Assignment_The_Battle_of_Students.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Battle of Students"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "* [Introduction](#introduction)\n",
    "* [Data](#data)\n",
    "* [Methodology](#methodology)\n",
    " * [Map of Starting Situation](#methodology-start)\n",
    " * [Merge Data](#methodology-data)\n",
    " * [Foursquare](#methodology-foursquare)\n",
    " * [Exploratory Data Analysis](#methodology-analysis)\n",
    "* [Results](#results)\n",
    "* [Discussion and Conclusions](#conclusions)\n",
    "\n",
    "**Note:** The final report of this project can be found [here](https://github.com/bneumayer/Coursera_Capstone/blob/master/tex/Assignment_The_Battle_of_Students_Final.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction <a name=\"introduction\"></a>\n",
    "\n",
    "### Background\n",
    "\n",
    "The city of Graz is the second biggest city in Austria with almost\n",
    "330,000 residents.\n",
    "It is a typical student city with roughly 60,000 students who mainly\n",
    "live in the city.\n",
    "The housing of students therefore is a significant economic\n",
    "factor in Graz.\n",
    "Accordingly, apartment-hunting is omnipresent and a time-consuming\n",
    "issue.\n",
    "\n",
    "Additionally, many companies in and around Graz\n",
    "are offering employment to graduates of the different universities and\n",
    "therefore, many move to Graz permanently after having finished their \n",
    "studies.\n",
    "Since interest rates are currently at a low, people planning to study \n",
    "and having an intention to stay afterwards may consider to invest into \n",
    "the future, take out a loan and buy property.\n",
    "\n",
    "### Problem\n",
    "\n",
    "When considering to rent an apartment or to buy property, preferences\n",
    "will differ significantly.\n",
    "An apartment can be chosen considering the current situation in a \n",
    "certain borough or neighbourhood but for the acquisition of property the\n",
    "outlook is more important.\n",
    "Questions to be answered are:\n",
    "* Where can I find affordable property?\n",
    "* What is the current state of the borough?\n",
    "* Will the value increase, stay or decrease over the years?\n",
    "\n",
    "This requires a comprehensive analysis of rents, prices, the structure\n",
    "of the different boroughs and the envrionmental circumstances.\n",
    "\n",
    "### Target Audience\n",
    "\n",
    "This data analysis project aims at upcoming students planning to move to\n",
    "Graz with little or no knowledge on the structural and environmental\n",
    "circumstances of the different boroughs.\n",
    "The outcome is a classification of all available boroughs into groups\n",
    "with similar characteristics regarding rents, property prices and\n",
    "demographics.\n",
    "Ultimately, the analysis provides conclusions on recommended boroughs\n",
    "for renting an apartment or buying property."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data <a name=\"data\"></a>\n",
    "\n",
    "* Information on the different boroughs of Graz including\n",
    "\tenvironmental and demographic characteristics can be obtaind\n",
    "\tfrom Wikipedia.\n",
    "* The website www.immobilienscout24.at provides rent and\n",
    "\tproperty price information for Graz down to \n",
    "\tborough-level.\n",
    "\tThis information is based on advertisements posted on \n",
    "\tand purchases made through this site.\n",
    "* The website www.immowert123.at is owned by a real estate \n",
    "\tagency evaluating and selling realty.\n",
    "\tThe agency provides estimates for selected objects in Graz,\n",
    "\tincluding object type, size and recommended price.\n",
    "* The Foursquare database is used to evaluate the\n",
    "\tvibrancy of a borough, which is based on the availability of venues \n",
    "\tin a certain area.\n",
    "\n",
    "The location data, i.e. latitude and longitude, for Graz and its boroughs\n",
    "is acquired using \\textit{geopy} with \\textit{Nominatim} and\\textit{ArcGIS}."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Get General Borough Information of Graz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from textwrap import wrap\n",
    "\n",
    "# Wiki entry with boroughs of Graz\n",
    "urlWiki = 'https://de.wikipedia.org/wiki/Liste_der_Stadtbezirke_von_Graz'\n",
    "\n",
    "# Read in data: take care of different number enconding\n",
    "GrazTable = pd.read_html(urlWiki, decimal=',', thousands='.')[1]\n",
    "\n",
    "# Skip last row (sum) and keep postal code, name, area and population density\n",
    "GrazTable = GrazTable.iloc[:-1, [7, 1, 4, 5]]\n",
    "# Name columns\n",
    "GrazTable.columns = list(['Postal Code', 'Borough', 'Area', 'Population Density'])\n",
    "# Separate postacl codes\n",
    "GrazTable['Postal Code'] = GrazTable['Postal Code'].apply(lambda r: wrap(r, 4)).str.join(', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8010</td>\n",
       "      <td>Innere Stadt</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8010, 8016</td>\n",
       "      <td>St. Leonhard</td>\n",
       "      <td>1.83</td>\n",
       "      <td>8650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8010, 8013, 8015, 8036</td>\n",
       "      <td>Geidorf</td>\n",
       "      <td>5.50</td>\n",
       "      <td>4452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Postal Code       Borough  Area  Population Density\n",
       "0                    8010  Innere Stadt  1.16                3287\n",
       "1              8010, 8016  St. Leonhard  1.83                8650\n",
       "2  8010, 8013, 8015, 8036       Geidorf  5.50                4452"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show data\n",
    "GrazTable.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "### 2.2 Get Borough Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from geopy.geocoders import ArcGIS\n",
