AnualStudio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "Edad=pd.read_csv(\"Edad.csv\", sep=';', decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>43.80</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  43.80\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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Edad.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "toted=Edad[Edad['Comunidades y Ciudades Autónomas']=='Total Nacional']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "totedh=toted[toted['Sexo']=='Hombres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "totedm=toted[toted['Sexo']=='Mujeres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "natality=pd.read_csv(\"NatalityRate.csv\", sep=';', decimal='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "nattot=natality[natality['Comunidades y Ciudades Autónomas']=='Total Nacional']\n",
    "nattot=nattot[nattot['Nacionalidad']=='Ambas nacionalidades']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comunidades y Ciudades Autónomas    object\n",
       "Nacionalidad                        object\n",
       "Periodo                              int64\n",
       "Total                               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nattot.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "growth=pd.read_csv(\"PopGrowth.csv\", sep=';', decimal='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "grtot=growth[growth['Comunidades y Ciudades Autónomas']=='Total Nacional']\n",
    "grtot=grtot[grtot['Periodo']>2001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "extranjeros=pd.read_csv(\"ProporcionExtranjeros.csv\", sep=';', decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "extranjeros=extranjeros[extranjeros['Periodo']<2022]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "extot=extranjeros[extranjeros['Comunidades y Ciudades Autónomas']=='Total Nacional']\n",
    "extot=extot[extot['Edad']=='Total edades']\n",
    "extot=extot[extot['Sexo']=='Ambos sexos']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "extotm=extranjeros[extranjeros['Comunidades y Ciudades Autónomas']=='Total Nacional']\n",
    "extotm=extotm[extotm['Edad']=='Total edades']\n",
    "extotm=extotm[extotm['Sexo']=='Mujeres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "extoth=extranjeros[extranjeros['Comunidades y Ciudades Autónomas']=='Total Nacional']\n",
    "extoth=extoth[extoth['Edad']=='Total edades']\n",
    "extoth=extoth[extoth['Sexo']=='Hombres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "extranjeros=pd.read_csv(\"ProporcionExtranjeros.csv\", sep=';', decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "exfertil=extranjeros.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "exfertil=exfertil[exfertil['Comunidades y Ciudades Autónomas']=='Total Nacional']\n",
    "exfertil=exfertil[exfertil['Sexo']=='Mujeres']\n",
    "exfertil = exfertil[exfertil['Edad'] != 'Total edades']\n",
    "exfertil['Edad']=exfertil.Edad.str.replace(' años', '').str.replace(' y más', '').str.replace(' año', '')\n",
    "exfertil['Edad'] = exfertil['Edad'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "exfertil = exfertil[(exfertil['Edad'] >= 18) & (exfertil['Edad'] <= 35)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "exfertil['Total'] = exfertil['Total'].str.replace(',', '.').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "exfertil=exfertil.groupby('Periodo')['Total'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>Edad</th>\n",
       "      <th>Periodo</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>18</td>\n",
       "      <td>2021</td>\n",
       "      <td>9.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>18</td>\n",
       "      <td>2020</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>18</td>\n",
       "      <td>2019</td>\n",
       "      <td>9.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>18</td>\n",
       "      <td>2018</td>\n",
       "      <td>9.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4688</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>18</td>\n",
       "      <td>2017</td>\n",
       "      <td>9.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5056</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>35</td>\n",
       "      <td>2006</td>\n",
       "      <td>12.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5057</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>35</td>\n",
       "      <td>2005</td>\n",
       "      <td>10.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5058</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>35</td>\n",
       "      <td>2004</td>\n",
       "      <td>9.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5059</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>35</td>\n",
       "      <td>2003</td>\n",
       "      <td>7.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5060</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Mujeres</td>\n",
       "      <td>35</td>\n",
       "      <td>2002</td>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Comunidades y Ciudades Autónomas     Sexo  Edad  Periodo  Total\n",
       "4684                   Total Nacional  Mujeres    18     2021   9.34\n",
       "4685                   Total Nacional  Mujeres    18     2020   9.67\n",
       "4686                   Total Nacional  Mujeres    18     2019   9.42\n",
       "4687                   Total Nacional  Mujeres    18     2018   9.42\n",
       "4688                   Total Nacional  Mujeres    18     2017   9.24\n",
       "...                               ...      ...   ...      ...    ...\n",
       "5056                   Total Nacional  Mujeres    35     2006  12.35\n",
       "5057                   Total Nacional  Mujeres    35     2005  10.93\n",
       "5058                   Total Nacional  Mujeres    35     2004   9.49\n",
       "5059                   Total Nacional  Mujeres    35     2003   7.62\n",
       "5060                   Total Nacional  Mujeres    35     2002   5.97\n",
       "\n",
       "[360 rows x 5 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exfertil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "masculinity=pd.read_csv(\"RatioMasculinidad.csv\", sep=';', decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masculinity = masculinity[(masculinity['Periodo'] >= 2002) & (masculinity['Periodo'] <= 2021)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "totmasc=masculinity[masculinity['Comunidades y Ciudades Autónomas']=='Total Nacional']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "married=pd.read_csv(\"Marriage.csv\", sep=';', decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gqsal\\AppData\\Local\\Temp\\ipykernel_10108\\3774494974.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  married.Total=married.Total.str.replace('.', '')\n"
     ]
    }
   ],
   "source": [
    "married.Total=married.Total.str.replace('.', '')\n",
    "married.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "married.Total=married.Total.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "married['Periodo'] = married['Periodo'].str[:-3]\n",
    "married['Periodo'] = married['Periodo'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "married = married.groupby(['Comunidades y Ciudades Autónomas', 'Periodo'])['Total'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "married = married[(married['Periodo'] >= 2002) & (married['Periodo'] <= 2021)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "martot=married[married['Comunidades y Ciudades Autónomas']=='Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "paro=pd.read_csv(\"Tasa de paro trimestral.csv\", sep=';', decimal='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "paro['Periodo'] = paro['Periodo'].str[:-2]\n",
    "paro['Periodo'] = paro['Periodo'].astype(int)\n",
    "paro = paro[(paro['Periodo'] >= 2002) & (paro['Periodo'] <= 2021)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Comunidades y Ciudades Autónomas</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Periodo</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Total</td>\n",
       "      <td>2021</td>\n",
       "      <td>13,33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Total</td>\n",
       "      <td>2021</td>\n",
       "      <td>14,57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Total</td>\n",
       "      <td>2021</td>\n",
       "      <td>15,26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Total</td>\n",
       "      <td>2021</td>\n",
       "      <td>15,98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ambos sexos</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>16,13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sexo Comunidades y Ciudades Autónomas   Edad  Periodo  Total\n",
       "4  Ambos sexos                   Total Nacional  Total     2021  13,33\n",
       "5  Ambos sexos                   Total Nacional  Total     2021  14,57\n",
       "6  Ambos sexos                   Total Nacional  Total     2021  15,26\n",
       "7  Ambos sexos                   Total Nacional  Total     2021  15,98\n",
       "8  Ambos sexos                   Total Nacional  Total     2020  16,13"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "parotot=paro[paro['Comunidades y Ciudades Autónomas']=='Total Nacional']\n",
    "parotot=parotot[parotot['Sexo']=='Ambos sexos']\n",
    "parotot=parotot[parotot['Edad']=='Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "parotot['Total'] = parotot['Total'].str.replace(',', '.').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "parotot = parotot.groupby(['Comunidades y Ciudades Autónomas', 'Periodo'])['Total'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "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>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>2002</td>\n",
       "      <td>11.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2003</td>\n",
       "      <td>11.4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2004</td>\n",
       "      <td>10.9650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2005</td>\n",
       "      <td>9.1525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2006</td>\n",
       "      <td>8.4525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2007</td>\n",
       "      <td>8.2325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2008</td>\n",
       "      <td>11.2450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2009</td>\n",
       "      <td>17.8550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2010</td>\n",
       "      <td>19.8575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2011</td>\n",
       "      <td>21.3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2012</td>\n",
       "      <td>24.7875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2013</td>\n",
       "      <td>26.0950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2014</td>\n",
       "      <td>24.4425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2015</td>\n",
       "      <td>22.0575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2016</td>\n",
       "      <td>19.6350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2017</td>\n",
       "      <td>17.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2018</td>\n",
       "      <td>15.2550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2019</td>\n",
       "      <td>14.1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2020</td>\n",
       "      <td>15.5325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2021</td>\n",
       "      <td>14.7850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Comunidades y Ciudades Autónomas  Periodo    Total\n",
       "0                    Total Nacional     2002  11.4500\n",
       "1                    Total Nacional     2003  11.4850\n",
       "2                    Total Nacional     2004  10.9650\n",
       "3                    Total Nacional     2005   9.1525\n",
       "4                    Total Nacional     2006   8.4525\n",
       "5                    Total Nacional     2007   8.2325\n",
       "6                    Total Nacional     2008  11.2450\n",
       "7                    Total Nacional     2009  17.8550\n",
       "8                    Total Nacional     2010  19.8575\n",
       "9                    Total Nacional     2011  21.3900\n",
       "10                   Total Nacional     2012  24.7875\n",
       "11                   Total Nacional     2013  26.0950\n",
       "12                   Total Nacional     2014  24.4425\n",
       "13                   Total Nacional     2015  22.0575\n",
       "14                   Total Nacional     2016  19.6350\n",
       "15                   Total Nacional     2017  17.2250\n",
       "16                   Total Nacional     2018  15.2550\n",
       "17                   Total Nacional     2019  14.1050\n",
       "18                   Total Nacional     2020  15.5325\n",
       "19                   Total Nacional     2021  14.7850"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parotot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "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": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paro['Comunidades y Ciudades Autónomas'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sexo', 'Comunidades y Ciudades Autónomas', 'Edad', 'Periodo', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paro.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [],
   "source": [
    "paradas=paro.copy()\n",
    "paradas.dropna(inplace=True)\n",
    "paradas=paradas[paradas['Sexo']=='Mujeres']\n",
    "paradas=paradas[paradas['Edad']!='Total']\n",
    "paradas=paradas[paradas['Edad']!='55 y más años']\n",
