TMF Precio electricidad -  feature engineering and basic VIZ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering & basic visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing electricity price for all years (2013-2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.options.display.max_columns = None\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dsc/CarpetaCompartida/TFM\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_2013 = \"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Definitivo_2013/2013/PFMDIASM_TOD_2013\"\n",
    "path_2014 = \"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Definitivo_2014/2014/PFMDIASM_TOD_2014\"\n",
    "path_2015 = \"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Definitivo_2015/2015/PFMDIASM_TOD_2015\"\n",
    "path_2016 = \"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Definitivo_2016/2016/PFMDIASM_TOD_2016\"\n",
    "path_2017 = \"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Definitivo_2017/2017/PFMDIASM_TOD_2017\"\n",
    "path_2018 = \"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Definitivos_2018/2018/PFMDIASM_TOD_2018\"\n",
    "path_2019 = \"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Definitivos_2019/2019/PFMDIASM_TOD_2019\"\n",
    "path_2020 = \"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Definitivos_2020/2020/PFMDIASM_TOD_2020\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using glob.iglob() it returns an iterator that I can easily convert into a pandas Dataframe\n",
    "\n",
    "data2013_import = glob.iglob(path_2013 + \"*xls\")\n",
    "dataframes13 = (pd.read_excel(excel, dtype= str,parse_dates= True, engine= None, header = 3) for excel in data2013_import)\n",
    "data2013  = pd.concat(dataframes13, ignore_index= True)\n",
    "\n",
    "data2014_import = glob.iglob(path_2014 + \"*xls\")\n",
    "dataframes14 = (pd.read_excel(excel, dtype= str,parse_dates= True, engine= None, header = 3) for excel in data2014_import)\n",
    "data2014  = pd.concat(dataframes14, ignore_index= True)\n",
    "\n",
    "data2015_import = glob.iglob(path_2015 + \"*xls\")\n",
    "dataframes15 = (pd.read_excel(excel, dtype= str,parse_dates= True, engine= None, header = 3) for excel in data2015_import)\n",
    "data2015  = pd.concat(dataframes15, ignore_index= True)\n",
    "\n",
    "data2016_import = glob.iglob(path_2016 + \"*xls\")\n",
    "dataframes16 = (pd.read_excel(excel, dtype= str,parse_dates= True, engine= None, header = 3) for excel in data2016_import)\n",
    "data2016  = pd.concat(dataframes16, ignore_index= True)\n",
    "\n",
    "data2017_import = glob.iglob(path_2017 + \"*xls\")\n",
    "dataframes17 = (pd.read_excel(excel, dtype= str,parse_dates= True, engine= None, header = 3) for excel in data2017_import)\n",
    "data2017  = pd.concat(dataframes17, ignore_index= True)\n",
    "\n",
    "data2018_import = glob.iglob(path_2018 + \"*xls\")\n",
    "dataframes18 = (pd.read_excel(excel, dtype= str,parse_dates= True, engine= None, header = 3) for excel in data2018_import)\n",
    "data2018  = pd.concat(dataframes18, ignore_index= True)\n",
    "\n",
    "data2019_import = glob.iglob(path_2019 + \"*xls\")\n",
    "dataframes19 = (pd.read_excel(excel, dtype= str,parse_dates= True, engine= None, header = 3) for excel in data2019_import)\n",
    "data2019  = pd.concat(dataframes19, ignore_index= True)\n",
    "\n",
    "data2020_import = glob.iglob(path_2020 + \"*xls\")\n",
    "dataframes20 = (pd.read_excel(excel, dtype= str,parse_dates= True, engine= None, header = 3) for excel in data2020_import)\n",
    "data2020  = pd.concat(dataframes20, ignore_index= True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Periodo</th>\n",
       "      <th>Energía\\n final \\nMWh</th>\n",
       "      <th>Mercado \\ndiario \\n€/MWh</th>\n",
       "      <th>Mercado \\nintradiario \\n€/MWh</th>\n",
       "      <th>Restricciones\\n€/MWh</th>\n",
       "      <th>Procesos \\nOS \\n€/MWh</th>\n",
       "      <th>Garantía potencia \\nPagos capacidad\\n€/MWh</th>\n",
       "      <th>Total\\n€/MWh</th>\n",
       "      <th>Coste\\ns.interrrumpibilidad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/06/13 Sábado</td>\n",
       "      <td>656315.602</td>\n",
       "      <td>33.02</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>4.81</td>\n",
       "      <td>1.49</td>\n",
       "      <td>3.9</td>\n",
       "      <td>42.78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/06/13 Domingo</td>\n",
       "      <td>649328.82</td>\n",
       "      <td>19.09</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>6.53</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.74</td>\n",
       "      <td>31.32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/06/13 Lunes</td>\n",
       "      <td>700500.79</td>\n",
       "      <td>38.51</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>5.44</td>\n",
       "      <td>1.94</td>\n",
       "      <td>5.45</td>\n",
       "      <td>51.18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/06/13 Martes</td>\n",
       "      <td>702582.423</td>\n",
       "      <td>47.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.42</td>\n",
       "      <td>56.96</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/06/13 Miércoles</td>\n",
       "      <td>701818.743</td>\n",
       "      <td>51.48</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.45</td>\n",
       "      <td>60.35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>27/01/20 Lunes</td>\n",
       "      <td>822863.04</td>\n",
       "      <td>39.87</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3.23</td>\n",
       "      <td>44.46</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>28/01/20 Martes</td>\n",
       "      <td>816667.2</td>\n",
       "      <td>39.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>3.2</td>\n",
       "      <td>43.83</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>29/01/20 Miércoles</td>\n",
       "      <td>818153.969</td>\n",
       "      <td>40.24</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.12</td>\n",
       "      <td>45.06</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>30/01/20 Jueves</td>\n",
       "      <td>793218.71</td>\n",
       "      <td>38.48</td>\n",
       "      <td>0</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.33</td>\n",
       "      <td>3.15</td>\n",
       "      <td>43.1</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>31/01/20 Viernes</td>\n",
       "      <td>772638.789</td>\n",
       "      <td>37.6</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.26</td>\n",
       "      <td>3.17</td>\n",
       "      <td>41.73</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2922 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Periodo Energía\\n final \\nMWh Mercado \\ndiario \\n€/MWh  \\\n",
       "0     01/06/13 Sábado               656315.602                    33.02   \n",
       "1     02/06/13 Domingo               649328.82                    19.09   \n",
       "2     03/06/13 Lunes                 700500.79                    38.51   \n",
       "3     04/06/13 Martes               702582.423                    47.12   \n",
       "4     05/06/13 Miércoles            701818.743                    51.48   \n",
       "...                  ...                   ...                      ...   \n",
       "2917  27/01/20 Lunes                 822863.04                    39.87   \n",
       "2918  28/01/20 Martes                 816667.2                    39.02   \n",
       "2919  29/01/20 Miércoles            818153.969                    40.24   \n",
       "2920  30/01/20 Jueves                793218.71                    38.48   \n",
       "2921  31/01/20 Viernes              772638.789                     37.6   \n",
       "\n",
       "     Mercado \\nintradiario \\n€/MWh Restricciones\\n€/MWh Procesos \\nOS \\n€/MWh  \\\n",
       "0                            -0.44                 4.81                  1.49   \n",
       "1                            -0.39                 6.53                  2.35   \n",
       "2                            -0.16                 5.44                  1.94   \n",
       "3                             0.01                 3.16                  1.25   \n",
       "4                            -0.16                  2.6                  0.97   \n",
       "...                            ...                  ...                   ...   \n",
       "2917                         -0.03                 1.15                  0.21   \n",
       "2918                         -0.02                 1.36                  0.24   \n",
       "2919                         -0.02                 1.38                   0.3   \n",
       "2920                             0                 1.11                  0.33   \n",
       "2921                         -0.02                 0.69                  0.26   \n",
       "\n",
       "     Garantía potencia \\nPagos capacidad\\n€/MWh Total\\n€/MWh  \\\n",
       "0                                           3.9        42.78   \n",
       "1                                          3.74        31.32   \n",
       "2                                          5.45        51.18   \n",
       "3                                          5.42        56.96   \n",
       "4                                          5.45        60.35   \n",
       "...                                         ...          ...   \n",
       "2917                                       3.23        44.46   \n",
       "2918                                        3.2        43.83   \n",
       "2919                                       3.12        45.06   \n",
       "2920                                       3.15         43.1   \n",
       "2921                                       3.17        41.73   \n",
       "\n",
       "     Coste\\ns.interrrumpibilidad  \n",
       "0                            NaN  \n",
       "1                            NaN  \n",
       "2                            NaN  \n",
       "3                            NaN  \n",
       "4                            NaN  \n",
       "...                          ...  \n",
       "2917                        0.03  \n",
       "2918                        0.03  \n",
       "2919                        0.03  \n",
       "2920                        0.03  \n",
       "2921                        0.03  \n",
       "\n",
       "[2922 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_prices = data2013.append([data2014, data2015, data2016, data2017, data2018, data2019, data2020],\n",
    "                                    ignore_index=True)\n",
    "electricity_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(electricity_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "electricity_prices[[\"Date\", \"Day\"]] = electricity_prices.Periodo.str.split(expand= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Periodo</th>\n",
       "      <th>Energía\\n final \\nMWh</th>\n",
       "      <th>Mercado \\ndiario \\n€/MWh</th>\n",
       "      <th>Mercado \\nintradiario \\n€/MWh</th>\n",
       "      <th>Restricciones\\n€/MWh</th>\n",
       "      <th>Procesos \\nOS \\n€/MWh</th>\n",
       "      <th>Garantía potencia \\nPagos capacidad\\n€/MWh</th>\n",
       "      <th>Total\\n€/MWh</th>\n",
       "      <th>Coste\\ns.interrrumpibilidad</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/06/13 Sábado</td>\n",
       "      <td>656315.602</td>\n",
       "      <td>33.02</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>4.81</td>\n",
       "      <td>1.49</td>\n",
       "      <td>3.9</td>\n",
       "      <td>42.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01/06/13</td>\n",
       "      <td>Sábado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/06/13 Domingo</td>\n",
       "      <td>649328.82</td>\n",
       "      <td>19.09</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>6.53</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.74</td>\n",
       "      <td>31.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02/06/13</td>\n",
       "      <td>Domingo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/06/13 Lunes</td>\n",
       "      <td>700500.79</td>\n",
       "      <td>38.51</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>5.44</td>\n",
       "      <td>1.94</td>\n",
       "      <td>5.45</td>\n",
       "      <td>51.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03/06/13</td>\n",
       "      <td>Lunes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/06/13 Martes</td>\n",
       "      <td>702582.423</td>\n",
       "      <td>47.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.42</td>\n",
       "      <td>56.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04/06/13</td>\n",
       "      <td>Martes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/06/13 Miércoles</td>\n",
       "      <td>701818.743</td>\n",
       "      <td>51.48</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.45</td>\n",
       "      <td>60.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05/06/13</td>\n",
       "      <td>Miércoles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Periodo Energía\\n final \\nMWh Mercado \\ndiario \\n€/MWh  \\\n",
       "0  01/06/13 Sábado               656315.602                    33.02   \n",
       "1  02/06/13 Domingo               649328.82                    19.09   \n",
       "2  03/06/13 Lunes                 700500.79                    38.51   \n",
       "3  04/06/13 Martes               702582.423                    47.12   \n",
       "4  05/06/13 Miércoles            701818.743                    51.48   \n",
       "\n",
       "  Mercado \\nintradiario \\n€/MWh Restricciones\\n€/MWh Procesos \\nOS \\n€/MWh  \\\n",
       "0                         -0.44                 4.81                  1.49   \n",
       "1                         -0.39                 6.53                  2.35   \n",
       "2                         -0.16                 5.44                  1.94   \n",
       "3                          0.01                 3.16                  1.25   \n",
       "4                         -0.16                  2.6                  0.97   \n",
       "\n",
       "  Garantía potencia \\nPagos capacidad\\n€/MWh Total\\n€/MWh  \\\n",
       "0                                        3.9        42.78   \n",
       "1                                       3.74        31.32   \n",
       "2                                       5.45        51.18   \n",
       "3                                       5.42        56.96   \n",
       "4                                       5.45        60.35   \n",
       "\n",
       "  Coste\\ns.interrrumpibilidad      Date        Day  \n",
       "0                         NaN  01/06/13     Sábado  \n",
       "1                         NaN  02/06/13    Domingo  \n",
       "2                         NaN  03/06/13      Lunes  \n",
       "3                         NaN  04/06/13     Martes  \n",
       "4                         NaN  05/06/13  Miércoles  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Energía\\n final \\nMWh</th>\n",
       "      <th>Mercado \\ndiario \\n€/MWh</th>\n",
       "      <th>Mercado \\nintradiario \\n€/MWh</th>\n",
       "      <th>Restricciones\\n€/MWh</th>\n",
       "      <th>Procesos \\nOS \\n€/MWh</th>\n",
       "      <th>Garantía potencia \\nPagos capacidad\\n€/MWh</th>\n",
       "      <th>Total\\n€/MWh</th>\n",
       "      <th>Coste\\ns.interrrumpibilidad</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/06/13 Sábado</td>\n",
       "      <td>656315.602</td>\n",
       "      <td>33.02</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>4.81</td>\n",
       "      <td>1.49</td>\n",
       "      <td>3.9</td>\n",
       "      <td>42.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>Sábado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/06/13 Domingo</td>\n",
       "      <td>649328.82</td>\n",
       "      <td>19.09</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>6.53</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.74</td>\n",
       "      <td>31.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>Domingo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/06/13 Lunes</td>\n",
       "      <td>700500.79</td>\n",
       "      <td>38.51</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>5.44</td>\n",
       "      <td>1.94</td>\n",
       "      <td>5.45</td>\n",
       "      <td>51.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>Lunes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/06/13 Martes</td>\n",
       "      <td>702582.423</td>\n",
       "      <td>47.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.42</td>\n",
       "      <td>56.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-04</td>\n",
       "      <td>Martes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/06/13 Miércoles</td>\n",
       "      <td>701818.743</td>\n",
       "      <td>51.48</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.45</td>\n",
       "      <td>60.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-05</td>\n",
       "      <td>Miércoles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Periodo Energía\\n final \\nMWh Mercado \\ndiario \\n€/MWh  \\\n",
       "0  01/06/13 Sábado               656315.602                    33.02   \n",
       "1  02/06/13 Domingo               649328.82                    19.09   \n",
       "2  03/06/13 Lunes                 700500.79                    38.51   \n",
       "3  04/06/13 Martes               702582.423                    47.12   \n",
       "4  05/06/13 Miércoles            701818.743                    51.48   \n",
       "\n",
       "  Mercado \\nintradiario \\n€/MWh Restricciones\\n€/MWh Procesos \\nOS \\n€/MWh  \\\n",
       "0                         -0.44                 4.81                  1.49   \n",
       "1                         -0.39                 6.53                  2.35   \n",
       "2                         -0.16                 5.44                  1.94   \n",
       "3                          0.01                 3.16                  1.25   \n",
       "4                         -0.16                  2.6                  0.97   \n",
       "\n",
       "  Garantía potencia \\nPagos capacidad\\n€/MWh Total\\n€/MWh  \\\n",
       "0                                        3.9        42.78   \n",
       "1                                       3.74        31.32   \n",
       "2                                       5.45        51.18   \n",
       "3                                       5.42        56.96   \n",
       "4                                       5.45        60.35   \n",
       "\n",
       "  Coste\\ns.interrrumpibilidad       Date        Day  \n",
       "0                         NaN 2013-06-01     Sábado  \n",
       "1                         NaN 2013-06-02    Domingo  \n",
       "2                         NaN 2013-06-03      Lunes  \n",
       "3                         NaN 2013-06-04     Martes  \n",
       "4                         NaN 2013-06-05  Miércoles  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_prices[\"Date\"] = pd.to_datetime(electricity_prices[\"Date\"], dayfirst=True)\n",
    "electricity_prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Brent crude oil future prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Último</th>\n",
       "      <th>Apertura</th>\n",
       "      <th>Máximo</th>\n",
       "      <th>Mínimo</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>% var.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.11.2021</td>\n",
       "      <td>81,99</td>\n",
       "      <td>82,85</td>\n",
       "      <td>82,85</td>\n",
       "      <td>81,26</td>\n",
       "      <td>-</td>\n",
       "      <td>-1,06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.11.2021</td>\n",
       "      <td>82,87</td>\n",
       "      <td>82,85</td>\n",
       "      <td>83,44</td>\n",
       "      <td>81,66</td>\n",
       "      <td>336,14K</td>\n",
       "      <td>0,28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.11.2021</td>\n",
       "      <td>82,64</td>\n",
       "      <td>84,99</td>\n",
       "      <td>85,50</td>\n",
       "      <td>82,08</td>\n",
       "      <td>366,31K</td>\n",
       "      <td>-2,52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09.11.2021</td>\n",
       "      <td>84,78</td>\n",
       "      <td>83,47</td>\n",
       "      <td>85,30</td>\n",
       "      <td>83,16</td>\n",
       "      <td>346,57K</td>\n",
       "      <td>1,62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08.11.2021</td>\n",
       "      <td>83,43</td>\n",
       "      <td>82,58</td>\n",
       "      <td>84,00</td>\n",
       "      <td>82,53</td>\n",
       "      <td>269,60K</td>\n",
       "      <td>0,83%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fecha Último Apertura Máximo Mínimo     Vol.  % var.\n",
       "0  12.11.2021  81,99    82,85  82,85  81,26        -  -1,06%\n",
       "1  11.11.2021  82,87    82,85  83,44  81,66  336,14K   0,28%\n",
       "2  10.11.2021  82,64    84,99  85,50  82,08  366,31K  -2,52%\n",
       "3  09.11.2021  84,78    83,47  85,30  83,16  346,57K   1,62%\n",
       "4  08.11.2021  83,43    82,58  84,00  82,53  269,60K   0,83%"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brent_prices = pd.read_csv(\"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Datos_históricos_Futuros_petróleo_Brent.csv\")\n",
    "brent_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "brent_prices[\"Fecha\"] = pd.to_datetime(brent_prices[\"Fecha\"], dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Fecha</th>\n",
       "      <th>Último</th>\n",
       "      <th>Apertura</th>\n",
       "      <th>Máximo</th>\n",
       "      <th>Mínimo</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>% var.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-11-12</td>\n",
       "      <td>81,99</td>\n",
       "      <td>82,85</td>\n",
       "      <td>82,85</td>\n",
       "      <td>81,26</td>\n",
       "      <td>-</td>\n",
       "      <td>-1,06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-11</td>\n",
       "      <td>82,87</td>\n",
       "      <td>82,85</td>\n",
       "      <td>83,44</td>\n",
       "      <td>81,66</td>\n",
       "      <td>336,14K</td>\n",
       "      <td>0,28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-11-10</td>\n",
       "      <td>82,64</td>\n",
       "      <td>84,99</td>\n",
       "      <td>85,50</td>\n",
       "      <td>82,08</td>\n",
       "      <td>366,31K</td>\n",
       "      <td>-2,52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-11-09</td>\n",
       "      <td>84,78</td>\n",
       "      <td>83,47</td>\n",
       "      <td>85,30</td>\n",
       "      <td>83,16</td>\n",
       "      <td>346,57K</td>\n",
       "      <td>1,62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-11-08</td>\n",
       "      <td>83,43</td>\n",
       "      <td>82,58</td>\n",
       "      <td>84,00</td>\n",
       "      <td>82,53</td>\n",
       "      <td>269,60K</td>\n",
       "      <td>0,83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>111,94</td>\n",
       "      <td>111,42</td>\n",
       "      <td>112,47</td>\n",
       "      <td>111,12</td>\n",
       "      <td>194,00K</td>\n",
       "      <td>0,48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>111,40</td>\n",
       "      <td>111,42</td>\n",
       "      <td>111,69</td>\n",
       "      <td>110,54</td>\n",
       "      <td>203,50K</td>\n",
       "      <td>0,08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>111,31</td>\n",
       "      <td>111,72</td>\n",
       "      <td>111,78</td>\n",
       "      <td>110,38</td>\n",
       "      <td>191,33K</td>\n",
       "      <td>-0,74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>112,14</td>\n",
       "      <td>112,20</td>\n",
       "      <td>112,42</td>\n",
       "      <td>111,63</td>\n",
       "      <td>190,29K</td>\n",
       "      <td>-0,29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>112,47</td>\n",
       "      <td>111,50</td>\n",
       "      <td>112,90</td>\n",
       "      <td>111,27</td>\n",
       "      <td>183,14K</td>\n",
       "      <td>1,22%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2290 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Fecha  Último Apertura  Máximo  Mínimo     Vol.  % var.\n",
       "0    2021-11-12   81,99    82,85   82,85   81,26        -  -1,06%\n",
       "1    2021-11-11   82,87    82,85   83,44   81,66  336,14K   0,28%\n",
       "2    2021-11-10   82,64    84,99   85,50   82,08  366,31K  -2,52%\n",
       "3    2021-11-09   84,78    83,47   85,30   83,16  346,57K   1,62%\n",
       "4    2021-11-08   83,43    82,58   84,00   82,53  269,60K   0,83%\n",
       "...         ...     ...      ...     ...     ...      ...     ...\n",
       "2285 2013-01-08  111,94   111,42  112,47  111,12  194,00K   0,48%\n",
       "2286 2013-01-07  111,40   111,42  111,69  110,54  203,50K   0,08%\n",
       "2287 2013-01-04  111,31   111,72  111,78  110,38  191,33K  -0,74%\n",
       "2288 2013-01-03  112,14   112,20  112,42  111,63  190,29K  -0,29%\n",
       "2289 2013-01-02  112,47   111,50  112,90  111,27  183,14K   1,22%\n",
       "\n",
       "[2290 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Column \"Vol.\" is the number of contracts that were sold that day\n",
    "brent_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fecha       datetime64[ns]\n",
       "Último              object\n",
       "Apertura            object\n",
       "Máximo              object\n",
       "Mínimo              object\n",
       "Vol.                object\n",
       "% var.              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brent_prices.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "brent_prices.rename(columns={\"Fecha\":\"Date\"}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Energía\\n final \\nMWh</th>\n",
       "      <th>Mercado \\ndiario \\n€/MWh</th>\n",
       "      <th>Mercado \\nintradiario \\n€/MWh</th>\n",
       "      <th>Restricciones\\n€/MWh</th>\n",
       "      <th>Procesos \\nOS \\n€/MWh</th>\n",
       "      <th>Garantía potencia \\nPagos capacidad\\n€/MWh</th>\n",
       "      <th>Total\\n€/MWh</th>\n",
       "      <th>Coste\\ns.interrrumpibilidad</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Último</th>\n",
       "      <th>Apertura</th>\n",
       "      <th>Máximo</th>\n",
       "      <th>Mínimo</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>% var.</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03/06/13 Lunes</td>\n",
       "      <td>700500.79</td>\n",
       "      <td>38.51</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>5.44</td>\n",
       "      <td>1.94</td>\n",
       "      <td>5.45</td>\n",
       "      <td>51.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>102,06</td>\n",
       "      <td>100,15</td>\n",
       "      <td>102,48</td>\n",
       "      <td>99,66</td>\n",
       "      <td>255,71K</td>\n",
       "      <td>1,66%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04/06/13 Martes</td>\n",
       "      <td>702582.423</td>\n",
       "      <td>47.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.42</td>\n",
       "      <td>56.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-04</td>\n",
       "      <td>Martes</td>\n",
       "      <td>103,24</td>\n",
       "      <td>102,02</td>\n",
       "      <td>103,58</td>\n",
       "      <td>101,47</td>\n",
       "      <td>300,89K</td>\n",
       "      <td>1,16%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05/06/13 Miércoles</td>\n",
       "      <td>701818.743</td>\n",
       "      <td>51.48</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.45</td>\n",
       "      <td>60.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-05</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>103,04</td>\n",
       "      <td>103,48</td>\n",
       "      <td>104,07</td>\n",
       "      <td>102,85</td>\n",
       "      <td>202,95K</td>\n",
       "      <td>-0,19%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06/06/13 Jueves</td>\n",
       "      <td>700746.514</td>\n",
       "      <td>49.47</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.67</td>\n",
       "      <td>5.44</td>\n",
       "      <td>57.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-06</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>103,61</td>\n",
       "      <td>102,94</td>\n",
       "      <td>103,95</td>\n",
       "      <td>102,81</td>\n",
       "      <td>173,41K</td>\n",
       "      <td>0,55%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07/06/13 Viernes</td>\n",
       "      <td>688082.805</td>\n",
       "      <td>49.33</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5.42</td>\n",
       "      <td>57.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>104,56</td>\n",
       "      <td>103,65</td>\n",
       "      <td>105,01</td>\n",
       "      <td>102,89</td>\n",
       "      <td>234,18K</td>\n",
       "      <td>0,92%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Periodo Energía\\n final \\nMWh Mercado \\ndiario \\n€/MWh  \\\n",
       "0  03/06/13 Lunes                 700500.79                    38.51   \n",
       "1  04/06/13 Martes               702582.423                    47.12   \n",
       "2  05/06/13 Miércoles            701818.743                    51.48   \n",
       "3  06/06/13 Jueves               700746.514                    49.47   \n",
       "4  07/06/13 Viernes              688082.805                    49.33   \n",
       "\n",
       "  Mercado \\nintradiario \\n€/MWh Restricciones\\n€/MWh Procesos \\nOS \\n€/MWh  \\\n",
       "0                         -0.16                 5.44                  1.94   \n",
       "1                          0.01                 3.16                  1.25   \n",
       "2                         -0.16                  2.6                  0.97   \n",
       "3                         -0.32                 2.35                  0.67   \n",
       "4                         -0.16                 2.24                  0.99   \n",
       "\n",
       "  Garantía potencia \\nPagos capacidad\\n€/MWh Total\\n€/MWh  \\\n",
       "0                                       5.45        51.18   \n",
       "1                                       5.42        56.96   \n",
       "2                                       5.45        60.35   \n",
       "3                                       5.44         57.6   \n",
       "4                                       5.42        57.82   \n",
       "\n",
       "  Coste\\ns.interrrumpibilidad       Date        Day  Último Apertura  Máximo  \\\n",
       "0                         NaN 2013-06-03      Lunes  102,06   100,15  102,48   \n",
       "1                         NaN 2013-06-04     Martes  103,24   102,02  103,58   \n",
       "2                         NaN 2013-06-05  Miércoles  103,04   103,48  104,07   \n",
       "3                         NaN 2013-06-06     Jueves  103,61   102,94  103,95   \n",
       "4                         NaN 2013-06-07    Viernes  104,56   103,65  105,01   \n",
       "\n",
       "   Mínimo     Vol.  % var. _merge  \n",
       "0   99,66  255,71K   1,66%   both  \n",
       "1  101,47  300,89K   1,16%   both  \n",
       "2  102,85  202,95K  -0,19%   both  \n",
       "3  102,81  173,41K   0,55%   both  \n",
       "4  102,89  234,18K   0,92%   both  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_brent = pd.merge(electricity_prices, brent_prices, on= \"Date\", sort=False, indicator= True)\n",