    "geolocator = ArcGIS(user_agent=\"graz_explorer\")\n",
    "\n",
    "# Arrays for latitude and longitude\n",
    "arrLat = np.zeros(GrazTable.shape[0])\n",
    "arrLon = np.zeros(GrazTable.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Innere Stadt\tlat: 47.072, long: 15.441\n",
      "1 St. Leonhard\tlat: 47.069, long: 15.456\n",
      "2 Geidorf\tlat: 47.085, long: 15.444\n",
      "3 Lend\t\tlat: 47.079, long: 15.419\n",
      "4 Gries\t\tlat: 47.057, long: 15.427\n",
      "5 Jakomini\tlat: 47.057, long: 15.451\n",
      "6 Liebenau\tlat: 47.032, long: 15.464\n",
      "7 St. Peter\tlat: 47.052, long: 15.478\n",
      "8 Waltendorf\tlat: 47.073, long: 15.482\n",
      "9 Ries\t\tlat: 47.081, long: 15.488\n",
      "10 Mariatrost\tlat: 47.107, long: 15.490\n",
      "11 Andritz\tlat: 47.113, long: 15.421\n",
      "12 Gösting\tlat: 47.107, long: 15.394\n",
      "13 Eggenberg\tlat: 47.071, long: 15.399\n",
      "14 Wetzelsdorf\tlat: 47.052, long: 15.401\n",
      "15 Straßgang\tlat: 47.031, long: 15.403\n",
      "16 Puntigam\tlat: 47.029, long: 15.435\n"
     ]
    }
   ],
   "source": [
    "# Loop over all rows and retrieve latitude and longitude for each postal code\n",
    "for idx, row in GrazTable.iterrows():\n",
    "    print(idx, end=\" \")\n",
    "    address = '{}, Graz, Austria'.format(row.Borough)\n",
    "    # Show borough\n",
    "    print(row.Borough, end= \"\")\n",
    "    # Add a tab when necessary\n",
    "    if len(row.Borough)<6: print('\\t', end=\"\")\n",
    "    \n",
    "    # Get location\n",
    "    location = geolocator.geocode(address)\n",
    "    \n",
    "    # Check if we got a valid result\n",
    "    if (location != None):\n",
    "        # Add location to output\n",
    "        print('\\tlat: {:.3f}, long: {:.3f}'.format(location.latitude, location.longitude))\n",
    "        # Store result\n",
    "        arrLat[idx] = location.latitude\n",
    "        arrLon[idx] = location.longitude        \n",
    "    else:\n",
    "        print(', None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8010</td>\n",
       "      <td>Inner City</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3287</td>\n",
       "      <td>47.07249</td>\n",
       "      <td>15.44114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8010, 8016</td>\n",
       "      <td>St. Leonhard</td>\n",
       "      <td>1.83</td>\n",
       "      <td>8650</td>\n",
       "      <td>47.06945</td>\n",
       "      <td>15.45639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8010, 8013, 8015, 8036</td>\n",
       "      <td>Geidorf</td>\n",
       "      <td>5.50</td>\n",
       "      <td>4452</td>\n",
       "      <td>47.08481</td>\n",
       "      <td>15.44361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Postal Code       Borough  Area  Population Density  Latitude  \\\n",
       "0                    8010    Inner City  1.16                3287  47.07249   \n",
       "1              8010, 8016  St. Leonhard  1.83                8650  47.06945   \n",
       "2  8010, 8013, 8015, 8036       Geidorf  5.50                4452  47.08481   \n",
       "\n",
       "   Longitude  \n",
       "0   15.44114  \n",
       "1   15.45639  \n",
       "2   15.44361  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add locations to table\n",
    "GrazTable['Latitude'] = arrLat\n",
    "GrazTable['Longitude'] = arrLon\n",
    "\n",
    "# Rename inner city for better understanding\n",
    "GrazTable['Borough'] = GrazTable['Borough'].str.replace('Innere Stadt', 'Inner City')\n",
    "\n",
    "GrazTable.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Rent and Realty Prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.1 First Data Source: Scout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 per sqm</th>\n",
       "      <th>Price per sqm (Scout)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inner City</td>\n",
       "      <td>12.90</td>\n",
       "      <td>3888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>St. Leonhard</td>\n",
       "      <td>11.32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Geidorf</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3811.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Rent per sqm  Price per sqm (Scout)\n",
       "0    Inner City         12.90                 3888.0\n",
       "1  St. Leonhard         11.32                    NaN\n",
       "2       Geidorf         10.56                 3811.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rent and realty prices provided by a realty portal\n",
    "urlHomeGraz = 'https://www.immobilienscout24.at/immobilienpreise/graz.html'\n",
    "\n",
    "# Rent\n",
    "TableGrazRent = pd.read_html(urlHomeGraz, decimal=',', thousands='.')[0]\n",
    "TableGrazRent.columns=list(['Borough', 'Rent per sqm'])\n",
    "\n",
    "# Realty prices\n",
    "TableGrazRealty = pd.read_html(urlHomeGraz, decimal=',', thousands='.')[1]\n",
    "TableGrazRealty.columns=list(['Borough', 'Price per sqm (Scout)'])\n",
    "\n",
    "# Merge data\n",
    "TableGrazHomeScout = TableGrazRent.merge(TableGrazRealty, on='Borough')\n",
    "# Set missing values to NaN\n",
    "TableGrazHomeScout.loc[TableGrazHomeScout['Price per sqm (Scout)']=='k.A.', 'Price per sqm (Scout)']=np.nan\n",
    "# Convert data to float\n",
    "TableGrazHomeScout['Price per sqm (Scout)'] = TableGrazHomeScout['Price per sqm (Scout)'].astype('float')\n",
    "# Rename inner city for better understanding\n",
    "TableGrazHomeScout['Borough'] = TableGrazHomeScout['Borough'].str.replace('Innere Stadt', 'Inner City')\n",
    "\n",
    "TableGrazHomeScout.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.2 Second Data Source: Immo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Base URL for all boroughs\n",
    "baseUrl = 'https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/'\n",
