Desafio Spike.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Punto 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>barra_mnemotecnico</th>\n",
       "      <th>barra_referencia_mnemotecnico</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>costo_en_dolares</th>\n",
       "      <th>costo_en_pesos</th>\n",
       "      <th>nombre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BA01T005SE075T005</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>62.24</td>\n",
       "      <td>41.47176</td>\n",
       "      <td>BA S/E LORETO 15KV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BA01T005SE175T005</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>62.56</td>\n",
       "      <td>41.68498</td>\n",
       "      <td>BA S/E RETIRO 13.2KV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BA03R002SE002R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>60.04</td>\n",
       "      <td>40.00585</td>\n",
       "      <td>BA S/E ALTAMIRANO 12.5KV BP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BA01R002SE012R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>61.65</td>\n",
       "      <td>41.07863</td>\n",
       "      <td>BA S/E CURACAVI 44KV B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA03R002SE013R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>59.55</td>\n",
       "      <td>39.67936</td>\n",
       "      <td>BA S/E EL MANZANO (ENEL DISTRIBUCIÓN) 23KV BP1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  barra_mnemotecnico barra_referencia_mnemotecnico       fecha  hora  \\\n",
       "0  BA01T005SE075T005             BA02T002SE032T002  2019-04-06    25   \n",
       "1  BA01T005SE175T005             BA02T002SE032T002  2019-04-06    25   \n",
       "2  BA03R002SE002R002             BA02T002SE032T002  2019-04-06    25   \n",
       "3  BA01R002SE012R002             BA02T002SE032T002  2019-04-06    25   \n",
       "4  BA03R002SE013R002             BA02T002SE032T002  2019-04-06    25   \n",
       "\n",
       "   costo_en_dolares  costo_en_pesos  \\\n",
       "0             62.24        41.47176   \n",
       "1             62.56        41.68498   \n",
       "2             60.04        40.00585   \n",
       "3             61.65        41.07863   \n",
       "4             59.55        39.67936   \n",
       "\n",
       "                                           nombre  \n",
       "0                              BA S/E LORETO 15KV  \n",
       "1                            BA S/E RETIRO 13.2KV  \n",
       "2                    BA S/E ALTAMIRANO 12.5KV BP1  \n",
       "3                         BA S/E CURACAVI 44KV B1  \n",
       "4  BA S/E EL MANZANO (ENEL DISTRIBUCIÓN) 23KV BP1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>mnemotecnico_barra</th>\n",
       "      <th>nombre_barra</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>costo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BA01T002SE002T002</td>\n",
       "      <td>BA S/E ALTO JAHUEL 110KV SECCION 1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>49.897334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BA01T002SE002T002</td>\n",
       "      <td>BA S/E ALTO JAHUEL 110KV SECCION 1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>50.471616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BA01T002SE002T002</td>\n",
       "      <td>BA S/E ALTO JAHUEL 110KV SECCION 1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>50.077821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BA01T002SE002T002</td>\n",
       "      <td>BA S/E ALTO JAHUEL 110KV SECCION 1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>49.796216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA01T002SE002T002</td>\n",
       "      <td>BA S/E ALTO JAHUEL 110KV SECCION 1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>5</td>\n",
       "      <td>49.796216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mnemotecnico_barra                        nombre_barra       fecha  hora  \\\n",
       "0  BA01T002SE002T002  BA S/E ALTO JAHUEL 110KV SECCION 1  2019-01-01     1   \n",
       "1  BA01T002SE002T002  BA S/E ALTO JAHUEL 110KV SECCION 1  2019-01-01     2   \n",
       "2  BA01T002SE002T002  BA S/E ALTO JAHUEL 110KV SECCION 1  2019-01-01     3   \n",
       "3  BA01T002SE002T002  BA S/E ALTO JAHUEL 110KV SECCION 1  2019-01-01     4   \n",
       "4  BA01T002SE002T002  BA S/E ALTO JAHUEL 110KV SECCION 1  2019-01-01     5   \n",
       "\n",
       "       costo  \n",
       "0  49.897334  \n",
       "1  50.471616  \n",
       "2  50.077821  \n",
       "3  49.796216  \n",
       "4  49.796216  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmr_data = pd.read_csv('data/costo_marginal_real.csv')\n",
    "cmp_data = pd.read_csv('data/costo_marginal_programado.csv')\n",
    "display(cmr_data.head(),cmp_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(cmr_data.duplicated().sum())\n",
    "print(cmp_data.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmp_data.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barra_mnemotecnico</th>\n",
       "      <th>barra_referencia_mnemotecnico</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>costo_en_dolares</th>\n",
       "      <th>costo_en_pesos</th>\n",
       "      <th>nombre</th>\n",
       "      <th>mnemotecnico_barra</th>\n",
       "      <th>nombre_barra</th>\n",
       "      <th>costo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BA01T005SE075T005</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>62.24</td>\n",
       "      <td>41.47176</td>\n",
       "      <td>BA S/E LORETO 15KV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BA01T005SE175T005</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>62.56</td>\n",
       "      <td>41.68498</td>\n",
       "      <td>BA S/E RETIRO 13.2KV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BA03R002SE002R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>60.04</td>\n",
       "      <td>40.00585</td>\n",
       "      <td>BA S/E ALTAMIRANO 12.5KV BP1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BA01R002SE012R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>61.65</td>\n",
       "      <td>41.07863</td>\n",
       "      <td>BA S/E CURACAVI 44KV B1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA03R002SE013R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>59.55</td>\n",
       "      <td>39.67936</td>\n",
       "      <td>BA S/E EL MANZANO (ENEL DISTRIBUCIÓN) 23KV BP1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  barra_mnemotecnico barra_referencia_mnemotecnico       fecha  hora  \\\n",
       "0  BA01T005SE075T005             BA02T002SE032T002  2019-04-06    25   \n",
       "1  BA01T005SE175T005             BA02T002SE032T002  2019-04-06    25   \n",
       "2  BA03R002SE002R002             BA02T002SE032T002  2019-04-06    25   \n",
       "3  BA01R002SE012R002             BA02T002SE032T002  2019-04-06    25   \n",
       "4  BA03R002SE013R002             BA02T002SE032T002  2019-04-06    25   \n",
       "\n",
       "   costo_en_dolares  costo_en_pesos  \\\n",
       "0             62.24        41.47176   \n",
       "1             62.56        41.68498   \n",
       "2             60.04        40.00585   \n",
       "3             61.65        41.07863   \n",
       "4             59.55        39.67936   \n",
       "\n",
       "                                           nombre mnemotecnico_barra  \\\n",
       "0                              BA S/E LORETO 15KV                NaN   \n",
       "1                            BA S/E RETIRO 13.2KV                NaN   \n",
       "2                    BA S/E ALTAMIRANO 12.5KV BP1                NaN   \n",
       "3                         BA S/E CURACAVI 44KV B1                NaN   \n",
       "4  BA S/E EL MANZANO (ENEL DISTRIBUCIÓN) 23KV BP1                NaN   \n",
       "\n",
       "  nombre_barra  costo  \n",
       "0          NaN    NaN  \n",
       "1          NaN    NaN  \n",
       "2          NaN    NaN  \n",
       "3          NaN    NaN  \n",
       "4          NaN    NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costo_marginal = pd.merge(cmr_data, cmp_data, left_on = ['barra_mnemotecnico','fecha','hora'],right_on = ['mnemotecnico_barra','fecha','hora'], how = 'left')\n",
    "costo_marginal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barra_mnemotecnico</th>\n",
       "      <th>barra_referencia_mnemotecnico</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>costo_en_dolares</th>\n",
       "      <th>costo_en_pesos</th>\n",
       "      <th>nombre</th>\n",
       "      <th>mnemotecnico_barra</th>\n",
       "      <th>nombre_barra</th>\n",
       "      <th>costo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [barra_mnemotecnico, barra_referencia_mnemotecnico, fecha, hora, costo_en_dolares, costo_en_pesos, nombre, mnemotecnico_barra, nombre_barra, costo]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costo_marginal[np.isnan(costo_marginal['costo_en_dolares'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barra_mnemotecnico</th>\n",
       "      <th>barra_referencia_mnemotecnico</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>costo_en_dolares</th>\n",
       "      <th>costo_en_pesos</th>\n",
       "      <th>costo</th>\n",
       "      <th>nombre</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BA01T005SE075T005</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>62.24</td>\n",
       "      <td>41.47176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BA S/E LORETO 15KV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BA01T005SE175T005</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>62.56</td>\n",
       "      <td>41.68498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BA S/E RETIRO 13.2KV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BA03R002SE002R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>60.04</td>\n",
       "      <td>40.00585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BA S/E ALTAMIRANO 12.5KV BP1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BA01R002SE012R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>61.65</td>\n",
       "      <td>41.07863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BA S/E CURACAVI 44KV B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA03R002SE013R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>59.55</td>\n",
       "      <td>39.67936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BA S/E EL MANZANO (ENEL DISTRIBUCIÓN) 23KV BP1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  barra_mnemotecnico barra_referencia_mnemotecnico       fecha  hora  \\\n",
       "0  BA01T005SE075T005             BA02T002SE032T002  2019-04-06    25   \n",
       "1  BA01T005SE175T005             BA02T002SE032T002  2019-04-06    25   \n",
       "2  BA03R002SE002R002             BA02T002SE032T002  2019-04-06    25   \n",
       "3  BA01R002SE012R002             BA02T002SE032T002  2019-04-06    25   \n",
       "4  BA03R002SE013R002             BA02T002SE032T002  2019-04-06    25   \n",
       "\n",
       "   costo_en_dolares  costo_en_pesos  costo  \\\n",
       "0             62.24        41.47176    NaN   \n",
       "1             62.56        41.68498    NaN   \n",
       "2             60.04        40.00585    NaN   \n",
       "3             61.65        41.07863    NaN   \n",
       "4             59.55        39.67936    NaN   \n",
       "\n",
       "                                           nombre  \n",
       "0                              BA S/E LORETO 15KV  \n",
       "1                            BA S/E RETIRO 13.2KV  \n",
       "2                    BA S/E ALTAMIRANO 12.5KV BP1  \n",
       "3                         BA S/E CURACAVI 44KV B1  \n",
       "4  BA S/E EL MANZANO (ENEL DISTRIBUCIÓN) 23KV BP1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costo_marginal = costo_marginal.loc[:,['barra_mnemotecnico','barra_referencia_mnemotecnico','fecha','hora','costo_en_dolares','costo_en_pesos','costo','nombre']]\n",