    "electricity_brent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "brent_prices.to_csv(\"historic_brent_prices.csv\", sep= \";\", index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In the brent_prices DataFrame, we're missing Saturday and Sunday prices (and bank holidays) \n",
    "#This is because the Brent Crude Futures Markets does not operate during weekends.\n",
    "#So Friday prices should also be Saturday and Sunday prices.\n",
    "\n",
    "electricity_brent[\"Day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>Sábado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>Domingo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>Lunes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-06-04</td>\n",
       "      <td>Martes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-06-05</td>\n",
       "      <td>Miércoles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>Lunes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>Martes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>Miércoles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>Jueves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Viernes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2922 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Day\n",
       "0    2013-06-01     Sábado\n",
       "1    2013-06-02    Domingo\n",
       "2    2013-06-03      Lunes\n",
       "3    2013-06-04     Martes\n",
       "4    2013-06-05  Miércoles\n",
       "...         ...        ...\n",
       "2917 2020-01-27      Lunes\n",
       "2918 2020-01-28     Martes\n",
       "2919 2020-01-29  Miércoles\n",
       "2920 2020-01-30     Jueves\n",
       "2921 2020-01-31    Viernes\n",
       "\n",
       "[2922 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_day = electricity_prices[[\"Date\", \"Day\"]]\n",
    "date_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Último</th>\n",
       "      <th>Apertura</th>\n",
       "      <th>Máximo</th>\n",
       "      <th>Mínimo</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>% var.</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>Sábado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>102,06</td>\n",
       "      <td>100,15</td>\n",
       "      <td>102,48</td>\n",
       "      <td>99,66</td>\n",
       "      <td>255,71K</td>\n",
       "      <td>1,66%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-06-04</td>\n",
       "      <td>Martes</td>\n",
       "      <td>103,24</td>\n",
       "      <td>102,02</td>\n",
       "      <td>103,58</td>\n",
       "      <td>101,47</td>\n",
       "      <td>300,89K</td>\n",
       "      <td>1,16%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-06-05</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>103,04</td>\n",
       "      <td>103,48</td>\n",
       "      <td>104,07</td>\n",
       "      <td>102,85</td>\n",
       "      <td>202,95K</td>\n",
       "      <td>-0,19%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>59,32</td>\n",
       "      <td>60,23</td>\n",
       "      <td>60,49</td>\n",
       "      <td>58,50</td>\n",
       "      <td>259,77K</td>\n",
       "      <td>-2,26%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>Martes</td>\n",
       "      <td>59,51</td>\n",
       "      <td>59,03</td>\n",
       "      <td>60,12</td>\n",
       "      <td>58,50</td>\n",
       "      <td>248,95K</td>\n",
       "      <td>0,32%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>59,81</td>\n",
       "      <td>59,86</td>\n",
       "      <td>60,62</td>\n",
       "      <td>59,26</td>\n",
       "      <td>142,68K</td>\n",
       "      <td>0,50%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>58,29</td>\n",
       "      <td>59,60</td>\n",
       "      <td>59,65</td>\n",
       "      <td>57,71</td>\n",
       "      <td>104,27K</td>\n",
       "      <td>-2,54%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>58,16</td>\n",
       "      <td>59,30</td>\n",
       "      <td>59,45</td>\n",
       "      <td>57,94</td>\n",
       "      <td>19,60K</td>\n",
       "      <td>-0,22%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2922 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Day  Último Apertura  Máximo  Mínimo     Vol.  % var.  \\\n",
       "0    2013-06-01     Sábado     NaN      NaN     NaN     NaN      NaN     NaN   \n",
       "1    2013-06-02    Domingo     NaN      NaN     NaN     NaN      NaN     NaN   \n",
       "2    2013-06-03      Lunes  102,06   100,15  102,48   99,66  255,71K   1,66%   \n",
       "3    2013-06-04     Martes  103,24   102,02  103,58  101,47  300,89K   1,16%   \n",
       "4    2013-06-05  Miércoles  103,04   103,48  104,07  102,85  202,95K  -0,19%   \n",
       "...         ...        ...     ...      ...     ...     ...      ...     ...   \n",
       "2917 2020-01-27      Lunes   59,32    60,23   60,49   58,50  259,77K  -2,26%   \n",
       "2918 2020-01-28     Martes   59,51    59,03   60,12   58,50  248,95K   0,32%   \n",
       "2919 2020-01-29  Miércoles   59,81    59,86   60,62   59,26  142,68K   0,50%   \n",
       "2920 2020-01-30     Jueves   58,29    59,60   59,65   57,71  104,27K  -2,54%   \n",
       "2921 2020-01-31    Viernes   58,16    59,30   59,45   57,94   19,60K  -0,22%   \n",
       "\n",
       "         _merge  \n",
       "0     left_only  \n",
       "1     left_only  \n",
       "2          both  \n",
       "3          both  \n",
       "4          both  \n",
       "...         ...  \n",
       "2917       both  \n",
       "2918       both  \n",
       "2919       both  \n",
       "2920       both  \n",
       "2921       both  \n",
       "\n",
       "[2922 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brent_prices_allweek = pd.merge(date_day, brent_prices, on= \"Date\", indicator = True, how= \"left\")\n",
    "brent_prices_allweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "856"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There were 856 Saturday, Sunday and bank holidays between 2013 and 2020 in Spain\n",
    "np.count_nonzero(brent_prices_allweek[\"_merge\"] != \"both\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>Sábado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>102,06</td>\n",
       "      <td>100,15</td>\n",
       "      <td>102,48</td>\n",
       "      <td>99,66</td>\n",
       "      <td>255,71K</td>\n",
       "      <td>1,66%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-06-04</td>\n",
       "      <td>Martes</td>\n",
       "      <td>103,24</td>\n",
       "      <td>102,02</td>\n",
       "      <td>103,58</td>\n",
       "      <td>101,47</td>\n",
       "      <td>300,89K</td>\n",
       "      <td>1,16%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-06-05</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>103,04</td>\n",
       "      <td>103,48</td>\n",
       "      <td>104,07</td>\n",
       "      <td>102,85</td>\n",
       "      <td>202,95K</td>\n",
       "      <td>-0,19%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>59,32</td>\n",
       "      <td>60,23</td>\n",
       "      <td>60,49</td>\n",
       "      <td>58,50</td>\n",
       "      <td>259,77K</td>\n",
       "      <td>-2,26%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>Martes</td>\n",
       "      <td>59,51</td>\n",
       "      <td>59,03</td>\n",
       "      <td>60,12</td>\n",
       "      <td>58,50</td>\n",
       "      <td>248,95K</td>\n",
       "      <td>0,32%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>59,81</td>\n",
       "      <td>59,86</td>\n",
       "      <td>60,62</td>\n",
       "      <td>59,26</td>\n",
       "      <td>142,68K</td>\n",
       "      <td>0,50%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>58,29</td>\n",
       "      <td>59,60</td>\n",
       "      <td>59,65</td>\n",
       "      <td>57,71</td>\n",
       "      <td>104,27K</td>\n",
       "      <td>-2,54%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>58,16</td>\n",
       "      <td>59,30</td>\n",
       "      <td>59,45</td>\n",
       "      <td>57,94</td>\n",
       "      <td>19,60K</td>\n",
       "      <td>-0,22%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2922 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Day  Último Apertura  Máximo  Mínimo     Vol.  % var.  \\\n",
       "0    2013-06-01     Sábado     NaN      NaN     NaN     NaN      NaN     NaN   \n",
       "1    2013-06-02    Domingo     NaN      NaN     NaN     NaN      NaN     NaN   \n",
       "2    2013-06-03      Lunes  102,06   100,15  102,48   99,66  255,71K   1,66%   \n",
       "3    2013-06-04     Martes  103,24   102,02  103,58  101,47  300,89K   1,16%   \n",
       "4    2013-06-05  Miércoles  103,04   103,48  104,07  102,85  202,95K  -0,19%   \n",
       "...         ...        ...     ...      ...     ...     ...      ...     ...   \n",
       "2917 2020-01-27      Lunes   59,32    60,23   60,49   58,50  259,77K  -2,26%   \n",
       "2918 2020-01-28     Martes   59,51    59,03   60,12   58,50  248,95K   0,32%   \n",
       "2919 2020-01-29  Miércoles   59,81    59,86   60,62   59,26  142,68K   0,50%   \n",
       "2920 2020-01-30     Jueves   58,29    59,60   59,65   57,71  104,27K  -2,54%   \n",
       "2921 2020-01-31    Viernes   58,16    59,30   59,45   57,94   19,60K  -0,22%   \n",
       "\n",
       "         _merge  \n",
       "0     left_only  \n",
       "1     left_only  \n",
       "2          both  \n",
       "3          both  \n",
       "4          both  \n",
       "...         ...  \n",
       "2917       both  \n",
       "2918       both  \n",
       "2919       both  \n",
       "2920       both  \n",
       "2921       both  \n",
       "\n",
       "[2922 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I'm going to use Friday's price for the weekend (as this is the latest price) and the previous day's price for the bank holidays\n",
    "brent_prices_allweek = brent_prices_allweek.fillna(method= \"ffill\")\n",
    "brent_prices_allweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        0\n",
       "Day         0\n",
       "Último      2\n",
       "Apertura    2\n",
       "Máximo      2\n",
       "Mínimo      2\n",
       "Vol.        2\n",
       "% var.      2\n",
       "_merge      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The 1st of January of 2013 still has NaN values, but I don't have historical data before that.\n",
    "#Therefore, I'll drop that row\n",
    "brent_prices_allweek.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Último</th>\n",
       "      <th>Apertura</th>\n",
       "      <th>Máximo</th>\n",
       "      <th>Mínimo</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>% var.</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>102,06</td>\n",
       "      <td>100,15</td>\n",
       "      <td>102,48</td>\n",
       "      <td>99,66</td>\n",
       "      <td>255,71K</td>\n",
       "      <td>1,66%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-06-04</td>\n",
       "      <td>Martes</td>\n",
       "      <td>103,24</td>\n",
       "      <td>102,02</td>\n",
       "      <td>103,58</td>\n",
       "      <td>101,47</td>\n",
       "      <td>300,89K</td>\n",
       "      <td>1,16%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-06-05</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>103,04</td>\n",
       "      <td>103,48</td>\n",
       "      <td>104,07</td>\n",
       "      <td>102,85</td>\n",
       "      <td>202,95K</td>\n",
       "      <td>-0,19%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-06-06</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>103,61</td>\n",
       "      <td>102,94</td>\n",
       "      <td>103,95</td>\n",
       "      <td>102,81</td>\n",
       "      <td>173,41K</td>\n",
       "      <td>0,55%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>104,56</td>\n",
       "      <td>103,65</td>\n",
       "      <td>105,01</td>\n",
       "      <td>102,89</td>\n",
       "      <td>234,18K</td>\n",
       "      <td>0,92%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>59,32</td>\n",
       "      <td>60,23</td>\n",
       "      <td>60,49</td>\n",
       "      <td>58,50</td>\n",
       "      <td>259,77K</td>\n",
       "      <td>-2,26%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>Martes</td>\n",
       "      <td>59,51</td>\n",
       "      <td>59,03</td>\n",
       "      <td>60,12</td>\n",
       "      <td>58,50</td>\n",
       "      <td>248,95K</td>\n",
       "      <td>0,32%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>59,81</td>\n",
       "      <td>59,86</td>\n",
       "      <td>60,62</td>\n",
       "      <td>59,26</td>\n",
       "      <td>142,68K</td>\n",
       "      <td>0,50%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>58,29</td>\n",
       "      <td>59,60</td>\n",
       "      <td>59,65</td>\n",
       "      <td>57,71</td>\n",
       "      <td>104,27K</td>\n",
       "      <td>-2,54%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>58,16</td>\n",
       "      <td>59,30</td>\n",
       "      <td>59,45</td>\n",
       "      <td>57,94</td>\n",
       "      <td>19,60K</td>\n",
       "      <td>-0,22%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2920 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Day  Último Apertura  Máximo  Mínimo     Vol.  % var.  \\\n",
       "2    2013-06-03      Lunes  102,06   100,15  102,48   99,66  255,71K   1,66%   \n",
       "3    2013-06-04     Martes  103,24   102,02  103,58  101,47  300,89K   1,16%   \n",
       "4    2013-06-05  Miércoles  103,04   103,48  104,07  102,85  202,95K  -0,19%   \n",
       "5    2013-06-06     Jueves  103,61   102,94  103,95  102,81  173,41K   0,55%   \n",
       "6    2013-06-07    Viernes  104,56   103,65  105,01  102,89  234,18K   0,92%   \n",
       "...         ...        ...     ...      ...     ...     ...      ...     ...   \n",
       "2917 2020-01-27      Lunes   59,32    60,23   60,49   58,50  259,77K  -2,26%   \n",
       "2918 2020-01-28     Martes   59,51    59,03   60,12   58,50  248,95K   0,32%   \n",
       "2919 2020-01-29  Miércoles   59,81    59,86   60,62   59,26  142,68K   0,50%   \n",
       "2920 2020-01-30     Jueves   58,29    59,60   59,65   57,71  104,27K  -2,54%   \n",
       "2921 2020-01-31    Viernes   58,16    59,30   59,45   57,94   19,60K  -0,22%   \n",
       "\n",
       "     _merge  \n",
       "2      both  \n",
       "3      both  \n",
       "4      both  \n",
       "5      both  \n",
       "6      both  \n",
       "...     ...  \n",
       "2917   both  \n",
       "2918   both  \n",
       "2919   both  \n",
       "2920   both  \n",
       "2921   both  \n",
       "\n",
       "[2920 rows x 9 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brent_prices_allweek = brent_prices_allweek.dropna()\n",
    "brent_prices_allweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Periodo</th>\n",
       "      <th>Energía\\n final \\nMWh</th>\n",
       "      <th>Mercado \\ndiario \\n€/MWh</th>\n",
       "      <th>Mercado \\nintradiario \\n€/MWh</th>\n",
       "      <th>Restricciones\\n€/MWh</th>\n",
       "      <th>Procesos \\nOS \\n€/MWh</th>\n",
       "      <th>Garantía potencia \\nPagos capacidad\\n€/MWh</th>\n",
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       "      <th>Date</th>\n",
       "      <th>Day_x</th>\n",
       "      <th>Day_y</th>\n",
       "      <th>Último</th>\n",
       "      <th>Apertura</th>\n",
       "      <th>Máximo</th>\n",
       "      <th>Mínimo</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>% var.</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03/06/13 Lunes</td>\n",
       "      <td>700500.79</td>\n",
       "      <td>38.51</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>5.44</td>\n",
       "      <td>1.94</td>\n",
       "      <td>5.45</td>\n",
       "      <td>51.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>102,06</td>\n",
       "      <td>100,15</td>\n",
       "      <td>102,48</td>\n",
       "      <td>99,66</td>\n",
       "      <td>255,71K</td>\n",
       "      <td>1,66%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04/06/13 Martes</td>\n",
       "      <td>702582.423</td>\n",
       "      <td>47.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.42</td>\n",
       "      <td>56.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-04</td>\n",
       "      <td>Martes</td>\n",
       "      <td>Martes</td>\n",
       "      <td>103,24</td>\n",
       "      <td>102,02</td>\n",
       "      <td>103,58</td>\n",
       "      <td>101,47</td>\n",
       "      <td>300,89K</td>\n",
       "      <td>1,16%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05/06/13 Miércoles</td>\n",
       "      <td>701818.743</td>\n",
       "      <td>51.48</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.45</td>\n",
       "      <td>60.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-05</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>103,04</td>\n",
       "      <td>103,48</td>\n",
       "      <td>104,07</td>\n",
       "      <td>102,85</td>\n",
       "      <td>202,95K</td>\n",
       "      <td>-0,19%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06/06/13 Jueves</td>\n",
       "      <td>700746.514</td>\n",
       "      <td>49.47</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.67</td>\n",
       "      <td>5.44</td>\n",
       "      <td>57.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-06</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>103,61</td>\n",
       "      <td>102,94</td>\n",
       "      <td>103,95</td>\n",
       "      <td>102,81</td>\n",
       "      <td>173,41K</td>\n",
       "      <td>0,55%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07/06/13 Viernes</td>\n",
       "      <td>688082.805</td>\n",
       "      <td>49.33</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5.42</td>\n",
       "      <td>57.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>104,56</td>\n",
       "      <td>103,65</td>\n",
       "      <td>105,01</td>\n",
       "      <td>102,89</td>\n",
       "      <td>234,18K</td>\n",
       "      <td>0,92%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>27/01/20 Lunes</td>\n",
       "      <td>822863.04</td>\n",
       "      <td>39.87</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3.23</td>\n",
       "      <td>44.46</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>59,32</td>\n",
       "      <td>60,23</td>\n",
       "      <td>60,49</td>\n",
       "      <td>58,50</td>\n",
       "      <td>259,77K</td>\n",
       "      <td>-2,26%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>28/01/20 Martes</td>\n",
       "      <td>816667.2</td>\n",
       "      <td>39.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>3.2</td>\n",
       "      <td>43.83</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>Martes</td>\n",
       "      <td>Martes</td>\n",
       "      <td>59,51</td>\n",
       "      <td>59,03</td>\n",
       "      <td>60,12</td>\n",
       "      <td>58,50</td>\n",
       "      <td>248,95K</td>\n",
       "      <td>0,32%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>29/01/20 Miércoles</td>\n",
       "      <td>818153.969</td>\n",
       "      <td>40.24</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.12</td>\n",
       "      <td>45.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>59,81</td>\n",
       "      <td>59,86</td>\n",
       "      <td>60,62</td>\n",
       "      <td>59,26</td>\n",
       "      <td>142,68K</td>\n",
       "      <td>0,50%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>30/01/20 Jueves</td>\n",
       "      <td>793218.71</td>\n",
       "      <td>38.48</td>\n",
       "      <td>0</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.33</td>\n",
       "      <td>3.15</td>\n",
       "      <td>43.1</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>58,29</td>\n",
       "      <td>59,60</td>\n",
       "      <td>59,65</td>\n",
       "      <td>57,71</td>\n",
       "      <td>104,27K</td>\n",
       "      <td>-2,54%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>31/01/20 Viernes</td>\n",
       "      <td>772638.789</td>\n",
       "      <td>37.6</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.26</td>\n",
       "      <td>3.17</td>\n",
       "      <td>41.73</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>58,16</td>\n",
       "      <td>59,30</td>\n",
       "      <td>59,45</td>\n",
       "      <td>57,94</td>\n",
       "      <td>19,60K</td>\n",
       "      <td>-0,22%</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2920 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Periodo Energía\\n final \\nMWh Mercado \\ndiario \\n€/MWh  \\\n",
       "0     03/06/13 Lunes                 700500.79                    38.51   \n",
       "1     04/06/13 Martes               702582.423                    47.12   \n",
       "2     05/06/13 Miércoles            701818.743                    51.48   \n",
       "3     06/06/13 Jueves               700746.514                    49.47   \n",
       "4     07/06/13 Viernes              688082.805                    49.33   \n",
       "...                  ...                   ...                      ...   \n",
       "2915  27/01/20 Lunes                 822863.04                    39.87   \n",
       "2916  28/01/20 Martes                 816667.2                    39.02   \n",
       "2917  29/01/20 Miércoles            818153.969                    40.24   \n",
       "2918  30/01/20 Jueves                793218.71                    38.48   \n",
       "2919  31/01/20 Viernes              772638.789                     37.6   \n",
       "\n",
       "     Mercado \\nintradiario \\n€/MWh Restricciones\\n€/MWh Procesos \\nOS \\n€/MWh  \\\n",
       "0                            -0.16                 5.44                  1.94   \n",
       "1                             0.01                 3.16                  1.25   \n",
       "2                            -0.16                  2.6                  0.97   \n",
       "3                            -0.32                 2.35                  0.67   \n",
       "4                            -0.16                 2.24                  0.99   \n",
       "...                            ...                  ...                   ...   \n",
       "2915                         -0.03                 1.15                  0.21   \n",
       "2916                         -0.02                 1.36                  0.24   \n",
       "2917                         -0.02                 1.38                   0.3   \n",
       "2918                             0                 1.11                  0.33   \n",
       "2919                         -0.02                 0.69                  0.26   \n",
       "\n",
       "     Garantía potencia \\nPagos capacidad\\n€/MWh Total\\n€/MWh  \\\n",
       "0                                          5.45        51.18   \n",
       "1                                          5.42        56.96   \n",
       "2                                          5.45        60.35   \n",
       "3                                          5.44         57.6   \n",
       "4                                          5.42        57.82   \n",
       "...                                         ...          ...   \n",
       "2915                                       3.23        44.46   \n",
       "2916                                        3.2        43.83   \n",
       "2917                                       3.12        45.06   \n",
       "2918                                       3.15         43.1   \n",
       "2919                                       3.17        41.73   \n",
       "\n",
       "     Coste\\ns.interrrumpibilidad       Date      Day_x      Day_y  Último  \\\n",
       "0                            NaN 2013-06-03      Lunes      Lunes  102,06   \n",
       "1                            NaN 2013-06-04     Martes     Martes  103,24   \n",
       "2                            NaN 2013-06-05  Miércoles  Miércoles  103,04   \n",
       "3                            NaN 2013-06-06     Jueves     Jueves  103,61   \n",
       "4                            NaN 2013-06-07    Viernes    Viernes  104,56   \n",
       "...                          ...        ...        ...        ...     ...   \n",
       "2915                        0.03 2020-01-27      Lunes      Lunes   59,32   \n",
       "2916                        0.03 2020-01-28     Martes     Martes   59,51   \n",
       "2917                        0.03 2020-01-29  Miércoles  Miércoles   59,81   \n",
       "2918                        0.03 2020-01-30     Jueves     Jueves   58,29   \n",
       "2919                        0.03 2020-01-31    Viernes    Viernes   58,16   \n",
       "\n",
       "     Apertura  Máximo  Mínimo     Vol.  % var. _merge  \n",
       "0      100,15  102,48   99,66  255,71K   1,66%   both  \n",
       "1      102,02  103,58  101,47  300,89K   1,16%   both  \n",
       "2      103,48  104,07  102,85  202,95K  -0,19%   both  \n",
       "3      102,94  103,95  102,81  173,41K   0,55%   both  \n",
       "4      103,65  105,01  102,89  234,18K   0,92%   both  \n",
       "...       ...     ...     ...      ...     ...    ...  \n",
       "2915    60,23   60,49   58,50  259,77K  -2,26%   both  \n",
       "2916    59,03   60,12   58,50  248,95K   0,32%   both  \n",
       "2917    59,86   60,62   59,26  142,68K   0,50%   both  \n",
       "2918    59,60   59,65   57,71  104,27K  -2,54%   both  \n",
       "2919    59,30   59,45   57,94   19,60K  -0,22%   both  \n",
       "\n",
       "[2920 rows x 19 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now that we have the Brent prices for all days of the week, let's merge both datasets again\n",
    "electricity_brent = pd.merge(electricity_prices, brent_prices_allweek, on= \"Date\", sort=False)\n",
    "electricity_brent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's get rid of some columns we don't need\n",
    "electricity_brent = electricity_brent.drop(columns= [\"Day_y\", \"_merge\", \"% var.\"])\n",
    "electricity_brent.rename(columns={\"Day_x\":\"Day\"}, inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, let's start looking at the weather data\n",
    "\n",
    "The first file that we'll look at, contains a list of weather stations across Spain (with their specific code) that will allow us to choose the weather stations data that we will use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Identificador_est</th>\n",
       "      <th>Nombre_est</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Longitud</th>\n",
       "      <th>Latitud</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>Coruña, A</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>58</td>\n",
       "      <td>432157N</td>\n",
       "      <td>082517W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1387E</td>\n",
       "      <td>A Coruña Aeropuerto</td>\n",
       "      <td>Culleredo</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>98</td>\n",
       "      <td>431825N</td>\n",
       "      <td>082219W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1390X</td>\n",
       "      <td>Carballo, Depuradora</td>\n",
       "      <td>Carballo</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>98</td>\n",
       "      <td>431213N</td>\n",
       "      <td>084239W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1393</td>\n",
       "      <td>Cabo Vilan</td>\n",
       "      <td>Camariñas</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>50</td>\n",
       "      <td>430938N</td>\n",
       "      <td>091239W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1399</td>\n",
       "      <td>Vimianzo</td>\n",
       "      <td>Vimianzo</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>287</td>\n",
       "      <td>430327N</td>\n",
       "      <td>090249W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>B640X</td>\n",
       "      <td>Petra</td>\n",
       "      <td>Petra</td>\n",
       "      <td>Illes Balears</td>\n",
       "      <td>95</td>\n",
       "      <td>393659N</td>\n",
       "      <td>030716E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>9293X</td>\n",
       "      <td>Alfaro, La Plana</td>\n",
       "      <td>Alfaro</td>\n",
       "      <td>La Rioja</td>\n",
       "      <td>371</td>\n",
       "      <td>421034N</td>\n",
       "      <td>014438W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>9445L</td>\n",
       "      <td>Formigal, Sarrios</td>\n",
       "      <td>Sallent de Gállego</td>\n",
       "      <td>Huesca</td>\n",
       "      <td>1800</td>\n",
       "      <td>424545N</td>\n",
       "      <td>002334W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>9574B</td>\n",
       "      <td>Caspe, Plana del Pilón</td>\n",
       "      <td>Caspe</td>\n",
       "      <td>Zaragoza</td>\n",
       "      <td>202</td>\n",
       "      <td>411433N</td>\n",
       "      <td>000407W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>1387D</td>\n",
       "      <td>A Coruña Bens</td>\n",
       "      <td>Coruña, A</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>132</td>\n",
       "      <td>432148N</td>\n",
       "      <td>082631W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>848 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Identificador_est              Nombre_est           Municipio  \\\n",
       "0                1387                A Coruña           Coruña, A   \n",
       "1               1387E     A Coruña Aeropuerto           Culleredo   \n",
       "2               1390X    Carballo, Depuradora            Carballo   \n",
       "3                1393              Cabo Vilan           Camariñas   \n",
       "4                1399                Vimianzo            Vimianzo   \n",
       "..                ...                     ...                 ...   \n",
       "843             B640X                   Petra               Petra   \n",
       "844             9293X        Alfaro, La Plana              Alfaro   \n",
       "845             9445L       Formigal, Sarrios  Sallent de Gállego   \n",
       "846             9574B  Caspe, Plana del Pilón               Caspe   \n",
       "847             1387D           A Coruña Bens           Coruña, A   \n",
       "\n",
       "         Provincia Altura Longitud  Latitud  \n",
       "0         A Coruña     58  432157N  082517W  \n",
       "1         A Coruña     98  431825N  082219W  \n",
       "2         A Coruña     98  431213N  084239W  \n",
       "3         A Coruña     50  430938N  091239W  \n",
       "4         A Coruña    287  430327N  090249W  \n",
       "..             ...    ...      ...      ...  \n",
       "843  Illes Balears     95  393659N  030716E  \n",
       "844       La Rioja    371  421034N  014438W  \n",
       "845         Huesca   1800  424545N  002334W  \n",
       "846       Zaragoza    202  411433N  000407W  \n",
       "847       A Coruña    132  432148N  082631W  \n",
       "\n",
       "[848 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The encoding = \"latin1\" is used so \"special characters\" like ñ are read well\n",
    "\n",
    "listado_estaciones = pd.read_csv(\"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Aemet2021-06-Por-Estaciones/ListadoEstaciones.csv\", \n",
    "                                names =[\"Identificador_est\", \"Nombre_est\", \"Municipio\", \"Provincia\", \"Altura\", \"Longitud\", \"Latitud\"],\n",
    "                                         header= 0, dtype = str, engine= \"python\", sep=\";\", error_bad_lines= False,\n",
    "                                encoding= \"latin1\")\n",
    "listado_estaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2462</td>\n",
       "      <td>Puerto de Navacerrada</td>\n",
       "      <td>Cercedilla</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>1894</td>\n",
