3-EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from datetime import datetime\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.set_option('display.max_rows', 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\Entregas\\\\EDA'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:\\\\Entregas\\\\EDA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\Entregas\\\\EDA'"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RENTAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "alq_alicante = pd.read_excel(\"raw/ALQ_COM_VALENCIANA.xlsx\", sheet_name=\"ALQ_ALICANTE\")\n",
    "alq_castellon = pd.read_excel(\"raw/ALQ_COM_VALENCIANA.xlsx\", sheet_name=\"ALQ_CASTELLON\")\n",
    "alq_valencia = pd.read_excel(\"raw/ALQ_COM_VALENCIANA.xlsx\", sheet_name=\"ALQ_VALENCIA\")\n",
    "idealista_table = pd.read_csv(\"idealista_localidades_cv.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VALENCIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mes', 'Precio_alq_Valencia', 'unidad', 'Variación_mensual_Valencia',\n",
       "       'Variación_trimestral_Valencia', 'Variación_anual_Valencia'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_valencia.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>Mes</th>\n",
       "      <th>Precio_alq_Valencia</th>\n",
       "      <th>Variación_mensual_Valencia</th>\n",
       "      <th>Variación_trimestral_Valencia</th>\n",
       "      <th>Variación_anual_Valencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9/1/22</td>\n",
       "      <td>9.60000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.07000</td>\n",
       "      <td>0.16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8/1/22</td>\n",
       "      <td>9.50000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7/1/22</td>\n",
       "      <td>9.10000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.06000</td>\n",
       "      <td>0.11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6/1/22</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/1/22</td>\n",
       "      <td>8.80000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.08000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>5/1/06</td>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>4/1/06</td>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>3/1/06</td>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2/1/06</td>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>1/1/06</td>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Mes  Precio_alq_Valencia  Variación_mensual_Valencia  \\\n",
       "0    9/1/22              9.60000                     0.01000   \n",
       "1    8/1/22              9.50000                     0.04000   \n",
       "2    7/1/22              9.10000                     0.02000   \n",
       "3    6/1/22              9.00000                     0.02000   \n",
       "4    5/1/22              8.80000                     0.02000   \n",
       "..      ...                  ...                         ...   \n",
       "196  5/1/06              9.70000                     0.00000   \n",
       "197  4/1/06              9.70000                     0.00000   \n",
       "198  3/1/06              9.70000                     0.00000   \n",
       "199  2/1/06              9.70000                     0.00000   \n",
       "200  1/1/06              9.70000                     0.00000   \n",
       "\n",
       "     Variación_trimestral_Valencia  Variación_anual_Valencia  \n",
       "0                          0.07000                   0.16000  \n",
       "1                          0.08000                   0.14000  \n",
       "2                          0.06000                   0.11000  \n",
       "3                          0.05000                   0.10000  \n",
       "4                          0.03000                   0.08000  \n",
       "..                             ...                       ...  \n",
       "196                        0.00000                   0.00000  \n",
       "197                        0.00000                   0.00000  \n",
       "198                        0.00000                   0.00000  \n",
       "199                        0.00000                   0.00000  \n",
       "200                        0.00000                   0.00000  \n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_valencia = alq_valencia.drop('unidad',axis=1)\n",
    "alq_valencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Precio_alq_Valencia</th>\n",
       "      <th>Variación_mensual_Valencia</th>\n",
       "      <th>Variación_trimestral_Valencia</th>\n",
       "      <th>Variación_anual_Valencia</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.60000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.07000</td>\n",
       "      <td>0.16000</td>\n",
       "      <td>2022-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.50000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.14000</td>\n",
       "      <td>2022-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.10000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.06000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>2022-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.00000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>2022-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.80000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>2022-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2006-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2006-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2006-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2006-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2006-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Precio_alq_Valencia  Variación_mensual_Valencia  \\\n",
       "0                9.60000                     0.01000   \n",
       "1                9.50000                     0.04000   \n",
       "2                9.10000                     0.02000   \n",
       "3                9.00000                     0.02000   \n",
       "4                8.80000                     0.02000   \n",
       "..                   ...                         ...   \n",
       "196              9.70000                     0.00000   \n",
       "197              9.70000                     0.00000   \n",
       "198              9.70000                     0.00000   \n",
       "199              9.70000                     0.00000   \n",
       "200              9.70000                     0.00000   \n",
       "\n",
       "     Variación_trimestral_Valencia  Variación_anual_Valencia       date  \n",
       "0                          0.07000                   0.16000 2022-09-01  \n",
       "1                          0.08000                   0.14000 2022-08-01  \n",
       "2                          0.06000                   0.11000 2022-07-01  \n",
       "3                          0.05000                   0.10000 2022-06-01  \n",
       "4                          0.03000                   0.08000 2022-05-01  \n",
       "..                             ...                       ...        ...  \n",
       "196                        0.00000                   0.00000 2006-05-01  \n",
       "197                        0.00000                   0.00000 2006-04-01  \n",
       "198                        0.00000                   0.00000 2006-03-01  \n",
       "199                        0.00000                   0.00000 2006-02-01  \n",
       "200                        0.00000                   0.00000 2006-01-01  \n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_valencia[\"date\"]= pd.to_datetime(alq_valencia.Mes)\n",
    "alq_valencia = alq_valencia.drop('Mes',axis=1)\n",
    "\n",
    "alq_valencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "alq_valencia.set_index(\"date\", inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "alq_valencia = alq_valencia.sort_index(ascending=True)\n",
    "alq_valencia = alq_valencia.rename(str.lower, axis='columns')\n",
    "first_price = alq_valencia.precio_alq_valencia[0]\n",
    "alq_valencia[\"precio_alq_valencia_normalizado\"]= alq_valencia.precio_alq_valencia.div(first_price).mul(100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precio_alq_valencia</th>\n",
       "      <th>variación_mensual_valencia</th>\n",
       "      <th>variación_trimestral_valencia</th>\n",
       "      <th>variación_anual_valencia</th>\n",
       "      <th>precio_alq_valencia_normalizado</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
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       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>8.80000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>90.72165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>9.00000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>92.78351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>9.10000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.06000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>93.81443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>9.50000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.14000</td>\n",
       "      <td>97.93814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>9.60000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.07000</td>\n",
       "      <td>0.16000</td>\n",
       "      <td>98.96907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precio_alq_valencia  variación_mensual_valencia  \\\n",
       "date                                                          \n",
       "2006-01-01              9.70000                     0.00000   \n",
       "2006-02-01              9.70000                     0.00000   \n",
       "2006-03-01              9.70000                     0.00000   \n",
       "2006-04-01              9.70000                     0.00000   \n",
       "2006-05-01              9.70000                     0.00000   \n",
       "...                         ...                         ...   \n",
       "2022-05-01              8.80000                     0.02000   \n",
       "2022-06-01              9.00000                     0.02000   \n",
       "2022-07-01              9.10000                     0.02000   \n",
       "2022-08-01              9.50000                     0.04000   \n",
       "2022-09-01              9.60000                     0.01000   \n",
       "\n",
       "            variación_trimestral_valencia  variación_anual_valencia  \\\n",
       "date                                                                  \n",
       "2006-01-01                        0.00000                   0.00000   \n",
       "2006-02-01                        0.00000                   0.00000   \n",
       "2006-03-01                        0.00000                   0.00000   \n",
       "2006-04-01                        0.00000                   0.00000   \n",
       "2006-05-01                        0.00000                   0.00000   \n",
       "...                                   ...                       ...   \n",
       "2022-05-01                        0.03000                   0.08000   \n",
       "2022-06-01                        0.05000                   0.10000   \n",
       "2022-07-01                        0.06000                   0.11000   \n",
       "2022-08-01                        0.08000                   0.14000   \n",
       "2022-09-01                        0.07000                   0.16000   \n",
       "\n",
       "            precio_alq_valencia_normalizado  \n",
       "date                                         \n",
       "2006-01-01                        100.00000  \n",
       "2006-02-01                        100.00000  \n",
       "2006-03-01                        100.00000  \n",
       "2006-04-01                        100.00000  \n",
       "2006-05-01                        100.00000  \n",
       "...                                     ...  \n",
       "2022-05-01                         90.72165  \n",
       "2022-06-01                         92.78351  \n",
       "2022-07-01                         93.81443  \n",
       "2022-08-01                         97.93814  \n",
       "2022-09-01                         98.96907  \n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_valencia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ALICANTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precio_alq_alicante</th>\n",
       "      <th>variación_mensual_alicante</th>\n",
       "      <th>variación_trimestral_alicante</th>\n",
       "      <th>variación_anual_alicante</th>\n",
       "      <th>precio_alq_alicante_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>9.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>9.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>9.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>9.20000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>102.22222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>8.80000</td>\n",
       "      <td>-0.05000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>97.77778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>8.00000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>88.88889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>8.20000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.06000</td>\n",
       "      <td>0.15000</td>\n",
       "      <td>91.11111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>8.50000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>94.44444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>8.60000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.07000</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>95.55556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>8.50000</td>\n",
       "      <td>-0.01000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>94.44444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precio_alq_alicante  variación_mensual_alicante  \\\n",
       "date                                                          \n",
       "2006-01-01              9.00000                     0.00000   \n",
       "2006-02-01              9.00000                     0.00000   \n",
       "2006-03-01              9.00000                     0.00000   \n",
       "2006-04-01              9.20000                     0.03000   \n",
       "2006-05-01              8.80000                    -0.05000   \n",
       "...                         ...                         ...   \n",
       "2022-05-01              8.00000                     0.02000   \n",
       "2022-06-01              8.20000                     0.03000   \n",
       "2022-07-01              8.50000                     0.04000   \n",
       "2022-08-01              8.60000                     0.01000   \n",
       "2022-09-01              8.50000                    -0.01000   \n",
       "\n",
       "            variación_trimestral_alicante  variación_anual_alicante  \\\n",
       "date                                                                  \n",
       "2006-01-01                        0.00000                   0.00000   \n",
       "2006-02-01                        0.00000                   0.00000   \n",
       "2006-03-01                        0.00000                   0.00000   \n",
       "2006-04-01                        0.00000                   0.00000   \n",
       "2006-05-01                        0.00000                   0.00000   \n",
       "...                                   ...                       ...   \n",
       "2022-05-01                        0.04000                   0.12000   \n",
       "2022-06-01                        0.06000                   0.15000   \n",
       "2022-07-01                        0.09000                   0.17000   \n",
       "2022-08-01                        0.07000                   0.18000   \n",
       "2022-09-01                        0.03000                   0.17000   \n",
       "\n",
       "            precio_alq_alicante_normalizado  \n",
       "date                                         \n",
       "2006-01-01                        100.00000  \n",
       "2006-02-01                        100.00000  \n",
       "2006-03-01                        100.00000  \n",
       "2006-04-01                        102.22222  \n",
       "2006-05-01                         97.77778  \n",
       "...                                     ...  \n",
       "2022-05-01                         88.88889  \n",
       "2022-06-01                         91.11111  \n",
       "2022-07-01                         94.44444  \n",
       "2022-08-01                         95.55556  \n",
       "2022-09-01                         94.44444  \n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_alicante = alq_alicante.drop('unidad',axis=1)\n",
    "alq_alicante[\"date\"]= pd.to_datetime(alq_alicante.Mes)\n",
    "alq_alicante = alq_alicante.drop('Mes',axis=1)\n",
    "alq_alicante.set_index(\"date\", inplace=True)\n",
    "alq_alicante = alq_alicante.sort_index(ascending=True)\n",
    "alq_alicante = alq_alicante.rename(str.lower, axis='columns')\n",
    "first_price = alq_alicante.precio_alq_alicante[0]\n",
    "alq_alicante[\"precio_alq_alicante_normalizado\"]= alq_alicante.precio_alq_alicante.div(first_price).mul(100)\n",
    "alq_alicante"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CASTELLON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precio_alq_castellon</th>\n",
       "      <th>variación_mensual_castellon</th>\n",
       "      <th>variación_trimestral_castellon</th>\n",
       "      <th>variación_anual_castellon</th>\n",
       "      <th>precio_alq_castellon_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>7.80000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>7.80000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>7.80000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>7.80000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>7.80000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>7.00000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>89.74359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>7.20000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>92.30769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>7.50000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>96.15385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>7.40000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.07000</td>\n",
       "      <td>0.16000</td>\n",
       "      <td>94.87179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>7.10000</td>\n",
       "      <td>-0.05000</td>\n",
       "      <td>-0.01000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>91.02564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precio_alq_castellon  variación_mensual_castellon  \\\n",
       "date                                                            \n",
       "2006-01-01               7.80000                      0.00000   \n",
       "2006-02-01               7.80000                      0.00000   \n",
