merge_all_datasets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import missingno as msno\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2044, 9)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>period</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>734.99</td>\n",
       "      <td>13.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>2</td>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>905.26</td>\n",
       "      <td>14.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>3</td>\n",
       "      <td>la Barceloneta</td>\n",
       "      <td>722.78</td>\n",
       "      <td>19.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>895.28</td>\n",
       "      <td>15.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>5</td>\n",
       "      <td>el Fort Pienc</td>\n",
       "      <td>871.08</td>\n",
       "      <td>12.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  quarter      period  district_code district_name  neighbourhood_code  \\\n",
       "0  2017        1  2017-01-01              1  Ciutat Vella                   1   \n",
       "1  2017        1  2017-01-01              1  Ciutat Vella                   2   \n",
       "2  2017        1  2017-01-01              1  Ciutat Vella                   3   \n",
       "3  2017        1  2017-01-01              1  Ciutat Vella                   4   \n",
       "4  2017        1  2017-01-01              2      Eixample                   5   \n",
       "\n",
       "                      neighbourhood_name  price_month(€)  price_m2_month(€)  \n",
       "0                               el Raval          734.99              13.50  \n",
       "1                         el Barri Gòtic          905.26              14.14  \n",
       "2                         la Barceloneta          722.78              19.46  \n",
       "3  Sant Pere, Santa Caterina i la Ribera          895.28              15.01  \n",
       "4                          el Fort Pienc          871.08              12.77  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the Barcelona rent dataset\n",
    "rent_prices_df = pd.read_csv('./data/cleaned/rent/rent_prices.csv')\n",
    "print(rent_prices_df.shape)\n",
    "rent_prices_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(444, 8)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>Over 12 months</th>\n",
       "      <th>Up to 6 months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>7562</td>\n",
       "      <td>18642</td>\n",
       "      <td>22445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>2</td>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>1986</td>\n",
       "      <td>5139</td>\n",
       "      <td>5855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>3</td>\n",
       "      <td>la Barceloneta</td>\n",
       "      <td>2128</td>\n",
       "      <td>6088</td>\n",
       "      <td>6212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>2949</td>\n",
       "      <td>8425</td>\n",
       "      <td>8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>5</td>\n",
       "      <td>el Fort Pienc</td>\n",
       "      <td>2848</td>\n",
       "      <td>8666</td>\n",
       "      <td>7607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  district_code district_name  neighbourhood_code  \\\n",
       "0  2015              1  Ciutat Vella                   1   \n",
       "1  2015              1  Ciutat Vella                   2   \n",
       "2  2015              1  Ciutat Vella                   3   \n",
       "3  2015              1  Ciutat Vella                   4   \n",
       "4  2015              2      Eixample                   5   \n",
       "\n",
       "                      neighbourhood_name  From 6 months to 12 motnhs  \\\n",
       "0                               el Raval                        7562   \n",
       "1                         el Barri Gòtic                        1986   \n",
       "2                         la Barceloneta                        2128   \n",
       "3  Sant Pere, Santa Caterina i la Ribera                        2949   \n",
       "4                          el Fort Pienc                        2848   \n",
       "\n",
       "   Over 12 months  Up to 6 months  \n",
       "0           18642           22445  \n",
       "1            5139            5855  \n",
       "2            6088            6212  \n",
       "3            8425            8625  \n",
       "4            8666            7607  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the Barcelona unemployment rate dataset\n",
    "unemployment_df = pd.read_csv('./data/cleaned/unemployment/unemployment.csv')\n",
    "print(unemployment_df.shape)\n",
    "unemployment_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(439, 15)\n"
     ]
    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>1 Spanish</th>\n",
       "      <th>1 foreigner</th>\n",
       "      <th>2 Spanish</th>\n",
       "      <th>2 foreigners</th>\n",
       "      <th>3 Spanish</th>\n",
       "      <th>3 foreigners</th>\n",
       "      <th>4 espanyols o més</th>\n",
       "      <th>more than 4 foreigners</th>\n",
       "      <th>no Spanish</th>\n",
       "      <th>no foreigner</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>6104.0</td>\n",
       "      <td>3066.0</td>\n",
       "      <td>3294.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>1588.0</td>\n",
       "      <td>1058.0</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>2229.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>2</td>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>2373.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>1273.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>3</td>\n",
       "      <td>la Barceloneta</td>\n",
       "      <td>2496.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>1491.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>3812.0</td>\n",
       "      <td>2108.0</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>5</td>\n",
       "      <td>el Fort Pienc</td>\n",
       "      <td>4216.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>3426.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>2189.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  district_code district_name  neighbourhood_code  \\\n",
       "0  2015              1  Ciutat Vella                   1   \n",
       "1  2015              1  Ciutat Vella                   2   \n",
       "2  2015              1  Ciutat Vella                   3   \n",
       "3  2015              1  Ciutat Vella                   4   \n",
       "4  2015              2      Eixample                   5   \n",
       "\n",
       "                      neighbourhood_name  1 Spanish  1 foreigner  2 Spanish  \\\n",
       "0                               el Raval     6104.0       3066.0     3294.0   \n",
       "1                         el Barri Gòtic     2373.0       1415.0     1273.0   \n",
       "2                         la Barceloneta     2496.0       1236.0     1491.0   \n",
       "3  Sant Pere, Santa Caterina i la Ribera     3812.0       2108.0     1964.0   \n",
       "4                          el Fort Pienc     4216.0       1415.0     3426.0   \n",
       "\n",
       "   2 foreigners  3 Spanish  3 foreigners  4 espanyols o més  \\\n",
       "0        1608.0     1588.0        1058.0             1451.0   \n",
       "1         723.0      570.0         343.0              478.0   \n",
       "2         638.0      734.0         277.0              585.0   \n",
       "3        1029.0      916.0         478.0              692.0   \n",
       "4         594.0     2189.0         362.0             1765.0   \n",
       "\n",
       "   more than 4 foreigners  no Spanish  no foreigner  \n",
       "0                  2229.0         NaN           NaN  \n",
       "1                   435.0         NaN           NaN  \n",
       "2                   257.0         NaN           NaN  \n",
       "3                   534.0         NaN           NaN  \n",
       "4                   441.0         NaN           NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the Barcelona demographics dataset\n",
    "demographics_df = pd.read_csv('./data/cleaned/demographics/demographics.csv')\n",
    "print(demographics_df.shape)\n",
    "demographics_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(439, 17)\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>district_code</th>\n",
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       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
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       "      <th>woman_bachelors_degree</th>\n",
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       "      <th>woman_junior_high_school</th>\n",
       "      <th>woman_senior_high_school</th>\n",
       "      <th>woman_unknown</th>\n",
       "      <th>woman_without education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>4455</td>\n",
       "      <td>9031</td>\n",
       "      <td>4966</td>\n",
       "      <td>3909</td>\n",
       "      <td>5</td>\n",
       "      <td>443</td>\n",
       "      <td>4563</td>\n",
       "      <td>5932</td>\n",
       "      <td>3732</td>\n",
       "      <td>3300</td>\n",
       "      <td>7</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>2</td>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>2534</td>\n",
       "      <td>1567</td>\n",
       "      <td>1323</td>\n",
       "      <td>1713</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>2721</td>\n",
       "      <td>1170</td>\n",
       "      <td>977</td>\n",
       "      <td>1515</td>\n",
       "      <td>1</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>3</td>\n",
       "      <td>la Barceloneta</td>\n",
       "      <td>1560</td>\n",
       "      <td>1782</td>\n",
       "      <td>1726</td>\n",
       "      <td>1438</td>\n",
       "      <td>2</td>\n",
       "      <td>244</td>\n",
       "      <td>1839</td>\n",
       "      <td>1786</td>\n",
       "      <td>1444</td>\n",
       "      <td>1259</td>\n",
       "      <td>1</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>3480</td>\n",
       "      <td>2225</td>\n",
       "      <td>1807</td>\n",
       "      <td>2263</td>\n",
       "      <td>2</td>\n",
       "      <td>190</td>\n",
       "      <td>4002</td>\n",
       "      <td>2020</td>\n",
       "      <td>1464</td>\n",
       "      <td>2067</td>\n",
       "      <td>5</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>5</td>\n",
       "      <td>el Fort Pienc</td>\n",
       "      <td>4637</td>\n",
       "      <td>1767</td>\n",
       "      <td>2341</td>\n",
       "      <td>3808</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "      <td>5462</td>\n",
       "      <td>2490</td>\n",
       "      <td>2690</td>\n",
       "      <td>3655</td>\n",
       "      <td>3</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  district_code district_name  neighbourhood_code  \\\n",
       "0  2015              1  Ciutat Vella                   1   \n",
       "1  2015              1  Ciutat Vella                   2   \n",
       "2  2015              1  Ciutat Vella                   3   \n",
       "3  2015              1  Ciutat Vella                   4   \n",
       "4  2015              2      Eixample                   5   \n",
       "\n",
       "                      neighbourhood_name  man_bachelors_degree  \\\n",
       "0                               el Raval                  4455   \n",
       "1                         el Barri Gòtic                  2534   \n",
       "2                         la Barceloneta                  1560   \n",
       "3  Sant Pere, Santa Caterina i la Ribera                  3480   \n",
       "4                          el Fort Pienc                  4637   \n",
       "\n",
       "   man_elementary_school  man_junior_high_school  man_senior_high_school  \\\n",
       "0                   9031                    4966                    3909   \n",
       "1                   1567                    1323                    1713   \n",
       "2                   1782                    1726                    1438   \n",
       "3                   2225                    1807                    2263   \n",
       "4                   1767                    2341                    3808   \n",
       "\n",
       "   man_unknown  man_without education  woman_bachelors_degree  \\\n",
