diff --git a/assignments/assignment_4/Assignment_4.ipynb b/assignments/assignment_4/Assignment_4.ipynb
index a5461648..dd8ef3e7 100644
--- a/assignments/assignment_4/Assignment_4.ipynb
+++ b/assignments/assignment_4/Assignment_4.ipynb
@@ -613,7 +613,9 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -640,7 +642,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(20, 20))\n",
-    "dpt_shp.plot( ax = ax )"
+    "dpt_shp.plot( ax = ax )\n"
    ]
   },
   {
@@ -698,7 +700,7 @@
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -712,7 +714,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/assignments/assignment_4/group_4_ass_4_2024.ipynb b/assignments/assignment_4/group_4_ass_4_2024.ipynb
new file mode 100644
index 00000000..c05c3524
--- /dev/null
+++ b/assignments/assignment_4/group_4_ass_4_2024.ipynb
@@ -0,0 +1,491 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: folium in c:\\users\\antoni\\anaconda3\\lib\\site-packages (0.15.1)\n",
+      "Requirement already satisfied: branca>=0.6.0 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from folium) (0.7.0)\n",
+      "Requirement already satisfied: jinja2>=2.9 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from folium) (3.1.2)\n",
+      "Requirement already satisfied: numpy in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from folium) (1.24.3)\n",
+      "Requirement already satisfied: requests in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from folium) (2.31.0)\n",
+      "Requirement already satisfied: xyzservices in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from folium) (2022.9.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from jinja2>=2.9->folium) (2.1.1)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from requests->folium) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from requests->folium) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from requests->folium) (1.26.16)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from requests->folium) (2023.11.17)\n",
+      "Requirement already satisfied: geopandas in c:\\users\\antoni\\anaconda3\\lib\\site-packages (0.14.2)\n",
+      "Requirement already satisfied: fiona>=1.8.21 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from geopandas) (1.9.5)\n",
+      "Requirement already satisfied: packaging in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from geopandas) (23.1)\n",
+      "Requirement already satisfied: pandas>=1.4.0 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from geopandas) (2.0.3)\n",
+      "Requirement already satisfied: pyproj>=3.3.0 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from geopandas) (3.6.1)\n",
+      "Requirement already satisfied: shapely>=1.8.0 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from geopandas) (2.0.2)\n",
+      "Requirement already satisfied: attrs>=19.2.0 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (22.1.0)\n",
+      "Requirement already satisfied: certifi in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (2023.11.17)\n",
+      "Requirement already satisfied: click~=8.0 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (8.0.4)\n",
+      "Requirement already satisfied: click-plugins>=1.0 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (1.1.1)\n",
+      "Requirement already satisfied: cligj>=0.5 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (0.7.2)\n",
+      "Requirement already satisfied: six in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (1.16.0)\n",
+      "Requirement already satisfied: setuptools in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from fiona>=1.8.21->geopandas) (68.0.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2023.3.post1)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (2023.3)\n",
+      "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from pandas>=1.4.0->geopandas) (1.24.3)\n",
+      "Requirement already satisfied: colorama in c:\\users\\antoni\\anaconda3\\lib\\site-packages (from click~=8.0->fiona>=1.8.21->geopandas) (0.4.6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "! pip install folium\n",
+    "! pip install geopandas\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#cargando las bibliotecas\n",
+    "import pandas as pd\n",
+    "#from pandas import Series, DataFrame\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt \n",
+    "import chardet\n",
+    "\n",
+    "import folium as fm\n",
+    "from folium import Marker, GeoJson\n",
+    "from folium.plugins import MarkerCluster, HeatMap, StripePattern\n",
+    "\n",
+    "import geopandas as gpd\n",