       "2006-03-01               7.80000                      0.00000   \n",
       "2006-04-01               7.80000                      0.00000   \n",
       "2006-05-01               7.80000                      0.00000   \n",
       "...                          ...                          ...   \n",
       "2022-05-01               7.00000                      0.03000   \n",
       "2022-06-01               7.20000                      0.03000   \n",
       "2022-07-01               7.50000                      0.04000   \n",
       "2022-08-01               7.40000                      0.00000   \n",
       "2022-09-01               7.10000                     -0.05000   \n",
       "\n",
       "            variación_trimestral_castellon  variación_anual_castellon  \\\n",
       "date                                                                    \n",
       "2006-01-01                         0.00000                    0.00000   \n",
       "2006-02-01                         0.00000                    0.00000   \n",
       "2006-03-01                         0.00000                    0.00000   \n",
       "2006-04-01                         0.00000                    0.00000   \n",
       "2006-05-01                         0.00000                    0.00000   \n",
       "...                                    ...                        ...   \n",
       "2022-05-01                         0.08000                    0.12000   \n",
       "2022-06-01                         0.09000                    0.13000   \n",
       "2022-07-01                         0.10000                    0.17000   \n",
       "2022-08-01                         0.07000                    0.16000   \n",
       "2022-09-01                        -0.01000                    0.11000   \n",
       "\n",
       "            precio_alq_castellon_normalizado  \n",
       "date                                          \n",
       "2006-01-01                         100.00000  \n",
       "2006-02-01                         100.00000  \n",
       "2006-03-01                         100.00000  \n",
       "2006-04-01                         100.00000  \n",
       "2006-05-01                         100.00000  \n",
       "...                                      ...  \n",
       "2022-05-01                          89.74359  \n",
       "2022-06-01                          92.30769  \n",
       "2022-07-01                          96.15385  \n",
       "2022-08-01                          94.87179  \n",
       "2022-09-01                          91.02564  \n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_castellon = alq_castellon.drop('unidad',axis=1)\n",
    "alq_castellon[\"date\"]= pd.to_datetime(alq_castellon.Mes)\n",
    "alq_castellon = alq_castellon.drop('Mes',axis=1)\n",
    "alq_castellon.set_index(\"date\", inplace=True)\n",
    "alq_castellon = alq_castellon.sort_index(ascending=True)\n",
    "alq_castellon = alq_castellon.rename(str.lower, axis='columns')\n",
    "first_price = alq_castellon.precio_alq_castellon[0]\n",
    "alq_castellon[\"precio_alq_castellon_normalizado\"]= alq_castellon.precio_alq_castellon.div(first_price).mul(100)\n",
    "alq_castellon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alq_castellon.set_axis([\"precio\",\"varmens\",\"vartri\", \"varanu\"],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SALES \n",
    "\n",
    "### VALENCIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fecha</th>\n",
       "      <th>precio_vta_valencia</th>\n",
       "      <th>variacion_vta_mensual_valencia</th>\n",
       "      <th>variacion_vta_trimetral_valencia</th>\n",
       "      <th>variacion_vta_anual_valencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sep-22</td>\n",
       "      <td>1303</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aug-22</td>\n",
       "      <td>1305</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.04000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jul-22</td>\n",
       "      <td>1278</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jun-22</td>\n",
       "      <td>1272</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May-22</td>\n",
       "      <td>1264</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.01000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>May-06</td>\n",
       "      <td>1806</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Apr-06</td>\n",
       "      <td>1763</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Mar-06</td>\n",
       "      <td>1585</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Feb-06</td>\n",
       "      <td>1580</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Jan-06</td>\n",
       "      <td>1580</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fecha  precio_vta_valencia  variacion_vta_mensual_valencia  \\\n",
       "0    Sep-22                 1303                         0.00000   \n",
       "1    Aug-22                 1305                         0.02000   \n",
       "2    Jul-22                 1278                         0.01000   \n",
       "3    Jun-22                 1272                         0.01000   \n",
       "4    May-22                 1264                         0.01000   \n",
       "..      ...                  ...                             ...   \n",
       "196  May-06                 1806                         0.03000   \n",
       "197  Apr-06                 1763                         0.11000   \n",
       "198  Mar-06                 1585                         0.00000   \n",
       "199  Feb-06                 1580                         0.00000   \n",
       "200  Jan-06                 1580                         0.00000   \n",
       "\n",
       "     variacion_vta_trimetral_valencia  variacion_vta_anual_valencia  \n",
       "0                             0.02000                       0.05000  \n",
       "1                             0.03000                       0.04000  \n",
       "2                             0.02000                       0.00000  \n",
       "3                             0.04000                       0.00000  \n",
       "4                             0.04000                       0.01000  \n",
       "..                                ...                           ...  \n",
       "196                           0.00000                       0.00000  \n",
       "197                           0.00000                       0.00000  \n",
       "198                           0.00000                       0.00000  \n",
       "199                           0.00000                       0.00000  \n",
       "200                           0.00000                       0.00000  \n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vta_valencia = pd.read_excel(\"raw/VTA_COM_VALENCIANA.xlsx\", sheet_name=\"VALENCIA\")\n",
    "vta_alicante = pd.read_excel(\"raw/VTA_COM_VALENCIANA.xlsx\", sheet_name=\"ALICANTE\")\n",
    "vta_castellon = pd.read_excel(\"raw/VTA_COM_VALENCIANA.xlsx\", sheet_name=\"CASTELLON\")\n",
    "\n",
    "\n",
    "vta_valencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fecha</th>\n",
       "      <th>precio_vta_valencia</th>\n",
       "      <th>variacion_vta_mensual_valencia</th>\n",
       "      <th>variacion_vta_trimetral_valencia</th>\n",
       "      <th>variacion_vta_anual_valencia</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sep-22</td>\n",
       "      <td>1303</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>Sep 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aug-22</td>\n",
       "      <td>1305</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>Aug 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jul-22</td>\n",
       "      <td>1278</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>Jul 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jun-22</td>\n",
       "      <td>1272</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>Jun 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May-22</td>\n",
       "      <td>1264</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>May 2022</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>May-06</td>\n",
       "      <td>1806</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>May 2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Apr-06</td>\n",
       "      <td>1763</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>Apr 2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Mar-06</td>\n",
       "      <td>1585</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>Mar 2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Feb-06</td>\n",
       "      <td>1580</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>Feb 2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Jan-06</td>\n",
       "      <td>1580</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>Jan 2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fecha  precio_vta_valencia  variacion_vta_mensual_valencia  \\\n",
       "0    Sep-22                 1303                         0.00000   \n",
       "1    Aug-22                 1305                         0.02000   \n",
       "2    Jul-22                 1278                         0.01000   \n",
       "3    Jun-22                 1272                         0.01000   \n",
       "4    May-22                 1264                         0.01000   \n",
       "..      ...                  ...                             ...   \n",
       "196  May-06                 1806                         0.03000   \n",
       "197  Apr-06                 1763                         0.11000   \n",
       "198  Mar-06                 1585                         0.00000   \n",
       "199  Feb-06                 1580                         0.00000   \n",
       "200  Jan-06                 1580                         0.00000   \n",
       "\n",
       "     variacion_vta_trimetral_valencia  variacion_vta_anual_valencia      date  \n",
       "0                             0.02000                       0.05000  Sep 2022  \n",
       "1                             0.03000                       0.04000  Aug 2022  \n",
       "2                             0.02000                       0.00000  Jul 2022  \n",
       "3                             0.04000                       0.00000  Jun 2022  \n",
       "4                             0.04000                       0.01000  May 2022  \n",
       "..                                ...                           ...       ...  \n",
       "196                           0.00000                       0.00000  May 2006  \n",
       "197                           0.00000                       0.00000  Apr 2006  \n",
       "198                           0.00000                       0.00000  Mar 2006  \n",
       "199                           0.00000                       0.00000  Feb 2006  \n",
       "200                           0.00000                       0.00000  Jan 2006  \n",
       "\n",
       "[201 rows x 6 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vta_valencia[\"date\"]=[x.replace(\"-\",\" 20\") for x in vta_valencia[\"fecha\"]]\n",
    "vta_valencia\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precio_vta_valencia</th>\n",
       "      <th>variacion_vta_mensual_valencia</th>\n",
       "      <th>variacion_vta_trimetral_valencia</th>\n",
       "      <th>variacion_vta_anual_valencia</th>\n",
       "      <th>precio_vta_valencia_normalizado</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>1580</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>1580</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>1585</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.31646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>1763</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>111.58228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>1806</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>114.30380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>1264</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>80.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>1272</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>80.50633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>1278</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>80.88608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>1305</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>82.59494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>1303</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>82.46835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precio_vta_valencia  variacion_vta_mensual_valencia  \\\n",
       "date                                                              \n",
       "2006-01-01                 1580                         0.00000   \n",
       "2006-02-01                 1580                         0.00000   \n",
       "2006-03-01                 1585                         0.00000   \n",
       "2006-04-01                 1763                         0.11000   \n",
       "2006-05-01                 1806                         0.03000   \n",
       "...                         ...                             ...   \n",
       "2022-05-01                 1264                         0.01000   \n",
       "2022-06-01                 1272                         0.01000   \n",
       "2022-07-01                 1278                         0.01000   \n",
       "2022-08-01                 1305                         0.02000   \n",
       "2022-09-01                 1303                         0.00000   \n",
       "\n",
       "            variacion_vta_trimetral_valencia  variacion_vta_anual_valencia  \\\n",
       "date                                                                         \n",
       "2006-01-01                           0.00000                       0.00000   \n",
       "2006-02-01                           0.00000                       0.00000   \n",
       "2006-03-01                           0.00000                       0.00000   \n",
       "2006-04-01                           0.00000                       0.00000   \n",
       "2006-05-01                           0.00000                       0.00000   \n",
       "...                                      ...                           ...   \n",
       "2022-05-01                           0.04000                       0.01000   \n",
       "2022-06-01                           0.04000                       0.00000   \n",
       "2022-07-01                           0.02000                       0.00000   \n",
       "2022-08-01                           0.03000                       0.04000   \n",
       "2022-09-01                           0.02000                       0.05000   \n",
       "\n",
       "            precio_vta_valencia_normalizado  \n",
       "date                                         \n",
       "2006-01-01                        100.00000  \n",
       "2006-02-01                        100.00000  \n",
       "2006-03-01                        100.31646  \n",
       "2006-04-01                        111.58228  \n",
       "2006-05-01                        114.30380  \n",
       "...                                     ...  \n",
       "2022-05-01                         80.00000  \n",
       "2022-06-01                         80.50633  \n",
       "2022-07-01                         80.88608  \n",
       "2022-08-01                         82.59494  \n",
       "2022-09-01                         82.46835  \n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vta_valencia[\"date\"]=pd.to_datetime(vta_valencia[\"date\"])\n",
    "vta_valencia.set_index(\"date\", inplace=True)\n",
    "vta_valencia = vta_valencia.sort_index(ascending=True)\n",
    "vta_valencia = vta_valencia.rename(str.lower, axis='columns')\n",
    "first_price = vta_valencia.precio_vta_valencia[0]\n",
    "vta_valencia[\"precio_vta_valencia_normalizado\"]= vta_valencia.precio_vta_valencia.div(first_price).mul(100)\n",
    "vta_valencia = vta_valencia.drop(\"fecha\", axis=1)\n",
    "vta_valencia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ALICANTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "vta_alicante = vta_alicante.rename(columns={'fecha':'date'})\n",
    "vta_alicante[\"date\"] = pd.to_datetime(vta_alicante.date)\n",
    "vta_alicante.set_index(\"date\", inplace=True)\n",
    "vta_alicante = vta_alicante.sort_index(ascending=True)\n",
    "vta_alicante = vta_alicante.rename(str.lower, axis='columns')\n",
    "first_price = vta_alicante.precio_vta_alicante[0]\n",
    "vta_alicante[\"precio_vta_alicante_normalizado\"]= vta_alicante.precio_vta_alicante.div(first_price).mul(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precio_vta_alicante</th>\n",
       "      <th>variacion_vta_mensual_alicante</th>\n",
       "      <th>variacion_vta_trimestral_alicante</th>\n",
       "      <th>variacion_vta_anual_alicante</th>\n",
       "      <th>precio_vta_alicante_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>1944</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>1975</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>101.59465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>1896</td>\n",
       "      <td>-0.04000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>97.53086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>1871</td>\n",
       "      <td>-0.01000</td>\n",
       "      <td>-0.04000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>96.24486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>1776</td>\n",
       "      <td>-0.05000</td>\n",
       "      <td>-0.10000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>91.35802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>1796</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>92.38683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>1815</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>93.36420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>1836</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.09000</td>\n",
       "      <td>94.44444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>1854</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>95.37037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>1872</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>96.29630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precio_vta_alicante  variacion_vta_mensual_alicante  \\\n",
       "date                                                              \n",
       "2006-01-01                 1944                         0.00000   \n",
       "2006-02-01                 1975                         0.02000   \n",
       "2006-03-01                 1896                        -0.04000   \n",
       "2006-04-01                 1871                        -0.01000   \n",
       "2006-05-01                 1776                        -0.05000   \n",
       "...                         ...                             ...   \n",
       "2022-05-01                 1796                         0.02000   \n",