       "0            5                    443                    4563   \n",
       "1            0                    124                    2721   \n",
       "2            2                    244                    1839   \n",
       "3            2                    190                    4002   \n",
       "4            2                    290                    5462   \n",
       "\n",
       "   woman_elementary_school  woman_junior_high_school  \\\n",
       "0                     5932                      3732   \n",
       "1                     1170                       977   \n",
       "2                     1786                      1444   \n",
       "3                     2020                      1464   \n",
       "4                     2490                      2690   \n",
       "\n",
       "   woman_senior_high_school  woman_unknown  woman_without education  \n",
       "0                      3300              7                      880  \n",
       "1                      1515              1                      268  \n",
       "2                      1259              1                      553  \n",
       "3                      2067              5                      470  \n",
       "4                      3655              3                      571  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the Barcelona education level dataset\n",
    "education_df = pd.read_csv('./data/cleaned/education/academic_level.csv')\n",
    "print(education_df.shape)\n",
    "education_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1572, 8)\n"
     ]
    },
    {
     "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>neighbourhood</th>\n",
       "      <th>period</th>\n",
       "      <th>airbnb_price(€)</th>\n",
       "      <th>n_airbnb_listings</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>airbnb_ratio_business</th>\n",
       "      <th>airbnb_professional_ratio</th>\n",
       "      <th>airbnb_mega_host_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baró de Viver</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>75.00</td>\n",
       "      <td>1</td>\n",
       "      <td>358.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baró de Viver</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>27.00</td>\n",
       "      <td>1</td>\n",
       "      <td>365.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baró de Viver</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>90.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baró de Viver</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>94.75</td>\n",
       "      <td>4</td>\n",
       "      <td>128.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baró de Viver</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>90.00</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   neighbourhood      period  airbnb_price(€)  n_airbnb_listings  \\\n",
       "0  Baró de Viver  2015-07-01            75.00                  1   \n",
       "1  Baró de Viver  2016-01-01            27.00                  1   \n",
       "2  Baró de Viver  2017-01-01            90.00                  1   \n",
       "3  Baró de Viver  2017-04-01            94.75                  4   \n",
       "4  Baró de Viver  2017-07-01            90.00                  1   \n",
       "\n",
       "   availability_365  airbnb_ratio_business  airbnb_professional_ratio  \\\n",
       "0            358.00                   1.00                        0.0   \n",
       "1            365.00                   0.00                        0.0   \n",
       "2              2.00                   1.00                        0.0   \n",
       "3            128.25                   0.25                        0.0   \n",
       "4              4.00                   1.00                        0.0   \n",
       "\n",
       "   airbnb_mega_host_ratio  \n",
       "0                     0.0  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                     0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb_df = pd.read_csv('./data/cleaned/airbnb/airbnb_simple.csv')\n",
    "print(airbnb_df.shape)\n",
    "airbnb_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start merging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge rent prices and unemployment datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2050, 12)\n"
     ]
    },
    {
     "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>period</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>Over 12 months</th>\n",
       "      <th>Up to 6 months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>734.99</td>\n",
       "      <td>13.50</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>14379.0</td>\n",
       "      <td>20385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>714.29</td>\n",
       "      <td>13.69</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>14379.0</td>\n",
       "      <td>20385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>764.85</td>\n",
       "      <td>13.97</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>14379.0</td>\n",
       "      <td>20385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>776.73</td>\n",
       "      <td>14.42</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>14379.0</td>\n",
       "      <td>20385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>2</td>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>905.26</td>\n",
       "      <td>14.14</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>3322.0</td>\n",
       "      <td>5632.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  quarter      period  district_code district_name  neighbourhood_code  \\\n",
       "0  2017      1.0  2017-01-01            1.0  Ciutat Vella                   1   \n",
       "1  2017      2.0  2017-04-01            1.0  Ciutat Vella                   1   \n",
       "2  2017      3.0  2017-07-01            1.0  Ciutat Vella                   1   \n",
       "3  2017      4.0  2017-10-01            1.0  Ciutat Vella                   1   \n",
       "4  2017      1.0  2017-01-01            1.0  Ciutat Vella                   2   \n",
       "\n",
       "  neighbourhood_name  price_month(€)  price_m2_month(€)  \\\n",
       "0           el Raval          734.99              13.50   \n",
       "1           el Raval          714.29              13.69   \n",
       "2           el Raval          764.85              13.97   \n",
       "3           el Raval          776.73              14.42   \n",
       "4     el Barri Gòtic          905.26              14.14   \n",
       "\n",
       "   From 6 months to 12 motnhs  Over 12 months  Up to 6 months  \n",
       "0                      6054.0         14379.0         20385.0  \n",
       "1                      6054.0         14379.0         20385.0  \n",
       "2                      6054.0         14379.0         20385.0  \n",
       "3                      6054.0         14379.0         20385.0  \n",
       "4                      1568.0          3322.0          5632.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge rent_prices and unemployment datasets\n",
    "merged = rent_prices_df.merge(unemployment_df.drop(columns=['district_code', 'district_name', 'neighbourhood_name']), \n",
    "                     on=['year','neighbourhood_code'], how='outer')\n",
    "print(merged.shape)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check how the merge behaved\n",
    "msno.matrix(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>period</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>Over 12 months</th>\n",
       "      <th>Up to 6 months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>2014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>589.55</td>\n",
       "      <td>10.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>2014</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>550.63</td>\n",
       "      <td>10.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>576.45</td>\n",
       "      <td>9.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>597.00</td>\n",
       "      <td>10.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>2</td>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>712.79</td>\n",
       "      <td>10.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Sant Martí</td>\n",
       "      <td>72</td>\n",
       "      <td>Sant Martí de Provençals</td>\n",
       "      <td>636.30</td>\n",
       "      <td>8.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Sant Martí</td>\n",
       "      <td>73</td>\n",
       "      <td>la Verneda i la Pau</td>\n",
       "      <td>568.68</td>\n",
       "      <td>8.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>2014</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Sant Martí</td>\n",
       "      <td>73</td>\n",
       "      <td>la Verneda i la Pau</td>\n",
       "      <td>569.14</td>\n",
       "      <td>8.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Sant Martí</td>\n",
       "      <td>73</td>\n",
       "      <td>la Verneda i la Pau</td>\n",
       "      <td>569.84</td>\n",
       "      <td>8.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Sant Martí</td>\n",
       "      <td>73</td>\n",
       "      <td>la Verneda i la Pau</td>\n",
       "      <td>552.58</td>\n",
       "      <td>8.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>292 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter      period  district_code district_name  \\\n",
       "292  2014      1.0  2014-01-01            1.0  Ciutat Vella   \n",
       "293  2014      2.0  2014-04-01            1.0  Ciutat Vella   \n",
       "294  2014      3.0  2014-07-01            1.0  Ciutat Vella   \n",
       "295  2014      4.0  2014-10-01            1.0  Ciutat Vella   \n",
       "296  2014      1.0  2014-01-01            1.0  Ciutat Vella   \n",
       "..    ...      ...         ...            ...           ...   \n",
       "579  2014      4.0  2014-10-01           10.0    Sant Martí   \n",
       "580  2014      1.0  2014-01-01           10.0    Sant Martí   \n",
       "581  2014      2.0  2014-04-01           10.0    Sant Martí   \n",
       "582  2014      3.0  2014-07-01           10.0    Sant Martí   \n",
       "583  2014      4.0  2014-10-01           10.0    Sant Martí   \n",
       "\n",
       "     neighbourhood_code        neighbourhood_name  price_month(€)  \\\n",
       "292                   1                  el Raval          589.55   \n",
       "293                   1                  el Raval          550.63   \n",
       "294                   1                  el Raval          576.45   \n",
       "295                   1                  el Raval          597.00   \n",
       "296                   2            el Barri Gòtic          712.79   \n",
       "..                  ...                       ...             ...   \n",
       "579                  72  Sant Martí de Provençals          636.30   \n",
       "580                  73       la Verneda i la Pau          568.68   \n",
       "581                  73       la Verneda i la Pau          569.14   \n",
       "582                  73       la Verneda i la Pau          569.84   \n",
       "583                  73       la Verneda i la Pau          552.58   \n",
       "\n",
       "     price_m2_month(€)  From 6 months to 12 motnhs  Over 12 months  \\\n",
       "292              10.76                         NaN             NaN   \n",
       "293              10.52                         NaN             NaN   \n",
       "294               9.84                         NaN             NaN   \n",
       "295              10.81                         NaN             NaN   \n",
       "296              10.58                         NaN             NaN   \n",
       "..                 ...                         ...             ...   \n",
       "579               8.86                         NaN             NaN   \n",
       "580               8.34                         NaN             NaN   \n",
       "581               8.26                         NaN             NaN   \n",
       "582               8.52                         NaN             NaN   \n",
       "583               8.14                         NaN             NaN   \n",
       "\n",
       "     Up to 6 months  \n",
       "292             NaN  \n",
       "293             NaN  \n",
