Coursera_Capstone_Project_Week4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "1. <a href=\"#item1\">Introduction</a>\n",
    "2. <a href=\"#item2\">Objective</a>  \n",
    "3. <a href=\"#item3\">Data</a>  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the capstone project of the Applied Data Science Course from Coursera.                                                    In this project we had to think about an own problem that could be solved using data from internet sources as well as from the foursquare database.\n",
    "\n",
    "Since I live in Stuttgart and the rents are pretty high in some areas in Stuttgart, I thought about looking into some characteristics that influence the rent. \n",
    "\n",
    "**Stakeholders**: This could also be interesting for city planners. The politician don't want the rents to soar in some areas, so nearly nobody can afford them. With this project, the city planners can see what makes the rents rise and can maybe take action if necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Objective\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this project is to figure out what circumstances have an big impact on the rent and what are just incidental.\n",
    "Therefore we will look for online sources with data regarding the population and other relevant factors in the different areas.\n",
    "The foursquare database will be used to get information about local venues. Statistical methods will then be used to find dependencies between the extracted features and the rent.\n",
    "\n",
    "The final objective is a set of features, that mainly influence the rent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data section will cover following:\n",
    "\n",
    "**1. First we will get the current rents per square meter for each borough.**\n",
    "\n",
    "**2. Second we will extract the postcodes together with coordiantes for each borough. **\n",
    "\n",
    "**3. Third we will get some data offered by the city of Stuttgart about the differen boroughs**\n",
    "\n",
    "**4. Then we will use the foursquare database to get the venues in the boroughs. We will try to cluster them so they can be used for the analysis later on.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Getting the rents**\n",
    "\n",
    "The current rents for each borough are taken from following website: https://www.wohnungsboerse.net/mietspiegel-Stuttgart/972\n",
    "\n",
    "To process it more easily, I put the data into an excel file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
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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>Borough</th>\n",
       "      <th>rent (€/m²)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bad Cannstatt</td>\n",
       "      <td>16.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birkach</td>\n",
       "      <td>14.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Botnang</td>\n",
       "      <td>14.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Degerloch</td>\n",
       "      <td>18.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dürrlewang</td>\n",
       "      <td>20.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Borough  rent (€/m²) \n",
       "0  Bad Cannstatt         16.34\n",
       "1        Birkach         14.87\n",
       "2        Botnang         14.04\n",
       "3      Degerloch         18.89\n",
       "4     Dürrlewang         20.64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#read the excel file into a data frame\n",
    "rents = pd.read_excel(\"Stg_rent_by_borough.xlsx\", sheet_name = \"Tabelle1\")\n",
    "# rename the rent column\n",
    "rents.rename(columns = {\"€ / m2\":\"rent (€/m²) \"},inplace = True)\n",
    "rents.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 2. Getting the postcodes**\n",
    "\n",
    "Next we need the postcodes for the different boroughs.\n",
    "I'll extract them from the following website using webscraping: https://www.suche-postleitzahl.org/stuttgart-plz-70173-70629.608e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "\n",
    "#get the html code from the respective website\n",
    "page = requests.get('https://www.suche-postleitzahl.org/stuttgart-plz-70173-70629.608e').text\n",
    "#store the html file in the soup variable with the lxml parser\n",
    "soup = BeautifulSoup(page, 'lxml')\n",
    "# extract the section from the soup with the respective table\n",
