{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "051e8802-b8c7-42ef-9ed5-4f8c421d2b7b",
   "metadata": {},
   "source": [
    "# Ejercicio Transacciones. Parte 2 - Integración de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9b47a97-53b1-441d-a40e-85062df0fa2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.options.display.float_format = '{:.2f}'.format #Desactivar notación científica en pandas:\n",
    "np.set_printoptions(suppress=True) #Desactivar notación científica en numpy:\n",
    "pd.set_option('display.max_columns', None) #comando para mostrar todas las columnas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "716b947e-3caf-4b81-85ca-c9a259ab882e",
   "metadata": {},
   "source": [
    "#### En el ejercicio previo hemos obtenido datos de diferentes fuentes y las hemos 'limpiado'.  Ahora integramos los datos en un tablón con el que realizar la analítica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1f6c328a-c127-4e2d-8642-07aa64a8adcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "sep = \";\"\n",
    "decimal = \",\"\n",
    "index=False\n",
    "encoding=\"UTF-8\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "116cfe26-9d79-4f46-af39-8072496793a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "datos1 = pd.read_csv('./data/transacciones2.csv', sep=sep, decimal=decimal, encoding=encoding)\n",
    "datos2 = pd.read_csv('./data/clima2.csv', sep=sep, decimal=decimal, encoding=encoding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "489aa2f1-930d-461c-9b56-da33b85e3134",
   "metadata": {},
   "source": [
    "Unimos ambas tablas por la fecha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f60a251-fe1f-4ff8-a51c-b5ee72ad1e37",
   "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>Fecha</th>\n",
       "      <th>Código Postal</th>\n",
       "      <th>Tipo de Comercio</th>\n",
       "      <th>Transacciones</th>\n",
       "      <th>Importe Medio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_parking</td>\n",
       "      <td>8</td>\n",
       "      <td>9.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_pharmacy</td>\n",
       "      <td>3</td>\n",
       "      <td>15.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_taxi</td>\n",
       "      <td>2</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_gas</td>\n",
       "      <td>2</td>\n",
       "      <td>52.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_fastfood</td>\n",
       "      <td>2</td>\n",
       "      <td>29.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fecha  Código Postal Tipo de Comercio  Transacciones  Importe Medio\n",
       "0  2015-01-01          48004       es_parking              8           9.24\n",
       "1  2015-01-01          48004      es_pharmacy              3          15.40\n",
       "2  2015-01-01          48004          es_taxi              2          19.80\n",
       "3  2015-01-01          48004           es_gas              2          52.98\n",
       "4  2015-01-01          48004      es_fastfood              2          29.25"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3757b88f-c41c-4142-bc31-92d759c34d6f",
   "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>Fecha</th>\n",
       "      <th>Temperatura media (_C)</th>\n",
       "      <th>Humedad media (%)</th>\n",
       "      <th>Precipitaciones media (l/m2)</th>\n",
       "      <th>Viento medio (km/h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>6.10</td>\n",
       "      <td>64.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>6.90</td>\n",
       "      <td>66.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>8.20</td>\n",
       "      <td>71.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>10.60</td>\n",
       "      <td>79.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>9.00</td>\n",
       "      <td>77.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fecha  Temperatura media (_C)  Humedad media (%)  \\\n",
       "0  2015-01-01                    6.10              64.00   \n",
       "1  2015-02-01                    6.90              66.00   \n",
       "2  2015-03-01                    8.20              71.00   \n",
       "3  2015-04-01                   10.60              79.00   \n",
       "4  2015-05-01                    9.00              77.00   \n",
       "\n",
       "   Precipitaciones media (l/m2)  Viento medio (km/h)   \n",
       "0                          0.00                 14.20  \n",
       "1                          0.00                 10.40  \n",
       "2                          0.00                  9.80  \n",
       "3                          0.00                  8.00  \n",
       "4                          0.00                 11.10  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "68dc4143-e01f-4452-ae9d-5e405b51dea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110948 entries, 0 to 110947\n",
      "Data columns (total 5 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   Fecha             110948 non-null  object \n",
      " 1   Código Postal     110948 non-null  int64  \n",
      " 2   Tipo de Comercio  110948 non-null  object \n",
      " 3   Transacciones     110948 non-null  int64  \n",
      " 4   Importe Medio     110948 non-null  float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "datos1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7d3ebd6-de6f-4d9f-ac5e-8e4d01ab7774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 365 entries, 0 to 364\n",
      "Data columns (total 5 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Fecha                         365 non-null    object \n",