       "294             NaN  \n",
       "295             NaN  \n",
       "296             NaN  \n",
       "..              ...  \n",
       "579             NaN  \n",
       "580             NaN  \n",
       "581             NaN  \n",
       "582             NaN  \n",
       "583             NaN  \n",
       "\n",
       "[292 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[merged['Up to 6 months'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**All these NaNs** are because there is no unemployment data **for 2014**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>year</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>Over 12 months</th>\n",
       "      <th>Up to 6 months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [year, district_code, district_name, neighbourhood_code, neighbourhood_name, From 6 months to 12 motnhs, Over 12 months, Up to 6 months]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_df[unemployment_df['year'] == 2014]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a little gap at the end of the graph, with a neighbourhood_code of 99!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>period</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>Over 12 months</th>\n",
       "      <th>Up to 6 months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2044</th>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2045</th>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2049</th>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>1577.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  quarter period  district_code district_name  neighbourhood_code  \\\n",
       "2044  2015      NaN    NaN            NaN           NaN                  99   \n",
       "2045  2016      NaN    NaN            NaN           NaN                  99   \n",
       "2046  2017      NaN    NaN            NaN           NaN                  99   \n",
       "2047  2018      NaN    NaN            NaN           NaN                  99   \n",
       "2048  2019      NaN    NaN            NaN           NaN                  99   \n",
       "2049  2020      NaN    NaN            NaN           NaN                  99   \n",
       "\n",
       "     neighbourhood_name  price_month(€)  price_m2_month(€)  \\\n",
       "2044                NaN             NaN                NaN   \n",
       "2045                NaN             NaN                NaN   \n",
       "2046                NaN             NaN                NaN   \n",
       "2047                NaN             NaN                NaN   \n",
       "2048                NaN             NaN                NaN   \n",
       "2049                NaN             NaN                NaN   \n",
       "\n",
       "      From 6 months to 12 motnhs  Over 12 months  Up to 6 months  \n",
       "2044                         9.0            20.0             7.0  \n",
       "2045                         8.0            15.0           100.0  \n",
       "2046                         8.0            17.0            63.0  \n",
       "2047                         9.0            24.0            82.0  \n",
       "2048                        41.0            50.0           104.0  \n",
       "2049                       428.0           141.0          1577.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[merged['neighbourhood_name'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge now the demographics dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2050, 22)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>period</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>...</th>\n",
       "      <th>1 Spanish</th>\n",
       "      <th>1 foreigner</th>\n",
       "      <th>2 Spanish</th>\n",
       "      <th>2 foreigners</th>\n",
       "      <th>3 Spanish</th>\n",
       "      <th>3 foreigners</th>\n",
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       "      <th>more than 4 foreigners</th>\n",
       "      <th>no Spanish</th>\n",
       "      <th>no foreigner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>734.99</td>\n",
       "      <td>13.50</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6030.0</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>3135.0</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>714.29</td>\n",
       "      <td>13.69</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6030.0</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>3135.0</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>764.85</td>\n",
       "      <td>13.97</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6030.0</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>3135.0</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>776.73</td>\n",
       "      <td>14.42</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6030.0</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>3135.0</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>2</td>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>905.26</td>\n",
       "      <td>14.14</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2371.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>1166.0</td>\n",
       "      <td>823.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  quarter      period  district_code district_name  neighbourhood_code  \\\n",
       "0  2017      1.0  2017-01-01            1.0  Ciutat Vella                   1   \n",
       "1  2017      2.0  2017-04-01            1.0  Ciutat Vella                   1   \n",
       "2  2017      3.0  2017-07-01            1.0  Ciutat Vella                   1   \n",
       "3  2017      4.0  2017-10-01            1.0  Ciutat Vella                   1   \n",
       "4  2017      1.0  2017-01-01            1.0  Ciutat Vella                   2   \n",
       "\n",
       "  neighbourhood_name  price_month(€)  price_m2_month(€)  \\\n",
       "0           el Raval          734.99              13.50   \n",
       "1           el Raval          714.29              13.69   \n",
       "2           el Raval          764.85              13.97   \n",
       "3           el Raval          776.73              14.42   \n",
       "4     el Barri Gòtic          905.26              14.14   \n",
       "\n",
       "   From 6 months to 12 motnhs  ...  1 Spanish  1 foreigner  2 Spanish  \\\n",
       "0                      6054.0  ...     6030.0       3353.0     3135.0   \n",
       "1                      6054.0  ...     6030.0       3353.0     3135.0   \n",
       "2                      6054.0  ...     6030.0       3353.0     3135.0   \n",
       "3                      6054.0  ...     6030.0       3353.0     3135.0   \n",
       "4                      1568.0  ...     2371.0       1485.0     1166.0   \n",
       "\n",
       "   2 foreigners  3 Spanish  3 foreigners  4 espanyols o més  \\\n",
       "0        1770.0     1563.0        1152.0             1423.0   \n",
       "1        1770.0     1563.0        1152.0             1423.0   \n",
       "2        1770.0     1563.0        1152.0             1423.0   \n",
       "3        1770.0     1563.0        1152.0             1423.0   \n",
       "4         823.0      541.0         381.0              433.0   \n",
       "\n",
       "   more than 4 foreigners  no Spanish  no foreigner  \n",
       "0                  2286.0         NaN           NaN  \n",
       "1                  2286.0         NaN           NaN  \n",
       "2                  2286.0         NaN           NaN  \n",
       "3                  2286.0         NaN           NaN  \n",
       "4                   495.0         NaN           NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge now the demographics dataset\n",
    "merged = merged.merge(demographics_df.drop(columns=['district_code', 'district_name', 'neighbourhood_name']), \n",
    "                     on=['year','neighbourhood_code'], how='outer')\n",
    "print(merged.shape)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check how the merge behaved\n",
    "msno.matrix(merged)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, there is no demographics data for 2014:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>year</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>1 Spanish</th>\n",
       "      <th>1 foreigner</th>\n",
       "      <th>2 Spanish</th>\n",
       "      <th>2 foreigners</th>\n",
       "      <th>3 Spanish</th>\n",
       "      <th>3 foreigners</th>\n",
       "      <th>4 espanyols o més</th>\n",
       "      <th>more than 4 foreigners</th>\n",
       "      <th>no Spanish</th>\n",
       "      <th>no foreigner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [year, district_code, district_name, neighbourhood_code, neighbourhood_name, 1 Spanish, 1 foreigner, 2 Spanish, 2 foreigners, 3 Spanish, 3 foreigners, 4 espanyols o més, more than 4 foreigners, no Spanish, no foreigner]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demographics_df[demographics_df['year'] == 2014]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the neighbourhood code 99, too, but in this case is empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>year</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>1 Spanish</th>\n",
       "      <th>1 foreigner</th>\n",
       "      <th>2 Spanish</th>\n",
       "      <th>2 foreigners</th>\n",
       "      <th>3 Spanish</th>\n",
       "      <th>3 foreigners</th>\n",
       "      <th>4 espanyols o més</th>\n",
       "      <th>more than 4 foreigners</th>\n",
       "      <th>no Spanish</th>\n",
       "      <th>no foreigner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2015</td>\n",
       "      <td>99</td>\n",
       "      <td>No consta</td>\n",
       "      <td>99</td>\n",
       "      <td>No consta</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  district_code district_name  neighbourhood_code neighbourhood_name  \\\n",
       "73  2015             99     No consta                  99          No consta   \n",
       "\n",
       "    1 Spanish  1 foreigner  2 Spanish  2 foreigners  3 Spanish  3 foreigners  \\\n",
       "73        0.0          0.0        0.0           0.0        0.0           0.0   \n",
       "\n",
       "    4 espanyols o més  more than 4 foreigners  no Spanish  no foreigner  \n",
       "73                0.0                     0.0         NaN           NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demographics_df[demographics_df['neighbourhood_code'] == 99]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, the *no Spanish*, *no Foreigner* columns are almost empty:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no Spanish      1758\n",
       "no foreigner    1758\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[['no Spanish', 'no foreigner']].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge now the academic level dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2050, 34)\n"
     ]
    },
    {
     "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>period</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>...</th>\n",
       "      <th>man_junior_high_school</th>\n",
       "      <th>man_senior_high_school</th>\n",
       "      <th>man_unknown</th>\n",
       "      <th>man_without education</th>\n",
       "      <th>woman_bachelors_degree</th>\n",
       "      <th>woman_elementary_school</th>\n",
       "      <th>woman_junior_high_school</th>\n",
       "      <th>woman_senior_high_school</th>\n",
       "      <th>woman_unknown</th>\n",
       "      <th>woman_without education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>734.99</td>\n",
       "      <td>13.50</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5529.0</td>\n",
       "      <td>4062.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>5082.0</td>\n",
       "      <td>5280.0</td>\n",
       "      <td>4143.0</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>638.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>714.29</td>\n",
       "      <td>13.69</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5529.0</td>\n",
       "      <td>4062.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>5082.0</td>\n",
       "      <td>5280.0</td>\n",
       "      <td>4143.0</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>638.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>764.85</td>\n",
       "      <td>13.97</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5529.0</td>\n",