    "costo_marginal.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tal como se puede leer desde el código anterior, existen registros duplicados los cuales por simplicidad en términos de ganar tiempo para intentar completar el desaifío, estos simplemente fueron eliminados. Además el merge fue realizado utlizando un `left join`, dado que el costo real (en mi impresión) es la más importante y por ende tener observaciones con costo real `NaN` no estaría aportando.\n",
    "\n",
    "Por otro lado, a continuación se muestran histogramas y boxplot respecto a los costos marginales reales y programados para tener alguna idea de como están distribuidos. Como se puede ver, las distribuciones son muy similares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize = (12,4))\n",
    "ax[0].hist(costo_marginal['costo_en_dolares'],bins = 150,density = True, alpha = 0.3)\n",
    "ax[0].hist(costo_marginal['costo'].dropna(), bins = 150, density = True, alpha = 0.3)\n",
    "ax[1].boxplot([costo_marginal['costo_en_dolares'],costo_marginal['costo'].dropna()], \n",
    "           labels = ['Costo Real','Costo Prog.'],\n",
    "          showfliers = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sin embargo, al aplicar el test de Kolmogorov-Smirnov para determinar si dicha similitud es estadísticamente significativa, nos encontramos con que no estan muestreados desde la misma distribución apesar de su similitud. Lo anterior se puede deber a la diferencia de peso que tiene en las colas la distribución (outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.048133464923792446, pvalue=0.0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.ks_2samp(costo_marginal['costo_en_dolares'], costo_marginal['costo'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En cuanto a las barras, un resumen de la cantidad de barras reales, programadas y cuanto es el porcentaje de las barras que tienen costo programado respecto a las con costo real, se tiene el siguiente código."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barras sin costo programado:  3409022\n",
      "Barras con costo programado:  913388\n",
      "Porcentaje de barras con costo programado:  21.13 %\n"
     ]
    }
   ],
   "source": [
    "print('Barras sin costo programado: ',costo_marginal['costo'].isna().sum())\n",
    "print('Barras con costo programado: ',costo_marginal.shape[0]-costo_marginal['costo'].isna().sum())\n",
    "print('Porcentaje de barras con costo programado: ',round((costo_marginal.shape[0]-costo_marginal['costo'].isna().sum())/costo_marginal.shape[0]*100,2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Punto 2\n",
    "\n",
    "A continuación creamos las variables sintéticas que están en el desafío"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>barra_mnemotecnico</th>\n",
       "      <th>barra_referencia_mnemotecnico</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>costo_en_dolares</th>\n",
       "      <th>costo_en_pesos</th>\n",
       "      <th>costo</th>\n",
       "      <th>nombre</th>\n",
       "      <th>desviacion</th>\n",
       "      <th>desviacion_pct</th>\n",
       "      <th>desviacion_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BA01G021SE018G021</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>58.02</td>\n",
       "      <td>38.65989</td>\n",
       "      <td>54.292145</td>\n",
       "      <td>BA S/E CENTRAL QUINTERO 220KV</td>\n",
       "      <td>3.727855</td>\n",
       "      <td>6.866289</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BA01T018SE001T018</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>63.60</td>\n",
       "      <td>42.37795</td>\n",
       "      <td>60.620648</td>\n",
       "      <td>BA S/E NUEVA PICHIRROPULLI 220KV B1</td>\n",
       "      <td>2.979352</td>\n",
       "      <td>4.914748</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BA02T005SE055T005</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>62.78</td>\n",
       "      <td>41.83157</td>\n",
       "      <td>59.177969</td>\n",
       "      <td>BA S/E SAN FRANCISCO DE MOSTAZAL 66KV T1</td>\n",
       "      <td>3.602031</td>\n",
       "      <td>6.086776</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BA01T002SE029T002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>58.55</td>\n",
       "      <td>39.01304</td>\n",
       "      <td>54.566605</td>\n",
       "      <td>BA S/E CENTRAL PEHUENCHE 220KV B1</td>\n",
       "      <td>3.983395</td>\n",
       "      <td>7.300060</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BA01R002SE041R002</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-04-06</td>\n",
       "      <td>25</td>\n",
       "      <td>60.39</td>\n",
       "      <td>40.23906</td>\n",
       "      <td>56.086260</td>\n",
       "      <td>BA S/E SAN CRISTOBAL 110KV B1</td>\n",
       "      <td>4.303740</td>\n",
       "      <td>7.673429</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4322387</th>\n",
       "      <td>BA05T005SE288T005</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>24</td>\n",
       "      <td>63.65</td>\n",
       "      <td>42.79953</td>\n",
       "      <td>132.934674</td>\n",
       "      <td>BA S/E SUR 110KV</td>\n",
       "      <td>-69.284673</td>\n",
       "      <td>-52.119339</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4322390</th>\n",
       "      <td>BA01L081SE066L081</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>24</td>\n",
       "      <td>73.18</td>\n",
       "      <td>49.20770</td>\n",
       "      <td>53.174872</td>\n",
       "      <td>BA S/E FPC 154KV BP1</td>\n",
       "      <td>20.005128</td>\n",
       "      <td>37.621395</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4322395</th>\n",
       "      <td>BA04T005SE071T005</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>24</td>\n",
       "      <td>67.20</td>\n",
       "      <td>45.18662</td>\n",
       "      <td>144.613620</td>\n",
       "      <td>BA S/E OVALLE 66KV</td>\n",
       "      <td>-77.413620</td>\n",
       "      <td>-53.531348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4322404</th>\n",
       "      <td>BA01L079SE004L079</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>24</td>\n",
       "      <td>77.61</td>\n",
       "      <td>52.18652</td>\n",
       "      <td>55.571525</td>\n",
       "      <td>BA S/E PUENTE ALTO (CMPC) 110KV</td>\n",
       "      <td>22.038475</td>\n",
       "      <td>39.657856</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4322409</th>\n",
       "      <td>BA12T025SE075T025</td>\n",
       "      <td>BA02T002SE032T002</td>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>24</td>\n",
       "      <td>63.54</td>\n",
       "      <td>42.72557</td>\n",
       "      <td>132.592876</td>\n",
       "      <td>BA S/E ESMERALDA 110KV</td>\n",
       "      <td>-69.052876</td>\n",
       "      <td>-52.078873</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>913353 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        barra_mnemotecnico barra_referencia_mnemotecnico       fecha  hora  \\\n",
       "13       BA01G021SE018G021             BA02T002SE032T002  2019-04-06    25   \n",
       "16       BA01T018SE001T018             BA02T002SE032T002  2019-04-06    25   \n",
       "20       BA02T005SE055T005             BA02T002SE032T002  2019-04-06    25   \n",
       "24       BA01T002SE029T002             BA02T002SE032T002  2019-04-06    25   \n",
       "28       BA01R002SE041R002             BA02T002SE032T002  2019-04-06    25   \n",
       "...                    ...                           ...         ...   ...   \n",
       "4322387  BA05T005SE288T005             BA02T002SE032T002  2019-01-23    24   \n",
       "4322390  BA01L081SE066L081             BA02T002SE032T002  2019-01-23    24   \n",
       "4322395  BA04T005SE071T005             BA02T002SE032T002  2019-01-23    24   \n",
       "4322404  BA01L079SE004L079             BA02T002SE032T002  2019-01-23    24   \n",
       "4322409  BA12T025SE075T025             BA02T002SE032T002  2019-01-23    24   \n",
       "\n",
       "         costo_en_dolares  costo_en_pesos       costo  \\\n",
       "13                  58.02        38.65989   54.292145   \n",
       "16                  63.60        42.37795   60.620648   \n",
       "20                  62.78        41.83157   59.177969   \n",
       "24                  58.55        39.01304   54.566605   \n",
       "28                  60.39        40.23906   56.086260   \n",
       "...                   ...             ...         ...   \n",
       "4322387             63.65        42.79953  132.934674   \n",
       "4322390             73.18        49.20770   53.174872   \n",
       "4322395             67.20        45.18662  144.613620   \n",
       "4322404             77.61        52.18652   55.571525   \n",
       "4322409             63.54        42.72557  132.592876   \n",
       "\n",
       "                                           nombre  desviacion  desviacion_pct  \\\n",
       "13                  BA S/E CENTRAL QUINTERO 220KV    3.727855        6.866289   \n",
       "16            BA S/E NUEVA PICHIRROPULLI 220KV B1    2.979352        4.914748   \n",
       "20       BA S/E SAN FRANCISCO DE MOSTAZAL 66KV T1    3.602031        6.086776   \n",
       "24              BA S/E CENTRAL PEHUENCHE 220KV B1    3.983395        7.300060   \n",
       "28                  BA S/E SAN CRISTOBAL 110KV B1    4.303740        7.673429   \n",
       "...                                           ...         ...             ...   \n",
       "4322387                          BA S/E SUR 110KV  -69.284673      -52.119339   \n",
       "4322390                      BA S/E FPC 154KV BP1   20.005128       37.621395   \n",
       "4322395                        BA S/E OVALLE 66KV  -77.413620      -53.531348   \n",
       "4322404           BA S/E PUENTE ALTO (CMPC) 110KV   22.038475       39.657856   \n",
       "4322409                    BA S/E ESMERALDA 110KV  -69.052876      -52.078873   \n",
       "\n",
       "         desviacion_cat  \n",
       "13                    0  \n",
       "16                    0  \n",
       "20                    0  \n",
       "24                    0  \n",
       "28                    0  \n",
       "...                 ...  \n",
       "4322387               1  \n",
       "4322390               1  \n",
       "4322395               1  \n",
       "4322404               1  \n",
       "4322409               1  \n",
       "\n",
       "[913353 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costo_marginal['desviacion'] = costo_marginal['costo_en_dolares'] - costo_marginal['costo']\n",
    "costo_marginal['desviacion_pct'] = (costo_marginal['costo_en_dolares'] - costo_marginal['costo'])/costo_marginal['costo']*100\n",
    "def costo_cat(value):\n",
    "    if abs(value) > 15:\n",
    "        res = 1\n",
    "    else :\n",
    "        res = 0\n",
    "    return res\n",
    "costo_marginal['desviacion_cat'] = costo_marginal['desviacion_pct'].apply(costo_cat)\n",
    "costo_marginal.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Punto 3\n",
    "\n",
    "A continuación creamos la función para generar los gráficos, tomando como parámetros el código de barra, fechas inicial y final "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "costo_marginal_agg = costo_marginal.groupby(['barra_mnemotecnico','fecha']).agg('mean')[['costo_en_dolares','costo']]\n",
    "costo_marginal_agg.reset_index(inplace = True)\n",
    "costo_marginal_agg['fecha'] = pd.to_datetime(costo_marginal_agg['fecha'])\n",
    "def time_plot_costo_barra(codigo_barra, fecha_inicial, fecha_final):\n",
    "    data_plot = costo_marginal_agg[(costo_marginal_agg['barra_mnemotecnico'] == codigo_barra) &\n",
    "                                  (costo_marginal_agg['fecha'] >= fecha_inicial) &\n",
    "                                  (costo_marginal_agg['fecha'] <= fecha_final)]\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(data_plot['fecha'],data_plot['costo_en_dolares'], label = 'CM_real', c = 'red', alpha = 0.4)\n",
    "    ax.plot(data_plot['fecha'],data_plot['costo'], label = 'CM_programado', c = 'blue', alpha = 0.4)\n",
