studies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import datetime\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: ''",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mUntitled-1.ipynb Cell 2\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#W1sdW50aXRsZWQ%3D?line=14'>15</a>\u001b[0m respuesta \u001b[39m=\u001b[39m requests\u001b[39m.\u001b[39mget(url_solicitud)\n\u001b[0;32m     <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#W1sdW50aXRsZWQ%3D?line=16'>17</a>\u001b[0m \u001b[39m# Procesar los datos recibidos y convertirlos a un DataFrame de pandas\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#W1sdW50aXRsZWQ%3D?line=17'>18</a>\u001b[0m datos \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(respuesta\u001b[39m.\u001b[39;49mtext, sep\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m;\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m     <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#W1sdW50aXRsZWQ%3D?line=19'>20</a>\u001b[0m \u001b[39m# Agregar los datos de la comunidad actual al DataFrame de datos totales\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:Untitled-1.ipynb?jupyter-notebook#W1sdW50aXRsZWQ%3D?line=20'>21</a>\u001b[0m datos[\u001b[39m'\u001b[39m\u001b[39mcomunidad\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m comunidad\n",
      "File \u001b[1;32mc:\\Users\\gqsal\\miniconda3\\envs\\ironhack\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[39m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\gqsal\\miniconda3\\envs\\ironhack\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\gqsal\\miniconda3\\envs\\ironhack\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    946\u001b[0m     defaults\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mdelimiter\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m,\u001b[39m\u001b[39m\"\u001b[39m},\n\u001b[0;32m    947\u001b[0m )\n\u001b[0;32m    948\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[1;32mc:\\Users\\gqsal\\miniconda3\\envs\\ironhack\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    602\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[0;32m    604\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m    607\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[0;32m    608\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n",
      "File \u001b[1;32mc:\\Users\\gqsal\\miniconda3\\envs\\ironhack\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1439\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m   1441\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n",
      "File \u001b[1;32mc:\\Users\\gqsal\\miniconda3\\envs\\ironhack\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1733\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[0;32m   1734\u001b[0m         mode \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(\n\u001b[0;32m   1736\u001b[0m     f,\n\u001b[0;32m   1737\u001b[0m     mode,\n\u001b[0;32m   1738\u001b[0m     encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   1739\u001b[0m     compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   1740\u001b[0m     memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[0;32m   1741\u001b[0m     is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[0;32m   1742\u001b[0m     errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[0;32m   1743\u001b[0m     storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   1744\u001b[0m )\n\u001b[0;32m   1745\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n",
      "File \u001b[1;32mc:\\Users\\gqsal\\miniconda3\\envs\\ironhack\\lib\\site-packages\\pandas\\io\\common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    851\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[0;32m    852\u001b[0m     \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    853\u001b[0m     \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    854\u001b[0m     \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[0;32m    855\u001b[0m         \u001b[39m# Encoding\u001b[39;00m\n\u001b[1;32m--> 856\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[0;32m    857\u001b[0m             handle,\n\u001b[0;32m    858\u001b[0m             ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[0;32m    859\u001b[0m             encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[0;32m    860\u001b[0m             errors\u001b[39m=\u001b[39;49merrors,\n\u001b[0;32m    861\u001b[0m             newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    862\u001b[0m         )\n\u001b[0;32m    863\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    864\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m    865\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: ''"
     ]
    }
   ],
   "source": [
    "# Especificar los parámetros de la solicitud\n",
    "comunidades = '01'\n",
    "fecha_inicio = '200201'  # Año y mes de inicio (en este caso, enero de 2002)\n",
    "fecha_fin = '202112'  # Año y mes de fin (en este caso, diciembre de 2021)\n",
    "variables = 'P5017'  # Lista de variables separadas por comas\n",
    "\n",
    "# Construir la URL de la solicitud y hacer la solicitud HTTP para cada comunidad\n",
    "datos_totales = pd.DataFrame()\n",
    "for comunidad in comunidades:\n",
    "    # Construir la URL de la solicitud\n",
    "    url_base = 'https://servicios.ine.es/wstempus/js/ES/DATOS_SERIE/'\n",
    "    url_solicitud = f'{url_base}{variables}/{comunidad}/{fecha_inicio}/{fecha_fin}'\n",
    "\n",
    "    # Hacer la solicitud HTTP y recibir la respuesta\n",
    "    respuesta = requests.get(url_solicitud)\n",
    "\n",
    "    # Procesar los datos recibidos y convertirlos a un DataFrame de pandas\n",
    "    datos = pd.read_csv(respuesta.text, sep=';')\n",