       "      <td>4062.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>5082.0</td>\n",
       "      <td>5280.0</td>\n",
       "      <td>4143.0</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>638.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>776.73</td>\n",
       "      <td>14.42</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5529.0</td>\n",
       "      <td>4062.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>5082.0</td>\n",
       "      <td>5280.0</td>\n",
       "      <td>4143.0</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>638.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>2</td>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>905.26</td>\n",
       "      <td>14.14</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3017.0</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>1619.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  quarter      period  district_code district_name  neighbourhood_code  \\\n",
       "0  2017      1.0  2017-01-01            1.0  Ciutat Vella                   1   \n",
       "1  2017      2.0  2017-04-01            1.0  Ciutat Vella                   1   \n",
       "2  2017      3.0  2017-07-01            1.0  Ciutat Vella                   1   \n",
       "3  2017      4.0  2017-10-01            1.0  Ciutat Vella                   1   \n",
       "4  2017      1.0  2017-01-01            1.0  Ciutat Vella                   2   \n",
       "\n",
       "  neighbourhood_name  price_month(€)  price_m2_month(€)  \\\n",
       "0           el Raval          734.99              13.50   \n",
       "1           el Raval          714.29              13.69   \n",
       "2           el Raval          764.85              13.97   \n",
       "3           el Raval          776.73              14.42   \n",
       "4     el Barri Gòtic          905.26              14.14   \n",
       "\n",
       "   From 6 months to 12 motnhs  ...  man_junior_high_school  \\\n",
       "0                      6054.0  ...                  5529.0   \n",
       "1                      6054.0  ...                  5529.0   \n",
       "2                      6054.0  ...                  5529.0   \n",
       "3                      6054.0  ...                  5529.0   \n",
       "4                      1568.0  ...                  1575.0   \n",
       "\n",
       "   man_senior_high_school  man_unknown  man_without education  \\\n",
       "0                  4062.0        359.0                  264.0   \n",
       "1                  4062.0        359.0                  264.0   \n",
       "2                  4062.0        359.0                  264.0   \n",
       "3                  4062.0        359.0                  264.0   \n",
       "4                  1802.0         68.0                   87.0   \n",
       "\n",
       "   woman_bachelors_degree  woman_elementary_school  woman_junior_high_school  \\\n",
       "0                  5082.0                   5280.0                    4143.0   \n",
       "1                  5082.0                   5280.0                    4143.0   \n",
       "2                  5082.0                   5280.0                    4143.0   \n",
       "3                  5082.0                   5280.0                    4143.0   \n",
       "4                  3017.0                   1084.0                    1112.0   \n",
       "\n",
       "   woman_senior_high_school  woman_unknown  woman_without education  \n",
       "0                    3439.0          271.0                    638.0  \n",
       "1                    3439.0          271.0                    638.0  \n",
       "2                    3439.0          271.0                    638.0  \n",
       "3                    3439.0          271.0                    638.0  \n",
       "4                    1619.0           56.0                    205.0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge now the academic_level dataset\n",
    "merged = merged.merge(education_df.drop(columns=['district_code', 'district_name', 'neighbourhood_name']), \n",
    "                     on=['year','neighbourhood_code'], how='outer')\n",
    "print(merged.shape)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check how the merge behaved\n",
    "msno.matrix(merged)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we don't have education data for 2014:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>year</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>man_bachelors_degree</th>\n",
       "      <th>man_elementary_school</th>\n",
       "      <th>man_junior_high_school</th>\n",
       "      <th>man_senior_high_school</th>\n",
       "      <th>man_unknown</th>\n",
       "      <th>man_without education</th>\n",
       "      <th>woman_bachelors_degree</th>\n",
       "      <th>woman_elementary_school</th>\n",
       "      <th>woman_junior_high_school</th>\n",
       "      <th>woman_senior_high_school</th>\n",
       "      <th>woman_unknown</th>\n",
       "      <th>woman_without education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [year, district_code, district_name, neighbourhood_code, neighbourhood_name, man_bachelors_degree, man_elementary_school, man_junior_high_school, man_senior_high_school, man_unknown, man_without education, woman_bachelors_degree, woman_elementary_school, woman_junior_high_school, woman_senior_high_school, woman_unknown, woman_without education]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_df[education_df['year'] == 2014]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the neighbourhood_code 99, empty again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>year</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>man_bachelors_degree</th>\n",
       "      <th>man_elementary_school</th>\n",
       "      <th>man_junior_high_school</th>\n",
       "      <th>man_senior_high_school</th>\n",
       "      <th>man_unknown</th>\n",
       "      <th>man_without education</th>\n",
       "      <th>woman_bachelors_degree</th>\n",
       "      <th>woman_elementary_school</th>\n",
       "      <th>woman_junior_high_school</th>\n",
       "      <th>woman_senior_high_school</th>\n",
       "      <th>woman_unknown</th>\n",
       "      <th>woman_without education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2015</td>\n",
       "      <td>99</td>\n",
       "      <td>No consta</td>\n",
       "      <td>99</td>\n",
       "      <td>No consta</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  district_code district_name  neighbourhood_code neighbourhood_name  \\\n",
       "73  2015             99     No consta                  99          No consta   \n",
       "\n",
       "    man_bachelors_degree  man_elementary_school  man_junior_high_school  \\\n",
       "73                     0                      0                       0   \n",
       "\n",
       "    man_senior_high_school  man_unknown  man_without education  \\\n",
       "73                       0            0                      0   \n",
       "\n",
       "    woman_bachelors_degree  woman_elementary_school  woman_junior_high_school  \\\n",
       "73                       0                        0                         0   \n",
       "\n",
       "    woman_senior_high_school  woman_unknown  woman_without education  \n",
       "73                         1              0                        0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_df[education_df['neighbourhood_code'] == 99]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the airbnb dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2122, 40)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
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       "      <th>district_code</th>\n",
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       "      <td>6054.0</td>\n",
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       "      <td>638.0</td>\n",
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       "      <td>2017-07-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1.0</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>764.85</td>\n",
       "      <td>13.97</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4143.0</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>80.244898</td>\n",
       "      <td>1372.0</td>\n",
       "      <td>162.528426</td>\n",
       "      <td>0.416181</td>\n",
       "      <td>0.049563</td>\n",
       "      <td>0.002915</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1.0</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>776.73</td>\n",
       "      <td>14.42</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4143.0</td>\n",
       "      <td>3439.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>82.460993</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>170.300000</td>\n",
       "      <td>0.404965</td>\n",
       "      <td>0.058156</td>\n",
       "      <td>0.010638</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017.0</td>\n",
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       "      <td>2017-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
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      ],
      "text/plain": [
       "     year  quarter      period  district_code district_name  \\\n",
       "0  2017.0      1.0  2017-01-01            1.0  Ciutat Vella   \n",
       "1  2017.0      2.0  2017-04-01            1.0  Ciutat Vella   \n",
       "2  2017.0      3.0  2017-07-01            1.0  Ciutat Vella   \n",
       "3  2017.0      4.0  2017-10-01            1.0  Ciutat Vella   \n",
       "4  2017.0      1.0  2017-01-01            1.0  Ciutat Vella   \n",
       "\n",
       "   neighbourhood_code neighbourhood_name  price_month(€)  price_m2_month(€)  \\\n",
       "0                 1.0           el Raval          734.99              13.50   \n",
       "1                 1.0           el Raval          714.29              13.69   \n",
       "2                 1.0           el Raval          764.85              13.97   \n",
       "3                 1.0           el Raval          776.73              14.42   \n",
       "4                 2.0     el Barri Gòtic          905.26              14.14   \n",
       "\n",
       "   From 6 months to 12 motnhs  ...  woman_junior_high_school  \\\n",
       "0                      6054.0  ...                    4143.0   \n",
       "1                      6054.0  ...                    4143.0   \n",
       "2                      6054.0  ...                    4143.0   \n",
       "3                      6054.0  ...                    4143.0   \n",
       "4                      1568.0  ...                    1112.0   \n",
       "\n",
       "   woman_senior_high_school  woman_unknown  woman_without education  \\\n",
       "0                    3439.0          271.0                    638.0   \n",
       "1                    3439.0          271.0                    638.0   \n",
       "2                    3439.0          271.0                    638.0   \n",
       "3                    3439.0          271.0                    638.0   \n",
       "4                    1619.0           56.0                    205.0   \n",
       "\n",
       "   airbnb_price(€)  n_airbnb_listings  availability_365  \\\n",
       "0        69.122576             1444.0        208.791551   \n",
       "1        75.554371             1407.0        172.835110   \n",
       "2        80.244898             1372.0        162.528426   \n",
       "3        82.460993             1410.0        170.300000   \n",
       "4        82.141653             1186.0        210.263912   \n",
       "\n",
       "   airbnb_ratio_business  airbnb_professional_ratio  airbnb_mega_host_ratio  \n",
       "0               0.461219                   0.076870                0.001385  \n",
       "1               0.439943                   0.073916                0.003554  \n",
       "2               0.416181                   0.049563                0.002915  \n",
       "3               0.404965                   0.058156                0.010638  \n",
       "4               0.429174                   0.076728                0.009275  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the airbnb dataset\n",
    "airbnb_df = airbnb_df.rename(columns={'neighbourhood':'neighbourhood_name'})\n",
    "merged = merged.merge(airbnb_df, on=['period', 'neighbourhood_name'], how='outer')\n",