    "\n",
    "# Available data\n",
    "urlSuffix = list(['innere-stadt', 'st-leonhard', 'geidorf', 'lend', 'gries', 'jakomini',\n",
    "                  'liebenau',   #liebenau\n",
    "                  'engelsdorf', #liebenau\n",
    "                  'st-peter', 'waltendorf',\n",
    "                  'ragnitz',    # ries\n",
    "                  'wenisbuch',  # mariatrost\n",
    "                  'andritz', 'goesting',\n",
    "                  'algersdorf', #eggenberg\n",
    "                  'baierdorf',  #eggenberg\n",
    "                  'wetzelsdorf', 'straszgang',\n",
    "                  'rudersdorf'  #puntigam\n",
    "                 ])\n",
    "\n",
    "urlSuffixNames = list(['Inner City', 'St. Leonhard', 'Geidorf', 'Lend', 'Gries', 'Jakomini', 'Liebenau', \n",
    "                       'Engelsdorf', 'St. Peter', 'Waltendorf', 'Ragnitz', 'Wenisbuch', 'Andritz', 'Gösting', \n",
    "                       'Algersdorf', 'Baierdorf', 'Wetzelsdorf', 'Straßgang', 'Rudersdorf'\n",
    "                      ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/st-peter\n",
      "St. Peter: Average Price per square metre: EUR 2906.95\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>Type</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Size</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price per sqm (Immo)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apartment</td>\n",
       "      <td>8042</td>\n",
       "      <td>45.10</td>\n",
       "      <td>120900.0</td>\n",
       "      <td>2680.709534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Garden Apartment</td>\n",
       "      <td>8042</td>\n",
       "      <td>55.00</td>\n",
       "      <td>174800.0</td>\n",
       "      <td>3178.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Investment Apartment</td>\n",
       "      <td>8042</td>\n",
       "      <td>44.83</td>\n",
       "      <td>125400.0</td>\n",
       "      <td>2797.233995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Penthouse</td>\n",
       "      <td>8042</td>\n",
       "      <td>113.00</td>\n",
       "      <td>335800.0</td>\n",
       "      <td>2971.681416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Type Postal Code    Size     Price  Price per sqm (Immo)\n",
       "0             Apartment        8042   45.10  120900.0           2680.709534\n",
       "1      Garden Apartment        8042   55.00  174800.0           3178.181818\n",
       "2  Investment Apartment        8042   44.83  125400.0           2797.233995\n",
       "3             Penthouse        8042  113.00  335800.0           2971.681416"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# Make a test output for one entry\n",
    "#\n",
    "testEntryNum = 8;\n",
    "# Use beautiful soup to parse website\n",
    "testUrl = baseUrl + urlSuffix[testEntryNum]\n",
    "rawData = requests.get(testUrl)\n",
    "data = rawData.text\n",
    "soup = BeautifulSoup(data, 'html.parser')\n",
    "#\n",
    "# Read in the second table and extract all rows\n",
    "table = soup.findAll('div', {'class': 'col-12'})[1]\n",
    "rows = table.findAll('div', {'class': 'row'})\n",
    "# Create a dataframe\n",
    "df = pd.DataFrame([row.text.strip().split('\\n') for row in rows], columns = list(['Type', 'Postal Code', 'Size', 'Price']))\n",
    "# Drop first row with German column names\n",
    "df.drop(index=0, axis=0, inplace=True)\n",
    "# Search for all occurences of the German word for flat (Wohnung)\n",
    "df = df[df['Type'].str.contains('Wohnung|wohnung', regex=True)].reset_index(drop=True)\n",
    "\n",
    "# Rename for better understanding\n",
    "df['Type'] = df['Type'].str.replace('Wohnung', 'Apartment')\n",
    "df['Type'] = df['Type'].str.replace('Vorsorgewohnung', 'Investment Apartment')\n",
    "df['Type'] = df['Type'].str.replace('Souterrainwohnung', 'Souterrain')\n",
    "df['Type'] = df['Type'].str.replace('Galeriewohnung', 'Gallery Apartment')\n",
    "df['Type'] = df['Type'].str.replace('Architektenwohnung', 'Architect Apartment')\n",
    "df['Type'] = df['Type'].str.replace('Terrassenwohnung', 'Terrace Apartment')\n",
    "df['Type'] = df['Type'].str.replace('Dachgeschoßwohnung', 'Penthouse')\n",
    "df['Type'] = df['Type'].str.replace('Gartenwohnung', 'Garden Apartment')\n",
    "\n",
    "# Get rid of special characters and convert prices to float, take care of number encoding\n",
    "df['Size'] = df['Size'].str.replace(',','.').str.slice(stop=-2).astype('float')\n",
    "df['Price'] = df['Price'].str.replace('.','').str.slice(start=2, stop=-1).astype('float')\n",
    "# Calculate average flat price\n",
    "df['Price per sqm (Immo)'] = (df['Price']/df['Size'])\n",
    "\n",
    "# Show the results of this test entry\n",
    "print(testUrl)\n",
    "print('{}: Average Price per square metre: EUR {:.2f}'.format(\n",
    "    urlSuffixNames[testEntryNum], df['Price per sqm (Immo)'].mean()))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/innere-stadt\t5397.78\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/st-leonhard\t2221.93\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/geidorf\t2078.25\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/lend\t\t1855.58\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/gries\t2234.76\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/jakomini\t2049.54\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/liebenau\t1854.78\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/engelsdorf\t2231.33\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/st-peter\t2906.95\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/waltendorf\t1819.11\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/ragnitz\t2303.90\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/wenisbuch\t2166.79\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/andritz\t1975.34\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/goesting\t1762.92\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/algersdorf\t2507.56\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/baierdorf\t1572.15\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/wetzelsdorf\t1754.38\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/straszgang\t2166.41\n",