    "paradas=paradas[paradas['Comunidades y Ciudades Autónomas']!='19 Melilla']\n",
    "paradas=paradas[paradas['Comunidades y Ciudades Autónomas']!='18 Ceuta']\n",
    "paradas=paradas[paradas['Total']!=\"..\"]\n",
    "paradas['Total'] = paradas['Total'].str.replace(',', '.').astype(float)\n",
    "paradas = paradas.groupby(['Comunidades y Ciudades Autónomas', 'Periodo'])['Total'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "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>Periodo</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>13 Madrid, Comunidad de</td>\n",
       "      <td>2017</td>\n",
       "      <td>28.7420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>12 Galicia</td>\n",
       "      <td>2006</td>\n",
       "      <td>19.6190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>11 Extremadura</td>\n",
       "      <td>2005</td>\n",
       "      <td>34.6165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>17 Rioja, La</td>\n",
       "      <td>2009</td>\n",
       "      <td>27.3050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>04 Balears, Illes</td>\n",
       "      <td>2002</td>\n",
       "      <td>16.1135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>10 Comunitat Valenciana</td>\n",
       "      <td>2005</td>\n",
       "      <td>19.4380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>04 Balears, Illes</td>\n",
       "      <td>2021</td>\n",
       "      <td>27.1805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>02 Aragón</td>\n",
       "      <td>2009</td>\n",
       "      <td>23.1445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>12 Galicia</td>\n",
       "      <td>2014</td>\n",
       "      <td>41.1195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>03 Asturias, Principado de</td>\n",
       "      <td>2015</td>\n",
       "      <td>32.7530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>13 Madrid, Comunidad de</td>\n",
       "      <td>2008</td>\n",
       "      <td>18.8630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>07 Castilla y León</td>\n",
       "      <td>2012</td>\n",
       "      <td>44.0740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>17 Rioja, La</td>\n",
       "      <td>2015</td>\n",
       "      <td>33.1095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>13 Madrid, Comunidad de</td>\n",
       "      <td>2007</td>\n",
       "      <td>17.2210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>08 Castilla - La Mancha</td>\n",
       "      <td>2017</td>\n",
       "      <td>43.1220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>04 Balears, Illes</td>\n",
       "      <td>2004</td>\n",
       "      <td>16.6255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>02 Aragón</td>\n",
       "      <td>2004</td>\n",
       "      <td>16.2060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>16 País Vasco</td>\n",
       "      <td>2009</td>\n",
       "      <td>23.8490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>12 Galicia</td>\n",
       "      <td>2018</td>\n",
       "      <td>31.4050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>06 Cantabria</td>\n",
       "      <td>2005</td>\n",
       "      <td>19.9925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Comunidades y Ciudades Autónomas  Periodo    Total\n",
       "255          13 Madrid, Comunidad de     2017  28.7420\n",
       "224                       12 Galicia     2006  19.6190\n",
       "203                   11 Extremadura     2005  34.6165\n",
       "327                     17 Rioja, La     2009  27.3050\n",
       "60                 04 Balears, Illes     2002  16.1135\n",
       "183          10 Comunitat Valenciana     2005  19.4380\n",
       "79                 04 Balears, Illes     2021  27.1805\n",
       "27                         02 Aragón     2009  23.1445\n",
       "232                       12 Galicia     2014  41.1195\n",
       "53        03 Asturias, Principado de     2015  32.7530\n",
       "246          13 Madrid, Comunidad de     2008  18.8630\n",
       "130               07 Castilla y León     2012  44.0740\n",
       "333                     17 Rioja, La     2015  33.1095\n",
       "245          13 Madrid, Comunidad de     2007  17.2210\n",
       "155          08 Castilla - La Mancha     2017  43.1220\n",
       "62                 04 Balears, Illes     2004  16.6255\n",
       "22                         02 Aragón     2004  16.2060\n",
       "307                    16 País Vasco     2009  23.8490\n",
       "236                       12 Galicia     2018  31.4050\n",
       "103                     06 Cantabria     2005  19.9925"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paradas.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "parofertil=paro.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Total', 'Menores de 25 años', '25 y más años', 'De 16 a 19 años',\n",
       "       'De 20 a 24 años', 'De 25 a 54 años', '55 y más años'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parofertil.Edad.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "parofertil=parofertil[parofertil['Comunidades y Ciudades Autónomas']=='Total Nacional']\n",
    "parofertil=parofertil[parofertil['Sexo']=='Mujeres']\n",
    "parofertil=parofertil[parofertil['Edad']!='Total']\n",
    "parofertil=parofertil[parofertil['Edad']!='55 y más años']\n",
    "parofertil['Total'] = parofertil['Total'].str.replace(',', '.').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "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>Sexo</th>\n",
       "      <th>Comunidades y Ciudades Autónomas</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Periodo</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23608</th>\n",
       "      <td>Mujeres</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Menores de 25 años</td>\n",
       "      <td>2021</td>\n",
       "      <td>30.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23609</th>\n",
       "      <td>Mujeres</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Menores de 25 años</td>\n",
       "      <td>2021</td>\n",
       "      <td>31.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23610</th>\n",
       "      <td>Mujeres</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Menores de 25 años</td>\n",
       "      <td>2021</td>\n",
       "      <td>39.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23611</th>\n",
       "      <td>Mujeres</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Menores de 25 años</td>\n",
       "      <td>2021</td>\n",
       "      <td>41.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23612</th>\n",
       "      <td>Mujeres</td>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Menores de 25 años</td>\n",
       "      <td>2020</td>\n",
       "      <td>41.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sexo Comunidades y Ciudades Autónomas                Edad  Periodo  \\\n",
       "23608  Mujeres                   Total Nacional  Menores de 25 años     2021   \n",
       "23609  Mujeres                   Total Nacional  Menores de 25 años     2021   \n",
       "23610  Mujeres                   Total Nacional  Menores de 25 años     2021   \n",
       "23611  Mujeres                   Total Nacional  Menores de 25 años     2021   \n",
       "23612  Mujeres                   Total Nacional  Menores de 25 años     2020   \n",
       "\n",
       "       Total  \n",
       "23608  30.78  \n",
       "23609  31.26  \n",
       "23610  39.90  \n",
       "23611  41.18  \n",
       "23612  41.63  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parofertil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "parofertil = parofertil.groupby(['Comunidades y Ciudades Autónomas', 'Periodo'])['Total'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>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>2002</td>\n",
       "      <td>23.7370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2003</td>\n",
       "      <td>24.1375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2004</td>\n",
       "      <td>22.9490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2005</td>\n",
       "      <td>20.2465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2006</td>\n",
       "      <td>19.2165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2007</td>\n",
       "      <td>18.9225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2008</td>\n",
       "      <td>22.6520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2009</td>\n",
       "      <td>31.3465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2010</td>\n",
       "      <td>34.9180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2011</td>\n",
       "      <td>37.6790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2012</td>\n",
       "      <td>43.6905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2013</td>\n",
       "      <td>46.2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2014</td>\n",
       "      <td>44.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2015</td>\n",
       "      <td>41.3930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2016</td>\n",
       "      <td>37.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2017</td>\n",
       "      <td>32.6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2018</td>\n",
       "      <td>29.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2019</td>\n",
       "      <td>28.9165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2020</td>\n",
       "      <td>33.9050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>2021</td>\n",
       "      <td>30.5425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Comunidades y Ciudades Autónomas  Periodo    Total\n",
       "0                    Total Nacional     2002  23.7370\n",
       "1                    Total Nacional     2003  24.1375\n",
       "2                    Total Nacional     2004  22.9490\n",
       "3                    Total Nacional     2005  20.2465\n",
       "4                    Total Nacional     2006  19.2165\n",
       "5                    Total Nacional     2007  18.9225\n",
       "6                    Total Nacional     2008  22.6520\n",
       "7                    Total Nacional     2009  31.3465\n",
       "8                    Total Nacional     2010  34.9180\n",
       "9                    Total Nacional     2011  37.6790\n",
       "10                   Total Nacional     2012  43.6905\n",
       "11                   Total Nacional     2013  46.2800\n",
       "12                   Total Nacional     2014  44.4300\n",
       "13                   Total Nacional     2015  41.3930\n",
       "14                   Total Nacional     2016  37.7800\n",
       "15                   Total Nacional     2017  32.6720\n",
       "16                   Total Nacional     2018  29.0750\n",
       "17                   Total Nacional     2019  28.9165\n",
       "18                   Total Nacional     2020  33.9050\n",
       "19                   Total Nacional     2021  30.5425"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parofertil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</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>Nacionalidad</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>Ambas nacionalidades</td>\n",
       "      <td>2021</td>\n",
       "      <td>7,12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2020</td>\n",
       "      <td>7,19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2019</td>\n",
       "      <td>7,62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2018</td>\n",
       "      <td>7,94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2017</td>\n",
       "      <td>8,41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2016</td>\n",
       "      <td>8,8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2015</td>\n",
       "      <td>9,02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2014</td>\n",
       "      <td>9,17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2013</td>\n",
       "      <td>9,11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2012</td>\n",
       "      <td>9,69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2011</td>\n",
       "      <td>10,07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2010</td>\n",
       "      <td>10,42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2009</td>\n",
       "      <td>10,65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2008</td>\n",
       "      <td>11,28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2007</td>\n",
       "      <td>10,86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2006</td>\n",
       "      <td>10,85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2005</td>\n",
       "      <td>10,65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2004</td>\n",
       "      <td>10,57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2003</td>\n",
       "      <td>10,44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Total Nacional</td>\n",
       "      <td>Ambas nacionalidades</td>\n",
       "      <td>2002</td>\n",
       "      <td>10,08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Comunidades y Ciudades Autónomas          Nacionalidad  Periodo  Total\n",
       "0                    Total Nacional  Ambas nacionalidades     2021   7,12\n",
       "1                    Total Nacional  Ambas nacionalidades     2020   7,19\n",
       "2                    Total Nacional  Ambas nacionalidades     2019   7,62\n",
       "3                    Total Nacional  Ambas nacionalidades     2018   7,94\n",
       "4                    Total Nacional  Ambas nacionalidades     2017   8,41\n",