    "    ax.legend(loc = 'upper right')\n",
    "    ax.set_xlabel('Fecha')\n",
    "    ax.set_ylabel('Costo Marginal promedio diario')\n",
    "    plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algunos ejemplos de como funciona. Aquí es importante mencionar que por simplicidad de código los datos fueron agrupados por barra y día, teniendo un costo marginal real promedio por hora. Lo anterior es por simplicidad y claridad del gráfico que se desplegará."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "display(time_plot_costo_barra('BA05T005SE288T005','2019-01-01','2019-06-30'))\n",
    "display(time_plot_costo_barra('BA12T025SE075T025','2019-03-01','2019-09-30'))\n",
    "display(time_plot_costo_barra('BA01L081SE066L081','2019-01-01','2019-09-30'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determinando y eliminando las arras con costo marginal real 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "barra_mnemotecnico\n",
       "BA01G049SE001G049    0.0\n",
       "BA01L038SE001L038    0.0\n",
       "BA02T003SE009T003    0.0\n",
       "BA02T005SE046T005    0.0\n",
       "BA02T005SE082T005    0.0\n",
       "Name: costo_en_dolares, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barra_en_cero = costo_marginal_agg.groupby(['barra_mnemotecnico']).agg('sum')['costo_en_dolares']\n",
    "barra_en_cero = barra_en_cero[barra_en_cero == 0]\n",
    "barra_en_cero.reset_index()\n",
    "barra_en_cero.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "costo_marginal.set_index('barra_mnemotecnico',inplace = True)\n",
    "costo_marginal.drop(index = barra_en_cero.index,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Punto 4\n",
    "\n",
    "Ahora cargaremos los datos que se utilizarán para hacer predicciones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>nemotecnico_se</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>gen_eolica_total_mwh</th>\n",
       "      <th>gen_geotermica_total_mwh</th>\n",
       "      <th>gen_hidraulica_total_mwh</th>\n",
       "      <th>gen_solar_total_mwh</th>\n",
       "      <th>gen_termica_total_mwh</th>\n",
       "      <th>cmg_real</th>\n",
       "      <th>cmg_prog</th>\n",
       "      <th>cmg_desv</th>\n",
       "      <th>cmg_desv_pct</th>\n",
       "      <th>n_barras</th>\n",
       "      <th>demanda_mwh</th>\n",
       "      <th>cap_inst_mw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00 UTC</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>55.627852</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2</td>\n",
       "      <td>1210767</td>\n",
       "      <td>13.20785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00 UTC</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>55.376645</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2</td>\n",
       "      <td>113232</td>\n",
       "      <td>13.20785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00 UTC</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>59.531891</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-5.59</td>\n",
       "      <td>2</td>\n",
       "      <td>1089415</td>\n",
       "      <td>13.20785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00 UTC</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>174.378923</td>\n",
       "      <td>-118.18</td>\n",
       "      <td>-67.77</td>\n",
       "      <td>2</td>\n",
       "      <td>1096867</td>\n",
       "      <td>13.20785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00 UTC</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>172.820310</td>\n",
       "      <td>-116.62</td>\n",
       "      <td>-67.48</td>\n",
       "      <td>2</td>\n",
       "      <td>1071851</td>\n",
       "      <td>13.20785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  nemotecnico_se                    fecha  hora  gen_eolica_total_mwh  \\\n",
       "0      SE031G216  2019-01-04 00:00:00 UTC     1                   NaN   \n",
       "1      SE031G216  2019-01-04 00:00:00 UTC     2                   NaN   \n",
       "2      SE031G216  2019-01-04 00:00:00 UTC     3                   NaN   \n",
       "3      SE031G216  2019-01-04 00:00:00 UTC     4                   NaN   \n",
       "4      SE031G216  2019-01-04 00:00:00 UTC     5                   NaN   \n",
       "\n",
       "   gen_geotermica_total_mwh  gen_hidraulica_total_mwh  gen_solar_total_mwh  \\\n",
       "0                       NaN                       NaN                  NaN   \n",
       "1                       NaN                       NaN                  NaN   \n",
       "2                       NaN                       NaN                  NaN   \n",
       "3                       NaN                       NaN                  NaN   \n",
       "4                       NaN                       NaN                  NaN   \n",
       "\n",
       "   gen_termica_total_mwh  cmg_real    cmg_prog  cmg_desv  cmg_desv_pct  \\\n",
       "0                    0.0      56.2   55.627852      0.57          1.02   \n",
       "1                    0.0      56.2   55.376645      0.82          1.48   \n",
       "2                    0.0      56.2   59.531891     -3.33         -5.59   \n",
       "3                    0.0      56.2  174.378923   -118.18        -67.77   \n",
       "4                    0.0      56.2  172.820310   -116.62        -67.48   \n",
       "\n",
       "   n_barras  demanda_mwh  cap_inst_mw  \n",
       "0         2      1210767     13.20785  \n",
       "1         2       113232     13.20785  \n",
       "2         2      1089415     13.20785  \n",
       "3         2      1096867     13.20785  \n",
       "4         2      1071851     13.20785  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_pred = pd.read_csv('data/base_para_prediccion.csv')\n",
    "datos_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algunas descripciones de datos, dimensión y tipos de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112779 entries, 0 to 112778\n",
      "Data columns (total 15 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   nemotecnico_se            112779 non-null  object \n",
      " 1   fecha                     112779 non-null  object \n",
      " 2   hora                      112779 non-null  int64  \n",
      " 3   gen_eolica_total_mwh      0 non-null       float64\n",
      " 4   gen_geotermica_total_mwh  0 non-null       float64\n",
      " 5   gen_hidraulica_total_mwh  34184 non-null   float64\n",
      " 6   gen_solar_total_mwh       41818 non-null   float64\n",
      " 7   gen_termica_total_mwh     61863 non-null   float64\n",
      " 8   cmg_real                  112779 non-null  float64\n",
      " 9   cmg_prog                  112779 non-null  float64\n",
      " 10  cmg_desv                  112779 non-null  float64\n",
      " 11  cmg_desv_pct              112779 non-null  float64\n",
      " 12  n_barras                  112779 non-null  int64  \n",
      " 13  demanda_mwh               112779 non-null  int64  \n",
      " 14  cap_inst_mw               112779 non-null  float64\n",
      "dtypes: float64(10), int64(3), object(2)\n",
      "memory usage: 12.9+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hora</th>\n",
       "      <th>gen_eolica_total_mwh</th>\n",
       "      <th>gen_geotermica_total_mwh</th>\n",
       "      <th>gen_hidraulica_total_mwh</th>\n",
       "      <th>gen_solar_total_mwh</th>\n",
       "      <th>gen_termica_total_mwh</th>\n",
       "      <th>cmg_real</th>\n",
       "      <th>cmg_prog</th>\n",
       "      <th>cmg_desv</th>\n",
       "      <th>cmg_desv_pct</th>\n",
       "      <th>n_barras</th>\n",
       "      <th>demanda_mwh</th>\n",
       "      <th>cap_inst_mw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>112779.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34184.000000</td>\n",
       "      <td>41818.000000</td>\n",
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       "      <td>1.127790e+05</td>\n",
       "      <td>112779.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.502993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.120053</td>\n",
       "      <td>3.764987</td>\n",
       "      <td>40.583146</td>\n",
       "      <td>61.170058</td>\n",
       "      <td>62.215400</td>\n",
       "      <td>-1.045338</td>\n",
       "      <td>inf</td>\n",
       "      <td>2.331844</td>\n",
       "      <td>6.357056e+07</td>\n",
       "      <td>128.719565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.924090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.838109</td>\n",
       "      <td>12.904388</td>\n",
       "      <td>101.083396</td>\n",
       "      <td>25.414147</td>\n",
       "      <td>25.307759</td>\n",
       "      <td>24.711575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.304096</td>\n",
       "      <td>3.948683e+08</td>\n",
       "      <td>167.948649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.713131</td>\n",
       "      <td>-562.470000</td>\n",
       "      <td>-3403.97</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-9.821095e+09</td>\n",
       "      <td>2.989800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.849787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.130000</td>\n",
       "      <td>49.906745</td>\n",
       "      <td>-3.790000</td>\n",
       "      <td>-7.07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.464800e+05</td>\n",
       "      <td>18.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.610000</td>\n",
       "      <td>55.420601</td>\n",
       "      <td>-0.310000</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.507282e+06</td>\n",
       "      <td>51.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1.571022</td>\n",
       "      <td>15.960000</td>\n",
       "      <td>63.500000</td>\n",
       "      <td>64.701951</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>2.92</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.332345e+07</td>\n",
       "      <td>184.685500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>86.050000</td>\n",
       "      <td>570.600800</td>\n",
       "      <td>240.360000</td>\n",
       "      <td>610.600000</td>\n",
       "      <td>175.020000</td>\n",
       "      <td>inf</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.004240e+10</td>\n",
       "      <td>597.782800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                hora  gen_eolica_total_mwh  gen_geotermica_total_mwh  \\\n",
       "count  112779.000000                   0.0                       0.0   \n",
       "mean       12.502993                   NaN                       NaN   \n",
       "std         6.924090                   NaN                       NaN   \n",
       "min         1.000000                   NaN                       NaN   \n",
       "25%         7.000000                   NaN                       NaN   \n",
       "50%        13.000000                   NaN                       NaN   \n",
       "75%        19.000000                   NaN                       NaN   \n",
       "max        25.000000                   NaN                       NaN   \n",
       "\n",
       "       gen_hidraulica_total_mwh  gen_solar_total_mwh  gen_termica_total_mwh  \\\n",
       "count              34184.000000         41818.000000           61863.000000   \n",
       "mean                  38.120053             3.764987              40.583146   \n",
       "std                   69.838109            12.904388             101.083396   \n",
       "min                    0.000000             0.000000               0.000000   \n",
       "25%                    0.849787             0.000000               0.000000   \n",
       "50%                    9.600000             0.000000               0.000000   \n",
       "75%                   40.000000             1.571022              15.960000   \n",
       "max                  472.000000            86.050000             570.600800   \n",
       "\n",
       "            cmg_real       cmg_prog       cmg_desv  cmg_desv_pct  \\\n",
