variables_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas\n",
    "!pip install geopandas\n",
    "!pip install matplotlib\n",
    "!pip install plotly\n",
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "code_show=true;\n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "}\n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "   \n",
    "HTML('''<script>\n",
    "code_show=true;\n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "}\n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First,import the dataframe with all the features\n",
    "- Simplify the columns\n",
    "- Imput the NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/rent_airbnb_all_features.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simplify the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum_columns_and_drop(df, columns, new_column, position):\n",
    "    # convert the list of column names into a numeric range\n",
    "    cols_range = []\n",
    "    for col in columns:\n",
    "        index_no = df.columns.get_loc(col)\n",
    "        cols_range.append(index_no)\n",
    "    cols_range = np.array(cols_range)\n",
    "    \n",
    "    # insert the new colum with the sum of all given columns\n",
    "    df.insert(position, new_column, df.iloc[:, cols_range].sum(axis=1))\n",
    "    # drop the old columns and return the dataframe\n",
    "    return df.drop(columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'quarter', 'district_code', 'district_name',\n",
       "       'neighbourhood_code', 'neighbourhood_name', 'price_month(€)',\n",
       "       'price_m2_month(€)', 'short-term unemployment',\n",
       "       'long-term unemployment', '1 Spanish', '1 foreigner',\n",
       "       'more than 2 foreigners', 'more than 2 Spanish', 'no education',\n",
       "       'medium education', 'bachelors degree', 'airbnb_price(€)',\n",
       "       'n_airbnb_listings', 'availability_365', 'airbnb_ratio_business',\n",
       "       'airbnb_professional_ratio', 'airbnb_mega_host_ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sum_columns_and_drop(df, ['2 Spanish', '3 Spanish', '4 espanyols o més'], 'more than 2 Spanish', 13)\n",
    "df = sum_columns_and_drop(df, ['2 foreigners', '3 foreigners', 'more than 4 foreigners'], 'more than 2 foreigners', 13)\n",
    "df = sum_columns_and_drop(df, ['Up to 6 months', 'From 6 months to 12 motnhs'], 'short-term unemployment', 9)\n",
    "df = df.rename(columns={'Over 12 months':'long-term unemployment'})\n",
    "df = sum_columns_and_drop(df, ['man_without education', 'woman_without education', 'woman_unknown', 'man_unknown'], 'no education', 14)\n",
    "df = sum_columns_and_drop(df, ['man_elementary_school', 'man_junior_high_school', 'man_senior_high_school',\n",
    "                               'woman_elementary_school', 'woman_junior_high_school', 'woman_senior_high_school'], 'medium education', 15)\n",
    "df = sum_columns_and_drop(df, ['man_bachelors_degree', 'woman_bachelors_degree'], 'bachelors degree', 16)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the population dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(439, 6)\n"
     ]
    },
    {
     "data": {
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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>district_code</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>Sant Andreu</td>\n",
       "      <td>58</td>\n",
       "      <td>Baró de Viver</td>\n",
       "      <td>2482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>Horta-Guinardó</td>\n",
       "      <td>34</td>\n",
       "      <td>Can Baró</td>\n",
       "      <td>8938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>Nou Barris</td>\n",
       "      <td>47</td>\n",
       "      <td>Can Peguera</td>\n",
       "      <td>2267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>Nou Barris</td>\n",
       "      <td>49</td>\n",
       "      <td>Canyelles</td>\n",
       "      <td>6946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>Nou Barris</td>\n",
       "      <td>55</td>\n",
       "      <td>Ciutat Meridiana</td>\n",
       "      <td>10156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  district_code   district_name  neighbourhood_code neighbourhood_name  \\\n",
       "0  2015              9     Sant Andreu                  58      Baró de Viver   \n",
       "1  2015              7  Horta-Guinardó                  34           Can Baró   \n",
       "2  2015              8      Nou Barris                  47        Can Peguera   \n",
       "3  2015              8      Nou Barris                  49          Canyelles   \n",
       "4  2015              8      Nou Barris                  55   Ciutat Meridiana   \n",
       "\n",
       "   population  \n",
       "0        2482  \n",