       "      <td>404735N</td>\n",
       "      <td>040038W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>3100B</td>\n",
       "      <td>Aranjuez</td>\n",
       "      <td>Aranjuez</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>540</td>\n",
       "      <td>400402N</td>\n",
       "      <td>033246W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>3104Y</td>\n",
       "      <td>Rascafría</td>\n",
       "      <td>Rascafría</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>1159</td>\n",
       "      <td>405323N</td>\n",
       "      <td>035318W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>3110C</td>\n",
       "      <td>Buitrago del Lozoya</td>\n",
       "      <td>Buitrago del Lozoya</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>1026</td>\n",
       "      <td>410025N</td>\n",
       "      <td>033649W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>3111D</td>\n",
       "      <td>Somosierra</td>\n",
       "      <td>Somosierra</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>1450</td>\n",
       "      <td>410808N</td>\n",
       "      <td>033449W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>3125Y</td>\n",
       "      <td>San Sebastián de Los Reyes</td>\n",
       "      <td>San Sebastián de los Reyes</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>671</td>\n",
       "      <td>403412N</td>\n",
       "      <td>033806W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>3126Y</td>\n",
       "      <td>Madrid, El Goloso</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>740</td>\n",
       "      <td>403341N</td>\n",
       "      <td>034243W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>3129</td>\n",
       "      <td>Madrid Aeropuerto</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>609</td>\n",
       "      <td>402800N</td>\n",
       "      <td>033320W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>3170Y</td>\n",
       "      <td>Alcala de Henares</td>\n",
       "      <td>Alcalá de Henares</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>605</td>\n",
       "      <td>403142N</td>\n",
       "      <td>031723W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>3182Y</td>\n",
       "      <td>Arganda del Rey</td>\n",
       "      <td>Arganda del Rey</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>533</td>\n",
       "      <td>401842N</td>\n",
       "      <td>032952W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>3191E</td>\n",
       "      <td>Colmenar Viejo</td>\n",
       "      <td>Colmenar Viejo</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>1004</td>\n",
       "      <td>404146N</td>\n",
       "      <td>034554W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>3194U</td>\n",
       "      <td>Madrid, Ciudad Universitaria</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>664</td>\n",
       "      <td>402706N</td>\n",
       "      <td>034327W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>3194Y</td>\n",
       "      <td>Pozuelo de Alarcón</td>\n",
       "      <td>Pozuelo de Alarcón</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>665</td>\n",
       "      <td>402654N</td>\n",
       "      <td>034848W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>3195</td>\n",
       "      <td>Madrid, Retiro</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>667</td>\n",
       "      <td>402443N</td>\n",
       "      <td>034041W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>3229Y</td>\n",
       "      <td>Tielmes</td>\n",
       "      <td>Tielmes</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>594</td>\n",
       "      <td>401450N</td>\n",
       "      <td>031842W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>3266A</td>\n",
       "      <td>Puerto Alto del León</td>\n",
       "      <td>Guadarrama</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>1532</td>\n",
       "      <td>404223N</td>\n",
       "      <td>040831W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>3268C</td>\n",
       "      <td>Alpedrete</td>\n",
       "      <td>Alpedrete</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>924</td>\n",
       "      <td>403935N</td>\n",
       "      <td>040103W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>3330Y</td>\n",
       "      <td>Rozas de Puerto Real</td>\n",
       "      <td>Rozas de Puerto Real</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>890</td>\n",
       "      <td>401847N</td>\n",
       "      <td>042914W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>3338</td>\n",
       "      <td>Robledo de Chavela</td>\n",
       "      <td>Robledo de Chavela</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>790</td>\n",
       "      <td>402540N</td>\n",
       "      <td>041500W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>3343Y</td>\n",
       "      <td>Valdemorillo</td>\n",
       "      <td>Valdemorillo</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>884</td>\n",
       "      <td>402948N</td>\n",
       "      <td>040340W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>3272M</td>\n",
       "      <td>Torrelodones</td>\n",
       "      <td>Torrelodones</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>879</td>\n",
       "      <td>403444N</td>\n",
       "      <td>035627W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>3344E</td>\n",
       "      <td>Villanueva de la Cañada</td>\n",
       "      <td>Villanueva de la Cañada</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>641</td>\n",
       "      <td>402615N</td>\n",
       "      <td>040119W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Identificador_est                    Nombre_est  \\\n",
       "496              2462         Puerto de Navacerrada   \n",
       "497             3100B                      Aranjuez   \n",
       "498             3104Y                     Rascafría   \n",
       "499             3110C           Buitrago del Lozoya   \n",
       "500             3111D                    Somosierra   \n",
       "501             3125Y    San Sebastián de Los Reyes   \n",
       "502             3126Y             Madrid, El Goloso   \n",
       "503              3129             Madrid Aeropuerto   \n",
       "504             3170Y             Alcala de Henares   \n",
       "505             3182Y               Arganda del Rey   \n",
       "506             3191E                Colmenar Viejo   \n",
       "507             3194U  Madrid, Ciudad Universitaria   \n",
       "508             3194Y            Pozuelo de Alarcón   \n",
       "509              3195                Madrid, Retiro   \n",
       "510             3229Y                       Tielmes   \n",
       "511             3266A          Puerto Alto del León   \n",
       "512             3268C                     Alpedrete   \n",
       "513             3330Y          Rozas de Puerto Real   \n",
       "514              3338            Robledo de Chavela   \n",
       "515             3343Y                  Valdemorillo   \n",
       "815             3272M                  Torrelodones   \n",
       "829             3344E       Villanueva de la Cañada   \n",
       "\n",
       "                      Municipio Provincia Altura Longitud  Latitud  \n",
       "496                  Cercedilla    Madrid   1894  404735N  040038W  \n",
       "497                    Aranjuez    Madrid    540  400402N  033246W  \n",
       "498                   Rascafría    Madrid   1159  405323N  035318W  \n",
       "499         Buitrago del Lozoya    Madrid   1026  410025N  033649W  \n",
       "500                  Somosierra    Madrid   1450  410808N  033449W  \n",
       "501  San Sebastián de los Reyes    Madrid    671  403412N  033806W  \n",
       "502                      Madrid    Madrid    740  403341N  034243W  \n",
       "503                      Madrid    Madrid    609  402800N  033320W  \n",
       "504           Alcalá de Henares    Madrid    605  403142N  031723W  \n",
       "505             Arganda del Rey    Madrid    533  401842N  032952W  \n",
       "506              Colmenar Viejo    Madrid   1004  404146N  034554W  \n",
       "507                      Madrid    Madrid    664  402706N  034327W  \n",
       "508          Pozuelo de Alarcón    Madrid    665  402654N  034848W  \n",
       "509                      Madrid    Madrid    667  402443N  034041W  \n",
       "510                     Tielmes    Madrid    594  401450N  031842W  \n",
       "511                  Guadarrama    Madrid   1532  404223N  040831W  \n",
       "512                   Alpedrete    Madrid    924  403935N  040103W  \n",
       "513        Rozas de Puerto Real    Madrid    890  401847N  042914W  \n",
       "514          Robledo de Chavela    Madrid    790  402540N  041500W  \n",
       "515                Valdemorillo    Madrid    884  402948N  040340W  \n",
       "815                Torrelodones    Madrid    879  403444N  035627W  \n",
       "829     Villanueva de la Cañada    Madrid    641  402615N  040119W  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's have a look at the stations in Madrid, for example\n",
    "in_madrid = listado_estaciones[\"Provincia\"] == \"Madrid\"\n",
    "estaciones_madrid = listado_estaciones[in_madrid]\n",
    "estaciones_madrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A Coruña',\n",
       " 'Albacete',\n",
       " 'Alacant/Alicante',\n",
       " 'Almería',\n",
       " 'Araba/Álava',\n",
       " 'Asturias',\n",
       " 'Ávila',\n",
       " 'Badajoz',\n",
       " 'Barcelona',\n",
       " 'Bizkaia',\n",
       " 'Burgos',\n",
       " 'Cáceres',\n",
       " 'Cádiz',\n",
       " 'Cantabria',\n",
       " 'Castelló/Castellón',\n",
       " 'Ceuta',\n",
       " 'Ciudad Real',\n",
       " 'Córdoba',\n",
       " 'Cuenca',\n",
       " 'Gipuzkoa',\n",
       " 'Girona',\n",
       " 'Granada',\n",
       " 'Guadalajara',\n",
       " 'Huelva',\n",
       " 'Huesca',\n",
       " 'Illes Balears',\n",
       " 'Jaén',\n",
       " 'La Rioja',\n",
       " 'Las Palmas',\n",
       " 'León',\n",
       " 'Lleida',\n",
       " 'Lugo',\n",
       " 'Madrid',\n",
       " 'Málaga',\n",
       " 'Melilla',\n",
       " 'Murcia',\n",
       " 'Navarra',\n",
       " 'Ourense',\n",
       " 'Palencia',\n",
       " 'Pontevedra',\n",
       " 'Salamanca',\n",
       " 'Segovia',\n",
       " 'Sevilla',\n",
       " 'Soria',\n",
       " 'Santa Cruz de Tenerife',\n",
       " 'Tarragona',\n",
       " 'Teruel',\n",
       " 'Toledo',\n",
       " 'València/Valencia',\n",
       " 'Valladolid',\n",
       " 'Zamora',\n",
       " 'Zaragoza']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's get all the province names in a list\n",
    "provincias = listado_estaciones[\"Provincia\"].unique()\n",
    "provincias = list(provincias)\n",
    "provincias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to get all the weather stations identification codes by province, so then I can do a median average by province. After that, the plan is to do a weighted average by each province population so the values for those provinces with more inhabitants value more.\n",
    "\n",
    "After trying to use groupby in different ways, and because of how the data is storage in the files, I've found that the easiest way to do this might be collecting all data from all separate files by station in one file and then in that file add a column with the province name. After that, I could do the median average by province that I mentioned before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Identificador_est</th>\n",
       "      <th>Nombre_est</th>\n",
       "      <th>Provincia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1387E</td>\n",
       "      <td>A Coruña Aeropuerto</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1390X</td>\n",
       "      <td>Carballo, Depuradora</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1393</td>\n",
       "      <td>Cabo Vilan</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1399</td>\n",
       "      <td>Vimianzo</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>B640X</td>\n",
       "      <td>Petra</td>\n",
       "      <td>Illes Balears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>9293X</td>\n",
       "      <td>Alfaro, La Plana</td>\n",
       "      <td>La Rioja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>9445L</td>\n",
       "      <td>Formigal, Sarrios</td>\n",
       "      <td>Huesca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>9574B</td>\n",
       "      <td>Caspe, Plana del Pilón</td>\n",
       "      <td>Zaragoza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>1387D</td>\n",
       "      <td>A Coruña Bens</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>848 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Identificador_est              Nombre_est      Provincia\n",
       "0                1387                A Coruña       A Coruña\n",
       "1               1387E     A Coruña Aeropuerto       A Coruña\n",
       "2               1390X    Carballo, Depuradora       A Coruña\n",
       "3                1393              Cabo Vilan       A Coruña\n",
       "4                1399                Vimianzo       A Coruña\n",
       "..                ...                     ...            ...\n",
       "843             B640X                   Petra  Illes Balears\n",
       "844             9293X        Alfaro, La Plana       La Rioja\n",
       "845             9445L       Formigal, Sarrios         Huesca\n",
       "846             9574B  Caspe, Plana del Pilón       Zaragoza\n",
       "847             1387D           A Coruña Bens       A Coruña\n",
       "\n",
       "[848 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listado_estaciones.drop(columns= [\"Municipio\", \"Altura\", \"Longitud\", \"Latitud\"], inplace= True)\n",
    "listado_estaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#grouped_df = listado_estaciones.groupby(by = [\"Provincia\"])\n",
    "\n",
    "#for provincia in provincias:\n",
    "#    \n",
    "#    by_province = grouped_df.get_group(provincia)\n",
    "#    \n",
    "#    print(by_province)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.13 s, sys: 1.85 s, total: 5.98 s\n",
      "Wall time: 6.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "weather_data_import = glob.iglob(\"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Aemet2021-06-Por-Estaciones/DatosEstaciones-2021-06/*csv\")\n",
    "dataframeweather = (pd.read_csv(csv, dtype= str,parse_dates= True, dayfirst= True, engine= None, header = 0,\n",
    "                                cache_dates= True,skip_blank_lines= False, sep = \";\") for csv in weather_data_import)\n",
    "all_weather_stations  = pd.concat(dataframeweather, ignore_index= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    }\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>Id</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>HTmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>HTmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Racha</th>\n",
       "      <th>HRacha</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>HVmax</th>\n",
       "      <th>TPrec</th>\n",
       "      <th>Prec1</th>\n",
       "      <th>Prec2</th>\n",
       "      <th>Prec3</th>\n",
       "      <th>Prec4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8203O</td>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>25.3</td>\n",
       "      <td>19:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>23:40</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>12:50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8203O</td>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>29.7</td>\n",
       "      <td>18:10</td>\n",
       "      <td>14.6</td>\n",
       "      <td>06:10</td>\n",
       "      <td>22.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>23:20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8203O</td>\n",
       "      <td>2014-07-09</td>\n",
       "      <td>24.7</td>\n",
       "      <td>15:00</td>\n",
       "      <td>15.7</td>\n",
       "      <td>02:20</td>\n",
       "      <td>20.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>17:50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8203O</td>\n",
       "      <td>2014-07-10</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19:40</td>\n",
       "      <td>13.0</td>\n",
       "      <td>07:00</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>16:50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8203O</td>\n",
       "      <td>2014-07-11</td>\n",
       "      <td>28.9</td>\n",
       "      <td>18:00</td>\n",
       "      <td>15.5</td>\n",
       "      <td>07:00</td>\n",
       "      <td>22.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>16:30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id       Fecha  Tmax  HTmax  Tmin  HTmin  Tmed Racha HRacha Vmax  HVmax  \\\n",
       "0  8203O  2014-07-07  25.3  19:00  15.8  23:40  20.5   NaN    NaN   44  12:50   \n",
       "1  8203O  2014-07-08  29.7  18:10  14.6  06:10  22.2   NaN    NaN   28  23:20   \n",
       "2  8203O  2014-07-09  24.7  15:00  15.7  02:20  20.2   NaN    NaN   27  17:50   \n",
       "3  8203O  2014-07-10  28.0  19:40  13.0  07:00  20.5   NaN    NaN   30  16:50   \n",
       "4  8203O  2014-07-11  28.9  18:00  15.5  07:00  22.2   NaN    NaN   24  16:30   \n",
       "\n",
       "  TPrec Prec1 Prec2 Prec3 Prec4  \n",
       "0     0     0     0     0     0  \n",
       "1     0     0     0     0     0  \n",
       "2     0     0     0     0     0  \n",
       "3     0     0     0     0     0  \n",
       "4     0     0     0     0     0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weather_stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_weather_stations.rename(columns={\"Id\": \"Identificador_est\"}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Identificador_est</th>\n",
       "      <th>Nombre_est</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>HTmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>HTmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Racha</th>\n",
       "      <th>HRacha</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>HVmax</th>\n",
       "      <th>TPrec</th>\n",
       "      <th>Prec1</th>\n",
       "      <th>Prec2</th>\n",
       "      <th>Prec3</th>\n",
       "      <th>Prec4</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>19.7</td>\n",
       "      <td>13:30</td>\n",
       "      <td>15.0</td>\n",
       "      <td>00:00</td>\n",
       "      <td>17.4</td>\n",
       "      <td>66</td>\n",
       "      <td>19:10</td>\n",
       "      <td>31</td>\n",
       "      <td>14:50</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-08</td>\n",
       "      <td>18.1</td>\n",
       "      <td>14:00</td>\n",
       "      <td>12.8</td>\n",
       "      <td>21:50</td>\n",
       "      <td>15.5</td>\n",
       "      <td>41</td>\n",
       "      <td>00:40</td>\n",
       "      <td>27</td>\n",
       "      <td>14:50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-09</td>\n",
       "      <td>15.1</td>\n",
       "      <td>15:20</td>\n",
       "      <td>11.4</td>\n",
       "      <td>09:30</td>\n",
       "      <td>13.3</td>\n",
       "      <td>27</td>\n",
       "      <td>09:10</td>\n",
       "      <td>20</td>\n",
       "      <td>13:30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-10</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15:00</td>\n",
       "      <td>8.2</td>\n",
       "      <td>07:10</td>\n",
       "      <td>11.8</td>\n",
       "      <td>28</td>\n",
       "      <td>16:10</td>\n",
       "      <td>19</td>\n",
       "      <td>16:10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-11</td>\n",
       "      <td>15.7</td>\n",
       "      <td>11:50</td>\n",
       "      <td>8.7</td>\n",
       "      <td>07:20</td>\n",
       "      <td>12.2</td>\n",
       "      <td>27</td>\n",
       "      <td>20:20</td>\n",
       "      <td>20</td>\n",
       "      <td>13:30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Identificador_est Nombre_est Provincia       Fecha  Tmax  HTmax  Tmin  \\\n",
       "0              1387   A Coruña  A Coruña  2013-05-07  19.7  13:30  15.0   \n",
       "1              1387   A Coruña  A Coruña  2013-05-08  18.1  14:00  12.8   \n",
       "2              1387   A Coruña  A Coruña  2013-05-09  15.1  15:20  11.4   \n",
       "3              1387   A Coruña  A Coruña  2013-05-10  15.5  15:00   8.2   \n",
       "4              1387   A Coruña  A Coruña  2013-05-11  15.7  11:50   8.7   \n",
       "\n",
       "   HTmin  Tmed Racha HRacha Vmax  HVmax TPrec Prec1 Prec2 Prec3 Prec4 _merge  \n",
       "0  00:00  17.4    66  19:10   31  14:50   1.2     1     0   0.2     0   both  \n",
       "1  21:50  15.5    41  00:40   27  14:50     0     0     0     0     0   both  \n",
       "2  09:30  13.3    27  09:10   20  13:30     0     0     0     0     0   both  \n",
       "3  07:10  11.8    28  16:10   19  16:10     0     0     0     0     0   both  \n",
       "4  07:20  12.2    27  20:20   20  13:30     0     0     0     0     0   both  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's now add the Province name to the dataframe with all the weather data\n",
    "all_weather_stations = pd.merge(listado_estaciones, all_weather_stations, on= \"Identificador_est\", sort=False, \n",
    "                                indicator= True)\n",
    "all_weather_stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Thankfully, all the stations' id were written exaclty the same in both files.\n",
    "np.count_nonzero(all_weather_stations[\"_merge\"] != \"both\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, I'm going to remove all the columns that we don't need. Also, I'll need to see whether there are any NaN values and what to do with them and also convert the Date column to date type and the number columns to numbers as now everything is saved as str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_weather_stations = all_weather_stations.drop({\"HTmax\", \"HTmin\", \"Racha\", \"HRacha\", \n",
    "                                                        \"HVmax\", \"Prec1\", \"Prec2\", \"Prec3\", \"Prec4\", \"_merge\" },\n",
    "                                                       axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Identificador_est    False\n",
       "Nombre_est           False\n",
       "Provincia            False\n",
       "Fecha                False\n",
       "Tmax                  True\n",
       "Tmin                  True\n",
       "Tmed                  True\n",
       "Vmax                  True\n",
       "TPrec                 True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weather_stations.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Identificador_est         0\n",
       "Nombre_est                0\n",
       "Provincia                 0\n",
       "Fecha                     0\n",
       "Tmax                 179680\n",
       "Tmin                 179680\n",
       "Tmed                 179680\n",
       "Vmax                 492229\n",
       "TPrec                205342\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weather_stations.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Identificador_est</th>\n",
       "      <th>Nombre_est</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Tmax</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-13</td>\n",
       "      <td>15.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-06-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-06-18</td>\n",
       "      <td>17.2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>14.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-07-18</td>\n",
       "      <td>25.4</td>\n",
       "      <td>16.1</td>\n",
       "      <td>20.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-08-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Identificador_est Nombre_est Provincia       Fecha  Tmax  Tmin  Tmed Vmax  \\\n",
       "6                1387   A Coruña  A Coruña  2013-05-13  15.1  10.9    13  NaN   \n",
       "41               1387   A Coruña  A Coruña  2013-06-17   NaN   NaN   NaN  NaN   \n",
       "42               1387   A Coruña  A Coruña  2013-06-18  17.2  12.5  14.9  NaN   \n",
       "72               1387   A Coruña  A Coruña  2013-07-18  25.4  16.1  20.8  NaN   \n",
       "100              1387   A Coruña  A Coruña  2013-08-15   NaN   NaN   NaN  NaN   \n",
       "\n",
       "    TPrec  \n",
       "6     NaN  \n",
       "41    NaN  \n",
       "42    7.8  \n",
       "72    NaN  \n",
       "100   NaN  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here we can see that sometimes, it's missing the whole day data. In other instances, it's one or some of the variables.\n",
    "nan_rows = all_weather_stations[all_weather_stations.isnull().any(1)]\n",
    "nan_rows.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What I'm going to do is check if the first row of each weather station have any NaN value. If this is not the case, I think a good idea to fill the NaN values is using the Foward Fill and use the latest valid observation, as this will be from the same weather stations and also, because the rows are in chronological order, weather conditions would also have been similar between days (or at least I think it's a fair assumption)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data_import = glob.iglob(\"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Aemet2021-06-Por-Estaciones/DatosEstaciones-2021-06/*csv\")\n",
    "dataframeweatherfirstrow = (pd.read_csv(csv, dtype= str,parse_dates= True, dayfirst= True, engine= None, header = 0,\n",
    "                                cache_dates= True,skip_blank_lines= False, sep = \";\",\n",
    "                                       nrows= 1) for csv in weather_data_import)\n",
    "weather_first_row = pd.concat(dataframeweatherfirstrow, ignore_index= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Tmin</th>\n",
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       "      <th>0</th>\n",
       "      <td>8203O</td>\n",
       "      <td>2014-07-07</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9283Y</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>23.1</td>\n",
       "      <td>12.4</td>\n",
       "      <td>17.8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0411X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>24.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3531X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>21.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>16.7</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4103X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>0201X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>23.8</td>\n",
       "      <td>15.9</td>\n",
       "      <td>19.8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>0429X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>25.5</td>\n",
       "      <td>15.4</td>\n",
       "      <td>20.4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>4325X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>31.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>22.7</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>0114X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>9443V</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>22.2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>849 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id       Fecha  Tmax  Tmin  Tmed Vmax TPrec\n",
       "0    8203O  2014-07-07  25.3  15.8  20.5   44     0\n",
       "1    9283Y  2013-05-07  23.1  12.4  17.8   10     0\n",
       "2    0411X  2013-05-07  24.4  13.1  18.8   15   NaN\n",
       "3    3531X  2013-05-07  21.9  11.5  16.7   12     0\n",
       "4    4103X  2013-05-07   NaN   NaN   NaN  NaN   NaN\n",
       "..     ...         ...   ...   ...   ...  ...   ...\n",
       "844  0201X  2013-05-07  23.8  15.9  19.8   10     0\n",
       "845  0429X  2013-05-07  25.5  15.4  20.4   11     0\n",
       "846  4325X  2013-05-07  31.0  14.4  22.7   14     0\n",
       "847  0114X  2013-05-07  23.0  11.1  17.1   13     0\n",
       "848  9443V  2013-05-07  27.0  17.4  22.2   14     0\n",
       "\n",
       "[849 rows x 7 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_first_row = weather_first_row.drop({\"HTmax\", \"HTmin\", \"Racha\", \"HRacha\", \n",
    "                                                        \"HVmax\", \"Prec1\", \"Prec2\", \"Prec3\", \"Prec4\"},\n",
    "                                                       axis = 1)\n",
    "weather_first_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\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>Id</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0411X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>24.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4103X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1014A</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>18.9</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1057B</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3305Y</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>24.6</td>\n",
       "      <td>12.4</td>\n",
       "      <td>18.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>B957</td>\n",
       "      <td>2015-06-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>9647X</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>23.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>16.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>0341X</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>3021Y</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>9677</td>\n",
       "      <td>2017-02-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id       Fecha  Tmax  Tmin  Tmed Vmax TPrec\n",
       "2    0411X  2013-05-07  24.4  13.1  18.8   15   NaN\n",
       "4    4103X  2013-05-07   NaN   NaN   NaN  NaN   NaN\n",
       "10   1014A  2013-05-07  23.0  14.8  18.9   14   NaN\n",
       "12   1057B  2013-05-07   NaN   NaN   NaN   22   0.5\n",
       "13   3305Y  2013-05-07  24.6  12.4  18.5  NaN     0\n",
       "..     ...         ...   ...   ...   ...  ...   ...\n",
       "818   B957  2015-06-02   NaN   NaN   NaN  NaN   NaN\n",
       "820  9647X  2013-05-07  23.1  10.1  16.6  NaN     0\n",
       "828  0341X  2015-01-13   NaN   NaN   NaN  NaN   NaN\n",
       "834  3021Y  2013-05-07   NaN   NaN   NaN  NaN   NaN\n",
       "838   9677  2017-02-22   NaN   NaN   NaN   26   NaN\n",
       "\n",
       "[217 rows x 7 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check how many files start with all NaN values\n",
    "all_nan = weather_first_row[weather_first_row.isnull().any(axis=1)]\n",
    "all_nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having had a look at this, and after checking some of the individual files, I think the best idea would be drop all the rows with NaN values. I don't think this will cause any dramatic impact in my models, as after this I'm going to do the mean by province. Therefore, even if data is missing from some weather stations for some dates, it should be there from the others. \n",
    "\n",
    "Also, after that I need to do the weighted average by population to get to a single row by day so I think that at this point is better not to start filling gaps with data that might not be accurate for those days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Identificador_est</th>\n",
       "      <th>Nombre_est</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>19.7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>31</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-08</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12.8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-09</td>\n",