       "count  112779.000000  112779.000000  112779.000000     112779.00   \n",
       "mean       61.170058      62.215400      -1.045338           inf   \n",
       "std        25.414147      25.307759      24.711575           NaN   \n",
       "min         0.000000      -1.713131    -562.470000      -3403.97   \n",
       "25%        49.130000      49.906745      -3.790000         -7.07   \n",
       "50%        54.610000      55.420601      -0.310000         -0.58   \n",
       "75%        63.500000      64.701951       1.670000          2.92   \n",
       "max       240.360000     610.600000     175.020000           inf   \n",
       "\n",
       "            n_barras   demanda_mwh    cap_inst_mw  \n",
       "count  112779.000000  1.127790e+05  112779.000000  \n",
       "mean        2.331844  6.357056e+07     128.719565  \n",
       "std         1.304096  3.948683e+08     167.948649  \n",
       "min         1.000000 -9.821095e+09       2.989800  \n",
       "25%         1.000000  7.464800e+05      18.420000  \n",
       "50%         2.000000  4.507282e+06      51.850000  \n",
       "75%         3.000000  1.332345e+07     184.685500  \n",
       "max         6.000000  1.004240e+10     597.782800  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(112779, 15)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(datos_pred.info())\n",
    "display(datos_pred.describe())\n",
    "display(datos_pred.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora cambiamos el tipo de la variable fecha a datetime, para poder generar las variables sintéticas que se piden en el desafío (año, mes, día del año, día de la semana, etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nemotecnico_se</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>gen_eolica_total_mwh</th>\n",
       "      <th>gen_geotermica_total_mwh</th>\n",
       "      <th>gen_hidraulica_total_mwh</th>\n",
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       "      <th>cmg_desv</th>\n",
       "      <th>cmg_desv_pct</th>\n",
       "      <th>n_barras</th>\n",
       "      <th>demanda_mwh</th>\n",
       "      <th>cap_inst_mw</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
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       "      <th>1</th>\n",
       "      <td>SE031G216</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
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       "      <td>56.2</td>\n",
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       "      <td>-3.33</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>2019-01-04 00:00:00+00:00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>174.378923</td>\n",
       "      <td>-118.18</td>\n",
       "      <td>-67.77</td>\n",
       "      <td>2</td>\n",
       "      <td>1096867</td>\n",
       "      <td>13.20785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>172.820310</td>\n",
       "      <td>-116.62</td>\n",
       "      <td>-67.48</td>\n",
       "      <td>2</td>\n",
       "      <td>1071851</td>\n",
       "      <td>13.20785</td>\n",
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      ],
      "text/plain": [
       "  nemotecnico_se                     fecha  hora  gen_eolica_total_mwh  \\\n",
       "0      SE031G216 2019-01-04 00:00:00+00:00     1                   NaN   \n",
       "1      SE031G216 2019-01-04 00:00:00+00:00     2                   NaN   \n",
       "2      SE031G216 2019-01-04 00:00:00+00:00     3                   NaN   \n",
       "3      SE031G216 2019-01-04 00:00:00+00:00     4                   NaN   \n",
       "4      SE031G216 2019-01-04 00:00:00+00:00     5                   NaN   \n",
       "\n",
       "   gen_geotermica_total_mwh  gen_hidraulica_total_mwh  gen_solar_total_mwh  \\\n",
       "0                       NaN                       NaN                  NaN   \n",
       "1                       NaN                       NaN                  NaN   \n",
       "2                       NaN                       NaN                  NaN   \n",
       "3                       NaN                       NaN                  NaN   \n",
       "4                       NaN                       NaN                  NaN   \n",
       "\n",
       "   gen_termica_total_mwh  cmg_real    cmg_prog  cmg_desv  cmg_desv_pct  \\\n",
       "0                    0.0      56.2   55.627852      0.57          1.02   \n",
       "1                    0.0      56.2   55.376645      0.82          1.48   \n",
       "2                    0.0      56.2   59.531891     -3.33         -5.59   \n",
       "3                    0.0      56.2  174.378923   -118.18        -67.77   \n",
       "4                    0.0      56.2  172.820310   -116.62        -67.48   \n",
       "\n",
       "   n_barras  demanda_mwh  cap_inst_mw  \n",
       "0         2      1210767     13.20785  \n",
       "1         2       113232     13.20785  \n",
       "2         2      1089415     13.20785  \n",
       "3         2      1096867     13.20785  \n",
       "4         2      1071851     13.20785  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "datos_pred['fecha'] = pd.to_datetime(datos_pred['fecha'])\n",
    "datos_pred.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>nemotecnico_se</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>gen_eolica_total_mwh</th>\n",
       "      <th>gen_geotermica_total_mwh</th>\n",
       "      <th>gen_hidraulica_total_mwh</th>\n",
       "      <th>gen_solar_total_mwh</th>\n",
       "      <th>gen_termica_total_mwh</th>\n",
       "      <th>cmg_real</th>\n",
       "      <th>cmg_prog</th>\n",
       "      <th>...</th>\n",
       "      <th>n_barras</th>\n",
       "      <th>demanda_mwh</th>\n",
       "      <th>cap_inst_mw</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekday_name</th>\n",
       "      <th>day_year</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>55.627852</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1210767</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>55.376645</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>113232</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>59.531891</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1089415</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>174.378923</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1096867</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>172.820310</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1071851</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  nemotecnico_se                     fecha  hora  gen_eolica_total_mwh  \\\n",
       "0      SE031G216 2019-01-04 00:00:00+00:00     1                   NaN   \n",
       "1      SE031G216 2019-01-04 00:00:00+00:00     2                   NaN   \n",
       "2      SE031G216 2019-01-04 00:00:00+00:00     3                   NaN   \n",
       "3      SE031G216 2019-01-04 00:00:00+00:00     4                   NaN   \n",
       "4      SE031G216 2019-01-04 00:00:00+00:00     5                   NaN   \n",
       "\n",
       "   gen_geotermica_total_mwh  gen_hidraulica_total_mwh  gen_solar_total_mwh  \\\n",
       "0                       NaN                       NaN                  NaN   \n",
       "1                       NaN                       NaN                  NaN   \n",
       "2                       NaN                       NaN                  NaN   \n",
       "3                       NaN                       NaN                  NaN   \n",
       "4                       NaN                       NaN                  NaN   \n",
       "\n",
       "   gen_termica_total_mwh  cmg_real    cmg_prog  ...  n_barras  demanda_mwh  \\\n",
       "0                    0.0      56.2   55.627852  ...         2      1210767   \n",
       "1                    0.0      56.2   55.376645  ...         2       113232   \n",
       "2                    0.0      56.2   59.531891  ...         2      1089415   \n",
       "3                    0.0      56.2  174.378923  ...         2      1096867   \n",
       "4                    0.0      56.2  172.820310  ...         2      1071851   \n",
       "\n",
       "   cap_inst_mw  year  month  day  weekday  weekday_name  day_year   weekend  \n",
       "0     13.20785  2019      1    4        4        Friday         4  Week day  \n",
       "1     13.20785  2019      1    4        4        Friday         4  Week day  \n",
       "2     13.20785  2019      1    4        4        Friday         4  Week day  \n",
       "3     13.20785  2019      1    4        4        Friday         4  Week day  \n",
       "4     13.20785  2019      1    4        4        Friday         4  Week day  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_pred['year'] = datos_pred['fecha'].apply(lambda x: x.year)\n",
    "datos_pred['month'] = datos_pred['fecha'].apply(lambda x: x.month)\n",
    "datos_pred['day'] = datos_pred['fecha'].apply(lambda x: x.day)\n",
    "datos_pred['weekday'] = datos_pred['fecha'].apply(lambda x: x.weekday)\n",
    "datos_pred['weekday_name'] = datos_pred['fecha'].apply(lambda x: x.strftime('%A'))\n",
    "datos_pred['day_year'] = datos_pred['fecha'].apply(lambda x: x.timetuple().tm_yday)\n",
    "\n",
    "def weekend(day):\n",
    "    if day <= 4:\n",
    "        res = 'Week day'\n",
    "    else:\n",
    "        res = 'Weekend day'\n",
    "    return res\n",
    "datos_pred['weekend'] = datos_pred['weekday'].apply(weekend)\n",
    "datos_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora generamos la funcion llamada `plot_series` que realiza los gráficos para una lista de fechas, para determinada subestación y forma de generación de energía, seguido de los ejemplos solicitados en el desafío"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def plot_series(substation, list_of_dates, value):\n",
    "    n = len(list_of_dates)\n",
    "    sub_data = datos_pred[datos_pred['nemotecnico_se'] == substation]\n",
    "    fig, ax = plt.subplots(n, figsize = (6,3*n), sharex = True)\n",
    "    if n == 1:\n",
    "        aux_data = sub_data[sub_data['fecha'] == list_of_dates[0]].sort_values('hora')\n",
    "        ax.plot(aux_data['hora'],aux_data[value], linestyle = 'solid')\n",
    "    else:\n",
    "        for k in range(0,n):\n",
    "            aux_data = sub_data[sub_data['fecha'] == list_of_dates[k]].sort_values('hora')\n",
    "            ax[k].plot(aux_data['hora'],aux_data[value], linestyle = 'solid')\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series('SE005T002',['2019-01-10', '2019-02-10', '2019-03-10'],'gen_solar_total_mwh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series('SE005T002',['2019-01-10', '2019-01-11', '2019-01-12', '2019-01-13', '2019-01-14'],'gen_solar_total_mwh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series('SE127T005',['2019-01-10', '2019-01-11', '2019-01-12', '2019-01-13', '2019-01-14'],'gen_solar_total_mwh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notamos de los gráficos anteriores que para las distintas subestaciones presentan comportamientos bastante similares, teniendo en cuenta que las generaciones comienzan alrededor de las 6-7 y terminando 21-22 horas. Lo anterior es bastante lógico pensando que estas gráficas corresponden a energía generada por luz solar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series('SE020G213',['2019-05-14', '2019-05-15', '2019-05-16', '2019-05-17'],'gen_termica_total_mwh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series('SE106G216',['2019-05-14', '2019-05-15', '2019-05-16', '2019-05-17'],'gen_termica_total_mwh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para los últimos ejemplos es un poco más complicado hacer una comparación dado que existen algunas gráficas que presentan una tendencia a la baja (desde el inicio al final del día) mientras que otros claramente tienen un valor de generación bastante más estable, excepto por caidas abruptas de la generación. Estas últimas deben ser consecuencia de algún proceso dentro de la generación térmica de la energía, dado que dichas caidas están presentes en varios gráficos.\n",
    "\n",