    "print(merged.shape)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
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       "      <td>764.85</td>\n",
       "      <td>13.97</td>\n",
       "      <td>6054.0</td>\n",
       "      <td>14379.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4143.0</td>\n",
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       "      <td>271.0</td>\n",
       "      <td>638.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>14379.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>2.0</td>\n",
       "      <td>el Barri Gòtic</td>\n",
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       "      <td>14.14</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>3322.0</td>\n",
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      "text/plain": [
       "     year     quarter  district_code district_name  neighbourhood_code  \\\n",
       "0  2017.0  2017-01-01            1.0  Ciutat Vella                 1.0   \n",
       "1  2017.0  2017-04-01            1.0  Ciutat Vella                 1.0   \n",
       "2  2017.0  2017-07-01            1.0  Ciutat Vella                 1.0   \n",
       "3  2017.0  2017-10-01            1.0  Ciutat Vella                 1.0   \n",
       "4  2017.0  2017-01-01            1.0  Ciutat Vella                 2.0   \n",
       "\n",
       "  neighbourhood_name  price_month(€)  price_m2_month(€)  \\\n",
       "0           el Raval          734.99              13.50   \n",
       "1           el Raval          714.29              13.69   \n",
       "2           el Raval          764.85              13.97   \n",
       "3           el Raval          776.73              14.42   \n",
       "4     el Barri Gòtic          905.26              14.14   \n",
       "\n",
       "   From 6 months to 12 motnhs  Over 12 months  ...  woman_junior_high_school  \\\n",
       "0                      6054.0         14379.0  ...                    4143.0   \n",
       "1                      6054.0         14379.0  ...                    4143.0   \n",
       "2                      6054.0         14379.0  ...                    4143.0   \n",
       "3                      6054.0         14379.0  ...                    4143.0   \n",
       "4                      1568.0          3322.0  ...                    1112.0   \n",
       "\n",
       "   woman_senior_high_school  woman_unknown  woman_without education  \\\n",
       "0                    3439.0          271.0                    638.0   \n",
       "1                    3439.0          271.0                    638.0   \n",
       "2                    3439.0          271.0                    638.0   \n",
       "3                    3439.0          271.0                    638.0   \n",
       "4                    1619.0           56.0                    205.0   \n",
       "\n",
       "   airbnb_price(€)  n_airbnb_listings  availability_365  \\\n",
       "0        69.122576             1444.0        208.791551   \n",
       "1        75.554371             1407.0        172.835110   \n",
       "2        80.244898             1372.0        162.528426   \n",
       "3        82.460993             1410.0        170.300000   \n",
       "4        82.141653             1186.0        210.263912   \n",
       "\n",
       "   airbnb_ratio_business  airbnb_professional_ratio  airbnb_mega_host_ratio  \n",
       "0               0.461219                   0.076870                0.001385  \n",
       "1               0.439943                   0.073916                0.003554  \n",
       "2               0.416181                   0.049563                0.002915  \n",
       "3               0.404965                   0.058156                0.010638  \n",
       "4               0.429174                   0.076728                0.009275  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = merged.drop(columns='quarter').rename(columns={'period':'quarter'})\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see how the merge went\n",
    "msno.matrix(merged.sort_values('quarter'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no data from the other datasets for 2021, that explains the gap in the bottom:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                          72\n",
       "quarter                        0\n",
       "district_code                 72\n",
       "district_name                 72\n",
       "neighbourhood_code            72\n",
       "neighbourhood_name             0\n",
       "price_month(€)                72\n",
       "price_m2_month(€)             72\n",
       "From 6 months to 12 motnhs    72\n",
       "Over 12 months                72\n",
       "Up to 6 months                72\n",
       "1 Spanish                     72\n",
       "1 foreigner                   72\n",
       "2 Spanish                     72\n",
       "2 foreigners                  72\n",
       "3 Spanish                     72\n",
       "3 foreigners                  72\n",
       "4 espanyols o més             72\n",
       "more than 4 foreigners        72\n",
       "no Spanish                    72\n",
       "no foreigner                  72\n",
       "man_bachelors_degree          72\n",
       "man_elementary_school         72\n",
       "man_junior_high_school        72\n",
       "man_senior_high_school        72\n",
       "man_unknown                   72\n",
       "man_without education         72\n",
       "woman_bachelors_degree        72\n",
       "woman_elementary_school       72\n",
       "woman_junior_high_school      72\n",
       "woman_senior_high_school      72\n",
       "woman_unknown                 72\n",
       "woman_without education       72\n",
       "airbnb_price(€)                0\n",
       "n_airbnb_listings              0\n",
       "availability_365               0\n",
       "airbnb_ratio_business          0\n",
       "airbnb_professional_ratio      0\n",
       "airbnb_mega_host_ratio         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[merged['quarter'] == '2021-01-01'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no airbnb data for certain neighbourhoods and certain quarters, like this (these airbnb values, we can input to zero):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>Over 12 months</th>\n",
       "      <th>...</th>\n",
       "      <th>woman_junior_high_school</th>\n",
       "      <th>woman_senior_high_school</th>\n",
       "      <th>woman_unknown</th>\n",
       "      <th>woman_without education</th>\n",
       "      <th>airbnb_price(€)</th>\n",
       "      <th>n_airbnb_listings</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>airbnb_ratio_business</th>\n",
       "      <th>airbnb_professional_ratio</th>\n",
       "      <th>airbnb_mega_host_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Horta-Guinardó</td>\n",
       "      <td>42.0</td>\n",
       "      <td>la Clota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Nou Barris</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Can Peguera</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>...</td>\n",
       "      <td>308.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year     quarter  district_code   district_name  neighbourhood_code  \\\n",
       "748  2019.0  2019-01-01            7.0  Horta-Guinardó                42.0   \n",
       "768  2019.0  2019-01-01            8.0      Nou Barris                47.0   \n",
       "\n",
       "    neighbourhood_name  price_month(€)  price_m2_month(€)  \\\n",
       "748           la Clota             NaN                NaN   \n",
       "768        Can Peguera             NaN                NaN   \n",
       "\n",
       "     From 6 months to 12 motnhs  Over 12 months  ...  \\\n",
       "748                        31.0            72.0  ...   \n",
       "768                       191.0           481.0  ...   \n",
       "\n",
       "     woman_junior_high_school  woman_senior_high_school  woman_unknown  \\\n",
       "748                      69.0                      57.0            5.0   \n",
       "768                     308.0                     179.0           26.0   \n",
       "\n",
       "     woman_without education  airbnb_price(€)  n_airbnb_listings  \\\n",
       "748                     11.0              NaN                NaN   \n",
       "768                     67.0              NaN                NaN   \n",
       "\n",
       "     availability_365  airbnb_ratio_business  airbnb_professional_ratio  \\\n",
       "748               NaN                    NaN                        NaN   \n",
       "768               NaN                    NaN                        NaN   \n",
       "\n",
       "     airbnb_mega_host_ratio  \n",
       "748                     NaN  \n",
       "768                     NaN  \n",
       "\n",
       "[2 rows x 39 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[(merged['quarter'] == '2019-01-01') & (merged['airbnb_price(€)'].isna())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for certain periods, like these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>neighbourhood_name</th>\n",
       "      <th>period</th>\n",
       "      <th>airbnb_price(€)</th>\n",
       "      <th>n_airbnb_listings</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>airbnb_ratio_business</th>\n",
       "      <th>airbnb_professional_ratio</th>\n",
       "      <th>airbnb_mega_host_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [neighbourhood_name, period, airbnb_price(€), n_airbnb_listings, availability_365, airbnb_ratio_business, airbnb_professional_ratio, airbnb_mega_host_ratio]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb_df[airbnb_df['period'] == '2016-04-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>period</th>\n",
       "      <th>airbnb_price(€)</th>\n",
       "      <th>n_airbnb_listings</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>airbnb_ratio_business</th>\n",
       "      <th>airbnb_professional_ratio</th>\n",
       "      <th>airbnb_mega_host_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [neighbourhood_name, period, airbnb_price(€), n_airbnb_listings, availability_365, airbnb_ratio_business, airbnb_professional_ratio, airbnb_mega_host_ratio]\n",
       "Index: []"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb_df[airbnb_df['period'] == '2016-07-01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary: \n",
    "\n",
    "+ We should drop rows with year 2014\n",
    "+ We should also drop rows with quarter greater than '2020-10-01' (the last date with information from rents)\n",
    "+ We should drop *no Spanish* and *no foreigner* columns\n",
    "+ We should drop the neighbourhood_code 99, because it's not useful for what we want (predict rent prices for neighbourhood)\n",
    "+ We should input to zero the airbnb data that we have no information of (certain neighbourhoods for certain periods)\n",
    "\n",
    "Question:\n",
    "+ What we do with the airbnb data missed for entire periods?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping of columns and rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = merged.drop(merged[merged['year'] == 2014].index, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = merged.drop(merged[merged['quarter'] > '2020-10-01'].index, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = merged.drop(['no Spanish', 'no foreigner'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = merged.drop(merged[merged['neighbourhood_code'] == 99].index, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputing aibnb data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2015-07-01', '2016-01-01', '2017-01-01', '2017-04-01',\n",
       "       '2017-07-01', '2017-10-01', '2018-01-01', '2018-04-01',\n",
       "       '2018-07-01', '2018-10-01', '2019-01-01', '2019-04-01',\n",
       "       '2019-07-01', '2019-10-01', '2020-01-01', '2020-04-01',\n",
       "       '2020-07-01', '2020-10-01', '2021-01-01', '2015-04-01',\n",
       "       '2015-10-01', '2016-10-01'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_airbnb_quarters = airbnb_df['period'].unique()\n",
    "valid_airbnb_quarters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['airbnb_price(€)', 'n_airbnb_listings', 'availability_365',\n",
       "       'airbnb_ratio_business', 'airbnb_professional_ratio',\n",
       "       'airbnb_mega_host_ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb_columns_to_fill = airbnb_df.columns.drop(['neighbourhood_name', 'period'])\n",