    "soup_part = soup.find('section', id='ortsteile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Stadtteil', 'Postleitzahl']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize an empty list for the headers of the data frame\n",
    "columns_headers = []\n",
    "# search for all entries in the first row of the table (the headings)\n",
    "for tr in soup_part.find_all('tr')[0].find_all('th'):\n",
    "    columns_headers.append(tr.text)\n",
    "columns_headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize two empty lists for the column values\n",
    "values_column1 = []\n",
    "values_column2 = []\n",
    "# loop through the table to get all the entries for the columns\n",
    "for td in soup_part.find_all('td')[::2]:\n",
    "    values_column1.append(td.text.strip())\n",
    "for td in soup_part.find_all('td')[1::2]:\n",
    "    values_column2.append(td.text)    \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have to switch \"Stuttgart-Mitte\" to just \"Mitte\" and so on, so it matches the names in the previous Data Frame\n",
    "for i,val in enumerate(values_column1):\n",
    "    if val.startswith('Stuttgart-'):\n",
    "        val = val[10:]\n",
    "        values_column1[i] = val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 2. Getting the coordinates**\n",
    "\n",
    "Apparently, they also have the coordinates saved on the website, so I'll extract those as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize two empty list for the longitude and latitude\n",
    "values_column3 = []\n",
    "values_column4 = []\n",
    "# loop thorugh the tabel and get the coordiantes\n",
    "for td in soup_part.find_all('td')[::2]:\n",
    "    values_column3.append(td['data-lat'])                            \n",
    "for td in soup_part.find_all('td')[::2]:\n",
    "    values_column4.append(td['data-lon'])\n",
    "\n",
    "#add the column names to the column_headers\n",
    "columns_headers.append('latitude')\n",
    "columns_headers.append('longitude')\n",
    "\n",
    "#converting the extracted strings to float values\n",
    "values_column3 = list(map(float,values_column3))\n",
    "values_column4 = list(map(float,values_column4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asemwald</td>\n",
       "      <td>70599</td>\n",
       "      <td>48.72498</td>\n",
       "      <td>9.19134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bad Cannstatt</td>\n",
       "      <td>70191, 70372, 70374, 70376, 70378</td>\n",
       "      <td>48.80322</td>\n",
       "      <td>9.21746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bergheim</td>\n",
       "      <td>70499</td>\n",
       "      <td>48.80154954</td>\n",
       "      <td>9.08995271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Birkach</td>\n",
       "      <td>70599</td>\n",
       "      <td>48.7193</td>\n",
       "      <td>9.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Botnang</td>\n",
       "      <td>70195</td>\n",
       "      <td>48.7789874</td>\n",
       "      <td>9.12889838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Borough                           Postcode     latitude   longitude\n",
       "0       Asemwald                              70599     48.72498     9.19134\n",
       "1  Bad Cannstatt  70191, 70372, 70374, 70376, 70378     48.80322     9.21746\n",
       "2       Bergheim                              70499  48.80154954  9.08995271\n",
       "3        Birkach                              70599      48.7193       9.208\n",
       "4        Botnang                              70195   48.7789874  9.12889838"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a data frame\n",
    "df_values = np.array([values_column1,values_column2,values_column3, values_column4]).transpose()\n",
    "postcodes = pd.DataFrame(data = df_values, columns = columns_headers)\n",
    "#change german to english\n",
    "postcodes.rename(columns = {\"Stadtteil\":\"Borough\",\"Postleitzahl\":\"Postcode\"}, inplace = True)\n",
    "postcodes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Getting data about the boroughs**\n",
    "\n",
    "Now I will extract some more Data. Following website provides an excel file with loads of statstics: https://statistik.stuttgart.de/statistiken/statistikatlas/atlas/atlas.html?indikator=i0&select=00\n",
    "\n",
    "You also can find it in my repository: https://github.com/dennis31197/Coursera_Capstone/blob/master/Stg_statistics_borough.xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Borough</th>\n",