      "https://www.immowert123.at/grundstueckspreise/steiermark/grazstadt/rudersdorf\t2366.67\n"
     ]
    }
   ],
   "source": [
    "# Perform the analysis shown above for all available URLs\n",
    "avgPriceArray = np.zeros(len(urlSuffix))\n",
    "\n",
    "# Loop over all entries\n",
    "for idx, url in enumerate(urlSuffix):\n",
    "    # Use beautiful soup to parse website\n",
    "    rawData = requests.get(baseUrl + url)\n",
    "    data = rawData.text\n",
    "    soup = BeautifulSoup(data, 'html.parser')\n",
    "    # Read in the second table and extract all rows\n",
    "    table = soup.findAll('div', {'class': 'col-12'})[1]\n",
    "    rows = table.findAll('div', {'class': 'row'})\n",
    "    # Create a dataframe\n",
    "    df = pd.DataFrame([row.text.strip().split('\\n') for row in rows], columns = \n",
    "                      list(['Type', 'Postal Code', 'Size', 'Price']))\n",
    "    # Drop first row with German column names\n",
    "    df.drop(index=0, axis=0, inplace=True)\n",
    "    # Search for all occurences of the German word for flat (Wohnung)\n",
    "    df = df[df['Type'].str.contains('Wohnung|wohnung',regex=True)].reset_index(drop=True)\n",
    "    # Get rid of special characters and convert prices to float, take care of number encoding\n",
    "    df['Size'] = df['Size'].str.replace(',','.').str.slice(stop=-2).astype('float')\n",
    "    df['Price'] = df['Price'].str.replace('.','').str.slice(start=2, stop=-1).astype('float')\n",
    "    df['Price per sqm'] = (df['Price']/df['Size'])\n",
    "    # Calculate average flat price\n",
    "    avgPrice = df['Price per sqm'].mean()\n",
    "    \n",
    "    # Show results\n",
    "    #\n",
    "    if len(url)<5:\n",
    "        print('{}\\t\\t{:.2f}'.format(baseUrl+url, avgPrice))\n",
    "    else:\n",
    "        print('{}\\t{:.2f}'.format(baseUrl+url, avgPrice))\n",
    "    \n",
    "    # Store the average flat price\n",
    "    avgPriceArray[idx] = avgPrice\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "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>Price per sqm (Immo)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inner City</td>\n",
       "      <td>5397.783378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>St. Leonhard</td>\n",
       "      <td>2221.927651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Geidorf</td>\n",
       "      <td>2078.248241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Price per sqm (Immo)\n",
       "0    Inner City           5397.783378\n",
       "1  St. Leonhard           2221.927651\n",
       "2       Geidorf           2078.248241"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TableGrazHomeImmo = pd.DataFrame({'Borough': urlSuffixNames,\n",
    "                                  'Price per sqm (Immo)': avgPriceArray})\n",
    "TableGrazHomeImmo.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Methodology <a name=\"methodology\"></a>\n",
    "\n",
    "In this chapter the data is visualised, further prepared, merged and finally clustered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Folium Map Describing the Situation <a name=\"methodology-start\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Engelsdorf\tlat: 47.030, long: 15.465\n",
      "1 Ragnitz\tlat: 47.075, long: 15.511\n",
      "2 Wenisbuch\tlat: 47.118, long: 15.477\n",
      "3 Algersdorf\tlat: 47.077, long: 15.391\n",
      "4 Baierdorf\tlat: 47.068, long: 15.392\n",
      "5 Rudersdorf\tlat: 47.021, long: 15.443\n"
     ]
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "# Location of Graz\n",
    "locGraz = geolocator.geocode('Graz, Austria')\n",
    "\n",
    "from geopy.geocoders import Nominatim\n",
    "geolocatorAdd = Nominatim(user_agent=\"graz_region_explorer\")\n",
    "\n",
    "# The second data source did not provide data for all boroughs but for additional regions.\n",
    "# Let's see where the additional regions are.\n",
    "#\n",
    "# Available data\n",
    "addRegions = list(['Engelsdorf', #liebenau\n",
    "                   'Ragnitz',    #ries\n",
    "                   'Wenisbuch',  #mariatrost\n",
    "                   'Algersdorf', #eggenberg\n",
    "                   'Baierdorf',  #eggenberg\n",
    "                   'Rudersdorf'  #puntigam\n",
    "                 ])\n",
    "\n",
    "arrAddLat = np.zeros(len(addRegions))\n",
    "arrAddLon = np.zeros(len(addRegions))\n",
    "\n",
    "# Loop over all regions and retrieve latitude and longitude for each postal code\n",
    "for idx, region in enumerate(addRegions):\n",
    "    print(idx, end=\" \")\n",
    "    # Use different geolocator for additional regions\n",
    "    address = '{}, Graz, Austria'.format(region)\n",
    "    # Show region\n",
    "    print(region, end= \"\")\n",
    "    # Add a tab when necessary\n",
    "    #if len(region)<6: print('\\t', end=\"\")\n",
    "    \n",
    "    # Get location\n",
    "    location = geolocatorAdd.geocode(address)\n",
    "    \n",
    "    # Check if we got a valid result\n",
    "    if (location != None):\n",
    "        # Add location to output\n",
    "        print('\\tlat: {:.3f}, long: {:.3f}'.format(location.latitude, location.longitude))\n",
    "        # Store result\n",
    "        arrAddLat[idx] = location.latitude\n",
    "        arrAddLon[idx] = location.longitude        \n",
    "    else:\n",
    "        print(', None')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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            popup_317dcf10e80b4784993ec81f80b7db57.setContent(html_a6f1cde56fd7403ba184d6ca68142965);
        

