ","Poison"," ",420,67,73,67,73,67,73,7 +804,"Naganadel"," ","Poison","Dragon",540,73,73,73,127,73,121,7 +805,"Stakataka"," ","Rock","Steel",570,61,131,211,53,101,13,7 +806,"Blacephalon"," ","Fire","Ghost",570,53,127,53,151,79,107,7 +807,"Zeraora"," ","Electric"," ",600,88,112,75,102,80,143,7 +808,"Meltan"," ","Steel"," ",300,46,65,65,55,35,34,7 +809,"Melmetal"," ","Steel"," ",600,135,143,143,80,65,34,7 +52,"Meowth","Galarian Meowth","Steel"," ",290,50,65,55,40,40,40,8 +58,"Growlithe","Hisuian Growlithe","Fire","Rock",350,60,75,45,65,50,55,8 +59,"Arcanine","Hisuian Arcanine","Fire","Rock",555,95,115,80,95,80,90,8 +77,"Ponyta","Galarian Ponyta","Psychic"," ",410,50,85,55,65,65,90,8 +78,"Rapidash","Galarian Rapidash","Psychic","Fairy",500,65,100,70,80,80,105,8 +79,"Slowpoke","Galarian Slowpoke","Psychic"," ",315,90,65,65,40,40,15,8 +80,"Slowbro","Galarian Slowbro","Poison","Psychic",490,95,100,95,100,70,30,8 +83,"Farfetch'd","Galarian Farfetch'd","Fighting"," ",377,52,95,55,58,62,55,8 +100,"Voltorb","Hisuian Voltorb","Electric","Grass",330,40,30,50,55,55,100,8 +101,"Electrode","Hisuian Electrode","Electric","Grass",490,60,50,70,80,80,150,8 +110,"Weezing","Galarian Weezing","Poison","Fairy",490,65,90,120,85,70,60,8 +122,"Mr. Mime","Galarian Mr. Mime","Ice","Psychic",460,50,65,65,90,90,100,8 +144,"Articuno","Galarian Articuno","Psychic","Flying",580,90,85,85,125,100,95,8 +145,"Zapdos","Galarian Zapdos","Fighting","Flying",580,90,125,90,85,90,100,8 +146,"Moltres","Galarian Moltres","Dark","Flying",580,90,85,90,100,125,90,8 +157,"Typhlosion","Hisuian Typhlosion","Fire","Ghost",534,73,84,78,119,85,95,8 +199,"Slowking","Galarian Slowking","Poison","Psychic",490,95,65,80,110,110,30,8 +211,"Qwilfish","Hisuian Qwilfish","Dark","Poison",440,65,95,85,55,55,85,8 +215,"Sneasel","Hisuian Sneasel","Fighting","Poison",430,55,95,55,35,75,115,8 +222,"Corsola","Galarian Corsola","Ghost"," ",410,60,55,100,65,100,30,8 +263,"Zigzagoon","Galarian Zigzagoon","Dark","Normal",240,38,30,41,30,41,60,8 +264,"Linoone","Galarian Linoone","Dark","Normal",420,78,70,61,50,61,100,8 +483,"Dialga","Origin Forme","Steel","Dragon",680,100,100,120,150,120,90,8 +484,"Palkia","Origin Forme","Water","Dragon",680,90,100,100,150,120,120,8 +503,"Samurott","Hisuian Samurott","Water","Dark",528,90,108,80,100,65,85,8 +549,"Lilligant","Hisuian Lilligant","Grass","Fighting",480,70,105,75,50,75,105,8 +550,"Basculin","White-Striped Form","Water"," ",460,70,92,65,80,55,98,8 +554,"Darumaka","Galarian Darumaka","Ice"," ",315,70,90,45,15,45,50,8 +555,"Darmanitan","Galarian Standard Mode","Ice"," ",480,105,140,55,30,55,95,8 +555,"Darmanitan","Galarian Zen Mode","Ice","Fire",540,105,160,55,30,55,135,8 +562,"Yamask","Galarian Yamask","Ground","Ghost",303,38,55,85,30,65,30,8 +570,"Zorua","Hisuian Zorua","Normal","Ghost",330,35,60,40,85,40,70,8 +571,"Zoroark","Hisuian Zoroark","Normal","Ghost",510,55,100,60,125,60,110,8 +618,"Stunfisk","Galarian Stunfisk","Ground","Steel",471,109,81,99,66,84,32,8 +628,"Braviary","Hisuian Braviary","Psychic","Flying",510,110,83,70,112,70,65,8 +705,"Sliggoo","Hisuian Sliggoo","Steel","Dragon",452,58,75,83,83,113,40,8 +706,"Goodra","Hisuian Goodra","Steel","Dragon",600,80,100,100,110,150,60,8 +713,"Avalugg","Hisuian Avalugg","Ice","Rock",514,95,127,184,34,36,38,8 +724,"Decidueye","Hisuian Decidueye","Grass","Fighting",530,88,112,80,95,95,60,8 +810,"Grookey"," ","Grass"," ",310,50,65,50,40,40,65,8 +811,"Thwackey"," ","Grass"," ",420,70,85,70,55,60,80,8 +812,"Rillaboom"," ","Grass"," ",530,100,125,90,60,70,85,8 +813,"Scorbunny"," ","Fire"," ",310,50,71,40,40,40,69,8 +814,"Raboot"," ","Fire"," ",420,65,86,60,55,60,94,8 +815,"Cinderace"," ","Fire"," ",530,80,116,75,65,75,119,8 +816,"Sobble"," ","Water"," ",310,50,40,40,70,40,70,8 +817,"Drizzile"," ","Water"," ",420,65,60,55,95,55,90,8 +818,"Inteleon"," ","Water"," ",530,70,85,65,125,65,120,8 +819,"Skwovet"," ","Normal"," ",275,70,55,55,35,35,25,8 +820,"Greedent"," ","Normal"," ",460,120,95,95,55,75,20,8 +821,"Rookidee"," ","Flying"," ",245,38,47,35,33,35,57,8 +822,"Corvisquire"," ","Flying"," ",365,68,67,55,43,55,77,8 +823,"Corviknight"," ","Flying","Steel",495,98,87,105,53,85,67,8 +824,"Blipbug"," ","Bug"," ",180,25,20,20,25,45,45,8 +825,"Dottler"," ","Bug","Psychic",335,50,35,80,50,90,30,8 +826,"Orbeetle"," ","Bug","Psychic",505,60,45,110,80,120,90,8 +827,"Nickit"," ","Dark"," ",245,40,28,28,47,52,50,8 +828,"Thievul"," ","Dark"," ",455,70,58,58,87,92,90,8 +829,"Gossifleur"," ","Grass"," ",250,40,40,60,40,60,10,8 +830,"Eldegoss"," ","Grass"," ",460,60,50,90,80,120,60,8 +831,"Wooloo"," ","Normal"," ",270,42,40,55,40,45,48,8 +832,"Dubwool"," ","Normal"," ",490,72,80,100,60,90,88,8 +833,"Chewtle"," ","Water"," ",284,50,64,50,38,38,44,8 +834,"Drednaw"," ","Water","Rock",485,90,115,90,48,68,74,8 +835,"Yamper"," ","Electric"," ",270,59,45,50,40,50,26,8 +836,"Boltund"," ","Electric"," ",490,69,90,60,90,60,121,8 +837,"Rolycoly"," ","Rock"," ",240,30,40,50,40,50,30,8 +838,"Carkol"," ","Rock","Fire",410,80,60,90,60,70,50,8 +839,"Coalossal"," ","Rock","Fire",510,110,80,120,80,90,30,8 +840,"Applin"," ","Grass","Dragon",260,40,40,80,40,40,20,8 +841,"Flapple"," ","Grass","Dragon",485,70,110,80,95,60,70,8 +842,"Appletun"," ","Grass","Dragon",485,110,85,80,100,80,30,8 +843,"Silicobra"," ","Ground"," ",315,52,57,75,35,50,46,8 +844,"Sandaconda"," ","Ground"," ",510,72,107,125,65,70,71,8 +845,"Cramorant"," ","Flying","Water",475,70,85,55,85,95,85,8 +846,"Arrokuda"," ","Water"," ",280,41,63,40,40,30,66,8 +847,"Barraskewda"," ","Water"," ",490,61,123,60,60,50,136,8 +848,"Toxel"," ","Electric","Poison",242,40,38,35,54,35,40,8 +849,"Toxtricity","Amped Form","Electric","Poison",502,75,98,70,114,70,75,8 +849,"Toxtricity","Low Key Form","Electric","Poison",502,75,98,70,114,70,75,8 +850,"Sizzlipede"," ","Fire","Bug",305,50,65,45,50,50,45,8 +851,"Centiskorch"," ","Fire","Bug",525,100,115,65,90,90,65,8 +852,"Clobbopus"," ","Fighting"," ",310,50,68,60,50,50,32,8 +853,"Grapploct"," ","Fighting"," ",480,80,118,90,70,80,42,8 +854,"Sinistea"," ","Ghost"," ",308,40,45,45,74,54,50,8 +855,"Polteageist"," ","Ghost"," ",508,60,65,65,134,114,70,8 +856,"Hatenna"," ","Psychic"," ",265,42,30,45,56,53,39,8 +857,"Hattrem"," ","Psychic"," ",370,57,40,65,86,73,49,8 +858,"Hatterene"," ","Psychic","Fairy",510,57,90,95,136,103,29,8 +859,"Impidimp"," ","Dark","Fairy",265,45,45,30,55,40,50,8 +860,"Morgrem"," ","Dark","Fairy",370,65,60,45,75,55,70,8 +861,"Grimmsnarl"," ","Dark","Fairy",510,95,120,65,95,75,60,8 +862,"Obstagoon"," ","Dark","Normal",520,93,90,101,60,81,95,8 +863,"Perrserker"," ","Steel"," ",440,70,110,100,50,60,50,8 +864,"Cursola"," ","Ghost"," ",510,60,95,50,145,130,30,8 +865,"Sirfetch'd"," ","Fighting"," ",507,62,135,95,68,82,65,8 +866,"Mr. Rime"," ","Ice","Psychic",520,80,85,75,110,100,70,8 +867,"Runerigus"," ","Ground","Ghost",483,58,95,145,50,105,30,8 +868,"Milcery"," ","Fairy"," ",270,45,40,40,50,61,34,8 +869,"Alcremie"," ","Fairy"," ",495,65,60,75,110,121,64,8 +870,"Falinks"," ","Fighting"," ",470,65,100,100,70,60,75,8 +871,"Pincurchin"," ","Electric"," ",435,48,101,95,91,85,15,8 +872,"Snom"," ","Ice","Bug",185,30,25,35,45,30,20,8 +873,"Frosmoth"," ","Ice","Bug",475,70,65,60,125,90,65,8 +874,"Stonjourner"," ","Rock"," ",470,100,125,135,20,20,70,8 +875,"Eiscue","Ice Face","Ice"," ",470,75,80,110,65,90,50,8 +875,"Eiscue","Noice Face","Ice"," ",470,75,80,70,65,50,130,8 +876,"Indeedee","Male","Psychic","Normal",475,60,65,55,105,95,95,8 +876,"Indeedee","Female","Psychic","Normal",475,70,55,65,95,105,85,8 +877,"Morpeko","Full Belly Mode","Electric","Dark",436,58,95,58,70,58,97,8 +877,"Morpeko","Hangry Mode","Electric","Dark",436,58,95,58,70,58,97,8 +878,"Cufant"," ","Steel"," ",330,72,80,49,40,49,40,8 +879,"Copperajah"," ","Steel"," ",500,122,130,69,80,69,30,8 +880,"Dracozolt"," ","Electric","Dragon",505,90,100,90,80,70,75,8 +881,"Arctozolt"," ","Electric","Ice",505,90,100,90,90,80,55,8 +882,"Dracovish"," ","Water","Dragon",505,90,90,100,70,80,75,8 +883,"Arctovish"," ","Water","Ice",505,90,90,100,80,90,55,8 +884,"Duraludon"," ","Steel","Dragon",535,70,95,115,120,50,85,8 +885,"Dreepy"," ","Dragon","Ghost",270,28,60,30,40,30,82,8 +886,"Drakloak"," ","Dragon","Ghost",410,68,80,50,60,50,102,8 +887,"Dragapult"," ","Dragon","Ghost",600,88,120,75,100,75,142,8 +888,"Zacian","Hero of Many Battles","Fairy"," ",660,92,120,115,80,115,138,8 +888,"Zacian","Crowned Sword","Fairy","Steel",700,92,150,115,80,115,148,8 +889,"Zamazenta","Hero of Many Battles","Fighting"," ",660,92,120,115,80,115,138,8 +889,"Zamazenta","Crowned Shield","Fighting","Steel",700,92,120,140,80,140,128,8 +890,"Eternatus"," ","Poison","Dragon",690,140,85,95,145,95,130,8 +890,"Eternatus","Eternamax","Poison","Dragon",1125,255,115,250,125,250,130,8 +891,"Kubfu"," ","Fighting"," ",385,60,90,60,53,50,72,8 +892,"Urshifu","Single Strike Style","Fighting","Dark",550,100,130,100,63,60,97,8 +892,"Urshifu","Rapid Strike Style","Fighting","Water",550,100,130,100,63,60,97,8 +893,"Zarude"," ","Dark","Grass",600,105,120,105,70,95,105,8 +894,"Regieleki"," ","Electric"," ",580,80,100,50,100,50,200,8 +895,"Regidrago"," ","Dragon"," ",580,200,100,50,100,50,80,8 +896,"Glastrier"," ","Ice"," ",580,100,145,130,65,110,30,8 +897,"Spectrier"," ","Ghost"," ",580,100,65,60,145,80,130,8 +898,"Calyrex"," ","Psychic","Grass",500,100,80,80,80,80,80,8 +898,"Calyrex","Ice Rider","Psychic","Ice",680,100,165,150,85,130,50,8 +898,"Calyrex","Shadow Rider","Psychic","Ghost",680,100,85,80,165,100,150,8 +899,"Wyrdeer"," ","Normal","Psychic",525,103,105,72,105,75,65,8 +900,"Kleavor"," ","Bug","Rock",500,70,135,95,45,70,85,8 +901,"Ursaluna"," ","Ground","Normal",550,130,140,105,45,80,50,8 +902,"Basculegion","Male","Water","Ghost",530,120,112,65,80,75,78,8 +902,"Basculegion","Female","Water","Ghost",530,120,92,65,100,75,78,8 +903,"Sneasler"," ","Fighting","Poison",510,80,130,60,40,80,120,8 +904,"Overqwil"," ","Dark","Poison",510,85,115,95,65,65,85,8 +905,"Enamorus","Incarnate Forme","Fairy","Flying",580,74,115,70,135,80,106,8 +905,"Enamorus","Therian Forme","Fairy","Flying",580,74,115,110,135,100,46,8 +128,"Tauros","Combat Breed","Fighting"," ",490,75,110,105,30,70,100,9 +128,"Tauros","Blaze Breed","Fighting","Fire",490,75,110,105,30,70,100,9 +128,"Tauros","Aqua Breed","Fighting","Water",490,75,110,105,30,70,100,9 +194,"Wooper","Paldean Wooper","Poison","Ground",210,55,45,45,25,25,15,9 +901,"Ursaluna","Bloodmoon","Ground","Normal",555,113,70,120,135,65,52,9 +906,"Sprigatito"," ","Grass"," ",310,40,61,54,45,45,65,9 +907,"Floragato"," ","Grass"," ",410,61,80,63,60,63,83,9 +908,"Meowscarada"," ","Grass","Dark",530,76,110,70,81,70,123,9 +909,"Fuecoco"," ","Fire"," ",310,67,45,59,63,40,36,9 +910,"Crocalor"," ","Fire"," ",411,81,55,78,90,58,49,9 +911,"Skeledirge"," ","Fire","Ghost",530,104,75,100,110,75,66,9 +912,"Quaxly"," ","Water"," ",310,55,65,45,50,45,50,9 +913,"Quaxwell"," ","Water"," ",410,70,85,65,65,60,65,9 +914,"Quaquaval"," ","Water","Fighting",530,85,120,80,85,75,85,9 +915,"Lechonk"," ","Normal"," ",254,54,45,40,35,45,35,9 +916,"Oinkologne","Male","Normal"," ",489,110,100,75,59,80,65,9 +916,"Oinkologne","Female","Normal"," ",489,115,90,70,59,90,65,9 +917,"Tarountula"," ","Bug"," ",210,35,41,45,29,40,20,9 +918,"Spidops"," ","Bug"," ",404,60,79,92,52,86,35,9 +919,"Nymble"," ","Bug"," ",210,33,46,40,21,25,45,9 +920,"Lokix"," ","Bug","Dark",450,71,102,78,52,55,92,9 +921,"Pawmi"," ","Electric"," ",240,45,50,20,40,25,60,9 +922,"Pawmo"," ","Electric","Fighting",350,60,75,40,50,40,85,9 +923,"Pawmot"," ","Electric","Fighting",490,70,115,70,70,60,105,9 +924,"Tandemaus"," ","Normal"," ",305,50,50,45,40,45,75,9 +925,"Maushold","Family of Four","Normal"," ",470,74,75,70,65,75,111,9 +925,"Maushold","Family of Three","Normal"," ",470,74,75,70,65,75,111,9 +926,"Fidough"," ","Fairy"," ",312,37,55,70,30,55,65,9 +927,"Dachsbun"," ","Fairy"," ",477,57,80,115,50,80,95,9 +928,"Smoliv"," ","Grass","Normal",260,41,35,45,58,51,30,9 +929,"Dolliv"," ","Grass","Normal",354,52,53,60,78,78,33,9 +930,"Arboliva"," ","Grass","Normal",510,78,69,90,125,109,39,9 +931,"Squawkabilly","Green Plumage","Normal","Flying",417,82,96,51,45,51,92,9 +931,"Squawkabilly","Blue Plumage","Normal","Flying",417,82,96,51,45,51,92,9 +931,"Squawkabilly","Yellow Plumage","Normal","Flying",417,82,96,51,45,51,92,9 +931,"Squawkabilly","White Plumage","Normal","Flying",417,82,96,51,45,51,92,9 +932,"Nacli"," ","Rock"," ",280,55,55,75,35,35,25,9 +933,"Naclstack"," ","Rock"," ",355,60,60,100,35,65,35,9 +934,"Garganacl"," ","Rock"," ",500,100,100,130,45,90,35,9 +935,"Charcadet"," ","Fire"," ",255,40,50,40,50,40,35,9 +936,"Armarouge"," ","Fire","Psychic",525,85,60,100,125,80,75,9 +937,"Ceruledge"," ","Fire","Ghost",525,75,125,80,60,100,85,9 +938,"Tadbulb"," ","Electric"," ",272,61,31,41,59,35,45,9 +939,"Bellibolt"," ","Electric"," ",495,109,64,91,103,83,45,9 +940,"Wattrel"," ","Electric","Flying",280,40,40,35,55,40,70,9 +941,"Kilowattrel"," ","Electric","Flying",490,70,70,60,105,60,125,9 +942,"Maschiff"," ","Dark"," ",340,60,78,60,40,51,51,9 +943,"Mabosstiff"," ","Dark"," ",505,80,120,90,60,70,85,9 +944,"Shroodle"," ","Poison","Normal",290,40,65,35,40,35,75,9 +945,"Grafaiai"," ","Poison","Normal",485,63,95,65,80,72,110,9 +946,"Bramblin"," ","Grass","Ghost",275,40,65,30,45,35,60,9 +947,"Brambleghast"," ","Grass","Ghost",480,55,115,70,80,70,90,9 +948,"Toedscool"," ","Ground","Grass",335,40,40,35,50,100,70,9 +949,"Toedscruel"," ","Ground","Grass",515,80,70,65,80,120,100,9 +950,"Klawf"," ","Rock"," ",450,70,100,115,35,55,75,9 +951,"Capsakid"," ","Grass"," ",304,50,62,40,62,40,50,9 +952,"Scovillain"," ","Grass","Fire",486,65,108,65,108,65,75,9 +953,"Rellor"," ","Bug"," ",270,41,50,60,31,58,30,9 +954,"Rabsca"," ","Bug","Psychic",470,75,50,85,115,100,45,9 +955,"Flittle"," ","Psychic"," ",255,30,35,30,55,30,75,9 +956,"Espathra"," ","Psychic"," ",481,95,60,60,101,60,105,9 +957,"Tinkatink"," ","Fairy","Steel",297,50,45,45,35,64,58,9 +958,"Tinkatuff"," ","Fairy","Steel",380,65,55,55,45,82,78,9 +959,"Tinkaton"," ","Fairy","Steel",506,85,75,77,70,105,94,9 +960,"Wiglett"," ","Water"," ",245,10,55,25,35,25,95,9 +961,"Wugtrio"," ","Water"," ",425,35,100,50,50,70,120,9 +962,"Bombirdier"," ","Flying","Dark",485,70,103,85,60,85,82,9 +963,"Finizen"," ","Water"," ",315,70,45,40,45,40,75,9 +964,"Palafin","Zero Form","Water"," ",457,100,70,72,53,62,100,9 +964,"Palafin","Hero Form","Water"," ",650,100,160,97,106,87,100,9 +965,"Varoom"," ","Steel","Poison",300,45,70,63,30,45,47,9 +966,"Revavroom"," ","Steel","Poison",500,80,119,90,54,67,90,9 +967,"Cyclizar"," ","Dragon","Normal",501,70,95,65,85,65,121,9 +968,"Orthworm"," ","Steel"," ",480,70,85,145,60,55,65,9 +969,"Glimmet"," ","Rock","Poison",350,48,35,42,105,60,60,9 +970,"Glimmora"," ","Rock","Poison",525,83,55,90,130,81,86,9 +971,"Greavard"," ","Ghost"," ",290,50,61,60,30,55,34,9 +972,"Houndstone"," ","Ghost"," ",488,72,101,100,50,97,68,9 +973,"Flamigo"," ","Flying","Fighting",500,82,115,74,75,64,90,9 +974,"Cetoddle"," ","Ice"," ",334,108,68,45,30,40,43,9 +975,"Cetitan"," ","Ice"," ",521,170,113,65,45,55,73,9 +976,"Veluza"," ","Water","Psychic",478,90,102,73,78,65,70,9 +977,"Dondozo"," ","Water"," ",530,150,100,115,65,65,35,9 +978,"Tatsugiri","Curly Form","Dragon","Water",475,68,50,60,120,95,82,9 +978,"Tatsugiri","Droopy Form","Dragon","Water",475,68,50,60,120,95,82,9 +978,"Tatsugiri","Stretchy Form","Dragon","Water",475,68,50,60,120,95,82,9 +979,"Annihilape"," ","Fighting","Ghost",535,110,115,80,50,90,90,9 +980,"Clodsire"," ","Poison","Ground",430,130,75,60,45,100,20,9 +981,"Farigiraf"," ","Normal","Psychic",520,120,90,70,110,70,60,9 +982,"Dudunsparce","Two-Segment Form","Normal"," ",520,125,100,80,85,75,55,9 +982,"Dudunsparce","Three-Segment Form","Normal"," ",520,125,100,80,85,75,55,9 +983,"Kingambit"," ","Dark","Steel",550,100,135,120,60,85,50,9 +984,"Great Tusk"," ","Ground","Fighting",570,115,131,131,53,53,87,9 +985,"Scream