       "      <th>residents per km²</th>\n",
       "      <th>sport and leisure area (m²/resident)</th>\n",
       "      <th>Average age</th>\n",
       "      <th>amount of non-german residents (%)</th>\n",
       "      <th>amount of married residents (%)</th>\n",
       "      <th>residents per household</th>\n",
       "      <th>living area per flat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mitte</td>\n",
       "      <td>6473</td>\n",
       "      <td>16.6</td>\n",
       "      <td>38.7</td>\n",
       "      <td>32.1</td>\n",
       "      <td>30.6</td>\n",
       "      <td>1.52</td>\n",
       "      <td>72.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nord</td>\n",
       "      <td>4823</td>\n",
       "      <td>36.3</td>\n",
       "      <td>41.3</td>\n",
       "      <td>26.1</td>\n",
       "      <td>43.4</td>\n",
       "      <td>1.81</td>\n",
       "      <td>82.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ost</td>\n",
       "      <td>7718</td>\n",
       "      <td>18.7</td>\n",
       "      <td>41.3</td>\n",
       "      <td>28.4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Süd</td>\n",
       "      <td>9321</td>\n",
       "      <td>10.4</td>\n",
       "      <td>40.3</td>\n",
       "      <td>24.3</td>\n",
       "      <td>38.9</td>\n",
       "      <td>1.68</td>\n",
       "      <td>75.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>West</td>\n",
       "      <td>12729</td>\n",
       "      <td>4.5</td>\n",
       "      <td>40.4</td>\n",
       "      <td>20.7</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>75.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Borough  residents per km²  sport and leisure area (m²/resident)  \\\n",
       "0   Mitte               6473                                  16.6   \n",
       "1    Nord               4823                                  36.3   \n",
       "2     Ost               7718                                  18.7   \n",
       "3     Süd               9321                                  10.4   \n",
       "4    West              12729                                   4.5   \n",
       "\n",
       "   Average age  amount of non-german residents (%)  \\\n",
       "0         38.7                                32.1   \n",
       "1         41.3                                26.1   \n",
       "2         41.3                                28.4   \n",
       "3         40.3                                24.3   \n",
       "4         40.4                                20.7   \n",
       "\n",
       "   amount of married residents (%)  residents per household  \\\n",
       "0                             30.6                     1.52   \n",
       "1                             43.4                     1.81   \n",
       "2                             41.0                     1.73   \n",
       "3                             38.9                     1.68   \n",
       "4                             36.0                     1.63   \n",
       "\n",
       "   living area per flat  \n",
       "0                  72.7  \n",
       "1                  82.6  \n",
       "2                  73.5  \n",
       "3                  75.8  \n",
       "4                  75.7  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics = pd.read_excel(\"Stg_statistics_borough.xlsx\", sheet_name = \"Tabelle1\")\n",
    "statistics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>rent (€/m²)</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>residents per km²</th>\n",
       "      <th>sport and leisure area (m²/resident)</th>\n",
       "      <th>Average age</th>\n",
       "      <th>amount of non-german residents (%)</th>\n",
       "      <th>amount of married residents (%)</th>\n",
       "      <th>residents per household</th>\n",
       "      <th>living area per flat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bad Cannstatt</td>\n",
       "      <td>16.34</td>\n",
       "      <td>70191, 70372, 70374, 70376, 70378</td>\n",
       "      <td>48.803220</td>\n",
       "      <td>9.217460</td>\n",
       "      <td>5745</td>\n",
       "      <td>32.3</td>\n",
       "      <td>40.9</td>\n",
       "      <td>31.7</td>\n",
       "      <td>46.9</td>\n",
       "      <td>1.88</td>\n",
       "      <td>74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birkach</td>\n",
       "      <td>14.87</td>\n",
       "      <td>70599</td>\n",
       "      <td>48.719300</td>\n",
       "      <td>9.208000</td>\n",