      " 1   Temperatura media (_C)        365 non-null    float64\n",
      " 2   Humedad media (%)             358 non-null    float64\n",
      " 3   Precipitaciones media (l/m2)  365 non-null    float64\n",
      " 4   Viento medio (km/h)           365 non-null    float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 14.4+ KB\n"
     ]
    }
   ],
   "source": [
    "datos2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16325837-6841-4af1-8e8b-25cf4120ee31",
   "metadata": {},
   "outputs": [],
   "source": [
    "resultado = pd.merge(\n",
    "    datos1,\n",
    "    datos2,\n",
    "    how = 'left',\n",
    "    on = 'Fecha',\n",
    "    sort = True)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c03f458-dc3a-4fb2-ae4e-ed494d94d043",
   "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>Fecha</th>\n",
       "      <th>Código Postal</th>\n",
       "      <th>Tipo de Comercio</th>\n",
       "      <th>Transacciones</th>\n",
       "      <th>Importe Medio</th>\n",
       "      <th>Temperatura media (_C)</th>\n",
       "      <th>Humedad media (%)</th>\n",
       "      <th>Precipitaciones media (l/m2)</th>\n",
       "      <th>Viento medio (km/h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_parking</td>\n",
       "      <td>8</td>\n",
       "      <td>9.24</td>\n",
       "      <td>6.10</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>14.20</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_pharmacy</td>\n",
       "      <td>3</td>\n",
       "      <td>15.40</td>\n",
       "      <td>6.10</td>\n",
       "      <td>64.00</td>\n",
       "      <td>0.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_taxi</td>\n",
       "      <td>2</td>\n",
       "      <td>19.80</td>\n",
       "      <td>6.10</td>\n",
       "      <td>64.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_gas</td>\n",
       "      <td>2</td>\n",
       "      <td>52.98</td>\n",
       "      <td>6.10</td>\n",
       "      <td>64.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>48004</td>\n",
       "      <td>es_fastfood</td>\n",
       "      <td>2</td>\n",
       "      <td>29.25</td>\n",
       "      <td>6.10</td>\n",
       "      <td>64.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>110943</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>48014</td>\n",
       "      <td>es_cafe</td>\n",
       "      <td>1</td>\n",
       "      <td>36.00</td>\n",
       "      <td>11.50</td>\n",
       "      <td>83.00</td>\n",
       "      <td>2.60</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110944</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>48014</td>\n",
       "      <td>es_hospital</td>\n",
       "      <td>1</td>\n",
       "      <td>50.00</td>\n",
       "      <td>11.50</td>\n",
       "      <td>83.00</td>\n",
       "      <td>2.60</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110945</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>48014</td>\n",
       "      <td>es_taxi</td>\n",
       "      <td>1</td>\n",
       "      <td>22.50</td>\n",
       "      <td>11.50</td>\n",
       "      <td>83.00</td>\n",
       "      <td>2.60</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110946</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>48014</td>\n",
       "      <td>es_wellness</td>\n",
       "      <td>1</td>\n",
       "      <td>22.00</td>\n",
       "      <td>11.50</td>\n",
       "      <td>83.00</td>\n",
       "      <td>2.60</td>\n",
       "      <td>8.90</td>\n",
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       "    <tr>\n",
       "      <th>110947</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>48014</td>\n",
       "      <td>es_leather</td>\n",
       "      <td>1</td>\n",
       "      <td>59.00</td>\n",
       "      <td>11.50</td>\n",
       "      <td>83.00</td>\n",
       "      <td>2.60</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110948 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fecha  Código Postal Tipo de Comercio  Transacciones  \\\n",
       "0       2015-01-01          48004       es_parking              8   \n",
       "1       2015-01-01          48004      es_pharmacy              3   \n",
       "2       2015-01-01          48004          es_taxi              2   \n",
       "3       2015-01-01          48004           es_gas              2   \n",
       "4       2015-01-01          48004      es_fastfood              2   \n",
       "...            ...            ...              ...            ...   \n",
       "110943  2015-12-31          48014          es_cafe              1   \n",
       "110944  2015-12-31          48014      es_hospital              1   \n",
       "110945  2015-12-31          48014          es_taxi              1   \n",
       "110946  2015-12-31          48014      es_wellness              1   \n",
       "110947  2015-12-31          48014       es_leather              1   \n",
       "\n",
       "        Importe Medio  Temperatura media (_C)  Humedad media (%)  \\\n",
       "0                9.24                    6.10              64.00   \n",
       "1               15.40                    6.10              64.00   \n",
       "2               19.80                    6.10              64.00   \n",
       "3               52.98                    6.10              64.00   \n",
       "4               29.25                    6.10              64.00   \n",
       "...               ...                     ...                ...   \n",
       "110943          36.00                   11.50              83.00   \n",