       "1        8938  \n",
       "2        2267  \n",
       "3        6946  \n",
       "4       10156  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df = pd.read_csv('./data/cleaned/population/population.csv')\n",
    "print(pop_df.shape)\n",
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge with the original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>short-term unemployment</th>\n",
       "      <th>long-term unemployment</th>\n",
       "      <th>...</th>\n",
       "      <th>no education</th>\n",
       "      <th>medium education</th>\n",
       "      <th>bachelors degree</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",
       "      <th>population</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>11574.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>667.0</td>\n",
       "      <td>11846.0</td>\n",
       "      <td>7482.0</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",
       "      <td>22305</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>11574.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>667.0</td>\n",
       "      <td>11846.0</td>\n",
       "      <td>7482.0</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",
       "      <td>22305</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>11574.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>667.0</td>\n",
       "      <td>11846.0</td>\n",
       "      <td>7482.0</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",
       "      <td>22305</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>11574.0</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>...</td>\n",
       "      <td>667.0</td>\n",
       "      <td>11846.0</td>\n",
       "      <td>7482.0</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",
       "      <td>22305</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>10754.0</td>\n",
       "      <td>7550.0</td>\n",
       "      <td>...</td>\n",
       "      <td>752.0</td>\n",
       "      <td>11759.0</td>\n",
       "      <td>7573.0</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",
       "      <td>22380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 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",
       "   short-term unemployment  long-term unemployment  ...  no education  \\\n",
       "0                  11574.0                  8425.0  ...         667.0   \n",
       "1                  11574.0                  8425.0  ...         667.0   \n",
       "2                  11574.0                  8425.0  ...         667.0   \n",
       "3                  11574.0                  8425.0  ...         667.0   \n",
       "4                  10754.0                  7550.0  ...         752.0   \n",
       "\n",
       "   medium education  bachelors degree  airbnb_price(€)  n_airbnb_listings  \\\n",
       "0           11846.0            7482.0              NaN                NaN   \n",
       "1           11846.0            7482.0        73.343300              903.0   \n",
       "2           11846.0            7482.0        75.124776             1114.0   \n",
       "3           11846.0            7482.0        72.410441             1111.0   \n",
       "4           11759.0            7573.0        72.739919              992.0   \n",
       "\n",
       "   availability_365  airbnb_ratio_business  airbnb_professional_ratio  \\\n",
       "0               NaN                    NaN                        NaN   \n",
       "1        262.921373               0.631229                   0.128461   \n",
       "2        259.641831               0.607720                   0.106822   \n",
       "3        271.629163               0.580558                   0.067507   \n",
       "4        260.639113               0.561492                   0.073589   \n",
       "\n",
       "   airbnb_mega_host_ratio  population  \n",
       "0                     NaN       22305  \n",
       "1                0.016611       22305  \n",
       "2                0.002693       22305  \n",
       "3                0.001800       22305  \n",
       "4                0.000000       22380  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.merge(pop_df[['year', 'neighbourhood_name', 'population']], on=('neighbourhood_name', 'year'), how='left')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some transformations for the map:\n",
    "- Normalize by the population of the neighbourhood\n",
    "- Imput the NaNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalize by the population of the neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>short-term unemployment</th>\n",
       "      <th>long-term unemployment</th>\n",
       "      <th>...</th>\n",
       "      <th>no education</th>\n",
       "      <th>medium education</th>\n",