       "5                    Total Nacional  Ambas nacionalidades     2016    8,8\n",
       "6                    Total Nacional  Ambas nacionalidades     2015   9,02\n",
       "7                    Total Nacional  Ambas nacionalidades     2014   9,17\n",
       "8                    Total Nacional  Ambas nacionalidades     2013   9,11\n",
       "9                    Total Nacional  Ambas nacionalidades     2012   9,69\n",
       "10                   Total Nacional  Ambas nacionalidades     2011  10,07\n",
       "11                   Total Nacional  Ambas nacionalidades     2010  10,42\n",
       "12                   Total Nacional  Ambas nacionalidades     2009  10,65\n",
       "13                   Total Nacional  Ambas nacionalidades     2008  11,28\n",
       "14                   Total Nacional  Ambas nacionalidades     2007  10,86\n",
       "15                   Total Nacional  Ambas nacionalidades     2006  10,85\n",
       "16                   Total Nacional  Ambas nacionalidades     2005  10,65\n",
       "17                   Total Nacional  Ambas nacionalidades     2004  10,57\n",
       "18                   Total Nacional  Ambas nacionalidades     2003  10,44\n",
       "19                   Total Nacional  Ambas nacionalidades     2002  10,08"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nattot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "total=nattot.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = total.loc[:, ['Periodo', 'Total']].rename(columns={'Total': 'Natality'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comunidades y Ciudades Autónomas    object\n",
       "Periodo                              int64\n",
       "Total                               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grtot.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = total.merge(totedh.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "total = total.rename(columns={'Total': 'Men_Age'})\n",
    "\n",
    "total = total.merge(totedm.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "total = total.rename(columns={'Total': 'Women_Age'})\n",
    "\n",
    "total = total.merge(grtot.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "total = total.rename(columns={'Total': 'Population_Growth'})\n",
    "\n",
    "total = total.merge(extot.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "total = total.rename(columns={'Total': 'Foreigners_Ratio'})\n",
    "\n",
    "total = total.merge(exfertil.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "total = total.rename(columns={'Total': 'Foreigner_Women'})\n",
    "\n",
    "total = total.merge(totmasc.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "total = total.rename(columns={'Total': 'Masculinity_Ratio'})\n",
    "\n",
    "total = total.merge(martot.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "total = total.rename(columns={'Total': 'Marriages'})\n",
    "\n",
    "total = total.merge(parotot.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "total = total.rename(columns={'Total': 'Unemployment_Ratio'})\n",
    "\n",
    "total = total.merge(parofertil.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "total = total.rename(columns={'Total': 'Unemployment_Women'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Periodo</th>\n",
       "      <th>Natality</th>\n",
       "      <th>Men_Age</th>\n",
       "      <th>Women_Age</th>\n",
       "      <th>Population_Growth</th>\n",
       "      <th>Foreigners_Ratio</th>\n",
       "      <th>Foreigner_Women</th>\n",
       "      <th>Masculinity_Ratio</th>\n",
       "      <th>Marriages</th>\n",
       "      <th>Unemployment_Ratio</th>\n",
       "      <th>Unemployment_Women</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>7,12</td>\n",
       "      <td>42.50</td>\n",
       "      <td>45.05</td>\n",
       "      <td>0,72</td>\n",
       "      <td>11,33</td>\n",
       "      <td>18.558333</td>\n",
       "      <td>96.09</td>\n",
       "      <td>143515</td>\n",
       "      <td>14.7850</td>\n",
       "      <td>30.5425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>7,19</td>\n",
       "      <td>42.28</td>\n",
       "      <td>44.83</td>\n",
       "      <td>1,4</td>\n",
       "      <td>11,04</td>\n",
       "      <td>18.393333</td>\n",
       "      <td>96.13</td>\n",
       "      <td>87481</td>\n",
       "      <td>15.5325</td>\n",
       "      <td>33.9050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>7,62</td>\n",
       "      <td>42.08</td>\n",
       "      <td>44.63</td>\n",
       "      <td>8,4</td>\n",
       "      <td>10,31</td>\n",
       "      <td>17.078333</td>\n",
       "      <td>96.16</td>\n",
       "      <td>161389</td>\n",
       "      <td>14.1050</td>\n",
       "      <td>28.9165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>7,94</td>\n",
       "      <td>41.88</td>\n",
       "      <td>44.43</td>\n",
       "      <td>5,96</td>\n",
       "      <td>9,78</td>\n",
       "      <td>16.013333</td>\n",
       "      <td>96.24</td>\n",
       "      <td>162743</td>\n",
       "      <td>15.2550</td>\n",
       "      <td>29.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>8,41</td>\n",
       "      <td>41.66</td>\n",
       "      <td>44.22</td>\n",
       "      <td>2,82</td>\n",
       "      <td>9,5</td>\n",
       "      <td>15.295556</td>\n",
       "      <td>96.38</td>\n",
       "      <td>168989</td>\n",
       "      <td>17.2250</td>\n",
       "      <td>32.6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>8,8</td>\n",
       "      <td>41.41</td>\n",
       "      <td>43.97</td>\n",
       "      <td>1,87</td>\n",
       "      <td>9,51</td>\n",
       "      <td>15.306667</td>\n",
       "      <td>96.51</td>\n",
       "      <td>171023</td>\n",
       "      <td>19.6350</td>\n",
       "      <td>37.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>9,02</td>\n",
       "      <td>41.16</td>\n",
       "      <td>43.73</td>\n",
       "      <td>-0,2</td>\n",
       "      <td>9,59</td>\n",
       "      <td>15.436111</td>\n",
       "      <td>96.63</td>\n",
       "      <td>165172</td>\n",
       "      <td>22.0575</td>\n",
       "      <td>41.3930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>9,17</td>\n",
       "      <td>40.86</td>\n",
       "      <td>43.43</td>\n",
       "      <td>-1,35</td>\n",
       "      <td>10,06</td>\n",
       "      <td>16.251667</td>\n",
       "      <td>96.80</td>\n",
       "      <td>159279</td>\n",
       "      <td>24.4425</td>\n",
       "      <td>44.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>9,11</td>\n",
       "      <td>40.52</td>\n",
       "      <td>43.10</td>\n",
       "      <td>-4,63</td>\n",
       "      <td>10,86</td>\n",
       "      <td>17.685556</td>\n",
       "      <td>97.08</td>\n",
       "      <td>153375</td>\n",
       "      <td>26.0950</td>\n",
       "      <td>46.2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>9,69</td>\n",
       "      <td>40.22</td>\n",
       "      <td>42.81</td>\n",
       "      <td>-1,93</td>\n",
       "      <td>11,18</td>\n",
       "      <td>18.182222</td>\n",
       "      <td>97.39</td>\n",
       "      <td>165101</td>\n",
       "      <td>24.7875</td>\n",
       "      <td>43.6905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011</td>\n",
       "      <td>10,07</td>\n",
       "      <td>39.95</td>\n",
       "      <td>42.56</td>\n",
       "      <td>3,23</td>\n",
       "      <td>11,38</td>\n",
       "      <td>18.380000</td>\n",
       "      <td>97.59</td>\n",
       "      <td>159798</td>\n",
       "      <td>21.3900</td>\n",
       "      <td>37.6790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>10,42</td>\n",
       "      <td>39.68</td>\n",
       "      <td>42.31</td>\n",
       "      <td>3,88</td>\n",
       "      <td>11,62</td>\n",
       "      <td>18.715556</td>\n",
       "      <td>97.78</td>\n",
       "      <td>167247</td>\n",
       "      <td>19.8575</td>\n",
       "      <td>34.9180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>10,65</td>\n",
       "      <td>39.44</td>\n",
       "      <td>42.09</td>\n",
       "      <td>5,33</td>\n",
       "      <td>11,65</td>\n",
       "      <td>18.657778</td>\n",
       "      <td>97.95</td>\n",
       "      <td>174062</td>\n",
       "      <td>17.8550</td>\n",
       "      <td>31.3465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2008</td>\n",
       "      <td>11,28</td>\n",
       "      <td>39.30</td>\n",
       "      <td>41.96</td>\n",
       "      <td>12,4</td>\n",
       "      <td>11,14</td>\n",
       "      <td>17.706111</td>\n",
       "      <td>97.89</td>\n",
       "      <td>194022</td>\n",
       "      <td>11.2450</td>\n",
       "      <td>22.6520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>10,86</td>\n",
       "      <td>39.21</td>\n",
       "      <td>41.89</td>\n",
       "      <td>19,55</td>\n",
       "      <td>9,94</td>\n",
       "      <td>15.581667</td>\n",
       "      <td>97.59</td>\n",
       "      <td>201579</td>\n",
       "      <td>8.2325</td>\n",
       "      <td>18.9225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2006</td>\n",
       "      <td>10,85</td>\n",
       "      <td>39.09</td>\n",
       "      <td>41.79</td>\n",
       "      <td>17,46</td>\n",
       "      <td>8,93</td>\n",
       "      <td>13.725556</td>\n",
       "      <td>97.44</td>\n",
       "      <td>203453</td>\n",
       "      <td>8.4525</td>\n",
       "      <td>19.2165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2005</td>\n",
       "      <td>10,65</td>\n",
       "      <td>38.97</td>\n",
       "      <td>41.68</td>\n",
       "      <td>16,34</td>\n",
       "      <td>7,92</td>\n",
       "      <td>12.060556</td>\n",
       "      <td>97.15</td>\n",
       "      <td>208146</td>\n",
       "      <td>9.1525</td>\n",
       "      <td>20.2465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2004</td>\n",
       "      <td>10,57</td>\n",
       "      <td>38.83</td>\n",
       "      <td>41.55</td>\n",
       "      <td>17,47</td>\n",
       "      <td>6,82</td>\n",
       "      <td>10.324444</td>\n",
       "      <td>96.77</td>\n",
       "      <td>216149</td>\n",
       "      <td>10.9650</td>\n",
       "      <td>22.9490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2003</td>\n",
       "      <td>10,44</td>\n",
       "      <td>38.72</td>\n",
       "      <td>41.45</td>\n",
       "      <td>17,05</td>\n",
       "      <td>5,65</td>\n",
       "      <td>8.466667</td>\n",
       "      <td>96.51</td>\n",
       "      <td>212300</td>\n",
       "      <td>11.4850</td>\n",
       "      <td>24.1375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2002</td>\n",
       "      <td>10,08</td>\n",
       "      <td>38.62</td>\n",
       "      <td>41.34</td>\n",
       "      <td>19,13</td>\n",
       "      <td>4,24</td>\n",
       "      <td>6.231111</td>\n",
       "      <td>96.16</td>\n",
       "      <td>211522</td>\n",
       "      <td>11.4500</td>\n",
       "      <td>23.7370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Periodo Natality  Men_Age  Women_Age Population_Growth Foreigners_Ratio  \\\n",
       "0      2021     7,12    42.50      45.05              0,72            11,33   \n",
       "1      2020     7,19    42.28      44.83               1,4            11,04   \n",
       "2      2019     7,62    42.08      44.63               8,4            10,31   \n",
       "3      2018     7,94    41.88      44.43              5,96             9,78   \n",
       "4      2017     8,41    41.66      44.22              2,82              9,5   \n",
       "5      2016      8,8    41.41      43.97              1,87             9,51   \n",
       "6      2015     9,02    41.16      43.73              -0,2             9,59   \n",
       "7      2014     9,17    40.86      43.43             -1,35            10,06   \n",
       "8      2013     9,11    40.52      43.10             -4,63            10,86   \n",
       "9      2012     9,69    40.22      42.81             -1,93            11,18   \n",
       "10     2011    10,07    39.95      42.56              3,23            11,38   \n",
       "11     2010    10,42    39.68      42.31              3,88            11,62   \n",
       "12     2009    10,65    39.44      42.09              5,33            11,65   \n",
       "13     2008    11,28    39.30      41.96              12,4            11,14   \n",
       "14     2007    10,86    39.21      41.89             19,55             9,94   \n",
       "15     2006    10,85    39.09      41.79             17,46             8,93   \n",
       "16     2005    10,65    38.97      41.68             16,34             7,92   \n",
       "17     2004    10,57    38.83      41.55             17,47             6,82   \n",
       "18     2003    10,44    38.72      41.45             17,05             5,65   \n",
       "19     2002    10,08    38.62      41.34             19,13             4,24   \n",
       "\n",
       "    Foreigner_Women  Masculinity_Ratio  Marriages  Unemployment_Ratio  \\\n",
       "0         18.558333              96.09     143515             14.7850   \n",
       "1         18.393333              96.13      87481             15.5325   \n",
       "2         17.078333              96.16     161389             14.1050   \n",
       "3         16.013333              96.24     162743             15.2550   \n",
       "4         15.295556              96.38     168989             17.2250   \n",
       "5         15.306667              96.51     171023             19.6350   \n",
       "6         15.436111              96.63     165172             22.0575   \n",