Tail"," ","Fairy","Psychic",570,115,65,99,65,115,111,9 +986,"Brute Bonnet"," ","Grass","Dark",570,111,127,99,79,99,55,9 +987,"Flutter Mane"," ","Ghost","Fairy",570,55,55,55,135,135,135,9 +988,"Slither Wing"," ","Bug","Fighting",570,85,135,79,85,105,81,9 +989,"Sandy Shocks"," ","Electric","Ground",570,85,81,97,121,85,101,9 +990,"Iron Treads"," ","Ground","Steel",570,90,112,120,72,70,106,9 +991,"Iron Bundle"," ","Ice","Water",570,56,80,114,124,60,136,9 +992,"Iron Hands"," ","Fighting","Electric",570,154,140,108,50,68,50,9 +993,"Iron Jugulis"," ","Dark","Flying",570,94,80,86,122,80,108,9 +994,"Iron Moth"," ","Fire","Poison",570,80,70,60,140,110,110,9 +995,"Iron Thorns"," ","Rock","Electric",570,100,134,110,70,84,72,9 +996,"Frigibax"," ","Dragon","Ice",320,65,75,45,35,45,55,9 +997,"Arctibax"," ","Dragon","Ice",423,90,95,66,45,65,62,9 +998,"Baxcalibur"," ","Dragon","Ice",600,115,145,92,75,86,87,9 +999,"Gimmighoul","Chest Form","Ghost"," ",300,45,30,70,75,70,10,9 +999,"Gimmighoul","Roaming Form","Ghost"," ",300,45,30,25,75,45,80,9 +1000,"Gholdengo"," ","Steel","Ghost",550,87,60,95,133,91,84,9 +1001,"Wo-Chien"," ","Dark","Grass",570,85,85,100,95,135,70,9 +1002,"Chien-Pao"," ","Dark","Ice",570,80,120,80,90,65,135,9 +1003,"Ting-Lu"," ","Dark","Ground",570,155,110,125,55,80,45,9 +1004,"Chi-Yu"," ","Dark","Fire",570,55,80,80,135,120,100,9 +1005,"Roaring Moon"," ","Dragon","Dark",590,105,139,71,55,101,119,9 +1006,"Iron Valiant"," ","Fairy","Fighting",590,74,130,90,120,60,116,9 +1007,"Koraidon"," ","Fighting","Dragon",670,100,135,115,85,100,135,9 +1008,"Miraidon"," ","Electric","Dragon",670,100,85,100,135,115,135,9 +1009,"Walking Wake"," ","Water","Dragon",590,99,83,91,125,83,109,9 +1010,"Iron Leaves"," ","Grass","Psychic",590,90,130,88,70,108,104,9 +1011,"Dipplin"," ","Grass","Dragon",485,80,80,110,95,80,40,9 +1012,"Poltchageist"," ","Grass","Ghost",308,40,45,45,74,54,50,9 +1013,"Sinistcha"," ","Grass","Ghost",508,71,60,106,121,80,70,9 +1014,"Okidogi"," ","Poison","Fighting",555,88,128,115,58,86,80,9 +1015,"Munkidori"," ","Poison","Psychic",555,88,75,66,130,90,106,9 +1016,"Fezandipiti"," ","Poison","Fairy",555,88,91,82,70,125,99,9 +1017,"Ogerpon","Teal Mask","Grass"," ",550,80,120,84,60,96,110,9 +1017,"Ogerpon","Wellspring Mask","Grass","Water",550,80,120,84,60,96,110,9 +1017,"Ogerpon","Hearthflame Mask","Grass","Fire",550,80,120,84,60,96,110,9 +1017,"Ogerpon","Cornerstone Mask","Grass","Rock",550,80,120,84,60,96,110,9 +1018,"Archaludon"," ","Steel","Dragon",600,90,105,130,125,65,85,9 +1019,"Hydrapple"," ","Grass","Dragon",540,106,80,110,120,80,44,9 +1020,"Gouging Fire"," ","Fire","Dragon",590,105,115,121,65,93,91,9 +1021,"Raging Bolt"," ","Electric","Dragon",590,125,73,91,137,89,75,9 +1022,"Iron Boulder"," ","Rock","Psychic",590,90,120,80,68,108,124,9 +1023,"Iron Crown"," ","Steel","Psychic",590,90,72,100,122,108,98,9 +1024,"Terapagos","Normal Form","Normal"," ",450,90,65,85,65,85,60,9 +1024,"Terapagos","Terastal Form","Normal"," ",600,95,95,110,105,110,85,9 +1024,"Terapagos","Stellar Form","Normal"," ",700,160,105,110,130,110,85,9 +1025,"Pecharunt"," ","Poison","Ghost",600,88,88,160,88,88,88,9 diff --git a/analisis_estrellas_estudiante.ipynb b/analisis_estrellas_estudiante.ipynb index 90aaa74..c89f918 100644 --- a/analisis_estrellas_estudiante.ipynb +++ b/analisis_estrellas_estudiante.ipynb @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "id": "code-01", "metadata": {}, "outputs": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "id": "5fd8be11", "metadata": {}, "outputs": [ @@ -124,7 +124,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['.git', '.gitignore', 'analisis_estrellas_estudiante.ipynb', 'autograder', 'banner_ecfm.sh', 'banner_ecfm_small.sh', 'cpp', 'Dockerfile', 'ejemplos', 'estudiantes.csv', 'examenes', 'Makefile', 'ordenamiento', 'Pandas.ipynb', 'pandita.ipynb', 'parciales', 'plantilla_tareas', 'README.md', 'resultado.csv', 'tareas']\n" + "['.git', '.gitignore', 'analisis_estrellas_estudiante.ipynb', 'autograder', 'banner_ecfm.sh', 'banner_ecfm_small.sh', 'cpp', 'Dockerfile', 'ejemplos', 'estudiantes.csv', 'examenes', 'Makefile', 'ordenamiento', 'Pandas.ipynb', 'parciales', 'plantilla_tareas', 'README.md', 'resultado.csv', 'tareas']\n" ] } ], @@ -135,30 +135,30 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "id": "code-02", "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '../data/star_dataset.csv'", + "evalue": "[Errno 2] No such file or directory: 'data/star_dataset.csv'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m stars = pd.read_csv(\u001b[33m'../data/star_dataset.csv'\u001b[39m)\n\u001b[32m 3\u001b[39m \n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# Muestra las primeras 5 filas del DataFrame\u001b[39;00m\n\u001b[32m 5\u001b[39m stars.head()\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m stars = pd.read_csv(\u001b[33m'data/star_dataset.csv'\u001b[39m)\n\u001b[32m 3\u001b[39m \n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# Muestra las primeras 5 filas del DataFrame\u001b[39;00m\n\u001b[32m 5\u001b[39m stars.head()\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:873\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, skip_blank_lines, parse_dates, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 861\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 862\u001b[39m dialect,\n\u001b[32m 863\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 869\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 870\u001b[39m )\n\u001b[32m 871\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:300\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 297\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 299\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 302\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 303\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1645\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1642\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1644\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1645\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1904\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1902\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1903\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1904\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1905\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1906\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1907\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1908\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1909\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1910\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1911\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1912\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1913\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1914\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1915\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\common.py:926\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 921\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 922\u001b[39m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m 923\u001b[39m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m 924\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m 925\u001b[39m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m926\u001b[39m handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 927\u001b[39m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 928\u001b[39m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 929\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 930\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 931\u001b[39m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 932\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 933\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 934\u001b[39m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m 935\u001b[39m handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n", - "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '../data/star_dataset.csv'" + "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'data/star_dataset.csv'" ] } ], "source": [ "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", - "stars = pd.read_csv('../data/star_dataset.csv')\n", + "stars = pd.read_csv('data/star_dataset.csv')\n", "\n", "# Muestra las primeras 5 filas del DataFrame\n", "stars.head()\n" diff --git a/puntos extra.ipynb b/puntos extra.ipynb new file mode 100644 index 0000000..a9c1987 --- /dev/null +++ b/puntos extra.ipynb @@ -0,0 +1,2023 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "812cce11", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "1441ccb0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset cargado con éxito.\n", + "Total de registros: 1215 | Total de columnas: 13\n", + "Tabla con los nulos ya procesados:\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDNameFormType1Type2TotalHPAttackDefenseSp. AtkSp. DefSpeedGeneration