       "2022-06-01                 1815                         0.01000   \n",
       "2022-07-01                 1836                         0.01000   \n",
       "2022-08-01                 1854                         0.01000   \n",
       "2022-09-01                 1872                         0.01000   \n",
       "\n",
       "            variacion_vta_trimestral_alicante  variacion_vta_anual_alicante  \\\n",
       "date                                                                          \n",
       "2006-01-01                            0.00000                       0.00000   \n",
       "2006-02-01                            0.00000                       0.00000   \n",
       "2006-03-01                            0.00000                       0.00000   \n",
       "2006-04-01                           -0.04000                       0.00000   \n",
       "2006-05-01                           -0.10000                       0.00000   \n",
       "...                                       ...                           ...   \n",
       "2022-05-01                            0.05000                       0.09000   \n",
       "2022-06-01                            0.05000                       0.08000   \n",
       "2022-07-01                            0.04000                       0.09000   \n",
       "2022-08-01                            0.03000                       0.10000   \n",
       "2022-09-01                            0.03000                       0.11000   \n",
       "\n",
       "            precio_vta_alicante_normalizado  \n",
       "date                                         \n",
       "2006-01-01                        100.00000  \n",
       "2006-02-01                        101.59465  \n",
       "2006-03-01                         97.53086  \n",
       "2006-04-01                         96.24486  \n",
       "2006-05-01                         91.35802  \n",
       "...                                     ...  \n",
       "2022-05-01                         92.38683  \n",
       "2022-06-01                         93.36420  \n",
       "2022-07-01                         94.44444  \n",
       "2022-08-01                         95.37037  \n",
       "2022-09-01                         96.29630  \n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vta_alicante"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CASTELLON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "vta_castellon = vta_castellon.rename(columns={'fecha':'date'})\n",
    "vta_castellon[\"date\"] = pd.to_datetime(vta_castellon.date)\n",
    "vta_castellon.set_index(\"date\", inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "vta_castellon = vta_castellon.sort_index(ascending=True)\n",
    "vta_castellon = vta_castellon.rename(str.lower, axis='columns')\n",
    "\n",
    "vta_castellon = vta_castellon[vta_castellon[\"precio_vta_castellon\"]>0]\n",
    "first_price = vta_castellon.precio_vta_castellon[0]\n",
    "vta_castellon[\"precio_vta_castellon_normalizado\"]= vta_castellon.precio_vta_castellon.div(first_price).mul(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>precio_vta_castellon</th>\n",
       "      <th>variacion_vta_mensual_castellon</th>\n",
       "      <th>variacion_vta_trimestral_castellon</th>\n",
       "      <th>variacion_vta_anual_castellon</th>\n",
       "      <th>precio_vta_castellon_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-09-01</th>\n",
       "      <td>1532</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-01</th>\n",
       "      <td>1680</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>109.66057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-01</th>\n",
       "      <td>1658</td>\n",
       "      <td>-0.01000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>108.22454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-01</th>\n",
       "      <td>1555</td>\n",
       "      <td>-0.06000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>101.50131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>1583</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>-0.06000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>103.32898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>1119</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>73.04178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>1127</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>73.56397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>1130</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>73.75979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>1134</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>74.02089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>1137</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>74.21671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            precio_vta_castellon  variacion_vta_mensual_castellon  \\\n",
       "date                                                                \n",
       "2006-09-01                  1532                          0.00000   \n",
       "2006-10-01                  1680                          0.10000   \n",
       "2006-11-01                  1658                         -0.01000   \n",
       "2006-12-01                  1555                         -0.06000   \n",
       "2007-01-01                  1583                          0.02000   \n",
       "...                          ...                              ...   \n",
       "2022-05-01                  1119                          0.01000   \n",
       "2022-06-01                  1127                          0.01000   \n",
       "2022-07-01                  1130                          0.00000   \n",
       "2022-08-01                  1134                          0.00000   \n",
       "2022-09-01                  1137                          0.00000   \n",
       "\n",
       "            variacion_vta_trimestral_castellon  variacion_vta_anual_castellon  \\\n",
       "date                                                                            \n",
       "2006-09-01                             0.00000                        0.00000   \n",
       "2006-10-01                             0.00000                        0.00000   \n",
       "2006-11-01                             0.00000                        0.00000   \n",
       "2006-12-01                             0.02000                        0.00000   \n",
       "2007-01-01                            -0.06000                        0.00000   \n",
       "...                                        ...                            ...   \n",
       "2022-05-01                             0.04000                        0.03000   \n",
       "2022-06-01                             0.03000                        0.03000   \n",
       "2022-07-01                             0.02000                        0.02000   \n",
       "2022-08-01                             0.01000                        0.03000   \n",
       "2022-09-01                             0.01000                        0.04000   \n",
       "\n",
       "            precio_vta_castellon_normalizado  \n",
       "date                                          \n",
       "2006-09-01                         100.00000  \n",
       "2006-10-01                         109.66057  \n",
       "2006-11-01                         108.22454  \n",
       "2006-12-01                         101.50131  \n",
       "2007-01-01                         103.32898  \n",
       "...                                      ...  \n",
       "2022-05-01                          73.04178  \n",
       "2022-06-01                          73.56397  \n",
       "2022-07-01                          73.75979  \n",
       "2022-08-01                          74.02089  \n",
       "2022-09-01                          74.21671  \n",
       "\n",
       "[193 rows x 5 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vta_castellon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "precio_alq_valencia                float64\n",
       "variación_mensual_valencia         float64\n",
       "variación_trimestral_valencia      float64\n",
       "variación_anual_valencia           float64\n",
       "precio_alq_valencia_normalizado    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_valencia.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "precio_vta_valencia                   int64\n",
       "variacion_vta_mensual_valencia      float64\n",
       "variacion_vta_trimetral_valencia    float64\n",
       "variacion_vta_anual_valencia        float64\n",
       "precio_vta_valencia_normalizado     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vta_valencia.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "vta_valencia.to_csv(\"N_vta_valencia.csv\")\n",
    "vta_alicante.to_csv(\"N_vta_alicante.csv\")\n",
    "vta_castellon.to_csv(\"N_vta_castellon.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "alq_alicante.to_csv(\"N_alq_alicante.csv\")\n",
    "alq_valencia.to_csv(\"N_alq_valencia.csv\")\n",
    "alq_castellon.to_csv(\"N_alq_castellon.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.getcwd()\n",
    "os.chdir(\"c:\\\\Entregas\\\\EDA\\\\\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\Entregas\\\\EDA'"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID CASES "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_cases = pd.read_csv(\"raw/COVID_nuevos_casos_notificados_CVAL.csv\", sep=\";\", encoding='latin-1' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>Nuevos casos notificados cada</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-4</th>\n",
       "      <td>954.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-7</th>\n",
       "      <td>1.20700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-14</th>\n",
       "      <td>2.22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>2.19500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-28</th>\n",
       "      <td>2.07100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>620 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nuevos casos notificados cada \n",
       "date                                      \n",
       "2020-02-21                         0.00000\n",
       "2020-02-22                         0.00000\n",
       "2020-02-23                         0.00000\n",
       "2020-02-24                         0.00000\n",
       "2020-02-25                         0.00000\n",
       "...                                    ...\n",
       "2022-10-4                        954.00000\n",
       "2022-10-7                          1.20700\n",
       "2022-10-14                         2.22000\n",
       "2022-10-21                         2.19500\n",
       "2022-10-28                         2.07100\n",
       "\n",
       "[620 rows x 1 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_cases= covid_cases.drop(\"Unnamed: 4\",axis=1) # drop de unnamed\n",
    "covid_cases.drop(covid_cases.columns[[0,1]],axis=1, inplace=True) #drop columnas 1 y 2 \n",
    "\n",
    "covid_cases[\"Fecha\"] = [x.replace(\"de\",\"\") for x in covid_cases[\"Fecha\"]] # removes \"de\" in Fecha\n",
    "\n",
    "dic_meses={ \"enero\":\"01\",\n",
    "            \"febrero\":\"02\",\n",
    "            \"marzo\":\"03\", \n",
    "            \"abril\":\"04\",\n",
    "            \"mayo\":\"05\", \n",
    "            \"junio\":\"06\" , \n",
    "            \"julio\": \"07\" ,\n",
    "            \"agosto\":\"08\", \n",
    "            \"septiembre\": \"09\",\n",
    "            \"setiembre\":\"09\", \n",
    "            \"octubre\" : \"10\", \n",
    "            \"noviembre\" : \"11\", \n",
    "            \"diciembre\": \"12\" } # set dictionary of months days \n",
    "\n",
    "covid_cases[\"Fecha\"] = [x.replace(x.split()[1], dic_meses[x.split()[1]]) for x in covid_cases[\"Fecha\"]] # changes month name by number\n",
    "covid_cases[\"date\"] = covid_cases[\"Fecha\"].str.split().str[2]+\"-\"+ covid_cases[\"Fecha\"].str.split().str[1]+\"-\"+covid_cases[\"Fecha\"].str.split().str[0] \n",
    "\n",
    "covid_cases.set_index(\"date\", inplace=True)\n",
    "covid_cases= covid_cases.drop(\"Fecha\",axis=1)\n",
    "\n",
    "covid_cases.to_csv(\"covid_cases.csv\")\n",
    "covid_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic_meses={\"enero\":\"01\", \"febrero\":\"02\",\"marzo\":\"03\", \"abril\":\"04\",\"mayo\":\"05\", \"junio\":\"06\" , \"julio\": \"07\" ,\"agosto\":\"08\", \"septiembre\": \"09\", \"setiembre\":\"09\", \"octubre\" : \"10\", \"noviembre\" : \"11\", \"diciembre\": \"12\" }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Fecha'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\soler\\anaconda3\\envs\\np_pd_soup_selenium\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3080\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3079\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3080\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m   3081\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[1;32mpandas\\_libs\\index.pyx:70\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\index.pyx:101\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:4554\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:4562\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Fecha'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [159], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m covid_cases[\u001b[39m\"\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m covid_cases[\u001b[39m\"\u001b[39;49m\u001b[39mFecha\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39mstr\u001b[39m.\u001b[39msplit()\u001b[39m.\u001b[39mstr[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m] \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m-\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m+\u001b[39m covid_cases[\u001b[39m\"\u001b[39m\u001b[39mFecha\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mstr\u001b[39m.\u001b[39msplit()\u001b[39m.\u001b[39mstr[\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mmap(dic_meses) \u001b[39m+\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m-\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m+\u001b[39m covid_cases[\u001b[39m\"\u001b[39m\u001b[39mFecha\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mstr\u001b[39m.\u001b[39msplit()\u001b[39m.\u001b[39mstr[\u001b[39m0\u001b[39m]\n\u001b[0;32m      2\u001b[0m covid_cases\n",
      "File \u001b[1;32mc:\\Users\\soler\\anaconda3\\envs\\np_pd_soup_selenium\\lib\\site-packages\\pandas\\core\\frame.py:3024\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3022\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m   3023\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3024\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[0;32m   3025\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3026\u001b[0m     indexer \u001b[39m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mc:\\Users\\soler\\anaconda3\\envs\\np_pd_soup_selenium\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3082\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3080\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3081\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m-> 3082\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m   3084\u001b[0m \u001b[39mif\u001b[39;00m tolerance \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   3085\u001b[0m     tolerance \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_convert_tolerance(tolerance, np\u001b[39m.\u001b[39masarray(key))\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Fecha'"
     ]
    }
   ],
   "source": [
    "covid_cases[\"date\"] = covid_cases[\"Fecha\"].str.split().str[-1] + \"-\"+ covid_cases[\"Fecha\"].str.split().str[1].map(dic_meses) +\"-\"+ covid_cases[\"Fecha\"].str.split().str[0]\n",
    "covid_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "######################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>Localidad</th>\n",
       "      <th>precio</th>\n",
       "      <th>maximo</th>\n",
       "      <th>Variacion_trimestral(%)</th>\n",
       "      <th>Variacion_anual(%)</th>\n",
       "      <th>Variacion_Maximo(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Comunitat Valenciana</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4.80000</td>\n",
       "      <td>15.80000</td>\n",
       "      <td>-4.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alicante/Alacant</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>3.10000</td>\n",
       "      <td>17.10000</td>\n",
       "      <td>-8.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alicante / Alacant</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.30000</td>\n",
       "      <td>21.30000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Benalua-La Florida-Babel-San Gabriel</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.20000</td>\n",
       "      <td>15.40000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Campoamor-Carolinas-Altozano</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.10000</td>\n",
       "      <td>23.20000</td>\n",
       "      <td>0.00000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Riba-Roja de Túria</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.1</td>\n",
       "      <td>-1.50000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-4.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Sagunto/Sagunt</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.40000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Torrent</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.20000</td>\n",
       "      <td>12.90000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Zona Avenida al Vedat</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>-4.40000</td>\n",
       "      <td>24.90000</td>\n",
       "      <td>-4.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Xirivella</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.10000</td>\n",
       "      <td>27.10000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Localidad precio maximo  \\\n",
       "0                    Comunitat Valenciana    8.9    9.4   \n",
       "1                        Alicante/Alacant    8.5    9.2   \n",
       "2                      Alicante / Alacant    9.0    9.0   \n",
       "3    Benalua-La Florida-Babel-San Gabriel    7.7    7.7   \n",
       "4            Campoamor-Carolinas-Altozano    8.0    8.0   \n",
       "..                                    ...    ...    ...   \n",
       "128                    Riba-Roja de Túria    6.8    7.1   \n",
       "129                        Sagunto/Sagunt    6.6    6.6   \n",
       "130                               Torrent    7.4    7.4   \n",
       "131                 Zona Avenida al Vedat    7.5    7.8   \n",
       "132                             Xirivella    8.3    8.3   \n",
       "\n",
       "     Variacion_trimestral(%)  Variacion_anual(%)  Variacion_Maximo(%)  \n",
       "0                    4.80000            15.80000             -4.90000  \n",