       "7         16.251667              96.80     159279             24.4425   \n",
       "8         17.685556              97.08     153375             26.0950   \n",
       "9         18.182222              97.39     165101             24.7875   \n",
       "10        18.380000              97.59     159798             21.3900   \n",
       "11        18.715556              97.78     167247             19.8575   \n",
       "12        18.657778              97.95     174062             17.8550   \n",
       "13        17.706111              97.89     194022             11.2450   \n",
       "14        15.581667              97.59     201579              8.2325   \n",
       "15        13.725556              97.44     203453              8.4525   \n",
       "16        12.060556              97.15     208146              9.1525   \n",
       "17        10.324444              96.77     216149             10.9650   \n",
       "18         8.466667              96.51     212300             11.4850   \n",
       "19         6.231111              96.16     211522             11.4500   \n",
       "\n",
       "    Unemployment_Women  \n",
       "0              30.5425  \n",
       "1              33.9050  \n",
       "2              28.9165  \n",
       "3              29.0750  \n",
       "4              32.6720  \n",
       "5              37.7800  \n",
       "6              41.3930  \n",
       "7              44.4300  \n",
       "8              46.2800  \n",
       "9              43.6905  \n",
       "10             37.6790  \n",
       "11             34.9180  \n",
       "12             31.3465  \n",
       "13             22.6520  \n",
       "14             18.9225  \n",
       "15             19.2165  \n",
       "16             20.2465  \n",
       "17             22.9490  \n",
       "18             24.1375  \n",
       "19             23.7370  "
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "total['Economic_Crisis'] = total['Periodo'].apply(lambda x: 1 if (x >= 2009) and (x <= 2015) else 0)\n",
    "total['Confination'] = total['Periodo'].apply(lambda x: 1 if x > 2019 else 0)\n",
    "total['Facebook'] = total['Periodo'].apply(lambda x: 1 if x > 2009 else 0)\n",
    "total['Instagram_Tinder'] = total['Periodo'].apply(lambda x: 1 if x > 2013 else 0)\n",
    "total['Law_Change'] = total['Periodo'].apply(lambda x: 1 if x > 2011 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Periodo                 int64\n",
       "Natality              float64\n",
       "Men_Age               float64\n",
       "Women_Age             float64\n",
       "Population_Growth     float64\n",
       "Foreigners_Ratio      float64\n",
       "Foreigner_Women       float64\n",
       "Masculinity_Ratio     float64\n",
       "Marriages               int32\n",
       "Unemployment_Ratio    float64\n",
       "Unemployment_Women    float64\n",
       "Economic_Crisis         int64\n",
       "Confination             int64\n",
       "Facebook                int64\n",
       "Instagram_Tinder        int64\n",
       "Law_Change              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "total['Natality'] = total['Natality'].str.replace(',', '.').astype(float)\n",
    "total['Population_Growth'] = total['Population_Growth'].str.replace(',', '.').astype(float)\n",
    "total['Foreigners_Ratio'] = total['Foreigners_Ratio'].str.replace(',', '.').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
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       "#T_d8d5f_row2_col15, #T_d8d5f_row3_col15 {\n",
       "  background-color: #ca3b37;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row3_col4 {\n",
       "  background-color: #6282ea;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row3_col5, #T_d8d5f_row9_col2, #T_d8d5f_row10_col5, #T_d8d5f_row11_col7 {\n",
       "  background-color: #f7af91;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row3_col6, #T_d8d5f_row5_col10, #T_d8d5f_row10_col3, #T_d8d5f_row13_col5 {\n",
       "  background-color: #f5a081;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row3_col7 {\n",
       "  background-color: #4e68d8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row3_col8 {\n",
       "  background-color: #4055c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row3_col9 {\n",
       "  background-color: #f7b497;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row3_col10, #T_d8d5f_row3_col12 {\n",
       "  background-color: #f6a283;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row3_col11 {\n",
       "  background-color: #b1cbfc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row3_col13, #T_d8d5f_row11_col9 {\n",
       "  background-color: #d55042;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row4_col0, #T_d8d5f_row6_col8 {\n",
       "  background-color: #516ddb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row4_col2, #T_d8d5f_row5_col4, #T_d8d5f_row14_col8 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row4_col3 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row4_col7 {\n",
       "  background-color: #d6dce4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row4_col8, #T_d8d5f_row7_col1 {\n",
       "  background-color: #da5a49;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row4_col12 {\n",
       "  background-color: #89acfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row4_col14 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row4_col15 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row5_col0 {\n",
       "  background-color: #ee8468;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row5_col1 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row5_col2 {\n",
       "  background-color: #f59d7e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row5_col7 {\n",
       "  background-color: #f6bea4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row5_col8, #T_d8d5f_row12_col8, #T_d8d5f_row13_col1 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row5_col11, #T_d8d5f_row7_col11 {\n",
       "  background-color: #f7b599;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row5_col12, #T_d8d5f_row11_col1 {\n",
       "  background-color: #ead4c8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row5_col14, #T_d8d5f_row6_col12 {\n",
       "  background-color: #eed0c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row5_col15, #T_d8d5f_row10_col14 {\n",
       "  background-color: #f6bda2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row6_col0, #T_d8d5f_row15_col10 {\n",
       "  background-color: #e9785d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row6_col1 {\n",
       "  background-color: #a5c3fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row6_col3, #T_d8d5f_row12_col2, #T_d8d5f_row13_col6 {\n",
       "  background-color: #f39475;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row6_col4 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row6_col7, #T_d8d5f_row6_col14, #T_d8d5f_row7_col6, #T_d8d5f_row14_col9 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row6_col9, #T_d8d5f_row6_col10 {\n",
       "  background-color: #f4987a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row6_col11, #T_d8d5f_row11_col13 {\n",
       "  background-color: #f7b79b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row6_col15, #T_d8d5f_row12_col14 {\n",
       "  background-color: #f7b396;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row7_col0, #T_d8d5f_row12_col11 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row7_col2 {\n",
       "  background-color: #7699f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row7_col3 {\n",
       "  background-color: #7597f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row7_col4 {\n",
       "  background-color: #e6d7cf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row7_col5 {\n",
       "  background-color: #f5c0a7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row7_col8 {\n",
       "  background-color: #f3c7b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row7_col9, #T_d8d5f_row11_col0 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row7_col10 {\n",
       "  background-color: #d1dae9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row7_col13, #T_d8d5f_row14_col11 {\n",
       "  background-color: #97b8ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row7_col14, #T_d8d5f_row15_col8 {\n",
       "  background-color: #5470de;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col0, #T_d8d5f_row13_col4 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col1 {\n",
       "  background-color: #dc5d4a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col2, #T_d8d5f_row8_col3 {\n",
       "  background-color: #506bda;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col4, #T_d8d5f_row9_col11 {\n",
       "  background-color: #d95847;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col5 {\n",
       "  background-color: #3d50c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col7 {\n",
       "  background-color: #efcebd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row8_col9 {\n",
       "  background-color: #779af7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col10, #T_d8d5f_row11_col4 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col11 {\n",
       "  background-color: #81a4fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col13, #T_d8d5f_row15_col1 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row8_col14 {\n",
       "  background-color: #6687ed;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row9_col1 {\n",
       "  background-color: #a7c5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row9_col4 {\n",
       "  background-color: #3e51c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row9_col5 {\n",
       "  background-color: #f7ac8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row9_col6 {\n",
       "  background-color: #f6a385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row9_col7, #T_d8d5f_row15_col11 {\n",
       "  background-color: #cedaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row9_col8 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row9_col14 {\n",
       "  background-color: #f1ccb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row9_col15 {\n",
       "  background-color: #f18f71;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row10_col0 {\n",
       "  background-color: #f39778;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row10_col1 {\n",
       "  background-color: #96b7ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row10_col7 {\n",
       "  background-color: #bad0f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row10_col11 {\n",
       "  background-color: #e36c55;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row10_col12 {\n",
       "  background-color: #c6d6f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row10_col13 {\n",
       "  background-color: #d85646;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row10_col15 {\n",
       "  background-color: #ea7b60;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row11_col2 {\n",
       "  background-color: #d5dbe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row11_col3 {\n",
       "  background-color: #d3dbe7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row11_col6 {\n",
       "  background-color: #f7b093;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row11_col8 {\n",
       "  background-color: #a3c2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row11_col12 {\n",
       "  background-color: #8db0fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row11_col14 {\n",
       "  background-color: #b6cefa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row11_col15 {\n",
       "  background-color: #e1dad6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row12_col0 {\n",
       "  background-color: #f49a7b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row12_col1 {\n",
       "  background-color: #7295f4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row12_col3 {\n",
       "  background-color: #f29274;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row12_col4 {\n",
       "  background-color: #aec9fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row12_col5, #T_d8d5f_row13_col12 {\n",
       "  background-color: #ebd3c6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row12_col6 {\n",
       "  background-color: #f0cdbb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row12_col7 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row12_col9 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row12_col10 {\n",
       "  background-color: #dddcdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row12_col13, #T_d8d5f_row15_col5 {\n",
       "  background-color: #f3c8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row12_col15, #T_d8d5f_row14_col12 {\n",
       "  background-color: #f6bfa6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row13_col2 {\n",
       "  background-color: #d24b40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row13_col7 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row13_col8 {\n",
       "  background-color: #455cce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row13_col10 {\n",
       "  background-color: #d75445;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row13_col11 {\n",
       "  background-color: #f4c6af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row13_col14, #T_d8d5f_row14_col13 {\n",
       "  background-color: #e97a5f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row13_col15, #T_d8d5f_row14_col15, #T_d8d5f_row15_col13, #T_d8d5f_row15_col14 {\n",
       "  background-color: #d65244;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row14_col1 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row14_col3 {\n",