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" + ], + "text/plain": [ + " ID Name Form Type1 Type2 Total HP Attack Defense Sp. Atk \\\n", + "0 1 Bulbasaur Grass Poison 318 45 49 49 65 \n", + "1 2 Ivysaur Grass Poison 405 60 62 63 80 \n", + "2 3 Venusaur Grass Poison 525 80 82 83 100 \n", + "3 4 Charmander Fire 309 39 52 43 60 \n", + "4 5 Charmeleon Fire 405 58 64 58 80 \n", + "\n", + " Sp. Def Speed Generation \n", + "0 65 45 1 \n", + "1 80 60 1 \n", + "2 100 80 1 \n", + "3 50 65 1 \n", + "4 65 80 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Lectura del archivo local 🤓\n", + "ruta = r'C:\\Users\\ruben\\OneDrive\\Escritorio\\tarea-1\\Pokemon.csv'\n", + "df = pd.read_csv(ruta)\n", + "\n", + "# Resumen dinámico: muestra tamaño y nombres de columnas \n", + "print(f\"Dataset cargado con éxito.\")\n", + "print(f\"Total de registros: {df.shape[0]} | Total de columnas: {df.shape[1]}\")\n", + "\n", + "# Verificamos los primeros 10 para ver cómo se llenaron los vacíos 🤓\n", + "print(\"Tabla con los nulos ya procesados:\")\n", + "display(df[['Name', 'Type1', 'Type2']].head(10))\n", + "# 5. Verificación final 🤓\n", + "print(\"\\n--- Conteo de nulos tras la limpieza ---\")\n", + "print(df.isnull().sum())\n", + "\n", + "# Mostramos los primeros registros para ver el cambio 🤓\n", + "display(df.head())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "7ec232bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 Pokémon con mayor cantidad de HP (Puntos de Salud):\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDNameFormType1Type2TotalHPAttackDefenseSp. AtkSp. DefSpeedGeneration
1054890EternatusEternamaxPoisonDragon11252551152501252501308
241242BlisseyNormal540255101075135552
112113ChanseyNormal4502505535105501
914799GuzzlordDarkDragon570223101539753437
828718ZygardeComplete FormeDragonGround7082161001219195857
1060895RegidragoDragon5802001005010050808
201202WobbuffetPsychic40519033583358332
320321WailordWater50017090459045603
1155975CetitanIce521170113654555739
612594AlomomolaWater47016575804045655
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" + ], + "text/plain": [ + " ID Name Form Type1 Type2 Total HP Attack \\\n", + "1054 890 Eternatus Eternamax Poison Dragon 1125 255 115 \n", + "241 242 Blissey Normal 540 255 10 \n", + "112 113 Chansey Normal 450 250 5 \n", + "914 799 Guzzlord Dark Dragon 570 223 101 \n", + "828 718 Zygarde Complete Forme Dragon Ground 708 216 100 \n", + "1060 895 Regidrago Dragon 580 200 100 \n", + "201 202 Wobbuffet Psychic 405 190 33 \n", + "320 321 Wailord Water 500 170 90 \n", + "1155 975 Cetitan Ice 521 170 113 \n", + "612 594 Alomomola Water 470 165 75 \n", + "\n", + " Defense Sp. Atk Sp. Def Speed Generation \n", + "1054 250 125 250 130 8 \n", + "241 10 75 135 55 2 \n", + "112 5 35 105 50 1 \n", + "914 53 97 53 43 7 \n", + "828 121 91 95 85 7 \n", + "1060 50 100 50 80 8 \n", + "201 58 33 58 33 2 \n", + "320 45 90 45 60 3 \n", + "1155 65 45 55 73 9 \n", + "612 80 40 45 65 5 " + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Ordenamos los top 10 de hp porque bueno, quiero un pokemon que aguante en mi team 🤓\n", + "df_hp = df.sort_values(by='HP', ascending=False)\n", + "\n", + "print(\"Top 10 Pokémon con mayor cantidad de HP (Puntos de Salud):\")\n", + "df_hp.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "55ee5b33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 Pokémon con mayor Ataque Físico:\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDNameFormType1Type2TotalHPAttackDefenseSp. AtkSp. DefSpeedGeneration
688150MewtwoMega Mewtwo XPsychicFighting7801061901001541001306
693214HeracrossMega HeracrossBugFighting6008018511540105756
913798KartanaGrassSteel5705918113159311097
389386DeoxysAttack FormePsychic6005018020180201503
717384RayquazaMega RayquazaDragonFlying7801051801001801001156
716383GroudonPrimal GroudonGroundFire77010018016015090906
669646KyuremBlack KyuremDragonIce70012517010012090955
719445GarchompMega GarchompDragonGround70010817011512095926
918800NecrozmaUltra NecrozmaPsychicDragon7549716797167971297
722475GalladeMega GalladePsychicFighting6186816595651151106
\n", + "
" + ], + "text/plain": [ + " ID Name Form Type1 Type2 Total HP Attack \\\n", + "688 150 Mewtwo Mega Mewtwo X Psychic Fighting 780 106 190 \n", + "693 214 Heracross Mega Heracross Bug Fighting 600 80 185 \n", + "913 798 Kartana Grass Steel 570 59 181 \n", + "389 386 Deoxys Attack Forme Psychic 600 50 180 \n", + "717 384 Rayquaza Mega Rayquaza Dragon Flying 780 105 180 \n", + "716 383 Groudon Primal Groudon Ground Fire 770 100 180 \n", + "669 646 Kyurem Black Kyurem Dragon Ice 700 125 170 \n", + "719 445 Garchomp Mega Garchomp Dragon Ground 700 108 170 \n", + "918 800 Necrozma Ultra Necrozma Psychic Dragon 754 97 167 \n", + "722 475 Gallade Mega Gallade Psychic Fighting 618 68 165 \n", + "\n", + " Defense Sp. Atk Sp. Def Speed Generation \n", + "688 100 154 100 130 6 \n", + "693 115 40 105 75 6 \n", + "913 131 59 31 109 7 \n", + "389 20 180 20 150 3 \n", + "717 100 180 100 115 6 \n", + "716 160 150 90 90 6 \n", + "669 100 120 90 95 5 \n", + "719 115 120 95 92 6 \n", + "918 97 167 97 129 7 \n", + "722 95 65 115 110 6 " + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# un buen equipo necesita 1 a 2 atacantes fisicos para poder barrer equipos mirare los top 10 🤓 (en esta casa odiamos las estrategias walleras)\n", + "df_ataque = df.sort_values(by='Attack', ascending=False)\n", + "\n", + "# Mostramos los primeros 100 con mayor ataque\n", + "print(\"Top 10 Pokémon con mayor Ataque Físico:\")\n", + "df_ataque.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "6001f016", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 Pokémon con mayor Defensa Física:\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDNameFormType1Type2TotalHPAttackDefenseSp. AtkSp. DefSpeedGeneration
1054890EternatusEternamaxPoisonDragon11252551152501252501308
691208SteelixMega SteelixSteelGround610751252305595306
212213ShuckleBugRock50520102301023052
702306AggronMega AggronSteel630701402306080506
923805StakatakaRockSteel5706113121153101137
379377RegirockRock5808010020050100503
207208SteelixSteelGround51075852005565302
795713AvaluggIce514951171844446286
965713AvaluggHisuian AvaluggIceRock514951271843436388
68280SlowbroMega SlowbroWaterPsychic590957518013080306
\n", + "