       "      <th>bachelors degree</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",
       "      <th>population</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>0.518897</td>\n",
       "      <td>0.377718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029904</td>\n",
       "      <td>0.531092</td>\n",
       "      <td>0.335440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22305</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>0.518897</td>\n",
       "      <td>0.377718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029904</td>\n",
       "      <td>0.531092</td>\n",
       "      <td>0.335440</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",
       "      <td>22305</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>0.518897</td>\n",
       "      <td>0.377718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029904</td>\n",
       "      <td>0.531092</td>\n",
       "      <td>0.335440</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",
       "      <td>22305</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>0.518897</td>\n",
       "      <td>0.377718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029904</td>\n",
       "      <td>0.531092</td>\n",
       "      <td>0.335440</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",
       "      <td>22305</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>0.480518</td>\n",
       "      <td>0.337355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033601</td>\n",
       "      <td>0.525424</td>\n",
       "      <td>0.338382</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",
       "      <td>22380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 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",
       "   short-term unemployment  long-term unemployment  ...  no education  \\\n",
       "0                 0.518897                0.377718  ...      0.029904   \n",
       "1                 0.518897                0.377718  ...      0.029904   \n",
       "2                 0.518897                0.377718  ...      0.029904   \n",
       "3                 0.518897                0.377718  ...      0.029904   \n",
       "4                 0.480518                0.337355  ...      0.033601   \n",
       "\n",
       "   medium education  bachelors degree  airbnb_price(€)  n_airbnb_listings  \\\n",
       "0          0.531092          0.335440              NaN                NaN   \n",
       "1          0.531092          0.335440        73.343300              903.0   \n",
       "2          0.531092          0.335440        75.124776             1114.0   \n",
       "3          0.531092          0.335440        72.410441             1111.0   \n",
       "4          0.525424          0.338382        72.739919              992.0   \n",
       "\n",
       "   availability_365  airbnb_ratio_business  airbnb_professional_ratio  \\\n",
       "0               NaN                    NaN                        NaN   \n",
       "1        262.921373               0.631229                   0.128461   \n",
       "2        259.641831               0.607720                   0.106822   \n",
       "3        271.629163               0.580558                   0.067507   \n",
       "4        260.639113               0.561492                   0.073589   \n",
       "\n",
       "   airbnb_mega_host_ratio  population  \n",
       "0                     NaN       22305  \n",
       "1                0.016611       22305  \n",
       "2                0.002693       22305  \n",
       "3                0.001800       22305  \n",
       "4                0.000000       22380  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_map = df.copy()\n",
    "\n",
    "population_features = ['short-term unemployment', 'long-term unemployment','1 Spanish', '1 foreigner',\n",
    "                       'more than 2 foreigners', 'more than 2 Spanish', 'no education', \n",
    "                       'medium education', 'bachelors degree']\n",
    "\n",
    "def norm_pop(row):\n",
    "    return row[population_features] / row['population']\n",
    "    \n",
    "df_map[population_features] = df.apply(norm_pop, axis=1)\n",
    "df_map.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imput the NaNs for the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.experimental import enable_iterative_imputer  # noqa\n",
    "# from sklearn.impute import IterativeImputer\n",
    "\n",
    "# X = df.drop(columns=['quarter', 'district_name', 'neighbourhood_name'], axis=1)\n",
    "# imp_iter = IterativeImputer(random_state=42)\n",
    "# imp_iter.fit(X[X.notna().all(axis=1)])\n",
    "\n",
    "# df_map = pd.DataFrame(imp_iter.transform(X), index=X.index, columns=X.columns)\n",
    "# df_map = df_map.assign(quarter=df['quarter'].values, \n",