       "      <td>15.1</td>\n",
       "      <td>11.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-10</td>\n",
       "      <td>15.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1387</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>2013-05-11</td>\n",
       "      <td>15.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>12.2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Identificador_est Nombre_est Provincia       Fecha  Tmax  Tmin  Tmed Vmax  \\\n",
       "0              1387   A Coruña  A Coruña  2013-05-07  19.7  15.0  17.4   31   \n",
       "1              1387   A Coruña  A Coruña  2013-05-08  18.1  12.8  15.5   27   \n",
       "2              1387   A Coruña  A Coruña  2013-05-09  15.1  11.4  13.3   20   \n",
       "3              1387   A Coruña  A Coruña  2013-05-10  15.5   8.2  11.8   19   \n",
       "4              1387   A Coruña  A Coruña  2013-05-11  15.7   8.7  12.2   20   \n",
       "\n",
       "  TPrec  \n",
       "0   1.2  \n",
       "1     0  \n",
       "2     0  \n",
       "3     0  \n",
       "4     0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weather_stations.dropna(axis=0, how= \"any\", inplace= True)\n",
    "all_weather_stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "735"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If I do this, I lose the information from 113 weather stations\n",
    "all_weather_stations[\"Identificador_est\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which ones are the weather stations that we've lost information from, just for clarification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_stations = pd.DataFrame(listado_estaciones[\"Identificador_est\"])\n",
    "\n",
    "remaining_stations = pd.DataFrame(all_weather_stations[\"Identificador_est\"].unique(), columns=[\"Identificador_est\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = total_stations.merge(remaining_stations, how= \"left\", indicator= True)\n",
    "np.count_nonzero(comparison[\"_merge\"] != \"both\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\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>Identificador_est</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1390X</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1399</td>\n",
       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1406X</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1410X</td>\n",
       "      <td>left_only</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1442U</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>8005X</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>B228D</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>1446X</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>9988B</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>9445L</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Identificador_est     _merge\n",
       "2               1390X  left_only\n",
       "4                1399  left_only\n",
       "6               1406X  left_only\n",
       "7               1410X  left_only\n",
       "11              1442U  left_only\n",
       "..                ...        ...\n",
       "820             8005X  left_only\n",
       "832             B228D  left_only\n",
       "833             1446X  left_only\n",
       "842             9988B  left_only\n",
       "845             9445L  left_only\n",
       "\n",
       "[113 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checks = comparison[\"_merge\"] == \"left_only\"\n",
    "gone_stations = comparison[checks]\n",
    "gone_stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "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>Identificador_est</th>\n",
       "      <th>Nombre_est</th>\n",
       "      <th>Provincia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1390X</td>\n",
       "      <td>Carballo, Depuradora</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1399</td>\n",
       "      <td>Vimianzo</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1406X</td>\n",
       "      <td>Mazaricos</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1410X</td>\n",
       "      <td>Sobrado</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1442U</td>\n",
       "      <td>Boiro</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1476R</td>\n",
       "      <td>Rois, Casas do Porto</td>\n",
       "      <td>A Coruña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7066Y</td>\n",
       "      <td>Yeste, Embalse Fuensanta</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7067Y</td>\n",
       "      <td>Nerpio</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7072Y</td>\n",
       "      <td>Yeste</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7103Y</td>\n",
       "      <td>Tobarra</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8013X</td>\n",
       "      <td>Novelda</td>\n",
       "      <td>Alacant/Alicante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8018X</td>\n",
       "      <td>Elche/Elx</td>\n",
       "      <td>Alacant/Alicante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5060X</td>\n",
       "      <td>Vélez Blanco</td>\n",
       "      <td>Almería</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6291B</td>\n",
       "      <td>El Ejido</td>\n",
       "      <td>Almería</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1044X</td>\n",
       "      <td>Aramaio</td>\n",
       "      <td>Araba/Álava</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9145X</td>\n",
       "      <td>Leza</td>\n",
       "      <td>Araba/Álava</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9178X</td>\n",
       "      <td>Campezo/Kanpezu</td>\n",
       "      <td>Araba/Álava</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0158X</td>\n",
       "      <td>Monistrol de Montserrat</td>\n",
       "      <td>Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0194D</td>\n",
       "      <td>Corbera, Pic d'Agulles</td>\n",
       "      <td>Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0260X</td>\n",
       "      <td>Fogars de Montclús</td>\n",
       "      <td>Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1069Y</td>\n",
       "      <td>Abadiño</td>\n",
       "      <td>Bizkaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1078C</td>\n",
       "      <td>Balmaseda</td>\n",
       "      <td>Bizkaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9012E</td>\n",
       "      <td>Santa Gadea de Alfoz</td>\n",
       "      <td>Burgos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5910X</td>\n",
       "      <td>Rota, Base Naval</td>\n",
       "      <td>Cádiz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5346X</td>\n",
       "      <td>Cardeña</td>\n",
       "      <td>Córdoba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3040Y</td>\n",
       "      <td>Beteta</td>\n",
       "      <td>Cuenca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4075Y</td>\n",
       "      <td>Villares del Saz</td>\n",
       "      <td>Cuenca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8245Y</td>\n",
       "      <td>Mira</td>\n",
       "      <td>Cuenca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1036A</td>\n",
       "      <td>Zarautz</td>\n",
       "      <td>Gipuzkoa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1037X</td>\n",
       "      <td>Legazpia</td>\n",
       "      <td>Gipuzkoa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1048X</td>\n",
       "      <td>Aretxabaleta</td>\n",
       "      <td>Gipuzkoa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>6258X</td>\n",
       "      <td>Lanjarón</td>\n",
       "      <td>Granada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>6281X</td>\n",
       "      <td>Válor</td>\n",
       "      <td>Granada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3085Y</td>\n",
       "      <td>Pastrana</td>\n",
       "      <td>Guadalajara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3140Y</td>\n",
       "      <td>Mandayona</td>\n",
       "      <td>Guadalajara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3209Y</td>\n",
       "      <td>Brihuega</td>\n",
       "      <td>Guadalajara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4541X</td>\n",
       "      <td>El Granado</td>\n",
       "      <td>Huelva</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4575X</td>\n",
       "      <td>Valverde del Camino</td>\n",
       "      <td>Huelva</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4584X</td>\n",
       "      <td>El Cerro de Andévalo</td>\n",
       "      <td>Huelva</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>9901X</td>\n",
       "      <td>Huesca</td>\n",
       "      <td>Huesca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>B334X</td>\n",
       "      <td>Llucmajor</td>\n",
       "      <td>Illes Balears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>B526X</td>\n",
       "      <td>Artà</td>\n",
       "      <td>Illes Balears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>B603X</td>\n",
       "      <td>Artà-Colònia de Sant Pere</td>\n",
       "      <td>Illes Balears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>B605X</td>\n",
       "      <td>Muro, S'Albufera</td>\n",
       "      <td>Illes Balears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>B684A</td>\n",
       "      <td>Escorca, Son Torrella</td>\n",
       "      <td>Illes Balears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>B860X</td>\n",
       "      <td>Ciutadella</td>\n",
       "      <td>Illes Balears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>B908X</td>\n",
       "      <td>Sant Joan de Labritja</td>\n",
       "      <td>Illes Balears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5165X</td>\n",
       "      <td>Torres</td>\n",
       "      <td>Jaén</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>9121X</td>\n",
       "      <td>Haro</td>\n",
       "      <td>La Rioja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>9136X</td>\n",
       "      <td>Anguiano</td>\n",
       "      <td>La Rioja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1178Y</td>\n",
       "      <td>Posada de Valdeón, Soto</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1561I</td>\n",
       "      <td>Vega de Espinareda</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>9590D</td>\n",
       "      <td>Illes de Cerdanya,Cap de Rec</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>9647X</td>\n",
       "      <td>Torà</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>9650X</td>\n",
       "      <td>Artesa de Segre</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>9689X</td>\n",
       "      <td>Torre de Cabdella</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>9772X</td>\n",
       "      <td>La Pobla de Cérvoles</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>9775X</td>\n",
       "      <td>El Soleràs</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1344X</td>\n",
       "      <td>Mondoñedo</td>\n",
       "      <td>Lugo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1347T</td>\n",
       "      <td>Burela</td>\n",
       "      <td>Lugo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1521I</td>\n",
       "      <td>O Páramo</td>\n",
       "      <td>Lugo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1658</td>\n",
       "      <td>Folgoso do Courel</td>\n",
       "      <td>Lugo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>3125Y</td>\n",
       "      <td>San Sebastián de Los Reyes</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>3194Y</td>\n",
       "      <td>Pozuelo de Alarcón</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>3229Y</td>\n",
       "      <td>Tielmes</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>3330Y</td>\n",
       "      <td>Rozas de Puerto Real</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>6143X</td>\n",
       "      <td>Coín</td>\n",
       "      <td>Málaga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>6175X</td>\n",
       "      <td>Rincon de la Victoria</td>\n",
       "      <td>Málaga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6199B</td>\n",
       "      <td>Vélez-Málaga</td>\n",
       "      <td>Málaga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>7023X</td>\n",
       "      <td>Fuente Álamo de Murcia</td>\n",
       "      <td>Murcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>7080X</td>\n",
       "      <td>Moratalla</td>\n",
       "      <td>Murcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>7127X</td>\n",
       "      <td>Bullas</td>\n",
       "      <td>Murcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>7158X</td>\n",
       "      <td>Archena</td>\n",
       "      <td>Murcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>7195X</td>\n",
       "      <td>Caravaca de La Cruz, Los Royos</td>\n",
       "      <td>Murcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>7211B</td>\n",
       "      <td>Puerto Lumbreras</td>\n",
       "      <td>Murcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>7250C</td>\n",
       "      <td>Abanilla</td>\n",
       "      <td>Murcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>9238X</td>\n",
       "      <td>Navascués/Nabaskoze</td>\n",
       "      <td>Navarra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>9257X</td>\n",
       "      <td>Esteribar, Eugui</td>\n",
       "      <td>Navarra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>9301X</td>\n",
       "      <td>Monteagudo</td>\n",
       "      <td>Navarra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1583X</td>\n",
       "      <td>O Barco de Valdeorras</td>\n",
       "      <td>Ourense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1696O</td>\n",
       "      <td>Beariz</td>\n",
       "      <td>Ourense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1706A</td>\n",
       "      <td>Allariz</td>\n",
       "      <td>Ourense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1738U</td>\n",
       "      <td>Muiños</td>\n",
       "      <td>Ourense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2969U</td>\n",
       "      <td>A Gudiña</td>\n",
       "      <td>Ourense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2978X</td>\n",
       "      <td>Verín</td>\n",
       "      <td>Ourense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1468X</td>\n",
       "      <td>A Estrada</td>\n",
       "      <td>Pontevedra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1719</td>\n",
       "      <td>A Cañiza</td>\n",
       "      <td>Pontevedra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1723X</td>\n",
       "      <td>Ponteareas, Canedo</td>\n",
       "      <td>Pontevedra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1730E</td>\n",
       "      <td>O Rosal</td>\n",
       "      <td>Pontevedra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2471Y</td>\n",
       "      <td>San Rafael</td>\n",
       "      <td>Segovia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>5612X</td>\n",
       "      <td>Lora de Estepa</td>\n",
       "      <td>Sevilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2084Y</td>\n",
       "      <td>Ucero</td>\n",
       "      <td>Soria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0034X</td>\n",
       "      <td>Valls</td>\n",
       "      <td>Tarragona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>9946X</td>\n",
       "      <td>Horta de Sant Joan</td>\n",
       "      <td>Tarragona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>3245Y</td>\n",
       "      <td>Tembleque</td>\n",
       "      <td>Toledo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3305Y</td>\n",
       "      <td>Navahermosa</td>\n",
       "      <td>Toledo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3362Y</td>\n",
       "      <td>Castillo de Bayuela</td>\n",
       "      <td>Toledo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3427Y</td>\n",
       "      <td>Oropesa</td>\n",
       "      <td>Toledo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>4236Y</td>\n",
       "      <td>Puerto del Rey</td>\n",
       "      <td>Toledo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>8290X</td>\n",
       "      <td>Enguera</td>\n",
       "      <td>València/Valencia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>8337X</td>\n",
       "      <td>Turís</td>\n",
       "      <td>València/Valencia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>8381X</td>\n",
       "      <td>Ademuz</td>\n",
       "      <td>València/Valencia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>8446Y</td>\n",
       "      <td>Sagunto</td>\n",
       "      <td>València/Valencia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2166Y</td>\n",
       "      <td>Peñafiel</td>\n",
       "      <td>Valladolid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2507Y</td>\n",
       "      <td>Rueda</td>\n",
       "      <td>Valladolid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>9354X</td>\n",
       "      <td>Alhama de Aragon</td>\n",
       "      <td>Zaragoza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>5051X</td>\n",
       "      <td>Huéscar</td>\n",
       "      <td>Granada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>9677</td>\n",
       "      <td>Rialp, Port Ainé</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>8005X</td>\n",
       "      <td>Fontanars dels Alforins</td>\n",
       "      <td>València/Valencia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>B228D</td>\n",
       "      <td>Palma, Dic de l'Oest</td>\n",
       "      <td>Illes Balears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1446X</td>\n",
       "      <td>Monterroso</td>\n",
       "      <td>Lugo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>9988B</td>\n",
       "      <td>CAP DE VAQUEIRA</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>9445L</td>\n",
       "      <td>Formigal, Sarrios</td>\n",
       "      <td>Huesca</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Identificador_est                      Nombre_est          Provincia\n",
       "0               1390X            Carballo, Depuradora           A Coruña\n",
       "1                1399                        Vimianzo           A Coruña\n",
       "2               1406X                       Mazaricos           A Coruña\n",
       "3               1410X                         Sobrado           A Coruña\n",
       "4               1442U                           Boiro           A Coruña\n",
       "5               1476R            Rois, Casas do Porto           A Coruña\n",
       "6               7066Y        Yeste, Embalse Fuensanta           Albacete\n",
       "7               7067Y                          Nerpio           Albacete\n",
       "8               7072Y                           Yeste           Albacete\n",
       "9               7103Y                         Tobarra           Albacete\n",
       "10              8013X                         Novelda   Alacant/Alicante\n",
       "11              8018X                       Elche/Elx   Alacant/Alicante\n",
       "12              5060X                    Vélez Blanco            Almería\n",
       "13              6291B                        El Ejido            Almería\n",
       "14              1044X                         Aramaio        Araba/Álava\n",
       "15              9145X                            Leza        Araba/Álava\n",
       "16              9178X                 Campezo/Kanpezu        Araba/Álava\n",
       "17              0158X         Monistrol de Montserrat          Barcelona\n",
       "18              0194D          Corbera, Pic d'Agulles          Barcelona\n",
       "19              0260X              Fogars de Montclús          Barcelona\n",
       "20              1069Y                         Abadiño            Bizkaia\n",
       "21              1078C                       Balmaseda            Bizkaia\n",
       "22              9012E            Santa Gadea de Alfoz             Burgos\n",
       "23              5910X                Rota, Base Naval              Cádiz\n",
       "24              5346X                         Cardeña            Córdoba\n",
       "25              3040Y                          Beteta             Cuenca\n",
       "26              4075Y                Villares del Saz             Cuenca\n",
       "27              8245Y                            Mira             Cuenca\n",
       "28              1036A                         Zarautz           Gipuzkoa\n",
       "29              1037X                        Legazpia           Gipuzkoa\n",
       "30              1048X                    Aretxabaleta           Gipuzkoa\n",
       "31              6258X                        Lanjarón            Granada\n",
       "32              6281X                           Válor            Granada\n",
       "33              3085Y                        Pastrana        Guadalajara\n",
       "34              3140Y                       Mandayona        Guadalajara\n",
       "35              3209Y                        Brihuega        Guadalajara\n",
       "36              4541X                      El Granado             Huelva\n",
       "37              4575X             Valverde del Camino             Huelva\n",
       "38              4584X            El Cerro de Andévalo             Huelva\n",
       "39              9901X                          Huesca             Huesca\n",
       "40              B334X                       Llucmajor      Illes Balears\n",
       "41              B526X                            Artà      Illes Balears\n",
       "42              B603X       Artà-Colònia de Sant Pere      Illes Balears\n",
       "43              B605X                Muro, S'Albufera      Illes Balears\n",
       "44              B684A           Escorca, Son Torrella      Illes Balears\n",
       "45              B860X                      Ciutadella      Illes Balears\n",
       "46              B908X           Sant Joan de Labritja      Illes Balears\n",
       "47              5165X                          Torres               Jaén\n",
       "48              9121X                            Haro           La Rioja\n",
       "49              9136X                        Anguiano           La Rioja\n",
       "50              1178Y         Posada de Valdeón, Soto               León\n",
       "51              1561I              Vega de Espinareda               León\n",
       "52              9590D    Illes de Cerdanya,Cap de Rec             Lleida\n",
       "53              9647X                            Torà             Lleida\n",
       "54              9650X                 Artesa de Segre             Lleida\n",
       "55              9689X               Torre de Cabdella             Lleida\n",
       "56              9772X            La Pobla de Cérvoles             Lleida\n",
       "57              9775X                      El Soleràs             Lleida\n",
       "58              1344X                       Mondoñedo               Lugo\n",
       "59              1347T                          Burela               Lugo\n",
       "60              1521I                        O Páramo               Lugo\n",
       "61               1658               Folgoso do Courel               Lugo\n",
       "62              3125Y      San Sebastián de Los Reyes             Madrid\n",
       "63              3194Y              Pozuelo de Alarcón             Madrid\n",
       "64              3229Y                         Tielmes             Madrid\n",
       "65              3330Y            Rozas de Puerto Real             Madrid\n",
       "66              6143X                            Coín             Málaga\n",
       "67              6175X           Rincon de la Victoria             Málaga\n",
       "68              6199B                    Vélez-Málaga             Málaga\n",
       "69              7023X          Fuente Álamo de Murcia             Murcia\n",
       "70              7080X                       Moratalla             Murcia\n",
       "71              7127X                          Bullas             Murcia\n",
       "72              7158X                         Archena             Murcia\n",
       "73              7195X  Caravaca de La Cruz, Los Royos             Murcia\n",
       "74              7211B                Puerto Lumbreras             Murcia\n",
       "75              7250C                        Abanilla             Murcia\n",
       "76              9238X             Navascués/Nabaskoze            Navarra\n",
       "77              9257X                Esteribar, Eugui            Navarra\n",
       "78              9301X                      Monteagudo            Navarra\n",
       "79              1583X           O Barco de Valdeorras            Ourense\n",
       "80              1696O                          Beariz            Ourense\n",
       "81              1706A                         Allariz            Ourense\n",
       "82              1738U                          Muiños            Ourense\n",
       "83              2969U                        A Gudiña            Ourense\n",
       "84              2978X                           Verín            Ourense\n",
       "85              1468X                       A Estrada         Pontevedra\n",
       "86               1719                        A Cañiza         Pontevedra\n",
       "87              1723X              Ponteareas, Canedo         Pontevedra\n",
       "88              1730E                         O Rosal         Pontevedra\n",
       "89              2471Y                      San Rafael            Segovia\n",
       "90              5612X                  Lora de Estepa            Sevilla\n",
       "91              2084Y                           Ucero              Soria\n",
       "92              0034X                           Valls          Tarragona\n",
       "93              9946X              Horta de Sant Joan          Tarragona\n",
       "94              3245Y                       Tembleque             Toledo\n",
       "95              3305Y                     Navahermosa             Toledo\n",
       "96              3362Y             Castillo de Bayuela             Toledo\n",
       "97              3427Y                         Oropesa             Toledo\n",
       "98              4236Y                  Puerto del Rey             Toledo\n",
       "99              8290X                         Enguera  València/Valencia\n",
       "100             8337X                           Turís  València/Valencia\n",
       "101             8381X                          Ademuz  València/Valencia\n",
       "102             8446Y                         Sagunto  València/Valencia\n",
       "103             2166Y                        Peñafiel         Valladolid\n",
       "104             2507Y                           Rueda         Valladolid\n",
       "105             9354X                Alhama de Aragon           Zaragoza\n",
       "106             5051X                         Huéscar            Granada\n",
       "107              9677                Rialp, Port Ainé             Lleida\n",
       "108             8005X         Fontanars dels Alforins  València/Valencia\n",
       "109             B228D            Palma, Dic de l'Oest      Illes Balears\n",
       "110             1446X                      Monterroso               Lugo\n",
       "111             9988B                 CAP DE VAQUEIRA             Lleida\n",
       "112             9445L               Formigal, Sarrios             Huesca"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"max_rows\", 113)\n",
    "gone_stations = gone_stations.merge(listado_estaciones)\n",
    "gone_stations.drop({\"_merge\"}, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option(\"display.max_rows\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To be able to group the data, let's change the data type\n",
    "all_weather_stations[\"Fecha\"] = pd.to_datetime(all_weather_stations[\"Fecha\"], dayfirst= True)\n",
    "all_weather_stations[\"Tmax\"] = pd.to_numeric(all_weather_stations[\"Tmax\"], downcast=\"float\")\n",
    "all_weather_stations[\"Tmin\"] = pd.to_numeric(all_weather_stations[\"Tmin\"], downcast=\"float\")\n",
    "all_weather_stations[\"Tmed\"] = pd.to_numeric(all_weather_stations[\"Tmed\"], downcast=\"float\")\n",
    "all_weather_stations[\"Vmax\"] = pd.to_numeric(all_weather_stations[\"Vmax\"], downcast=\"float\")\n",
    "all_weather_stations[\"TPrec\"] = pd.to_numeric(all_weather_stations[\"TPrec\"], downcast=\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Provincia</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-05-07</th>\n",
       "      <td>A Coruña</td>\n",
       "      <td>17.650000</td>\n",
       "      <td>12.950000</td>\n",
       "      <td>15.049999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-07</th>\n",
       "      <td>Alacant/Alicante</td>\n",
       "      <td>29.299999</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>23.200001</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-07</th>\n",
       "      <td>Albacete</td>\n",
       "      <td>25.450001</td>\n",
       "      <td>13.450001</td>\n",
       "      <td>19.750000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-07</th>\n",
       "      <td>Almería</td>\n",
       "      <td>24.250000</td>\n",
       "      <td>16.200001</td>\n",
       "      <td>20.950001</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-07</th>\n",
       "      <td>Araba/Álava</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>17.200001</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Provincia       Tmax       Tmin       Tmed  Vmax  TPrec\n",
       "Fecha                                                                     \n",
       "2013-05-07          A Coruña  17.650000  12.950000  15.049999  35.0    9.6\n",
       "2013-05-07  Alacant/Alicante  29.299999  17.400000  23.200001  18.5    0.0\n",
       "2013-05-07          Albacete  25.450001  13.450001  19.750000  15.0    0.0\n",
       "2013-05-07           Almería  24.250000  16.200001  20.950001  19.5    0.0\n",
       "2013-05-07       Araba/Álava  21.100000  13.300000  17.200001  18.0    0.8"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here I finally get one row by day/province with the mean of all weather stations\n",
    "all_weather_stations = all_weather_stations.groupby([\"Fecha\", \"Provincia\"]).median().reset_index()\n",
    "all_weather_stations.set_index(\"Fecha\", inplace=True)\n",
    "all_weather_stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.dates.RRuleLocator at 0x7ff55ebb2760>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Temperatures clearly look seasonal\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "\n",
    "all_weather_stations[\"Tmax\"].plot(ax=ax)\n",
    "\n",
    "mdates.RRuleLocator(\"o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's add the population info to the weather DataFrame so I can do the weighted average by population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "population_path = \"/home/dsc/CarpetaCompartida/TFM/Data/Precios de la electricidad/Cifras oficiales de población resultantes de la revisión del Padrón municipal 2020.csv\"\n",
    "population = pd.read_csv(population_path, dtype = str, engine= \"python\", sep=\";\", encoding= \"latin1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "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>Provincias</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Periodo</th>\n",
       "      <th>Total</th>\n",
       "      <th>Code</th>\n",
       "      <th>Provincia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02 Albacete</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>388.270</td>\n",