       "  background-color: #c53334;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row14_col5 {\n",
       "  background-color: #e4d9d2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row14_col6 {\n",
       "  background-color: #edd1c2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row14_col10 {\n",
       "  background-color: #f7b99e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row15_col0 {\n",
       "  background-color: #cc403a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row15_col2, #T_d8d5f_row15_col3 {\n",
       "  background-color: #c83836;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row15_col4 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row15_col6 {\n",
       "  background-color: #f7bca1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d8d5f_row15_col9 {\n",
       "  background-color: #f18d6f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d8d5f_row15_col12 {\n",
       "  background-color: #f2cbb7;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d8d5f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d8d5f_level0_col0\" class=\"col_heading level0 col0\" >Periodo</th>\n",
       "      <th id=\"T_d8d5f_level0_col1\" class=\"col_heading level0 col1\" >Natality</th>\n",
       "      <th id=\"T_d8d5f_level0_col2\" class=\"col_heading level0 col2\" >Men_Age</th>\n",
       "      <th id=\"T_d8d5f_level0_col3\" class=\"col_heading level0 col3\" >Women_Age</th>\n",
       "      <th id=\"T_d8d5f_level0_col4\" class=\"col_heading level0 col4\" >Population_Growth</th>\n",
       "      <th id=\"T_d8d5f_level0_col5\" class=\"col_heading level0 col5\" >Foreigners_Ratio</th>\n",
       "      <th id=\"T_d8d5f_level0_col6\" class=\"col_heading level0 col6\" >Foreigner_Women</th>\n",
       "      <th id=\"T_d8d5f_level0_col7\" class=\"col_heading level0 col7\" >Masculinity_Ratio</th>\n",
       "      <th id=\"T_d8d5f_level0_col8\" class=\"col_heading level0 col8\" >Marriages</th>\n",
       "      <th id=\"T_d8d5f_level0_col9\" class=\"col_heading level0 col9\" >Unemployment_Ratio</th>\n",
       "      <th id=\"T_d8d5f_level0_col10\" class=\"col_heading level0 col10\" >Unemployment_Women</th>\n",
       "      <th id=\"T_d8d5f_level0_col11\" class=\"col_heading level0 col11\" >Economic_Crisis</th>\n",
       "      <th id=\"T_d8d5f_level0_col12\" class=\"col_heading level0 col12\" >Confination</th>\n",
       "      <th id=\"T_d8d5f_level0_col13\" class=\"col_heading level0 col13\" >Facebook</th>\n",
       "      <th id=\"T_d8d5f_level0_col14\" class=\"col_heading level0 col14\" >Instagram_Tinder</th>\n",
       "      <th id=\"T_d8d5f_level0_col15\" class=\"col_heading level0 col15\" >Law_Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row0\" class=\"row_heading level0 row0\" >Periodo</th>\n",
       "      <td id=\"T_d8d5f_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row0_col1\" class=\"data row0 col1\" >-0.901711</td>\n",
       "      <td id=\"T_d8d5f_row0_col2\" class=\"data row0 col2\" >0.988566</td>\n",
       "      <td id=\"T_d8d5f_row0_col3\" class=\"data row0 col3\" >0.986464</td>\n",
       "      <td id=\"T_d8d5f_row0_col4\" class=\"data row0 col4\" >-0.754303</td>\n",
       "      <td id=\"T_d8d5f_row0_col5\" class=\"data row0 col5\" >0.618818</td>\n",
       "      <td id=\"T_d8d5f_row0_col6\" class=\"data row0 col6\" >0.669995</td>\n",
       "      <td id=\"T_d8d5f_row0_col7\" class=\"data row0 col7\" >-0.462705</td>\n",
       "      <td id=\"T_d8d5f_row0_col8\" class=\"data row0 col8\" >-0.845897</td>\n",
       "      <td id=\"T_d8d5f_row0_col9\" class=\"data row0 col9\" >0.462932</td>\n",
       "      <td id=\"T_d8d5f_row0_col10\" class=\"data row0 col10\" >0.538173</td>\n",
       "      <td id=\"T_d8d5f_row0_col11\" class=\"data row0 col11\" >0.063628</td>\n",
       "      <td id=\"T_d8d5f_row0_col12\" class=\"data row0 col12\" >0.520266</td>\n",
       "      <td id=\"T_d8d5f_row0_col13\" class=\"data row0 col13\" >0.849591</td>\n",
       "      <td id=\"T_d8d5f_row0_col14\" class=\"data row0 col14\" >0.849591</td>\n",
       "      <td id=\"T_d8d5f_row0_col15\" class=\"data row0 col15\" >0.867110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row1\" class=\"row_heading level0 row1\" >Natality</th>\n",
       "      <td id=\"T_d8d5f_row1_col0\" class=\"data row1 col0\" >-0.901711</td>\n",
       "      <td id=\"T_d8d5f_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row1_col2\" class=\"data row1 col2\" >-0.948284</td>\n",
       "      <td id=\"T_d8d5f_row1_col3\" class=\"data row1 col3\" >-0.951376</td>\n",
       "      <td id=\"T_d8d5f_row1_col4\" class=\"data row1 col4\" >0.586990</td>\n",
       "      <td id=\"T_d8d5f_row1_col5\" class=\"data row1 col5\" >-0.265860</td>\n",
       "      <td id=\"T_d8d5f_row1_col6\" class=\"data row1 col6\" >-0.333662</td>\n",
       "      <td id=\"T_d8d5f_row1_col7\" class=\"data row1 col7\" >0.774758</td>\n",
       "      <td id=\"T_d8d5f_row1_col8\" class=\"data row1 col8\" >0.766627</td>\n",
       "      <td id=\"T_d8d5f_row1_col9\" class=\"data row1 col9\" >-0.312767</td>\n",
       "      <td id=\"T_d8d5f_row1_col10\" class=\"data row1 col10\" >-0.414206</td>\n",
       "      <td id=\"T_d8d5f_row1_col11\" class=\"data row1 col11\" >0.136409</td>\n",
       "      <td id=\"T_d8d5f_row1_col12\" class=\"data row1 col12\" >-0.615293</td>\n",
       "      <td id=\"T_d8d5f_row1_col13\" class=\"data row1 col13\" >-0.756473</td>\n",
       "      <td id=\"T_d8d5f_row1_col14\" class=\"data row1 col14\" >-0.861208</td>\n",
       "      <td id=\"T_d8d5f_row1_col15\" class=\"data row1 col15\" >-0.859099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row2\" class=\"row_heading level0 row2\" >Men_Age</th>\n",
       "      <td id=\"T_d8d5f_row2_col0\" class=\"data row2 col0\" >0.988566</td>\n",
       "      <td id=\"T_d8d5f_row2_col1\" class=\"data row2 col1\" >-0.948284</td>\n",
       "      <td id=\"T_d8d5f_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row2_col3\" class=\"data row2 col3\" >0.999838</td>\n",
       "      <td id=\"T_d8d5f_row2_col4\" class=\"data row2 col4\" >-0.696892</td>\n",
       "      <td id=\"T_d8d5f_row2_col5\" class=\"data row2 col5\" >0.494626</td>\n",
       "      <td id=\"T_d8d5f_row2_col6\" class=\"data row2 col6\" >0.551493</td>\n",
       "      <td id=\"T_d8d5f_row2_col7\" class=\"data row2 col7\" >-0.588978</td>\n",
       "      <td id=\"T_d8d5f_row2_col8\" class=\"data row2 col8\" >-0.810791</td>\n",
       "      <td id=\"T_d8d5f_row2_col9\" class=\"data row2 col9\" >0.401948</td>\n",
       "      <td id=\"T_d8d5f_row2_col10\" class=\"data row2 col10\" >0.489562</td>\n",
       "      <td id=\"T_d8d5f_row2_col11\" class=\"data row2 col11\" >-0.033011</td>\n",
       "      <td id=\"T_d8d5f_row2_col12\" class=\"data row2 col12\" >0.539430</td>\n",
       "      <td id=\"T_d8d5f_row2_col13\" class=\"data row2 col13\" >0.827186</td>\n",
       "      <td id=\"T_d8d5f_row2_col14\" class=\"data row2 col14\" >0.899441</td>\n",
       "      <td id=\"T_d8d5f_row2_col15\" class=\"data row2 col15\" >0.889239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row3\" class=\"row_heading level0 row3\" >Women_Age</th>\n",
       "      <td id=\"T_d8d5f_row3_col0\" class=\"data row3 col0\" >0.986464</td>\n",
       "      <td id=\"T_d8d5f_row3_col1\" class=\"data row3 col1\" >-0.951376</td>\n",
       "      <td id=\"T_d8d5f_row3_col2\" class=\"data row3 col2\" >0.999838</td>\n",
       "      <td id=\"T_d8d5f_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row3_col4\" class=\"data row3 col4\" >-0.685977</td>\n",
       "      <td id=\"T_d8d5f_row3_col5\" class=\"data row3 col5\" >0.484721</td>\n",
       "      <td id=\"T_d8d5f_row3_col6\" class=\"data row3 col6\" >0.541756</td>\n",
       "      <td id=\"T_d8d5f_row3_col7\" class=\"data row3 col7\" >-0.598755</td>\n",
       "      <td id=\"T_d8d5f_row3_col8\" class=\"data row3 col8\" >-0.806827</td>\n",
       "      <td id=\"T_d8d5f_row3_col9\" class=\"data row3 col9\" >0.387559</td>\n",
       "      <td id=\"T_d8d5f_row3_col10\" class=\"data row3 col10\" >0.476492</td>\n",
       "      <td id=\"T_d8d5f_row3_col11\" class=\"data row3 col11\" >-0.047951</td>\n",
       "      <td id=\"T_d8d5f_row3_col12\" class=\"data row3 col12\" >0.547211</td>\n",
       "      <td id=\"T_d8d5f_row3_col13\" class=\"data row3 col13\" >0.819554</td>\n",
       "      <td id=\"T_d8d5f_row3_col14\" class=\"data row3 col14\" >0.902029</td>\n",
       "      <td id=\"T_d8d5f_row3_col15\" class=\"data row3 col15\" >0.886104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row4\" class=\"row_heading level0 row4\" >Population_Growth</th>\n",
       "      <td id=\"T_d8d5f_row4_col0\" class=\"data row4 col0\" >-0.754303</td>\n",
       "      <td id=\"T_d8d5f_row4_col1\" class=\"data row4 col1\" >0.586990</td>\n",
       "      <td id=\"T_d8d5f_row4_col2\" class=\"data row4 col2\" >-0.696892</td>\n",
       "      <td id=\"T_d8d5f_row4_col3\" class=\"data row4 col3\" >-0.685977</td>\n",
       "      <td id=\"T_d8d5f_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row4_col5\" class=\"data row4 col5\" >-0.676287</td>\n",
       "      <td id=\"T_d8d5f_row4_col6\" class=\"data row4 col6\" >-0.711246</td>\n",
       "      <td id=\"T_d8d5f_row4_col7\" class=\"data row4 col7\" >0.102994</td>\n",
       "      <td id=\"T_d8d5f_row4_col8\" class=\"data row4 col8\" >0.786348</td>\n",
       "      <td id=\"T_d8d5f_row4_col9\" class=\"data row4 col9\" >-0.907113</td>\n",
       "      <td id=\"T_d8d5f_row4_col10\" class=\"data row4 col10\" >-0.930670</td>\n",
       "      <td id=\"T_d8d5f_row4_col11\" class=\"data row4 col11\" >-0.622372</td>\n",
       "      <td id=\"T_d8d5f_row4_col12\" class=\"data row4 col12\" >-0.263883</td>\n",
       "      <td id=\"T_d8d5f_row4_col13\" class=\"data row4 col13\" >-0.872245</td>\n",
       "      <td id=\"T_d8d5f_row4_col14\" class=\"data row4 col14\" >-0.500852</td>\n",
       "      <td id=\"T_d8d5f_row4_col15\" class=\"data row4 col15\" >-0.760162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row5\" class=\"row_heading level0 row5\" >Foreigners_Ratio</th>\n",
       "      <td id=\"T_d8d5f_row5_col0\" class=\"data row5 col0\" >0.618818</td>\n",
       "      <td id=\"T_d8d5f_row5_col1\" class=\"data row5 col1\" >-0.265860</td>\n",
       "      <td id=\"T_d8d5f_row5_col2\" class=\"data row5 col2\" >0.494626</td>\n",
       "      <td id=\"T_d8d5f_row5_col3\" class=\"data row5 col3\" >0.484721</td>\n",
       "      <td id=\"T_d8d5f_row5_col4\" class=\"data row5 col4\" >-0.676287</td>\n",
       "      <td id=\"T_d8d5f_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row5_col6\" class=\"data row5 col6\" >0.996891</td>\n",
       "      <td id=\"T_d8d5f_row5_col7\" class=\"data row5 col7\" >0.395729</td>\n",
       "      <td id=\"T_d8d5f_row5_col8\" class=\"data row5 col8\" >-0.659998</td>\n",
       "      <td id=\"T_d8d5f_row5_col9\" class=\"data row5 col9\" >0.499923</td>\n",
       "      <td id=\"T_d8d5f_row5_col10\" class=\"data row5 col10\" >0.485319</td>\n",
       "      <td id=\"T_d8d5f_row5_col11\" class=\"data row5 col11\" >0.473252</td>\n",
       "      <td id=\"T_d8d5f_row5_col12\" class=\"data row5 col12\" >0.261767</td>\n",
       "      <td id=\"T_d8d5f_row5_col13\" class=\"data row5 col13\" >0.548350</td>\n",
       "      <td id=\"T_d8d5f_row5_col14\" class=\"data row5 col14\" >0.212364</td>\n",
       "      <td id=\"T_d8d5f_row5_col15\" class=\"data row5 col15\" >0.348548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row6\" class=\"row_heading level0 row6\" >Foreigner_Women</th>\n",
       "      <td id=\"T_d8d5f_row6_col0\" class=\"data row6 col0\" >0.669995</td>\n",
       "      <td id=\"T_d8d5f_row6_col1\" class=\"data row6 col1\" >-0.333662</td>\n",
       "      <td id=\"T_d8d5f_row6_col2\" class=\"data row6 col2\" >0.551493</td>\n",
       "      <td id=\"T_d8d5f_row6_col3\" class=\"data row6 col3\" >0.541756</td>\n",
       "      <td id=\"T_d8d5f_row6_col4\" class=\"data row6 col4\" >-0.711246</td>\n",
       "      <td id=\"T_d8d5f_row6_col5\" class=\"data row6 col5\" >0.996891</td>\n",
       "      <td id=\"T_d8d5f_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row6_col7\" class=\"data row6 col7\" >0.330226</td>\n",
       "      <td id=\"T_d8d5f_row6_col8\" class=\"data row6 col8\" >-0.708314</td>\n",
       "      <td id=\"T_d8d5f_row6_col9\" class=\"data row6 col9\" >0.525654</td>\n",
       "      <td id=\"T_d8d5f_row6_col10\" class=\"data row6 col10\" >0.518122</td>\n",
       "      <td id=\"T_d8d5f_row6_col11\" class=\"data row6 col11\" >0.465372</td>\n",
       "      <td id=\"T_d8d5f_row6_col12\" class=\"data row6 col12\" >0.293576</td>\n",
       "      <td id=\"T_d8d5f_row6_col13\" class=\"data row6 col13\" >0.598605</td>\n",
       "      <td id=\"T_d8d5f_row6_col14\" class=\"data row6 col14\" >0.266543</td>\n",
       "      <td id=\"T_d8d5f_row6_col15\" class=\"data row6 col15\" >0.406242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row7\" class=\"row_heading level0 row7\" >Masculinity_Ratio</th>\n",
       "      <td id=\"T_d8d5f_row7_col0\" class=\"data row7 col0\" >-0.462705</td>\n",
       "      <td id=\"T_d8d5f_row7_col1\" class=\"data row7 col1\" >0.774758</td>\n",
       "      <td id=\"T_d8d5f_row7_col2\" class=\"data row7 col2\" >-0.588978</td>\n",
       "      <td id=\"T_d8d5f_row7_col3\" class=\"data row7 col3\" >-0.598755</td>\n",
       "      <td id=\"T_d8d5f_row7_col4\" class=\"data row7 col4\" >0.102994</td>\n",
       "      <td id=\"T_d8d5f_row7_col5\" class=\"data row7 col5\" >0.395729</td>\n",
       "      <td id=\"T_d8d5f_row7_col6\" class=\"data row7 col6\" >0.330226</td>\n",