+    "from geopandas import GeoSeries\n",
+    "from shapely.geometry import Point, LineString\n",
+    "\n",
+    "\n",
+    "import branca.colormap as br "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    div#notebook-container    { width: 95%; }\n",
+       "    div#menubar-container     { width: 65%; }\n",
+       "    div#maintoolbar-container { width: 99%; }a\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "display(HTML(data=\"\"\"\n",
+    "<style>\n",
+    "    div#notebook-container    { width: 95%; }\n",
+    "    div#menubar-container     { width: 65%; }\n",
+    "    div#maintoolbar-container { width: 99%; }a\n",
+    "</style>\n",
+    "\"\"\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'c:\\\\Users\\\\Antoni\\\\Documents\\\\GitHub\\\\QLAB_Summer_Python\\\\assignments\\\\assignment_4'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get data of labor\n",
+    "sex_work = pd.read_stata(r'../../../QLAB_Summer_Python/_data/8_trab_sex_20.dta' )\n",
+    "sex_work[ 'dpt_code' ] = sex_work.cod_ubigeo.str[ :2 ].copy()\n",
+    "sex_work[ 'prov_code' ] = sex_work.cod_ubigeo.str[ :4 ].copy()\n",
+    "\n",
+    "# Sex work\n",
+    "women_work = sex_work[ sex_work.sex == 'Mujer' ].copy().reset_index( drop = True )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get data from lima\n",
+    "women_work[ 'month' ] = pd.to_datetime( women_work.month , format = '%B' ) \\\n",
+    "                                    .dt.strftime( '%m' ) \\\n",
+    "                                    .astype( int )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sort by department and month\n",
+    "women_work.sort_values([ 'dpt_code', 'month'], inplace = True )\n",
+    "\n",
+    "# Get the total number of women workers by dpt\n",
+    "dpt_women_work = women_work.groupby( [ 'dpt_code', 'month'], as_index = False )[['empl']] \\\n",
+    "                            .sum() \\\n",
+    "                            .rename( columns = {'empl' :'women_empl'})\n",
+    "\n",
+    "# Sort by dpt code and month\n",
+    "dpt_women_work.sort_values([ 'dpt_code', 'month'], inplace = True )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = dpt_women_work.groupby( ['dpt_code'],as_index = False )[['women_empl']].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#SHAPEFILE\n",
+    "dpt_shp = gpd.read_file(r'../../../QLAB_Summer_Python/_data/INEI_LIMITE_DEPARTAMENTAL/INEI_LIMITE_DEPARTAMENTAL.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df3 = dpt_shp.merge( df2, left_on = 'CCDD', right_on = 'dpt_code'  )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'fm' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 7\u001b[0m\n\u001b[0;32m      5\u001b[0m zoom_start \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m#Then we use this information to locate Peru\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m map1 \u001b[38;5;241m=\u001b[39m fm\u001b[38;5;241m.\u001b[39mMap(location \u001b[38;5;241m=\u001b[39m [lat_palacio, long_palacio], tiles\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcartodbpositron\u001b[39m\u001b[38;5;124m'\u001b[39m, zoom_start \u001b[38;5;241m=\u001b[39m zoom_start)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m#Finally we specify the geographical separation by department and the information that will be used to distinguish according to colors.\u001b[39;00m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m#Because there is no color scheme in follium that goes from white to red, we use the one that goes from yellow to red\u001b[39;00m\n\u001b[0;32m     10\u001b[0m fm\u001b[38;5;241m.\u001b[39mChoropleth( \n\u001b[0;32m     11\u001b[0m     geo_data\u001b[38;5;241m=\u001b[39mdf3,\n\u001b[0;32m     12\u001b[0m     data\u001b[38;5;241m=\u001b[39mdf3,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     23\u001b[0m     nan_fill_color \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhite\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     24\u001b[0m     )\u001b[38;5;241m.\u001b[39madd_to(map1)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'fm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "#Map 1\n",
+    "#First we put Lima as the center of the map,using lat and long\n",
+    "lat_palacio = -12.0757538\n",
+    "long_palacio = -76.9863174\n",
+    "zoom_start = 5\n",
+    "#Then we use this information to locate Peru\n",
+    "map1 = fm.Map(location = [lat_palacio, long_palacio], tiles='cartodbpositron', zoom_start = zoom_start)\n",
+    "#Finally we specify the geographical separation by department and the information that will be used to distinguish according to colors.\n",