       "      <td>02</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03 Alicante/Alacant</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.879.888</td>\n",
       "      <td>03</td>\n",
       "      <td>Alicante/Alacant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04 Almería</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>727.945</td>\n",
       "      <td>04</td>\n",
       "      <td>Almería</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01 Araba/Álava</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>333.940</td>\n",
       "      <td>01</td>\n",
       "      <td>Araba/Álava</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33 Asturias</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.018.784</td>\n",
       "      <td>33</td>\n",
       "      <td>Asturias</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>05 Ávila</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>157.664</td>\n",
       "      <td>05</td>\n",
       "      <td>Ávila</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>06 Badajoz</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>672.137</td>\n",
       "      <td>06</td>\n",
       "      <td>Badajoz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>07 Balears, Illes</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.171.543</td>\n",
       "      <td>07</td>\n",
       "      <td>Balears, Illes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>08 Barcelona</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>5.743.402</td>\n",
       "      <td>08</td>\n",
       "      <td>Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48 Bizkaia</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.159.443</td>\n",
       "      <td>48</td>\n",
       "      <td>Bizkaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>09 Burgos</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>357.650</td>\n",
       "      <td>09</td>\n",
       "      <td>Burgos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10 Cáceres</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>391.850</td>\n",
       "      <td>10</td>\n",
       "      <td>Cáceres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11 Cádiz</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.244.049</td>\n",
       "      <td>11</td>\n",
       "      <td>Cádiz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>39 Cantabria</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>582.905</td>\n",
       "      <td>39</td>\n",
       "      <td>Cantabria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12 Castellón/Castelló</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>585.590</td>\n",
       "      <td>12</td>\n",
       "      <td>Castellón/Castelló</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13 Ciudad Real</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>495.045</td>\n",
       "      <td>13</td>\n",
       "      <td>Ciudad Real</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14 Córdoba</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>781.451</td>\n",
       "      <td>14</td>\n",
       "      <td>Córdoba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15 Coruña, A</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.121.815</td>\n",
       "      <td>15</td>\n",
       "      <td>Coruña, A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16 Cuenca</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>196.139</td>\n",
       "      <td>16</td>\n",
       "      <td>Cuenca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20 Gipuzkoa</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>727.121</td>\n",
       "      <td>20</td>\n",
       "      <td>Gipuzkoa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17 Girona</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>781.788</td>\n",
       "      <td>17</td>\n",
       "      <td>Girona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>18 Granada</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>919.168</td>\n",
       "      <td>18</td>\n",
       "      <td>Granada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19 Guadalajara</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>261.995</td>\n",
       "      <td>19</td>\n",
       "      <td>Guadalajara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21 Huelva</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>524.278</td>\n",
       "      <td>21</td>\n",
       "      <td>Huelva</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22 Huesca</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>222.687</td>\n",
       "      <td>22</td>\n",
       "      <td>Huesca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23 Jaén</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>631.381</td>\n",
       "      <td>23</td>\n",
       "      <td>Jaén</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>24 León</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>456.439</td>\n",
       "      <td>24</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>25 Lleida</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>438.517</td>\n",
       "      <td>25</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>27 Lugo</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>327.946</td>\n",
       "      <td>27</td>\n",
       "      <td>Lugo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28 Madrid</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>6.779.888</td>\n",
       "      <td>28</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>29 Málaga</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.685.920</td>\n",
       "      <td>29</td>\n",
       "      <td>Málaga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>30 Murcia</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.511.251</td>\n",
       "      <td>30</td>\n",
       "      <td>Murcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>31 Navarra</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>661.197</td>\n",
       "      <td>31</td>\n",
       "      <td>Navarra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>32 Ourense</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>306.650</td>\n",
       "      <td>32</td>\n",
       "      <td>Ourense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34 Palencia</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>160.321</td>\n",
       "      <td>34</td>\n",
       "      <td>Palencia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35 Palmas, Las</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.131.065</td>\n",
       "      <td>35</td>\n",
       "      <td>Palmas, Las</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36 Pontevedra</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>945.408</td>\n",
       "      <td>36</td>\n",
       "      <td>Pontevedra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>26 Rioja, La</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>319.914</td>\n",
       "      <td>26</td>\n",
       "      <td>Rioja, La</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>37 Salamanca</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>329.245</td>\n",
       "      <td>37</td>\n",
       "      <td>Salamanca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38 Santa Cruz de Tenerife</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.044.887</td>\n",
       "      <td>38</td>\n",
       "      <td>Santa Cruz de Tenerife</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40 Segovia</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>153.478</td>\n",
       "      <td>40</td>\n",
       "      <td>Segovia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41 Sevilla</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.950.219</td>\n",
       "      <td>41</td>\n",
       "      <td>Sevilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42 Soria</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>88.884</td>\n",
       "      <td>42</td>\n",
       "      <td>Soria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43 Tarragona</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>816.772</td>\n",
       "      <td>43</td>\n",
       "      <td>Tarragona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44 Teruel</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>134.176</td>\n",
       "      <td>44</td>\n",
       "      <td>Teruel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45 Toledo</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>703.772</td>\n",
       "      <td>45</td>\n",
       "      <td>Toledo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46 Valencia/València</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>2.591.875</td>\n",
       "      <td>46</td>\n",
       "      <td>Valencia/València</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47 Valladolid</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>520.649</td>\n",
       "      <td>47</td>\n",
       "      <td>Valladolid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49 Zamora</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>170.588</td>\n",
       "      <td>49</td>\n",
       "      <td>Zamora</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50 Zaragoza</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>972.528</td>\n",
       "      <td>50</td>\n",
       "      <td>Zaragoza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51 Ceuta</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>84.202</td>\n",
       "      <td>51</td>\n",
       "      <td>Ceuta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52 Melilla</td>\n",
       "      <td>Total</td>\n",
       "      <td>2020</td>\n",
       "      <td>87.076</td>\n",
       "      <td>52</td>\n",
       "      <td>Melilla</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Provincias   Sexo Periodo      Total Code  \\\n",
       "0                 02 Albacete  Total    2020    388.270   02   \n",
       "1         03 Alicante/Alacant  Total    2020  1.879.888   03   \n",
       "2                  04 Almería  Total    2020    727.945   04   \n",
       "3              01 Araba/Álava  Total    2020    333.940   01   \n",
       "4                 33 Asturias  Total    2020  1.018.784   33   \n",
       "5                    05 Ávila  Total    2020    157.664   05   \n",
       "6                  06 Badajoz  Total    2020    672.137   06   \n",
       "7           07 Balears, Illes  Total    2020  1.171.543   07   \n",
       "8                08 Barcelona  Total    2020  5.743.402   08   \n",
       "9                  48 Bizkaia  Total    2020  1.159.443   48   \n",
       "10                  09 Burgos  Total    2020    357.650   09   \n",
       "11                 10 Cáceres  Total    2020    391.850   10   \n",
       "12                   11 Cádiz  Total    2020  1.244.049   11   \n",
       "13               39 Cantabria  Total    2020    582.905   39   \n",
       "14      12 Castellón/Castelló  Total    2020    585.590   12   \n",
       "15             13 Ciudad Real  Total    2020    495.045   13   \n",
       "16                 14 Córdoba  Total    2020    781.451   14   \n",
       "17               15 Coruña, A  Total    2020  1.121.815   15   \n",
       "18                  16 Cuenca  Total    2020    196.139   16   \n",
       "19                20 Gipuzkoa  Total    2020    727.121   20   \n",
       "20                  17 Girona  Total    2020    781.788   17   \n",
       "21                 18 Granada  Total    2020    919.168   18   \n",
       "22             19 Guadalajara  Total    2020    261.995   19   \n",
       "23                  21 Huelva  Total    2020    524.278   21   \n",
       "24                  22 Huesca  Total    2020    222.687   22   \n",
       "25                    23 Jaén  Total    2020    631.381   23   \n",
       "26                    24 León  Total    2020    456.439   24   \n",
       "27                  25 Lleida  Total    2020    438.517   25   \n",
       "28                    27 Lugo  Total    2020    327.946   27   \n",
       "29                  28 Madrid  Total    2020  6.779.888   28   \n",
       "30                  29 Málaga  Total    2020  1.685.920   29   \n",
       "31                  30 Murcia  Total    2020  1.511.251   30   \n",
       "32                 31 Navarra  Total    2020    661.197   31   \n",
       "33                 32 Ourense  Total    2020    306.650   32   \n",
       "34                34 Palencia  Total    2020    160.321   34   \n",
       "35             35 Palmas, Las  Total    2020  1.131.065   35   \n",
       "36              36 Pontevedra  Total    2020    945.408   36   \n",
       "37               26 Rioja, La  Total    2020    319.914   26   \n",
       "38               37 Salamanca  Total    2020    329.245   37   \n",
       "39  38 Santa Cruz de Tenerife  Total    2020  1.044.887   38   \n",
       "40                 40 Segovia  Total    2020    153.478   40   \n",
       "41                 41 Sevilla  Total    2020  1.950.219   41   \n",
       "42                   42 Soria  Total    2020     88.884   42   \n",
       "43               43 Tarragona  Total    2020    816.772   43   \n",
       "44                  44 Teruel  Total    2020    134.176   44   \n",
       "45                  45 Toledo  Total    2020    703.772   45   \n",
       "46       46 Valencia/València  Total    2020  2.591.875   46   \n",
       "47              47 Valladolid  Total    2020    520.649   47   \n",
       "48                  49 Zamora  Total    2020    170.588   49   \n",
       "49                50 Zaragoza  Total    2020    972.528   50   \n",
       "50                   51 Ceuta  Total    2020     84.202   51   \n",
       "51                 52 Melilla  Total    2020     87.076   52   \n",
       "\n",
       "                 Provincia  \n",
       "0                 Albacete  \n",
       "1         Alicante/Alacant  \n",
       "2                  Almería  \n",
       "3              Araba/Álava  \n",
       "4                 Asturias  \n",
       "5                    Ávila  \n",
       "6                  Badajoz  \n",
       "7           Balears, Illes  \n",
       "8                Barcelona  \n",
       "9                  Bizkaia  \n",
       "10                  Burgos  \n",
       "11                 Cáceres  \n",
       "12                   Cádiz  \n",
       "13               Cantabria  \n",
       "14      Castellón/Castelló  \n",
       "15             Ciudad Real  \n",
       "16                 Córdoba  \n",
       "17               Coruña, A  \n",
       "18                  Cuenca  \n",
       "19                Gipuzkoa  \n",
       "20                  Girona  \n",
       "21                 Granada  \n",
       "22             Guadalajara  \n",
       "23                  Huelva  \n",
       "24                  Huesca  \n",
       "25                    Jaén  \n",
       "26                    León  \n",
       "27                  Lleida  \n",
       "28                    Lugo  \n",
       "29                  Madrid  \n",
       "30                  Málaga  \n",
       "31                  Murcia  \n",
       "32                 Navarra  \n",
       "33                 Ourense  \n",
       "34                Palencia  \n",
       "35             Palmas, Las  \n",
       "36              Pontevedra  \n",
       "37               Rioja, La  \n",
       "38               Salamanca  \n",
       "39  Santa Cruz de Tenerife  \n",
       "40                 Segovia  \n",
       "41                 Sevilla  \n",
       "42                   Soria  \n",
       "43               Tarragona  \n",
       "44                  Teruel  \n",
       "45                  Toledo  \n",
       "46       Valencia/València  \n",
       "47              Valladolid  \n",
       "48                  Zamora  \n",
       "49                Zaragoza  \n",
       "50                   Ceuta  \n",
       "51                 Melilla  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population[[\"Code\", \"Provincia\"]] = population.Provincias.str.split(\" \",1, expand=True)\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Total</th>\n",
       "      <th>Provincia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>388.270</td>\n",
       "      <td>Albacete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.879.888</td>\n",
       "      <td>Alicante/Alacant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>727.945</td>\n",
       "      <td>Almería</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>333.940</td>\n",
       "      <td>Araba/Álava</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.018.784</td>\n",
       "      <td>Asturias</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>157.664</td>\n",
       "      <td>Ávila</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>672.137</td>\n",
       "      <td>Badajoz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.171.543</td>\n",
       "      <td>Balears, Illes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.743.402</td>\n",
       "      <td>Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.159.443</td>\n",
       "      <td>Bizkaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>357.650</td>\n",
       "      <td>Burgos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>391.850</td>\n",
       "      <td>Cáceres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.244.049</td>\n",
       "      <td>Cádiz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>582.905</td>\n",
       "      <td>Cantabria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>585.590</td>\n",
       "      <td>Castellón/Castelló</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>495.045</td>\n",
       "      <td>Ciudad Real</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>781.451</td>\n",
       "      <td>Córdoba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.121.815</td>\n",
       "      <td>Coruña, A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>196.139</td>\n",
       "      <td>Cuenca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>727.121</td>\n",
       "      <td>Gipuzkoa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>781.788</td>\n",
       "      <td>Girona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>919.168</td>\n",
       "      <td>Granada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>261.995</td>\n",
       "      <td>Guadalajara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>524.278</td>\n",
       "      <td>Huelva</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>222.687</td>\n",
       "      <td>Huesca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>631.381</td>\n",
       "      <td>Jaén</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>456.439</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>438.517</td>\n",
       "      <td>Lleida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>327.946</td>\n",
       "      <td>Lugo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6.779.888</td>\n",
       "      <td>Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.685.920</td>\n",
       "      <td>Málaga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1.511.251</td>\n",
       "      <td>Murcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>661.197</td>\n",
       "      <td>Navarra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>306.650</td>\n",
       "      <td>Ourense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>160.321</td>\n",
       "      <td>Palencia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.131.065</td>\n",
       "      <td>Palmas, Las</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>945.408</td>\n",
       "      <td>Pontevedra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>319.914</td>\n",
       "      <td>Rioja, La</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>329.245</td>\n",
       "      <td>Salamanca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.044.887</td>\n",
       "      <td>Santa Cruz de Tenerife</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>153.478</td>\n",
       "      <td>Segovia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1.950.219</td>\n",
       "      <td>Sevilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>88.884</td>\n",
       "      <td>Soria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>816.772</td>\n",
       "      <td>Tarragona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>134.176</td>\n",
       "      <td>Teruel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>703.772</td>\n",
       "      <td>Toledo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2.591.875</td>\n",
       "      <td>Valencia/València</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>520.649</td>\n",
       "      <td>Valladolid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>170.588</td>\n",
       "      <td>Zamora</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>972.528</td>\n",
       "      <td>Zaragoza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>84.202</td>\n",
       "      <td>Ceuta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>87.076</td>\n",
       "      <td>Melilla</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total               Provincia\n",
       "0     388.270                Albacete\n",
       "1   1.879.888        Alicante/Alacant\n",
       "2     727.945                 Almería\n",
       "3     333.940             Araba/Álava\n",
       "4   1.018.784                Asturias\n",
       "5     157.664                   Ávila\n",
       "6     672.137                 Badajoz\n",
       "7   1.171.543          Balears, Illes\n",
       "8   5.743.402               Barcelona\n",
       "9   1.159.443                 Bizkaia\n",
       "10    357.650                  Burgos\n",
       "11    391.850                 Cáceres\n",
       "12  1.244.049                   Cádiz\n",
       "13    582.905               Cantabria\n",
       "14    585.590      Castellón/Castelló\n",
       "15    495.045             Ciudad Real\n",
       "16    781.451                 Córdoba\n",
       "17  1.121.815               Coruña, A\n",
       "18    196.139                  Cuenca\n",
       "19    727.121                Gipuzkoa\n",
       "20    781.788                  Girona\n",
       "21    919.168                 Granada\n",
       "22    261.995             Guadalajara\n",
       "23    524.278                  Huelva\n",
       "24    222.687                  Huesca\n",
       "25    631.381                    Jaén\n",
       "26    456.439                    León\n",
       "27    438.517                  Lleida\n",
       "28    327.946                    Lugo\n",
       "29  6.779.888                  Madrid\n",
       "30  1.685.920                  Málaga\n",
       "31  1.511.251                  Murcia\n",
       "32    661.197                 Navarra\n",
       "33    306.650                 Ourense\n",
       "34    160.321                Palencia\n",
       "35  1.131.065             Palmas, Las\n",
       "36    945.408              Pontevedra\n",
       "37    319.914               Rioja, La\n",
       "38    329.245               Salamanca\n",
       "39  1.044.887  Santa Cruz de Tenerife\n",
       "40    153.478                 Segovia\n",
       "41  1.950.219                 Sevilla\n",
       "42     88.884                   Soria\n",
       "43    816.772               Tarragona\n",
       "44    134.176                  Teruel\n",
       "45    703.772                  Toledo\n",
       "46  2.591.875       Valencia/València\n",
       "47    520.649              Valladolid\n",
       "48    170.588                  Zamora\n",
       "49    972.528                Zaragoza\n",
       "50     84.202                   Ceuta\n",
       "51     87.076                 Melilla"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = population.drop([\"Provincias\", \"Sexo\", \"Periodo\", \"Code\"], axis = 1)\n",
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I need to check if all provinces name are the same between the population DataFrame and the weather DataFrame. If some names are different, I will rename them so both are the same in both DataFrames and I can easily work with them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Albacete', 'Alicante/Alacant', 'Almería', 'Araba/Álava',\n",
       "       'Asturias', 'Ávila', 'Badajoz', 'Balears, Illes', 'Barcelona',\n",
       "       'Bizkaia', 'Burgos', 'Cáceres', 'Cádiz', 'Cantabria',\n",
       "       'Castellón/Castelló', 'Ciudad Real', 'Córdoba', 'Coruña, A',\n",
       "       'Cuenca', 'Gipuzkoa', 'Girona', 'Granada', 'Guadalajara', 'Huelva',\n",
       "       'Huesca', 'Jaén', 'León', 'Lleida', 'Lugo', 'Madrid', 'Málaga',\n",
       "       'Murcia', 'Navarra', 'Ourense', 'Palencia', 'Palmas, Las',\n",
       "       'Pontevedra', 'Rioja, La', 'Salamanca', 'Santa Cruz de Tenerife',\n",
       "       'Segovia', 'Sevilla', 'Soria', 'Tarragona', 'Teruel', 'Toledo',\n",
       "       'Valencia/València', 'Valladolid', 'Zamora', 'Zaragoza', 'Ceuta',\n",
       "       'Melilla'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population[\"Provincia\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A Coruña', 'Alacant/Alicante', 'Albacete', 'Almería',\n",
       "       'Araba/Álava', 'Asturias', 'Badajoz', 'Barcelona', 'Bizkaia',\n",
       "       'Burgos', 'Cantabria', 'Castelló/Castellón', 'Ceuta',\n",
       "       'Ciudad Real', 'Cuenca', 'Cáceres', 'Cádiz', 'Córdoba', 'Gipuzkoa',\n",
       "       'Girona', 'Granada', 'Guadalajara', 'Huelva', 'Huesca',\n",
       "       'Illes Balears', 'Jaén', 'La Rioja', 'Las Palmas', 'León',\n",
       "       'Lleida', 'Lugo', 'Madrid', 'Murcia', 'Málaga', 'Navarra',\n",
       "       'Ourense', 'Palencia', 'Pontevedra', 'Salamanca',\n",
       "       'Santa Cruz de Tenerife', 'Segovia', 'Sevilla', 'Soria',\n",
       "       'Tarragona', 'Teruel', 'Toledo', 'Valladolid', 'València/Valencia',\n",
       "       'Zamora', 'Zaragoza', 'Ávila', 'Melilla'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weather_stations[\"Provincia\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "provincias_weather = all_weather_stations[\"Provincia\"]\n",
    "provincias_weather = pd.DataFrame(provincias_weather)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Total</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>1.879.888</td>\n",
       "      <td>Alicante/Alacant</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17770</th>\n",
       "      <td>1.171.543</td>\n",
       "      <td>Balears, Illes</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35557</th>\n",
       "      <td>585.590</td>\n",
       "      <td>Castellón/Castelló</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41488</th>\n",
       "      <td>1.121.815</td>\n",
       "      <td>Coruña, A</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91884</th>\n",
       "      <td>1.131.065</td>\n",
       "      <td>Palmas, Las</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94848</th>\n",
       "      <td>319.914</td>\n",
       "      <td>Rioja, La</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118564</th>\n",
       "      <td>2.591.875</td>\n",
       "      <td>Valencia/València</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total           Provincia     _merge\n",
       "2959    1.879.888    Alicante/Alacant  left_only\n",
       "17770   1.171.543      Balears, Illes  left_only\n",
       "35557     585.590  Castellón/Castelló  left_only\n",
       "41488   1.121.815           Coruña, A  left_only\n",
       "91884   1.131.065         Palmas, Las  left_only\n",
       "94848     319.914           Rioja, La  left_only\n",
       "118564  2.591.875   Valencia/València  left_only"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#So there are 7 provinces that are called slighty different between the 2.\n",
    "\n",
    "different_names = population.merge(right = provincias_weather, how= \"left\", indicator=True)\n",
    "\n",
    "different_names = different_names[different_names['_merge']=='left_only']\n",
    "different_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to change the name for all the 7 provinces, even if some of them won't be used in my models because they're not part of the Spanish peninsula. I'll get rid of those later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I'm changing the name in the population dataframe so it matches the weather stations one, for those that are not the same\n",
    "population.loc[17, \"Provincia\"] = \"A Coruña\"\n",
    "population.loc[1, \"Provincia\"] = \"Alacant/Alicante\"\n",
    "population.loc[14, \"Provincia\"] = \"Castelló/Castellón\"\n",
    "population.loc[37, \"Provincia\"] = \"La Rioja\"\n",
    "population.loc[46, \"Provincia\"] = \"València/Valencia\"\n",
    "population.loc[7, \"Provincia\"] = \"Illes Balears\"\n",
    "population.loc[35, \"Provincia\"] = \"Las Palmas\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The reset index here is because otherwise I lose the Date when I do the merge (and I really don't want that...)\n",
    "\n",
    "all_weather_stations.reset_index(inplace= True)\n",
    "all_weather_stations_population = all_weather_stations.merge(right= population, on= \"Provincia\", how= \"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Fecha</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "      <th>Población</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>17.650000</td>\n",
       "      <td>12.950000</td>\n",
       "      <td>15.049999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1.121.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>Alacant/Alicante</td>\n",
       "      <td>29.299999</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>23.200001</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.879.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>25.450001</td>\n",
       "      <td>13.450001</td>\n",
       "      <td>19.750000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>388.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>Almería</td>\n",
       "      <td>24.250000</td>\n",
       "      <td>16.200001</td>\n",
       "      <td>20.950001</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>727.945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>Araba/Álava</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>17.200001</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>333.940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fecha         Provincia       Tmax       Tmin       Tmed  Vmax  TPrec  \\\n",
       "0 2013-05-07          A Coruña  17.650000  12.950000  15.049999  35.0    9.6   \n",
       "1 2013-05-07  Alacant/Alicante  29.299999  17.400000  23.200001  18.5    0.0   \n",
       "2 2013-05-07          Albacete  25.450001  13.450001  19.750000  15.0    0.0   \n",
       "3 2013-05-07           Almería  24.250000  16.200001  20.950001  19.5    0.0   \n",
       "4 2013-05-07       Araba/Álava  21.100000  13.300000  17.200001  18.0    0.8   \n",
       "\n",
       "   Población  \n",
       "0  1.121.815  \n",
       "1  1.879.888  \n",
       "2    388.270  \n",
       "3    727.945  \n",
       "4    333.940  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weather_stations_population.rename(columns= {\"Total\": \"Población\"}, inplace=True)\n",
    "all_weather_stations_population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fecha        False\n",
       "Provincia    False\n",
       "Tmax         False\n",
       "Tmin         False\n",
       "Tmed         False\n",
       "Vmax         False\n",
       "TPrec        False\n",
       "Población    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking once more that there are no NaN values now\n",