    "\n",
    "    # Agregar los datos de la comunidad actual al DataFrame de datos totales\n",
    "    datos['comunidad'] = comunidad\n",
    "    datos_totales = pd.concat([datos_totales, datos], ignore_index=True)\n",
    "\n",
    "\n",
    "# Mostrar los primeros 10 registros de los datos totales\n",
    "print(datos_totales.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COD': 'EPA87',\n",
       " 'Nombre': 'Total Nacional. Ambos sexos. 16 y más años. Ocupados. Personas. ',\n",
       " 'FK_Unidad': 3,\n",
       " 'FK_Escala': 4,\n",
       " 'Notas': [{'texto': 'http://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176918&menu=ultiDatos&idp=1254735976595#~#http://www.ine.es/consul/serie.do?s=EPA87&nult=100&TB_iframe=true&position=center&width=700&height=500&L=0&nocab=1',\n",
       "   'Fk_TipoNota': 6,\n",
       "   'textoTipo': None}],\n",
       " 'Data': [{'Fecha': 1009839600000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 19,\n",
       "   'Anyo': 2002,\n",
       "   'Valor': 16482.3,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1017612000000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 20,\n",
       "   'Anyo': 2002,\n",
       "   'Valor': 16766.9,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1025474400000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 21,\n",
       "   'Anyo': 2002,\n",
       "   'Valor': 16919.3,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1033423200000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 22,\n",
       "   'Anyo': 2002,\n",
       "   'Valor': 16991.9,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1041375600000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 19,\n",
       "   'Anyo': 2003,\n",
       "   'Valor': 17092.7,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1049148000000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 20,\n",
       "   'Anyo': 2003,\n",
       "   'Valor': 17423.2,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1057010400000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 21,\n",
       "   'Anyo': 2003,\n",
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       "   'Secreto': False},\n",
       "  {'Fecha': 1064959200000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 22,\n",
       "   'Anyo': 2003,\n",
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       "   'Secreto': False},\n",
       "  {'Fecha': 1072911600000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 19,\n",
       "   'Anyo': 2004,\n",
       "   'Valor': 17770.2,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1080770400000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 20,\n",
       "   'Anyo': 2004,\n",
       "   'Valor': 18018.9,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1088632800000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 21,\n",
       "   'Anyo': 2004,\n",
       "   'Valor': 18289.1,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1096581600000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 22,\n",
       "   'Anyo': 2004,\n",
       "   'Valor': 18490.8,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1104534000000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 19,\n",
       "   'Anyo': 2005,\n",
       "   'Valor': 18736.1,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1112306400000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 20,\n",
       "   'Anyo': 2005,\n",
       "   'Valor': 19160.6,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1120168800000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 21,\n",
       "   'Anyo': 2005,\n",
       "   'Valor': 19422.1,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1128117600000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 22,\n",
       "   'Anyo': 2005,\n",
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       "   'Secreto': False},\n",
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       "   'Anyo': 2018,\n",
       "   'Valor': 19564.6,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1546297200000,\n",
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       "   'Anyo': 2019,\n",
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       "   'Secreto': False},\n",
       "  {'Fecha': 1554069600000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 20,\n",
       "   'Anyo': 2019,\n",
       "   'Valor': 19804.9,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1561932000000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 21,\n",
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       "   'Valor': 19874.3,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1569880800000,\n",
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       "   'Anyo': 2019,\n",
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       "   'Secreto': False},\n",
       "  {'Fecha': 1585692000000,\n",
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       "   'Secreto': False},\n",
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       "   'Secreto': False},\n",
       "  {'Fecha': 1617228000000,\n",
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       "   'FK_Periodo': 20,\n",
       "   'Anyo': 2021,\n",
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       "   'Secreto': False},\n",
       "  {'Fecha': 1625090400000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 21,\n",
       "   'Anyo': 2021,\n",
       "   'Valor': 20031.0,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1633039200000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 22,\n",
       "   'Anyo': 2021,\n",
       "   'Valor': 20184.9,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1640991600000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 19,\n",