    "#                district_name=df['district_name'].values, \n",
    "#                neighbourhood_name=df['neighbourhood_name'].values)\n",
    "\n",
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "X = df_map.drop(columns=['quarter', 'district_name', 'neighbourhood_name'], axis=1)\n",
    "imputer = KNNImputer(n_neighbors=3)\n",
    "imputer.fit(X[X.notna().all(axis=1)])\n",
    "\n",
    "df_map = pd.DataFrame(imputer.transform(X), index=X.index, columns=X.columns)\n",
    "df_map = df_map.assign(quarter=df['quarter'].values, \n",
    "                         district_name=df['district_name'].values, \n",
    "                         neighbourhood_name=df['neighbourhood_name'].values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Then, import the geojson for Barcelona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(73, 47)\n",
      "Index(['ID_ANNEX', 'ANNEXDESCR', 'ID_TEMA', 'TEMA_DESCR', 'ID_CONJUNT',\n",
      "       'CONJ_DESCR', 'ID_SUBCONJ', 'SCONJ_DESC', 'ID_ELEMENT', 'ELEM_DESCR',\n",
      "       'NIVELL', 'NDESCR_CA', 'NDESCR_ES', 'NDESCR_EN', 'TERME', 'DISTRICTE',\n",
      "       'BARRI', 'AEB', 'SEC_CENS', 'GRANBARRI', 'ZUA', 'AREA_I', 'LITERAL',\n",
      "       'PERIMETRE', 'AREA', 'ORD_REPRES', 'CODI_UA', 'TIPUS_UA', 'NOM', 'WEB1',\n",
      "       'WEB2', 'WEB3', 'DOCUMENTA', 'RANGESCALA', 'TIPUS_POL', 'GRUIX_ID',\n",
      "       'GRUIXDIMEN', 'ESTIL_ID', 'ESTIL_QGIS', 'VALOR1QGIS', 'VALOR2QGIS',\n",
      "       'COL_FARCIT', 'FCOL_DESCR', 'FHEX_COLOR', 'COL_DESCR', 'HEX_COLOR7',\n",
      "       'geometry'],\n",
      "      dtype='object')\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>NOM</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>el Raval</td>\n",
       "      <td>POLYGON ((2.16471 41.38593, 2.16936 41.38554, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>el Barri Gòtic</td>\n",
       "      <td>POLYGON ((2.17701 41.38525, 2.17873 41.38396, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>la Barceloneta</td>\n",
       "      <td>POLYGON ((2.19623 41.38745, 2.19631 41.38745, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>la Dreta de l'Eixample</td>\n",
       "      <td>POLYGON ((2.17091 41.40182, 2.17221 41.40083, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>l'Antiga Esquerra de l'Eixample</td>\n",
       "      <td>POLYGON ((2.15736 41.39331, 2.15847 41.39245, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               NOM  \\\n",
       "0                         el Raval   \n",
       "1                   el Barri Gòtic   \n",
       "2                   la Barceloneta   \n",
       "3           la Dreta de l'Eixample   \n",
       "4  l'Antiga Esquerra de l'Eixample   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((2.16471 41.38593, 2.16936 41.38554, ...  \n",
       "1  POLYGON ((2.17701 41.38525, 2.17873 41.38396, ...  \n",
       "2  POLYGON ((2.19623 41.38745, 2.19631 41.38745, ...  \n",
       "3  POLYGON ((2.17091 41.40182, 2.17221 41.40083, ...  \n",
       "4  POLYGON ((2.15736 41.39331, 2.15847 41.39245, ...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the geojson for Barcelona\n",
    "barcelona = geopandas.read_file(\"./data/geojson/neighborhoods.geojson\")\n",
    "print(barcelona.shape)\n",
    "print(barcelona.columns)\n",
    "barcelona.drop(barcelona.columns.difference(['NOM', 'geometry']), axis=1, inplace=True)\n",
    "barcelona.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A neighbourhood in the geojson (el Poble-sec) has a different name in the dataframe (el Poble Sec):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>short-term unemployment</th>\n",
       "      <th>long-term unemployment</th>\n",
       "      <th>...</th>\n",
       "      <th>no education</th>\n",
       "      <th>medium education</th>\n",
       "      <th>bachelors degree</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",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sants-Montjuïc</td>\n",
       "      <td>11.0</td>\n",
       "      <td>el Poble Sec</td>\n",
       "      <td>578.76</td>\n",
       "      <td>10.27</td>\n",
       "      <td>21260.0</td>\n",
       "      <td>14091.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1477.0</td>\n",
       "      <td>25493.0</td>\n",
       "      <td>8273.0</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",
       "      <td>40217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year     quarter  district_code   district_name  neighbourhood_code  \\\n",
       "1488  2015.0  2015-01-01            3.0  Sants-Montjuïc                11.0   \n",
       "\n",
       "     neighbourhood_name  price_month(€)  price_m2_month(€)  \\\n",
       "1488       el Poble Sec          578.76              10.27   \n",
       "\n",