        marker_25423bc598154c959f163b90e94010f7.bindPopup(popup_317dcf10e80b4784993ec81f80b7db57)
        ;

        
    
    
            var marker_64111c9401f34d92b573b421138717ae = L.marker(
                [47.0754743, 15.5114771],
                {}
            ).addTo(map_e30869e4f75b453691d249bc8c18bbf7);
        
    
            var icon_260e0c10984540828baae5633727b723 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "cadetblue", "prefix": "glyphicon"}
            );
            marker_64111c9401f34d92b573b421138717ae.setIcon(icon_260e0c10984540828baae5633727b723);
        
    
        var popup_d8676317dd2a414982bbe17d314f4900 = L.popup({"maxWidth": "100%"});

        
            var html_8a2858356f47495d84181e42f6492e2c = $(`<div id="html_8a2858356f47495d84181e42f6492e2c" style="width: 100.0%; height: 100.0%;">Ragnitz</div>`)[0];
            popup_d8676317dd2a414982bbe17d314f4900.setContent(html_8a2858356f47495d84181e42f6492e2c);
        

        marker_64111c9401f34d92b573b421138717ae.bindPopup(popup_d8676317dd2a414982bbe17d314f4900)
        ;

        
    
    
            var marker_326f6629745941dcbf3665c87b689f80 = L.marker(
                [47.1183046, 15.4773184],
                {}
            ).addTo(map_e30869e4f75b453691d249bc8c18bbf7);
        
    
            var icon_149b33fb704c4a339f4edeb08b9408a6 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "cadetblue", "prefix": "glyphicon"}
            );
            marker_326f6629745941dcbf3665c87b689f80.setIcon(icon_149b33fb704c4a339f4edeb08b9408a6);
        
    
        var popup_f3c4006d5632424f8d3473408b682b80 = L.popup({"maxWidth": "100%"});

        
            var html_62d76e4ce4794736b009dbc58e94a1ed = $(`<div id="html_62d76e4ce4794736b009dbc58e94a1ed" style="width: 100.0%; height: 100.0%;">Wenisbuch</div>`)[0];
            popup_f3c4006d5632424f8d3473408b682b80.setContent(html_62d76e4ce4794736b009dbc58e94a1ed);
        

        marker_326f6629745941dcbf3665c87b689f80.bindPopup(popup_f3c4006d5632424f8d3473408b682b80)
        ;

        
    
    
            var marker_5489da06be5d4d24b5ded6531ee3303b = L.marker(
                [47.07687975, 15.3905706916335],
                {}
            ).addTo(map_e30869e4f75b453691d249bc8c18bbf7);
        
    
            var icon_fe5ed63743514802b99d47548dea950e = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "cadetblue", "prefix": "glyphicon"}
            );
            marker_5489da06be5d4d24b5ded6531ee3303b.setIcon(icon_fe5ed63743514802b99d47548dea950e);
        
    
        var popup_df69948fdc5c41ed865d7807dde3cea4 = L.popup({"maxWidth": "100%"});

        
            var html_18ae5b0e1ac64c7d8236874e3a442bea = $(`<div id="html_18ae5b0e1ac64c7d8236874e3a442bea" style="width: 100.0%; height: 100.0%;">Algersdorf</div>`)[0];
            popup_df69948fdc5c41ed865d7807dde3cea4.setContent(html_18ae5b0e1ac64c7d8236874e3a442bea);
        

        marker_5489da06be5d4d24b5ded6531ee3303b.bindPopup(popup_df69948fdc5c41ed865d7807dde3cea4)
        ;

        
    
    
            var marker_9e0d97e93d1d41f2b59f89b174a23025 = L.marker(
                [47.0684597, 15.3921235],
                {}
            ).addTo(map_e30869e4f75b453691d249bc8c18bbf7);
        
    
            var icon_f001a43a41854c559d90f46e184730ca = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "cadetblue", "prefix": "glyphicon"}
            );
            marker_9e0d97e93d1d41f2b59f89b174a23025.setIcon(icon_f001a43a41854c559d90f46e184730ca);
        
    
        var popup_ccbd84909641462188c253d56350bb36 = L.popup({"maxWidth": "100%"});

        
            var html_c224928a757343488f9a7eba944f91d8 = $(`<div id="html_c224928a757343488f9a7eba944f91d8" style="width: 100.0%; height: 100.0%;">Baierdorf</div>`)[0];
            popup_ccbd84909641462188c253d56350bb36.setContent(html_c224928a757343488f9a7eba944f91d8);
        

        marker_9e0d97e93d1d41f2b59f89b174a23025.bindPopup(popup_ccbd84909641462188c253d56350bb36)
        ;

        
    
    
            var marker_72c81808f83c40278bfdc4d863abc214 = L.marker(
                [47.0208196, 15.4429021],
                {}
            ).addTo(map_e30869e4f75b453691d249bc8c18bbf7);
        
    
            var icon_d3e0fef834db41e3b4f3aac16032ce86 = L.AwesomeMarkers.icon(
                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "cadetblue", "prefix": "glyphicon"}
            );
            marker_72c81808f83c40278bfdc4d863abc214.setIcon(icon_d3e0fef834db41e3b4f3aac16032ce86);
        
    
        var popup_8fecef0f0d75489989e7ec6fdf09c4b6 = L.popup({"maxWidth": "100%"});

        
            var html_be7c5b21dac44db7bffc6eb9f3e4ac02 = $(`<div id="html_be7c5b21dac44db7bffc6eb9f3e4ac02" style="width: 100.0%; height: 100.0%;">Rudersdorf</div>`)[0];
            popup_8fecef0f0d75489989e7ec6fdf09c4b6.setContent(html_be7c5b21dac44db7bffc6eb9f3e4ac02);
        

        marker_72c81808f83c40278bfdc4d863abc214.bindPopup(popup_8fecef0f0d75489989e7ec6fdf09c4b6)
        ;