       "   'Anyo': 2022,\n",
       "   'Valor': 20084.7,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1648764000000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 20,\n",
       "   'Anyo': 2022,\n",
       "   'Valor': 20468.0,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1656626400000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 21,\n",
       "   'Anyo': 2022,\n",
       "   'Valor': 20545.7,\n",
       "   'Secreto': False},\n",
       "  {'Fecha': 1664575200000,\n",
       "   'FK_TipoDato': 1,\n",
       "   'FK_Periodo': 22,\n",
       "   'Anyo': 2022,\n",
       "   'Valor': 20463.9,\n",
       "   'Secreto': False}]}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_plantilla = 'http://servicios.ine.es/wstempus/js/ES/DATOS_SERIE/{codigo}?nult={num_datos}'\n",
    "codigo = \"EPA87\"\n",
    "num_datos = 300\n",
    "\n",
    "url = url_plantilla.format(codigo=codigo, num_datos=num_datos)\n",
    "respuesta = requests.get(url)\n",
    "datos = respuesta.json()\n",
    "\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "edad=pd.read_csv(\"Edad.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comunidades y Ciudades Autónomas</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Periodo</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>2022</td>\n",
       "      <td>44,07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>2021</td>\n",
       "      <td>43,8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>2020</td>\n",
       "      <td>43,58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>2019</td>\n",
       "      <td>43,38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>2018</td>\n",
       "      <td>43,18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Comunidades y Ciudades Autónomas         Sexo  Periodo  Total\n",
       "0                   Total Nacional  Ambos sexos     2022  44,07\n",
       "1                   Total Nacional  Ambos sexos     2021   43,8\n",
       "2                   Total Nacional  Ambos sexos     2020  43,58\n",
       "3                   Total Nacional  Ambos sexos     2019  43,38\n",
       "4                   Total Nacional  Ambos sexos     2018  43,18"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edad.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012,\n",
       "       2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001,\n",
       "       2000, 1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990,\n",
       "       1989, 1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979,\n",
       "       1978, 1977, 1976, 1975], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edad.Periodo.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Comunidades y Ciudades Autónomas', 'Sexo', 'Periodo', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edad.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Total Nacional', '01 Andalucía', '02 Aragón',\n",
       "       '03 Asturias, Principado de', '04 Balears, Illes', '05 Canarias',\n",
       "       '06 Cantabria', '07 Castilla y León', '08 Castilla - La Mancha',\n",
       "       '09 Cataluña', '10 Comunitat Valenciana', '11 Extremadura',\n",
       "       '12 Galicia', '13 Madrid, Comunidad de', '14 Murcia, Región de',\n",
       "       '15 Navarra, Comunidad Foral de', '16 País Vasco', '17 Rioja, La',\n",
       "       '18 Ceuta', '19 Melilla'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edad['Comunidades y Ciudades Autónomas'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "married=pd.read_csv(\"Marriage.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Comunidades y Ciudades Autónomas', 'Periodo', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "married.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2021M12\n",
       "1        2021M11\n",
       "2        2021M10\n",
       "3        2021M09\n",
       "4        2021M08\n",
       "          ...   \n",
       "11839    1975M05\n",
       "11840    1975M04\n",
       "11841    1975M03\n",
       "11842    1975M02\n",
       "11843    1975M01\n",
       "Name: Periodo, Length: 11844, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "married.Periodo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Total', '01 Andalucía', '02 Aragón', '03 Asturias, Principado de',\n",
       "       '04 Balears, Illes', '05 Canarias', '06 Cantabria',\n",
       "       '07 Castilla y León', '08 Castilla - La Mancha', '09 Cataluña',\n",
       "       '10 Comunitat Valenciana', '11 Extremadura', '12 Galicia',\n",
       "       '13 Madrid, Comunidad de', '14 Murcia, Región de',\n",
       "       '15 Navarra, Comunidad Foral de', '16 País Vasco', '17 Rioja, La',\n",
       "       '18 Ceuta', '19 Melilla', 'No residente'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "married['Comunidades y Ciudades Autónomas'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "growth=pd.read_csv(\"PopGrowth.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Comunidades y Ciudades Autónomas', 'Periodo', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "growth.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2021\n",
       "1      2020\n",
       "2      2019\n",
       "3      2018\n",
       "4      2017\n",
       "       ... \n",
       "935    1979\n",
       "936    1978\n",
       "937    1977\n",
       "938    1976\n",
       "939    1975\n",
       "Name: Periodo, Length: 940, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "growth.Periodo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "born=pd.read_csv(\"NatalityRate.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Comunidades y Ciudades Autónomas', 'Nacionalidad', 'Periodo', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "born.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2021\n",