    "airbnb_columns_to_fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.loc[(merged['quarter'].isin(valid_airbnb_quarters) & (merged['airbnb_price(€)'].isna())), airbnb_columns_to_fill] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(merged)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reorder the dataframe: one neighbourhood after the other, and then the dates ordered for each neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>Over 12 months</th>\n",
       "      <th>...</th>\n",
       "      <th>woman_junior_high_school</th>\n",
       "      <th>woman_senior_high_school</th>\n",
       "      <th>woman_unknown</th>\n",
       "      <th>woman_without education</th>\n",
       "      <th>airbnb_price(€)</th>\n",
       "      <th>n_airbnb_listings</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>airbnb_ratio_business</th>\n",
       "      <th>airbnb_professional_ratio</th>\n",
       "      <th>airbnb_mega_host_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Ciutat Vella</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Sant Pere, Santa Caterina i la Ribera</th>\n",
       "      <th>1472</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>697.16</td>\n",
       "      <td>11.51</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1473</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>708.03</td>\n",
       "      <td>12.02</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>73.343300</td>\n",
       "      <td>903.0</td>\n",
       "      <td>262.921373</td>\n",
       "      <td>0.631229</td>\n",
       "      <td>0.128461</td>\n",
       "      <td>0.016611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>729.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>75.124776</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>259.641831</td>\n",
       "      <td>0.607720</td>\n",
       "      <td>0.106822</td>\n",
       "      <td>0.002693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1475</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>751.60</td>\n",
       "      <td>13.43</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>72.410441</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>271.629163</td>\n",
       "      <td>0.580558</td>\n",
       "      <td>0.067507</td>\n",
       "      <td>0.001800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>759.16</td>\n",
       "      <td>12.94</td>\n",
       "      <td>2563.0</td>\n",
       "      <td>7550.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1509.0</td>\n",
       "      <td>2196.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>72.739919</td>\n",
       "      <td>992.0</td>\n",
       "      <td>260.639113</td>\n",
       "      <td>0.561492</td>\n",
       "      <td>0.073589</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Sarrià-Sant Gervasi</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">les Tres Torres</th>\n",
       "      <th>679</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Sarrià-Sant Gervasi</td>\n",
       "      <td>24.0</td>\n",
       "      <td>les Tres Torres</td>\n",
       "      <td>1745.00</td>\n",
       "      <td>17.40</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1446.0</td>\n",
       "      <td>...</td>\n",
       "      <td>813.0</td>\n",
       "      <td>2212.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>153.696970</td>\n",
       "      <td>33.0</td>\n",
       "      <td>227.242424</td>\n",
       "      <td>0.575758</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Sarrià-Sant Gervasi</td>\n",
       "      <td>24.0</td>\n",
       "      <td>les Tres Torres</td>\n",
       "      <td>1662.40</td>\n",
       "      <td>16.30</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>...</td>\n",
       "      <td>856.0</td>\n",
       "      <td>2176.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>153.176471</td>\n",
       "      <td>34.0</td>\n",
       "      <td>224.911765</td>\n",
       "      <td>0.558824</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Sarrià-Sant Gervasi</td>\n",
       "      <td>24.0</td>\n",
       "      <td>les Tres Torres</td>\n",
       "      <td>1679.10</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>...</td>\n",
       "      <td>856.0</td>\n",
       "      <td>2176.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>208.971429</td>\n",
       "      <td>35.0</td>\n",
       "      <td>179.200000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Sarrià-Sant Gervasi</td>\n",
       "      <td>24.0</td>\n",
       "      <td>les Tres Torres</td>\n",
       "      <td>1699.40</td>\n",
       "      <td>15.90</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>...</td>\n",
       "      <td>856.0</td>\n",
       "      <td>2176.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>108.628571</td>\n",
       "      <td>35.0</td>\n",
       "      <td>231.342857</td>\n",
       "      <td>0.542857</td>\n",
       "      <td>0.457143</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Sarrià-Sant Gervasi</td>\n",
       "      <td>24.0</td>\n",
       "      <td>les Tres Torres</td>\n",
       "      <td>1729.50</td>\n",
       "      <td>16.80</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>...</td>\n",
       "      <td>856.0</td>\n",
       "      <td>2176.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>110.421053</td>\n",
       "      <td>38.0</td>\n",
       "      <td>206.526316</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.131579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1752 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  year  \\\n",
       "district_name       neighbourhood_name                                   \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472  2015.0   \n",
       "                                                          1473  2015.0   \n",
       "                                                          1474  2015.0   \n",
       "                                                          1475  2015.0   \n",
       "                                                          888   2016.0   \n",
       "...                                                                ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679   2019.0   \n",
       "                                                          1260  2020.0   \n",
       "                                                          1261  2020.0   \n",
       "                                                          1262  2020.0   \n",
       "                                                          1263  2020.0   \n",
       "\n",
       "                                                                   quarter  \\\n",
       "district_name       neighbourhood_name                                       \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472  2015-01-01   \n",
       "                                                          1473  2015-04-01   \n",
       "                                                          1474  2015-07-01   \n",
       "                                                          1475  2015-10-01   \n",
       "                                                          888   2016-01-01   \n",
       "...                                                                    ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679   2019-10-01   \n",
       "                                                          1260  2020-01-01   \n",
       "                                                          1261  2020-04-01   \n",
       "                                                          1262  2020-07-01   \n",
       "                                                          1263  2020-10-01   \n",
       "\n",
       "                                                                district_code  \\\n",
       "district_name       neighbourhood_name                                          \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472            1.0   \n",
       "                                                          1473            1.0   \n",
       "                                                          1474            1.0   \n",
       "                                                          1475            1.0   \n",
       "                                                          888             1.0   \n",
       "...                                                                       ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679             5.0   \n",
       "                                                          1260            5.0   \n",
       "                                                          1261            5.0   \n",
       "                                                          1262            5.0   \n",
       "                                                          1263            5.0   \n",
       "\n",
       "                                                                      district_name  \\\n",
       "district_name       neighbourhood_name                                                \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472         Ciutat Vella   \n",
       "                                                          1473         Ciutat Vella   \n",
       "                                                          1474         Ciutat Vella   \n",
       "                                                          1475         Ciutat Vella   \n",
       "                                                          888          Ciutat Vella   \n",
       "...                                                                             ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679   Sarrià-Sant Gervasi   \n",
       "                                                          1260  Sarrià-Sant Gervasi   \n",
       "                                                          1261  Sarrià-Sant Gervasi   \n",
       "                                                          1262  Sarrià-Sant Gervasi   \n",
       "                                                          1263  Sarrià-Sant Gervasi   \n",
       "\n",
       "                                                                neighbourhood_code  \\\n",
       "district_name       neighbourhood_name                                               \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472                 4.0   \n",
       "                                                          1473                 4.0   \n",
       "                                                          1474                 4.0   \n",
       "                                                          1475                 4.0   \n",
       "                                                          888                  4.0   \n",
       "...                                                                            ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                 24.0   \n",
       "                                                          1260                24.0   \n",
       "                                                          1261                24.0   \n",
       "                                                          1262                24.0   \n",
       "                                                          1263                24.0   \n",
       "\n",
       "                                                                                   neighbourhood_name  \\\n",
       "district_name       neighbourhood_name                                                                  \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472  Sant Pere, Santa Caterina i la Ribera   \n",
       "                                                          1473  Sant Pere, Santa Caterina i la Ribera   \n",
       "                                                          1474  Sant Pere, Santa Caterina i la Ribera   \n",
       "                                                          1475  Sant Pere, Santa Caterina i la Ribera   \n",
       "                                                          888   Sant Pere, Santa Caterina i la Ribera   \n",
       "...                                                                                               ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                         les Tres Torres   \n",