       "      <td id=\"T_d8d5f_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row7_col8\" class=\"data row7 col8\" >0.286930</td>\n",
       "      <td id=\"T_d8d5f_row7_col9\" class=\"data row7 col9\" >0.054436</td>\n",
       "      <td id=\"T_d8d5f_row7_col10\" class=\"data row7 col10\" >-0.055635</td>\n",
       "      <td id=\"T_d8d5f_row7_col11\" class=\"data row7 col11\" >0.473414</td>\n",
       "      <td id=\"T_d8d5f_row7_col12\" class=\"data row7 col12\" >-0.425705</td>\n",
       "      <td id=\"T_d8d5f_row7_col13\" class=\"data row7 col13\" >-0.351704</td>\n",
       "      <td id=\"T_d8d5f_row7_col14\" class=\"data row7 col14\" >-0.707959</td>\n",
       "      <td id=\"T_d8d5f_row7_col15\" class=\"data row7 col15\" >-0.590785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row8\" class=\"row_heading level0 row8\" >Marriages</th>\n",
       "      <td id=\"T_d8d5f_row8_col0\" class=\"data row8 col0\" >-0.845897</td>\n",
       "      <td id=\"T_d8d5f_row8_col1\" class=\"data row8 col1\" >0.766627</td>\n",
       "      <td id=\"T_d8d5f_row8_col2\" class=\"data row8 col2\" >-0.810791</td>\n",
       "      <td id=\"T_d8d5f_row8_col3\" class=\"data row8 col3\" >-0.806827</td>\n",
       "      <td id=\"T_d8d5f_row8_col4\" class=\"data row8 col4\" >0.786348</td>\n",
       "      <td id=\"T_d8d5f_row8_col5\" class=\"data row8 col5\" >-0.659998</td>\n",
       "      <td id=\"T_d8d5f_row8_col6\" class=\"data row8 col6\" >-0.708314</td>\n",
       "      <td id=\"T_d8d5f_row8_col7\" class=\"data row8 col7\" >0.286930</td>\n",
       "      <td id=\"T_d8d5f_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row8_col9\" class=\"data row8 col9\" >-0.549039</td>\n",
       "      <td id=\"T_d8d5f_row8_col10\" class=\"data row8 col10\" >-0.624286</td>\n",
       "      <td id=\"T_d8d5f_row8_col11\" class=\"data row8 col11\" >-0.269033</td>\n",
       "      <td id=\"T_d8d5f_row8_col12\" class=\"data row8 col12\" >-0.660457</td>\n",
       "      <td id=\"T_d8d5f_row8_col13\" class=\"data row8 col13\" >-0.779386</td>\n",
       "      <td id=\"T_d8d5f_row8_col14\" class=\"data row8 col14\" >-0.601474</td>\n",
       "      <td id=\"T_d8d5f_row8_col15\" class=\"data row8 col15\" >-0.690913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row9\" class=\"row_heading level0 row9\" >Unemployment_Ratio</th>\n",
       "      <td id=\"T_d8d5f_row9_col0\" class=\"data row9 col0\" >0.462932</td>\n",
       "      <td id=\"T_d8d5f_row9_col1\" class=\"data row9 col1\" >-0.312767</td>\n",
       "      <td id=\"T_d8d5f_row9_col2\" class=\"data row9 col2\" >0.401948</td>\n",
       "      <td id=\"T_d8d5f_row9_col3\" class=\"data row9 col3\" >0.387559</td>\n",
       "      <td id=\"T_d8d5f_row9_col4\" class=\"data row9 col4\" >-0.907113</td>\n",
       "      <td id=\"T_d8d5f_row9_col5\" class=\"data row9 col5\" >0.499923</td>\n",
       "      <td id=\"T_d8d5f_row9_col6\" class=\"data row9 col6\" >0.525654</td>\n",
       "      <td id=\"T_d8d5f_row9_col7\" class=\"data row9 col7\" >0.054436</td>\n",
       "      <td id=\"T_d8d5f_row9_col8\" class=\"data row9 col8\" >-0.549039</td>\n",
       "      <td id=\"T_d8d5f_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row9_col10\" class=\"data row9 col10\" >0.988152</td>\n",
       "      <td id=\"T_d8d5f_row9_col11\" class=\"data row9 col11\" >0.819490</td>\n",
       "      <td id=\"T_d8d5f_row9_col12\" class=\"data row9 col12\" >-0.062993</td>\n",
       "      <td id=\"T_d8d5f_row9_col13\" class=\"data row9 col13\" >0.754923</td>\n",
       "      <td id=\"T_d8d5f_row9_col14\" class=\"data row9 col14\" >0.248814</td>\n",
       "      <td id=\"T_d8d5f_row9_col15\" class=\"data row9 col15\" >0.579142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row10\" class=\"row_heading level0 row10\" >Unemployment_Women</th>\n",
       "      <td id=\"T_d8d5f_row10_col0\" class=\"data row10 col0\" >0.538173</td>\n",
       "      <td id=\"T_d8d5f_row10_col1\" class=\"data row10 col1\" >-0.414206</td>\n",
       "      <td id=\"T_d8d5f_row10_col2\" class=\"data row10 col2\" >0.489562</td>\n",
       "      <td id=\"T_d8d5f_row10_col3\" class=\"data row10 col3\" >0.476492</td>\n",
       "      <td id=\"T_d8d5f_row10_col4\" class=\"data row10 col4\" >-0.930670</td>\n",
       "      <td id=\"T_d8d5f_row10_col5\" class=\"data row10 col5\" >0.485319</td>\n",
       "      <td id=\"T_d8d5f_row10_col6\" class=\"data row10 col6\" >0.518122</td>\n",
       "      <td id=\"T_d8d5f_row10_col7\" class=\"data row10 col7\" >-0.055635</td>\n",
       "      <td id=\"T_d8d5f_row10_col8\" class=\"data row10 col8\" >-0.624286</td>\n",
       "      <td id=\"T_d8d5f_row10_col9\" class=\"data row10 col9\" >0.988152</td>\n",
       "      <td id=\"T_d8d5f_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row10_col11\" class=\"data row10 col11\" >0.755090</td>\n",
       "      <td id=\"T_d8d5f_row10_col12\" class=\"data row10 col12\" >0.039227</td>\n",
       "      <td id=\"T_d8d5f_row10_col13\" class=\"data row10 col13\" >0.800334</td>\n",
       "      <td id=\"T_d8d5f_row10_col14\" class=\"data row10 col14\" >0.347575</td>\n",
       "      <td id=\"T_d8d5f_row10_col15\" class=\"data row10 col15\" >0.664657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row11\" class=\"row_heading level0 row11\" >Economic_Crisis</th>\n",
       "      <td id=\"T_d8d5f_row11_col0\" class=\"data row11 col0\" >0.063628</td>\n",
       "      <td id=\"T_d8d5f_row11_col1\" class=\"data row11 col1\" >0.136409</td>\n",
       "      <td id=\"T_d8d5f_row11_col2\" class=\"data row11 col2\" >-0.033011</td>\n",
       "      <td id=\"T_d8d5f_row11_col3\" class=\"data row11 col3\" >-0.047951</td>\n",
       "      <td id=\"T_d8d5f_row11_col4\" class=\"data row11 col4\" >-0.622372</td>\n",
       "      <td id=\"T_d8d5f_row11_col5\" class=\"data row11 col5\" >0.473252</td>\n",
       "      <td id=\"T_d8d5f_row11_col6\" class=\"data row11 col6\" >0.465372</td>\n",
       "      <td id=\"T_d8d5f_row11_col7\" class=\"data row11 col7\" >0.473414</td>\n",
       "      <td id=\"T_d8d5f_row11_col8\" class=\"data row11 col8\" >-0.269033</td>\n",
       "      <td id=\"T_d8d5f_row11_col9\" class=\"data row11 col9\" >0.819490</td>\n",
       "      <td id=\"T_d8d5f_row11_col10\" class=\"data row11 col10\" >0.755090</td>\n",
       "      <td id=\"T_d8d5f_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row11_col12\" class=\"data row11 col12\" >-0.244600</td>\n",
       "      <td id=\"T_d8d5f_row11_col13\" class=\"data row11 col13\" >0.385164</td>\n",
       "      <td id=\"T_d8d5f_row11_col14\" class=\"data row11 col14\" >-0.171184</td>\n",
       "      <td id=\"T_d8d5f_row11_col15\" class=\"data row11 col15\" >0.104828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row12\" class=\"row_heading level0 row12\" >Confination</th>\n",
       "      <td id=\"T_d8d5f_row12_col0\" class=\"data row12 col0\" >0.520266</td>\n",
       "      <td id=\"T_d8d5f_row12_col1\" class=\"data row12 col1\" >-0.615293</td>\n",
       "      <td id=\"T_d8d5f_row12_col2\" class=\"data row12 col2\" >0.539430</td>\n",
       "      <td id=\"T_d8d5f_row12_col3\" class=\"data row12 col3\" >0.547211</td>\n",
       "      <td id=\"T_d8d5f_row12_col4\" class=\"data row12 col4\" >-0.263883</td>\n",
       "      <td id=\"T_d8d5f_row12_col5\" class=\"data row12 col5\" >0.261767</td>\n",
       "      <td id=\"T_d8d5f_row12_col6\" class=\"data row12 col6\" >0.293576</td>\n",
       "      <td id=\"T_d8d5f_row12_col7\" class=\"data row12 col7\" >-0.425705</td>\n",
       "      <td id=\"T_d8d5f_row12_col8\" class=\"data row12 col8\" >-0.660457</td>\n",
       "      <td id=\"T_d8d5f_row12_col9\" class=\"data row12 col9\" >-0.062993</td>\n",
       "      <td id=\"T_d8d5f_row12_col10\" class=\"data row12 col10\" >0.039227</td>\n",
       "      <td id=\"T_d8d5f_row12_col11\" class=\"data row12 col11\" >-0.244600</td>\n",
       "      <td id=\"T_d8d5f_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row12_col13\" class=\"data row12 col13\" >0.272166</td>\n",
       "      <td id=\"T_d8d5f_row12_col14\" class=\"data row12 col14\" >0.408248</td>\n",
       "      <td id=\"T_d8d5f_row12_col15\" class=\"data row12 col15\" >0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row13\" class=\"row_heading level0 row13\" >Facebook</th>\n",
       "      <td id=\"T_d8d5f_row13_col0\" class=\"data row13 col0\" >0.849591</td>\n",
       "      <td id=\"T_d8d5f_row13_col1\" class=\"data row13 col1\" >-0.756473</td>\n",
       "      <td id=\"T_d8d5f_row13_col2\" class=\"data row13 col2\" >0.827186</td>\n",
       "      <td id=\"T_d8d5f_row13_col3\" class=\"data row13 col3\" >0.819554</td>\n",
       "      <td id=\"T_d8d5f_row13_col4\" class=\"data row13 col4\" >-0.872245</td>\n",
       "      <td id=\"T_d8d5f_row13_col5\" class=\"data row13 col5\" >0.548350</td>\n",
       "      <td id=\"T_d8d5f_row13_col6\" class=\"data row13 col6\" >0.598605</td>\n",
       "      <td id=\"T_d8d5f_row13_col7\" class=\"data row13 col7\" >-0.351704</td>\n",
       "      <td id=\"T_d8d5f_row13_col8\" class=\"data row13 col8\" >-0.779386</td>\n",
       "      <td id=\"T_d8d5f_row13_col9\" class=\"data row13 col9\" >0.754923</td>\n",
       "      <td id=\"T_d8d5f_row13_col10\" class=\"data row13 col10\" >0.800334</td>\n",
       "      <td id=\"T_d8d5f_row13_col11\" class=\"data row13 col11\" >0.385164</td>\n",
       "      <td id=\"T_d8d5f_row13_col12\" class=\"data row13 col12\" >0.272166</td>\n",
       "      <td id=\"T_d8d5f_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row13_col14\" class=\"data row13 col14\" >0.666667</td>\n",
       "      <td id=\"T_d8d5f_row13_col15\" class=\"data row13 col15\" >0.816497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row14\" class=\"row_heading level0 row14\" >Instagram_Tinder</th>\n",
       "      <td id=\"T_d8d5f_row14_col0\" class=\"data row14 col0\" >0.849591</td>\n",
       "      <td id=\"T_d8d5f_row14_col1\" class=\"data row14 col1\" >-0.861208</td>\n",
       "      <td id=\"T_d8d5f_row14_col2\" class=\"data row14 col2\" >0.899441</td>\n",
       "      <td id=\"T_d8d5f_row14_col3\" class=\"data row14 col3\" >0.902029</td>\n",
       "      <td id=\"T_d8d5f_row14_col4\" class=\"data row14 col4\" >-0.500852</td>\n",
       "      <td id=\"T_d8d5f_row14_col5\" class=\"data row14 col5\" >0.212364</td>\n",
       "      <td id=\"T_d8d5f_row14_col6\" class=\"data row14 col6\" >0.266543</td>\n",
       "      <td id=\"T_d8d5f_row14_col7\" class=\"data row14 col7\" >-0.707959</td>\n",
       "      <td id=\"T_d8d5f_row14_col8\" class=\"data row14 col8\" >-0.601474</td>\n",
       "      <td id=\"T_d8d5f_row14_col9\" class=\"data row14 col9\" >0.248814</td>\n",
       "      <td id=\"T_d8d5f_row14_col10\" class=\"data row14 col10\" >0.347575</td>\n",
       "      <td id=\"T_d8d5f_row14_col11\" class=\"data row14 col11\" >-0.171184</td>\n",
       "      <td id=\"T_d8d5f_row14_col12\" class=\"data row14 col12\" >0.408248</td>\n",
       "      <td id=\"T_d8d5f_row14_col13\" class=\"data row14 col13\" >0.666667</td>\n",
       "      <td id=\"T_d8d5f_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
       "      <td id=\"T_d8d5f_row14_col15\" class=\"data row14 col15\" >0.816497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8d5f_level0_row15\" class=\"row_heading level0 row15\" >Law_Change</th>\n",
       "      <td id=\"T_d8d5f_row15_col0\" class=\"data row15 col0\" >0.867110</td>\n",
       "      <td id=\"T_d8d5f_row15_col1\" class=\"data row15 col1\" >-0.859099</td>\n",
       "      <td id=\"T_d8d5f_row15_col2\" class=\"data row15 col2\" >0.889239</td>\n",
       "      <td id=\"T_d8d5f_row15_col3\" class=\"data row15 col3\" >0.886104</td>\n",
       "      <td id=\"T_d8d5f_row15_col4\" class=\"data row15 col4\" >-0.760162</td>\n",
       "      <td id=\"T_d8d5f_row15_col5\" class=\"data row15 col5\" >0.348548</td>\n",
       "      <td id=\"T_d8d5f_row15_col6\" class=\"data row15 col6\" >0.406242</td>\n",
       "      <td id=\"T_d8d5f_row15_col7\" class=\"data row15 col7\" >-0.590785</td>\n",
       "      <td id=\"T_d8d5f_row15_col8\" class=\"data row15 col8\" >-0.690913</td>\n",
       "      <td id=\"T_d8d5f_row15_col9\" class=\"data row15 col9\" >0.579142</td>\n",
       "      <td id=\"T_d8d5f_row15_col10\" class=\"data row15 col10\" >0.664657</td>\n",
       "      <td id=\"T_d8d5f_row15_col11\" class=\"data row15 col11\" >0.104828</td>\n",
       "      <td id=\"T_d8d5f_row15_col12\" class=\"data row15 col12\" >0.333333</td>\n",
       "      <td id=\"T_d8d5f_row15_col13\" class=\"data row15 col13\" >0.816497</td>\n",
       "      <td id=\"T_d8d5f_row15_col14\" class=\"data row15 col14\" >0.816497</td>\n",
       "      <td id=\"T_d8d5f_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f4261f88e0>"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = total.corr()\n",