       "      short-term unemployment  long-term unemployment  ...  no education  \\\n",
       "1488                  21260.0                 14091.0  ...        1477.0   \n",
       "\n",
       "      medium education  bachelors degree  airbnb_price(€)  n_airbnb_listings  \\\n",
       "1488           25493.0            8273.0              NaN                NaN   \n",
       "\n",
       "      availability_365  airbnb_ratio_business  airbnb_professional_ratio  \\\n",
       "1488               NaN                    NaN                        NaN   \n",
       "\n",
       "      airbnb_mega_host_ratio  population  \n",
       "1488                     NaN       40217  \n",
       "\n",
       "[1 rows x 24 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['neighbourhood_name'] == 'el Poble Sec'].head(1)"
   ]
  },
  {
   "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>NOM</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>el Poble-sec</td>\n",
       "      <td>POLYGON ((2.17624 41.37286, 2.17619 41.37280, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            NOM                                           geometry\n",
       "7  el Poble-sec  POLYGON ((2.17624 41.37286, 2.17619 41.37280, ..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barcelona[barcelona['NOM'] == 'el Poble-sec']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the name in the barcelona geodataframe to do the merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "barcelona = barcelona.replace('el Poble-sec', 'el Poble Sec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge the dataframe and the geodataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>district_code</th>\n",
       "      <th>neighbourhood_code</th>\n",
       "      <th>price_month(€)</th>\n",
       "      <th>price_m2_month(€)</th>\n",
       "      <th>short-term unemployment</th>\n",
       "      <th>long-term unemployment</th>\n",
       "      <th>1 Spanish</th>\n",
       "      <th>1 foreigner</th>\n",
       "      <th>more than 2 foreigners</th>\n",
       "      <th>...</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",
       "      <th>population</th>\n",
       "      <th>quarter</th>\n",
       "      <th>district_name</th>\n",
       "      <th>neighbourhood_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((2.18345 41.39061, 2.18459 41.38976, ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>697.16</td>\n",
       "      <td>11.51</td>\n",
       "      <td>0.518897</td>\n",
       "      <td>0.377718</td>\n",
       "      <td>0.170903</td>\n",
       "      <td>0.094508</td>\n",
       "      <td>0.091504</td>\n",
       "      <td>...</td>\n",
       "      <td>73.626172</td>\n",
       "      <td>1042.666667</td>\n",
       "      <td>264.730789</td>\n",
       "      <td>0.606502</td>\n",
       "      <td>0.100930</td>\n",
       "      <td>0.007035</td>\n",
       "      <td>22305.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((2.18345 41.39061, 2.18459 41.38976, ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>708.03</td>\n",
       "      <td>12.02</td>\n",
       "      <td>0.518897</td>\n",
       "      <td>0.377718</td>\n",
       "      <td>0.170903</td>\n",
       "      <td>0.094508</td>\n",
       "      <td>0.091504</td>\n",
       "      <td>...</td>\n",
       "      <td>73.343300</td>\n",
       "      <td>903.000000</td>\n",
       "      <td>262.921373</td>\n",
       "      <td>0.631229</td>\n",
       "      <td>0.128461</td>\n",
       "      <td>0.016611</td>\n",
       "      <td>22305.0</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  district_code  \\\n",
       "0  POLYGON ((2.18345 41.39061, 2.18459 41.38976, ...            1.0   \n",
       "1  POLYGON ((2.18345 41.39061, 2.18459 41.38976, ...            1.0   \n",
       "\n",
       "   neighbourhood_code  price_month(€)  price_m2_month(€)  \\\n",
       "0                 4.0          697.16              11.51   \n",
       "1                 4.0          708.03              12.02   \n",
       "\n",
       "   short-term unemployment  long-term unemployment  1 Spanish  1 foreigner  \\\n",
       "0                 0.518897                0.377718   0.170903     0.094508   \n",
       "1                 0.518897                0.377718   0.170903     0.094508   \n",
       "\n",
       "   more than 2 foreigners  ...  airbnb_price(€)  n_airbnb_listings  \\\n",
       "0                0.091504  ...        73.626172        1042.666667   \n",
       "1                0.091504  ...        73.343300         903.000000   \n",
       "\n",
       "   availability_365  airbnb_ratio_business  airbnb_professional_ratio  \\\n",
       "0        264.730789               0.606502                   0.100930   \n",