       "      <td>5945</td>\n",
       "      <td>14.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>44.7</td>\n",
       "      <td>1.86</td>\n",
       "      <td>78.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Botnang</td>\n",
       "      <td>14.04</td>\n",
       "      <td>70195</td>\n",
       "      <td>48.778987</td>\n",
       "      <td>9.128898</td>\n",
       "      <td>6987</td>\n",
       "      <td>16.3</td>\n",
       "      <td>45.6</td>\n",
       "      <td>17.3</td>\n",
       "      <td>52.6</td>\n",
       "      <td>1.93</td>\n",
       "      <td>86.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Degerloch</td>\n",
       "      <td>18.89</td>\n",
       "      <td>70597</td>\n",
       "      <td>48.746795</td>\n",
       "      <td>9.170746</td>\n",
       "      <td>4526</td>\n",
       "      <td>30.4</td>\n",
       "      <td>44.7</td>\n",
       "      <td>18.4</td>\n",
       "      <td>49.1</td>\n",
       "      <td>1.87</td>\n",
       "      <td>87.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Feuerbach</td>\n",
       "      <td>14.81</td>\n",
       "      <td>70192, 70469, 70499</td>\n",
       "      <td>48.811301</td>\n",
       "      <td>9.159111</td>\n",
       "      <td>5321</td>\n",
       "      <td>13.1</td>\n",
       "      <td>42.3</td>\n",
       "      <td>28.9</td>\n",
       "      <td>49.4</td>\n",
       "      <td>1.90</td>\n",
       "      <td>79.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Borough  rent (€/m²)                            Postcode   latitude  \\\n",
       "0  Bad Cannstatt         16.34  70191, 70372, 70374, 70376, 70378  48.803220   \n",
       "1        Birkach         14.87                              70599  48.719300   \n",
       "2        Botnang         14.04                              70195  48.778987   \n",
       "3      Degerloch         18.89                              70597  48.746795   \n",
       "4      Feuerbach         14.81                70192, 70469, 70499  48.811301   \n",
       "\n",
       "   longitude  residents per km²  sport and leisure area (m²/resident)  \\\n",
       "0   9.217460               5745                                  32.3   \n",
       "1   9.208000               5945                                  14.0   \n",
       "2   9.128898               6987                                  16.3   \n",
       "3   9.170746               4526                                  30.4   \n",
       "4   9.159111               5321                                  13.1   \n",
       "\n",
       "   Average age  amount of non-german residents (%)  \\\n",
       "0         40.9                                31.7   \n",
       "1         43.1                                19.6   \n",
       "2         45.6                                17.3   \n",
       "3         44.7                                18.4   \n",
       "4         42.3                                28.9   \n",
       "\n",
       "   amount of married residents (%)  residents per household  \\\n",
       "0                             46.9                     1.88   \n",
       "1                             44.7                     1.86   \n",
       "2                             52.6                     1.93   \n",
       "3                             49.1                     1.87   \n",
       "4                             49.4                     1.90   \n",
       "\n",
       "   living area per flat  \n",
       "0                  74.4  \n",
       "1                  78.8  \n",
       "2                  86.4  \n",
       "3                  87.4  \n",
       "4                  79.6  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge1 = pd.merge(rents,postcodes, how = 'inner', on='Borough')\n",
    "Bouroughs_S = pd.merge(df_merge1, statistics, how = 'inner', on = 'Borough')\n",
    "\n",
    "#change to data type of the coordinates to floats\n",
    "Bouroughs_S['longitude'] = Bouroughs_S['longitude'].astype(dtype='float64')\n",
    "Bouroughs_S['latitude'] = Bouroughs_S['latitude'].astype(dtype='float64')\n",
    "\n",
    "Bouroughs_S.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1bb9deb5cf8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "# create map of Stuttgart using latitude and longitude values\n",
    "map_stuttgart = folium.Map(location=[values_column3[0], values_column4[0]], zoom_start=11)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, label in zip(Bouroughs_S['latitude'], Bouroughs_S['longitude'], Bouroughs_S['Borough']):\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7).add_to(map_stuttgart)  \n",