        
    
</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 0x26cc090eba8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapGraz = folium.Map(location=[locGraz.latitude, locGraz.longitude], zoom_start=12)\n",
    "\n",
    "# Radius used for foursquare\n",
    "RADIUS = 350\n",
    "\n",
    "for name, lat, lon, area in zip(GrazTable['Borough'], GrazTable['Latitude'], GrazTable['Longitude'], GrazTable['Area']):\n",
    "    label = folium.Popup(name, parse_html=True)\n",
    "    \n",
    "    # Show a circle for the area investigated using foursquare\n",
    "    folium.Circle(\n",
    "    [lat, lon],\n",
    "    radius = np.max([RADIUS * np.sqrt(area), 500]),\n",
    "    color = None,\n",
    "    fill=True,\n",
    "    fill_opacity=0.7,\n",
    "    fill_color = 'grey'\n",
    "    ).add_to(mapGraz)\n",
    "    \n",
    "    # Mark the center of a borough\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        popup = label,\n",
    "        radius = 5,\n",
    "        fill=True,\n",
    "        fill_opacity = 0.7,\n",
    "        parse_html = False\n",
    "    ).add_to(mapGraz)\n",
    "\n",
    "# Add additional regions to see how they fit to the shown boroughs\n",
    "for name, lat, lon in zip(addRegions, arrAddLat, arrAddLon):\n",
    "    label = folium.Popup(name, parse_html=True)\n",
    "    \n",
    "    # Show a marker for additional region\n",
    "    folium.Marker(\n",
    "        [lat,lon],\n",
    "        popup=label,        \n",
    "        icon=folium.Icon(color='cadetblue')\n",
    "    ).add_to(mapGraz)\n",
    "\n",
    "# Show the map\n",
    "mapGraz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Merge Rent and Price Data <a name=\"methodology-data\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.1 Reorder Results from Second Data Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# New dataframe\n",
    "TableGrazHomeImmo_reord = TableGrazHomeImmo\n",
    "\n",
    "# From our map we know that we can combine the data from:\n",
    "#\n",
    "# Liebenau and Engelsdorf\n",
    "TableGrazHomeImmo_reord.loc[TableGrazHomeImmo_reord['Borough']=='Liebenau', 'Price per sqm (Immo)'] = \\\n",
    "TableGrazHomeImmo_reord.loc[TableGrazHomeImmo_reord['Borough'].str.contains('Engelsdorf|Liebenau'), \\\n",
    "                            'Price per sqm (Immo)'].mean()\n",
    "TableGrazHomeImmo_reord = TableGrazHomeImmo_reord[TableGrazHomeImmo_reord['Borough']!='Engelsdorf'].reset_index(drop=True)\n",
    "# Baierndorf and Algersdorf\n",
    "TableGrazHomeImmo_reord.loc[TableGrazHomeImmo_reord['Borough']=='Baierdorf', 'Price per sqm (Immo)'] = \\\n",
    "TableGrazHomeImmo_reord.loc[TableGrazHomeImmo_reord['Borough'].str.contains('Baierdorf|Algersdorf'), \\\n",
    "                            'Price per sqm (Immo)'].mean()\n",
    "TableGrazHomeImmo_reord.loc[TableGrazHomeImmo_reord['Borough']=='Baierdorf', 'Borough'] = 'Eggenberg'\n",
    "TableGrazHomeImmo_reord = TableGrazHomeImmo_reord[TableGrazHomeImmo_reord['Borough']!='Algersdorf'].reset_index(drop=True)\n",
    "\n",
    "# Further, we can rename\n",
    "#\n",
    "# Rudersdorf to Puntigam\n",
    "TableGrazHomeImmo_reord.loc[TableGrazHomeImmo_reord['Borough']=='Rudersdorf', 'Borough'] = 'Puntigam'\n",
    "# Ragnitz to Ries\n",
    "TableGrazHomeImmo_reord.loc[TableGrazHomeImmo_reord['Borough']=='Ragnitz', 'Borough'] = 'Ries'\n",
    "# Wenisbuch to Mariatrost\n",
    "TableGrazHomeImmo_reord.loc[TableGrazHomeImmo_reord['Borough']=='Wenisbuch', 'Borough'] = 'Mariatrost'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.2 Merge Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 entries in merged dataset\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>Borough</th>\n",
       "      <th>Rent per sqm</th>\n",
       "      <th>Price per sqm (Scout)</th>\n",
       "      <th>Price per sqm (Immo)</th>\n",
       "      <th>Average Price per sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inner City</td>\n",
       "      <td>12.90</td>\n",
       "      <td>3888.0</td>\n",
       "      <td>5397.783378</td>\n",
       "      <td>4642.891689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>St. Leonhard</td>\n",
       "      <td>11.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2221.927651</td>\n",
       "      <td>2221.927651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Geidorf</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3811.0</td>\n",
       "      <td>2078.248241</td>\n",
       "      <td>2944.624120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Rent per sqm  Price per sqm (Scout)  Price per sqm (Immo)  \\\n",
       "0    Inner City         12.90                 3888.0           5397.783378   \n",
       "1  St. Leonhard         11.32                    NaN           2221.927651   \n",
       "2       Geidorf         10.56                 3811.0           2078.248241   \n",
       "\n",
       "   Average Price per sqm  \n",
       "0            4642.891689  \n",
       "1            2221.927651  \n",
       "2            2944.624120  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TableGrazHome_merged = TableGrazHomeScout.merge(TableGrazHomeImmo_reord, on='Borough')\n",
    "TableGrazHome_merged['Average Price per sqm'] = np.mean(TableGrazHome_merged.loc[:, ['Price per sqm (Scout)', \\\n",
    "                                                                                     'Price per sqm (Immo)']], axis=1)\n",
    "\n",
    "print('{} entries in merged dataset'.format(TableGrazHome_merged.shape[0]))\n",
    "TableGrazHome_merged.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Foursquare <a name=\"methodology-foursquare\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json # library to handle JSON files\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "CLIENT_ID = '' # removed for upload\n",
    "CLIENT_SECRET = '' # removed for upload\n",