    "all_weather_stations_population.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before I do the weighted average by population so I get to 1 row by day, I need to remove all those Provinces that are not part of the Spanish peninsula (Balearic and Canary Islands, Ceuta and Melilla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "indexOut1 = all_weather_stations_population[(all_weather_stations_population[\"Provincia\"] == \"Ceuta\")].index\n",
    "indexOut2 = all_weather_stations_population[(all_weather_stations_population[\"Provincia\"] == \"Illes Balears\")].index\n",
    "indexOut3 = all_weather_stations_population[(all_weather_stations_population[\"Provincia\"] == \"Las Palmas\")].index\n",
    "indexOut4 = all_weather_stations_population[(all_weather_stations_population[\"Provincia\"] == \"Melilla\")].index\n",
    "indexOut5 = all_weather_stations_population[(all_weather_stations_population[\"Provincia\"] == \"Santa Cruz de Tenerife\")].index\n",
    "\n",
    "indexOut = indexOut1.append([indexOut2, indexOut3, indexOut4, indexOut5])\n",
    "\n",
    "all_weather_stations_population.drop(indexOut, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Fecha</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "      <th>Población</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>A Coruña</td>\n",
       "      <td>17.650000</td>\n",
       "      <td>12.950000</td>\n",
       "      <td>15.049999</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1.121.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>Alacant/Alicante</td>\n",
       "      <td>29.299999</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>23.200001</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.879.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>25.450001</td>\n",
       "      <td>13.450001</td>\n",
       "      <td>19.750000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>388.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>Almería</td>\n",
       "      <td>24.250000</td>\n",
       "      <td>16.200001</td>\n",
       "      <td>20.950001</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>727.945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>Araba/Álava</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>17.200001</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>333.940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fecha         Provincia       Tmax       Tmin       Tmed  Vmax  TPrec  \\\n",
       "0 2013-05-07          A Coruña  17.650000  12.950000  15.049999  35.0    9.6   \n",
       "1 2013-05-07  Alacant/Alicante  29.299999  17.400000  23.200001  18.5    0.0   \n",
       "2 2013-05-07          Albacete  25.450001  13.450001  19.750000  15.0    0.0   \n",
       "3 2013-05-07           Almería  24.250000  16.200001  20.950001  19.5    0.0   \n",
       "4 2013-05-07       Araba/Álava  21.100000  13.300000  17.200001  18.0    0.8   \n",
       "\n",
       "   Población  \n",
       "0  1.121.815  \n",
       "1  1.879.888  \n",
       "2    388.270  \n",
       "3    727.945  \n",
       "4    333.940  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weather_stations_population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0 0 0\n"
     ]
    }
   ],
   "source": [
    "#In here I'm just checking that in fact all those provinces have been deleted\n",
    "ce = all_weather_stations_population[\"Provincia\"] == \"Ceuta\"\n",
    "ba = all_weather_stations_population[\"Provincia\"] == \"Illes Balears\"\n",
    "pa = all_weather_stations_population[\"Provincia\"] == \"Las Palmas\"\n",
    "me =all_weather_stations_population[\"Provincia\"] == \"Melilla\"\n",
    "te = all_weather_stations_population[\"Provincia\"] == \"Santa Cruz de Tenerife\"\n",
    "\n",
    "print(np.count_nonzero(ce), np.count_nonzero(ba), np.count_nonzero(pa), np.count_nonzero(me), np.count_nonzero(te))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fecha        datetime64[ns]\n",
       "Provincia            object\n",
       "Tmax                float32\n",
       "Tmin                float32\n",
       "Tmed                float32\n",
       "Vmax                float32\n",
       "TPrec               float32\n",
       "Población            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_weather_stations_population.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_weather_stations_population['Población'] = all_weather_stations_population['Población'].str.replace('.', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_weather_stations_population[\"Población\"] = pd.to_numeric(all_weather_stations_population[\"Población\"], downcast=\"float\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to do the weighted average by population, I'm going to use the following functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grouped_weighted_avg_Tmax(values, weights, by):\n",
    "    return pd.DataFrame((values * weights).groupby(by).sum() / weights.groupby(by).sum(), columns= [\"Tmax\"])\n",
    "\n",
    "def grouped_weighted_avg_Tmin(values, weights, by):\n",
    "    return pd.DataFrame((values * weights).groupby(by).sum() / weights.groupby(by).sum(), columns= [\"Tmin\"])\n",
    "\n",
    "def grouped_weighted_avg_Tmed(values, weights, by):\n",
    "    return pd.DataFrame((values * weights).groupby(by).sum() / weights.groupby(by).sum(), columns= [\"Tmed\"])\n",
    "\n",
    "def grouped_weighted_avg_Vmax(values, weights, by):\n",
    "    return pd.DataFrame((values * weights).groupby(by).sum() / weights.groupby(by).sum(), columns= [\"Vmax\"])\n",
    "\n",
    "def grouped_weighted_avg_Tprec(values, weights, by):\n",
    "    return pd.DataFrame((values * weights).groupby(by).sum() / weights.groupby(by).sum(), columns= [\"TPrec\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_av_Tmax = grouped_weighted_avg_Tmax(values= all_weather_stations_population[\"Tmax\"],\n",
    "                                 weights= all_weather_stations_population[\"Población\"],\n",
    "                                 by= all_weather_stations_population[\"Fecha\"])\n",
    "weighted_av_Tmin = grouped_weighted_avg_Tmin(values= all_weather_stations_population[\"Tmin\"],\n",
    "                                 weights= all_weather_stations_population[\"Población\"],\n",
    "                                 by= all_weather_stations_population[\"Fecha\"])\n",
    "weighted_av_Tmed = grouped_weighted_avg_Tmed(values= all_weather_stations_population[\"Tmed\"],\n",
    "                                 weights= all_weather_stations_population[\"Población\"],\n",
    "                                 by= all_weather_stations_population[\"Fecha\"])\n",
    "weighted_av_Vmax = grouped_weighted_avg_Vmax(values= all_weather_stations_population[\"Vmax\"],\n",
    "                                 weights= all_weather_stations_population[\"Población\"],\n",
    "                                 by= all_weather_stations_population[\"Fecha\"])\n",
    "weighted_av_Tprec = grouped_weighted_avg_Tprec(values= all_weather_stations_population[\"TPrec\"],\n",
    "                                 weights= all_weather_stations_population[\"Población\"],\n",
    "                                 by= all_weather_stations_population[\"Fecha\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-05-07</th>\n",
       "      <td>24.372623</td>\n",
       "      <td>13.598554</td>\n",
       "      <td>19.108723</td>\n",
       "      <td>17.469931</td>\n",
       "      <td>0.548212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-08</th>\n",
       "      <td>24.171618</td>\n",
       "      <td>13.924416</td>\n",
       "      <td>19.068588</td>\n",
       "      <td>16.027378</td>\n",
       "      <td>0.335008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-09</th>\n",
       "      <td>23.007523</td>\n",
       "      <td>13.323574</td>\n",
       "      <td>18.142879</td>\n",
       "      <td>17.443392</td>\n",
       "      <td>2.003548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-10</th>\n",
       "      <td>22.321642</td>\n",
       "      <td>12.264773</td>\n",
       "      <td>17.353886</td>\n",
       "      <td>17.460852</td>\n",
       "      <td>0.044821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-11</th>\n",
       "      <td>21.945469</td>\n",
       "      <td>10.597996</td>\n",
       "      <td>16.243876</td>\n",
       "      <td>17.480986</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Tmax       Tmin       Tmed       Vmax     TPrec\n",
       "Fecha                                                           \n",
       "2013-05-07  24.372623  13.598554  19.108723  17.469931  0.548212\n",
       "2013-05-08  24.171618  13.924416  19.068588  16.027378  0.335008\n",
       "2013-05-09  23.007523  13.323574  18.142879  17.443392  2.003548\n",
       "2013-05-10  22.321642  12.264773  17.353886  17.460852  0.044821\n",
       "2013-05-11  21.945469  10.597996  16.243876  17.480986  0.000000"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_variables_list = [weighted_av_Tmax, weighted_av_Tmin, weighted_av_Tmed,\n",
    "                          weighted_av_Vmax, weighted_av_Tprec]\n",
    "\n",
    "weather_variables = weather_variables_list[0].join(weather_variables_list[1:])\n",
    "weather_variables.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-05-07</th>\n",
       "      <td>24.372623</td>\n",
       "      <td>13.598554</td>\n",
       "      <td>19.108723</td>\n",
       "      <td>17.469931</td>\n",
       "      <td>0.548212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-08</th>\n",
       "      <td>24.171618</td>\n",
       "      <td>13.924416</td>\n",
       "      <td>19.068588</td>\n",
       "      <td>16.027378</td>\n",
       "      <td>0.335008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-09</th>\n",
       "      <td>23.007523</td>\n",
       "      <td>13.323574</td>\n",
       "      <td>18.142879</td>\n",
       "      <td>17.443392</td>\n",
       "      <td>2.003548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-10</th>\n",
       "      <td>22.321642</td>\n",
       "      <td>12.264773</td>\n",
       "      <td>17.353886</td>\n",
       "      <td>17.460852</td>\n",
       "      <td>0.044821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-11</th>\n",
       "      <td>21.945469</td>\n",
       "      <td>10.597996</td>\n",
       "      <td>16.243876</td>\n",
       "      <td>17.480986</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Tmax       Tmin       Tmed       Vmax     TPrec\n",
       "Date                                                            \n",
       "2013-05-07  24.372623  13.598554  19.108723  17.469931  0.548212\n",
       "2013-05-08  24.171618  13.924416  19.068588  16.027378  0.335008\n",
       "2013-05-09  23.007523  13.323574  18.142879  17.443392  2.003548\n",
       "2013-05-10  22.321642  12.264773  17.353886  17.460852  0.044821\n",
       "2013-05-11  21.945469  10.597996  16.243876  17.480986  0.000000"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_variables.rename_axis(\"Date\", axis= \"index\", inplace=True)\n",
    "weather_variables.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining weather data to electricity and Brent prices dataframe\n",
    "Now, let's add all the weather variables to the dataframe with the electricity and Brent prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "electricity_brent.set_index(\"Date\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "electricity_brent.drop(\"Periodo\", inplace= True, axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Energía\\n final \\nMWh', 'Mercado \\ndiario \\n€/MWh',\n",
       "       'Mercado \\nintradiario \\n€/MWh', 'Restricciones\\n€/MWh',\n",
       "       'Procesos \\nOS \\n€/MWh', 'Garantía potencia \\nPagos capacidad\\n€/MWh',\n",
       "       'Total\\n€/MWh', 'Coste\\ns.interrrumpibilidad', 'Day', 'Último',\n",
       "       'Apertura', 'Máximo', 'Mínimo', 'Vol.'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_brent.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's rename the columns so they make more sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "electricity_brent.rename(columns= {\"Energía\\n final \\nMWh\": \"Energía final MWh\", \n",
    "                                  \"Mercado \\ndiario \\n€/MWh\": \"Mercado diario €/MWh\",\n",
    "                                 \"Mercado \\nintradiario \\n€/MWh\": \"Mercado intradiario €/MWh\",\n",
    "                                 \"Restricciones\\n€/MWh\": \"Restricciones €/MWh\",\n",
    "                                 \"Procesos \\nOS \\n€/MWh\": \"Procesos OS €/MWh\",\n",
    "                                 \"Garantía potencia \\nPagos capacidad\\n€/MWh\": \"Garantía potencia Pagos capacidad €/MWh\",\n",
    "                                 \"Total\\n€/MWh\": \"Total €/MWh\", \"Coste\\ns.interrrumpibilidad\":\"Coste s.interrrumpibilidad\",\n",
    "                                   \"Último\": \"Precio cierre Brent\", \n",
    "                                   \"Apertura\": \"Precio apertura Brent\",\"Máximo\": \"Precio Máximo Brent\",\n",
    "                                   \"Mínimo\": \"Precio mínimo Brent\", \"Vol.\": \"N. de operaciones del día\"}, \n",
    "                         inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Energía final MWh</th>\n",
       "      <th>Mercado diario €/MWh</th>\n",
       "      <th>Mercado intradiario €/MWh</th>\n",
       "      <th>Restricciones €/MWh</th>\n",
       "      <th>Procesos OS €/MWh</th>\n",
       "      <th>Garantía potencia Pagos capacidad €/MWh</th>\n",
       "      <th>Total €/MWh</th>\n",
       "      <th>Coste s.interrrumpibilidad</th>\n",
       "      <th>Day</th>\n",
       "      <th>Precio cierre Brent</th>\n",
       "      <th>Precio apertura Brent</th>\n",
       "      <th>Precio Máximo Brent</th>\n",
       "      <th>Precio mínimo Brent</th>\n",
       "      <th>N. de operaciones del día</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-06-03</th>\n",
       "      <td>700500.79</td>\n",
       "      <td>38.51</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>5.44</td>\n",
       "      <td>1.94</td>\n",
       "      <td>5.45</td>\n",
       "      <td>51.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>102,06</td>\n",
       "      <td>100,15</td>\n",
       "      <td>102,48</td>\n",
       "      <td>99,66</td>\n",
       "      <td>255,71K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-04</th>\n",
       "      <td>702582.423</td>\n",
       "      <td>47.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.42</td>\n",
       "      <td>56.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Martes</td>\n",
       "      <td>103,24</td>\n",
       "      <td>102,02</td>\n",
       "      <td>103,58</td>\n",
       "      <td>101,47</td>\n",
       "      <td>300,89K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-05</th>\n",
       "      <td>701818.743</td>\n",
       "      <td>51.48</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.45</td>\n",
       "      <td>60.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>103,04</td>\n",
       "      <td>103,48</td>\n",
       "      <td>104,07</td>\n",
       "      <td>102,85</td>\n",
       "      <td>202,95K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-06</th>\n",
       "      <td>700746.514</td>\n",
       "      <td>49.47</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.67</td>\n",
       "      <td>5.44</td>\n",
       "      <td>57.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>103,61</td>\n",
       "      <td>102,94</td>\n",
       "      <td>103,95</td>\n",
       "      <td>102,81</td>\n",
       "      <td>173,41K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-07</th>\n",
       "      <td>688082.805</td>\n",
       "      <td>49.33</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5.42</td>\n",
       "      <td>57.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>104,56</td>\n",
       "      <td>103,65</td>\n",
       "      <td>105,01</td>\n",
       "      <td>102,89</td>\n",
       "      <td>234,18K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Energía final MWh Mercado diario €/MWh Mercado intradiario €/MWh  \\\n",
       "Date                                                                          \n",
       "2013-06-03         700500.79                38.51                     -0.16   \n",
       "2013-06-04        702582.423                47.12                      0.01   \n",
       "2013-06-05        701818.743                51.48                     -0.16   \n",
       "2013-06-06        700746.514                49.47                     -0.32   \n",
       "2013-06-07        688082.805                49.33                     -0.16   \n",
       "\n",
       "           Restricciones €/MWh Procesos OS €/MWh  \\\n",
       "Date                                               \n",
       "2013-06-03                5.44              1.94   \n",
       "2013-06-04                3.16              1.25   \n",
       "2013-06-05                 2.6              0.97   \n",
       "2013-06-06                2.35              0.67   \n",
       "2013-06-07                2.24              0.99   \n",
       "\n",
       "           Garantía potencia Pagos capacidad €/MWh Total €/MWh  \\\n",
       "Date                                                             \n",
       "2013-06-03                                    5.45       51.18   \n",
       "2013-06-04                                    5.42       56.96   \n",
       "2013-06-05                                    5.45       60.35   \n",
       "2013-06-06                                    5.44        57.6   \n",
       "2013-06-07                                    5.42       57.82   \n",
       "\n",
       "           Coste s.interrrumpibilidad        Day Precio cierre Brent  \\\n",
       "Date                                                                   \n",
       "2013-06-03                        NaN      Lunes              102,06   \n",
       "2013-06-04                        NaN     Martes              103,24   \n",
       "2013-06-05                        NaN  Miércoles              103,04   \n",
       "2013-06-06                        NaN     Jueves              103,61   \n",
       "2013-06-07                        NaN    Viernes              104,56   \n",
       "\n",
       "           Precio apertura Brent Precio Máximo Brent Precio mínimo Brent  \\\n",
       "Date                                                                       \n",
       "2013-06-03                100,15              102,48               99,66   \n",
       "2013-06-04                102,02              103,58              101,47   \n",
       "2013-06-05                103,48              104,07              102,85   \n",
       "2013-06-06                102,94              103,95              102,81   \n",
       "2013-06-07                103,65              105,01              102,89   \n",
       "\n",
       "           N. de operaciones del día  \n",
       "Date                                  \n",
       "2013-06-03                   255,71K  \n",
       "2013-06-04                   300,89K  \n",
       "2013-06-05                   202,95K  \n",
       "2013-06-06                   173,41K  \n",
       "2013-06-07                   234,18K  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_brent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Energía final MWh</th>\n",
       "      <th>Mercado diario €/MWh</th>\n",
       "      <th>Mercado intradiario €/MWh</th>\n",
       "      <th>Restricciones €/MWh</th>\n",
       "      <th>Procesos OS €/MWh</th>\n",
       "      <th>Garantía potencia Pagos capacidad €/MWh</th>\n",
       "      <th>Total €/MWh</th>\n",
       "      <th>Coste s.interrrumpibilidad</th>\n",
       "      <th>Day</th>\n",
       "      <th>Precio cierre Brent</th>\n",
       "      <th>Precio apertura Brent</th>\n",
       "      <th>Precio Máximo Brent</th>\n",
       "      <th>Precio mínimo Brent</th>\n",
       "      <th>N. de operaciones del día</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-06-03</th>\n",
       "      <td>700500.79</td>\n",
       "      <td>38.51</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>5.44</td>\n",
       "      <td>1.94</td>\n",
       "      <td>5.45</td>\n",
       "      <td>51.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>102,06</td>\n",
       "      <td>100,15</td>\n",
       "      <td>102,48</td>\n",
       "      <td>99,66</td>\n",
       "      <td>255,71K</td>\n",
       "      <td>24.673298</td>\n",
       "      <td>9.800121</td>\n",
       "      <td>17.411293</td>\n",
       "      <td>17.170864</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-04</th>\n",
       "      <td>702582.423</td>\n",
       "      <td>47.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.42</td>\n",
       "      <td>56.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Martes</td>\n",
       "      <td>103,24</td>\n",
       "      <td>102,02</td>\n",
       "      <td>103,58</td>\n",
       "      <td>101,47</td>\n",
       "      <td>300,89K</td>\n",
       "      <td>25.979870</td>\n",
       "      <td>10.501701</td>\n",
       "      <td>18.384163</td>\n",
       "      <td>17.234720</td>\n",
       "      <td>0.007118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-05</th>\n",
       "      <td>701818.743</td>\n",
       "      <td>51.48</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.45</td>\n",
       "      <td>60.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>103,04</td>\n",
       "      <td>103,48</td>\n",
       "      <td>104,07</td>\n",
       "      <td>102,85</td>\n",
       "      <td>202,95K</td>\n",
       "      <td>25.768139</td>\n",
       "      <td>12.123443</td>\n",
       "      <td>19.021616</td>\n",
       "      <td>19.147642</td>\n",
       "      <td>0.053441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-06</th>\n",
       "      <td>700746.514</td>\n",
       "      <td>49.47</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.67</td>\n",
       "      <td>5.44</td>\n",
       "      <td>57.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>103,61</td>\n",
       "      <td>102,94</td>\n",
       "      <td>103,95</td>\n",
       "      <td>102,81</td>\n",
       "      <td>173,41K</td>\n",
       "      <td>23.760830</td>\n",
       "      <td>12.517649</td>\n",
       "      <td>18.125957</td>\n",
       "      <td>20.303318</td>\n",
       "      <td>0.745070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-07</th>\n",
       "      <td>688082.805</td>\n",
       "      <td>49.33</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5.42</td>\n",
       "      <td>57.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>104,56</td>\n",
       "      <td>103,65</td>\n",
       "      <td>105,01</td>\n",
       "      <td>102,89</td>\n",
       "      <td>234,18K</td>\n",
       "      <td>21.150854</td>\n",
       "      <td>11.924727</td>\n",
       "      <td>16.672276</td>\n",
       "      <td>20.615791</td>\n",
       "      <td>1.826179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>822863.04</td>\n",
       "      <td>39.87</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3.23</td>\n",
       "      <td>44.46</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Lunes</td>\n",
       "      <td>59,32</td>\n",
       "      <td>60,23</td>\n",
       "      <td>60,49</td>\n",
       "      <td>58,50</td>\n",
       "      <td>259,77K</td>\n",
       "      <td>12.427604</td>\n",
       "      <td>3.589538</td>\n",
       "      <td>8.053116</td>\n",
       "      <td>20.110352</td>\n",
       "      <td>1.507235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>816667.2</td>\n",
       "      <td>39.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>3.2</td>\n",
       "      <td>43.83</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Martes</td>\n",
       "      <td>59,51</td>\n",
       "      <td>59,03</td>\n",
       "      <td>60,12</td>\n",
       "      <td>58,50</td>\n",
       "      <td>248,95K</td>\n",
       "      <td>14.772270</td>\n",
       "      <td>7.194873</td>\n",
       "      <td>10.979397</td>\n",
       "      <td>18.389824</td>\n",
       "      <td>0.385379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>818153.969</td>\n",
       "      <td>40.24</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.12</td>\n",
       "      <td>45.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Miércoles</td>\n",
       "      <td>59,81</td>\n",
       "      <td>59,86</td>\n",
       "      <td>60,62</td>\n",
       "      <td>59,26</td>\n",
       "      <td>142,68K</td>\n",
       "      <td>15.774078</td>\n",
       "      <td>7.631106</td>\n",
       "      <td>11.899413</td>\n",
       "      <td>14.971382</td>\n",
       "      <td>2.780003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>793218.71</td>\n",
       "      <td>38.48</td>\n",
       "      <td>0</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.33</td>\n",
       "      <td>3.15</td>\n",
       "      <td>43.1</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Jueves</td>\n",
       "      <td>58,29</td>\n",
       "      <td>59,60</td>\n",
       "      <td>59,65</td>\n",
       "      <td>57,71</td>\n",
       "      <td>104,27K</td>\n",
       "      <td>15.723001</td>\n",
       "      <td>8.100962</td>\n",
       "      <td>11.918385</td>\n",
       "      <td>16.169188</td>\n",
       "      <td>0.726683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>772638.789</td>\n",
       "      <td>37.6</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.26</td>\n",
       "      <td>3.17</td>\n",
       "      <td>41.73</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Viernes</td>\n",
       "      <td>58,16</td>\n",
       "      <td>59,30</td>\n",
       "      <td>59,45</td>\n",
       "      <td>57,94</td>\n",
       "      <td>19,60K</td>\n",
       "      <td>17.513662</td>\n",
       "      <td>8.646461</td>\n",
       "      <td>13.110268</td>\n",
       "      <td>15.740695</td>\n",
       "      <td>1.236663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2783 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Energía final MWh Mercado diario €/MWh Mercado intradiario €/MWh  \\\n",
       "Date                                                                          \n",
       "2013-06-03         700500.79                38.51                     -0.16   \n",
       "2013-06-04        702582.423                47.12                      0.01   \n",
       "2013-06-05        701818.743                51.48                     -0.16   \n",
       "2013-06-06        700746.514                49.47                     -0.32   \n",
       "2013-06-07        688082.805                49.33                     -0.16   \n",
       "...                      ...                  ...                       ...   \n",
       "2020-01-27         822863.04                39.87                     -0.03   \n",
       "2020-01-28          816667.2                39.02                     -0.02   \n",
       "2020-01-29        818153.969                40.24                     -0.02   \n",
       "2020-01-30         793218.71                38.48                         0   \n",
       "2020-01-31        772638.789                 37.6                     -0.02   \n",
       "\n",
       "           Restricciones €/MWh Procesos OS €/MWh  \\\n",
       "Date                                               \n",
       "2013-06-03                5.44              1.94   \n",
       "2013-06-04                3.16              1.25   \n",
       "2013-06-05                 2.6              0.97   \n",
       "2013-06-06                2.35              0.67   \n",
       "2013-06-07                2.24              0.99   \n",
       "...                        ...               ...   \n",
       "2020-01-27                1.15              0.21   \n",
       "2020-01-28                1.36              0.24   \n",
       "2020-01-29                1.38               0.3   \n",
       "2020-01-30                1.11              0.33   \n",
       "2020-01-31                0.69              0.26   \n",
       "\n",
       "           Garantía potencia Pagos capacidad €/MWh Total €/MWh  \\\n",
       "Date                                                             \n",
       "2013-06-03                                    5.45       51.18   \n",
       "2013-06-04                                    5.42       56.96   \n",
       "2013-06-05                                    5.45       60.35   \n",
       "2013-06-06                                    5.44        57.6   \n",
       "2013-06-07                                    5.42       57.82   \n",
       "...                                            ...         ...   \n",
       "2020-01-27                                    3.23       44.46   \n",
       "2020-01-28                                     3.2       43.83   \n",
       "2020-01-29                                    3.12       45.06   \n",
       "2020-01-30                                    3.15        43.1   \n",
       "2020-01-31                                    3.17       41.73   \n",
       "\n",
       "           Coste s.interrrumpibilidad        Day Precio cierre Brent  \\\n",
       "Date                                                                   \n",
       "2013-06-03                        NaN      Lunes              102,06   \n",
       "2013-06-04                        NaN     Martes              103,24   \n",
       "2013-06-05                        NaN  Miércoles              103,04   \n",
       "2013-06-06                        NaN     Jueves              103,61   \n",
       "2013-06-07                        NaN    Viernes              104,56   \n",
       "...                               ...        ...                 ...   \n",
       "2020-01-27                       0.03      Lunes               59,32   \n",
       "2020-01-28                       0.03     Martes               59,51   \n",
       "2020-01-29                       0.03  Miércoles               59,81   \n",
       "2020-01-30                       0.03     Jueves               58,29   \n",
       "2020-01-31                       0.03    Viernes               58,16   \n",
       "\n",
       "           Precio apertura Brent Precio Máximo Brent Precio mínimo Brent  \\\n",
       "Date                                                                       \n",
       "2013-06-03                100,15              102,48               99,66   \n",
       "2013-06-04                102,02              103,58              101,47   \n",
       "2013-06-05                103,48              104,07              102,85   \n",
       "2013-06-06                102,94              103,95              102,81   \n",
       "2013-06-07                103,65              105,01              102,89   \n",
       "...                          ...                 ...                 ...   \n",
       "2020-01-27                 60,23               60,49               58,50   \n",
       "2020-01-28                 59,03               60,12               58,50   \n",
       "2020-01-29                 59,86               60,62               59,26   \n",
       "2020-01-30                 59,60               59,65               57,71   \n",
       "2020-01-31                 59,30               59,45               57,94   \n",
       "\n",
       "           N. de operaciones del día       Tmax       Tmin       Tmed  \\\n",
       "Date                                                                    \n",
       "2013-06-03                   255,71K  24.673298   9.800121  17.411293   \n",