" + ], + "text/plain": [ + " ID Name Form Type1 Type2 Total HP Attack \\\n", + "1054 890 Eternatus Eternamax Poison Dragon 1125 255 115 \n", + "691 208 Steelix Mega Steelix Steel Ground 610 75 125 \n", + "212 213 Shuckle Bug Rock 505 20 10 \n", + "702 306 Aggron Mega Aggron Steel 630 70 140 \n", + "923 805 Stakataka Rock Steel 570 61 131 \n", + "379 377 Regirock Rock 580 80 100 \n", + "207 208 Steelix Steel Ground 510 75 85 \n", + "795 713 Avalugg Ice 514 95 117 \n", + "965 713 Avalugg Hisuian Avalugg Ice Rock 514 95 127 \n", + "682 80 Slowbro Mega Slowbro Water Psychic 590 95 75 \n", + "\n", + " Defense Sp. Atk Sp. Def Speed Generation \n", + "1054 250 125 250 130 8 \n", + "691 230 55 95 30 6 \n", + "212 230 10 230 5 2 \n", + "702 230 60 80 50 6 \n", + "923 211 53 101 13 7 \n", + "379 200 50 100 50 3 \n", + "207 200 55 65 30 2 \n", + "795 184 44 46 28 6 \n", + "965 184 34 36 38 8 \n", + "682 180 130 80 30 6 " + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#top 10 en defensa fisica, aunque no me gusto las estrategias wall, un buen equipo siemopre necesita al menos 1 o 2 \n", + "df_defensa = df.sort_values(by='Defense', ascending=False)\n", + "\n", + "print(\"Top 10 Pokémon con mayor Defensa Física:\")\n", + "df_defensa.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "bf22b0f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 Pokémon con mayor Ataque Especial (Sp. Atk):\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDNameFormType1Type2TotalHPAttackDefenseSp. AtkSp. DefSpeedGeneration
689150MewtwoMega Mewtwo YPsychic780106150701941201406
717384RayquazaMega RayquazaDragonFlying7801051801001801001156
715382KyogrePrimal KyogreWater77010015090180160906
389386DeoxysAttack FormePsychic6005018020180201503
68165AlakazamMega AlakazamPsychic6005550651751051506
911796XurkitreeElectric57083897117371837
804720HoopaHoopa UnboundPsychicDark6808016060170130806
668646KyuremWhite KyuremDragonIce70012512090170100955
68394GengarMega GengarGhostPoison600606580170951306
918800NecrozmaUltra NecrozmaPsychicDragon7549716797167971297
\n", + "
" + ], + "text/plain": [ + " ID Name Form Type1 Type2 Total HP Attack \\\n", + "689 150 Mewtwo Mega Mewtwo Y Psychic 780 106 150 \n", + "717 384 Rayquaza Mega Rayquaza Dragon Flying 780 105 180 \n", + "715 382 Kyogre Primal Kyogre Water 770 100 150 \n", + "389 386 Deoxys Attack Forme Psychic 600 50 180 \n", + "681 65 Alakazam Mega Alakazam Psychic 600 55 50 \n", + "911 796 Xurkitree Electric 570 83 89 \n", + "804 720 Hoopa Hoopa Unbound Psychic Dark 680 80 160 \n", + "668 646 Kyurem White Kyurem Dragon Ice 700 125 120 \n", + "683 94 Gengar Mega Gengar Ghost Poison 600 60 65 \n", + "918 800 Necrozma Ultra Necrozma Psychic Dragon 754 97 167 \n", + "\n", + " Defense Sp. Atk Sp. Def Speed Generation \n", + "689 70 194 120 140 6 \n", + "717 100 180 100 115 6 \n", + "715 90 180 160 90 6 \n", + "389 20 180 20 150 3 \n", + "681 65 175 105 150 6 \n", + "911 71 173 71 83 7 \n", + "804 60 170 130 80 6 \n", + "668 90 170 100 95 5 \n", + "683 80 170 95 130 6 \n", + "918 97 167 97 129 7 " + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sp. Atk top 10, obviamente si me ponen una wall fisica, llevar solo full team fisico sseria bobo\n", + "df_sp_atk = df.sort_values(by='Sp. Atk', ascending=False)\n", + "\n", + "print(\"Top 10 Pokémon con mayor Ataque Especial (Sp. Atk):\")\n", + "df_sp_atk.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "0ed85104", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 Pokémon con mayor Defensa Especial (Sp. Def):\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDNameFormType1Type2TotalHPAttackDefenseSp. AtkSp. DefSpeedGeneration
1054890EternatusEternamaxPoisonDragon11252551152501252501308
212213ShuckleBugRock50520102301023052
380378RegiceIce5808050100100200503
390386DeoxysDefense FormePsychic600507016070160903
715382KyogrePrimal KyogreWater77010015090180160906
248249LugiaPsychicFlying68010690130901541102
249250Ho-ohFireFlying68010613090110154902
745671FlorgesFairy552786568112154756
779703CarbinkRockFairy500505015050150506
782706GoodraDragon6009010070110150806
\n", + "
" + ], + "text/plain": [ + " ID Name Form Type1 Type2 Total HP Attack \\\n", + "1054 890 Eternatus Eternamax Poison Dragon 1125 255 115 \n", + "212 213 Shuckle Bug Rock 505 20 10 \n", + "380 378 Regice Ice 580 80 50 \n", + "390 386 Deoxys Defense Forme Psychic 600 50 70 \n", + "715 382 Kyogre Primal Kyogre Water 770 100 150 \n", + "248 249 Lugia Psychic Flying 680 106 90 \n", + "249 250 Ho-oh Fire Flying 680 106 130 \n", + "745 671 Florges Fairy 552 78 65 \n", + "779 703 Carbink Rock Fairy 500 50 50 \n", + "782 706 Goodra Dragon 600 90 100 \n", + "\n", + " Defense Sp. Atk Sp. Def Speed Generation \n", + "1054 250 125 250 130 8 \n", + "212 230 10 230 5 2 \n", + "380 100 100 200 50 3 \n", + "390 160 70 160 90 3 \n", + "715 90 180 160 90 6 \n", + "248 130 90 154 110 2 \n", + "249 90 110 154 90 2 \n", + "745 68 112 154 75 6 \n", + "779 150 50 150 50 6 \n", + "782 70 110 150 80 6 " + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#top 10 def. special. vere si agrego un defensor especial porque si no me harian pupita\n", + "df_sp_def = df.sort_values(by='Sp. Def', ascending=False)\n", + "\n", + "print(\"Top 10 Pokémon con mayor Defensa Especial (Sp. Def):\")\n", + "df_sp_def.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "72952de3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 Pokémon más rápidos (Speed):\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDNameFormType1Type2TotalHPAttackDefenseSp. AtkSp. DefSpeedGeneration
1059894RegielekiElectric5808010050100502008
391386DeoxysSpeed FormePsychic60050959095901803
290291NinjaskBugFlying45661904550501603
910795PheromosaBugFighting5707113737137371517
389386DeoxysAttack FormePsychic6005018020180201503
68165AlakazamMega AlakazamPsychic6005550651751051506
388386DeoxysNormal FormePsychic6005015050150501503
100101ElectrodeElectric49060507080801501
1065898CalyrexShadow RiderPsychicGhost68010085801651001508
937101ElectrodeHisuian ElectrodeElectricGrass49060507080801508
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" + ], + "text/plain": [ + " ID Name Form Type1 Type2 Total HP \\\n", + "1059 894 Regieleki Electric 580 80 \n", + "391 386 Deoxys Speed Forme Psychic 600 50 \n", + "290 291 Ninjask Bug Flying 456 61 \n", + "910 795 Pheromosa Bug Fighting 570 71 \n", + "389 386 Deoxys Attack Forme Psychic 600 50 \n", + "681 65 Alakazam Mega Alakazam Psychic 600 55 \n", + "388 386 Deoxys Normal Forme Psychic 600 50 \n", + "100 101 Electrode Electric 490 60 \n", + "1065 898 Calyrex Shadow Rider Psychic Ghost 680 100 \n", + "937 101 Electrode Hisuian Electrode Electric Grass 490 60 \n", + "\n", + " Attack Defense Sp. Atk Sp. Def Speed Generation \n", + "1059 100 50 100 50 200 8 \n", + "391 95 90 95 90 180 3 \n", + "290 90 45 50 50 160 3 \n", + "910 137 37 137 37 151 7 \n", + "389 180 20 180 20 150 3 \n", + "681 50 65 175 105 150 6 \n", + "388 150 50 150 50 150 3 \n", + "100 50 70 80 80 150 1 \n", + "1065 85 80 165 100 150 8 \n", + "937 50 70 80 80 150 8 " + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# top 10 en velo. para mi estrategia favorita (equipos hiper ofense)🤓 necesito mucha velo, chequemos los mejores para ver de hacer pupita al contrincante \n", + "df_velocidad = df.sort_values(by='Speed', ascending=False)\n", + "\n", + "print(\"Top 10 Pokémon más rápidos (Speed):\")\n", + "df_velocidad.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "94334e4d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Comparativa de Ataque por Generación (Promedio, Máximo y Desviación Estándar):\n" + ] + }, + { + "data": { + "text/html": [ + "
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meanmaxstd
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983.85000016029.702100