    "# Punto 5\n",
    "\n",
    "Acá crearemos la variable `target` tal como se especifica en el documento del desafío, así como también la variable `en_total_mwh`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>nemotecnico_se</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>gen_eolica_total_mwh</th>\n",
       "      <th>gen_geotermica_total_mwh</th>\n",
       "      <th>gen_hidraulica_total_mwh</th>\n",
       "      <th>gen_solar_total_mwh</th>\n",
       "      <th>gen_termica_total_mwh</th>\n",
       "      <th>cmg_real</th>\n",
       "      <th>cmg_prog</th>\n",
       "      <th>...</th>\n",
       "      <th>demanda_mwh</th>\n",
       "      <th>cap_inst_mw</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekday_name</th>\n",
       "      <th>day_year</th>\n",
       "      <th>weekend</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>55.627852</td>\n",
       "      <td>...</td>\n",
       "      <td>1210767</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>55.376645</td>\n",
       "      <td>...</td>\n",
       "      <td>113232</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>59.531891</td>\n",
       "      <td>...</td>\n",
       "      <td>1089415</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>174.378923</td>\n",
       "      <td>...</td>\n",
       "      <td>1096867</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>172.820310</td>\n",
       "      <td>...</td>\n",
       "      <td>1071851</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  nemotecnico_se                     fecha  hora  gen_eolica_total_mwh  \\\n",
       "0      SE031G216 2019-01-04 00:00:00+00:00     1                   NaN   \n",
       "1      SE031G216 2019-01-04 00:00:00+00:00     2                   NaN   \n",
       "2      SE031G216 2019-01-04 00:00:00+00:00     3                   NaN   \n",
       "3      SE031G216 2019-01-04 00:00:00+00:00     4                   NaN   \n",
       "4      SE031G216 2019-01-04 00:00:00+00:00     5                   NaN   \n",
       "\n",
       "   gen_geotermica_total_mwh  gen_hidraulica_total_mwh  gen_solar_total_mwh  \\\n",
       "0                       NaN                       NaN                  NaN   \n",
       "1                       NaN                       NaN                  NaN   \n",
       "2                       NaN                       NaN                  NaN   \n",
       "3                       NaN                       NaN                  NaN   \n",
       "4                       NaN                       NaN                  NaN   \n",
       "\n",
       "   gen_termica_total_mwh  cmg_real    cmg_prog  ...  demanda_mwh  cap_inst_mw  \\\n",
       "0                    0.0      56.2   55.627852  ...      1210767     13.20785   \n",
       "1                    0.0      56.2   55.376645  ...       113232     13.20785   \n",
       "2                    0.0      56.2   59.531891  ...      1089415     13.20785   \n",
       "3                    0.0      56.2  174.378923  ...      1096867     13.20785   \n",
       "4                    0.0      56.2  172.820310  ...      1071851     13.20785   \n",
       "\n",
       "   year  month  day  weekday  weekday_name  day_year   weekend target  \n",
       "0  2019      1    4        4        Friday         4  Week day      0  \n",
       "1  2019      1    4        4        Friday         4  Week day      0  \n",
       "2  2019      1    4        4        Friday         4  Week day      0  \n",
       "3  2019      1    4        4        Friday         4  Week day      1  \n",
       "4  2019      1    4        4        Friday         4  Week day      1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def costo_cat(value):\n",
    "    if abs(value) <= 15:\n",
    "        res = 0\n",
    "    else :\n",
    "        res = 1\n",
    "    return res\n",
    "\n",
    "datos_pred['target'] = datos_pred['cmg_desv_pct'].apply(costo_cat)\n",
    "datos_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nemotecnico_se</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>gen_eolica_total_mwh</th>\n",
       "      <th>gen_geotermica_total_mwh</th>\n",
       "      <th>gen_hidraulica_total_mwh</th>\n",
       "      <th>gen_solar_total_mwh</th>\n",
       "      <th>gen_termica_total_mwh</th>\n",
       "      <th>cmg_real</th>\n",
       "      <th>cmg_prog</th>\n",
       "      <th>...</th>\n",
       "      <th>cap_inst_mw</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekday_name</th>\n",
       "      <th>day_year</th>\n",
       "      <th>weekend</th>\n",
       "      <th>target</th>\n",
       "      <th>en_total_mwh</th>\n",
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       "      <th>0</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
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       "      <td>55.627852</td>\n",
       "      <td>...</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>55.376645</td>\n",
       "      <td>...</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>59.531891</td>\n",
       "      <td>...</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>174.378923</td>\n",
       "      <td>...</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>172.820310</td>\n",
       "      <td>...</td>\n",
       "      <td>13.20785</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>4</td>\n",
       "      <td>Week day</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  nemotecnico_se                     fecha  hora  gen_eolica_total_mwh  \\\n",
       "0      SE031G216 2019-01-04 00:00:00+00:00     1                   NaN   \n",
       "1      SE031G216 2019-01-04 00:00:00+00:00     2                   NaN   \n",
       "2      SE031G216 2019-01-04 00:00:00+00:00     3                   NaN   \n",
       "3      SE031G216 2019-01-04 00:00:00+00:00     4                   NaN   \n",
       "4      SE031G216 2019-01-04 00:00:00+00:00     5                   NaN   \n",
       "\n",
       "   gen_geotermica_total_mwh  gen_hidraulica_total_mwh  gen_solar_total_mwh  \\\n",
       "0                       NaN                       NaN                  NaN   \n",
       "1                       NaN                       NaN                  NaN   \n",
       "2                       NaN                       NaN                  NaN   \n",
       "3                       NaN                       NaN                  NaN   \n",
       "4                       NaN                       NaN                  NaN   \n",
       "\n",
       "   gen_termica_total_mwh  cmg_real    cmg_prog  ...  cap_inst_mw  year  month  \\\n",
       "0                    0.0      56.2   55.627852  ...     13.20785  2019      1   \n",
       "1                    0.0      56.2   55.376645  ...     13.20785  2019      1   \n",
       "2                    0.0      56.2   59.531891  ...     13.20785  2019      1   \n",
       "3                    0.0      56.2  174.378923  ...     13.20785  2019      1   \n",
       "4                    0.0      56.2  172.820310  ...     13.20785  2019      1   \n",
       "\n",
       "   day  weekday  weekday_name  day_year   weekend  target en_total_mwh  \n",
       "0    4        4        Friday         4  Week day       0          0.0  \n",
       "1    4        4        Friday         4  Week day       0          0.0  \n",
       "2    4        4        Friday         4  Week day       0          0.0  \n",
       "3    4        4        Friday         4  Week day       1          0.0  \n",
       "4    4        4        Friday         4  Week day       1          0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_pred['en_total_mwh'] = datos_pred.iloc[:,3:8].sum(axis = 1)\n",
    "datos_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Punto 7\n",
    "\n",
    "Cargamos y hacemos el merge con los datos de clima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>fecha</th>\n",
       "      <th>subestacion</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>ALLSKY_SFC_SW_DWN</th>\n",
       "      <th>KT</th>\n",
       "      <th>PRECTOT</th>\n",
       "      <th>RH2M</th>\n",
       "      <th>T2M</th>\n",
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       "      <th>WS10M_MIN</th>\n",
       "      <th>WS50M</th>\n",
       "      <th>WS50M_MAX</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5219</th>\n",
       "      <td>2019-01-15 00:00:00 UTC</td>\n",
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       "      <td>4.45</td>\n",
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       "    <tr>\n",
       "      <th>5220</th>\n",
       "      <td>2019-01-10 00:00:00 UTC</td>\n",
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       "      <td>-37.095143</td>\n",
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       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.71</td>\n",
       "      <td>15.83</td>\n",
       "      <td>1.31</td>\n",
       "      <td>26.74</td>\n",
       "      <td>4.63</td>\n",
       "      <td>0.73</td>\n",
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       "      <td>1.27</td>\n",
       "      <td>3.34</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.95</td>\n",
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       "    <tr>\n",
       "      <th>5221</th>\n",
       "      <td>2019-01-15 00:00:00 UTC</td>\n",
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       "      <td>0.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.09</td>\n",
       "      <td>20.75</td>\n",
       "      <td>1.52</td>\n",
       "      <td>29.44</td>\n",
       "      <td>12.03</td>\n",
       "      <td>0.97</td>\n",
       "      <td>22.97</td>\n",
       "      <td>2.54</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.55</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0.73</td>\n",
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       "    <tr>\n",
       "      <th>5222</th>\n",
       "      <td>2019-01-15 00:00:00 UTC</td>\n",
       "      <td>SE004L079</td>\n",
       "      <td>-33.607927</td>\n",
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       "      <td>0.79</td>\n",
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       "      <td>0.97</td>\n",
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       "      <td>2.54</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.55</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0.73</td>\n",
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       "    <tr>\n",
       "      <th>5223</th>\n",
       "      <td>2019-01-15 00:00:00 UTC</td>\n",
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       "      <td>-33.981594</td>\n",
       "      <td>-70.701865</td>\n",
       "      <td>9.48</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.09</td>\n",
       "      <td>20.75</td>\n",
       "      <td>1.52</td>\n",
       "      <td>29.44</td>\n",
       "      <td>12.03</td>\n",
       "      <td>0.97</td>\n",
       "      <td>22.97</td>\n",
       "      <td>2.54</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.55</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0.73</td>\n",
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       "    <tr>\n",
       "      <th>5224</th>\n",
       "      <td>2019-06-12 00:00:00 UTC</td>\n",
       "      <td>SE009G004</td>\n",
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       "      <td>0.10</td>\n",
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       "      <td>94.75</td>\n",
       "      <td>6.55</td>\n",
       "      <td>5.74</td>\n",
       "      <td>8.75</td>\n",
       "      <td>3.94</td>\n",
       "      <td>1.49</td>\n",
       "      <td>6.48</td>\n",
       "      <td>4.83</td>\n",
       "      <td>6.79</td>\n",
       "      <td>2.01</td>\n",
       "      <td>8.02</td>\n",
       "      <td>11.16</td>\n",
       "      <td>3.84</td>\n",
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       "    <tr>\n",
       "      <th>5225</th>\n",
       "      <td>2019-06-04 00:00:00 UTC</td>\n",
       "      <td>SE009G004</td>\n",