    "matrix.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = smf.ols(\"Natality ~ Periodo + Men_Age + Women_Age + Population_Growth + Foreigners_Ratio + Foreigner_Women + Masculinity_Ratio + Marriages + Unemployment_Ratio + Unemployment_Women + Economic_Crisis + Confination + Facebook + Instagram_Tinder + Law_Change\", data=total).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Natality</td>     <th>  R-squared:         </th> <td>   0.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   139.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 15 Mar 2023</td> <th>  Prob (F-statistic):</th> <td>0.000115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:06:06</td>     <th>  Log-Likelihood:    </th> <td>  29.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>  -26.94</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     4</td>      <th>  BIC:               </th> <td>  -11.01</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    15</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>-1285.4837</td> <td> 4103.242</td> <td>   -0.313</td> <td> 0.770</td> <td>-1.27e+04</td> <td> 1.01e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Periodo</th>            <td>    0.6871</td> <td>    2.371</td> <td>    0.290</td> <td> 0.786</td> <td>   -5.896</td> <td>    7.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Men_Age</th>            <td>    0.4180</td> <td>    8.736</td> <td>    0.048</td> <td> 0.964</td> <td>  -23.838</td> <td>   24.674</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Women_Age</th>          <td>   -4.3751</td> <td>   10.806</td> <td>   -0.405</td> <td> 0.706</td> <td>  -34.378</td> <td>   25.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Population_Growth</th>  <td>   -0.0606</td> <td>    0.073</td> <td>   -0.833</td> <td> 0.452</td> <td>   -0.262</td> <td>    0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foreigners_Ratio</th>   <td>   -0.6226</td> <td>    1.842</td> <td>   -0.338</td> <td> 0.752</td> <td>   -5.737</td> <td>    4.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foreigner_Women</th>    <td>    0.1707</td> <td>    1.304</td> <td>    0.131</td> <td> 0.902</td> <td>   -3.450</td> <td>    3.791</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Masculinity_Ratio</th>  <td>    0.8896</td> <td>    2.340</td> <td>    0.380</td> <td> 0.723</td> <td>   -5.606</td> <td>    7.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marriages</th>          <td> 9.277e-06</td> <td> 6.23e-06</td> <td>    1.489</td> <td> 0.211</td> <td>-8.02e-06</td> <td> 2.66e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Unemployment_Ratio</th> <td>   -0.2638</td> <td>    0.324</td> <td>   -0.813</td> <td> 0.462</td> <td>   -1.165</td> <td>    0.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Unemployment_Women</th> <td>    0.1267</td> <td>    0.122</td> <td>    1.036</td> <td> 0.359</td> <td>   -0.213</td> <td>    0.466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Economic_Crisis</th>    <td>   -0.1738</td> <td>    0.321</td> <td>   -0.542</td> <td> 0.617</td> <td>   -1.064</td> <td>    0.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Confination</th>        <td>   -0.1377</td> <td>    0.595</td> <td>   -0.232</td> <td> 0.828</td> <td>   -1.788</td> <td>    1.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Facebook</th>           <td>    0.1198</td> <td>    0.233</td> <td>    0.515</td> <td> 0.634</td> <td>   -0.526</td> <td>    0.765</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Instagram_Tinder</th>   <td>    0.6458</td> <td>    0.277</td> <td>    2.334</td> <td> 0.080</td> <td>   -0.123</td> <td>    1.414</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Law_Change</th>         <td>   -0.1736</td> <td>    0.344</td> <td>   -0.505</td> <td> 0.640</td> <td>   -1.129</td> <td>    0.781</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.517</td> <th>  Durbin-Watson:     </th> <td>   2.743</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.284</td> <th>  Jarque-Bera (JB):  </th> <td>   1.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.007</td> <th>  Prob(JB):          </th> <td>   0.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.832</td> <th>  Cond. No.          </th> <td>2.62e+10</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.62e+10. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Natality   R-squared:                       0.998\n",
       "Model:                            OLS   Adj. R-squared:                  0.991\n",
       "Method:                 Least Squares   F-statistic:                     139.4\n",
       "Date:                Wed, 15 Mar 2023   Prob (F-statistic):           0.000115\n",
       "Time:                        17:06:06   Log-Likelihood:                 29.472\n",
       "No. Observations:                  20   AIC:                            -26.94\n",
       "Df Residuals:                       4   BIC:                            -11.01\n",
       "Df Model:                          15                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept          -1285.4837   4103.242     -0.313      0.770   -1.27e+04    1.01e+04\n",
       "Periodo                0.6871      2.371      0.290      0.786      -5.896       7.271\n",
       "Men_Age                0.4180      8.736      0.048      0.964     -23.838      24.674\n",
       "Women_Age             -4.3751     10.806     -0.405      0.706     -34.378      25.628\n",
       "Population_Growth     -0.0606      0.073     -0.833      0.452      -0.262       0.141\n",
       "Foreigners_Ratio      -0.6226      1.842     -0.338      0.752      -5.737       4.492\n",
       "Foreigner_Women        0.1707      1.304      0.131      0.902      -3.450       3.791\n",
       "Masculinity_Ratio      0.8896      2.340      0.380      0.723      -5.606       7.385\n",
       "Marriages           9.277e-06   6.23e-06      1.489      0.211   -8.02e-06    2.66e-05\n",
       "Unemployment_Ratio    -0.2638      0.324     -0.813      0.462      -1.165       0.637\n",
       "Unemployment_Women     0.1267      0.122      1.036      0.359      -0.213       0.466\n",
       "Economic_Crisis       -0.1738      0.321     -0.542      0.617      -1.064       0.716\n",
       "Confination           -0.1377      0.595     -0.232      0.828      -1.788       1.513\n",
       "Facebook               0.1198      0.233      0.515      0.634      -0.526       0.765\n",
       "Instagram_Tinder       0.6458      0.277      2.334      0.080      -0.123       1.414\n",
       "Law_Change            -0.1736      0.344     -0.505      0.640      -1.129       0.781\n",
       "==============================================================================\n",
       "Omnibus:                        2.517   Durbin-Watson:                   2.743\n",
       "Prob(Omnibus):                  0.284   Jarque-Bera (JB):                1.136\n",
       "Skew:                          -0.007   Prob(JB):                        0.567\n",
       "Kurtosis:                       1.832   Cond. No.                     2.62e+10\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.62e+10. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      VIF Factor            features\n",
      "0   4.036794e+07             Periodo\n",
      "1   2.915694e+04            Natality\n",
      "2   1.253099e+08             Men_Age\n",
      "3   1.765511e+08           Women_Age\n",
      "4   4.435491e+02   Population_Growth\n",
      "5   2.637542e+05    Foreigners_Ratio\n",
      "6   1.815771e+05     Foreigner_Women\n",
      "7   2.232450e+07   Masculinity_Ratio\n",
      "8   2.274114e+03           Marriages\n",
      "9   1.165671e+04  Unemployment_Ratio\n",
      "10  9.042788e+03  Unemployment_Women\n",
      "11  3.206793e+01     Economic_Crisis\n",
      "12  2.677452e+01         Confination\n",
      "13  4.481259e+01            Facebook\n",
      "14  9.363840e+01    Instagram_Tinder\n",
      "15  6.068460e+01          Law_Change\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(total.values, i) for i in range(total.shape[1])]\n",
    "vif[\"features\"] = total.columns\n",
    "\n",
    "print(vif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = smf.ols(\"Natality ~  Women_Age + Masculinity_Ratio + Marriages + Unemployment_Women + Economic_Crisis + Instagram_Tinder\", data=total).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Natality</td>     <th>  R-squared:         </th> <td>   0.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   673.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 15 Mar 2023</td> <th>  Prob (F-statistic):</th> <td>1.95e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:30:53</td>     <th>  Log-Likelihood:    </th> <td>  24.285</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>  -34.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    13</td>      <th>  BIC:               </th> <td>  -27.60</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>  -49.0905</td> <td>    6.065</td> <td>   -8.095</td> <td> 0.000</td> <td>  -62.192</td> <td>  -35.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Women_Age</th>          <td>   -0.9349</td> <td>    0.062</td> <td>  -14.988</td> <td> 0.000</td> <td>   -1.070</td> <td>   -0.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Masculinity_Ratio</th>  <td>    1.0018</td> <td>    0.058</td> <td>   17.373</td> <td> 0.000</td> <td>    0.877</td> <td>    1.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marriages</th>          <td>  5.08e-06</td> <td> 1.47e-06</td> <td>    3.460</td> <td> 0.004</td> <td> 1.91e-06</td> <td> 8.25e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Unemployment_Women</th> <td>    0.0190</td> <td>    0.006</td> <td>    2.958</td> <td> 0.011</td> <td>    0.005</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Economic_Crisis</th>    <td>   -0.4091</td> <td>    0.115</td> <td>   -3.558</td> <td> 0.004</td> <td>   -0.657</td> <td>   -0.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Instagram_Tinder</th>   <td>    0.7775</td> <td>    0.117</td> <td>    6.641</td> <td> 0.000</td> <td>    0.525</td> <td>    1.030</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 5.150</td> <th>  Durbin-Watson:     </th> <td>   3.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.076</td> <th>  Jarque-Bera (JB):  </th> <td>   2.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.759</td> <th>  Prob(JB):          </th> <td>   0.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.057</td> <th>  Cond. No.          </th> <td>5.38e+07</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.38e+07. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Natality   R-squared:                       0.997\n",
       "Model:                            OLS   Adj. R-squared:                  0.995\n",
       "Method:                 Least Squares   F-statistic:                     673.5\n",
       "Date:                Wed, 15 Mar 2023   Prob (F-statistic):           1.95e-15\n",
       "Time:                        17:30:53   Log-Likelihood:                 24.285\n",
       "No. Observations:                  20   AIC:                            -34.57\n",
       "Df Residuals:                      13   BIC:                            -27.60\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept            -49.0905      6.065     -8.095      0.000     -62.192     -35.989\n",
       "Women_Age             -0.9349      0.062    -14.988      0.000      -1.070      -0.800\n",
       "Masculinity_Ratio      1.0018      0.058     17.373      0.000       0.877       1.126\n",
       "Marriages            5.08e-06   1.47e-06      3.460      0.004    1.91e-06    8.25e-06\n",
       "Unemployment_Women     0.0190      0.006      2.958      0.011       0.005       0.033\n",
       "Economic_Crisis       -0.4091      0.115     -3.558      0.004      -0.657      -0.161\n",
       "Instagram_Tinder       0.7775      0.117      6.641      0.000       0.525       1.030\n",
       "==============================================================================\n",
       "Omnibus:                        5.150   Durbin-Watson:                   3.096\n",
       "Prob(Omnibus):                  0.076   Jarque-Bera (JB):                2.849\n",
       "Skew:                           0.759   Prob(JB):                        0.241\n",
       "Kurtosis:                       4.057   Cond. No.                     5.38e+07\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.38e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tabla(name):\n",
    "    #Years\n",
    "    ccaa = pd.DataFrame({'Periodo': range(2002, 2022)})\n",
    "\n",
    "    #Natality Rate per 1000 people\n",
    "    nat=natality[natality['Comunidades y Ciudades Autónomas']==name]\n",
    "    nat=nat[nat['Nacionalidad']=='Ambas nacionalidades']\n",
    "    nat['Total'] = nat['Total'].str.replace(',', '.').astype(float)\n",
    "    ccaa = ccaa.merge(nat.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "    ccaa = ccaa.rename(columns={'Total': 'Natality'})\n",
    "\n",
    "    #Mean Age of Women\n",
    "    women=Edad[Edad['Comunidades y Ciudades Autónomas']==name]\n",
    "    women=women[women['Sexo']=='Mujeres']\n",
    "    ccaa = ccaa.merge(women.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "    ccaa = ccaa.rename(columns={'Total': 'Women_Age'})\n",
    "\n",
    "    #Masculinity Ratio\n",
    "    man=masculinity[masculinity['Comunidades y Ciudades Autónomas']==name]\n",
    "    ccaa = ccaa.merge(man.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "    ccaa = ccaa.rename(columns={'Total': 'Masculinity_Ratio'})\n",
    "\n",
    "    #Number of total Marriages\n",
    "    happy=married[married['Comunidades y Ciudades Autónomas']==name]\n",
    "    happy = happy.groupby(['Periodo'])['Total'].sum().reset_index()\n",
    "    ccaa = ccaa.merge(happy.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "    ccaa = ccaa.rename(columns={'Total': 'Marriages'})\n",
    "\n",
    "    # Unemployment Rate of Fertile Women\n",
    "    unemployed=paradas[paradas['Comunidades y Ciudades Autónomas']==name]\n",
    "    ccaa = ccaa.merge(unemployed.loc[:, ['Periodo', 'Total']], on='Periodo', how='left')\n",
    "    ccaa = ccaa.rename(columns={'Total': 'Unemployment_Women'})\n",
    "\n",
    "    #Boolean: Economic Crisis of 2008\n",
    "    ccaa['Economic_Crisis'] = ccaa['Periodo'].apply(lambda x: 1 if (x >= 2009) and (x <= 2015) else 0)\n",
    "\n",
    "    #Boolean: Arrival of trending social network\n",