       "1        262.921373               0.631229                   0.128461   \n",
       "\n",
       "   airbnb_mega_host_ratio  population     quarter  district_name  \\\n",
       "0                0.007035     22305.0  2015-01-01   Ciutat Vella   \n",
       "1                0.016611     22305.0  2015-04-01   Ciutat Vella   \n",
       "\n",
       "                      neighbourhood_name  \n",
       "0  Sant Pere, Santa Caterina i la Ribera  \n",
       "1  Sant Pere, Santa Caterina i la Ribera  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = barcelona.merge(df_map, right_on='neighbourhood_name', left_on='NOM', how='right')\n",
    "merged = merged.drop(columns=['NOM', 'year'])\n",
    "merged.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the variables for every neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68f90feef42d49349be3ef71953d94ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='distr', options=('Ciutat Vella', 'Eixample', 'Gràcia', 'Horta-Guin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "districts = df['district_name'].unique().tolist()\n",
    "variables = df.columns.drop(['year', 'quarter', 'district_code', 'district_name', 'neighbourhood_code', 'neighbourhood_name']).to_list()\n",
    "\n",
    "@interact\n",
    "def my_plot(distr = districts, \n",
    "            var=variables):\n",
    "    \n",
    "    fig = px.line(df[df['district_name'] == distr], x='quarter', y=var, color='neighbourhood_name',\n",
    "                  template='simple_white')\n",
    "    fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map of the behaviour of variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e88e2b51df4495d83014e8f6ca4f07a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectionSlider(description='quarter', layout=Layout(width='600px'), options=('2015-01-0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighbourhoods = df_map['neighbourhood_name'].unique().tolist()\n",
    "features = df_map.columns.drop(['year', 'quarter', 'district_code', 'district_name', 'neighbourhood_code', 'neighbourhood_name'])\n",
    "quarters = merged['quarter'].sort_values().unique().tolist()\n",
    "\n",
    "@interact(quarter=widgets.SelectionSlider(options=quarters, \n",
    "                                          value=quarters[0],#merged['quarter'].to_list()[0], \n",
    "                                          layout=widgets.Layout(width='600px')\n",
    "                                         )\n",
    "         )\n",
    "\n",
    "def select_features_and_quarter(quarter, feature=features,show_neighb=neighbourhoods):\n",
    "                                \n",
    "    quarter_df = merged[merged['quarter'] == quarter].copy()\n",
    "    \n",
    "    # fix the min and max values of the range color\n",
    "\n",
    "\n",
    "    quantiles = df_map[feature].quantile([0.25,0.75])\n",
    "    iqr = quantiles[0.75] - quantiles[0.25]\n",
    "    factor = 0.3\n",
    "    if feature == 'n_airbnb_listings':\n",
    "        factor = 5\n",
    "    elif feature == 'price_month(€)':\n",
    "        factor = 0.2\n",
    "    elif feature == 'mega_host_ratio':\n",
    "        factor = 2\n",
    "    elif feature in ['more than 2 foreigners', 'more than 2 Spanish', '1 foreigner', '1 Spanish']:\n",
    "        factor = 3\n",
    "    lim_outliers_min = quantiles[0.25] - factor*iqr\n",
    "    lim_outliers_max = quantiles[0.75] + factor*iqr\n",
    "    vmin = df_map[feature].min()\n",
    "    vmax = df_map[feature].max()\n",
    "        \n",
    "    vmin = vmin if vmin > lim_outliers_min else lim_outliers_min\n",
    "    vmax = vmax if vmax < lim_outliers_max else lim_outliers_max\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(10,10))\n",
    "    ax.set_aspect('equal')\n",
    "    quarter_df.plot(column=feature, legend=True, ax=ax, vmin=vmin, vmax=vmax, cmap='coolwarm') \n",
    "    plt.title(f'{feature} in Barcelona ({quarter})')\n",
    "    \n",
    "    # label the chosen neighbourhood\n",
    "    quarter_df['coords'] = quarter_df['geometry'].apply(lambda x: x.representative_point().coords[:])\n",
    "    quarter_df['coords'] = [coords[0] for coords in quarter_df['coords']]\n",
    "    \n",
    "    for idx, row in quarter_df.iterrows():\n",
    "        if row['neighbourhood_name'] == show_neighb:\n",
    "#         if row['neighbourhood_name'] in [\"la Dreta de l'Eixample\", 'el Barri Gòtic', \n",
    "#                                          'la Sagrada Família', 'Pedralbes', '']:\n",
    "            plt.annotate(text=row['neighbourhood_name'], xy=row['coords'], color='k', fontsize='large',fontweight='normal',\n",
    "                 horizontalalignment='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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