    "    \n",
    "map_stuttgart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Getting data from foursquare**\n",
    "\n",
    "Now it's time to get some data from Foursquare, that could influence the rents.                                                       \n",
    "Number of restaurants, parks, shooping location, tram stations.                                                                 Therefore, I let's get the top 100 venues in a radius of 3km around the coordinates for each bourough\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.io.json import json_normalize\n",
    "CLIENT_ID = 'WDZVSRMBUM4GRNNKUTSV0AWYLLMHLNHZUGZDIOE25V5MLE1F' \n",
    "CLIENT_SECRET = 'WD4NYWRDESXQLLL21JGE1NASVBZVMURHWRTEAPOP1LP5SJMK' \n",
    "VERSION = '20180604'\n",
    "LIMIT = 150\n",
    "radius = 3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "def venues_categories(venue_request):\n",
    "    '''\n",
    "    returns a list with the categories with all the venues of the json file\n",
    "    '''\n",
    "    #determine how many venues are in the file\n",
    "    amount_venues = len(venue_request['response']['groups'][0]['items'])\n",
    "    #initiate empty list\n",
    "    category_list = []\n",
    "    #loop through the file and append the short names of the category to the list\n",
    "    for i in range(0,amount_venues):\n",
    "        category_list.append(venue_request['response']['groups'][0]['items'][i]['venue']['categories'][0]['shortName'])\n",
    "        \n",
    "    return category_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_boroughs = len(Bouroughs_S['Borough'])\n",
    "categories_list = []\n",
    "for i in range(0, number_boroughs):\n",
    "    # loop thourgh all the boroughs in our dataset and save latitude and longitude\n",
    "    latitude = Bouroughs_S.loc[i,'latitude']\n",
    "    longitude = Bouroughs_S.loc[i,'longitude']\n",
    "\n",
    "    #get the url for the call\n",
    "    url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "        CLIENT_ID, \n",
    "        CLIENT_SECRET, \n",
    "        VERSION, \n",
    "        latitude, \n",
    "        longitude, \n",
    "        radius, \n",
    "        LIMIT)\n",
    "    \n",
    "    #make the call\n",
    "    results = requests.get(url).json()\n",
    "    \n",
    "    #save the categories of the venues into a list, the index of the borough in our data frame is also the same index in this list\n",
    "    categories_list.append(venues_categories(results))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can expolore the different categories in the different boroughs and try to cluster and condense them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Concert Hall',\n",
       " 'Gastropub',\n",
       " 'Hardware',\n",
       " 'Museum',\n",
       " 'Restaurant',\n",
       " 'Lounge',\n",
       " 'German',\n",
       " 'Burgers',\n",
       " 'Park',\n",
       " 'Deli / Bodega',\n",
       " 'German',\n",
       " 'Soccer',\n",
       " 'Hotel',\n",
       " 'Racetrack',\n",
       " 'Concert Hall',\n",
       " 'Soccer Field',\n",
       " 'Bakery',\n",
       " 'Middle Eastern',\n",
       " 'History Museum',\n",
       " 'Restaurant',\n",
       " 'Hotel',\n",
       " 'Bakery',\n",
       " 'Gym',\n",
       " 'Hotel',\n",
       " 'Vineyard',\n",
       " 'Fried Chicken',\n",
       " 'German',\n",
       " 'Bakery',\n",
       " 'Greek',\n",
       " 'Pool',\n",
       " 'Sporting Goods',\n",
       " 'Wine Shop',\n",
       " 'Pool',\n",
       " 'Park',\n",
       " 'German',\n",
       " 'Pub',\n",
       " 'German',\n",
       " 'Grocery Store',\n",
       " 'Italian',\n",
       " 'Lounge',\n",
       " 'Supermarket',\n",
       " 'Museum',\n",
       " 'Gym',\n",
       " 'Sports Bar',\n",
       " 'Event Space',\n",
       " 'Schnitzel',\n",
       " 'Hotel',\n",
       " 'Supermarket',\n",
       " 'Supermarket',\n",
       " 'German',\n",
       " 'Fried Chicken',\n",
       " 'Hotel',\n",
       " 'Stadium',\n",
       " 'Hardware',\n",
       " 'Fast Food',\n",
       " 'Fast Food',\n",
       " 'Chinese',\n",
       " 'Harbor / Marina',\n",
       " 'Supermarket',\n",
       " 'Soccer Field',\n",
       " 'Hotel',\n",
       " 'Bakery',\n",
       " 'Music Venue',\n",
       " 'German Pop-Up Restaurant']"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories_list[19]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking into it, I want to extract the following features:\n",