       "                                                          1260                        les Tres Torres   \n",
       "                                                          1261                        les Tres Torres   \n",
       "                                                          1262                        les Tres Torres   \n",
       "                                                          1263                        les Tres Torres   \n",
       "\n",
       "                                                                price_month(€)  \\\n",
       "district_name       neighbourhood_name                                           \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472          697.16   \n",
       "                                                          1473          708.03   \n",
       "                                                          1474          729.00   \n",
       "                                                          1475          751.60   \n",
       "                                                          888           759.16   \n",
       "...                                                                        ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679          1745.00   \n",
       "                                                          1260         1662.40   \n",
       "                                                          1261         1679.10   \n",
       "                                                          1262         1699.40   \n",
       "                                                          1263         1729.50   \n",
       "\n",
       "                                                                price_m2_month(€)  \\\n",
       "district_name       neighbourhood_name                                              \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472              11.51   \n",
       "                                                          1473              12.02   \n",
       "                                                          1474              14.00   \n",
       "                                                          1475              13.43   \n",
       "                                                          888               12.94   \n",
       "...                                                                           ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679               17.40   \n",
       "                                                          1260              16.30   \n",
       "                                                          1261              18.00   \n",
       "                                                          1262              15.90   \n",
       "                                                          1263              16.80   \n",
       "\n",
       "                                                                From 6 months to 12 motnhs  \\\n",
       "district_name       neighbourhood_name                                                       \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472                      2949.0   \n",
       "                                                          1473                      2949.0   \n",
       "                                                          1474                      2949.0   \n",
       "                                                          1475                      2949.0   \n",
       "                                                          888                       2563.0   \n",
       "...                                                                                    ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                        687.0   \n",
       "                                                          1260                      1277.0   \n",
       "                                                          1261                      1277.0   \n",
       "                                                          1262                      1277.0   \n",
       "                                                          1263                      1277.0   \n",
       "\n",
       "                                                                Over 12 months  \\\n",
       "district_name       neighbourhood_name                                           \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472          8425.0   \n",
       "                                                          1473          8425.0   \n",
       "                                                          1474          8425.0   \n",
       "                                                          1475          8425.0   \n",
       "                                                          888           7550.0   \n",
       "...                                                                        ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679           1446.0   \n",
       "                                                          1260          1842.0   \n",
       "                                                          1261          1842.0   \n",
       "                                                          1262          1842.0   \n",
       "                                                          1263          1842.0   \n",
       "\n",
       "                                                                ...  \\\n",
       "district_name       neighbourhood_name                          ...   \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472  ...   \n",
       "                                                          1473  ...   \n",
       "                                                          1474  ...   \n",
       "                                                          1475  ...   \n",
       "                                                          888   ...   \n",
       "...                                                             ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679   ...   \n",
       "                                                          1260  ...   \n",
       "                                                          1261  ...   \n",
       "                                                          1262  ...   \n",
       "                                                          1263  ...   \n",
       "\n",
       "                                                                woman_junior_high_school  \\\n",
       "district_name       neighbourhood_name                                                     \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472                    1464.0   \n",
       "                                                          1473                    1464.0   \n",
       "                                                          1474                    1464.0   \n",
       "                                                          1475                    1464.0   \n",
       "                                                          888                     1509.0   \n",
       "...                                                                                  ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                      813.0   \n",
       "                                                          1260                     856.0   \n",
       "                                                          1261                     856.0   \n",
       "                                                          1262                     856.0   \n",
       "                                                          1263                     856.0   \n",
       "\n",
       "                                                                woman_senior_high_school  \\\n",
       "district_name       neighbourhood_name                                                     \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472                    2067.0   \n",
       "                                                          1473                    2067.0   \n",
       "                                                          1474                    2067.0   \n",
       "                                                          1475                    2067.0   \n",
       "                                                          888                     2196.0   \n",
       "...                                                                                  ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                     2212.0   \n",
       "                                                          1260                    2176.0   \n",
       "                                                          1261                    2176.0   \n",
       "                                                          1262                    2176.0   \n",
       "                                                          1263                    2176.0   \n",
       "\n",
       "                                                                woman_unknown  \\\n",
       "district_name       neighbourhood_name                                          \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472            5.0   \n",
       "                                                          1473            5.0   \n",
       "                                                          1474            5.0   \n",
       "                                                          1475            5.0   \n",
       "                                                          888           121.0   \n",
       "...                                                                       ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679           213.0   \n",
       "                                                          1260          167.0   \n",
       "                                                          1261          167.0   \n",
       "                                                          1262          167.0   \n",
       "                                                          1263          167.0   \n",
       "\n",
       "                                                                woman_without education  \\\n",
       "district_name       neighbourhood_name                                                    \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472                    470.0   \n",
       "                                                          1473                    470.0   \n",
       "                                                          1474                    470.0   \n",
       "                                                          1475                    470.0   \n",
       "                                                          888                     376.0   \n",
       "...                                                                                 ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                      36.0   \n",
       "                                                          1260                     36.0   \n",
       "                                                          1261                     36.0   \n",
       "                                                          1262                     36.0   \n",
       "                                                          1263                     36.0   \n",
       "\n",
       "                                                                airbnb_price(€)  \\\n",
       "district_name       neighbourhood_name                                            \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472              NaN   \n",
       "                                                          1473        73.343300   \n",
       "                                                          1474        75.124776   \n",
       "                                                          1475        72.410441   \n",
       "                                                          888         72.739919   \n",
       "...                                                                         ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679        153.696970   \n",
       "                                                          1260       153.176471   \n",