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" + ], + "text/plain": [ + " mean max std\n", + "Generation \n", + "1 72.913907 134 26.755421\n", + "2 68.260000 134 28.419164\n", + "3 73.936170 180 31.108155\n", + "4 79.127119 165 30.615494\n", + "5 82.442424 170 30.512363\n", + "6 94.923664 190 39.940844\n", + "7 86.508197 181 32.388970\n", + "8 86.197279 165 30.502108\n", + "9 83.850000 160 29.702100" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# me causo curiosidad cual generacion es la que tiene mayor estadisticas de \"attaque\" creo que va ser 6.ta por sus megas 🤓\n", + "comparativa_gen = df.groupby('Generation')['Attack'].agg(['mean', 'max', 'std'])\n", + "\n", + "print(\"Comparativa de Ataque por Generación (Promedio, Máximo y Desviación Estándar):\")\n", + "display(comparativa_gen)\n", + "\n", + "# Graficamos el promedio y el máximo🤓\n", + "comparativa_gen[['mean', 'max']].plot(kind='bar', figsize=(12, 6), color=['skyblue', 'salmon'])\n", + "\n", + "plt.title('Comparativa de Ataque: Promedio vs Máximo por Generación')\n", + "plt.xlabel('Generación')\n", + "plt.ylabel('Puntos de Ataque')\n", + "plt.legend(['Ataque Promedio (mean)', 'Ataque Máximo (max)'])\n", + "plt.grid(axis='y', linestyle='--', alpha=0.3)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "b22be009", + "metadata": {}, + "outputs": [], + "source": [ + "# eh de decir que gracias a esta tarea ahora podre hacer mi pokedex propia muajajajajjajajaja 🤓 (esos torneos no se ganaran solos)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.14.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 72b09ea326c02046419f8a7a237f1c8f2b7ce6cf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Mon, 27 Apr 2026 20:49:11 -0600 Subject: [PATCH 10/12] listooo --- analisis_estrellas_estudiante.ipynb | 628 +++++++++++++++-- star_dataset.csv | 1001 +++++++++++++++++++++++++++ 2 files changed, 1570 insertions(+), 59 deletions(-) create mode 100644 star_dataset.csv diff --git a/analisis_estrellas_estudiante.ipynb b/analisis_estrellas_estudiante.ipynb index c89f918..66a876b 100644 --- a/analisis_estrellas_estudiante.ipynb +++ b/analisis_estrellas_estudiante.ipynb @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "code-01", "metadata": {}, "outputs": [ @@ -116,52 +116,138 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "5fd8be11", + "execution_count": null, + "id": "code-02", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['.git', '.gitignore', 'analisis_estrellas_estudiante.ipynb', 'autograder', 'banner_ecfm.sh', 'banner_ecfm_small.sh', 'cpp', 'Dockerfile', 'ejemplos', 'estudiantes.csv', 'examenes', 'Makefile', 'ordenamiento', 'Pandas.ipynb', 'parciales', 'plantilla_tareas', 'README.md', 'resultado.csv', 'tareas']\n" + "¡Éxito! Archivo cargado desde: ./star_dataset.csv\n" ] - } - ], - "source": [ - "import os\n", - "print(os.listdir('.')) # Esto te dirá qué archivos hay en la carpeta donde estás parado" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "code-02", - "metadata": {}, - "outputs": [ + }, { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'data/star_dataset.csv'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m stars = pd.read_csv(\u001b[33m'data/star_dataset.csv'\u001b[39m)\n\u001b[32m 3\u001b[39m \n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# Muestra las primeras 5 filas del DataFrame\u001b[39;00m\n\u001b[32m 5\u001b[39m stars.head()\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:873\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, skip_blank_lines, parse_dates, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 861\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 862\u001b[39m dialect,\n\u001b[32m 863\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 869\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 870\u001b[39m )\n\u001b[32m 871\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:300\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 297\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 299\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 302\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 303\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1645\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1642\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1644\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1645\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1904\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1902\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1903\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1904\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1905\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1906\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1907\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1908\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1909\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1910\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1911\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1912\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1913\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1914\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1915\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\ruben\\AppData\\Local\\Python\\pythoncore-3.14-64\\Lib\\site-packages\\pandas\\io\\common.py:926\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 921\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 922\u001b[39m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m 923\u001b[39m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m 924\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m 925\u001b[39m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m926\u001b[39m handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 927\u001b[39m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 928\u001b[39m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 929\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 930\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 931\u001b[39m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 932\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 933\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 934\u001b[39m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m 935\u001b[39m handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n", - "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'data/star_dataset.csv'" - ] + "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" } ], "source": [ - "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", - "stars = pd.read_csv('data/star_dataset.csv')\n", + "import os\n", "\n", - "# Muestra las primeras 5 filas del DataFrame\n", - "stars.head()\n" + "# Intentamos buscar el archivo de forma inteligente, no me salia asi que tuve que reescribir esta celda \n", + "# a si que obligue a python a encontrar el archivo \n", + "posibles_rutas = [\n", + " 'data/star_dataset.csv',\n", + " '../data/star_dataset.csv',\n", + " './star_dataset.csv'\n", + "]\n", + "\n", + "stars = None\n", + "for ruta in posibles_rutas:\n", + " if os.path.exists(ruta):\n", + " stars = pd.read_csv(ruta)\n", + " print(f\"¡Éxito! Archivo cargado desde: {ruta}\")\n", + " break\n", + "\n", + "if stars is not None:\n", + " display(stars.head())\n", + "else:\n", + " print(\"Aún no lo encuentro. Rubén, prueba arrastrando el archivo csv directamente a la carpeta donde está tu notebook.\")" ] }, { @@ -192,10 +278,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "code-03a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensiones: (1000, 6)\n", + "Columnas: ['Name', 'Distance (ly)', 'Luminosity (L/Lo)', 'Radius (R/Ro)', 'Temperature (K)', 'Spectral Class']\n", + "\n", + "Tipos de datos:\n", + "Name str\n", + "Distance (ly) float64\n", + "Luminosity (L/Lo) float64\n", + "Radius (R/Ro) float64\n", + "Temperature (K) float64\n", + "Spectral Class str\n", + "dtype: object\n" + ] + } + ], "source": [ "# Celda 3a: dimensiones, nombres de columnas y tipos de datos\n", "print(f\"Dimensiones: {stars.shape}\")\n", @@ -209,10 +313,114 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "code-03b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Distance (ly)Luminosity (L/Lo)Radius (R/Ro)Temperature (K)
count1000.0000001000.0000001000.0000001000.000000
mean295.50532719644.90944286.9606969983.486779
std541.47840342223.595017213.8500057906.973529
min3.877798-4.9931410.0680872750.183163
25%11.71685310.4410391.6644793940.020856
50%52.031435171.0978095.8454447379.007975
75%322.86587410500.57711733.71977812055.975095
max2600.490723196004.854081887.09793628044.279272
\n", + "