    "1. Restaurants\n",
    "2. Sport Activities\n",
    "3. Leisure Activities\n",
    "4. Tram stopps\n",
    "5. Shopping\n",
    "\n",
    "Therefore I will create a list with the most common category names for these features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurants = \"restaurants,german,greek,swabian,spanish,french,food & drink,japanese,african,korean,turkish,vietnamese,sushi,italien,beer garden,thai,chinese,burgers,mediterranean,steakhouse,fast food\".split(\",\")\n",
    "sports = \"gym,athletics & sports,gym / fitness,climbing gym,soccer field,soccer,sports club\".split(\",\")\n",
    "leisure =  \"garden,pool,park,history museum,art museum,castle,mountain,playground,zoo exhibit,forest,movie theater,garden center,theater,hookah bar,museum,zoo,café,pub,nightclub,concert hall,exhibit\".split(\",\")\n",
    "transport = \"train station,metro,tram,bus station,bus stop\".split(\",\")\n",
    "shopping =\"supermarket,grocery store,drugstore,bakery,mall\".split(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize some empty lists\n",
    "number_restaurants = []\n",
    "number_sports = []\n",
    "number_leisure = []\n",
    "number_transport = []\n",
    "number_shopping = []\n",
    "\n",
    "for i in range(0,number_boroughs):\n",
    "    #initialize counters\n",
    "    restaurant_count = 0\n",
    "    sports_count = 0\n",
    "    leisure_count = 0\n",
    "    transport_count = 0\n",
    "    shopping_count = 0\n",
    "    \n",
    "    #loop through all values for one borough and increase the counter if a venue falls into one of the features\n",
    "    for j in range(0,len(categories_list[i])):\n",
    "        if categories_list[i][j].lower() in restaurants:\n",
    "            restaurant_count += 1 \n",
    "        elif categories_list[i][j].lower() in sports:\n",
    "            sports_count += 1\n",
    "        elif categories_list[i][j].lower() in leisure:\n",
    "            leisure_count += 1\n",
    "        elif categories_list[i][j].lower() in transport:\n",
    "            transport_count +=1\n",
    "        elif categories_list[i][j].lower() in shopping:\n",
    "            shopping_count +=1\n",
    "        else: pass\n",
    "    #fill the list with the counters              \n",
    "    number_restaurants.append(restaurant_count)\n",
    "    number_sports.append(sports_count)\n",
    "    number_leisure.append(leisure_count)\n",
    "    number_transport.append(transport_count)\n",
    "    number_shopping.append(shopping_count)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding the newe data to our data frame\n",
    "Bouroughs_S[\"number of restaurants\"] = number_restaurants\n",
    "Bouroughs_S[\"number of sports\"] = number_sports\n",
    "Bouroughs_S[\"number of leisure\"] = number_leisure\n",
    "Bouroughs_S[\"number of transport\"] = number_transport\n",
    "Bouroughs_S[\"number of shopping\"] = number_shopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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>Borough</th>\n",
       "      <th>rent (€/m²)</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>residents per km²</th>\n",
       "      <th>sport and leisure area (m²/resident)</th>\n",
       "      <th>Average age</th>\n",
       "      <th>amount of non-german residents (%)</th>\n",
       "      <th>amount of married residents (%)</th>\n",
       "      <th>residents per household</th>\n",
       "      <th>living area per flat</th>\n",
       "      <th>number of restaurants</th>\n",
       "      <th>number of sports</th>\n",
       "      <th>number of leisure</th>\n",
       "      <th>number of transport</th>\n",
       "      <th>number of shopping</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bad Cannstatt</td>\n",
       "      <td>16.34</td>\n",
       "      <td>70191, 70372, 70374, 70376, 70378</td>\n",
       "      <td>48.803220</td>\n",
       "      <td>9.217460</td>\n",
       "      <td>5745</td>\n",
       "      <td>32.3</td>\n",
       "      <td>40.9</td>\n",
       "      <td>31.7</td>\n",
       "      <td>46.9</td>\n",
       "      <td>1.88</td>\n",
       "      <td>74.4</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birkach</td>\n",
       "      <td>14.87</td>\n",
       "      <td>70599</td>\n",
       "      <td>48.719300</td>\n",
       "      <td>9.208000</td>\n",
       "      <td>5945</td>\n",