    "VERSION = '20180605' # Foursquare API version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a venue limit\n",
    "LIMIT = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, area, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng, area in zip(names, latitudes, longitudes, area):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\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",
    "            lat, \n",
    "            lng, \n",
    "            np.max([radius * np.sqrt(area), 500]), \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Borough', \n",
    "                  'Borough Latitude', \n",
    "                  'Borough Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner City\n",
      "St. Leonhard\n",
      "Geidorf\n",
      "Lend\n",
      "Gries\n",
      "Jakomini\n",
      "Liebenau\n",
      "St. Peter\n",
      "Waltendorf\n",
      "Ries\n",
      "Mariatrost\n",
      "Andritz\n",
      "Gösting\n",
      "Eggenberg\n",
      "Wetzelsdorf\n",
      "Straßgang\n",
      "Puntigam\n"
     ]
    }
   ],
   "source": [
    "GrazVenues = getNearbyVenues(names=GrazTable['Borough'],\n",
    "                             latitudes=GrazTable['Latitude'],\n",
    "                             longitudes=GrazTable['Longitude'],\n",
    "                             area=GrazTable['Area'],\n",
    "                             radius=RADIUS\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data set size: (348, 7)\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>Borough</th>\n",
       "      <th>Borough Latitude</th>\n",
       "      <th>Borough Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inner City</td>\n",
       "      <td>47.07249</td>\n",
       "      <td>15.44114</td>\n",
       "      <td>Eis-Greissler</td>\n",
       "      <td>47.072120</td>\n",
       "      <td>15.438734</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Inner City</td>\n",
       "      <td>47.07249</td>\n",
       "      <td>15.44114</td>\n",
       "      <td>Delikatessen Frankowitsch</td>\n",
       "      <td>47.070623</td>\n",
       "      <td>15.441221</td>\n",
       "      <td>Gourmet Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Inner City</td>\n",
       "      <td>47.07249</td>\n",
       "      <td>15.44114</td>\n",
       "      <td>Café Mitte</td>\n",
       "      <td>47.072975</td>\n",
       "      <td>15.440947</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Borough  Borough Latitude  Borough Longitude                      Venue  \\\n",
       "0  Inner City          47.07249           15.44114              Eis-Greissler   \n",
       "1  Inner City          47.07249           15.44114  Delikatessen Frankowitsch   \n",
       "2  Inner City          47.07249           15.44114                 Café Mitte   \n",
       "\n",
       "   Venue Latitude  Venue Longitude  Venue Category  \n",
       "0       47.072120        15.438734  Ice Cream Shop  \n",
       "1       47.070623        15.441221    Gourmet Shop  \n",
       "2       47.072975        15.440947             Bar  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Data set size: {}'.format(GrazVenues.shape))\n",
    "GrazVenues.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Exploratory Data Analysis <a name=\"methodology-analysis\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to check if our variables are independent. Of special importance will be if vibrance correlates with rent or price. In that case there would not be much left to analyse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4.1 Rent and Realty Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rc('font', size=20)\n",
    "plt.figure(figsize=(15,6))\n",
    "# Have a quick look at the correlation between rent and price\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(x=TableGrazHome_merged['Average Price per sqm'], y=TableGrazHome_merged['Rent per sqm'], s=150)\n",
    "plt.xlabel(r'Realty Price (EUR/m$^2$)', fontsize=24)\n",
    "plt.ylabel(r'Rent (EUR/m$^2$)', fontsize=24)\n",
    "# \n",
    "plt.subplot(1,2,2)\n",
    "sns.regplot(x=TableGrazHome_merged['Average Price per sqm'], y=TableGrazHome_merged['Rent per sqm'])\n",
    "plt.xlabel(r'Realty Price (EUR/m$^2$)', fontsize=24)\n",
    "plt.ylabel(r'Rent (EUR/m$^2$)', fontsize=24)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4.2 Rent, Prices and Vibrancy of Boroughs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group venues by Borough\n",
    "GrazVenues_grouped = GrazVenues.groupby('Borough')[['Venue']].count()\n",
    "# Sort in ascending order\n",
    "GrazVenues_grouped_srt = GrazVenues_grouped.sort_values(by='Venue', ascending=True)\n",
    "\n",
    "# Sort prices by same order \n",
    "TableGrazHome_merged_srt = TableGrazHome_merged.copy()\n",
    "TableGrazHome_merged_srt.set_index('Borough', inplace=True)\n",
    "TableGrazHome_merged_srt = TableGrazHome_merged_srt.loc[GrazVenues_grouped_srt.index, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare venues, rent and realty price\n",
    "plt.rc('font', size=18)\n",
    "plt.figure(figsize=[13,13])\n",
    "#\n",
    "plt.subplot(2,2,1)\n",
    "plt.barh(np.arange(GrazTable.shape[0]), GrazVenues_grouped_srt['Venue'])\n",
    "plt.yticks(np.arange(GrazTable.shape[0]), GrazVenues_grouped_srt.index)\n",
    "plt.xlabel('Venues');\n",
    "#\n",
    "plt.subplot(2,2,2)\n",
    "plt.barh(np.arange(GrazTable.shape[0]), TableGrazHome_merged_srt['Rent per sqm'])\n",
    "plt.yticks(np.arange(GrazTable.shape[0]), TableGrazHome_merged_srt.index)\n",
    "plt.xlim((9, 13))\n",
    "plt.xlabel(r'Rent (EUR/m$^2$)');\n",
    "#\n",
    "plt.subplot(2,2,3)\n",
    "plt.barh(np.arange(GrazTable.shape[0]), TableGrazHome_merged_srt['Average Price per sqm'])\n",
    "plt.yticks(np.arange(GrazTable.shape[0]), TableGrazHome_merged_srt.index)\n",
    "plt.xlim((1900, 4800))\n",
    "plt.xlabel(r'Realty Price (EUR/m$^2$)');\n",
    "\n",