       "110944          50.00                   11.50              83.00   \n",
       "110945          22.50                   11.50              83.00   \n",
       "110946          22.00                   11.50              83.00   \n",
       "110947          59.00                   11.50              83.00   \n",
       "\n",
       "        Precipitaciones media (l/m2)  Viento medio (km/h)   \n",
       "0                               0.00                 14.20  \n",
       "1                               0.00                 14.20  \n",
       "2                               0.00                 14.20  \n",
       "3                               0.00                 14.20  \n",
       "4                               0.00                 14.20  \n",
       "...                              ...                   ...  \n",
       "110943                          2.60                  8.90  \n",
       "110944                          2.60                  8.90  \n",
       "110945                          2.60                  8.90  \n",
       "110946                          2.60                  8.90  \n",
       "110947                          2.60                  8.90  \n",
       "\n",
       "[110948 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "467a0a8e-bd71-44ad-8968-8d5ae634ca13",
   "metadata": {},
   "outputs": [],
   "source": [
    "resultado.to_csv('./data/tran_cli.csv', sep=sep, decimal=decimal, index=index, encoding=encoding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b459013f-cc84-4f66-921c-67974925d250",
   "metadata": {},
   "source": [
    "Ahora le unimos festivos..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f68912b-b5bb-4b23-b01c-b24e368ad782",
   "metadata": {},
   "outputs": [],
   "source": [
    "datos3 = pd.read_csv('./data/festivos2.csv', sep=sep, decimal=decimal, encoding=encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "38cb61de-8fcc-49a8-a929-8b06ed84ad25",
   "metadata": {},
   "outputs": [],
   "source": [
    "resultado2 = pd.merge(\n",
    "    resultado,\n",
    "    datos3,\n",
    "    how = 'left',\n",
    "    on = 'Fecha',\n",
    "    sort = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e847be61-b68c-4473-b0dc-eb158a8e17e6",
   "metadata": {},
   "outputs": [
    {
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       "             Fecha  Código Postal Tipo de Comercio  Transacciones  \\\n",
       "0       2015-01-01          48004       es_parking              8   \n",
       "1       2015-01-01          48004      es_pharmacy              3   \n",
       "2       2015-01-01          48004          es_taxi              2   \n",
       "3       2015-01-01          48004           es_gas              2   \n",
       "4       2015-01-01          48004      es_fastfood              2   \n",
       "...            ...            ...              ...            ...   \n",
       "110943  2015-12-31          48014          es_cafe              1   \n",
       "110944  2015-12-31          48014      es_hospital              1   \n",
       "110945  2015-12-31          48014          es_taxi              1   \n",
       "110946  2015-12-31          48014      es_wellness              1   \n",
       "110947  2015-12-31          48014       es_leather              1   \n",
       "\n",
       "        Importe Medio  Temperatura media (_C)  Humedad media (%)  \\\n",
       "0                9.24                    6.10              64.00   \n",
       "1               15.40                    6.10              64.00   \n",
       "2               19.80                    6.10              64.00   \n",
       "3               52.98                    6.10              64.00   \n",
       "4               29.25                    6.10              64.00   \n",
       "...               ...                     ...                ...   \n",
       "110943          36.00                   11.50              83.00   \n",
       "110944          50.00                   11.50              83.00   \n",
       "110945          22.50                   11.50              83.00   \n",
       "110946          22.00                   11.50              83.00   \n",
       "110947          59.00                   11.50              83.00   \n",
       "\n",
       "        Precipitaciones media (l/m2)  Viento medio (km/h)  Descripcion  \n",
       "0                               0.00                 14.20  Anio nuevo  \n",
       "1                               0.00                 14.20  Anio nuevo  \n",
       "2                               0.00                 14.20  Anio nuevo  \n",
       "3                               0.00                 14.20  Anio nuevo  \n",
       "4                               0.00                 14.20  Anio nuevo  \n",
       "...                              ...                   ...         ...  \n",
       "110943                          2.60                  8.90         NaN  \n",
       "110944                          2.60                  8.90         NaN  \n",
       "110945                          2.60                  8.90         NaN  \n",
       "110946                          2.60                  8.90         NaN  \n",
       "110947                          2.60                  8.90         NaN  \n",
       "\n",
       "[110948 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a894b93c-77ad-4791-b97d-f72eb5dc1e07",
   "metadata": {},
   "source": [