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       "      <td>0.10</td>\n",
       "      <td>8.36</td>\n",
       "      <td>73.99</td>\n",
       "      <td>7.84</td>\n",
       "      <td>3.53</td>\n",
       "      <td>11.69</td>\n",
       "      <td>5.81</td>\n",
       "      <td>1.73</td>\n",
       "      <td>7.68</td>\n",
       "      <td>3.51</td>\n",
       "      <td>4.81</td>\n",
       "      <td>2.10</td>\n",
       "      <td>6.15</td>\n",
       "      <td>7.52</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5226</th>\n",
       "      <td>2019-06-27 00:00:00 UTC</td>\n",
       "      <td>SE009G004</td>\n",
       "      <td>-37.360811</td>\n",
       "      <td>-71.866829</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.10</td>\n",
       "      <td>45.22</td>\n",
       "      <td>97.97</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.25</td>\n",
       "      <td>5.54</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.46</td>\n",
       "      <td>5.22</td>\n",
       "      <td>7.44</td>\n",
       "      <td>2.11</td>\n",
       "      <td>8.57</td>\n",
       "      <td>11.72</td>\n",
       "      <td>3.98</td>\n",
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       "    <tr>\n",
       "      <th>5227</th>\n",
       "      <td>2019-05-04 00:00:00 UTC</td>\n",
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       "      <td>0.14</td>\n",
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       "      <td>74.90</td>\n",
       "      <td>14.46</td>\n",
       "      <td>10.12</td>\n",
       "      <td>18.52</td>\n",
       "      <td>12.22</td>\n",
       "      <td>2.44</td>\n",
       "      <td>14.14</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.61</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0.17</td>\n",
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       "    <tr>\n",
       "      <th>5228</th>\n",
       "      <td>2019-05-23 00:00:00 UTC</td>\n",
       "      <td>SE005T003</td>\n",
       "      <td>-42.626987</td>\n",
       "      <td>-73.805673</td>\n",
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       "      <td>0.29</td>\n",
       "      <td>0.08</td>\n",
       "      <td>91.30</td>\n",
       "      <td>8.64</td>\n",
       "      <td>7.27</td>\n",
       "      <td>10.94</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.76</td>\n",
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       "      <td>1.12</td>\n",
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       "      <td>5.61</td>\n",
       "      <td>1.53</td>\n",
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       "    <tr>\n",
       "      <th>5229</th>\n",
       "      <td>2019-05-17 00:00:00 UTC</td>\n",
       "      <td>SE003T011</td>\n",
       "      <td>-35.685919</td>\n",
       "      <td>-71.377335</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.14</td>\n",
       "      <td>89.42</td>\n",
       "      <td>7.64</td>\n",
       "      <td>5.98</td>\n",
       "      <td>10.42</td>\n",
       "      <td>4.31</td>\n",
       "      <td>1.57</td>\n",
       "      <td>7.60</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.64</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.03</td>\n",
       "      <td>4.04</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5230</th>\n",
       "      <td>2019-06-20 00:00:00 UTC</td>\n",
       "      <td>SE009G004</td>\n",
       "      <td>-37.360811</td>\n",
       "      <td>-71.866829</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>51.71</td>\n",
       "      <td>7.66</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>12.12</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.39</td>\n",
       "      <td>5.79</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1.97</td>\n",
       "      <td>4.80</td>\n",
       "      <td>5.65</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5231</th>\n",
       "      <td>2019-06-04 00:00:00 UTC</td>\n",
       "      <td>SE039R002</td>\n",
       "      <td>-33.417179</td>\n",
       "      <td>-70.689370</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.01</td>\n",
       "      <td>25.83</td>\n",
       "      <td>14.00</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>21.12</td>\n",
       "      <td>10.55</td>\n",
       "      <td>0.35</td>\n",
       "      <td>9.25</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.23</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5232</th>\n",
       "      <td>2019-06-04 00:00:00 UTC</td>\n",
       "      <td>SE005R002</td>\n",
       "      <td>-33.267621</td>\n",
       "      <td>-70.730832</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.01</td>\n",
       "      <td>25.83</td>\n",
       "      <td>14.00</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>21.12</td>\n",
       "      <td>10.55</td>\n",
       "      <td>0.35</td>\n",
       "      <td>9.25</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.23</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5233</th>\n",
       "      <td>2019-03-30 00:00:00 UTC</td>\n",
       "      <td>SE015R002</td>\n",
       "      <td>-33.547230</td>\n",
       "      <td>-70.544867</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>28.77</td>\n",
       "      <td>19.32</td>\n",
       "      <td>0.62</td>\n",
       "      <td>26.46</td>\n",
       "      <td>13.80</td>\n",
       "      <td>1.50</td>\n",
       "      <td>19.21</td>\n",
       "      <td>2.09</td>\n",
       "      <td>3.41</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2.34</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5234</th>\n",
       "      <td>2019-03-30 00:00:00 UTC</td>\n",
       "      <td>SE004L079</td>\n",
       "      <td>-33.607927</td>\n",
       "      <td>-70.557676</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>28.77</td>\n",
       "      <td>19.32</td>\n",
       "      <td>0.62</td>\n",
       "      <td>26.46</td>\n",
       "      <td>13.80</td>\n",
       "      <td>1.50</td>\n",
       "      <td>19.21</td>\n",
       "      <td>2.09</td>\n",
       "      <td>3.41</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2.34</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5235</th>\n",
       "      <td>2019-03-30 00:00:00 UTC</td>\n",
       "      <td>SE055T005</td>\n",
       "      <td>-33.981594</td>\n",
       "      <td>-70.701865</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>28.77</td>\n",
       "      <td>19.32</td>\n",
       "      <td>0.62</td>\n",
       "      <td>26.46</td>\n",
       "      <td>13.80</td>\n",
       "      <td>1.50</td>\n",
       "      <td>19.21</td>\n",
       "      <td>2.09</td>\n",
       "      <td>3.41</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2.34</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5236</th>\n",
       "      <td>2019-05-25 00:00:00 UTC</td>\n",
       "      <td>SE170T005</td>\n",
       "      <td>-35.363701</td>\n",
       "      <td>-72.402716</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.37</td>\n",
       "      <td>81.99</td>\n",
       "      <td>10.94</td>\n",
       "      <td>8.00</td>\n",
       "      <td>13.16</td>\n",
       "      <td>9.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>11.02</td>\n",
       "      <td>3.61</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.24</td>\n",
       "      <td>4.59</td>\n",
       "      <td>7.23</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237</th>\n",
       "      <td>2019-06-04 00:00:00 UTC</td>\n",
       "      <td>SE015R002</td>\n",
       "      <td>-33.547230</td>\n",
       "      <td>-70.544867</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.01</td>\n",
       "      <td>34.77</td>\n",
       "      <td>13.31</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>20.08</td>\n",
       "      <td>10.09</td>\n",
       "      <td>0.41</td>\n",
       "      <td>9.06</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>2.36</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238</th>\n",
       "      <td>2019-06-04 00:00:00 UTC</td>\n",
       "      <td>SE004L079</td>\n",
       "      <td>-33.607927</td>\n",
       "      <td>-70.557676</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.01</td>\n",
       "      <td>34.77</td>\n",
       "      <td>13.31</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>20.08</td>\n",
       "      <td>10.09</td>\n",
       "      <td>0.41</td>\n",
       "      <td>9.06</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>2.36</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5239</th>\n",
       "      <td>2019-06-04 00:00:00 UTC</td>\n",
       "      <td>SE055T005</td>\n",
       "      <td>-33.981594</td>\n",
       "      <td>-70.701865</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.01</td>\n",
       "      <td>34.77</td>\n",
       "      <td>13.31</td>\n",
       "      <td>-2.05</td>\n",
       "      <td>20.08</td>\n",
       "      <td>10.09</td>\n",
       "      <td>0.41</td>\n",
       "      <td>9.06</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>2.36</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5240</th>\n",
       "      <td>2019-05-07 00:00:00 UTC</td>\n",
       "      <td>SE004T002</td>\n",
       "      <td>-39.549899</td>\n",
       "      <td>-72.912208</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>86.93</td>\n",
       "      <td>8.95</td>\n",
       "      <td>6.88</td>\n",
       "      <td>15.32</td>\n",
       "      <td>5.72</td>\n",
       "      <td>1.15</td>\n",
       "      <td>8.71</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.19</td>\n",
       "      <td>2.81</td>\n",
       "      <td>4.55</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5241</th>\n",
       "      <td>2019-03-30 00:00:00 UTC</td>\n",
       "      <td>SE039R002</td>\n",
       "      <td>-33.417179</td>\n",
       "      <td>-70.689370</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.28</td>\n",
       "      <td>28.92</td>\n",
       "      <td>19.09</td>\n",
       "      <td>0.49</td>\n",
       "      <td>26.13</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.59</td>\n",
       "      <td>18.85</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.13</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5242</th>\n",
       "      <td>2019-03-30 00:00:00 UTC</td>\n",
       "      <td>SE005R002</td>\n",
       "      <td>-33.267621</td>\n",
       "      <td>-70.730832</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.28</td>\n",
       "      <td>28.92</td>\n",
       "      <td>19.09</td>\n",
       "      <td>0.49</td>\n",
       "      <td>26.13</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.59</td>\n",
       "      <td>18.85</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.13</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5243</th>\n",
       "      <td>2019-06-03 00:00:00 UTC</td>\n",
       "      <td>SE036T002</td>\n",
       "      <td>-34.251969</td>\n",
       "      <td>-70.631584</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.03</td>\n",
       "      <td>11.94</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>20.67</td>\n",
       "      <td>6.78</td>\n",
       "      <td>0.60</td>\n",
       "      <td>7.71</td>\n",
       "      <td>1.97</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.51</td>\n",
       "      <td>3.95</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5244</th>\n",
       "      <td>2019-06-03 00:00:00 UTC</td>\n",
       "      <td>SE067T005</td>\n",
       "      <td>-34.119671</td>\n",
       "      <td>-70.753847</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.03</td>\n",
       "      <td>11.94</td>\n",
       "      <td>-2.41</td>\n",
       "      <td>20.67</td>\n",
       "      <td>6.78</td>\n",
       "      <td>0.60</td>\n",
       "      <td>7.71</td>\n",
       "      <td>1.97</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.51</td>\n",
       "      <td>3.95</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5245</th>\n",