" + ], + "text/plain": [ + " Distance (ly) Luminosity (L/Lo) Radius (R/Ro) Temperature (K)\n", + "count 1000.000000 1000.000000 1000.000000 1000.000000\n", + "mean 295.505327 19644.909442 86.960696 9983.486779\n", + "std 541.478403 42223.595017 213.850005 7906.973529\n", + "min 3.877798 -4.993141 0.068087 2750.183163\n", + "25% 11.716853 10.441039 1.664479 3940.020856\n", + "50% 52.031435 171.097809 5.845444 7379.007975\n", + "75% 322.865874 10500.577117 33.719778 12055.975095\n", + "max 2600.490723 196004.854081 887.097936 28044.279272" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Celda 3b: resumen estadístico de las columnas numéricas\n", "display(stars.describe())\n", @@ -221,10 +429,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "code-03c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Valores nulos por columna:\n", + "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": [ "# Celda 3c: cuenta los valores nulos por columna\n", "print(\"\\nValores nulos por columna:\")\n", @@ -257,10 +481,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "code-04a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectral Class\n", + "A7V 74\n", + "A1V 73\n", + "A9II 48\n", + "B1III 45\n", + "M3.5V 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", + "B0.5IV 30\n", + "F5IV-V 30\n", + "B6Vep 29\n", + "M2.1V 27\n", + "B7V 26\n", + "M1.5Iab 26\n", + "A2Ia 25\n", + "K1V 24\n", + "M6V 22\n", + "K5III 18\n", + "B8Ia 17\n", + "Name: count, dtype: int64\n", + "Spectral Class\n", + "A7V 74\n", + "A1V 73\n", + "A9II 48\n", + "B1III 45\n", + "M3.5V 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", + "B0.5IV 30\n", + "F5IV-V 30\n", + "B6Vep 29\n", + "M2.1V 27\n", + "B7V 26\n", + "M1.5Iab 26\n", + "A2Ia 25\n", + "K1V 24\n", + "M6V 22\n", + "K5III 18\n", + "B8Ia 17\n", + "Name: count, dtype: int64\n" + ] + } + ], "source": [ "# Celda 4a: Cuenta las estrellas por tipo y guarda en 'conteo'\n", "conteo = stars['Spectral Class'].value_counts()\n", @@ -273,10 +564,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "code-04b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(8, 4))\n", "\n", @@ -338,10 +659,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "code-05a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperatura media (for loop) : 7,550.2 K\n" + ] + } + ], "source": [ "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", "tipo_objetivo = 'A7V'\n", @@ -380,10 +709,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "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", + "Verificación para 'A7V': False\n" + ] + } + ], "source": [ "# Celda 5b: Enfoque vectorizado con pandas\n", "temp_por_tipo = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False)\n", @@ -399,10 +769,78 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "code-05b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(9, 5))\n", "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", @@ -455,10 +893,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "code-06", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(9, 6))\n", "\n", @@ -514,10 +981,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "code-07a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tipo de array: \n", + "Media: 9983.49\n", + "Mediana: 7379.01\n", + "Desv. Estándar: 7903.02\n", + "Mínimo: 2750.18316322717 | Máximo: 28044.279271635794\n" + ] + } + ], "source": [ "# Celda 7a: Extrae arrays y calcula estadísticas\n", "temperaturas = stars['Temperature (K)'].values\n", @@ -533,10 +1012,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "code-07b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Percentiles del Radio:\n", + "P25: 1.664 R/Ro\n", + "P50: 5.845 R/Ro\n", + "P75: 33.720 R/Ro\n", + "P90: 369.927 R/Ro\n", + "\n", + "Comparación K vs C (primeros 5):\n", + "7509.29424736388 K -> 7236.1 °C\n", + "8503.284796430231 K -> 8230.1 °C\n", + "3165.9596392449016 K -> 2892.8 °C\n", + "6048.326914763769 K -> 5775.2 °C\n", + "3130.6020692351385 K -> 2857.5 °C\n" + ] + } + ], "source": [ "niveles = [25, 50, 75, 90]\n", "\n", @@ -588,10 +1087,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "code-08", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tipos = stars['Spectral Class'].unique()\n", "colores = sns.color_palette('tab10', len(tipos))\n", diff --git a/star_dataset.csv b/star_dataset.csv new file mode 100644 index 0000000..a6ba6ab --- /dev/null +++ b/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 +Alnilam,1999.5922378259786,53697.14296034396,32.48838765422113,27489.295431556606,B0Ia +Bellatrix,239.79573031543998,6401.125654435876,5.829045687901594,22632.624354404084,B2III +Hadar,349.8378240849879,50002.534526391784,8.94744777918556,25006.352397614006,B1III +Rigil Kentaurus,4.039455513108716,0.2620564024777934,1.3098243183774203,5823.975302815448,G2V 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+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 e94e786dbd5c203ee346c8af3503d2635ed9667d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Wed, 29 Apr 2026 15:56:57 -0600 Subject: [PATCH 11/12] . --- analisis_estrellas_estudiante.ipynb | 95 ++++++++++------------------- 1 file changed, 33 insertions(+), 62 deletions(-) diff --git a/analisis_estrellas_estudiante.ipynb b/analisis_estrellas_estudiante.ipynb index 66a876b..b62e76b 100644 --- a/analisis_estrellas_estudiante.ipynb +++ b/analisis_estrellas_estudiante.ipynb @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 29, "id": "code-01", "metadata": {}, "outputs": [ @@ -84,13 +84,13 @@ } ], "source": [ - "# Importa las cuatro librerías con sus alias convencionales\n", + "# Importa las cuatro librerías 🤓\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:s\n", + "# Una vez que las importes,checo versiones 🤓\n", "print(f'numpy {np.__version__}')\n", "print(f'pandas {pd.__version__}')\n", "print(f'seaborn {sns.__version__}')" @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "code-02", "metadata": {}, "outputs": [ @@ -229,8 +229,7 @@ "source": [ "import os\n", "\n", - "# Intentamos buscar el archivo de forma inteligente, no me salia asi que tuve que reescribir esta celda \n", - "# a si que obligue a python a encontrar el archivo \n", + "# Intentamos buscar el archivo de forma inteligente, no me salia asi que tuve que obligar a python a encontrar el archivo 🤓\n", "posibles_rutas = [\n", " 'data/star_dataset.csv',\n", " '../data/star_dataset.csv',\n", @@ -278,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 31, "id": "code-03a", "metadata": {}, "outputs": [ @@ -301,7 +300,7 @@ } ], "source": [ - "# Celda 3a: dimensiones, nombres de columnas y tipos de datos\n", + "# dimensiones 🤓\n", "print(f\"Dimensiones: {stars.shape}\")\n", "print(f\"Columnas: {stars.columns.tolist()}\")\n", "print(\"\\nTipos de datos:\")\n", @@ -313,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 32, "id": "code-03b", "metadata": {}, "outputs": [ @@ -422,14 +421,14 @@ } ], "source": [ - "# Celda 3b: resumen estadístico de las columnas numéricas\n", + "#resumen 🤓\n", "display(stars.describe())\n", "\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 33, "id": "code-03c", "metadata": {}, "outputs": [ @@ -450,7 +449,7 @@ } ], "source": [ - "# Celda 3c: cuenta los valores nulos por columna\n", + "#cuenta de valores nulos por columna 🤓\n", "print(\"\\nValores nulos por columna:\")\n", "print(stars.isnull().sum())\n" ] @@ -481,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 34, "id": "code-04a", "metadata": {}, "outputs": [ @@ -553,7 +552,7 @@ } ], "source": [ - "# Celda 4a: Cuenta las estrellas por tipo y guarda en 'conteo'\n", + "# Cuento las estrellas por tipo y guarda en 'conteo' 🤓\n", "conteo = stars['Spectral Class'].value_counts()\n", "print(conteo)\n", "\n", @@ -564,19 +563,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 35, "id": "code-04b", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "image/png": 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", @@ -586,22 +576,11 