+    "#Because there is no color scheme in follium that goes from white to red, we use the one that goes from yellow to red\n",
+    "fm.Choropleth( \n",
+    "    geo_data=df3,\n",
+    "    data=df3,\n",
+    "    columns=[\"dpt_code\",\"women_empl\"],\n",
+    "    key_on=\"feature.properties.dpt_code\",\n",
+    "    fill_color=\"YlOrRd\",\n",
+    "    fill_opacity=0.8,\n",
+    "    line_opacity=0.8,\n",
+    "    smooth_factor=0.2,\n",
+    "    legend_name='Women employers',\n",
+    "    Highlight= True,\n",
+    "    line_color = \"#000000\",\n",
+    "    overlay=True,\n",
+    "    nan_fill_color = \"White\"\n",
+    "    ).add_to(map1)\n",
+    "map1\n",
+    "\n",
+    "## MAP 1\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'fm' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m long_palacio \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m76.9863174\u001b[39m\n\u001b[0;32m      4\u001b[0m zoom_start \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m\n\u001b[1;32m----> 5\u001b[0m map2 \u001b[38;5;241m=\u001b[39m fm\u001b[38;5;241m.\u001b[39mMap(location \u001b[38;5;241m=\u001b[39m [lat_palacio, long_palacio], tiles\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcartodbpositron\u001b[39m\u001b[38;5;124m'\u001b[39m, zoom_start \u001b[38;5;241m=\u001b[39m zoom_start)\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m#In this case we have to create bins to separate the colors by ranges\u001b[39;00m\n\u001b[0;32m      7\u001b[0m bins \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m675.33\u001b[39m, \u001b[38;5;241m20000\u001b[39m, \u001b[38;5;241m40000\u001b[39m, \u001b[38;5;241m60000\u001b[39m, \u001b[38;5;241m100000\u001b[39m, \u001b[38;5;241m656477.25\u001b[39m]  \n",
+      "\u001b[1;31mNameError\u001b[0m: name 'fm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "##Map2\n",
+    "lat_palacio = -12.0757538\n",
+    "long_palacio = -76.9863174\n",
+    "zoom_start = 5\n",
+    "map2 = fm.Map(location = [lat_palacio, long_palacio], tiles='cartodbpositron', zoom_start = zoom_start)\n",
+    "#In this case we have to create bins to separate the colors by ranges\n",
+    "bins = [675.33, 20000, 40000, 60000, 100000, 656477.25]  \n",
+    "#Finally we specify the geographical separation by department and the information that will be used to distinguish according to colors.\n",
+    "fm.Choropleth(\n",
+    "    geo_data=df3,\n",
+    "    data=df3,\n",
+    "    columns=[\"dpt_code\", \"women_empl\"],\n",
+    "    key_on=\"feature.properties.dpt_code\",\n",
+    "    fill_color='YlOrRd',\n",
+    "    fill_opacity=0.8,\n",
+    "    line_opacity=0.8,\n",
+    "    smooth_factor=0.2,\n",
+    "    legend_name='Women employers',\n",
+    "    line_color=\"#000000\",\n",
+    "    bins=bins,  # Specify the bins\n",
+    "    nan_fill_color=\"White\",\n",
+    "    highlight=True,\n",
+    "    overlay=True,\n",
+    "    threshold_scale=bins,  # We adjust the range\n",
+    ").add_to(map2)\n",
+    "\n",
+    "map2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df3' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#mapa 3\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m#mapa 3\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m df3_1\u001b[38;5;241m=\u001b[39mdf3[ df3\u001b[38;5;241m.\u001b[39mCCDD \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m15\u001b[39m\u001b[38;5;124m\"\u001b[39m ]\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m#seteamos el palacio de gobierno\u001b[39;00m\n\u001b[0;32m      5\u001b[0m lat_palacio \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m12.0757538\u001b[39m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'df3' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "#mapa 3\n",
+    "#mapa 3\n",
+    "df3_1=df3[ df3.CCDD != \"15\" ]\n",
+    "#seteamos el palacio de gobierno\n",
+    "lat_palacio = -12.0757538\n",
+    "long_palacio = -76.9863174\n",
+    "zoom_start = 5\n",
+    "map3 = fm.Map(location = [lat_palacio, long_palacio], tiles='cartodbpositron', zoom_start = zoom_start)\n",
+    "#In this case we have to create bins to separate the colors by ranges\n",
+    "bins = [675.33, 20000, 40000, 60000, 100000, 656477.25]  \n",
+    "#Finally we specify the geographical separation by department and the information that will be used to distinguish according to colors.\n",
+    "fm.Choropleth(\n",
+    "    geo_data=df3_1,\n",
+    "    data=df3_1,\n",
+    "    columns=[\"dpt_code\", \"women_empl\"],\n",
+    "    key_on=\"feature.properties.dpt_code\",\n",