    "¿Véis algo raro en la descripción de los festivos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c41f9a6a-bf0c-4408-8182-4884a9edb561",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creo una nueva variable a partir de la descripción\n",
    "filtro = ~(resultado2.Descripcion.isna()) # ~ invierte el resultado de una serie booleana.  Lo que es True lo convierte en False\n",
    "resultado2['Festivo'] = 0\n",
    "resultado2['Festivo'] = resultado2['Festivo'].astype('boolean')\n",
    "\n",
    "# Ahora tenemos un filtro que nos permite seleccionar las filas que son festivas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a74c076a-45ba-455c-8e3f-64043fffa53a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          True\n",
       "1          True\n",
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       "3          True\n",
       "4          True\n",
       "          ...  \n",
       "110943    False\n",
       "110944    False\n",
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       "110946    False\n",
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       "Name: Descripcion, Length: 110948, dtype: bool"
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     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "filtro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0eeff4ea-cebc-484a-902e-099c77a1b5f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "110943    False\n",
       "110944    False\n",
       "110945    False\n",
       "110946    False\n",
       "110947    False\n",
       "Name: Festivo, Length: 110948, dtype: boolean"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado2['Festivo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "60190125-56f0-4443-8271-915124a9bc5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "resultado2['Festivo'][filtro] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "eeafb56a-1250-4214-a717-5526bff6be92",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "             Fecha  Código Postal Tipo de Comercio  Transacciones  \\\n",
       "0       2015-01-01          48004       es_parking              8   \n",
       "1       2015-01-01          48004      es_pharmacy              3   \n",
       "2       2015-01-01          48004          es_taxi              2   \n",
       "3       2015-01-01          48004           es_gas              2   \n",
       "4       2015-01-01          48004      es_fastfood              2   \n",
       "...            ...            ...              ...            ...   \n",
       "110943  2015-12-31          48014          es_cafe              1   \n",
       "110944  2015-12-31          48014      es_hospital              1   \n",
       "110945  2015-12-31          48014          es_taxi              1   \n",
       "110946  2015-12-31          48014      es_wellness              1   \n",
       "110947  2015-12-31          48014       es_leather              1   \n",
       "\n",
       "        Importe Medio  Temperatura media (_C)  Humedad media (%)  \\\n",
       "0                9.24                    6.10              64.00   \n",
       "1               15.40                    6.10              64.00   \n",
       "2               19.80                    6.10              64.00   \n",
       "3               52.98                    6.10              64.00   \n",
       "4               29.25                    6.10              64.00   \n",
       "...               ...                     ...                ...   \n",
       "110943          36.00                   11.50              83.00   \n",
       "110944          50.00                   11.50              83.00   \n",
       "110945          22.50                   11.50              83.00   \n",
       "110946          22.00                   11.50              83.00   \n",
       "110947          59.00                   11.50              83.00   \n",
       "\n",
       "        Precipitaciones media (l/m2)  Viento medio (km/h)  Descripcion  \\\n",
       "0                               0.00                 14.20  Anio nuevo   \n",
       "1                               0.00                 14.20  Anio nuevo   \n",
       "2                               0.00                 14.20  Anio nuevo   \n",
       "3                               0.00                 14.20  Anio nuevo   \n",
       "4                               0.00                 14.20  Anio nuevo   \n",
       "...                              ...                   ...         ...   \n",
       "110943                          2.60                  8.90         NaN   \n",
       "110944                          2.60                  8.90         NaN   \n",
       "110945                          2.60                  8.90         NaN   \n",
       "110946                          2.60                  8.90         NaN   \n",
       "110947                          2.60                  8.90         NaN   \n",
       "\n",
       "        Festivo  \n",
       "0          True  \n",
       "1          True  \n",
       "2          True  \n",
       "3          True  \n",
       "4          True  \n",
       "...         ...  \n",
       "110943    False  \n",
       "110944    False  \n",
       "110945    False  \n",
       "110946    False  \n",
       "110947    False  \n",
       "\n",
       "[110948 rows x 11 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6a3ec3df-2fc4-4c68-a9f0-3948aa33d62b",
   "metadata": {},
   "outputs": [],
   "source": [
    "resultado2.to_csv('./data/tran_cli_fest.csv', sep=sep, decimal=decimal, index=index, encoding=encoding)"
   ]
  }
 ],
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