       "      <td>2019-05-30 00:00:00 UTC</td>\n",
       "      <td>SE082T005</td>\n",
       "      <td>-30.690328</td>\n",
       "      <td>-70.954981</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.23</td>\n",
       "      <td>83.22</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.45</td>\n",
       "      <td>7.44</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.96</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.46</td>\n",
       "      <td>6.87</td>\n",
       "      <td>1.71</td>\n",
       "      <td>4.35</td>\n",
       "      <td>7.19</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5246</th>\n",
       "      <td>2019-05-07 00:00:00 UTC</td>\n",
       "      <td>SE008G010</td>\n",
       "      <td>-32.749506</td>\n",
       "      <td>-71.481002</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.01</td>\n",
       "      <td>60.68</td>\n",
       "      <td>14.95</td>\n",
       "      <td>7.48</td>\n",
       "      <td>21.80</td>\n",
       "      <td>10.45</td>\n",
       "      <td>1.56</td>\n",
       "      <td>15.52</td>\n",
       "      <td>2.37</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.72</td>\n",
       "      <td>4.29</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5247</th>\n",
       "      <td>2019-06-09 00:00:00 UTC</td>\n",
       "      <td>SE003T011</td>\n",
       "      <td>-35.685919</td>\n",
       "      <td>-71.377335</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>46.51</td>\n",
       "      <td>8.77</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>15.99</td>\n",
       "      <td>5.62</td>\n",
       "      <td>0.78</td>\n",
       "      <td>5.31</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2.54</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5248</th>\n",
       "      <td>2019-05-20 00:00:00 UTC</td>\n",
       "      <td>SE009G004</td>\n",
       "      <td>-37.360811</td>\n",
       "      <td>-71.866829</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.53</td>\n",
       "      <td>67.34</td>\n",
       "      <td>4.19</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>11.47</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.23</td>\n",
       "      <td>1.52</td>\n",
       "      <td>4.05</td>\n",
       "      <td>5.80</td>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        fecha subestacion        lat        lon  \\\n",
       "5219  2019-01-15 00:00:00 UTC   SE005R002 -33.267621 -70.730832   \n",
       "5220  2019-01-10 00:00:00 UTC   SE009T002 -37.095143 -72.321635   \n",
       "5221  2019-01-15 00:00:00 UTC   SE015R002 -33.547230 -70.544867   \n",
       "5222  2019-01-15 00:00:00 UTC   SE004L079 -33.607927 -70.557676   \n",
       "5223  2019-01-15 00:00:00 UTC   SE055T005 -33.981594 -70.701865   \n",
       "5224  2019-06-12 00:00:00 UTC   SE009G004 -37.360811 -71.866829   \n",
       "5225  2019-06-04 00:00:00 UTC   SE009G004 -37.360811 -71.866829   \n",
       "5226  2019-06-27 00:00:00 UTC   SE009G004 -37.360811 -71.866829   \n",
       "5227  2019-05-04 00:00:00 UTC   SE127T005 -34.867935 -71.141328   \n",
       "5228  2019-05-23 00:00:00 UTC   SE005T003 -42.626987 -73.805673   \n",
       "5229  2019-05-17 00:00:00 UTC   SE003T011 -35.685919 -71.377335   \n",
       "5230  2019-06-20 00:00:00 UTC   SE009G004 -37.360811 -71.866829   \n",
       "5231  2019-06-04 00:00:00 UTC   SE039R002 -33.417179 -70.689370   \n",
       "5232  2019-06-04 00:00:00 UTC   SE005R002 -33.267621 -70.730832   \n",
       "5233  2019-03-30 00:00:00 UTC   SE015R002 -33.547230 -70.544867   \n",
       "5234  2019-03-30 00:00:00 UTC   SE004L079 -33.607927 -70.557676   \n",
       "5235  2019-03-30 00:00:00 UTC   SE055T005 -33.981594 -70.701865   \n",
       "5236  2019-05-25 00:00:00 UTC   SE170T005 -35.363701 -72.402716   \n",
       "5237  2019-06-04 00:00:00 UTC   SE015R002 -33.547230 -70.544867   \n",
       "5238  2019-06-04 00:00:00 UTC   SE004L079 -33.607927 -70.557676   \n",
       "5239  2019-06-04 00:00:00 UTC   SE055T005 -33.981594 -70.701865   \n",
       "5240  2019-05-07 00:00:00 UTC   SE004T002 -39.549899 -72.912208   \n",
       "5241  2019-03-30 00:00:00 UTC   SE039R002 -33.417179 -70.689370   \n",
       "5242  2019-03-30 00:00:00 UTC   SE005R002 -33.267621 -70.730832   \n",
       "5243  2019-06-03 00:00:00 UTC   SE036T002 -34.251969 -70.631584   \n",
       "5244  2019-06-03 00:00:00 UTC   SE067T005 -34.119671 -70.753847   \n",
       "5245  2019-05-30 00:00:00 UTC   SE082T005 -30.690328 -70.954981   \n",
       "5246  2019-05-07 00:00:00 UTC   SE008G010 -32.749506 -71.481002   \n",
       "5247  2019-06-09 00:00:00 UTC   SE003T011 -35.685919 -71.377335   \n",
       "5248  2019-05-20 00:00:00 UTC   SE009G004 -37.360811 -71.866829   \n",
       "\n",
       "      ALLSKY_SFC_SW_DWN    KT  PRECTOT   RH2M    T2M  T2MDEW  T2M_MAX  \\\n",
       "5219               9.48  0.79     0.00  28.41  20.16    1.17    28.52   \n",
       "5220               9.48  0.78     0.00  37.71  15.83    1.31    26.74   \n",
       "5221               9.48  0.79     0.00  28.09  20.75    1.52    29.44   \n",
       "5222               9.48  0.79     0.00  28.09  20.75    1.52    29.44   \n",
       "5223               9.48  0.79     0.00  28.09  20.75    1.52    29.44   \n",
       "5224               0.39  0.10    21.17  94.75   6.55    5.74     8.75   \n",
       "5225               0.39  0.10     8.36  73.99   7.84    3.53    11.69   \n",
       "5226               0.39  0.10    45.22  97.97   3.56    3.25     5.54   \n",
       "5227               0.78  0.14     2.90  74.90  14.46   10.12    18.52   \n",
       "5228               1.06  0.29     0.08  91.30   8.64    7.27    10.94   \n",
       "5229               1.06  0.21     4.14  89.42   7.64    5.98    10.42   \n",
       "5230               1.31  0.33     0.00  51.71   7.66   -1.66    12.12   \n",
       "5231               1.56  0.33     0.01  25.83  14.00   -5.39    21.12   \n",
       "5232               1.56  0.33     0.01  25.83  14.00   -5.39    21.12   \n",
       "5233               1.56  0.19     0.19  28.77  19.32    0.62    26.46   \n",
       "5234               1.56  0.19     0.19  28.77  19.32    0.62    26.46   \n",
       "5235               1.56  0.19     0.19  28.77  19.32    0.62    26.46   \n",
       "5236               1.56  0.33     1.37  81.99  10.94    8.00    13.16   \n",
       "5237               1.56  0.33     0.01  34.77  13.31   -2.05    20.08   \n",
       "5238               1.56  0.33     0.01  34.77  13.31   -2.05    20.08   \n",
       "5239               1.56  0.33     0.01  34.77  13.31   -2.05    20.08   \n",
       "5240               1.56  0.32     0.00  86.93   8.95    6.88    15.32   \n",
       "5241               1.56  0.19     0.28  28.92  19.09    0.49    26.13   \n",
       "5242               1.56  0.19     0.28  28.92  19.09    0.49    26.13   \n",
       "5243               1.81  0.39     0.00  37.03  11.94   -2.41    20.67   \n",
       "5244               1.81  0.39     0.00  37.03  11.94   -2.41    20.67   \n",
       "5245               1.81  0.34     1.23  83.22   3.02    0.45     7.44   \n",
       "5246               1.81  0.31     0.01  60.68  14.95    7.48    21.80   \n",
       "5247               1.81  0.42     0.00  46.51   8.77   -2.12    15.99   \n",
       "5248               1.81  0.40     0.53  67.34   4.19   -1.29    11.47   \n",
       "\n",
       "      T2M_MIN   TQV     TS  WS10M  WS10M_MAX  WS10M_MIN  WS50M  WS50M_MAX  \\\n",
       "5219    11.90  0.89  22.65   2.75       5.32       0.33   2.73       4.45   \n",
       "5220     4.63  0.73  15.97   1.90       2.61       1.27   3.34       5.23   \n",
       "5221    12.03  0.97  22.97   2.54       4.82       0.60   2.55       4.11   \n",
       "5222    12.03  0.97  22.97   2.54       4.82       0.60   2.55       4.11   \n",
       "5223    12.03  0.97  22.97   2.54       4.82       0.60   2.55       4.11   \n",
       "5224     3.94  1.49   6.48   4.83       6.79       2.01   8.02      11.16   \n",
       "5225     5.81  1.73   7.68   3.51       4.81       2.10   6.15       7.52   \n",
       "5226     2.40  1.57   3.46   5.22       7.44       2.11   8.57      11.72   \n",
       "5227    12.22  2.44  14.14   1.85       3.61       0.11   3.00       4.99   \n",
       "5228     6.75  1.76   8.73   2.45       3.90       1.12   3.61       5.61   \n",
       "5229     4.31  1.57   7.60   1.91       2.64       0.47   3.03       4.04   \n",
       "5230     3.28  0.39   5.79   2.42       2.88       1.97   4.80       5.65   \n",
       "5231    10.55  0.35   9.25   1.64       2.44       0.78   2.23       3.42   \n",
       "5232    10.55  0.35   9.25   1.64       2.44       0.78   2.23       3.42   \n",
       "5233    13.80  1.50  19.21   2.09       3.41       0.51   2.34       3.30   \n",
       "5234    13.80  1.50  19.21   2.09       3.41       0.51   2.34       3.30   \n",
       "5235    13.80  1.50  19.21   2.09       3.41       0.51   2.34       3.30   \n",
       "5236     9.06  1.06  11.02   3.61       5.72       0.24   4.59       7.23   \n",
       "5237    10.09  0.41   9.06   1.71       2.45       0.58   2.36       3.46   \n",
       "5238    10.09  0.41   9.06   1.71       2.45       0.58   2.36       3.46   \n",
       "5239    10.09  0.41   9.06   1.71       2.45       0.58   2.36       3.46   \n",
       "5240     5.72  1.15   8.71   1.56       2.00       1.19   2.81       4.55   \n",
       "5241    14.23  1.59  18.85   2.03       3.55       0.48   2.28       3.13   \n",
       "5242    14.23  1.59  18.85   2.03       3.55       0.48   2.28       3.13   \n",
       "5243     6.78  0.60   7.71   1.97       3.04       0.50   2.51       3.95   \n",
       "5244     6.78  0.60   7.71   1.97       3.04       0.50   2.51       3.95   \n",
       "5245     0.31  0.96   3.38   3.46       6.87       1.71   4.35       7.19   \n",
       "5246    10.45  1.56  15.52   2.37       3.96       0.84   2.72       4.29   \n",
       "5247     5.62  0.78   5.31   1.17       2.54       0.17   1.59       2.93   \n",
       "5248    -1.06  0.84   3.80   2.28       3.23       1.52   4.05       5.80   \n",
       "\n",
       "      WS50M_MIN  \n",
       "5219       0.48  \n",
       "5220       1.95  \n",
       "5221       0.73  \n",
       "5222       0.73  \n",
       "5223       0.73  \n",
       "5224       3.84  \n",
       "5225       5.08  \n",
       "5226       3.98  \n",
       "5227       0.17  \n",
       "5228       1.53  \n",
       "5229       0.55  \n",
       "5230       4.16  \n",
       "5231       1.18  \n",
       "5232       1.18  \n",
       "5233       0.68  \n",
       "5234       0.68  \n",
       "5235       0.68  \n",
       "5236       0.43  \n",
       "5237       1.25  \n",
       "5238       1.25  \n",
       "5239       1.25  \n",
       "5240       1.80  \n",
       "5241       0.82  \n",
       "5242       0.82  \n",
       "5243       0.49  \n",
       "5244       0.49  \n",
       "5245       2.61  \n",
       "5246       0.67  \n",
       "5247       0.25  \n",
       "5248       2.79  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clima = pd.read_csv('data/datos_clima.csv')\n",
    "clima.tail(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "los datos de clima estan por día, por lo que el merge se realizará mediante un left join hacia los datos de predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>nemotecnico_se</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>gen_eolica_total_mwh</th>\n",
       "      <th>gen_geotermica_total_mwh</th>\n",
       "      <th>gen_hidraulica_total_mwh</th>\n",
       "      <th>gen_solar_total_mwh</th>\n",
       "      <th>gen_termica_total_mwh</th>\n",
       "      <th>cmg_real</th>\n",
       "      <th>cmg_prog</th>\n",
       "      <th>...</th>\n",
       "      <th>T2M_MAX</th>\n",
       "      <th>T2M_MIN</th>\n",
       "      <th>TQV</th>\n",
       "      <th>TS</th>\n",
       "      <th>WS10M</th>\n",
       "      <th>WS10M_MAX</th>\n",
       "      <th>WS10M_MIN</th>\n",
       "      <th>WS50M</th>\n",
       "      <th>WS50M_MAX</th>\n",