+    "    fill_color='YlOrRd',\n",
+    "    fill_opacity=0.8,\n",
+    "    line_opacity=0.8,\n",
+    "    smooth_factor=0.2,\n",
+    "    legend_name='Women employers',\n",
+    "    line_color=\"#000000\",\n",
+    "    bins=bins,  # Specify the bins\n",
+    "    nan_fill_color=\"White\",\n",
+    "    highlight=True,\n",
+    "    overlay=True,\n",
+    "    threshold_scale=bins,  # We adjust the range\n",
+    ").add_to(map3)\n",
+    "\n",
+    "map3\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#mapa 4\n",
+    "#generamos la nueva variable en un data frame nuevo:\n",
+    "df4 = sex_work.groupby( ['dpt_code', 'month', 'sex'], as_index = False )[['empl']].sum() \\\n",
+    "        .pivot( index = [ 'dpt_code', 'month' ] , columns = 'sex',values='empl') \\\n",
+    "        .reset_index()\n",
+    "\n",
+    "df4[ 'prop_wom' ] = ( df4.Mujer * 100 / df4.Hombre )\n",
+    "df5 = dpt_shp.merge( df4, left_on = 'CCDD', right_on = 'dpt_code'  )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MAP 4\n",
+    "\n",
+    "La creación del mapa 4 vuelve al archivo muy pesado. Por eso la siguiente parte del codigo está comentado. Quitar el comentario y correr en el propio ordenador para poder observar el mapa y se creará un archivo html con el mapa también.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' # Lista de meses\\nmeses = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\\n# Coordenadas para centrar el mapa (puedes ajustarlas según tus necesidades)\\nlat_palacio = -12.0757538\\nlong_palacio = -76.9863174\\n#zoom start\\nzoom_start = 5\\n# Crear el mapa inicial\\nmap3 = fm.Map(location=[lat_palacio, long_palacio], tiles=\\'cartodbpositron\\', zoom_start=zoom_start)\\n\\n\\n# Iterar sobre cada mes\\nfor a in meses:\\n    # Seleccionar el DataFrame correspondiente al mes\\n    df_month = df5.loc[df5[\\'month\\']==a]\\n    \\n    # Crear la capa Choropleth para el mes actual\\n    fm.Choropleth(\\n        geo_data=df_month,\\n        data=df_month,\\n        columns= [\"dpt_code\", \"prop_wom\"],\\n        key_on= \"feature.properties.dpt_code\",\\n        fill_opacity= 0.8,\\n        line_opacity= 0.8,\\n        smooth_factor= 0.2,\\n        line_color= \"#000000\",\\n        overlay= True,\\n        nan_fill_color= \"White\",\\n        fill_color=\"YlOrRd\",\\n        legend_name= \\'Women employers\\',\\n        name=a,\\n        Highlight=True,\\n        show=False,\\n    ).add_to(map3)\\n\\n# Añadir el control de capas\\nfm.LayerControl(collapsed=False, autoZIndex=False).add_to(map3)\\n\\n# Mostrar el mapa\\nmap3\\n\\nmap3.save(\"mapa4.html\") '"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\" # Lista de meses\n",
+    "meses = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n",
+    "# Coordenadas para centrar el mapa (puedes ajustarlas según tus necesidades)\n",
+    "lat_palacio = -12.0757538\n",
+    "long_palacio = -76.9863174\n",
+    "#zoom start\n",
+    "zoom_start = 5\n",
+    "# Crear el mapa inicial\n",
+    "map3 = fm.Map(location=[lat_palacio, long_palacio], tiles='cartodbpositron', zoom_start=zoom_start)\n",
+    "\n",
+    "\n",
+    "# Iterar sobre cada mes\n",
+    "for a in meses:\n",
+    "    # Seleccionar el DataFrame correspondiente al mes\n",
+    "    df_month = df5.loc[df5['month']==a]\n",
+    "    \n",
+    "    # Crear la capa Choropleth para el mes actual\n",
+    "    fm.Choropleth(\n",
+    "        geo_data=df_month,\n",
+    "        data=df_month,\n",
+    "        columns= [\"dpt_code\", \"prop_wom\"],\n",
+    "        key_on= \"feature.properties.dpt_code\",\n",
+    "        fill_opacity= 0.8,\n",
+    "        line_opacity= 0.8,\n",
+    "        smooth_factor= 0.2,\n",
+    "        line_color= \"#000000\",\n",
+    "        overlay= True,\n",
+    "        nan_fill_color= \"White\",\n",
+    "        fill_color=\"YlOrRd\",\n",
+    "        legend_name= 'Women employers',\n",
+    "        name=a,\n",
+    "        Highlight=True,\n",
+    "        show=False,\n",
+    "    ).add_to(map3)\n",
+    "\n",
+    "# Añadir el control de capas\n",
+    "fm.LayerControl(collapsed=False, autoZIndex=False).add_to(map3)\n",
+    "\n",
+    "# Mostrar el mapa\n",
+    "map3\n",
+    "\n",
+    "map3.save(\"mapa4.html\") \"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#mapa 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#mapa 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#mapa 7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#mapa 8"
+   ]
+  }
+ ],
+ "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}