       "1                    3.10000            17.10000             -8.20000  \n",
       "2                    8.30000            21.30000              0.00000  \n",
       "3                    8.20000            15.40000              0.00000  \n",
       "4                   11.10000            23.20000              0.00000  \n",
       "..                       ...                 ...                  ...  \n",
       "128                 -1.50000             0.00000             -4.80000  \n",
       "129                  8.40000             0.00000              0.00000  \n",
       "130                  2.20000            12.90000              0.00000  \n",
       "131                 -4.40000            24.90000             -4.40000  \n",
       "132                  9.10000            27.10000              0.00000  \n",
       "\n",
       "[133 rows x 6 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idealista = pd.read_csv(\"tabla_idealista.csv\")\n",
    "\n",
    "idealista = idealista.drop(\"Unnamed: 0\", axis=1)\n",
    "\n",
    "idealista[\"precio\"] = [x.split()[0].replace(\",\",\".\") for x in idealista[\"Precio_(€/m2)\"]]\n",
    "idealista[\"maximo\"] = [x.split()[0].replace(\",\",\".\") for x in idealista[\"Maximo_historico(€/m2)\"]]\n",
    "idealista.drop(idealista.columns[[1,4]],axis=1, inplace=True)\n",
    "\n",
    "idealista = idealista[[\"Localidad\",\"precio\", \"maximo\",\"Variacion_trimestral(%)\",\"Variacion_anual(%)\",\"Variacion_Maximo(%)\"]]\n",
    "idealista.to_csv(\"idealista_localidades_cv.csv\")\n",
    "idealista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# idealista[\"Precio_(€/m2)\"].str.split()[0][0].replace(\",\",\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EURIBOR EVOLUTION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "euribor = pd.read_csv(\"raw/FIN_evolucion_del_euribor_mensual.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic_inicio_mes={ \"enero\":\"01-01\", \n",
    "            \"febrero\":\"02-01\", \n",
    "            \"marzo\":\"03-01\", \n",
    "            \"abril\":\"04-01\",\n",
    "            \"mayo\":\"05-01\", \n",
    "            \"junio\":\"06-01\" , \n",
    "            \"julio\": \"07-01\" ,\n",
    "            \"agosto\":\"08-01\", \n",
    "            \"septiembre\": \"09-01\",\n",
    "            \"setiembre\":\"09-01\", \n",
    "            \"octubre\" : \"10-01\", \n",
    "            \"noviembre\" : \"11-01\", \n",
    "            \"diciembre\": \"12-01\" }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>Euribor</th>\n",
       "      <th>euribor_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>2.91000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>3.11000</td>\n",
       "      <td>106.87285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>3.22000</td>\n",
       "      <td>110.65292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>3.31000</td>\n",
       "      <td>113.74570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-06-01</th>\n",
       "      <td>3.40000</td>\n",
       "      <td>116.83849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>0.29000</td>\n",
       "      <td>9.96564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>0.85000</td>\n",
       "      <td>29.20962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>0.99000</td>\n",
       "      <td>34.02062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>1.25000</td>\n",
       "      <td>42.95533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>2.23000</td>\n",
       "      <td>76.63230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Euribor  euribor_normalizado\n",
       "date                                    \n",
       "2006-02-01  2.91000            100.00000\n",
       "2006-03-01  3.11000            106.87285\n",
       "2006-04-01  3.22000            110.65292\n",
       "2006-05-01  3.31000            113.74570\n",
       "2006-06-01  3.40000            116.83849\n",
       "...             ...                  ...\n",
       "2022-05-01  0.29000              9.96564\n",
       "2022-06-01  0.85000             29.20962\n",
       "2022-07-01  0.99000             34.02062\n",
       "2022-08-01  1.25000             42.95533\n",
       "2022-09-01  2.23000             76.63230\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euribor[\"Periodo\"] = euribor[\"Periodo\"].str.lower()\n",
    "euribor[\"mes\"] = [x.replace(x.split()[0],dic_inicio_mes[x.split()[0]])for x in euribor[\"Periodo\"]]\n",
    "euribor[\"Año\"] = euribor[\"Año\"].apply(str)\n",
    "euribor[\"date\"] = euribor[\"Año\"]+\"-\"+euribor[\"mes\"]\n",
    "\n",
    "euribor = euribor.drop(euribor.columns[[0,1,3]], axis=1)\n",
    "euribor = euribor[euribor.date > \"2006-01-01\"]\n",
    "euribor[\"date\"]= pd.to_datetime(euribor[\"date\"])\n",
    "euribor.set_index(\"date\", inplace=True)\n",
    "first_price = euribor.Euribor[0]\n",
    "euribor[\"euribor_normalizado\"]= euribor.Euribor.div(first_price).mul(100)\n",
    "euribor.to_csv(\"N_euribor_mes.csv\")\n",
    "euribor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MORTGAGE EXECUTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Total Nacional                Comunidad Periodo  Total\n",
      "0           Total  10 Comunidad Valenciana  2022Q2    817\n",
      "1           Total  10 Comunidad Valenciana  2022Q1    747\n",
      "2           Total  10 Comunidad Valenciana  2021Q4    793\n",
      "3           Total  10 Comunidad Valenciana  2021Q3    590\n",
      "4           Total  10 Comunidad Valenciana  2021Q2    762\n",
      "..            ...                      ...     ...    ...\n",
      "29          Total  10 Comunidad Valenciana  2015Q1   2060\n",
      "30          Total  10 Comunidad Valenciana  2014Q4   2151\n",
      "31          Total  10 Comunidad Valenciana  2014Q3   1732\n",
      "32          Total  10 Comunidad Valenciana  2014Q2   2337\n",
      "33          Total  10 Comunidad Valenciana  2014Q1   1988\n",
      "\n",
      "[34 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\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>comunidad</th>\n",
       "      <th>total</th>\n",
       "      <th>executions_normalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>1988</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>2337</td>\n",
       "      <td>117.55533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>1732</td>\n",
       "      <td>87.12274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>2151</td>\n",
       "      <td>108.19920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>2060</td>\n",
       "      <td>103.62173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>762</td>\n",
       "      <td>38.32998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>590</td>\n",
       "      <td>29.67807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>793</td>\n",
       "      <td>39.88934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>747</td>\n",
       "      <td>37.57545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>817</td>\n",
       "      <td>41.09658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       comunidad  total  executions_normalized\n",
       "date                                                          \n",
       "2014-01-01  Comunidad Valenciana   1988              100.00000\n",
       "2014-04-01  Comunidad Valenciana   2337              117.55533\n",
       "2014-07-01  Comunidad Valenciana   1732               87.12274\n",
       "2014-10-01  Comunidad Valenciana   2151              108.19920\n",
       "2015-01-01  Comunidad Valenciana   2060              103.62173\n",
       "...                          ...    ...                    ...\n",
       "2021-04-01  Comunidad Valenciana    762               38.32998\n",
       "2021-07-01  Comunidad Valenciana    590               29.67807\n",
       "2021-10-01  Comunidad Valenciana    793               39.88934\n",
       "2022-01-01  Comunidad Valenciana    747               37.57545\n",
       "2022-04-01  Comunidad Valenciana    817               41.09658\n",
       "\n",
       "[34 rows x 3 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mortgages = pd.read_csv(\"raw/FIN_ejecuciones_hipotecarias_comunidad_valenciana.csv\")\n",
    "print(mortgages)\n",
    "mortgages[\"Periodo\"] = pd.to_datetime(mortgages[\"Periodo\"])\n",
    "mortgages[\"Comunidad\"] = mortgages[\"Comunidad\"].str[3:]\n",
    "mortgages = mortgages.drop(\"Total Nacional\", axis=1)\n",
    "mortgages.rename(columns= {\"Periodo\":\"date\"}, inplace=True)\n",
    "mortgages.set_index(\"date\", inplace=True)\n",
    "mortgages = mortgages.sort_index(ascending=True)\n",
    "mortgages = mortgages.rename(str.lower, axis=\"columns\")\n",
    "first_value = mortgages.total[0]\n",
    "mortgages[\"executions_normalized\"]= mortgages.total.div(first_value).mul(100) \n",
    "mortgages.to_csv(\"N_Ejecuciones_hipotecarias_cv.csv\")\n",
    "mortgages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MORTGAGE TYPE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>tipo de interes</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>75.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>73.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>72.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>75.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>72.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-01</th>\n",
       "      <td>Variable</td>\n",
       "      <td>96.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>Variable</td>\n",
       "      <td>97.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-01</th>\n",
       "      <td>Variable</td>\n",
       "      <td>97.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-01</th>\n",
       "      <td>Variable</td>\n",
       "      <td>96.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>Variable</td>\n",
       "      <td>96.60000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>326 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           tipo de interes    total\n",
       "date                               \n",
       "2022-07-01            Fijo 75.40000\n",
       "2022-06-01            Fijo 73.00000\n",
       "2022-05-01            Fijo 72.80000\n",
       "2022-04-01            Fijo 75.30000\n",
       "2022-03-01            Fijo 72.70000\n",
       "...                    ...      ...\n",
       "2009-05-01        Variable 96.90000\n",
       "2009-04-01        Variable 97.40000\n",
       "2009-03-01        Variable 97.00000\n",
       "2009-02-01        Variable 96.90000\n",
       "2009-01-01        Variable 96.60000\n",
       "\n",
       "[326 rows x 2 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mort_type = pd.read_csv(\"raw/FIN_interes _fijo_vs_variable.csv\")\n",
    "# print(mort_type)\n",
    "mort_type = mort_type.rename(str.lower, axis=\"columns\")\n",
    "mort_type[\"date\"] = pd.to_datetime(mort_type[\"periodo\"])\n",
    "mort_type = mort_type.drop(mort_type.columns[[1,2]],axis=1)\n",
    "mort_type.set_index(mort_type[\"date\"],inplace=True)\n",
    "# mort_type =mort_type.sort_index(ascending=True)\n",
    "mort_type = mort_type.drop(mort_type.columns[[2]],axis=1)\n",
    "mort_type.to_csv(\"emisiones_hipotecas_tipo.csv\")\n",
    "mort_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RATES\n",
    "\n",
    "### IPC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>grupos_ipc_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>14.60000</td>\n",
       "      <td>alimentos y bebidas no alcoholicas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>14.50000</td>\n",
       "      <td>comunitat valenciana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>14.40000</td>\n",
       "      <td>alimentos y bebidas no alcoholicas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>13.70000</td>\n",
       "      <td>alimentos y bebidas no alcoholicas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>11.60000</td>\n",
       "      <td>alimentos y bebidas no alcoholicas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-05-01</th>\n",
       "      <td>1.30000</td>\n",
       "      <td>transporte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-04-01</th>\n",
       "      <td>2.30000</td>\n",
       "      <td>transporte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-01</th>\n",
       "      <td>1.50000</td>\n",
       "      <td>transporte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-02-01</th>\n",
       "      <td>0.10000</td>\n",
       "      <td>transporte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>transporte</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>747 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              total                     grupos_ipc_type\n",
       "date                                                   \n",
       "2022-09-01 14.60000  alimentos y bebidas no alcoholicas\n",
       "2022-08-01 14.50000                comunitat valenciana\n",
       "2022-07-01 14.40000  alimentos y bebidas no alcoholicas\n",
       "2022-06-01 13.70000  alimentos y bebidas no alcoholicas\n",
       "2022-05-01 11.60000  alimentos y bebidas no alcoholicas\n",
       "...             ...                                 ...\n",
       "2002-05-01  1.30000                          transporte\n",
       "2002-04-01  2.30000                          transporte\n",
       "2002-03-01  1.50000                          transporte\n",
       "2002-02-01  0.10000                          transporte\n",
       "2002-01-01      NaN                          transporte\n",
       "\n",
       "[747 rows x 2 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipc_type = pd.read_csv(\"raw/IND_ipc_CV_evolucion.csv\")\n",
    "ipc_type = ipc_type.rename(str.lower, axis=\"columns\")\n",
    "\n",
    "ipc_type = ipc_type.drop(\"tipo de dato\", axis=1)\n",
    "ipc_type[\"date\"] = pd.to_datetime(ipc_type[\"periodo\"])\n",
    "ipc_type = ipc_type.drop(ipc_type.columns[[0,2]], axis=1)\n",
    "ipc_type[\"grupos_ipc_type\"] = ipc_type[\"grupos ecoicop\"].str[3:].str.lower()\n",
    "ipc_type = ipc_type.drop(\"grupos ecoicop\", axis=1)\n",
    "ipc_type.set_index(\"date\", inplace=True)\n",
    "\n",
    "ipc_type.to_csv(\"ipc_type_cv_tipos.csv\")\n",
    "ipc_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IPC GENERAL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grupos ecoicop</th>\n",
       "      <th>tipo de dato</th>\n",
       "      <th>total</th>\n",
       "      <th>ipc_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>-0.40000</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>99.60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>99.60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>100.29720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>101.70136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>1.90000</td>\n",
       "      <td>136.30225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>-0.30000</td>\n",
       "      <td>138.89199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>0.30000</td>\n",
       "      <td>138.47532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>-0.70000</td>\n",
       "      <td>138.89074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>137.91851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            grupos ecoicop       tipo de dato    total ipc_normalizado\n",
       "date                                                                  \n",
       "2006-01-01  Índice general  Variación mensual -0.40000             100\n",
       "2006-02-01  Índice general  Variación mensual  0.00000        99.60000\n",
       "2006-03-01  Índice general  Variación mensual  0.70000        99.60000\n",
       "2006-04-01  Índice general  Variación mensual  1.40000       100.29720\n",
       "2006-05-01  Índice general  Variación mensual  0.40000       101.70136\n",
       "...                    ...                ...      ...             ...\n",
       "2022-06-01  Índice general  Variación mensual  1.90000       136.30225\n",
       "2022-07-01  Índice general  Variación mensual -0.30000       138.89199\n",
       "2022-08-01  Índice general  Variación mensual  0.30000       138.47532\n",
       "2022-09-01  Índice general  Variación mensual -0.70000       138.89074\n",
       "2022-10-01  Índice general  Variación mensual  0.40000       137.91851\n",
       "\n",
       "[202 rows x 4 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipc = pd.read_csv(\"raw/IND_IPC_GENERAL.csv\", sep=\";\", encoding=\"latin-1\")\n",
    "ipc = ipc.rename(str.lower, axis=\"columns\")\n",
    "ipc[\"date\"] = ipc.periodo.apply(lambda x: x.replace(\"M\",\"-\"))\n",
    "ipc[\"date\"] = pd.to_datetime(ipc.date)\n",
    "ipc = ipc.set_index(\"date\")\n",
    "ipc = ipc.sort_index(ascending=True)\n",
    "ipc = ipc[\"2006-01-01\":] # set time according to others DF \n",
    "ipc = ipc.drop(\"periodo\", axis=1)\n",
    "ipc.total = ipc.total.apply((lambda x : x.replace(\",\",\".\")))\n",
    "ipc.total = ipc.total.astype(float)\n",
    "#### NORMALIZE\n",
    "ipc = ipc.assign(ipc_normalizado = 'NAN')\n",
    "ipc = ipc.reset_index()\n",
    "ipc.loc[0,\"ipc_normalizado\"] = 100\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
    "\n",
    "for i in range(1,len(ipc)):\n",
    "    ipc.loc[i,\"ipc_normalizado\"] = ipc.loc[i-1,\"ipc_normalizado\"]+ipc.loc[i-1,\"ipc_normalizado\"]*ipc.loc[i-1,\"total\"]/100\n",
    "\n",
    "ipc = ipc.set_index(\"date\")\n",
    "\n",
    "ipc.to_csv(\"N_ipc_general.csv\")\n",
    "ipc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "smia = pd.read_csv(\"raw/IND_SMI_ANUAL.csv\", sep=\",\")\n",
    "smia = smia.sort_index(ascending=False)\n",
    "smia[\"date\"] = pd.to_datetime(smia[\"date\"])\n",
    "smia.set_index(\"date\", inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>smi</th>\n",
       "      <th>variacion_anual_smi</th>\n",
       "      <th>smi_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-09-01</th>\n",
       "      <td>570.60000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-01</th>\n",
       "      <td>600.00000</td>\n",
       "      <td>0.01052</td>\n",
       "      <td>105.15247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09-01</th>\n",
       "      <td>624.00000</td>\n",
       "      <td>0.01040</td>\n",
       "      <td>109.35857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-01</th>\n",
       "      <td>633.30000</td>\n",
       "      <td>0.01015</td>\n",
       "      <td>110.98843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>641.40000</td>\n",
       "      <td>0.01013</td>\n",
       "      <td>112.40799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>641.40000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>112.40799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-01</th>\n",
       "      <td>645.30000</td>\n",
       "      <td>0.01006</td>\n",
       "      <td>113.09148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-01</th>\n",