       "      <td>14.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>44.7</td>\n",
       "      <td>1.86</td>\n",
       "      <td>78.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Botnang</td>\n",
       "      <td>14.04</td>\n",
       "      <td>70195</td>\n",
       "      <td>48.778987</td>\n",
       "      <td>9.128898</td>\n",
       "      <td>6987</td>\n",
       "      <td>16.3</td>\n",
       "      <td>45.6</td>\n",
       "      <td>17.3</td>\n",
       "      <td>52.6</td>\n",
       "      <td>1.93</td>\n",
       "      <td>86.4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Degerloch</td>\n",
       "      <td>18.89</td>\n",
       "      <td>70597</td>\n",
       "      <td>48.746795</td>\n",
       "      <td>9.170746</td>\n",
       "      <td>4526</td>\n",
       "      <td>30.4</td>\n",
       "      <td>44.7</td>\n",
       "      <td>18.4</td>\n",
       "      <td>49.1</td>\n",
       "      <td>1.87</td>\n",
       "      <td>87.4</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Feuerbach</td>\n",
       "      <td>14.81</td>\n",
       "      <td>70192, 70469, 70499</td>\n",
       "      <td>48.811301</td>\n",
       "      <td>9.159111</td>\n",
       "      <td>5321</td>\n",
       "      <td>13.1</td>\n",
       "      <td>42.3</td>\n",
       "      <td>28.9</td>\n",
       "      <td>49.4</td>\n",
       "      <td>1.90</td>\n",
       "      <td>79.6</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Borough  rent (€/m²)                            Postcode   latitude  \\\n",
       "0  Bad Cannstatt         16.34  70191, 70372, 70374, 70376, 70378  48.803220   \n",
       "1        Birkach         14.87                              70599  48.719300   \n",
       "2        Botnang         14.04                              70195  48.778987   \n",
       "3      Degerloch         18.89                              70597  48.746795   \n",
       "4      Feuerbach         14.81                70192, 70469, 70499  48.811301   \n",
       "\n",
       "   longitude  residents per km²  sport and leisure area (m²/resident)  \\\n",
       "0   9.217460               5745                                  32.3   \n",
       "1   9.208000               5945                                  14.0   \n",
       "2   9.128898               6987                                  16.3   \n",
       "3   9.170746               4526                                  30.4   \n",
       "4   9.159111               5321                                  13.1   \n",
       "\n",
       "   Average age  amount of non-german residents (%)  \\\n",
       "0         40.9                                31.7   \n",
       "1         43.1                                19.6   \n",
       "2         45.6                                17.3   \n",
       "3         44.7                                18.4   \n",
       "4         42.3                                28.9   \n",
       "\n",
       "   amount of married residents (%)  residents per household  \\\n",
       "0                             46.9                     1.88   \n",
       "1                             44.7                     1.86   \n",
       "2                             52.6                     1.93   \n",
       "3                             49.1                     1.87   \n",
       "4                             49.4                     1.90   \n",
       "\n",
       "   living area per flat  number of restaurants  number of sports  \\\n",
       "0                  74.4                     16                 4   \n",
       "1                  78.8                      2                 0   \n",
       "2                  86.4                     12                 3   \n",
       "3                  87.4                     26                 7   \n",
       "4                  79.6                     15                 2   \n",
       "\n",
       "   number of leisure  number of transport  number of shopping  \n",
       "0                 30                    0                   8  \n",
       "1                  5                    1                   5  \n",
       "2                 11                    2                   4  \n",
       "3                 17                    1                   4  \n",
       "4                 12                    2                  13  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bouroughs_S.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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