       "1       2020\n",
       "2       2019\n",
       "3       2018\n",
       "4       2017\n",
       "        ... \n",
       "1195    2006\n",
       "1196    2005\n",
       "1197    2004\n",
       "1198    2003\n",
       "1199    2002\n",
       "Name: Periodo, Length: 1200, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "born.Periodo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "extranjeros=pd.read_csv(\"ProporcionExtranjeros.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Comunidades y Ciudades Autónomas', 'Sexo', 'Edad', 'Periodo', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extranjeros.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012,\n",
       "       2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extranjeros.Periodo.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex=pd.read_csv(\"Proporción de población nacida en el extranjero por comunidad autónoma, según sexo, edad y nacionalidad (desde 2002).csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Comunidades y Ciudades Autónomas', 'Sexo', 'Edad', 'Nacionalidad',\n",
       "       'Periodo', 'Total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Ambas nacionalidades', 'Española', 'Extranjera'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex.Nacionalidad.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "man=pd.read_csv(\"RatioMasculinidad.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Comunidades y Ciudades Autónomas', 'Periodo', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2022\n",
       "1      2021\n",
       "2      2020\n",
       "3      2019\n",
       "4      2018\n",
       "       ... \n",
       "955    1979\n",
       "956    1978\n",
       "957    1977\n",
       "958    1976\n",
       "959    1975\n",
       "Name: Periodo, Length: 960, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man.Periodo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "bornmonth=pd.read_csv(\"Movimiento Natural de la Población Nacimientos.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Comunidades y Ciudades Autónomas', 'Periodo', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bornmonth.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total                             564\n",
       "11 Extremadura                    564\n",
       "19 Melilla                        564\n",
       "18 Ceuta                          564\n",
       "17 Rioja, La                      564\n",
       "16 País Vasco                     564\n",
       "15 Navarra, Comunidad Foral de    564\n",
       "14 Murcia, Región de              564\n",
       "13 Madrid, Comunidad de           564\n",
       "12 Galicia                        564\n",
       "10 Comunitat Valenciana           564\n",
       "01 Andalucía                      564\n",
       "09 Cataluña                       564\n",
       "08 Castilla - La Mancha           564\n",
       "07 Castilla y León                564\n",
       "06 Cantabria                      564\n",
       "05 Canarias                       564\n",
       "04 Balears, Illes                 564\n",
       "03 Asturias, Principado de        564\n",
       "02 Aragón                         564\n",
       "No residente                      564\n",
       "Name: Comunidades y Ciudades Autónomas, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bornmonth['Comunidades y Ciudades Autónomas'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2021M12\n",
       "1        2021M11\n",
       "2        2021M10\n",
       "3        2021M09\n",
       "4        2021M08\n",
       "          ...   \n",
       "11839    1975M05\n",
       "11840    1975M04\n",
       "11841    1975M03\n",
       "11842    1975M02\n",
       "11843    1975M01\n",
       "Name: Periodo, Length: 11844, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bornmonth.Periodo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        28.574\n",
       "1        28.452\n",
       "2        30.135\n",
       "3        30.229\n",
       "4        29.640\n",
       "          ...  \n",
       "11839    72.000\n",
       "11840    60.000\n",
       "11841    55.000\n",
       "11842    55.000\n",
       "11843    77.000\n",
       "Name: Total, Length: 11844, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bornmonth.Total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases=pd.read_csv(\"ccaa_covid19_casos_long.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fecha', 'cod_ine', 'CCAA', 'total'], dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Andalucía', 'Aragón', 'Asturias', 'Baleares', 'Canarias',\n",
       "       'Cantabria', 'Castilla La Mancha', 'Castilla y León', 'Cataluña',\n",
       "       'Ceuta', 'C. Valenciana', 'Extremadura', 'Galicia', 'Madrid',\n",
       "       'Melilla', 'Murcia', 'Navarra', 'País Vasco', 'La Rioja'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases.CCAA.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "deaths=pd.read_csv(\"ccaa_covid19_fallecidos_long.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fecha', 'cod_ine', 'CCAA', 'total'], dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deaths.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2022\n",
       "1      2021\n",
       "2      2020\n",
       "3      2019\n",
       "4      2018\n",
       "       ... \n",
       "955    1979\n",
       "956    1978\n",
       "957    1977\n",
       "958    1976\n",
       "959    1975\n",
       "Name: Periodo, Length: 960, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man.Periodo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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