       "      <th>WS50M_MIN</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>56.2</td>\n",
       "      <td>55.627852</td>\n",
       "      <td>...</td>\n",
       "      <td>25.07</td>\n",
       "      <td>17.16</td>\n",
       "      <td>3.33</td>\n",
       "      <td>23.89</td>\n",
       "      <td>3.56</td>\n",
       "      <td>6.31</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.40</td>\n",
       "      <td>5.49</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>56.2</td>\n",
       "      <td>55.376645</td>\n",
       "      <td>...</td>\n",
       "      <td>25.07</td>\n",
       "      <td>17.16</td>\n",
       "      <td>3.33</td>\n",
       "      <td>23.89</td>\n",
       "      <td>3.56</td>\n",
       "      <td>6.31</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.40</td>\n",
       "      <td>5.49</td>\n",
       "      <td>1.47</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>56.2</td>\n",
       "      <td>59.531891</td>\n",
       "      <td>...</td>\n",
       "      <td>25.07</td>\n",
       "      <td>17.16</td>\n",
       "      <td>3.33</td>\n",
       "      <td>23.89</td>\n",
       "      <td>3.56</td>\n",
       "      <td>6.31</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.40</td>\n",
       "      <td>5.49</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>56.2</td>\n",
       "      <td>174.378923</td>\n",
       "      <td>...</td>\n",
       "      <td>25.07</td>\n",
       "      <td>17.16</td>\n",
       "      <td>3.33</td>\n",
       "      <td>23.89</td>\n",
       "      <td>3.56</td>\n",
       "      <td>6.31</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.40</td>\n",
       "      <td>5.49</td>\n",
       "      <td>1.47</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SE031G216</td>\n",
       "      <td>2019-01-04 00:00:00+00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>56.2</td>\n",
       "      <td>172.820310</td>\n",
       "      <td>...</td>\n",
       "      <td>25.07</td>\n",
       "      <td>17.16</td>\n",
       "      <td>3.33</td>\n",
       "      <td>23.89</td>\n",
       "      <td>3.56</td>\n",
       "      <td>6.31</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.40</td>\n",
       "      <td>5.49</td>\n",
       "      <td>1.47</td>\n",
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       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112774</th>\n",
       "      <td>SE170T005</td>\n",
       "      <td>2019-06-30 00:00:00+00:00</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.775</td>\n",
       "      <td>44.1</td>\n",
       "      <td>41.362200</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.92</td>\n",
       "      <td>9.04</td>\n",
       "      <td>5.17</td>\n",
       "      <td>6.95</td>\n",
       "      <td>2.79</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.40</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112775</th>\n",
       "      <td>SE170T005</td>\n",
       "      <td>2019-06-30 00:00:00+00:00</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.925</td>\n",
       "      <td>44.1</td>\n",
       "      <td>42.217780</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.92</td>\n",
       "      <td>9.04</td>\n",
       "      <td>5.17</td>\n",
       "      <td>6.95</td>\n",
       "      <td>2.79</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.40</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112776</th>\n",
       "      <td>SE170T005</td>\n",
       "      <td>2019-06-30 00:00:00+00:00</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.975</td>\n",
       "      <td>44.1</td>\n",
       "      <td>42.197451</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.92</td>\n",
       "      <td>9.04</td>\n",
       "      <td>5.17</td>\n",
       "      <td>6.95</td>\n",
       "      <td>2.79</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.40</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112777</th>\n",
       "      <td>SE170T005</td>\n",
       "      <td>2019-06-30 00:00:00+00:00</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.750</td>\n",
       "      <td>44.1</td>\n",
       "      <td>41.473556</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.92</td>\n",
       "      <td>9.04</td>\n",
       "      <td>5.17</td>\n",
       "      <td>6.95</td>\n",
       "      <td>2.79</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.40</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112778</th>\n",
       "      <td>SE170T005</td>\n",
       "      <td>2019-06-30 00:00:00+00:00</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.700</td>\n",
       "      <td>44.1</td>\n",
       "      <td>36.197329</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.92</td>\n",
       "      <td>9.04</td>\n",
       "      <td>5.17</td>\n",
       "      <td>6.95</td>\n",
       "      <td>2.79</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.40</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112779 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       nemotecnico_se                     fecha  hora  gen_eolica_total_mwh  \\\n",
       "0           SE031G216 2019-01-04 00:00:00+00:00     1                   NaN   \n",
       "1           SE031G216 2019-01-04 00:00:00+00:00     2                   NaN   \n",
       "2           SE031G216 2019-01-04 00:00:00+00:00     3                   NaN   \n",
       "3           SE031G216 2019-01-04 00:00:00+00:00     4                   NaN   \n",
       "4           SE031G216 2019-01-04 00:00:00+00:00     5                   NaN   \n",
       "...               ...                       ...   ...                   ...   \n",
       "112774      SE170T005 2019-06-30 00:00:00+00:00    20                   NaN   \n",
       "112775      SE170T005 2019-06-30 00:00:00+00:00    21                   NaN   \n",
       "112776      SE170T005 2019-06-30 00:00:00+00:00    22                   NaN   \n",
       "112777      SE170T005 2019-06-30 00:00:00+00:00    23                   NaN   \n",
       "112778      SE170T005 2019-06-30 00:00:00+00:00    24                   NaN   \n",
       "\n",
       "        gen_geotermica_total_mwh  gen_hidraulica_total_mwh  \\\n",
       "0                            NaN                       NaN   \n",
       "1                            NaN                       NaN   \n",
       "2                            NaN                       NaN   \n",
       "3                            NaN                       NaN   \n",
       "4                            NaN                       NaN   \n",
       "...                          ...                       ...   \n",
       "112774                       NaN                       NaN   \n",
       "112775                       NaN                       NaN   \n",
       "112776                       NaN                       NaN   \n",
       "112777                       NaN                       NaN   \n",
       "112778                       NaN                       NaN   \n",
       "\n",
       "        gen_solar_total_mwh  gen_termica_total_mwh  cmg_real    cmg_prog  ...  \\\n",
       "0                       NaN                  0.000      56.2   55.627852  ...   \n",
       "1                       NaN                  0.000      56.2   55.376645  ...   \n",
       "2                       NaN                  0.000      56.2   59.531891  ...   \n",
       "3                       NaN                  0.000      56.2  174.378923  ...   \n",
       "4                       NaN                  0.000      56.2  172.820310  ...   \n",
       "...                     ...                    ...       ...         ...  ...   \n",
       "112774                  NaN                  0.775      44.1   41.362200  ...   \n",
       "112775                  NaN                  0.925      44.1   42.217780  ...   \n",
       "112776                  NaN                  0.975      44.1   42.197451  ...   \n",
       "112777                  NaN                  0.750      44.1   41.473556  ...   \n",
       "112778                  NaN                  0.700      44.1   36.197329  ...   \n",
       "\n",
       "        T2M_MAX  T2M_MIN   TQV     TS  WS10M  WS10M_MAX  WS10M_MIN  WS50M  \\\n",
       "0         25.07    17.16  3.33  23.89   3.56       6.31       1.50   3.40   \n",
       "1         25.07    17.16  3.33  23.89   3.56       6.31       1.50   3.40   \n",
       "2         25.07    17.16  3.33  23.89   3.56       6.31       1.50   3.40   \n",
       "3         25.07    17.16  3.33  23.89   3.56       6.31       1.50   3.40   \n",
       "4         25.07    17.16  3.33  23.89   3.56       6.31       1.50   3.40   \n",
       "...         ...      ...   ...    ...    ...        ...        ...    ...   \n",
       "112774    13.05     5.72  0.92   9.04   5.17       6.95       2.79   6.56   \n",
       "112775    13.05     5.72  0.92   9.04   5.17       6.95       2.79   6.56   \n",
       "112776    13.05     5.72  0.92   9.04   5.17       6.95       2.79   6.56   \n",
       "112777    13.05     5.72  0.92   9.04   5.17       6.95       2.79   6.56   \n",
       "112778    13.05     5.72  0.92   9.04   5.17       6.95       2.79   6.56   \n",
       "\n",
       "        WS50M_MAX WS50M_MIN  \n",
       "0            5.49      1.47  \n",
       "1            5.49      1.47  \n",
       "2            5.49      1.47  \n",
       "3            5.49      1.47  \n",
       "4            5.49      1.47  \n",
       "...           ...       ...  \n",
       "112774       8.40      3.35  \n",
       "112775       8.40      3.35  \n",
       "112776       8.40      3.35  \n",
       "112777       8.40      3.35  \n",
       "112778       8.40      3.35  \n",
       "\n",
       "[112779 rows x 43 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clima['fecha'] = pd.to_datetime(clima['fecha'])\n",
    "base_pred_clima = pd.merge(datos_pred,clima,left_on=['fecha','nemotecnico_se'],\n",
    "                          right_on = ['fecha','subestacion'],how='left')\n",
    "base_pred_clima"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comentario Final\n",
    "\n",
    "Lamentablemente solo llegué hasta esto luego de las 5 horas y en particular creo que es importante que ustedes puedan ver lo que alcancé a realizar en el tiempo que se estimaba, por ello lo deje hasta acá.\n",
    "\n",
    "Solo como comentarios de cómo hubiese seguido, es modelando mediantes series de tiempo (ARIMA, SARIMA en los casos paramétricos o SSA para no-paramétrico) para cada subestación, tratando de predecir la variable target. En términos de predicción, como se requiere (en el punto 5) tendríamos que hacer un shift en la variable target de modo que el modelo se entrene utilizando los registos de una hora x pero el target correspondería a una hora x+1, así sabremos que al entregarle variables de una hora en especifica nuestra predicción nos estará entregando lo que ocurrirá en la hora siguiente.\n",
    "\n",
    "Para validar, típicamente podríamos tomar las horas finales del día y ajustar el modelo con (por ejemplo) las primeras 20 horas de un día y validar con las que restan, para así poder medir el performance del modelo.\n",
    "\n",
    "Otra forma sería mirandolo desde un punto de vista de clasificación (variable target 0 y 1), mediante algún modelo ensamblado como XGBoost u otros métodos de Machina Learning.\n",
    "\n",
    "En el punto 6, creo que debiesemos cambiar el shift de solo una hora a 12, porque tendremos los datos actualizados en bloques de 12 horas, pero ahi habría que ver el modelo y su performance por lo que sin verlo en específico no podría decir mucho más, al igual que con el punto 7 dado que vuelve a comprar hacia atrás ahora con las fechas.\n",
    "\n",
    "Finalmente concluir que el problema es super interesante y muy útil claramente, tener un modelo preciso que anticipe desvíos de la planificación, sobre todo si se tiene el tiempo para reaccionar podría generar un impacto positivo en temas de manejo de costos."
   ]
  }
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