       "      <td>645.30000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>113.09148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-01</th>\n",
       "      <td>648.60000</td>\n",
       "      <td>0.01005</td>\n",
       "      <td>113.66982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>655.20000</td>\n",
       "      <td>0.01010</td>\n",
       "      <td>114.82650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-01</th>\n",
       "      <td>707.60000</td>\n",
       "      <td>0.01080</td>\n",
       "      <td>124.00981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>735.90000</td>\n",
       "      <td>0.01040</td>\n",
       "      <td>128.96951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>900.00000</td>\n",
       "      <td>0.01223</td>\n",
       "      <td>157.72871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>950.00000</td>\n",
       "      <td>0.01056</td>\n",
       "      <td>166.49141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>950.00000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>166.49141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>965.00000</td>\n",
       "      <td>0.01016</td>\n",
       "      <td>169.12022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>965.00000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>169.12022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 smi  variacion_anual_smi  smi_normalizado\n",
       "date                                                      \n",
       "2006-09-01 570.60000              0.00000        100.00000\n",
       "2007-09-01 600.00000              0.01052        105.15247\n",
       "2008-09-01 624.00000              0.01040        109.35857\n",
       "2009-09-01 633.30000              0.01015        110.98843\n",
       "2010-09-01 641.40000              0.01013        112.40799\n",
       "2011-09-01 641.40000              0.01000        112.40799\n",
       "2012-09-01 645.30000              0.01006        113.09148\n",
       "2013-09-01 645.30000              0.01000        113.09148\n",
       "2014-09-01 648.60000              0.01005        113.66982\n",
       "2015-09-01 655.20000              0.01010        114.82650\n",
       "2016-09-01 707.60000              0.01080        124.00981\n",
       "2017-09-01 735.90000              0.01040        128.96951\n",
       "2018-09-01 900.00000              0.01223        157.72871\n",
       "2019-09-01 950.00000              0.01056        166.49141\n",
       "2020-09-01 950.00000              0.01000        166.49141\n",
       "2021-09-01 965.00000              0.01016        169.12022\n",
       "2022-09-01 965.00000              0.01000        169.12022"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_smia = smia[\"smi\"].iloc[0]\n",
    "first_smia\n",
    "smia[\"smi_normalizado\"] = smia.smi.div(first_smia).mul(100)\n",
    "smia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "smia.to_csv(\"N_smi_anual.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('2006-09-01','2022-09-01', freq='MS').to_frame()\n",
    "prueba = pd.merge(smia,dates, left_index=True, right_index=True, how=\"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>smi</th>\n",
       "      <th>variacion_mensual_smi</th>\n",
       "      <th>smi_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-09-01</th>\n",
       "      <td>570.60000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-01</th>\n",
       "      <td>573.05000</td>\n",
       "      <td>0.00088</td>\n",
       "      <td>100.42937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-01</th>\n",
       "      <td>575.50000</td>\n",
       "      <td>0.00175</td>\n",
       "      <td>100.85875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-01</th>\n",
       "      <td>577.95000</td>\n",
       "      <td>0.00263</td>\n",
       "      <td>101.28812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>580.40000</td>\n",
       "      <td>0.00351</td>\n",
       "      <td>101.71749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-01</th>\n",
       "      <td>582.85000</td>\n",
       "      <td>0.00438</td>\n",
       "      <td>102.14686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-01</th>\n",
       "      <td>585.30000</td>\n",
       "      <td>0.00526</td>\n",
       "      <td>102.57624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-01</th>\n",
       "      <td>587.75000</td>\n",
       "      <td>0.00614</td>\n",
       "      <td>103.00561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-05-01</th>\n",
       "      <td>590.20000</td>\n",
       "      <td>0.00701</td>\n",
       "      <td>103.43498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-01</th>\n",
       "      <td>592.65000</td>\n",
       "      <td>0.00789</td>\n",
       "      <td>103.86435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-01</th>\n",
       "      <td>595.10000</td>\n",
       "      <td>0.00877</td>\n",
       "      <td>104.29373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08-01</th>\n",
       "      <td>597.55000</td>\n",
       "      <td>0.00964</td>\n",
       "      <td>104.72310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-01</th>\n",
       "      <td>600.00000</td>\n",
       "      <td>0.01052</td>\n",
       "      <td>105.15247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 smi  variacion_mensual_smi  smi_normalizado\n",
       "date                                                        \n",
       "2006-09-01 570.60000                0.00000        100.00000\n",
       "2006-10-01 573.05000                0.00088        100.42937\n",
       "2006-11-01 575.50000                0.00175        100.85875\n",
       "2006-12-01 577.95000                0.00263        101.28812\n",
       "2007-01-01 580.40000                0.00351        101.71749\n",
       "2007-02-01 582.85000                0.00438        102.14686\n",
       "2007-03-01 585.30000                0.00526        102.57624\n",
       "2007-04-01 587.75000                0.00614        103.00561\n",
       "2007-05-01 590.20000                0.00701        103.43498\n",
       "2007-06-01 592.65000                0.00789        103.86435\n",
       "2007-07-01 595.10000                0.00877        104.29373\n",
       "2007-08-01 597.55000                0.00964        104.72310\n",
       "2007-09-01 600.00000                0.01052        105.15247"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prueba = pd.merge(smia,dates, left_index=True, right_index=True, how=\"outer\")\n",
    "smi = prueba.interpolate(method=\"linear\")\n",
    "smi.index.name = \"date\"\n",
    "smi.rename(columns= {\"variacion_anual_smi\":\"variacion_mensual_smi\",\n",
    "                    \"var_anual_smi_b100\":\"CONTROL var_mensual_smi_b100\"}, inplace=True)\n",
    "smi.drop(0, axis=1, inplace=True)\n",
    "smi = smi.sort_index(axis=0, ascending=True)\n",
    "\n",
    "# smi[\"smi_vmb100\"] = 100*((smi[\"smi\"])/(smi[\"smi\"][0]))\n",
    "# smi[\"smi_vmb100\"][0] = 100 \n",
    "# smi[\"smi_vmb100\"]\n",
    "# smi[\"smi_vmb100\"] = [smi[\"smi\"][0]*(1+smi[\"variacion_mensual_smi\"][0])\n",
    "smi.head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100.  110.  121.  133.1]\n"
     ]
    }
   ],
   "source": [
    "start = 100\n",
    "q = 1.1\n",
    "count = 4\n",
    "stop = start * q ** (count-1)\n",
    "values = np.geomspace(start, stop, count)\n",
    "print(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TURISM\n",
    "\n",
    "### TURISTS INCOMING EVOLUTION\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>Nº acumulado de los turistas extranjeros</th>\n",
       "      <th>turists_normalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-08-01</th>\n",
       "      <td>3871506</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08-01</th>\n",
       "      <td>4015058</td>\n",
       "      <td>103.70791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>4138387</td>\n",
       "      <td>106.89347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-01</th>\n",
       "      <td>3618784</td>\n",
       "      <td>93.47226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>3559217</td>\n",
       "      <td>91.93366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>3782255</td>\n",
       "      <td>97.69467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>3766909</td>\n",
       "      <td>97.29829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-01</th>\n",
       "      <td>4200007</td>\n",
       "      <td>108.48510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-01</th>\n",
       "      <td>4408860</td>\n",
       "      <td>113.87972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>4608748</td>\n",
       "      <td>119.04277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>5446771</td>\n",
       "      <td>140.68869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>6278054</td>\n",
       "      <td>162.16051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>6415183</td>\n",
       "      <td>165.70252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>6657865</td>\n",
       "      <td>171.97093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-01</th>\n",
       "      <td>2054836</td>\n",
       "      <td>53.07588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>1970637</td>\n",
       "      <td>50.90104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>5760685</td>\n",
       "      <td>148.79701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nº acumulado de los turistas extranjeros  turists_normalized\n",
       "date                                                                    \n",
       "2006-08-01                                   3871506           100.00000\n",
       "2007-08-01                                   4015058           103.70791\n",
       "2008-08-01                                   4138387           106.89347\n",
       "2009-08-01                                   3618784            93.47226\n",
       "2010-08-01                                   3559217            91.93366\n",
       "2011-08-01                                   3782255            97.69467\n",
       "2012-08-01                                   3766909            97.29829\n",
       "2013-08-01                                   4200007           108.48510\n",
       "2014-08-01                                   4408860           113.87972\n",
       "2015-08-01                                   4608748           119.04277\n",
       "2016-08-01                                   5446771           140.68869\n",
       "2017-08-01                                   6278054           162.16051\n",
       "2018-08-01                                   6415183           165.70252\n",
       "2019-08-01                                   6657865           171.97093\n",
       "2020-08-01                                   2054836            53.07588\n",
       "2021-08-01                                   1970637            50.90104\n",
       "2022-08-01                                   5760685           148.79701"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nturi = pd.read_csv(\"raw/TUR_evolucion_del_numero_de_turistas_extranjeros_en_comunidad_valenciana__en_lo_que_va_de_año.csv\",sep=\",\")\n",
    "first_nturi = nturi[\"Nº acumulado de los turistas extranjeros\"][0]\n",
    "nturi[\"turists_normalized\"] = nturi[\"Nº acumulado de los turistas extranjeros\"].div(first_nturi).mul(100)\n",
    "nturi[\"date\"] = nturi[\"Año\"].astype(str)+\"-08-01\"\n",
    "nturi.date = pd.to_datetime(nturi.date)\n",
    "nturi.set_index(\"date\", inplace=True)\n",
    "nturi = nturi.drop(nturi.columns[[0,1]], axis=1)\n",
    "nturi.to_csv(\"N_numero_turistas_ingresados.csv\")\n",
    "nturi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MIGRATION\n",
    "\n",
    "### TO COMUNIDAD VALENCIANA MIGRATION EVOLUTION\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comunidades y ciudades autonomas de origen</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Melilla</td>\n",
       "      <td>164.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Comunidad Madrid de</td>\n",
       "      <td>7929.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Region de Murcia</td>\n",
       "      <td>4400.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Canarias</td>\n",
       "      <td>1842.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Castilla y Leon</td>\n",
       "      <td>2099.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>Ceuta</td>\n",
       "      <td>199.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>Canarias</td>\n",
       "      <td>1392.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>La Rioja</td>\n",
       "      <td>227.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>Melilla</td>\n",
       "      <td>272.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>Andalucia</td>\n",
       "      <td>4680.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Comunidades y ciudades autonomas de origen      Total\n",
       "date                                                            \n",
       "2008-08-01                                    Melilla  164.00000\n",
       "2008-08-01                        Comunidad Madrid de 7929.00000\n",
       "2008-08-01                           Region de Murcia 4400.00000\n",
       "2008-08-01                                   Canarias 1842.00000\n",
       "2008-08-01                            Castilla y Leon 2099.00000\n",
       "...                                               ...        ...\n",
       "2021-08-01                                      Ceuta  199.00000\n",
       "2021-08-01                                   Canarias 1392.00000\n",
       "2021-08-01                                   La Rioja  227.00000\n",
       "2021-08-01                                    Melilla  272.00000\n",
       "2021-08-01                                  Andalucia 4680.00000\n",
       "\n",
       "[266 rows x 2 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "migration = pd.read_csv(\"raw/migraciones interiores hacia valencia.csv\")\n",
    "# migration[\"origen\"] = migration[\"Comunidades y ciudades autonomas de origen\"].str[3:]\n",
    "migration[\"date\"] = migration.Periodo.astype(str) + \"-08-01\"\n",
    "migration = migration.drop(\"Comunidades y ciudades autonomas de destino\", axis=1)\n",
    "migration = migration.drop(\"Periodo\", axis=1)\n",
    "migration = migration.drop(\"Sexo\", axis=1)\n",
    "# migration = migration.drop(\"Comunidades y ciudades autonomas de origen\", axis=1)\n",
    "migration[\"Comunidades y ciudades autonomas de origen\"] = migration[\"Comunidades y ciudades autonomas de origen\"].str[3:]\n",
    "migration.set_index(\"date\", inplace=True)\n",
    "migration.index = pd.to_datetime(migration.index)\n",
    "migration = migration.sort_index(ascending=True)\n",
    "migration\n",
    "\n",
    "# migration.to_csv(\"N_internal_migration_norm.csv\")\n",
    "# migration_firsts = migration.sort_index(ascending=True).head(20)\n",
    "# migration.sort_values(\"Comunidades y ciudades autonomas de origen\", ascending=True)\n",
    "# test = migration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comunidades y ciudades autonomas de origen</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Melilla</td>\n",
       "      <td>164.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Comunidad Madrid de</td>\n",
       "      <td>7929.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Region de Murcia</td>\n",
       "      <td>4400.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Canarias</td>\n",
       "      <td>1842.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Castilla y Leon</td>\n",
       "      <td>2099.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Comunidades y ciudades autonomas de origen      Total\n",
       "date                                                            \n",
       "2008-08-01                                    Melilla  164.00000\n",
       "2008-08-01                        Comunidad Madrid de 7929.00000\n",
       "2008-08-01                           Region de Murcia 4400.00000\n",
       "2008-08-01                                   Canarias 1842.00000\n",
       "2008-08-01                            Castilla y Leon 2099.00000"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "migration.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_migration =  migration.groupby([\"date\",\"Comunidades y ciudades autonomas de origen\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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></th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>Comunidades y ciudades autonomas de origen</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2008-08-01</th>\n",
       "      <th>Andalucia</th>\n",
       "      <td>6215.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aragon</th>\n",
       "      <td>2044.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canarias</th>\n",
       "      <td>1842.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cantabria</th>\n",
       "      <td>286.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Castilla - La Mancha</th>\n",
       "      <td>4729.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2021-08-01</th>\n",
       "      <th>La Rioja</th>\n",
       "      <td>227.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melilla</th>\n",
       "      <td>272.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pais Vasco</th>\n",
       "      <td>999.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Principado de Asturias</th>\n",
       "      <td>483.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region de Murcia</th>\n",
       "      <td>3692.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           Total\n",
       "date       Comunidades y ciudades autonomas de origen           \n",
       "2008-08-01 Andalucia                                  6215.00000\n",
       "           Aragon                                     2044.00000\n",
       "           Canarias                                   1842.00000\n",
       "           Cantabria                                   286.00000\n",
       "           Castilla - La Mancha                       4729.00000\n",
       "...                                                          ...\n",
       "2021-08-01 La Rioja                                    227.00000\n",
       "           Melilla                                     272.00000\n",
       "           Pais Vasco                                  999.00000\n",
       "           Principado de Asturias                      483.00000\n",
       "           Region de Murcia                           3692.00000\n",
       "\n",
       "[266 rows x 1 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_migration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_values = groupby_migration.loc[(\"2008\"),\"Total\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6215., 2044., 1842.,  286., 4729., 2099., 7141.,   95.,  359.,\n",