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "plt.figure(figsize=(8, 4))\n", "\n", - "# Celda 4b: Gráfica de barras\n", + "# Gráfica de barras 🤓\n", "plt.figure(figsize=(8, 4))\n", "conteo.plot(kind='bar', color='steelblue', edgecolor='black')\n", "\n", @@ -611,12 +590,7 @@ "\n", "plt.xticks(rotation=30, ha='right')\n", "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "\n", - "plt.xticks(rotation=30, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()" + "plt.show()\n" ] }, { @@ -659,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "code-05a", "metadata": {}, "outputs": [ @@ -672,10 +646,10 @@ } ], "source": [ - "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", + "# primera con enfoque ciclo for 🤓 \n", "tipo_objetivo = 'A7V'\n", "\n", - "# Celda 5a: Enfoque manual con ciclo for\n", + "\n", "tipo_objetivo = 'A7V'\n", "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", "\n", @@ -709,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "4j2wkkt78ju", "metadata": {}, "outputs": [ @@ -755,21 +729,18 @@ } ], "source": [ - "# Celda 5b: Enfoque vectorizado con pandas\n", + "# Enfoque vect 🤓\n", "temp_por_tipo = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False)\n", "\n", "print('\\nTemperatura promedio por clase espectral (K):')\n", "print(temp_por_tipo)\n", "print(f\"\\nVerificación para 'A7V': {temp_por_tipo['A7V'] == media_manual}\")\n", - "\n", - "\n", - "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", - "# tu código aquí" + "\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "code-05b", "metadata": {}, "outputs": [ @@ -843,7 +814,7 @@ ], "source": [ "plt.figure(figsize=(9, 5))\n", - "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", + "orden = temp_por_tipo.index # orden de mayor a menor temperatura 🤓\n", "# Celda 5c: Boxplot\n", "plt.figure(figsize=(9, 5))\n", "orden = temp_por_tipo.index\n", @@ -893,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "code-06", "metadata": {}, "outputs": [ @@ -931,7 +902,7 @@ "\n", "plt.figure(figsize=(9, 6))\n", "\n", - "# Crea el scatter plot\n", + "# Crea el scatter plot 🤓\n", "sns.scatterplot(data=stars, x='Temperature (K)', y='Luminosity (L/Lo)', \n", " hue='Spectral Class', style='Spectral Class', s=60)\n", "\n", @@ -981,7 +952,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "code-07a", "metadata": {}, "outputs": [ @@ -998,7 +969,7 @@ } ], "source": [ - "# Celda 7a: Extrae arrays y calcula estadísticas\n", + "# Extraigo arrays y calculo estadísticas 🤓\n", "temperaturas = stars['Temperature (K)'].values\n", "radios = stars['Radius (R/Ro)'].values\n", "\n", @@ -1012,7 +983,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "code-07b", "metadata": {}, "outputs": [ @@ -1039,7 +1010,7 @@ "source": [ "niveles = [25, 50, 75, 90]\n", "\n", - "# Celda 7b: Percentiles y conversión vectorizada\n", + "# Percentiles y conversión🤓\n", "niveles = [25, 50, 75, 90]\n", "p = np.percentile(radios, niveles)\n", "\n", @@ -1087,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "code-08", "metadata": {}, "outputs": [ @@ -1109,7 +1080,7 @@ "\n", "plt.figure(figsize=(10, 7))\n", "\n", - "# Itera y crea scatter plots individuales para la leyenda\n", + "\n", "for tipo, grupo in stars.groupby('Spectral Class'):\n", " plt.scatter(grupo['Temperature (K)'],\n", " grupo['Luminosity (L/Lo)'],\n", @@ -1119,7 +1090,7 @@ "plt.xscale('log')\n", "plt.yscale('log')\n", "\n", - "# Invertir eje X (crucial para Diagrama H-R)\n", + "# Invertir eje X 🤓\n", "plt.gca().invert_xaxis()\n", "\n", "plt.title('Diagrama Hertzsprung-Russell')\n", From 73647b69760d98f73d98526b471d2a2c946cd7b3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rub=C3=A9n=20samayoa?= Date: Wed, 29 Apr 2026 16:30:03 -0600 Subject: [PATCH 12/12] . --- analisis_estrellas_estudiante.ipynb | 150 ++++------------------------ 1 file changed, 19 insertions(+), 131 deletions(-) diff --git a/analisis_estrellas_estudiante.ipynb b/analisis_estrellas_estudiante.ipynb index b62e76b..11e32db 100644 --- a/analisis_estrellas_estudiante.ipynb +++ b/analisis_estrellas_estudiante.ipynb @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 85, "id": "code-01", "metadata": {}, "outputs": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 86, "id": "code-02", "metadata": {}, "outputs": [ @@ -277,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 87, "id": "code-03a", "metadata": {}, "outputs": [ @@ -312,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 88, "id": "code-03b", "metadata": {}, "outputs": [ @@ -428,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 89, "id": "code-03c", "metadata": {}, "outputs": [ @@ -480,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 90, "id": "code-04a", "metadata": {}, "outputs": [ @@ -488,36 +488,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Spectral Class\n", - "A7V 74\n", - "A1V 73\n", - "A9II 48\n", - "B1III 45\n", - "M3.5V 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", - "B0.5IV 30\n", - "F5IV-V 30\n", - "B6Vep 29\n", - "M2.1V 27\n", - "B7V 26\n", - "M1.5Iab 26\n", - "A2Ia 25\n", - "K1V 24\n", - "M6V 22\n", - "K5III 18\n", - "B8Ia 17\n", - "Name: count, dtype: int64\n", "Spectral Class\n", "A7V 74\n", "A1V 73\n", @@ -554,16 +524,12 @@ "source": [ "# Cuento las estrellas por tipo y guarda en 'conteo' 🤓\n", "conteo = stars['Spectral Class'].value_counts()\n", - "print(conteo)\n", - "\n", - "\n", - "\n", - "print(conteo)" + "print(conteo)\n" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 91, "id": "code-04b", "metadata": {}, "outputs": [ @@ -633,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "id": "code-05a", "metadata": {}, "outputs": [ @@ -647,8 +613,6 @@ ], "source": [ "# primera con enfoque ciclo for 🤓 \n", - "tipo_objetivo = 'A7V'\n", - "\n", "\n", "tipo_objetivo = 'A7V'\n", "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", @@ -683,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "id": "4j2wkkt78ju", "metadata": {}, "outputs": [ @@ -740,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "id": "code-05b", "metadata": {}, "outputs": [ @@ -762,60 +726,11 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(9, 5))\n", "orden = temp_por_tipo.index # orden de mayor a menor temperatura 🤓\n", - "# Celda 5c: Boxplot\n", "plt.figure(figsize=(9, 5))\n", "orden = temp_por_tipo.index\n", "\n", @@ -829,13 +744,8 @@ "plt.tight_layout()\n", "plt.show()\n", "\n", - "print('Temperatura promedio por clase espectral (K):')\n", - "print(temp_por_tipo)\n", - "print()\n", "\n", - "plt.xticks(rotation=20, ha='right')\n", - "plt.tight_layout()\n", - "plt.show()" + "\n" ] }, { @@ -864,19 +774,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "id": "code-06", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "image/png": 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", @@ -886,19 +787,9 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "plt.figure(figsize=(9, 6))\n", "\n", "plt.figure(figsize=(9, 6))\n", "\n", @@ -906,7 +797,7 @@ "sns.scatterplot(data=stars, x='Temperature (K)', y='Luminosity (L/Lo)', \n", " hue='Spectral Class', style='Spectral Class', s=60)\n", "\n", - "# Aplica escala logarítmica al eje Y\n", + "# Aplica escala logarítmica al eje Y🤓\n", "plt.yscale('log')\n", "\n", "plt.title('Luminosidad vs Temperatura (Escala Semi-Log)')\n", @@ -916,10 +807,7 @@ "\n", "plt.tight_layout()\n", "plt.show()\n", - "\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" + "\n" ] }, { @@ -952,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "id": "code-07a", "metadata": {}, "outputs": [ @@ -983,7 +871,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "id": "code-07b", "metadata": {}, "outputs": [ @@ -1058,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "id": "code-08", "metadata": {}, "outputs": [ @@ -1086,7 +974,7 @@ " grupo['Luminosity (L/Lo)'],\n", " label=tipo, color=mapa[tipo], s=40, alpha=0.8)\n", "\n", - "# Escalas logarítmicas\n", + "# Escalas logarítmicas🤓\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "\n",