    "    ccaa['Instagram_Tinder'] = ccaa['Periodo'].apply(lambda x: 1 if x > 2012 else 0)\n",
    "\n",
    "    return ccaa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "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": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natality['Comunidades y Ciudades Autónomas'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "andalucia=tabla('01 Andalucía')\n",
    "aragon=tabla('02 Aragón')\n",
    "asturias=tabla('03 Asturias, Principado de')\n",
    "baleares=tabla('04 Balears, Illes')\n",
    "cantabria=tabla('06 Cantabria')\n",
    "leon=tabla('07 Castilla y León')\n",
    "mancha=tabla('08 Castilla - La Mancha')\n",
    "catalunya=tabla('09 Cataluña')\n",
    "valencia=tabla('10 Comunitat Valenciana')\n",
    "extremadura=tabla('11 Extremadura')\n",
    "galicia=tabla('12 Galicia')\n",
    "madrid=tabla('13 Madrid, Comunidad de')\n",
    "murcia=tabla('14 Murcia, Región de')\n",
    "navarra=tabla('15 Navarra, Comunidad Foral de')\n",
    "euskadi=tabla('16 País Vasco')\n",
    "rioja=tabla('17 Rioja, La')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "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>Periodo</th>\n",
       "      <th>Natality</th>\n",
       "      <th>Women_Age</th>\n",
       "      <th>Masculinity_Ratio</th>\n",
       "      <th>Marriages</th>\n",
       "      <th>Unemployment_Women</th>\n",
       "      <th>Economic_Crisis</th>\n",
       "      <th>Instagram_Tinder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>10.31</td>\n",
       "      <td>42.35</td>\n",
       "      <td>99.16</td>\n",
       "      <td>2828</td>\n",
       "      <td>16.638000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>10.80</td>\n",
       "      <td>42.36</td>\n",
       "      <td>99.23</td>\n",
       "      <td>2868</td>\n",
       "      <td>14.156000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>10.85</td>\n",
       "      <td>42.39</td>\n",
       "      <td>99.32</td>\n",
       "      <td>2920</td>\n",
       "      <td>12.984000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>10.46</td>\n",
       "      <td>42.50</td>\n",
       "      <td>99.46</td>\n",
       "      <td>2671</td>\n",
       "      <td>12.214500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>10.98</td>\n",
       "      <td>42.57</td>\n",
       "      <td>99.54</td>\n",
       "      <td>2727</td>\n",
       "      <td>13.114000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>10.82</td>\n",
       "      <td>42.60</td>\n",
       "      <td>99.58</td>\n",
       "      <td>2705</td>\n",
       "      <td>14.133500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>11.30</td>\n",
       "      <td>42.58</td>\n",
       "      <td>100.09</td>\n",
       "      <td>2681</td>\n",
       "      <td>19.160000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>10.86</td>\n",
       "      <td>42.58</td>\n",
       "      <td>100.23</td>\n",
       "      <td>2492</td>\n",
       "      <td>25.375000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>10.67</td>\n",
       "      <td>42.71</td>\n",
       "      <td>100.01</td>\n",
       "      <td>2285</td>\n",
       "      <td>24.640000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>10.57</td>\n",
       "      <td>42.89</td>\n",
       "      <td>99.73</td>\n",
       "      <td>2218</td>\n",
       "      <td>28.448500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>10.53</td>\n",
       "      <td>43.05</td>\n",
       "      <td>99.43</td>\n",
       "      <td>2232</td>\n",
       "      <td>34.771000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43.28</td>\n",
       "      <td>98.99</td>\n",
       "      <td>2097</td>\n",
       "      <td>39.591000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>9.72</td>\n",
       "      <td>43.58</td>\n",
       "      <td>98.57</td>\n",
       "      <td>2301</td>\n",
       "      <td>32.554500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>9.44</td>\n",
       "      <td>43.80</td>\n",
       "      <td>98.36</td>\n",
       "      <td>2616</td>\n",
       "      <td>31.262500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>9.24</td>\n",
       "      <td>44.00</td>\n",
       "      <td>98.26</td>\n",
       "      <td>2383</td>\n",
       "      <td>28.903158</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>8.96</td>\n",
       "      <td>44.16</td>\n",
       "      <td>98.03</td>\n",
       "      <td>2295</td>\n",
       "      <td>33.182500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>8.35</td>\n",
       "      <td>44.29</td>\n",
       "      <td>97.77</td>\n",
       "      <td>2050</td>\n",
       "      <td>20.644500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>8.23</td>\n",
       "      <td>44.39</td>\n",
       "      <td>97.62</td>\n",
       "      <td>2050</td>\n",
       "      <td>22.426500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>7.66</td>\n",
       "      <td>44.53</td>\n",
       "      <td>97.70</td>\n",
       "      <td>1279</td>\n",
       "      <td>17.713333</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021</td>\n",
       "      <td>7.78</td>\n",
       "      <td>44.70</td>\n",
       "      <td>97.64</td>\n",
       "      <td>1932</td>\n",
       "      <td>19.814211</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Periodo  Natality  Women_Age  Masculinity_Ratio  Marriages  \\\n",
       "0      2002     10.31      42.35              99.16       2828   \n",
       "1      2003     10.80      42.36              99.23       2868   \n",
       "2      2004     10.85      42.39              99.32       2920   \n",
       "3      2005     10.46      42.50              99.46       2671   \n",
       "4      2006     10.98      42.57              99.54       2727   \n",
       "5      2007     10.82      42.60              99.58       2705   \n",
       "6      2008     11.30      42.58             100.09       2681   \n",
       "7      2009     10.86      42.58             100.23       2492   \n",
       "8      2010     10.67      42.71             100.01       2285   \n",
       "9      2011     10.57      42.89              99.73       2218   \n",
       "10     2012     10.53      43.05              99.43       2232   \n",
       "11     2013      9.54      43.28              98.99       2097   \n",
       "12     2014      9.72      43.58              98.57       2301   \n",
       "13     2015      9.44      43.80              98.36       2616   \n",
       "14     2016      9.24      44.00              98.26       2383   \n",
       "15     2017      8.96      44.16              98.03       2295   \n",
       "16     2018      8.35      44.29              97.77       2050   \n",
       "17     2019      8.23      44.39              97.62       2050   \n",
       "18     2020      7.66      44.53              97.70       1279   \n",
       "19     2021      7.78      44.70              97.64       1932   \n",
       "\n",
       "    Unemployment_Women  Economic_Crisis  Instagram_Tinder  \n",
       "0            16.638000                0                 0  \n",
       "1            14.156000                0                 0  \n",
       "2            12.984000                0                 0  \n",
       "3            12.214500                0                 0  \n",
       "4            13.114000                0                 0  \n",
       "5            14.133500                0                 0  \n",
       "6            19.160000                0                 0  \n",
       "7            25.375000                1                 0  \n",
       "8            24.640000                1                 0  \n",
       "9            28.448500                1                 0  \n",
       "10           34.771000                1                 0  \n",
       "11           39.591000                1                 1  \n",
       "12           32.554500                1                 1  \n",
       "13           31.262500                1                 1  \n",
       "14           28.903158                0                 1  \n",
       "15           33.182500                0                 1  \n",
       "16           20.644500                0                 1  \n",
       "17           22.426500                0                 1  \n",
       "18           17.713333                0                 1  \n",
       "19           19.814211                0                 1  "
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "navarra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ols (var):\n",
    "    return smf.ols(\"Natality ~  Women_Age + Masculinity_Ratio + Marriages + Unemployment_Women + Economic_Crisis + Instagram_Tinder\", data=var).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultsand= ols(andalucia)\n",
    "resultsara= ols(aragon)\n",
    "resultsast = ols(asturias)\n",
    "resultsbal = ols(baleares)\n",
    "resultscan = ols(cantabria)\n",
    "resultsleo = ols(leon)\n",
    "resultsman = ols(mancha)\n",
    "resultscat = ols(catalunya)\n",
    "resultsval = ols(valencia)\n",
    "resultsext = ols(extremadura)\n",
    "resultsgal = ols(galicia)\n",
    "resultsmad = ols(madrid)\n",
    "resultsmur = ols(murcia)\n",
    "resultsnav = ols(navarra)\n",
    "resultseus = ols(euskadi)\n",
    "resultsrio = ols(rioja)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Natality</td>     <th>  R-squared:         </th> <td>   0.958</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.938</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   49.09</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 15 Mar 2023</td> <th>  Prob (F-statistic):</th> <td>3.47e-08</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:54:19</td>     <th>  Log-Likelihood:    </th> <td>-0.073918</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>   14.15</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    13</td>      <th>  BIC:               </th> <td>   21.12</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>  -14.5696</td> <td>   21.992</td> <td>   -0.663</td> <td> 0.519</td> <td>  -62.080</td> <td>   32.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Women_Age</th>          <td>   -0.4777</td> <td>    0.261</td> <td>   -1.834</td> <td> 0.090</td> <td>   -1.041</td> <td>    0.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Masculinity_Ratio</th>  <td>    0.4379</td> <td>    0.126</td> <td>    3.469</td> <td> 0.004</td> <td>    0.165</td> <td>    0.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marriages</th>          <td>    0.0005</td> <td>    0.001</td> <td>    0.729</td> <td> 0.479</td> <td>   -0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Unemployment_Women</th> <td>    0.0203</td> <td>    0.014</td> <td>    1.399</td> <td> 0.185</td> <td>   -0.011</td> <td>    0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Economic_Crisis</th>    <td>    0.0021</td> <td>    0.301</td> <td>    0.007</td> <td> 0.994</td> <td>   -0.648</td> <td>    0.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Instagram_Tinder</th>   <td>    0.2912</td> <td>    0.366</td> <td>    0.796</td> <td> 0.440</td> <td>   -0.499</td> <td>    1.081</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.726</td> <th>  Durbin-Watson:     </th> <td>   2.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.256</td> <th>  Jarque-Bera (JB):  </th> <td>   1.444</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.339</td> <th>  Prob(JB):          </th> <td>   0.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.872</td> <th>  Cond. No.          </th> <td>3.89e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.89e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Natality   R-squared:                       0.958\n",
       "Model:                            OLS   Adj. R-squared:                  0.938\n",
       "Method:                 Least Squares   F-statistic:                     49.09\n",
       "Date:                Wed, 15 Mar 2023   Prob (F-statistic):           3.47e-08\n",
       "Time:                        19:54:19   Log-Likelihood:              -0.073918\n",
       "No. Observations:                  20   AIC:                             14.15\n",
       "Df Residuals:                      13   BIC:                             21.12\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept            -14.5696     21.992     -0.663      0.519     -62.080      32.941\n",
       "Women_Age             -0.4777      0.261     -1.834      0.090      -1.041       0.085\n",
       "Masculinity_Ratio      0.4379      0.126      3.469      0.004       0.165       0.711\n",
       "Marriages              0.0005      0.001      0.729      0.479      -0.001       0.002\n",
       "Unemployment_Women     0.0203      0.014      1.399      0.185      -0.011       0.052\n",
       "Economic_Crisis        0.0021      0.301      0.007      0.994      -0.648       0.652\n",
       "Instagram_Tinder       0.2912      0.366      0.796      0.440      -0.499       1.081\n",
       "==============================================================================\n",
       "Omnibus:                        2.726   Durbin-Watson:                   2.193\n",
       "Prob(Omnibus):                  0.256   Jarque-Bera (JB):                1.444\n",
       "Skew:                          -0.339   Prob(JB):                        0.486\n",
       "Kurtosis:                       1.872   Cond. No.                     3.89e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.89e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsrio.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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