       "       7929.,    0.,  583., 1036., 2086.,  288.,  164.,  930.,  559.,\n",
       "       4400.])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 266 entries, (Timestamp('2008-08-01 00:00:00'), 'Andalucia') to (Timestamp('2021-08-01 00:00:00'), 'Region de Murcia')\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Total   266 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 11.1+ KB\n"
     ]
    }
   ],
   "source": [
    "groupby_migration.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "266"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(first_values) * 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_migration[\"el_bicho_a_dividir\"] = list(first_values) * 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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></th>\n",
       "      <th>Total</th>\n",
       "      <th>el_bicho_a_dividir</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>Comunidades y ciudades autonomas de origen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2008-08-01</th>\n",
       "      <th>Andalucia</th>\n",
       "      <td>6215.00000</td>\n",
       "      <td>6215.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aragon</th>\n",
       "      <td>2044.00000</td>\n",
       "      <td>2044.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canarias</th>\n",
       "      <td>1842.00000</td>\n",
       "      <td>1842.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cantabria</th>\n",
       "      <td>286.00000</td>\n",
       "      <td>286.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Castilla - La Mancha</th>\n",
       "      <td>4729.00000</td>\n",
       "      <td>4729.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2021-08-01</th>\n",
       "      <th>La Rioja</th>\n",
       "      <td>227.00000</td>\n",
       "      <td>288.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melilla</th>\n",
       "      <td>272.00000</td>\n",
       "      <td>164.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pais Vasco</th>\n",
       "      <td>999.00000</td>\n",
       "      <td>930.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Principado de Asturias</th>\n",
       "      <td>483.00000</td>\n",
       "      <td>559.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region de Murcia</th>\n",
       "      <td>3692.00000</td>\n",
       "      <td>4400.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           Total  \\\n",
       "date       Comunidades y ciudades autonomas de origen              \n",
       "2008-08-01 Andalucia                                  6215.00000   \n",
       "           Aragon                                     2044.00000   \n",
       "           Canarias                                   1842.00000   \n",
       "           Cantabria                                   286.00000   \n",
       "           Castilla - La Mancha                       4729.00000   \n",
       "...                                                          ...   \n",
       "2021-08-01 La Rioja                                    227.00000   \n",
       "           Melilla                                     272.00000   \n",
       "           Pais Vasco                                  999.00000   \n",
       "           Principado de Asturias                      483.00000   \n",
       "           Region de Murcia                           3692.00000   \n",
       "\n",
       "                                                       el_bicho_a_dividir  \n",
       "date       Comunidades y ciudades autonomas de origen                      \n",
       "2008-08-01 Andalucia                                           6215.00000  \n",
       "           Aragon                                              2044.00000  \n",
       "           Canarias                                            1842.00000  \n",
       "           Cantabria                                            286.00000  \n",
       "           Castilla - La Mancha                                4729.00000  \n",
       "...                                                                   ...  \n",
       "2021-08-01 La Rioja                                             288.00000  \n",
       "           Melilla                                              164.00000  \n",
       "           Pais Vasco                                           930.00000  \n",
       "           Principado de Asturias                               559.00000  \n",
       "           Region de Murcia                                    4400.00000  \n",
       "\n",
       "[266 rows x 2 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_migration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby_migration[\"normalized_migration\"] = groupby_migration[\"Total\"] / groupby_migration[\"el_bicho_a_dividir\"] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>el_bicho_a_dividir</th>\n",
       "      <th>normalized_migration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>Comunidades y ciudades autonomas de origen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">2008-08-01</th>\n",
       "      <th>Andalucia</th>\n",
       "      <td>6215.00000</td>\n",
       "      <td>6215.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aragon</th>\n",
       "      <td>2044.00000</td>\n",
       "      <td>2044.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canarias</th>\n",
       "      <td>1842.00000</td>\n",
       "      <td>1842.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cantabria</th>\n",
       "      <td>286.00000</td>\n",
       "      <td>286.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Castilla - La Mancha</th>\n",
       "      <td>4729.00000</td>\n",
       "      <td>4729.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Castilla y Leon</th>\n",
       "      <td>2099.00000</td>\n",
       "      <td>2099.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cataluna</th>\n",
       "      <td>7141.00000</td>\n",
       "      <td>7141.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ceuta</th>\n",
       "      <td>95.00000</td>\n",
       "      <td>95.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comunidad Foral de Navarra</th>\n",
       "      <td>359.00000</td>\n",
       "      <td>359.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comunidad Madrid de</th>\n",
       "      <td>7929.00000</td>\n",
       "      <td>7929.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comunidad Valenciana</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extremadura</th>\n",
       "      <td>583.00000</td>\n",
       "      <td>583.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galicia</th>\n",
       "      <td>1036.00000</td>\n",
       "      <td>1036.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Islas Balears</th>\n",
       "      <td>2086.00000</td>\n",
       "      <td>2086.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>La Rioja</th>\n",
       "      <td>288.00000</td>\n",
       "      <td>288.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melilla</th>\n",
       "      <td>164.00000</td>\n",
       "      <td>164.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pais Vasco</th>\n",
       "      <td>930.00000</td>\n",
       "      <td>930.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Principado de Asturias</th>\n",
       "      <td>559.00000</td>\n",
       "      <td>559.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region de Murcia</th>\n",
       "      <td>4400.00000</td>\n",
       "      <td>4400.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-01</th>\n",
       "      <th>Andalucia</th>\n",
       "      <td>4877.00000</td>\n",
       "      <td>6215.00000</td>\n",
       "      <td>78.47144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           Total  \\\n",
       "date       Comunidades y ciudades autonomas de origen              \n",
       "2008-08-01 Andalucia                                  6215.00000   \n",
       "           Aragon                                     2044.00000   \n",
       "           Canarias                                   1842.00000   \n",
       "           Cantabria                                   286.00000   \n",
       "           Castilla - La Mancha                       4729.00000   \n",
       "           Castilla y Leon                            2099.00000   \n",
       "           Cataluna                                   7141.00000   \n",
       "           Ceuta                                        95.00000   \n",
       "           Comunidad Foral de Navarra                  359.00000   \n",
       "           Comunidad Madrid de                        7929.00000   \n",
       "           Comunidad Valenciana                          0.00000   \n",
       "           Extremadura                                 583.00000   \n",
       "           Galicia                                    1036.00000   \n",
       "           Islas Balears                              2086.00000   \n",
       "           La Rioja                                    288.00000   \n",
       "           Melilla                                     164.00000   \n",
       "           Pais Vasco                                  930.00000   \n",
       "           Principado de Asturias                      559.00000   \n",
       "           Region de Murcia                           4400.00000   \n",
       "2009-08-01 Andalucia                                  4877.00000   \n",
       "\n",
       "                                                       el_bicho_a_dividir  \\\n",
       "date       Comunidades y ciudades autonomas de origen                       \n",
       "2008-08-01 Andalucia                                           6215.00000   \n",
       "           Aragon                                              2044.00000   \n",
       "           Canarias                                            1842.00000   \n",
       "           Cantabria                                            286.00000   \n",
       "           Castilla - La Mancha                                4729.00000   \n",
       "           Castilla y Leon                                     2099.00000   \n",
       "           Cataluna                                            7141.00000   \n",
       "           Ceuta                                                 95.00000   \n",
       "           Comunidad Foral de Navarra                           359.00000   \n",
       "           Comunidad Madrid de                                 7929.00000   \n",
       "           Comunidad Valenciana                                   0.00000   \n",
       "           Extremadura                                          583.00000   \n",
       "           Galicia                                             1036.00000   \n",
       "           Islas Balears                                       2086.00000   \n",
       "           La Rioja                                             288.00000   \n",
       "           Melilla                                              164.00000   \n",
       "           Pais Vasco                                           930.00000   \n",
       "           Principado de Asturias                               559.00000   \n",
       "           Region de Murcia                                    4400.00000   \n",
       "2009-08-01 Andalucia                                           6215.00000   \n",
       "\n",
       "                                                       normalized_migration  \n",
       "date       Comunidades y ciudades autonomas de origen                        \n",
       "2008-08-01 Andalucia                                              100.00000  \n",
       "           Aragon                                                 100.00000  \n",
       "           Canarias                                               100.00000  \n",
       "           Cantabria                                              100.00000  \n",
       "           Castilla - La Mancha                                   100.00000  \n",
       "           Castilla y Leon                                        100.00000  \n",
       "           Cataluna                                               100.00000  \n",
       "           Ceuta                                                  100.00000  \n",
       "           Comunidad Foral de Navarra                             100.00000  \n",
       "           Comunidad Madrid de                                    100.00000  \n",
       "           Comunidad Valenciana                                         NaN  \n",
       "           Extremadura                                            100.00000  \n",
       "           Galicia                                                100.00000  \n",
       "           Islas Balears                                          100.00000  \n",
       "           La Rioja                                               100.00000  \n",
       "           Melilla                                                100.00000  \n",
       "           Pais Vasco                                             100.00000  \n",
       "           Principado de Asturias                                 100.00000  \n",
       "           Region de Murcia                                       100.00000  \n",
       "2009-08-01 Andalucia                                               78.47144  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_migration.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_migration = groupby_migration.reset_index(level=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>Comunidades y ciudades autonomas de origen</th>\n",
       "      <th>Total</th>\n",
       "      <th>el_bicho_a_dividir</th>\n",
       "      <th>normalized_migration</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Andalucia</td>\n",
       "      <td>6215.00000</td>\n",
       "      <td>6215.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Aragon</td>\n",
       "      <td>2044.00000</td>\n",
       "      <td>2044.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Canarias</td>\n",
       "      <td>1842.00000</td>\n",
       "      <td>1842.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Cantabria</td>\n",
       "      <td>286.00000</td>\n",
       "      <td>286.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Castilla - La Mancha</td>\n",
       "      <td>4729.00000</td>\n",
       "      <td>4729.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>La Rioja</td>\n",
       "      <td>227.00000</td>\n",
       "      <td>288.00000</td>\n",
       "      <td>78.81944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>Melilla</td>\n",
       "      <td>272.00000</td>\n",
       "      <td>164.00000</td>\n",
       "      <td>165.85366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>Pais Vasco</td>\n",
       "      <td>999.00000</td>\n",
       "      <td>930.00000</td>\n",
       "      <td>107.41935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>Principado de Asturias</td>\n",
       "      <td>483.00000</td>\n",
       "      <td>559.00000</td>\n",
       "      <td>86.40429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>Region de Murcia</td>\n",
       "      <td>3692.00000</td>\n",
       "      <td>4400.00000</td>\n",
       "      <td>83.90909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Comunidades y ciudades autonomas de origen      Total  \\\n",
       "date                                                               \n",
       "2008-08-01                                  Andalucia 6215.00000   \n",
       "2008-08-01                                     Aragon 2044.00000   \n",
       "2008-08-01                                   Canarias 1842.00000   \n",
       "2008-08-01                                  Cantabria  286.00000   \n",
       "2008-08-01                       Castilla - La Mancha 4729.00000   \n",
       "...                                               ...        ...   \n",
       "2021-08-01                                   La Rioja  227.00000   \n",
       "2021-08-01                                    Melilla  272.00000   \n",
       "2021-08-01                                 Pais Vasco  999.00000   \n",
       "2021-08-01                     Principado de Asturias  483.00000   \n",
       "2021-08-01                           Region de Murcia 3692.00000   \n",
       "\n",
       "            el_bicho_a_dividir  normalized_migration  \n",
       "date                                                  \n",
       "2008-08-01          6215.00000             100.00000  \n",
       "2008-08-01          2044.00000             100.00000  \n",
       "2008-08-01          1842.00000             100.00000  \n",
       "2008-08-01           286.00000             100.00000  \n",
       "2008-08-01          4729.00000             100.00000  \n",
       "...                        ...                   ...  \n",
       "2021-08-01           288.00000              78.81944  \n",
       "2021-08-01           164.00000             165.85366  \n",
       "2021-08-01           930.00000             107.41935  \n",
       "2021-08-01           559.00000              86.40429  \n",
       "2021-08-01          4400.00000              83.90909  \n",
       "\n",
       "[266 rows x 4 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_migration.to_csv(\"Nsaldo_migratorio_interno_acomval.csv\")\n",
    "clean_migration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*lista de DATAFRAMES*\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Localidad</th>\n",
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       "      <td>15.80000</td>\n",
       "      <td>-4.90000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>17.10000</td>\n",
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       "      <td>21.30000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>8.00000</td>\n",
       "      <td>11.10000</td>\n",
       "      <td>23.20000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <th>...</th>\n",
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       "      <th>128</th>\n",
       "      <td>128</td>\n",
       "      <td>Riba-Roja de Túria</td>\n",
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       "      <td>-1.50000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>129</td>\n",
       "      <td>Sagunto/Sagunt</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>130</td>\n",
       "      <td>Torrent</td>\n",
       "      <td>7.40000</td>\n",
       "      <td>7.40000</td>\n",
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       "      <td>12.90000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>131</td>\n",
       "      <td>Zona Avenida al Vedat</td>\n",
       "      <td>7.50000</td>\n",
       "      <td>7.80000</td>\n",
       "      <td>-4.40000</td>\n",