       "                                                          1261       208.971429   \n",
       "                                                          1262       108.628571   \n",
       "                                                          1263       110.421053   \n",
       "\n",
       "                                                                n_airbnb_listings  \\\n",
       "district_name       neighbourhood_name                                              \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472                NaN   \n",
       "                                                          1473              903.0   \n",
       "                                                          1474             1114.0   \n",
       "                                                          1475             1111.0   \n",
       "                                                          888               992.0   \n",
       "...                                                                           ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                33.0   \n",
       "                                                          1260               34.0   \n",
       "                                                          1261               35.0   \n",
       "                                                          1262               35.0   \n",
       "                                                          1263               38.0   \n",
       "\n",
       "                                                                availability_365  \\\n",
       "district_name       neighbourhood_name                                             \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472               NaN   \n",
       "                                                          1473        262.921373   \n",
       "                                                          1474        259.641831   \n",
       "                                                          1475        271.629163   \n",
       "                                                          888         260.639113   \n",
       "...                                                                          ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679         227.242424   \n",
       "                                                          1260        224.911765   \n",
       "                                                          1261        179.200000   \n",
       "                                                          1262        231.342857   \n",
       "                                                          1263        206.526316   \n",
       "\n",
       "                                                                airbnb_ratio_business  \\\n",
       "district_name       neighbourhood_name                                                  \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472                    NaN   \n",
       "                                                          1473               0.631229   \n",
       "                                                          1474               0.607720   \n",
       "                                                          1475               0.580558   \n",
       "                                                          888                0.561492   \n",
       "...                                                                               ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                0.575758   \n",
       "                                                          1260               0.558824   \n",
       "                                                          1261               0.571429   \n",
       "                                                          1262               0.542857   \n",
       "                                                          1263               0.578947   \n",
       "\n",
       "                                                                airbnb_professional_ratio  \\\n",
       "district_name       neighbourhood_name                                                      \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472                        NaN   \n",
       "                                                          1473                   0.128461   \n",
       "                                                          1474                   0.106822   \n",
       "                                                          1475                   0.067507   \n",
       "                                                          888                    0.073589   \n",
       "...                                                                                   ...   \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                    0.484848   \n",
       "                                                          1260                   0.500000   \n",
       "                                                          1261                   0.428571   \n",
       "                                                          1262                   0.457143   \n",
       "                                                          1263                   0.421053   \n",
       "\n",
       "                                                                airbnb_mega_host_ratio  \n",
       "district_name       neighbourhood_name                                                  \n",
       "Ciutat Vella        Sant Pere, Santa Caterina i la Ribera 1472                     NaN  \n",
       "                                                          1473                0.016611  \n",
       "                                                          1474                0.002693  \n",
       "                                                          1475                0.001800  \n",
       "                                                          888                 0.000000  \n",
       "...                                                                                ...  \n",
       "Sarrià-Sant Gervasi les Tres Torres                       679                 0.181818  \n",
       "                                                          1260                0.176471  \n",
       "                                                          1261                0.142857  \n",
       "                                                          1262                0.142857  \n",
       "                                                          1263                0.131579  \n",
       "\n",
       "[1752 rows x 37 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = merged.groupby(by=['district_name','neighbourhood_name']).apply(lambda df: df.sort_values('quarter'))\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "      <th>From 6 months to 12 motnhs</th>\n",
       "      <th>Over 12 months</th>\n",
       "      <th>...</th>\n",
       "      <th>woman_junior_high_school</th>\n",
       "      <th>woman_senior_high_school</th>\n",
       "      <th>woman_unknown</th>\n",
       "      <th>woman_without education</th>\n",
       "      <th>airbnb_price(€)</th>\n",
       "      <th>n_airbnb_listings</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>airbnb_ratio_business</th>\n",
       "      <th>airbnb_professional_ratio</th>\n",
       "      <th>airbnb_mega_host_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>697.16</td>\n",
       "      <td>11.51</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>708.03</td>\n",
       "      <td>12.02</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>73.343300</td>\n",
       "      <td>903.0</td>\n",
       "      <td>262.921373</td>\n",
       "      <td>0.631229</td>\n",
       "      <td>0.128461</td>\n",
       "      <td>0.016611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>729.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>75.124776</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>259.641831</td>\n",
       "      <td>0.607720</td>\n",
       "      <td>0.106822</td>\n",
       "      <td>0.002693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>751.60</td>\n",
       "      <td>13.43</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>72.410441</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>271.629163</td>\n",
       "      <td>0.580558</td>\n",
       "      <td>0.067507</td>\n",
       "      <td>0.001800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>759.16</td>\n",
       "      <td>12.94</td>\n",
       "      <td>2563.0</td>\n",
       "      <td>7550.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1509.0</td>\n",
       "      <td>2196.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>72.739919</td>\n",
       "      <td>992.0</td>\n",
       "      <td>260.639113</td>\n",
       "      <td>0.561492</td>\n",
       "      <td>0.073589</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year     quarter  district_code district_name  neighbourhood_code  \\\n",
       "0  2015.0  2015-01-01            1.0  Ciutat Vella                 4.0   \n",
       "1  2015.0  2015-04-01            1.0  Ciutat Vella                 4.0   \n",
       "2  2015.0  2015-07-01            1.0  Ciutat Vella                 4.0   \n",
       "3  2015.0  2015-10-01            1.0  Ciutat Vella                 4.0   \n",
       "4  2016.0  2016-01-01            1.0  Ciutat Vella                 4.0   \n",
       "\n",
       "                      neighbourhood_name  price_month(€)  price_m2_month(€)  \\\n",
       "0  Sant Pere, Santa Caterina i la Ribera          697.16              11.51   \n",
       "1  Sant Pere, Santa Caterina i la Ribera          708.03              12.02   \n",
       "2  Sant Pere, Santa Caterina i la Ribera          729.00              14.00   \n",
       "3  Sant Pere, Santa Caterina i la Ribera          751.60              13.43   \n",
       "4  Sant Pere, Santa Caterina i la Ribera          759.16              12.94   \n",
       "\n",
       "   From 6 months to 12 motnhs  Over 12 months  ...  woman_junior_high_school  \\\n",
       "0                      2949.0          8425.0  ...                    1464.0   \n",
       "1                      2949.0          8425.0  ...                    1464.0   \n",
       "2                      2949.0          8425.0  ...                    1464.0   \n",
       "3                      2949.0          8425.0  ...                    1464.0   \n",
       "4                      2563.0          7550.0  ...                    1509.0   \n",
       "\n",
       "   woman_senior_high_school  woman_unknown  woman_without education  \\\n",
       "0                    2067.0            5.0                    470.0   \n",
       "1                    2067.0            5.0                    470.0   \n",
       "2                    2067.0            5.0                    470.0   \n",
       "3                    2067.0            5.0                    470.0   \n",
       "4                    2196.0          121.0                    376.0   \n",
       "\n",
       "   airbnb_price(€)  n_airbnb_listings  availability_365  \\\n",
       "0              NaN                NaN               NaN   \n",
       "1        73.343300              903.0        262.921373   \n",
       "2        75.124776             1114.0        259.641831   \n",
       "3        72.410441             1111.0        271.629163   \n",
       "4        72.739919              992.0        260.639113   \n",
       "\n",
       "   airbnb_ratio_business  airbnb_professional_ratio  airbnb_mega_host_ratio  \n",
       "0                    NaN                        NaN                     NaN  \n",
       "1               0.631229                   0.128461                0.016611  \n",
       "2               0.607720                   0.106822                0.002693  \n",
       "3               0.580558                   0.067507                0.001800  \n",
       "4               0.561492                   0.073589                0.000000  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#and reset index\n",
    "merged = merged.reset_index(drop=True)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged.to_csv('./data/rent_airbnb_all_features.csv', index=False)"
   ]
  }
 ],
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