       "2013-06-04                   300,89K  25.979870  10.501701  18.384163   \n",
       "2013-06-05                   202,95K  25.768139  12.123443  19.021616   \n",
       "2013-06-06                   173,41K  23.760830  12.517649  18.125957   \n",
       "2013-06-07                   234,18K  21.150854  11.924727  16.672276   \n",
       "...                              ...        ...        ...        ...   \n",
       "2020-01-27                   259,77K  12.427604   3.589538   8.053116   \n",
       "2020-01-28                   248,95K  14.772270   7.194873  10.979397   \n",
       "2020-01-29                   142,68K  15.774078   7.631106  11.899413   \n",
       "2020-01-30                   104,27K  15.723001   8.100962  11.918385   \n",
       "2020-01-31                    19,60K  17.513662   8.646461  13.110268   \n",
       "\n",
       "                 Vmax     TPrec  \n",
       "Date                             \n",
       "2013-06-03  17.170864  0.000000  \n",
       "2013-06-04  17.234720  0.007118  \n",
       "2013-06-05  19.147642  0.053441  \n",
       "2013-06-06  20.303318  0.745070  \n",
       "2013-06-07  20.615791  1.826179  \n",
       "...               ...       ...  \n",
       "2020-01-27  20.110352  1.507235  \n",
       "2020-01-28  18.389824  0.385379  \n",
       "2020-01-29  14.971382  2.780003  \n",
       "2020-01-30  16.169188  0.726683  \n",
       "2020-01-31  15.740695  1.236663  \n",
       "\n",
       "[2783 rows x 19 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_brent_weather = electricity_brent.merge(weather_variables, on = \"Date\")\n",
    "electricity_brent_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Energía final MWh                           object\n",
       "Mercado diario €/MWh                        object\n",
       "Mercado intradiario €/MWh                   object\n",
       "Restricciones €/MWh                         object\n",
       "Procesos OS €/MWh                           object\n",
       "Garantía potencia Pagos capacidad €/MWh     object\n",
       "Total €/MWh                                 object\n",
       "Coste s.interrrumpibilidad                  object\n",
       "Day                                         object\n",
       "Precio cierre Brent                         object\n",
       "Precio apertura Brent                       object\n",
       "Precio Máximo Brent                         object\n",
       "Precio mínimo Brent                         object\n",
       "N. de operaciones del día                   object\n",
       "Tmax                                       float32\n",
       "Tmin                                       float32\n",
       "Tmed                                       float32\n",
       "Vmax                                       float32\n",
       "TPrec                                      float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_brent_weather.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's replace dots and commas accordingly in the dataframe. Dots should be used for decimals separators, and commas as thousands separators. Also, let's convert all numerics values to numeric type.\n",
    "I'm also going to delete the column referring to the number of contracts sold that day in the Brent oil future market as I don't think this is relevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "electricity_brent_weather['Precio cierre Brent'] = electricity_brent_weather[\"Precio cierre Brent\"].str.replace(\",\", \".\")\n",
    "electricity_brent_weather['Precio apertura Brent'] = electricity_brent_weather[\"Precio apertura Brent\"].str.replace(\",\", \".\")\n",
    "electricity_brent_weather['Precio Máximo Brent'] = electricity_brent_weather[\"Precio Máximo Brent\"].str.replace(\",\", \".\")\n",
    "electricity_brent_weather['Precio mínimo Brent'] = electricity_brent_weather[\"Precio mínimo Brent\"].str.replace(\",\", \".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "electricity_brent_weather[\"Energía final MWh\"] = pd.to_numeric(electricity_brent_weather[\"Energía final MWh\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Mercado diario €/MWh\"] = pd.to_numeric(electricity_brent_weather[\"Mercado diario €/MWh\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Mercado intradiario €/MWh\"] = pd.to_numeric(electricity_brent_weather[\"Mercado intradiario €/MWh\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Restricciones €/MWh\"] = pd.to_numeric(electricity_brent_weather[\"Restricciones €/MWh\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Procesos OS €/MWh\"] = pd.to_numeric(electricity_brent_weather[\"Procesos OS €/MWh\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Garantía potencia Pagos capacidad €/MWh\"] = pd.to_numeric(electricity_brent_weather[\"Garantía potencia Pagos capacidad €/MWh\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Total €/MWh\"] = pd.to_numeric(electricity_brent_weather[\"Total €/MWh\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Coste s.interrrumpibilidad\"] = pd.to_numeric(electricity_brent_weather[\"Coste s.interrrumpibilidad\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Precio cierre Brent\"] = pd.to_numeric(electricity_brent_weather[\"Precio cierre Brent\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Precio apertura Brent\"] = pd.to_numeric(electricity_brent_weather[\"Precio apertura Brent\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Precio Máximo Brent\"] = pd.to_numeric(electricity_brent_weather[\"Precio Máximo Brent\"], downcast=\"float\")\n",
    "electricity_brent_weather[\"Precio mínimo Brent\"] = pd.to_numeric(electricity_brent_weather[\"Precio mínimo Brent\"], downcast=\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "electricity_brent_weather.drop(columns=\"N. de operaciones del día\", inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable \"Coste s.interrrumpibilidad\" is only there from February 2014. Therefore, let's replace the NaN values in that column with 0 for the period before February 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Energía final MWh                          False\n",
       "Mercado diario €/MWh                       False\n",
       "Mercado intradiario €/MWh                  False\n",
       "Restricciones €/MWh                        False\n",
       "Procesos OS €/MWh                          False\n",
       "Garantía potencia Pagos capacidad €/MWh    False\n",
       "Total €/MWh                                False\n",
       "Coste s.interrrumpibilidad                  True\n",
       "Day                                        False\n",
       "Precio cierre Brent                        False\n",
       "Precio apertura Brent                      False\n",
       "Precio Máximo Brent                        False\n",
       "Precio mínimo Brent                        False\n",
       "Tmax                                       False\n",
       "Tmin                                       False\n",
       "Tmed                                       False\n",
       "Vmax                                       False\n",
       "TPrec                                      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_brent_weather.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Energía final MWh                          False\n",
       "Mercado diario €/MWh                       False\n",
       "Mercado intradiario €/MWh                  False\n",
       "Restricciones €/MWh                        False\n",
       "Procesos OS €/MWh                          False\n",
       "Garantía potencia Pagos capacidad €/MWh    False\n",
       "Total €/MWh                                False\n",
       "Coste s.interrrumpibilidad                 False\n",
       "Day                                        False\n",
       "Precio cierre Brent                        False\n",
       "Precio apertura Brent                      False\n",
       "Precio Máximo Brent                        False\n",
       "Precio mínimo Brent                        False\n",
       "Tmax                                       False\n",
       "Tmin                                       False\n",
       "Tmed                                       False\n",
       "Vmax                                       False\n",
       "TPrec                                      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_brent_weather.fillna(0, inplace= True)\n",
    "electricity_brent_weather.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Energía final MWh</th>\n",
       "      <th>Mercado diario €/MWh</th>\n",
       "      <th>Mercado intradiario €/MWh</th>\n",
       "      <th>Restricciones €/MWh</th>\n",
       "      <th>Procesos OS €/MWh</th>\n",
       "      <th>Garantía potencia Pagos capacidad €/MWh</th>\n",
       "      <th>Total €/MWh</th>\n",
       "      <th>Coste s.interrrumpibilidad</th>\n",
       "      <th>Precio cierre Brent</th>\n",
       "      <th>Precio apertura Brent</th>\n",
       "      <th>Precio Máximo Brent</th>\n",
       "      <th>Precio mínimo Brent</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "      <td>2783.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>726284.937500</td>\n",
       "      <td>46.979504</td>\n",
       "      <td>-0.039206</td>\n",
       "      <td>2.064229</td>\n",
       "      <td>0.919504</td>\n",
       "      <td>3.368509</td>\n",
       "      <td>54.253700</td>\n",
       "      <td>0.960992</td>\n",
       "      <td>65.658798</td>\n",
       "      <td>65.668953</td>\n",
       "      <td>66.488586</td>\n",
       "      <td>64.793839</td>\n",
       "      <td>21.548716</td>\n",
       "      <td>10.296162</td>\n",
       "      <td>15.981436</td>\n",
       "      <td>18.154850</td>\n",
       "      <td>1.308443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>65468.472656</td>\n",
       "      <td>13.825276</td>\n",
       "      <td>0.113314</td>\n",
       "      <td>1.253099</td>\n",
       "      <td>0.741643</td>\n",
       "      <td>1.579078</td>\n",
       "      <td>13.798329</td>\n",
       "      <td>0.795205</td>\n",
       "      <td>23.072765</td>\n",
       "      <td>23.036779</td>\n",
       "      <td>22.989422</td>\n",
       "      <td>23.087326</td>\n",
       "      <td>6.946665</td>\n",
       "      <td>5.559977</td>\n",
       "      <td>6.172229</td>\n",
       "      <td>4.133570</td>\n",
       "      <td>2.287739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>549004.000000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>-1.200000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>6.360000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.330000</td>\n",
       "      <td>19.900000</td>\n",
       "      <td>21.290001</td>\n",
       "      <td>15.980000</td>\n",
       "      <td>6.146241</td>\n",
       "      <td>-3.613261</td>\n",
       "      <td>1.278752</td>\n",
       "      <td>9.203861</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>684016.687500</td>\n",
       "      <td>39.669998</td>\n",
       "      <td>-0.070000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>46.560001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.029999</td>\n",
       "      <td>49.110001</td>\n",
       "      <td>49.799999</td>\n",
       "      <td>48.209999</td>\n",
       "      <td>15.294067</td>\n",
       "      <td>5.531762</td>\n",
       "      <td>10.473571</td>\n",
       "      <td>15.608832</td>\n",
       "      <td>0.013913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>726161.125000</td>\n",
       "      <td>48.169998</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>55.360001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.529999</td>\n",
       "      <td>60.750000</td>\n",
       "      <td>61.790001</td>\n",
       "      <td>59.610001</td>\n",
       "      <td>21.367491</td>\n",
       "      <td>10.105659</td>\n",
       "      <td>15.716086</td>\n",
       "      <td>17.623846</td>\n",
       "      <td>0.317259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>772514.312500</td>\n",
       "      <td>56.105000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>63.670000</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>74.920002</td>\n",
       "      <td>74.730003</td>\n",
       "      <td>75.785000</td>\n",
       "      <td>73.910000</td>\n",
       "      <td>27.544918</td>\n",
       "      <td>15.270383</td>\n",
       "      <td>21.462419</td>\n",
       "      <td>19.970531</td>\n",
       "      <td>1.580784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>927416.187500</td>\n",
       "      <td>93.919998</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>10.160000</td>\n",
       "      <td>8.010000</td>\n",
       "      <td>7.820000</td>\n",
       "      <td>103.209999</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>116.610001</td>\n",
       "      <td>115.930000</td>\n",
       "      <td>117.339996</td>\n",
       "      <td>115.019997</td>\n",
       "      <td>36.400291</td>\n",
       "      <td>21.160681</td>\n",
       "      <td>28.862202</td>\n",
       "      <td>39.631035</td>\n",
       "      <td>21.939543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Energía final MWh  Mercado diario €/MWh  Mercado intradiario €/MWh  \\\n",
       "count        2783.000000           2783.000000                2783.000000   \n",
       "mean       726284.937500             46.979504                  -0.039206   \n",
       "std         65468.472656             13.825276                   0.113314   \n",
       "min        549004.000000              0.550000                  -1.200000   \n",
       "25%        684016.687500             39.669998                  -0.070000   \n",
       "50%        726161.125000             48.169998                  -0.020000   \n",
       "75%        772514.312500             56.105000                   0.010000   \n",
       "max        927416.187500             93.919998                   0.510000   \n",
       "\n",
       "       Restricciones €/MWh  Procesos OS €/MWh  \\\n",
       "count          2783.000000        2783.000000   \n",
       "mean              2.064229           0.919504   \n",
       "std               1.253099           0.741643   \n",
       "min               0.090000           0.000000   \n",
       "25%               1.140000           0.410000   \n",
       "50%               1.850000           0.700000   \n",
       "75%               2.720000           1.190000   \n",
       "max              10.160000           8.010000   \n",
       "\n",
       "       Garantía potencia Pagos capacidad €/MWh  Total €/MWh  \\\n",
       "count                              2783.000000  2783.000000   \n",
       "mean                                  3.368509    54.253700   \n",
       "std                                   1.579078    13.798329   \n",
       "min                                   1.450000     6.360000   \n",
       "25%                                   2.260000    46.560001   \n",
       "50%                                   2.670000    55.360001   \n",
       "75%                                   4.170000    63.670000   \n",
       "max                                   7.820000   103.209999   \n",
       "\n",
       "       Coste s.interrrumpibilidad  Precio cierre Brent  Precio apertura Brent  \\\n",
       "count                 2783.000000          2783.000000            2783.000000   \n",
       "mean                     0.960992            65.658798              65.668953   \n",
       "std                      0.795205            23.072765              23.036779   \n",
       "min                      0.000000            19.330000              19.900000   \n",
       "25%                      0.000000            49.029999              49.110001   \n",
       "50%                      1.000000            60.529999              60.750000   \n",
       "75%                      1.780000            74.920002              74.730003   \n",
       "max                      2.210000           116.610001             115.930000   \n",
       "\n",
       "       Precio Máximo Brent  Precio mínimo Brent         Tmax         Tmin  \\\n",
       "count          2783.000000          2783.000000  2783.000000  2783.000000   \n",
       "mean             66.488586            64.793839    21.548716    10.296162   \n",
       "std              22.989422            23.087326     6.946665     5.559977   \n",
       "min              21.290001            15.980000     6.146241    -3.613261   \n",
       "25%              49.799999            48.209999    15.294067     5.531762   \n",
       "50%              61.790001            59.610001    21.367491    10.105659   \n",
       "75%              75.785000            73.910000    27.544918    15.270383   \n",
       "max             117.339996           115.019997    36.400291    21.160681   \n",
       "\n",
       "              Tmed         Vmax        TPrec  \n",
       "count  2783.000000  2783.000000  2783.000000  \n",
       "mean     15.981436    18.154850     1.308443  \n",
       "std       6.172229     4.133570     2.287739  \n",
       "min       1.278752     9.203861     0.000000  \n",
       "25%      10.473571    15.608832     0.013913  \n",
       "50%      15.716086    17.623846     0.317259  \n",
       "75%      21.462419    19.970531     1.580784  \n",
       "max      28.862202    39.631035    21.939543  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electricity_brent_weather.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's do some visualisations before I start using some models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#At first sight, it doesn't look to me that the energy prices depend on seasonality, but I'll investigate this later\n",
    "\n",
    "media = electricity_brent_weather[\"Total €/MWh\"].mean()\n",
    "\n",
    "plt.plot(electricity_brent_weather[\"Total €/MWh\"].values)\n",
    "plt.axhline(y = media,color ='r', linestyle = '-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CyPS4cMuM4chw61+xEM5PfbgHB2qasXJvdViNmwEG9d42dXBs3SUIIs0g4Z4kZLNMrzzFZCPXgI3E5m577ti7RxBEmkHCPUnIXi89c70AgAfe3BpWcLsY0/O5J657BEGkGSTck4WkmgvNHYjMFTIYwYQqSX6CIGRIuCcRIX9zvZHPYzPGpJS/9m0IgiBkSLgnCcUsY2FeEUU9bAQ0gzGbZLg0BgRBEAAJ96QRTrm23R0m+MnxWIIgui0k3JOEIY2AJKx3HW8Ie5yAtHOCICKFhHuSsDOnbCqvU/fbHyf7wtvOp5LcJwhCgoR7ErHSvFvbnWuZROTnTnYZgiBMkHBPErJZRhbyLW0BbX/Y4xykPGnuBEHIkHBPEnZZIUVKYPvjjPncrZ4ClOOdIAgzJNyThJ0AbhP53m1sKy4ma/wEQRCRQcI9iejZHfVtdS1KjdVA0E50M2OEqs1DgjxpCIKQIeGeLGzMMq+tOwIAWLzxqPVh5rSQtm0IgiB0SLgnCSXSVMFKVNeqhbQtjwtXigk0ododOd7QirMeWY7dx5xjJYjuCQn3JMFk6W7B0J65tsdFUomJ6H4s23YcFXWteP7z/Z3dFaILQsI9SYTzaPnDtybaHqfZ6h2eDqS4d18OnWju7C4QXRAS7knEakIVAEp65qAwO8PymBDN3bINqfPdkf+sOQwA+HxPTSf3hOiKkHBPEnZ+7gDw+3nWWjtgtNUThMy0ET07uwtEF4aEe5JwSiMwYXCRw3Hh87mH20ekJ8W5ctEXugAIIyTck4RscxfmmXtmjcTfbjzD8bgjtS14dV05jte3KuexjFAluiNBSaD7beMkiO4KCfckYtau5p0xGOeP7hPRsYdONIcxz9DN3d2QM1eES2MRLRsO1+L/3toa9/MSyYOEe5KQzTLRvEGP7JsHAGjSEoyF6uk0n9o9kTX3dn98H+73vLwBf/v8AA5UN8X1vETyIOGeREKEegRC+alvTQIANLb6498hIqUJSqaYtjhr2PtUoU7vg6lLTMKdMXY3Y2wrY2wLY+xFxlgWY6yYMbaUMbZb/d8jXp1NZZTsjtGT63UDABp97Y6TZjSf1v2Qzey3/3td3M4rm2LoukpdOizcGWMDAdwBoIxzPg6AG8A8APMBLOeclwJYrq53e4zl8iIn36v4vzf6VLOM1YQqmWW6JQFJ8n65/0TczisXkKGEdKlLrGYZD4BsxpgHQA6AowAuB7BI3b8IwNwYPyN9MKlBkeRhF5r7g29vg5NDBN2C3Y9EuT+2tpPmng50WLhzzo8A+C2AQwAqANRxzj8A0JdzXqG2qQAQmTtImhNJuTwrPO7wPxEV6+iemNNEx2Py83hDKxZ+sldbNwv3tQdP4Knlu2P+HCLxxGKW6QFFSx8GYACAXMbYtVEcfytjbA1jbE1VVVVHu5EyGLM7dlwdsi+QTSpWdyPAOTKlh//Hu2K/j+56aQP+8ql1IrLW9gCuenYlnli6i663FCAWs8yFAPZzzqs45+0AXgMwDcAxxlh/AFD/H7c6mHO+kHNexjkv6927dwzdSA2s8r/Ey1ZONvfuSZPPj1yvG49fPR5AfK6Dk83thnXZ5v72pgpt+a1NFVjw9rbYP5BIGLEI90MApjLGcpgiuWYC2A5gMYAb1DY3AHgzti6mD3p2xxggSU6o1LX4UZidgYtP6w8A8LXH5g4ZDHJsr6g3bJMVdJ9fn2i948X1eO4zSjXclfF09EDO+ZeMsf8CWAfAD2A9gIUA8gD8hzF2M5QHwDXx6GiqYzDLSNuixe51mF6SuweLNx7FyL55GN2vAHUt7SjMyYTXo+hoW47WxXTuRSsPOO6vNWn1RNcmJm8ZzvkvOeejOefjOOfXcc59nPMazvlMznmp+j9+PlopjFNWyHA89a2JmDCoEADQ0hYI2U+6fPfhjhfXY/aTn2JfVSMOn2jGgMIseFzKFfDmhqOob+24AC4/2aItP3rlaQCM1+zj7+/s8LmJ5EMRqkmDdSj9AABcNmEALpswAABs3SFpfiv9kb1jLnjiYzT6/CjKyTDM51TWtXb4/JkeXRz0yvMCkGsQ0AWWapBwTxLWwUeR69xuVTsLBC3sqmSH7xY0mLTyqgYfsjLcjm2iIcujn0tcUkKmC9/30wYWdvj8RHIh4Z5EYtF+NOFOGlS35fcW/uVm4V4fQw4iETD3yU/OD9n33Kf7ABjfHsQ1SXRNSLgnCUP6gQ4IaF1zt95PIj/9yTYJcgBYd/AkAOCt288GADTEINyPqTUD+hZ6dc1d3bf+cC0AwC+9OQaCHHuON3b484jEQsI9SVhNqEaj97iZvVmG9Kf0xh8IoqKuxeBnLvjqgOKvUJSj5CBq93fMHbKhtV0LXsp0u7SoZ6GI7FBdJAuzMzB1eLF23PcWfYXZT34SkzkoEjjnlFs+Ski4JwljJabocYXT3Mlck7b831vbcNYjH+LQiWYAwJK7ztH23TR9GAD5za5j18G5j68AAPTJ9ypzQSbNffopvQAAD11xGl669SyM6V8AADhQ04wdlQ1YHcfEZVb8+u1tuOCJFXSdR0GH/dyJ6DFn2ItmHlRo7kGLi5vmU9ObdzYrGntuphvTTumFnAz9tv3heSMAQHOH7Gi5vRNNbQCAu2eNBKC/DYrLzR/kGNQjGyP75gMAWtqM5p8mCxfdePK3zw8AAOpb/ChU31IIZ0hzTxKx+LkDsWtmROoiBG9TWwA5mW7keHXbe06mIujdmnC3frU7UttiKO5hxxw12lX35FKOOVbfqrlHAkCzSZgnS6NubKOiNZFCwj1JdLTMnoC8ZQgAyHC7UJyTqa1nZSi3sMel/PcHQq+PV9YcxvRHP7SNQBWC+Y4LTkFhtqIVm18GK+taMbBHtrb+l+vLTOeIahgdZocpPUIgSLZ4O0i4JwkGFqLdRJOqVxPuFjcvWWW6D5keF1wuhpX3XoDnri/TNGy32/7N7oNtxwAAB2uaLc/5nDqRaqU4iE0+f1BLcwAAEwYXGdpZmQsTwc2L1gBQxnmsvhVzfv8pSu9/LymfnWqQzT1ZxCiBXcxZcyeFPj0xKwQDCrMAAP0Ls9G/UNeknWzuBVmKNt5D0vhlHnp3OwBjnV6zK2R7IGhIL2wmkdZC4REk8/tlu/CHD/do629tPKpFcRMKpLknEW76Hw3i5rWym0YT6UqkFuaH9il98i3bOUUwi4n8cHln5OtId4VU1tsDQWSYhPuEQYW4YuJAAInT3CvqWnDNn1Zq62cOU9wwzbnr2zroAprOkHBPEgwIlerReMuE8YagWpfpiflXPW+Ude0D4U1ldX0IO3xdi7Vwz8lUJmh/cO4IbZuefkA5ts0fNOSeAYA3bz8bP7lolKFdvPlg6zFteXBxNgYUKW8r5gplyTILpRJklkkSjDHtAuzIjSD83C1dIWPrGtGFMV8r5nQDApeLwcVCbe6r9tVg8cajABQt2Io8rweXnz4A/VSTD6BfU0frWvDtv6xCU1vA0p7v0lx0IxpO1Ly4+pC2nOFyaQ+vXnlGE1MbTaqGQJp7kijM9qDWpDlFY03JVbWroT1z4tktoosjZOZdF5Zi+69nO7b1SMJP8Lulu7Tlz/fUWGrvymSp6aGhXpt3v7wRX+ytAaDUVzUj0sskSnM+o0Qxw2z+1dfgcjHNLDlxSA9Du/tf34LW9sT62qcaJNyTRP/CbNQ2t1vmY4+EiUN64OlvT8KPvzbKcj+9laYnQmhmuF3IzrTW2gVuFwvRrjPcRg2itrkt5DifPwBvhlEUWHlyNfksagkkWHN/+avDKM7NRH5WBjwupvnxi3EO65Wrta1u9CWmEykKCfck0SdfCQD5ZHfHihi7XQyXjO+PIguPB5pPTV+ieWh7XCzEz10EOf2PGslqFv6cc7S2W2juFpgnVAFdc3cyNd772mZ8tMOylHJY2gJBbbJUfniJ/2/cNl1ra/Xw6YrsqKzXTGWJhIR7kjhd9Qs+3qBrF/GUyaS5pzeuCJ/g72+tNKw3t/kxaUgRRqu5YMzmExH96jVNllp93C8vG2vbL2EusfJaeXH1IXz3719F1H8rbpg2FIAu3F9fX66Zm+R+p0ow0+wnP8UdL65P+OeQcE8SInTbn4AL0PwKXdXgS3iWPiI5CGEciWzP8bpDTDdNvgByvR4pq6jxmMkLlgGwjmwV/HT2KGz8xdcwuDh0vkePvwC+3FeDkT9/D0u2VIa06wjibcCtRt+6XQz+IMfdL2/U2niknPIdzavTWSR6joCEe5LwqLZPf4AnXMs+46FluPB3Hyf2Q4ikIK6VSPT280b2QUNrO3Yda8BdL61HS1sATT4/cjM9EBYVu9xEjT6jMtDk0wOa/ue8U2yTdTGX6CfHGjW3/PpDJyPobXhEV8WDyc0YvtynBzQVZHkMBUNSRXMXkHBPE4S9skKqcRnP4COzn/uxeh+2Hq2L7FjOsaOynpKSJZhfv7UNd720PipXWNEyErNMXpYHja1+3LzoK7yx4ShW7atBc5uiuYvjy09apyAwFwIRXjV5XmdvaTlbqTjGzl0TUDxuzn38I4MXjx3iehQPJreLGVwe75szBowxPPnN0wEAC97ehrUH4/NgSQaidGGiIOGeJISG8fzn++MfcCTd97I3xCV/+AxVDeE9CFbsrMLsJz/Fa+vK49svwsDzn+/HGxuOYl91U8THRGOWyc/yoKktgJa2oHZsdaMPuV63dv3d+sJawzFnDe8JALh+WolhuxA8884Y7PiZ4qHR0OrHwk+UUnwt7QEEgxw/enE9Hnpnm6H9f9eW42BNM/5gUTLQjBi7iPHwSJ4/Zw4rxkWn9gOgBDcBwMbyOlz17Bcpk/O9hTT39EC2DQoSMaEqyqEJHn9/R9hjd1Q2AACVTEsS0ZgPopFT+WoOmWY1Le6SLZXw+YNYf6jWNt+6N8OFCYOLDOl8AeDKSQPxvbOH4S41v7sd4qGzYqfuBbb+0EnUtrTjrY1HtepOgk93VWvLYjLXDk1zVz9k73H9ofjy989Cj1zFc8zsxeNLkVQEiTbLsK7wlCvLz+drJk/u7G4knFX7apCXlYHi3AwcqmnGGSXFcSkyvPWokgb11AEFONncjp2VxrSo4wcVaSHmVuyrbsLx+lYM6pGDQVJa147CAew61oCTTW0YN7Aw7Kt9d2HVPiUY6LRBRVpQWjj8QY41B05gaM9c9JciSK043uDDvqrQB3RJr1wEg1yr5DRV1dYBYHtlAwLBIMYNKIx0GAaCHFi9vwYZHpdW4q8gOwPDeuVio0nRmDq8J7ZV1KNeNd9MGFxkWRdWYB67+P5cjGHKML3UX1NbAJvL9c8qKym2VKa6CmIcpw4sRH6M9wb7+OO1nPMyq32kuSeRPK8HboYEV7MOPbld2LnguFoY2RWnq6Gh1Y+TqlYWTjvrLhhfwaOwuUdxrdgpCsU5mSg2aeYd+gALxCe2+4NgjGlvD3ZvgfGY1xk/yPggMo96e0W99jbaldl6pE4rSp4IuoZKNWoUsGJFZ/ci4SxYuBLBIDBzTB888t4ObPv1RVqQSSw8uHAlghz4z/fPwrrtx7Sc14JrJg/C49dMsD1+3vx3lPPMHYfrpg6NuT879p/AvD8rmfyGFOdg4fWTMbpfQczn7QzaA0EEgtxxkjAcT3+0B4+/v1Nbf/tHZ2PcwMg05YamNsx7cCl+ddlY3KjWS7Vj++5qXPvXL0O273t4Dlwuhg/e2ob/rDmMLf93kbZvwXOr4GsP4r8/nBbhaIzwIMe8+94FAIzul4+inAwEg8BqizS9z35nEh55b4f2BrH07hko7Wud5RIA6hp9mLdgGf7v66fihmklqN50FDsqGjD1ImOU9u7DtZj39Ochxx949BK0tgfgdjHLAKzO4uZfLNHMZEOKc/DJT8/v+MkcJmO6zoi7AVkZbrT6E2tns1LEzKHlMp9KEbPeON0Acsj7oRPNmP3kp3E5b2dw4e8+xugHlqCm0RdiZogUWbAD0SnLXJtQDW9myPWGPoDyvR5tQtLtsopQjTxAygr50Iq6ViXQiHPNbCJfCxsO1xriL8Ip8QHThOql4wfgxxeFpt8o7ZtneXxVgw+jH1iCa58LfeB1JnJGy+mn9HRoGRsk3JOI1+NCa3sgMVYZbviHX1yqRxM6hZZf99fV2nK8kj9ZaUk7U+A12QpRvWjygmW4/OnP8cmu6NJH/MOitF0037PuChm+bX5W6FvgFZMGasuNvgBa2gMGb5Ig5zGlr5AfOhMGF8HFGKobffCpZqh2KThqaM9cNPr8GN5byQcTzkQjUtO7w3TQ7u33h/9UPIO+3B/6FtGZyN9/Iid/SbgnkVyvx5D/Ipoye07I5xEXjjzhZA4ttyNebu7mvN8AcOlTqau9y1z//OrwjST+/PG+kG3RCHetbSR+7l490OjTn56P75w5BP87S9d0RfrcLUf0CfdgjJq7zFPfmoj6Vj8O1jRjY3lojEVNow/tAa5Vhgr3PQS0CNWO9WdNF/V55wBunFaCU/rkJdRjhoR7EinIyghbDaejCN95q9slUnux08322e5qPLlsF36/LLx/stVp2h3C27syk4caU8vKBS0i4UitPpk9XM1gGNU3EUWEakG2rsEOLs7BQ1ecZhlZKk/ucs7jNpFemJ2BGofMjE+ogUsF2dbC/cpnPsf3pPkika8mHg8fqwpmnQljSuBYcwezxEYCCfckkp/lQUOrP+65r+Vr30rRy43Q3crJLfbav36JJ5ftxv9btsu2oo9AHt+Y/qk5kSrYYLKzF+dah+HbIbKBAsC16mR1oiJUczI9uGn6MPzWZvJcTJY/s2KPNq54au4AUH7S2TMLADJVO/zJZuN1tO5QLZZtP4YDapDXCtUEFqm7cIGFWUpwoCbywLGEo/6o2ZnuDqcAjwQS7klE2Cd/s2Snup6IT1En4MDw2FWnAdBvJitk//dIlZvdx5zt50J2nTW8J5761kQAwNdTsHgx5zzELhxt+pIzVZ/yHjkZ2sRfNEpkNBGqAPCLy8bi6smDLPeJAtIrdlZh7tOf43h9q2pzT65P+N4qRdD++JWNmkYt7OMAcN5vVwAAHnhjC4DIxr5zwWy8e+c5tvu7Uhk+DuX+zMl0JzRKNSbhzhgrYoz9lzG2gzG2nTF2FmOsmDG2lDG2W/3fI/yZugenDkicFiuuXVlznzmmr7LN4bhcrwez1TDuvRYBMABQZ9KwGqWkUlaIG+m700vQp0DRXM2+yanA7y1C5KMVEkIuDeuVq2nI0XnLGM8TC+a5kAue+FjV3ONw8igQdWCrGny47d/r8NA72/CeQybJo7XhfcG9HjcG9cjBnocuxrJ7zsVY0xtjF5LtAFLDLPN7AEs456MBTACwHcB8AMs556UAlqvrBEKTM8ULg1lG2iY2O13Y/oBe+PgfKw9atvEFjBdgfauzcBef52Isoj50VZ5U5xcuHNMXn8+/AED0tlsR1r/w+jLtd+qYt0zsEjjTNDPZ6PMrNvc4nLtELf8YzoTyxfwL0LdAj7R9b0tlSIoCAPjrZ/q2+jBmQBmP24VT+uTh3TvPwSjJh74rXX7CLJed4cae440Jm1TtsHBnjBUAmAHgrwDAOW/jnNcCuBzAIrXZIgBzY+ti+pDIkGhx8eqaHtNet51svP4gt/RukTFPhn4VxrVMNiVofehSt1dkZKnxAbecMwz9VYEU7bxckHMUZHnQK8+reTVF5S0TjJ/qbhXvEOQ8Zs39g7tn4M3bzgYAvCeZRmRT3A/PG4FFN03BgKJsnD+qT9hzPvi2nnDM42BWdOKtH51tO//Q2TBAy41zvD4x5QFj0dyHA6gC8DfG2HrG2HOMsVwAfTnnFQCg/rf8JRljtzLG1jDG1lRVdaz0XKrRL0xukI5icIWEJFi1bfb4A8ab22qCp93kixtuglbWNlNZc++Z68XF4/rhzOE9Na07EOVAgpxrQTiuSH4QG+JilrHwKQwGY089PbJvvuaVM7JvPqYMK0ZuplurynTawEJcN3Uozh2pmGNG9cuPeKJ9TP8C3H5+aYf6lelxaW/LXen6E105pY8yB/PcZ6HusvEgFuHuATAJwLOc84kAmhCFCYZzvpBzXsY5L+vdu3cM3Ugdhvc2RtIlYh5LttGK89td2O9vrURLe8BQl3Xp9mO4/vnVhsyF5iyGgaDzrKJRc1f7EPkQugx1Le3oX6gkUmOMwcVEzdEA3txwJCKvlyDnWhCOEPLRaP/6HErsF4sc75Cf5YHX44qL5m7mP98/C1t/PVvLvX7nzFIMKDImpLN7i71qknEy+LqpQ8MWBncikq/t3tc2o0RNwZEMOFf6Jb6CZduOJeRzYhHu5QDKOecitve/UIT9McZYfwBQ/3esMm6a8sLNUxJyXrOgUTR3YRKx5vtqbu+sDDf++G3Fq+WOF9fjk11V2C/lHDdH0f3l0/1Ye9DeNCP6omju0U8idgXaA0E0+vwokvzEXUyp4fn75btx50sbDGlu7QhyXTCLm7kjQUzxEMBZkpAMBBVPIM7jF0xnRigFGRZmP6taqwC06FXBuIHxcUJwMguK4K6mMI4C8YQxhqsnK7nyZ4/rn5DP6LBw55xXAjjMGBMhcDMBbAOwGMAN6rYbALwZUw/TjHNK4/+WYjWhquyI7HivxxWSokB2Abz0qc8AGL19rnp2pe35NDOxQXNPLekuJvQKsyXh7mIIcn2CTyTAcoIbNOPobe7yBHmsFGRl4LqpQ5HpduGKiQPhD3LVbBT7uS0/T/3urNIi7LRwp/3N1eMNAXeLb5+O8YOKYupDNGbBTRZRtYlA3AtuF8OAwqywcSMdJdaf9UcA/sUY2wTgdAAPA3gUwCzG2G4As9R1woJ4akz6hKomDvR9Ya7sXnmZ2uShQAj397fqLmrzLx4dWV8kbxnztlTh0fdCi5y4mCKYi9WJsFcjqFwVCOreKC7tQRc5WuKwOF0rD84dh10PXYw++Vla/xLl5/7w3NOwYO44TBxcZNvm3otHa3MB2RlutKiFRqaUFMcs2IHoHoptSarBqrwtKZSVFGNgHGooWBFTvlnO+QYAVoniZ8Zy3nSnV54X1Y2+BAUxKchasxVyHulzSnuHaC3iQpczIZoTgvkDQUOGO4GVzT2VeHWtLrTlIC+3apYR2rz8na3efwI7jzWEpEwOct09UPdz7xzNXUZEbFY1+nBqgn6kwpwMLSrXjm+UDcYj6oM0O8ONKyYNwpHaFtx/yVjH46LF7iuXy1ImujKSAfUr/4Ma5JcIKEK1E1h2zwy8cPOUuOaYDgligvMr6W3/WqctF+dmhpgKmtUEZ/J9b3aZXPDOdstXyqCkbeo29zBJooIcJfPfscyimEw+2VWF/31lIwBF2MjRnopZhocUYS4/2Yxv/HmlFlEpI2dd1PzcLRTEF1cfQsn8d7Q/8b1Gk/I3Gl5ffwSAUlilM4sWFUhmr1P65GFgUTYeuXJ8HKt3OQ9ODtxLVnm+ZL3EknDvBIpyMuNqe5dvfN0VUvJzt7ic5ARmWRluQw4UAFi5T6l1KXt2eFwM/QuzNCH/9y8O4MpnQoskQDPLhPfYEXyxV/m8pz7c49wwwXy5v0ZbfueOsw3frYsxBINK0WlA94Nfd6hWa/P2pqP4ySsb8fRHe/DMij0IGswyxgnuMQ8sQcn8d8A5x8emydkzH16mtI1jhKod8cwtEy1y0NNQNRAqEdjN+VTW6T7mvmRp7gmcxJbpGpWYiLgRqeZunkAtKyk2rNe3KLZP2cedc2DlvTNR3ehD2QJF+Ig8ITLigeBysYh87Re8vQ1H1VKApX2sCy8ki6c/2qstD+tl9NxwuxiqG9vw1QFFcxcauOzWd/u/1xuOGdk3TxNg5ghVkVdk+fbjGD+4EEuk+Y3WduXk8YxQlXnym6fjrpc3GPqVTF75wVnYXmGs9ZsI23845UJWcpJZWDsZ3zlp7mlCSIRqGB9z8wSqmaoGRaORfdyFUJI9SKyQ3ff0KFnrtieb2vDcZ/vx7mZFsMVjEi0Wrp06BADwk4tGhQgbFwPe2VyhrYuAJic/7F3HGnWzjI2JqqnNH5LWoKc6aRtt4rBIKZEeXJ2huZ9RUozrzypJ+Oc4jay60Yd7X9usrSfPLJMcwwwJ9zRAvoB1Xxnm+OonApdusqnLKTSaNin1gFjKcLscC4Do9ntZc7e+oCc+uNSw3tlV67Mz3MjOcOO2808J2ScL+9H98lU/cR62gr1mllG/MvODzucPwq8K91MHFOD6s4Zq32GizDLy79fJX3mn8eF2YwiOL8ElMGWS8ZWTcE8XNGEQqulZac0j1fSzP7GoSQnoAlrW3MdIRa6ddA/dlOD8WnzYwk+8s1OzKjVFrfeJtxkAmHOaEngS5NAEsx1uk809yIEPJBOMzx/UNPfFt58NF2PaOeMZoSpjFO6dK90//en5+DSWItEOOH1vcnIyAPC1J88VMhmQcE8D7IKYnAKIAkHlBpdNCrnq8uDibM2eXKZWInr86vGGtt8sG2zbH0OEqmkSUabWlErYrQYJdSaRFK9gTJ8I9AeDtpkizV4y4qwBznHrC3r+8jZVc/e4GNwuBo+LaXEGiTLLyMFCne2uOrg4B4OLEzeZClgL1ErJHTjT40p48XqtLyCbO9ERJJu7tsniwg4EgyEmkLkTlWLKRdm6a2SeGl14uikQxazd1re2Y8PhWlQ3+rQHgytMJ+Sgkc9+dj48LhaRPZJzjn+uOpiQKjZBOcLEhn/efKYm3INBYzIxkcZB6afyX3wP4kG3s9I4kejzB9Darptm3G4WUiQk3rKgv5TELjsjff0qnL43uWSi1+NKmuYOJMdbhoR7mqBNqBpcIe3b+4M8JPf2T2ePxuLbp6MoJ0MT7kEbs8D0U3oZ1sf/6gPMffpzlC1YFqJtMgYcqGkOmUiUTT6DeuSAscheWd/bUomfv7EFY36xBM1t8c8HEk5znzaip2Zq8QeDmlB47X+m4dLxA3DlxIEY3U/PJS5s7eLrPmIqRdfmD+L5z3UTgZsx+PxBPPDGFi31Q7zNMvL5rNIDpBtOSsODl5+KPK8nabllogliiwUS7mmAYULV4AppH0AUCPKQ6NLC7AyMH1QExnTziG5iMR4/wSGk3BxVyTmweONRvPTVYUM7Idz/dO1k9TNYRBf+/0gBWE98sCts+2iIJEsiY8yguW8qrwWga8O/++bpWHLXDM3MZdbczZWF2vxBragHAHys1g59YdVB/TM7OJ5IyIkh62JXx2nORygh15QN1uobR0u46/WZFXuw6IsDxmNAZhkiCrQIVXXd4Appcf1Zae4CkdoWkN0ajW1753lDjhPYVa3/fE+1sQ+qJ47Ic88QfTEM86RYrDjVFH3ztun407WTABht7nurm1DSM0dLDyxoUs1GIk2B+LpXHzBm1HxmxV5UN/pwwWil9MHWo0azjficRCHqvKYjkQhRF2PI83rClo80EwhynPObj/Cnj/fatvnNkp345eKtBpdLgLxliAgxRKhqmruzVS8Q4LZuhy7GNDuypf0cSoCSsFmaJxTFxJT5xvKbKjoJm3uGW/cmieSNNV5pYK1w8paZMLhIS88qhHuAcxyqacaQnrkh7b8/YzgAvUhGOHOPSC/89o/ONmjyABJaSNk8n5KOWF1W4rplDMjLykBDlMLd5w+g/GSLZZI5AGiQAqREWmGAvGWIKBE2RbkSk2CDlPxL4KS5tweCqKxTTAcBHno+gXg4bCw3nr9JzUtjFmZLtlaiZP47ON7Qqn0OIFUIYtaukJxzwwNky5F6nDmsOKRdtCzbdgxlC5Zi4Sd7tfHK+ded0IR7kOPQiWYMKQ7N7HfvnDFYdNMUrH3gwpB9VpWIRHDYuIGFhnJ1AJDpTl/TSSJxUnHk6N98rweNrdGl3g0npA/WOKSEToJdhoR7GmBrc1cvoOU7lGCNYJDjgidW4Gf/3YSKuhZbTfLT3dWobmxTz6eaWCweBHMnKjUy71bD2AU/VxNo2WnA5zz2EQBduHvCaLZPf7QHw+971zDhxaHnl9+hep8cqW3B1//4GbYcqbM8T3sgCL80ifvhzuOobmzDw+/uwNRHlmvjjeS208wyAY6G1nYUZWdatjt3ZG/kZ4VG9M4a21dbHqsK+qmSeaSHVCTkp7NHYfa4fhH0irDDyjauJ7gDcr3uqM0y4RTwJ5fZzwclwyyT/tPk3RVmvIB+t3QXWtr82FfVhH1VTcj3eiwr5MgEg1yzgbstBO+gHopv8gEbDcVOA/b5g+Cc4+6XNwLQzTLMRnP/5yrllba+tR25Xg9yM904bWAhPt2tTDy+vu4I7p1TgE2Ha7GpvA5Pf7QHz6qTtDKl97+HMf0LNK3Yb8rfvaOyXp1QjUBzV9us3FuDIHdOQSCQfblPH1yI+RePxhklPTBpSA/UNrdrBZMB5YH346+NxJCeuYZC0/Hki/kXJMTbqEuhxXqEIqfq8LhdiDade7iAu2XbQ4vQJctTBiDhnjaYJ1QB45vfH5bvNrTPynRj/MBCx3O2+gOOZd7kQBgrnFIJ/PaDndpyz1yv+hmhNnd/IKgFm8hClwF46dazMOnBpdqYRbZKpxwh2yvqsftYA77cfwLtpjmAJl/A0eYu41EfSD99dRMAPQAsUrIzPAY/a1mwC26/oGOFoSPFXNc0HXH6KeV0yi4bxcIJp+bCzJeV4cK8M4ZgkSmVNXnLEBFhuFDkXOoOV1BVgw99pUAWK1raArZ+7pEgvEf2PzIHN04rMewT2RcHFmVrWq/iLWO8Y2T3SXMitOLcTPQryMLJJsWEJIT/hzucy/b+8F/r8PM3tmipewUfbKuM2OZuzq2TE2X+8WTmMSGU2+J4fSuO1uoxBhz6g9zFWPSpLxyaCw+aouxMFGR5QuotJAMS7mmClSskAK0knBV2mvW0EYrtt7ktYOvnHglCk2YO/us3TBuqLTPGtP6vO3QSm8vrDJGaeiUjfXxFORlYskXJ0yIHqny0017A7zmuFGg40dRm2P7nj/cpNvcIxmouXCJMS5HSHTxUugLyg3rKw8sx7dEPNXOc7PYqcvVHg1NglKiz+8dvTzRoX9r9SRGqRGRYuUIqmMPYAWhudnbeMt85UxG4jT6/rc+6HQNtXvXbAtY3wremDNGWmeRff+UzX+CyP35meQyH0RdduAnKruC/iyC4qba5PaRARCBC4b7PlMd+QGFkJo7fXjMBr//PNC0rJ5Es9Otv2fZjyhZu1tyjO6NT+7qWdoztXxBSJ0FAZhkiasxl2b5z5hDD/qKcDBSo4eZ2mntJL0XgXfz7T/XCG2Guxjmn9UNWhgt/++4ZlvvbLWarVvz4PIMniUtNPyBr+c1S/hg5Da7ozbmjeuvRotJxm4/UGc5j5UFT19Ku5U0XvLnhKIZEkMTqa2N175U3b5uOKRG6Zl49eRAmDukRUVsidsR1Ir84rtyrVNsKcl2Ddrs6YnO3b+8Pct1RIMJj4g0J9zSBm/6LC+qns0drbaYMK8a/vzcVOV7Fxu12Wf/8cmpfzV0szJXyzHcmY8eDF2Nk33zL/WaPj5xMt6FghNJnxe7ZKiVwemyJHiBiMD1JGpfVZDJg9FZYb+Hr3+jzo6dFpO2ECAqGDJb82icMLkpIFSEiduSfRVTWkstPMuk6snrLdcKpdcAijoRzHnJ/JhLylkkDrJIvWsmaf958JjI9LuRkOmvuLhfDeaN6o6axDav3K6Hy0eT8vnNmKXrlGTXiGSN748Cjl+DBt7fhr5/tt/S0EZp7k417HpeeYELjcjE90MqsFcnFu0+a7OuCUX3zcefMUuRneXDu4ysAICQ61ArGGCYMLsIpvTu3LCARGRzQ3D5FKgd57sbliiw62nBOh/b+YBAel5hzCt2fDF2AhHuaIASb1YTNS7dOxdqDJ7VJQOG2Z2dzBxTNesWROmw+YsyLYmbDL2aFaDx3zxrpeF4AyLLwsRcJy95Yf8TyWM0sI2lcbsnLwax4HZPyddsV1BjTvwDjTC6hPXKdywgKXv/htE7PhU44I98HzWrkdCDIsbOyAQs/2afdE7KS4EQwyNHY5kdBVoajiSUYDL2/FJNjR0bRMcgskwZY2vSkjVOH9zSUjRMXtNPFac7xbae5F+VkWpo27BAfebSuNWQfY4rgXvDOdsP2s9TITU1xl2zuTDXLcM41If/GbdMBAJvLdTt7wCbxVnZm6C0Q6eSoy+Xsbkp0HTgHmtWJ9xdXH8ZFT34CQMnICRiVBCf+8OFujP/VBzjZ1OZolvEHg1oshPyAkVNyJxoS7mmK07WT51U000afva+1OQ1svK5F4YZoBWNARW2o0Nc086D+diLbSpU2+oMjT51TWCKVsvMHObweFw48egmeu77MsY9n2Hg4EKmHuE5ONPksbeoir5GsJDjx/lbF0+ZIbYujFm5pc4+i3/GAhHuaYaG4hzCmvzLp6eSn22BKomSVfqAjTBxSZLvPxRhW7qvR1vPUwCBzrVUl/4vu5QAoDwC9SEhoXwMB/WaTbzp58vYnF43CrTOGW+bRIVKbtzZVWG5f/r/nAoDkcWXc//r6cqw9qKdoFopDk8/veP+IsolA+KpoiYKEexqwraIeOyobAET22qd5CzhcaBWS2WTZPTNCCnt0lFvOGW7fL9P66vtn4vszhuMWNXVukIdq7kzT3LmhrN2N00oMFYbkLJjyVyMyMQLAbeefgvvmjOnAqIiuivip3zEJ9/NH9cYt5wzT8v2I57ms3e+rasTdL2/EVc+uRKtq0hHOCE1tfkc/dztvGa1fNKFKREK5WrbNHwhGpLmLfU4FIE4fXIQvVU+ZU/pYuzd2BCetWLbrXz15EHIyPbh3zhi8vr4cgK5VyTZ3zSwTDM3yJyJsmWpPFZqUnFfeXC6QSDNMl9uGX8xChtuFXFO6CKEkfOPPK3HfnDHok+/FBU98rO2/6e9f4d+3TNVqCq/adwLbKxosP5JzjvaA5C0Tr7FECQn3NGJ7RUNI+gEr9lcr0ZXH6322be68sBTeDDduml4Svw6qrL5/JjKsfOylPu+QikjraQckVYmFmmVkzT3X60EgyOHzB5GV4VY1d6WxKBJy0al62l2ie2AXGSy07A2Ha/GNP68M2f+FGviUp2ruCz/ZZ/sZZz/2EY7UtuDUAUYvLA5jMZ1EQ2aZNKK60RfRxXOGGk0pzB1W5GR6cM+skQkJk++Tn2WZBVHW3B+9cnzIfqvXYJdklpELc4vI03UHTwIwVp4SHhKZHiqAke7I98Gl4/vbtgs3zSI8tooc3GQP1TTj5a8O4YianMzKDKj1i9IPEJHw8BWnAVByrFhVYjJz2fj+WPPzC7ucV4jo8tWTBxl8z3Whz/X0CuIYySyjae4uhinDlJtxb3UTdh9rwNpDJ7WbTRPucZpHIFKDBXPH2e6zc/VdevcM9MzNxIg+SnSrXVEWAPjxKxvxs1f1WqnmlBucOzsxxBu6utOAs1W7cUtbIKLZeMZYRFGYycauTqiVu6MexAR1HzfY3EX+nKO1LZj1/z7BnuONmnA/c7jyUJs3ZXACRkF0JWSZbc7kGY775oxGad98LbgOcBbO5sLnb6uTuNY1jhNPzMKdMeZmjK1njL2trhczxpYyxnar/ylLUoLJUgNxZOGYirE1wi1x/CCjrZJJAtwcgeuSkoaJfS7GNEH+7Aq9Mr0wywztmYsDj17S5d5ciPgjp/HNcHhTE4Vb5CylcvoALQI8BsVbfjCkilnmTgBySOF8AMs556UAlqvrRALJVvO0tNpovqmC6H+2Ke+MsIfKQSZmV8iApLm7mLUr6IGappBtRHojV+VyqgwmTChyllCR1VHkPAKMk/qzT1Uyg9pV4Vp2z4yQbckMZIpJuDPGBgG4BMBz0ubLASxSlxcBmBvLZxDhycn0INPjQkVdq2STTj3VXRQoFr7EOqHaue4KqfznXJ9wFWXTzESbr5tIfVoNb7P294Qo4CFr93Lhdqv8RT5/ALNP7afVEp5geuM0uxAblJMU8JZ5EsBPAcgzB3055xUAoP7vY3UgY+xWxtgaxtiaqqqqGLvRvXG7GEb0zsOB6ibHrJCpwur9NYZ1o+auLMuJwwDhCqlr7k5J0Yjug908jhlRTMbjZpg1VnGR1SJMYYyxELQHuKGo+16pgMuBRy/RllPOW4YxdimA45zztR05nnO+kHNexjkv6927d0e7QagUZWegrqU9qfmiE8VVkwcZ1uWIWnMErjzZKuyriuYe+g2QwO9+NLRap482I1JyjOqbrykMGaa8M4B+/f3txjPw5LzTlXoC6jnsUlXLJPPlMZYgpukAvs4YmwMgC0ABY+yfAI4xxvpzzisYY/0BOFcrJuKC28Wwct9JTC5R5q9TOVthf1NWRk1zBw+Z0NImW4PyhGqoa9u/vncmSvtQ7vXuxlWTB2Hj4Vo8cOlYx3aXnz4Q00b0Qu98L15XU067JD91bjLLnD+6j7ZP9tKyEt6dZSLtsObOOb+Xcz6Ic14CYB6ADznn1wJYDOAGtdkNAN6MuZdEWD7bUw1AKfIMpLbmbnZZk7VzgWaWkbxl7Gzuq++fiemn9EKfgqyE9ZnomuR5PfjdN0+3DJoz0ztfcQ8WQl0380npgE3ahVGrV1xwX/3hNMvzp0M+90cBzGKM7QYwS10nEswdM0sN66mouIs6pOZ0w+JJJacY0CsxyT7w1jb3Pvkk1InIEV4yIgeRixnLWMqKQ5BzreIX58CN04dh8lCj97fhXpQUkEQTl9wynPMVAFaoyzUAZsbjvETk3Dx9GP6wfLe2nopmmT9fOxnHG3wh/sguS5s7DP8DQWPK31QcP9E1uGfWSJxoasNZI5QoZzmIKagmohOIbJPC28ZpWsfg5x7nPltBicPShILs1P8pe+RmWr4+a8kHeOjN4ZZeofXEYfqxwheZICJlUI8c/P27U7R1cxCTlWBu9QvhHrrXqLgnzy6T+hKBAKBoF/0KslBZ34rhvXM7uztxRdPcAbT7jTeHR8r0GOTG9tt+fRGyKDkYESMu1a7+wsoDeEaKeJbxqS6XThq5lStvIiHhnkb894dnYeXeGlxTll45U2SPmHWHlCyPWWoUa3amiM4NGrJCAlbBUAQRPYqfO8cDb261bSMiYa3qFVj6ucercw7Q1Z9GDOqRg2vKcjq7G3FHCO0DNU24//UtAPTkXyJVQUtbQLvBUjE6l+i6uCSPGDsiSf3BkTp+7gSRFPaqRbXldKqlami3JtzbA1i+XSleTMFKRDyRfdkBoJ+FS63P0eYuZ4UMXwYzXlDKX6LLk5URajcXAlyYZVraA8j1etAn30vCnYgrsrfMgMIsfGCREKyyXqk5bHXpCVfJ97dUSueMfz/NkHAnujx+h4xfogj2hkO1aPL5DUU+CCIeuBgQUOsNf/OMISjI0qsx3XvxaOW/+lZpJbQPn2wGADy2ZEfqZIUkiGQQcBDuffK9yM5wY0dlPZp8/pDCxwQRK4zpdXczPEbpLa43XXMPle7i8j3eoNcsToliHQSRaMy+7y/eMlVbZoyhrKQHWtoDaPQFkOcl10civrgYQ01jGwCEFHYXxT0uUeuzWtnS5YIhqZ5+gCDiymXj+xsKeIjIQUFWhhvrD9WiutGHqoa2ZHePSHMYY9hR2QAAyMsyvhm6XUp8iYhQtdLI5clYjuQ5upNwJ7o8jDFcdKqSY3vu6QNC9su5aLYdrUtav4jugSyGJw0JrRrqcTOt6LrVhKpsVnxDzThJZhmCUBGTqiLVqoxcZd6sWRFErLS06T7sWRmhIjPD7dJqsFoFMclTRp/tqQnZnyhIuBMpgagkv786tA7qu5t1F7M8mlAl4szOYw3astcinYXbxbQJVyuNXM6JNFKtKUCukARh4kRTqE1dNsvcdeHIZHaH6GZ4PaEi0+PSzTKWE6qScE9mFDUJdyIlWPHj8+BiwA/OHRGyb+F1ZdryjJFUspFIHFYBdR4300yDVq6QAcksIx4CyYDeYYmUoKRXLvY9conlvl754avsEEQ8MFcJA5TMpM1q/VQrcws3aO4B23bxhoQ7kfJQpSUiGfzkolGWqS2afH7sVvMfhfOW0c0yiYfMMkTKUxxBfUyCiJXvTi+x3C4EO2BtS5dt7u9J+WUSDQl3Ii2gqnpEookkIZ3VdRi0MLOTWYYgImTdz2cZ/N0JIt64I5DI1rllkpkuTIc0dyIt6JGbiT4WebYJIl50WHNXhfs1kwfp7cgVkiAIonOZNqInsjJcERXYcHKF9MrRrWSWIQiC6FwWXl+muTqGw8kV0iq6NZGQcCcIgnAgz+uJOK2FlXYvXCFlH3lyhSQIgujiyEK7sTVUwxf7M9wuy2MSBQl3giCIGPjsZ+djyrBiAHq9VJk/fnsS7phZirH9C7RtVjlq4g0Jd4IgiBjok5+FaWoBmRYL2/zAomzcM2skPJK3jdciR028IeFOEAQRIyIzaZOU+92MXKGPNHeCIIgUICdTmXB1yvooT7ZaZZeMN+QtQxAEESNXTx6EzeV1uOvCUts2sg98MjR3Eu4EQRAxkpXhxmNXj3dsIwe4JkNz7/DjgzE2mDH2EWNsO2NsK2PsTnV7MWNsKWNst/o/tKIsQRBEN8OdZLNMLO8GfgD/yzkfA2AqgNsYY2MBzAewnHNeCmC5uk4QBNGtYUk2y3T4EzjnFZzzdepyA4DtAAYCuBzAIrXZIgBzY+wjQRBEyiOqMAFdX3PXYIyVAJgI4EsAfTnnFYDyAADQJx6fQRAEkcrIAU65mSkg3BljeQBeBXAX57w+iuNuZYytYYytqaqqirUbBEEQXZrWdkVzP39U74gyTMZKTN4yjLEMKIL9X5zz19TNxxhj/TnnFYyx/gCOWx3LOV8IYCEAlJWVdU42e4IgiCQxd+JA7K1qwh0z7d0l40ks3jIMwF8BbOec/07atRjADeryDQDe7Hj3CIIg0gOvx4375oyJOMNkrMTyKdMBXAdgM2Nsg7rtPgCPAvgPY+xmAIcAXBNTDwmCIIio6bBw55x/Bvu0xDM7el6CIAgidii3DEEQRBpCwp0gCCINIeFOEASRhpBwJwiCSENIuBMEQaQhJNwJgiDSEMZ55weHMsaqAByM4RS9AFTHqTtdiXQdF0BjS0XSdVxA6o5tKOe8t9WOLiHcY4UxtoZzXtbZ/Yg36TougMaWiqTruID0HBuZZQiCINIQEu4EQRBpSLoI94Wd3YEEka7jAmhsqUi6jgtIw7Glhc2dIAiCMJIumjtBEAQhQcKdIAgiDUlp4c4Ym80Y28kY28MYm9/Z/YkWxtgBxthmxtgGxtgadVsxY2wpY2y3+r+H1P5edaw7GWMXdV7PQ2GMPc8YO84Y2yJti3osjLHJ6neyhzH2B5aMemRhsBnbrxhjR9TfbgNjbI60LyXGxhgbzBj7iDG2nTG2lTF2p7o95X83h7Gl/O8WMZzzlPwD4AawF8BwAJkANgIY29n9inIMBwD0Mm37DYD56vJ8AI+py2PVMXoBDFPH7u7sMUj9ngFgEoAtsYwFwGoAZ0GpFfAegIu76Nh+BeDHFm1TZmwA+gOYpC7nA9il9j/lfzeHsaX87xbpXypr7lMA7OGc7+OctwF4CcDlndyneHA5gEXq8iIAc6XtL3HOfZzz/QD2QPkOugSc808AnDBtjmosas3dAs75Sq7cVf+Qjuk0bMZmR8qMjXNewTlfpy43ANgOYCDS4HdzGJsdKTO2SEll4T4QwGFpvRzOP15XhAP4gDG2ljF2q7qtL+e8AlAuUAB91O2pON5oxzJQXTZv76rczhjbpJpthOkiJcfGGCsBMBHAl0iz3800NiCNfjcnUlm4W9m9Us2vczrnfBKAiwHcxhib4dA2HcYrsBtLKo3xWQAjAJwOoALAE+r2lBsbYywPwKsA7uKc1zs1tdiWamNLm98tHKks3MsBDJbWBwE42kl96RCc86Pq/+MAXodiZjmmvgpC/X9cbZ6K4412LOXqsnl7l4NzfoxzHuCcBwH8BbqJLKXGxhjLgCL8/sU5f03dnBa/m9XY0uV3i4RUFu5fAShljA1jjGUCmAdgcSf3KWIYY7mMsXyxDOBrALZAGcMNarMbALypLi8GMI8x5mWMDQNQCmWipysT1VhUE0ADY2yq6pFwvXRMl0IIP5UroPx2QAqNTe3HXwFs55z/TtqV8r+b3djS4XeLmM6e0Y3lD8AcKLPgewHc39n9ibLvw6HMzm8EsFX0H0BPAMsB7Fb/F0vH3K+OdSe62Iw9gBehvOa2Q9F2bu7IWACUQbnh9gL4I9Qo6i44thcAbAawCYpg6J9qYwNwNhQTwyYAG9S/OenwuzmMLeV/t0j/KP0AQRBEGpLKZhmCIAjCBhLuBEEQaQgJd4IgiDSEhDtBEEQaQsKdIAgiDSHhThAEkYaQcCcIgkhD/j+92BHujuRYxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compared to the electricity prices,the Brent oil future prices look much less dependant on seasonality\n",
    "\n",
    "media = electricity_brent_weather[\"Precio cierre Brent\"].mean()\n",
    "\n",
    "plt.plot(electricity_brent_weather[\"Precio cierre Brent\"].values)\n",
    "plt.axhline(y = media,color ='r', linestyle = '-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Energía final MWh</th>\n",
       "      <th>Mercado diario €/MWh</th>\n",
       "      <th>Mercado intradiario €/MWh</th>\n",
       "      <th>Restricciones €/MWh</th>\n",
       "      <th>Procesos OS €/MWh</th>\n",
       "      <th>Garantía potencia Pagos capacidad €/MWh</th>\n",
       "      <th>Total €/MWh</th>\n",
       "      <th>Coste s.interrrumpibilidad</th>\n",
       "      <th>Precio cierre Brent</th>\n",
       "      <th>Precio apertura Brent</th>\n",
       "      <th>Precio Máximo Brent</th>\n",
       "      <th>Precio mínimo Brent</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-05-31</th>\n",
       "      <td>680088.6250</td>\n",
       "      <td>43.075600</td>\n",
       "      <td>-0.195200</td>\n",
       "      <td>3.443600</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>4.943200</td>\n",
       "      <td>52.467602</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.411598</td>\n",
       "      <td>103.517998</td>\n",
       "      <td>104.028801</td>\n",
       "      <td>102.283203</td>\n",
       "      <td>20.059738</td>\n",
       "      <td>9.461501</td>\n",
       "      <td>14.843591</td>\n",
       "      <td>20.259188</td>\n",
       "      <td>1.413655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-30</th>\n",
       "      <td>694027.0625</td>\n",
       "      <td>42.568928</td>\n",
       "      <td>-0.222857</td>\n",
       "      <td>2.901429</td>\n",
       "      <td>1.466786</td>\n",
       "      <td>5.719643</td>\n",
       "      <td>52.433212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.355003</td>\n",
       "      <td>103.243927</td>\n",
       "      <td>104.275002</td>\n",
       "      <td>102.317856</td>\n",
       "      <td>25.993834</td>\n",
       "      <td>13.237323</td>\n",
       "      <td>19.710827</td>\n",
       "      <td>18.583956</td>\n",
       "      <td>0.745985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-31</th>\n",
       "      <td>757750.8750</td>\n",
       "      <td>51.859032</td>\n",
       "      <td>-0.037419</td>\n",
       "      <td>1.263871</td>\n",
       "      <td>1.267097</td>\n",
       "      <td>6.573548</td>\n",
       "      <td>60.928066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.559998</td>\n",
       "      <td>107.169357</td>\n",
       "      <td>108.048706</td>\n",
       "      <td>106.428711</td>\n",
       "      <td>31.171972</td>\n",
       "      <td>17.577093</td>\n",
       "      <td>24.457556</td>\n",
       "      <td>18.039492</td>\n",
       "      <td>0.254679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>705887.1250</td>\n",
       "      <td>48.723225</td>\n",
       "      <td>-0.073548</td>\n",
       "      <td>2.016452</td>\n",
       "      <td>1.386452</td>\n",
       "      <td>4.474839</td>\n",
       "      <td>56.526131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.358391</td>\n",
       "      <td>109.903221</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>109.267738</td>\n",
       "      <td>30.553507</td>\n",
       "      <td>17.467529</td>\n",
       "      <td>24.083385</td>\n",
       "      <td>17.851673</td>\n",
       "      <td>0.319438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-30</th>\n",
       "      <td>713595.0625</td>\n",
       "      <td>51.093334</td>\n",
       "      <td>-0.072333</td>\n",
       "      <td>2.319000</td>\n",
       "      <td>1.423333</td>\n",
       "      <td>4.957000</td>\n",
       "      <td>59.721001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.513336</td>\n",
       "      <td>111.502335</td>\n",
       "      <td>112.328667</td>\n",
       "      <td>110.504333</td>\n",
       "      <td>27.135378</td>\n",
       "      <td>15.221637</td>\n",
       "      <td>21.295116</td>\n",
       "      <td>17.077494</td>\n",
       "      <td>0.731561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>738332.6875</td>\n",
       "      <td>36.452579</td>\n",
       "      <td>-0.001935</td>\n",
       "      <td>1.580968</td>\n",
       "      <td>0.330968</td>\n",
       "      <td>1.922581</td>\n",
       "      <td>40.285484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.090321</td>\n",
       "      <td>45.215485</td>\n",
       "      <td>45.604515</td>\n",
       "      <td>44.554195</td>\n",
       "      <td>31.063057</td>\n",
       "      <td>17.644073</td>\n",
       "      <td>24.404476</td>\n",
       "      <td>18.251814</td>\n",
       "      <td>0.653450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>715004.3125</td>\n",
       "      <td>42.311001</td>\n",
       "      <td>-0.014333</td>\n",
       "      <td>1.739667</td>\n",
       "      <td>0.356000</td>\n",
       "      <td>2.131667</td>\n",
       "      <td>46.523666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.877998</td>\n",
       "      <td>42.077000</td>\n",
       "      <td>42.662666</td>\n",
       "      <td>41.251667</td>\n",
       "      <td>27.048923</td>\n",
       "      <td>14.647007</td>\n",
       "      <td>20.938395</td>\n",
       "      <td>17.335526</td>\n",
       "      <td>0.950422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>694828.5625</td>\n",
       "      <td>36.901611</td>\n",
       "      <td>-0.033226</td>\n",
       "      <td>2.141936</td>\n",
       "      <td>0.459032</td>\n",
       "      <td>2.031935</td>\n",
       "      <td>41.501289</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.439678</td>\n",
       "      <td>41.678387</td>\n",
       "      <td>42.201614</td>\n",
       "      <td>40.735161</td>\n",
       "      <td>20.802197</td>\n",
       "      <td>9.428974</td>\n",
       "      <td>15.177327</td>\n",
       "      <td>18.228868</td>\n",
       "      <td>1.878773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>703864.1875</td>\n",
       "      <td>42.434666</td>\n",
       "      <td>-0.034667</td>\n",
       "      <td>2.133000</td>\n",
       "      <td>0.475333</td>\n",
       "      <td>2.238667</td>\n",
       "      <td>47.249332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.558334</td>\n",
       "      <td>44.250668</td>\n",
       "      <td>45.190666</td>\n",
       "      <td>43.685001</td>\n",
       "      <td>17.687389</td>\n",
       "      <td>8.121656</td>\n",
       "      <td>12.934454</td>\n",
       "      <td>15.024754</td>\n",
       "      <td>2.016017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>770570.6875</td>\n",
       "      <td>42.619999</td>\n",
       "      <td>-0.016452</td>\n",
       "      <td>1.811935</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>2.701936</td>\n",
       "      <td>47.556774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.375160</td>\n",
       "      <td>50.168064</td>\n",
       "      <td>50.908066</td>\n",
       "      <td>49.624195</td>\n",
       "      <td>12.785348</td>\n",
       "      <td>4.331613</td>\n",
       "      <td>8.582540</td>\n",
       "      <td>19.794085</td>\n",
       "      <td>2.049257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Energía final MWh  Mercado diario €/MWh  \\\n",
       "Date                                                  \n",
       "2013-05-31        680088.6250             43.075600   \n",
       "2013-06-30        694027.0625             42.568928   \n",
       "2013-07-31        757750.8750             51.859032   \n",
       "2013-08-31        705887.1250             48.723225   \n",
       "2013-09-30        713595.0625             51.093334   \n",
       "...                       ...                   ...   \n",
       "2020-08-31        738332.6875             36.452579   \n",
       "2020-09-30        715004.3125             42.311001   \n",
       "2020-10-31        694828.5625             36.901611   \n",
       "2020-11-30        703864.1875             42.434666   \n",
       "2020-12-31        770570.6875             42.619999   \n",
       "\n",
       "            Mercado intradiario €/MWh  Restricciones €/MWh  Procesos OS €/MWh  \\\n",
       "Date                                                                            \n",
       "2013-05-31                  -0.195200             3.443600           1.200000   \n",
       "2013-06-30                  -0.222857             2.901429           1.466786   \n",
       "2013-07-31                  -0.037419             1.263871           1.267097   \n",
       "2013-08-31                  -0.073548             2.016452           1.386452   \n",
       "2013-09-30                  -0.072333             2.319000           1.423333   \n",
       "...                               ...                  ...                ...   \n",
       "2020-08-31                  -0.001935             1.580968           0.330968   \n",
       "2020-09-30                  -0.014333             1.739667           0.356000   \n",
       "2020-10-31                  -0.033226             2.141936           0.459032   \n",
       "2020-11-30                  -0.034667             2.133000           0.475333   \n",
       "2020-12-31                  -0.016452             1.811935           0.440000   \n",
       "\n",
       "            Garantía potencia Pagos capacidad €/MWh  Total €/MWh  \\\n",
       "Date                                                               \n",
       "2013-05-31                                 4.943200    52.467602   \n",
       "2013-06-30                                 5.719643    52.433212   \n",
       "2013-07-31                                 6.573548    60.928066   \n",
       "2013-08-31                                 4.474839    56.526131   \n",
       "2013-09-30                                 4.957000    59.721001   \n",
       "...                                             ...          ...   \n",
       "2020-08-31                                 1.922581    40.285484   \n",
       "2020-09-30                                 2.131667    46.523666   \n",
       "2020-10-31                                 2.031935    41.501289   \n",
       "2020-11-30                                 2.238667    47.249332   \n",
       "2020-12-31                                 2.701936    47.556774   \n",
       "\n",
       "            Coste s.interrrumpibilidad  Precio cierre Brent  \\\n",
       "Date                                                          \n",
       "2013-05-31                         0.0           103.411598   \n",
       "2013-06-30                         0.0           103.355003   \n",
       "2013-07-31                         0.0           107.559998   \n",
       "2013-08-31                         0.0           110.358391   \n",
       "2013-09-30                         0.0           111.513336   \n",
       "...                                ...                  ...   \n",
       "2020-08-31                         0.0            45.090321   \n",
       "2020-09-30                         0.0            41.877998   \n",
       "2020-10-31                         0.0            41.439678   \n",
       "2020-11-30                         0.0            44.558334   \n",
       "2020-12-31                         0.0            50.375160   \n",
       "\n",
       "            Precio apertura Brent  Precio Máximo Brent  Precio mínimo Brent  \\\n",
       "Date                                                                          \n",
       "2013-05-31             103.517998           104.028801           102.283203   \n",
       "2013-06-30             103.243927           104.275002           102.317856   \n",
       "2013-07-31             107.169357           108.048706           106.428711   \n",
       "2013-08-31             109.903221           111.000000           109.267738   \n",
       "2013-09-30             111.502335           112.328667           110.504333   \n",
       "...                           ...                  ...                  ...   \n",
       "2020-08-31              45.215485            45.604515            44.554195   \n",
       "2020-09-30              42.077000            42.662666            41.251667   \n",
       "2020-10-31              41.678387            42.201614            40.735161   \n",
       "2020-11-30              44.250668            45.190666            43.685001   \n",
       "2020-12-31              50.168064            50.908066            49.624195   \n",
       "\n",
       "                 Tmax       Tmin       Tmed       Vmax     TPrec  \n",
       "Date                                                              \n",
       "2013-05-31  20.059738   9.461501  14.843591  20.259188  1.413655  \n",
       "2013-06-30  25.993834  13.237323  19.710827  18.583956  0.745985  \n",
       "2013-07-31  31.171972  17.577093  24.457556  18.039492  0.254679  \n",
       "2013-08-31  30.553507  17.467529  24.083385  17.851673  0.319438  \n",
       "2013-09-30  27.135378  15.221637  21.295116  17.077494  0.731561  \n",
       "...               ...        ...        ...        ...       ...  \n",
       "2020-08-31  31.063057  17.644073  24.404476  18.251814  0.653450  \n",
       "2020-09-30  27.048923  14.647007  20.938395  17.335526  0.950422  \n",
       "2020-10-31  20.802197   9.428974  15.177327  18.228868  1.878773  \n",
       "2020-11-30  17.687389   8.121656  12.934454  15.024754  2.016017  \n",
       "2020-12-31  12.785348   4.331613   8.582540  19.794085  2.049257  \n",
       "\n",
       "[92 rows x 17 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months = electricity_brent_weather.resample(\"M\").mean()\n",
    "months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Energía final MWh</th>\n",
       "      <th>Mercado diario €/MWh</th>\n",
       "      <th>Mercado intradiario €/MWh</th>\n",
       "      <th>Restricciones €/MWh</th>\n",
       "      <th>Procesos OS €/MWh</th>\n",
       "      <th>Garantía potencia Pagos capacidad €/MWh</th>\n",
       "      <th>Total €/MWh</th>\n",
       "      <th>Coste s.interrrumpibilidad</th>\n",
       "      <th>Precio cierre Brent</th>\n",
       "      <th>Precio apertura Brent</th>\n",
       "      <th>Precio Máximo Brent</th>\n",
       "      <th>Precio mínimo Brent</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>Vmax</th>\n",
       "      <th>TPrec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>726303.312500</td>\n",
       "      <td>46.897961</td>\n",
       "      <td>-0.039743</td>\n",
       "      <td>2.070693</td>\n",
       "      <td>0.921931</td>\n",
       "      <td>3.375674</td>\n",
       "      <td>54.183868</td>\n",
       "      <td>0.957191</td>\n",
       "      <td>65.772285</td>\n",
       "      <td>65.782791</td>\n",
       "      <td>66.603165</td>\n",
       "      <td>64.905998</td>\n",
       "      <td>21.507051</td>\n",
       "      <td>10.264194</td>\n",
       "      <td>15.944671</td>\n",
       "      <td>18.173595</td>\n",
       "      <td>1.312593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40405.843750</td>\n",
       "      <td>11.473991</td>\n",
       "      <td>0.050458</td>\n",
       "      <td>1.020913</td>\n",
       "      <td>0.616351</td>\n",
       "      <td>1.444641</td>\n",
       "      <td>11.561060</td>\n",
       "      <td>0.799834</td>\n",
       "      <td>23.120914</td>\n",
       "      <td>23.081236</td>\n",
       "      <td>23.038614</td>\n",
       "      <td>23.133762</td>\n",
       "      <td>6.505588</td>\n",
       "      <td>5.219283</td>\n",
       "      <td>5.857212</td>\n",
       "      <td>2.009020</td>\n",
       "      <td>0.864627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>610884.625000</td>\n",
       "      <td>17.713333</td>\n",
       "      <td>-0.310968</td>\n",
       "      <td>0.513226</td>\n",
       "      <td>0.241935</td>\n",
       "      <td>1.922581</td>\n",
       "      <td>24.261333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.365334</td>\n",
       "      <td>27.713333</td>\n",
       "      <td>29.641666</td>\n",
       "      <td>25.717667</td>\n",
       "      <td>11.808302</td>\n",
       "      <td>1.768613</td>\n",
       "      <td>6.919828</td>\n",
       "      <td>13.237708</td>\n",
       "      <td>0.169755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>695817.828125</td>\n",
       "      <td>42.141719</td>\n",
       "      <td>-0.048565</td>\n",
       "      <td>1.160726</td>\n",
       "      <td>0.454274</td>\n",
       "      <td>2.238917</td>\n",
       "      <td>47.390638</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.204894</td>\n",
       "      <td>49.354140</td>\n",
       "      <td>50.133759</td>\n",
       "      <td>48.381038</td>\n",
       "      <td>15.525868</td>\n",
       "      <td>5.585818</td>\n",
       "      <td>10.782392</td>\n",
       "      <td>17.166286</td>\n",
       "      <td>0.586456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>723264.187500</td>\n",
       "      <td>48.099678</td>\n",
       "      <td>-0.026070</td>\n",
       "      <td>2.026392</td>\n",
       "      <td>0.768559</td>\n",
       "      <td>2.817419</td>\n",
       "      <td>55.816000</td>\n",
       "      <td>1.001452</td>\n",
       "      <td>60.518064</td>\n",
       "      <td>60.221737</td>\n",
       "      <td>61.443001</td>\n",
       "      <td>59.390097</td>\n",
       "      <td>21.021845</td>\n",
       "      <td>10.066199</td>\n",
       "      <td>15.726924</td>\n",
       "      <td>18.213499</td>\n",
       "      <td>1.216838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>754270.437500</td>\n",
       "      <td>53.656770</td>\n",
       "      <td>-0.012000</td>\n",
       "      <td>2.755242</td>\n",
       "      <td>1.152097</td>\n",
       "      <td>4.726146</td>\n",
       "      <td>61.471292</td>\n",
       "      <td>1.770217</td>\n",
       "      <td>75.266424</td>\n",
       "      <td>75.356714</td>\n",
       "      <td>76.169289</td>\n",
       "      <td>74.368408</td>\n",
       "      <td>27.139271</td>\n",
       "      <td>14.922043</td>\n",
       "      <td>21.115708</td>\n",
       "      <td>19.205523</td>\n",
       "      <td>1.823197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>818443.625000</td>\n",
       "      <td>72.460327</td>\n",
       "      <td>0.030645</td>\n",
       "      <td>5.480000</td>\n",
       "      <td>2.890000</td>\n",
       "      <td>6.744839</td>\n",
       "      <td>79.733223</td>\n",
       "      <td>2.109000</td>\n",
       "      <td>112.042000</td>\n",
       "      <td>112.026001</td>\n",
       "      <td>112.713333</td>\n",
       "      <td>111.419998</td>\n",
       "      <td>32.668228</td>\n",
       "      <td>19.156334</td>\n",
       "      <td>25.957901</td>\n",
       "      <td>25.483311</td>\n",
       "      <td>3.975250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Energía final MWh  Mercado diario €/MWh  Mercado intradiario €/MWh  \\\n",
       "count          92.000000             92.000000                  92.000000   \n",
       "mean       726303.312500             46.897961                  -0.039743   \n",
       "std         40405.843750             11.473991                   0.050458   \n",
       "min        610884.625000             17.713333                  -0.310968   \n",
       "25%        695817.828125             42.141719                  -0.048565   \n",
       "50%        723264.187500             48.099678                  -0.026070   \n",
       "75%        754270.437500             53.656770                  -0.012000   \n",
       "max        818443.625000             72.460327                   0.030645   \n",
       "\n",
       "       Restricciones €/MWh  Procesos OS €/MWh  \\\n",
       "count            92.000000          92.000000   \n",
       "mean              2.070693           0.921931   \n",
       "std               1.020913           0.616351   \n",
       "min               0.513226           0.241935   \n",
       "25%               1.160726           0.454274   \n",
       "50%               2.026392           0.768559   \n",
       "75%               2.755242           1.152097   \n",
       "max               5.480000           2.890000   \n",
       "\n",
       "       Garantía potencia Pagos capacidad €/MWh  Total €/MWh  \\\n",
       "count                                92.000000    92.000000   \n",
       "mean                                  3.375674    54.183868   \n",
       "std                                   1.444641    11.561060   \n",
       "min                                   1.922581    24.261333   \n",
       "25%                                   2.238917    47.390638   \n",
       "50%                                   2.817419    55.816000   \n",
       "75%                                   4.726146    61.471292   \n",
       "max                                   6.744839    79.733223   \n",
       "\n",
       "       Coste s.interrrumpibilidad  Precio cierre Brent  Precio apertura Brent  \\\n",
       "count                   92.000000            92.000000              92.000000   \n",
       "mean                     0.957191            65.772285              65.782791   \n",
       "std                      0.799834            23.120914              23.081236   \n",
       "min                      0.000000            27.365334              27.713333   \n",
       "25%                      0.000000            49.204894              49.354140   \n",
       "50%                      1.001452            60.518064              60.221737   \n",
       "75%                      1.770217            75.266424              75.356714   \n",
       "max                      2.109000           112.042000             112.026001   \n",
       "\n",
       "       Precio Máximo Brent  Precio mínimo Brent       Tmax       Tmin  \\\n",
       "count            92.000000            92.000000  92.000000  92.000000   \n",
       "mean             66.603165            64.905998  21.507051  10.264194   \n",
       "std              23.038614            23.133762   6.505588   5.219283   \n",
       "min              29.641666            25.717667  11.808302   1.768613   \n",
       "25%              50.133759            48.381038  15.525868   5.585818   \n",
       "50%              61.443001            59.390097  21.021845  10.066199   \n",
       "75%              76.169289            74.368408  27.139271  14.922043   \n",
       "max             112.713333           111.419998  32.668228  19.156334   \n",
       "\n",
       "            Tmed       Vmax      TPrec  \n",
       "count  92.000000  92.000000  92.000000  \n",
       "mean   15.944671  18.173595   1.312593  \n",
       "std     5.857212   2.009020   0.864627  \n",
       "min     6.919828  13.237708   0.169755  \n",
       "25%    10.782392  17.166286   0.586456  \n",
       "50%    15.726924  18.213499   1.216838  \n",
       "75%    21.115708  19.205523   1.823197  \n",
       "max    25.957901  25.483311   3.975250  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ff53ceb02e0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "media = months[\"Total €/MWh\"].mean()\n",
    "plt.plot(months[\"Total €/MWh\"].values)\n",
    "plt.axhline(y = media, color ='r', linestyle = '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff534dec1f0>]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(months[\"Tmax\"].values)\n",
    "plt.plot(months[\"Tmin\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#It looks like the energy prices follow a normal distribution\n",
    "sns.distplot(electricity_brent_weather[\"Total €/MWh\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#It looks as if the Brent oil future prices followed 2 different distributions\n",
    "sns.distplot(electricity_brent_weather[\"Precio cierre Brent\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#In here we can see how all the variables are correlated\n",
    "sns.heatmap(electricity_brent_weather.corr(), annot=True)\n",
    "plt.gcf().set_size_inches(15,13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the correlations, we can see that the Total electricity price is very strongly correlated with the price of \"Mercado diario\". This makes sense as both prices are practically the same, the only difference being that the Total one is adjusted by the price of \"Mercado intradiario\", \"Restricciones\", \"Procesos OS\", \"Garantía potencia de pagos capacidad\" and \"Coste s. interrumpibilidad\". It would make sense to not use all these variables when modelling, as they will lead to over-fitting.\n",
    "\n",
    "The other variable that seem to be very correlated with the Total electricity price is the wind (Vmax), although both variables have a negative correlation. This means that when the gust of winds are stronger in a day, the electricity price goes down."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to save the electricity_brent_weather DataFrame in a csv, so I can open it in another notebook if necessary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "electricity_brent_weather.to_csv(\"electricity_brent_weather1320.csv\", sep= \";\", index = True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}