       "      <td>24.90000</td>\n",
       "      <td>-4.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>132</td>\n",
       "      <td>Xirivella</td>\n",
       "      <td>8.30000</td>\n",
       "      <td>8.30000</td>\n",
       "      <td>9.10000</td>\n",
       "      <td>27.10000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                             Localidad  precio  maximo  \\\n",
       "0             0                  Comunitat Valenciana 8.90000 9.40000   \n",
       "1             1                      Alicante/Alacant 8.50000 9.20000   \n",
       "2             2                    Alicante / Alacant 9.00000 9.00000   \n",
       "3             3  Benalua-La Florida-Babel-San Gabriel 7.70000 7.70000   \n",
       "4             4          Campoamor-Carolinas-Altozano 8.00000 8.00000   \n",
       "..          ...                                   ...     ...     ...   \n",
       "128         128                    Riba-Roja de Túria 6.80000 7.10000   \n",
       "129         129                        Sagunto/Sagunt 6.60000 6.60000   \n",
       "130         130                               Torrent 7.40000 7.40000   \n",
       "131         131                 Zona Avenida al Vedat 7.50000 7.80000   \n",
       "132         132                             Xirivella 8.30000 8.30000   \n",
       "\n",
       "     Variacion_trimestral(%)  Variacion_anual(%)  Variacion_Maximo(%)  \n",
       "0                    4.80000            15.80000             -4.90000  \n",
       "1                    3.10000            17.10000             -8.20000  \n",
       "2                    8.30000            21.30000              0.00000  \n",
       "3                    8.20000            15.40000              0.00000  \n",
       "4                   11.10000            23.20000              0.00000  \n",
       "..                       ...                 ...                  ...  \n",
       "128                 -1.50000             0.00000             -4.80000  \n",
       "129                  8.40000             0.00000              0.00000  \n",
       "130                  2.20000            12.90000              0.00000  \n",
       "131                 -4.40000            24.90000             -4.40000  \n",
       "132                  9.10000            27.10000              0.00000  \n",
       "\n",
       "[133 rows x 7 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_valencia # rent prices for Valencia\n",
    "alq_alicante # rent prices for Alicante\n",
    "alq_castellon # rent prices for Castellon\n",
    "vta_valencia # sell prices for Valencia\n",
    "vta_alicante # sell prices for Alicante\n",
    "vta_castellon # sell prices for Castellon\n",
    "euribor # EURIBOR evolution\n",
    "mortgages # mortgages excecutions \n",
    "mort_type # mortgages emission types \n",
    "ipc # IPC evolution\n",
    "smia # minimum interpersonal salary anual evolution\n",
    "covid_cases # covid cases dettected by day\n",
    "nturi # number of turists entered CV by year \n",
    "clean_migration # internal migration to CV \n",
    "idealista_table # idealista scapping with selenium by city\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precio_alq_valencia</th>\n",
       "      <th>variación_mensual_valencia</th>\n",
       "      <th>variación_trimestral_valencia</th>\n",
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       "      <th>2006-01-01</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>9.70000</td>\n",
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       "      <td>0.00000</td>\n",
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       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>9.70000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
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       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
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       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>9.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "            precio_alq_valencia  variación_mensual_valencia  \\\n",
       "date                                                          \n",
       "2006-01-01              9.70000                     0.00000   \n",
       "2006-02-01              9.70000                     0.00000   \n",
       "2006-03-01              9.70000                     0.00000   \n",
       "2006-04-01              9.70000                     0.00000   \n",
       "2006-05-01              9.70000                     0.00000   \n",
       "\n",
       "            variación_trimestral_valencia  variación_anual_valencia  \\\n",
       "date                                                                  \n",
       "2006-01-01                        0.00000                   0.00000   \n",
       "2006-02-01                        0.00000                   0.00000   \n",
       "2006-03-01                        0.00000                   0.00000   \n",
       "2006-04-01                        0.00000                   0.00000   \n",
       "2006-05-01                        0.00000                   0.00000   \n",
       "\n",
       "            precio_alq_valencia_normalizado  \n",
       "date                                         \n",
       "2006-01-01                        100.00000  \n",
       "2006-02-01                        100.00000  \n",
       "2006-03-01                        100.00000  \n",
       "2006-04-01                        100.00000  \n",
       "2006-05-01                        100.00000  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_valencia.head(5) # rent prices for Valencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2006-01-01</th>\n",
       "      <td>9.00000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <th>2006-02-01</th>\n",
       "      <td>9.00000</td>\n",
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       "      <td>0.00000</td>\n",
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       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>9.00000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <th>2006-04-01</th>\n",
       "      <td>9.20000</td>\n",
       "      <td>0.03000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>102.22222</td>\n",
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       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>8.80000</td>\n",
       "      <td>-0.05000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>97.77778</td>\n",
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      ],
      "text/plain": [
       "            precio_alq_alicante  variación_mensual_alicante  \\\n",
       "date                                                          \n",
       "2006-01-01              9.00000                     0.00000   \n",
       "2006-02-01              9.00000                     0.00000   \n",
       "2006-03-01              9.00000                     0.00000   \n",
       "2006-04-01              9.20000                     0.03000   \n",
       "2006-05-01              8.80000                    -0.05000   \n",
       "\n",
       "            variación_trimestral_alicante  variación_anual_alicante  \\\n",
       "date                                                                  \n",
       "2006-01-01                        0.00000                   0.00000   \n",
       "2006-02-01                        0.00000                   0.00000   \n",
       "2006-03-01                        0.00000                   0.00000   \n",
       "2006-04-01                        0.00000                   0.00000   \n",
       "2006-05-01                        0.00000                   0.00000   \n",
       "\n",
       "            precio_alq_alicante_normalizado  \n",
       "date                                         \n",
       "2006-01-01                        100.00000  \n",
       "2006-02-01                        100.00000  \n",
       "2006-03-01                        100.00000  \n",
       "2006-04-01                        102.22222  \n",
       "2006-05-01                         97.77778  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_alicante.head(5) # rent prices for Alicante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>precio_alq_castellon</th>\n",
       "      <th>variación_mensual_castellon</th>\n",
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       "      <th>2006-01-01</th>\n",
       "      <td>7.80000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <th>2006-02-01</th>\n",
       "      <td>7.80000</td>\n",
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       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
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       "      <td>0.00000</td>\n",
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       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>7.80000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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      "text/plain": [
       "            precio_alq_castellon  variación_mensual_castellon  \\\n",
       "date                                                            \n",
       "2006-01-01               7.80000                      0.00000   \n",
       "2006-02-01               7.80000                      0.00000   \n",
       "2006-03-01               7.80000                      0.00000   \n",
       "2006-04-01               7.80000                      0.00000   \n",
       "2006-05-01               7.80000                      0.00000   \n",
       "\n",
       "            variación_trimestral_castellon  variación_anual_castellon  \\\n",
       "date                                                                    \n",
       "2006-01-01                         0.00000                    0.00000   \n",
       "2006-02-01                         0.00000                    0.00000   \n",
       "2006-03-01                         0.00000                    0.00000   \n",
       "2006-04-01                         0.00000                    0.00000   \n",
       "2006-05-01                         0.00000                    0.00000   \n",
       "\n",
       "            precio_alq_castellon_normalizado  \n",
       "date                                          \n",
       "2006-01-01                         100.00000  \n",
       "2006-02-01                         100.00000  \n",
       "2006-03-01                         100.00000  \n",
       "2006-04-01                         100.00000  \n",
       "2006-05-01                         100.00000  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alq_castellon.head(5) # rent prices for Castellon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>precio_vta_valencia</th>\n",
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       "      <th>2006-01-01</th>\n",
       "      <td>1580</td>\n",
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       "      <th>2006-02-01</th>\n",
       "      <td>1580</td>\n",
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       "      <th>2006-03-01</th>\n",
       "      <td>1585</td>\n",
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       "      <td>0.00000</td>\n",
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       "      <th>2006-04-01</th>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <th>2006-05-01</th>\n",
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       "      <td>0.03000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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      ],
      "text/plain": [
       "            precio_vta_valencia  variacion_vta_mensual_valencia  \\\n",
       "date                                                              \n",
       "2006-01-01                 1580                         0.00000   \n",
       "2006-02-01                 1580                         0.00000   \n",
       "2006-03-01                 1585                         0.00000   \n",
       "2006-04-01                 1763                         0.11000   \n",
       "2006-05-01                 1806                         0.03000   \n",
       "\n",
       "            variacion_vta_trimetral_valencia  variacion_vta_anual_valencia  \\\n",
       "date                                                                         \n",
       "2006-01-01                           0.00000                       0.00000   \n",
       "2006-02-01                           0.00000                       0.00000   \n",
       "2006-03-01                           0.00000                       0.00000   \n",
       "2006-04-01                           0.00000                       0.00000   \n",
       "2006-05-01                           0.00000                       0.00000   \n",
       "\n",
       "            precio_vta_valencia_normalizado  \n",
       "date                                         \n",
       "2006-01-01                        100.00000  \n",
       "2006-02-01                        100.00000  \n",
       "2006-03-01                        100.31646  \n",
       "2006-04-01                        111.58228  \n",
       "2006-05-01                        114.30380  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vta_valencia.head(5) # sell prices for Valencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>precio_vta_alicante</th>\n",
       "      <th>variacion_vta_mensual_alicante</th>\n",
       "      <th>variacion_vta_trimestral_alicante</th>\n",
       "      <th>variacion_vta_anual_alicante</th>\n",
       "      <th>precio_vta_alicante_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>1944</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>1975</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>101.59465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>1896</td>\n",
       "      <td>-0.04000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>97.53086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>1871</td>\n",
       "      <td>-0.01000</td>\n",
       "      <td>-0.04000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>96.24486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>1776</td>\n",
       "      <td>-0.05000</td>\n",
       "      <td>-0.10000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>91.35802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            precio_vta_alicante  variacion_vta_mensual_alicante  \\\n",
       "date                                                              \n",
       "2006-01-01                 1944                         0.00000   \n",
       "2006-02-01                 1975                         0.02000   \n",
       "2006-03-01                 1896                        -0.04000   \n",
       "2006-04-01                 1871                        -0.01000   \n",
       "2006-05-01                 1776                        -0.05000   \n",
       "\n",
       "            variacion_vta_trimestral_alicante  variacion_vta_anual_alicante  \\\n",
       "date                                                                          \n",
       "2006-01-01                            0.00000                       0.00000   \n",
       "2006-02-01                            0.00000                       0.00000   \n",
       "2006-03-01                            0.00000                       0.00000   \n",
       "2006-04-01                           -0.04000                       0.00000   \n",
       "2006-05-01                           -0.10000                       0.00000   \n",
       "\n",
       "            precio_vta_alicante_normalizado  \n",
       "date                                         \n",
       "2006-01-01                        100.00000  \n",
       "2006-02-01                        101.59465  \n",
       "2006-03-01                         97.53086  \n",
       "2006-04-01                         96.24486  \n",
       "2006-05-01                         91.35802  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vta_alicante.head(5) # sell prices for Alicante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precio_vta_castellon</th>\n",
       "      <th>variacion_vta_mensual_castellon</th>\n",
       "      <th>variacion_vta_trimestral_castellon</th>\n",
       "      <th>variacion_vta_anual_castellon</th>\n",
       "      <th>precio_vta_castellon_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-09-01</th>\n",
       "      <td>1532</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-01</th>\n",
       "      <td>1680</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>109.66057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-11-01</th>\n",
       "      <td>1658</td>\n",
       "      <td>-0.01000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>108.22454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-01</th>\n",
       "      <td>1555</td>\n",
       "      <td>-0.06000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>101.50131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>1583</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>-0.06000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>103.32898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            precio_vta_castellon  variacion_vta_mensual_castellon  \\\n",
       "date                                                                \n",
       "2006-09-01                  1532                          0.00000   \n",
       "2006-10-01                  1680                          0.10000   \n",
       "2006-11-01                  1658                         -0.01000   \n",
       "2006-12-01                  1555                         -0.06000   \n",
       "2007-01-01                  1583                          0.02000   \n",
       "\n",
       "            variacion_vta_trimestral_castellon  variacion_vta_anual_castellon  \\\n",
       "date                                                                            \n",
       "2006-09-01                             0.00000                        0.00000   \n",
       "2006-10-01                             0.00000                        0.00000   \n",
       "2006-11-01                             0.00000                        0.00000   \n",
       "2006-12-01                             0.02000                        0.00000   \n",
       "2007-01-01                            -0.06000                        0.00000   \n",
       "\n",
       "            precio_vta_castellon_normalizado  \n",
       "date                                          \n",
       "2006-09-01                         100.00000  \n",
       "2006-10-01                         109.66057  \n",
       "2006-11-01                         108.22454  \n",
       "2006-12-01                         101.50131  \n",
       "2007-01-01                         103.32898  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vta_castellon.head(5) # sell prices for Castellon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Euribor</th>\n",
       "      <th>euribor_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>2.91000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>3.11000</td>\n",
       "      <td>106.87285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>3.22000</td>\n",
       "      <td>110.65292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>3.31000</td>\n",
       "      <td>113.74570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-06-01</th>\n",
       "      <td>3.40000</td>\n",
       "      <td>116.83849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Euribor  euribor_normalizado\n",
       "date                                    \n",
       "2006-02-01  2.91000            100.00000\n",
       "2006-03-01  3.11000            106.87285\n",
       "2006-04-01  3.22000            110.65292\n",
       "2006-05-01  3.31000            113.74570\n",
       "2006-06-01  3.40000            116.83849"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euribor.head(5) # EURIBOR evolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comunidad</th>\n",
       "      <th>total</th>\n",
       "      <th>executions_normalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>1988</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>2337</td>\n",
       "      <td>117.55533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>1732</td>\n",
       "      <td>87.12274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>2151</td>\n",
       "      <td>108.19920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>Comunidad Valenciana</td>\n",
       "      <td>2060</td>\n",
       "      <td>103.62173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       comunidad  total  executions_normalized\n",
       "date                                                          \n",
       "2014-01-01  Comunidad Valenciana   1988              100.00000\n",
       "2014-04-01  Comunidad Valenciana   2337              117.55533\n",
       "2014-07-01  Comunidad Valenciana   1732               87.12274\n",
       "2014-10-01  Comunidad Valenciana   2151              108.19920\n",
       "2015-01-01  Comunidad Valenciana   2060              103.62173"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mortgages.head(5) # mortgages excecutions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tipo de interes</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>75.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>73.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>72.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>75.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>Fijo</td>\n",
       "      <td>72.70000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tipo de interes    total\n",
       "date                               \n",
       "2022-07-01            Fijo 75.40000\n",
       "2022-06-01            Fijo 73.00000\n",
       "2022-05-01            Fijo 72.80000\n",
       "2022-04-01            Fijo 75.30000\n",
       "2022-03-01            Fijo 72.70000"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mort_type.head(5) # mortgages emission types "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grupos ecoicop</th>\n",
       "      <th>tipo de dato</th>\n",
       "      <th>total</th>\n",
       "      <th>ipc_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>-0.40000</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>99.60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-03-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>99.60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>100.29720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-05-01</th>\n",
       "      <td>Índice general</td>\n",
       "      <td>Variación mensual</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>101.70136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            grupos ecoicop       tipo de dato    total ipc_normalizado\n",
       "date                                                                  \n",
       "2006-01-01  Índice general  Variación mensual -0.40000             100\n",
       "2006-02-01  Índice general  Variación mensual  0.00000        99.60000\n",
       "2006-03-01  Índice general  Variación mensual  0.70000        99.60000\n",
       "2006-04-01  Índice general  Variación mensual  1.40000       100.29720\n",
       "2006-05-01  Índice general  Variación mensual  0.40000       101.70136"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipc.head(5) # IPC evolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smi</th>\n",
       "      <th>variacion_anual_smi</th>\n",
       "      <th>smi_normalizado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-09-01</th>\n",
       "      <td>570.60000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-01</th>\n",
       "      <td>600.00000</td>\n",
       "      <td>0.01052</td>\n",
       "      <td>105.15247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09-01</th>\n",
       "      <td>624.00000</td>\n",
       "      <td>0.01040</td>\n",
       "      <td>109.35857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-01</th>\n",
       "      <td>633.30000</td>\n",
       "      <td>0.01015</td>\n",
       "      <td>110.98843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>641.40000</td>\n",
       "      <td>0.01013</td>\n",
       "      <td>112.40799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 smi  variacion_anual_smi  smi_normalizado\n",
       "date                                                      \n",
       "2006-09-01 570.60000              0.00000        100.00000\n",
       "2007-09-01 600.00000              0.01052        105.15247\n",
       "2008-09-01 624.00000              0.01040        109.35857\n",
       "2009-09-01 633.30000              0.01015        110.98843\n",
       "2010-09-01 641.40000              0.01013        112.40799"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smia.head(5) # minimum interpersonal salary anual evolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>Nuevos casos notificados cada</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nuevos casos notificados cada \n",
       "date                                      \n",
       "2020-02-21                         0.00000\n",
       "2020-02-22                         0.00000\n",
       "2020-02-23                         0.00000\n",
       "2020-02-24                         0.00000\n",
       "2020-02-25                         0.00000"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_cases.head(5) # covid cases dettected by day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>Nº acumulado de los turistas extranjeros</th>\n",
       "      <th>turists_normalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-08-01</th>\n",
       "      <td>3871506</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08-01</th>\n",
       "      <td>4015058</td>\n",
       "      <td>103.70791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>4138387</td>\n",
       "      <td>106.89347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-01</th>\n",
       "      <td>3618784</td>\n",
       "      <td>93.47226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>3559217</td>\n",
       "      <td>91.93366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nº acumulado de los turistas extranjeros  turists_normalized\n",
       "date                                                                    \n",
       "2006-08-01                                   3871506           100.00000\n",
       "2007-08-01                                   4015058           103.70791\n",
       "2008-08-01                                   4138387           106.89347\n",
       "2009-08-01                                   3618784            93.47226\n",
       "2010-08-01                                   3559217            91.93366"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nturi.head(5) # number of turists entered CV by year "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comunidades y ciudades autonomas de origen</th>\n",
       "      <th>Total</th>\n",
       "      <th>el_bicho_a_dividir</th>\n",
       "      <th>normalized_migration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Andalucia</td>\n",
       "      <td>6215.00000</td>\n",
       "      <td>6215.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Aragon</td>\n",
       "      <td>2044.00000</td>\n",
       "      <td>2044.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Canarias</td>\n",
       "      <td>1842.00000</td>\n",
       "      <td>1842.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Cantabria</td>\n",
       "      <td>286.00000</td>\n",
       "      <td>286.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>Castilla - La Mancha</td>\n",
       "      <td>4729.00000</td>\n",
       "      <td>4729.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Comunidades y ciudades autonomas de origen      Total  \\\n",
       "date                                                               \n",
       "2008-08-01                                  Andalucia 6215.00000   \n",
       "2008-08-01                                     Aragon 2044.00000   \n",
       "2008-08-01                                   Canarias 1842.00000   \n",
       "2008-08-01                                  Cantabria  286.00000   \n",
       "2008-08-01                       Castilla - La Mancha 4729.00000   \n",
       "\n",
       "            el_bicho_a_dividir  normalized_migration  \n",
       "date                                                  \n",
       "2008-08-01          6215.00000             100.00000  \n",
       "2008-08-01          2044.00000             100.00000  \n",
       "2008-08-01          1842.00000             100.00000  \n",
       "2008-08-01           286.00000             100.00000  \n",
       "2008-08-01          4729.00000             100.00000  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_migration.head(5) # internal migration to CV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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>Unnamed: 0</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>precio</th>\n",
       "      <th>maximo</th>\n",
       "      <th>Variacion_trimestral(%)</th>\n",
       "      <th>Variacion_anual(%)</th>\n",
       "      <th>Variacion_Maximo(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Comunitat Valenciana</td>\n",
       "      <td>8.90000</td>\n",
       "      <td>9.40000</td>\n",
       "      <td>4.80000</td>\n",
       "      <td>15.80000</td>\n",
       "      <td>-4.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Alicante/Alacant</td>\n",
       "      <td>8.50000</td>\n",
       "      <td>9.20000</td>\n",
       "      <td>3.10000</td>\n",
       "      <td>17.10000</td>\n",
       "      <td>-8.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Alicante / Alacant</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>8.30000</td>\n",
       "      <td>21.30000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Benalua-La Florida-Babel-San Gabriel</td>\n",
       "      <td>7.70000</td>\n",
       "      <td>7.70000</td>\n",
       "      <td>8.20000</td>\n",
       "      <td>15.40000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Campoamor-Carolinas-Altozano</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>11.10000</td>\n",
       "      <td>23.20000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                             Localidad  precio  maximo  \\\n",
       "0           0                  Comunitat Valenciana 8.90000 9.40000   \n",
       "1           1                      Alicante/Alacant 8.50000 9.20000   \n",
       "2           2                    Alicante / Alacant 9.00000 9.00000   \n",
       "3           3  Benalua-La Florida-Babel-San Gabriel 7.70000 7.70000   \n",
       "4           4          Campoamor-Carolinas-Altozano 8.00000 8.00000   \n",
       "\n",
       "   Variacion_trimestral(%)  Variacion_anual(%)  Variacion_Maximo(%)  \n",
       "0                  4.80000            15.80000             -4.90000  \n",
       "1                  3.10000            17.10000             -8.20000  \n",
       "2                  8.30000            21.30000              0.00000  \n",
       "3                  8.20000            15.40000              0.00000  \n",
       "4                 11.10000            23.20000              0.00000  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idealista_table.head(5) # idealista scapping with selenium by city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparation = pd.concat(\n",
    "        [alq_valencia[\"precio_alq_valencia_normalizado\"],\n",
    "        alq_alicante[\"precio_alq_alicante_normalizado\"],\n",
    "        alq_castellon[\"precio_alq_castellon_normalizado\"],\n",
    "        vta_valencia[\"precio_vta_valencia_normalizado\"],\n",
    "        vta_alicante[\"precio_vta_alicante_normalizado\"],\n",
    "        vta_castellon[\"precio_vta_castellon_normalizado\"],\n",
    "        euribor[\"euribor_normalizado\"],\n",
    "        mortgages[\"executions_normalized\"],\n",
    "        ipc[\"ipc_normalizado\"],\n",
    "        smia[\"smi_normalizado\"],\n",
    "        nturi[\"turists_normalized\"],\n",
    "        ], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# comparation = pd.concat(\n",
    "#         [alq_valencia[\"precio_alq_valencia_normalizado\"],\n",
    "#         alq_alicante[\"precio_alq_alicante_normalizado\"],\n",
    "#         alq_castellon[\"precio_alq_castellon_normalizado\"],\n",
    "#         vta_valencia[\"precio_vta_valencia_normalizado\"],\n",
    "#         vta_alicante[\"precio_vta_alicante_normalizado\"],\n",
    "#         vta_castellon[\"precio_vta_castellon_normalizado\"],\n",
    "#         euribor[\"euribor_normalizado\"],\n",
    "#         mortgages[\"executions_normalized\"],\n",
    "#         ipc[\"ipc_normalizado\"],\n",
    "#         smia[\"smi_normalizado\"],\n",
    "#         nturi[\"turists_normalized\"],\n",
    "#         ], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparation = pd.concat(\n",
    "        [alq_valencia.loc['2008-08':'2022-08'][\"precio_alq_valencia_normalizado\"],\n",
    "        alq_alicante.loc['2008-08':'2022-08'][\"precio_alq_alicante_normalizado\"],\n",
    "        alq_castellon.loc['2008-08':'2022-08'][\"precio_alq_castellon_normalizado\"],\n",
    "        vta_valencia.loc['2008-08':'2022-08'][\"precio_vta_valencia_normalizado\"],\n",
    "        vta_alicante.loc['2008-08':'2022-08'][\"precio_vta_alicante_normalizado\"],\n",
    "        vta_castellon.loc['2008-08':'2022-08'][\"precio_vta_castellon_normalizado\"],\n",
    "        euribor.loc['2008-08':'2022-08'][\"euribor_normalizado\"],\n",
    "        mortgages.loc['2008-08':'2022-08'][\"executions_normalized\"],\n",
    "        ipc.loc['2008-08':'2022-08'][\"ipc_normalizado\"],\n",
    "        smia.loc['2008-08':'2022-08'][\"smi_normalizado\"],\n",
    "        nturi.loc['2008-08':'2022-08'][\"turists_normalized\"],\n",
    "        ], axis=1)\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(comparation.corr(),\n",
    "            vmin=-1,\n",
    "            vmax=1,\n",
    "            cmap=sns.diverging_palette(145, 280, s=85, l=25, n=7),\n",
    "            square=True,\n",
    "            linewidths=.1,\n",
    "            annot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "desde = \"2018-08\"\n",
    "hasta = \"2022-08\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "desde = \"2015-08\"\n",
    "hasta = \"2020-08\"\n",
    "\n",
    "comparation = pd.concat(\n",
    "        [alq_valencia.loc[desde:hasta][\"precio_alq_valencia_normalizado\"],\n",
    "        alq_alicante.loc[desde:hasta][\"precio_alq_alicante_normalizado\"],\n",
    "        alq_castellon.loc[desde:hasta][\"precio_alq_castellon_normalizado\"],\n",
    "        vta_valencia.loc[desde:hasta][\"precio_vta_valencia_normalizado\"],\n",
    "        vta_alicante.loc[desde:hasta][\"precio_vta_alicante_normalizado\"],\n",
    "        vta_castellon.loc[desde:hasta][\"precio_vta_castellon_normalizado\"],\n",
    "        euribor.loc[desde:hasta][\"euribor_normalizado\"],\n",
    "        mortgages.loc[desde:hasta][\"executions_normalized\"],\n",
    "        ipc.loc[desde:hasta][\"ipc_normalizado\"],\n",
    "        smia.loc[desde:hasta][\"smi_normalizado\"],\n",
    "        nturi.loc[desde:hasta][\"turists_normalized\"],\n",
    "        ], axis=1)\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "\n",
    "sns.heatmap(comparation.corr(),\n",
    "            vmin=-1,\n",
    "            vmax=1,\n",
    "            cmap=sns.diverging_palette(5, 250, s=80, l=25, n=10),\n",
    "            square=True,\n",
    "            linewidths=.1,\n",
    "            annot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b78b066250>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year = alq_alicante.index[(alq_alicante.index >= '2008-08')&(alq_alicante.index <= '2022-08')]\n",
    "turistas = nturi.loc['2008-08':'2022-08'][\"turists_normalized\"].resample(rule=\"m\", axis=0).mean().interpolate()\n",
    "valencia_AL = alq_valencia.resample(rule=\"m\", axis=0).mean().loc['2008-08':'2022-08'][\"precio_alq_valencia_normalizado\"]#.interpolate()\n",
    "alicante_AL = alq_alicante.resample(rule=\"m\", axis=0).mean().loc['2008-08':'2022-08'][\"precio_alq_alicante_normalizado\"]#.interpolate()\n",
    "castellon_AL = alq_castellon.loc['2008-08':'2022-08'][\"precio_alq_castellon_normalizado\"]#.interpolate()\n",
    "plt.plot(year, turistas, 'g', label=\"TURISTAS\")\n",
    "plt.plot(year, valencia_AL, 'b', label=\"ALQU VALENCIA\")\n",
    "plt.plot(year, alicante_AL,'r', label=\"ALQU ALICANTE\" )\n",
    "plt.plot(year, castellon_AL,'c', label=\"ALQU CASTELLON\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b78b0a5430>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year = alq_alicante.index[(alq_alicante.index >= '2008-08')&(alq_alicante.index <= '2022-08')]\n",
    "turistas = nturi.loc['2008-08':'2022-08'][\"turists_normalized\"].resample(rule=\"m\", axis=0).mean().interpolate()\n",
    "valencia_VTA = vta_valencia.resample(rule=\"m\", axis=0).mean().loc['2008-08':'2022-08'][\"precio_vta_valencia_normalizado\"]#.interpolate()\n",
    "alicante_VTA = vta_alicante.resample(rule=\"m\", axis=0).mean().loc['2008-08':'2022-08'][\"precio_vta_alicante_normalizado\"]#.interpolate()\n",
    "castellon_VTA = vta_castellon.loc['2008-08':'2022-08'][\"precio_vta_castellon_normalizado\"]#.interpolate()\n",
    "plt.plot(year, turistas, 'g', label=\"TURISTAS\")\n",
    "plt.plot(year, valencia_VTA, 'b', label=\"VTA VALENCIA\")\n",
    "plt.plot(year, alicante_VTA,'r', label=\"VTA ALICANTE\" )\n",
    "plt.plot(year, castellon_VTA,'c', label=\"VTA CASTELLON\" )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('np_pd_soup_selenium')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "f1402436f837d0854c2ee0b97b88cc241d377f201d0e2d2b7459a5699470ad84"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}