    "plt.subplots_adjust(wspace=0.45, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4.3 Population Density and Vibrancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df_tmp = GrazTable.merge(GrazVenues_grouped, on='Borough')[['Population Density', 'Venue']]\n",
    "\n",
    "plt.rc('font', size=20) \n",
    "plt.figure(figsize=(15,6))\n",
    "# Have a quick look at the correlation between rent and price\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(x=df_tmp['Population Density'], y=df_tmp['Venue'], s=150)\n",
    "plt.xlabel('Population Density', fontsize=24)\n",
    "plt.ylabel('Venues', fontsize=24)\n",
    "# \n",
    "plt.subplot(1,2,2)\n",
    "sns.regplot(x=df_tmp['Population Density'], y=df_tmp['Venue'])\n",
    "plt.xlabel('Population Density', fontsize=24)\n",
    "plt.ylabel('Venues', fontsize=24)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Data Clustering <a name=\"methodology-clustering\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.1 Prepare Data"
   ]
  },
  {
   "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>Rent per sqm</th>\n",
       "      <th>Average Price per sqm</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "      <th>Venue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borough</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>Inner City</th>\n",
       "      <td>12.90</td>\n",
       "      <td>4642.891689</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3287</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Leonhard</th>\n",
       "      <td>11.32</td>\n",
       "      <td>2221.927651</td>\n",
       "      <td>1.83</td>\n",
       "      <td>8650</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geidorf</th>\n",
       "      <td>10.56</td>\n",
       "      <td>2944.624120</td>\n",
       "      <td>5.50</td>\n",
       "      <td>4452</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rent per sqm  Average Price per sqm  Area  Population Density  \\\n",
       "Borough                                                                       \n",
       "Inner City           12.90            4642.891689  1.16                3287   \n",
       "St. Leonhard         11.32            2221.927651  1.83                8650   \n",
       "Geidorf              10.56            2944.624120  5.50                4452   \n",
       "\n",
       "              Venue  \n",
       "Borough              \n",
       "Inner City       96  \n",
       "St. Leonhard     20  \n",
       "Geidorf          24  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Put entire information together\n",
    "TableGrazHome_cluster = TableGrazHome_merged.merge(GrazTable, on='Borough')\n",
    "TableGrazHome_cluster = TableGrazHome_cluster.merge(GrazVenues_grouped_srt, on='Borough')\n",
    "TableGrazHome_cluster = TableGrazHome_cluster.drop(\n",
    "    columns={'Price per sqm (Scout)', 'Price per sqm (Immo)', 'Postal Code', 'Latitude', 'Longitude'}).set_index('Borough')\n",
    "TableGrazHome_cluster.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll define two data sets\n",
    "\n",
    "# Rent:\n",
    "#\n",
    "# We will use\n",
    "df_rent = TableGrazHome_cluster.drop(columns={'Average Price per sqm'})\n",
    "\n",
    "# Realty\n",
    "df_realty = TableGrazHome_cluster.drop(columns={'Rent per sqm'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize our data\n",
    "rent_cluster = StandardScaler().fit_transform(df_rent)\n",
    "realty_cluster = StandardScaler().fit_transform(df_realty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K-means clustering\n",
    "numClust = 5\n",
    "kMeansGraz = KMeans(n_clusters=numClust, init='k-means++', n_init=12, max_iter=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rent\n",
    "kMeansGraz_rent = kMeansGraz.fit(rent_cluster)\n",
    "\n",
    "# Realty\n",
    "kMeansGraz_realty = kMeansGraz.fit(realty_cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.2 Check the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Same clusters for rent and realty.\n"
     ]
    }
   ],
   "source": [
    "# Check if there is a difference between the clustering\n",
    "if (np.sum(kMeansGraz_rent.labels_ - kMeansGraz_realty.labels_)):\n",
    "    print('Different clusters for rent and realty data.')\n",
    "else:\n",
    "    print('Same clusters for rent and realty.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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 per sqm</th>\n",
       "      <th>Average Price per sqm</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Venue Count</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inner City</td>\n",
       "      <td>12.90</td>\n",
       "      <td>4642.891689</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3287</td>\n",
       "      <td>47.07249</td>\n",
       "      <td>15.44114</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>St. Leonhard</td>\n",
       "      <td>11.32</td>\n",
       "      <td>2221.927651</td>\n",
       "      <td>1.83</td>\n",
       "      <td>8650</td>\n",
       "      <td>47.06945</td>\n",
       "      <td>15.45639</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Geidorf</td>\n",
       "      <td>10.56</td>\n",
       "      <td>2944.624120</td>\n",
       "      <td>5.50</td>\n",
       "      <td>4452</td>\n",
       "      <td>47.08481</td>\n",
       "      <td>15.44361</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Rent per sqm  Average Price per sqm  Area  \\\n",
       "0    Inner City         12.90            4642.891689  1.16   \n",
       "1  St. Leonhard         11.32            2221.927651  1.83   \n",
       "2       Geidorf         10.56            2944.624120  5.50   \n",
       "\n",
       "   Population Density  Latitude  Longitude  Venue Count  Cluster  \n",
       "0                3287  47.07249   15.44114           96        1  \n",
       "1                8650  47.06945   15.45639           20        3  \n",
       "2                4452  47.08481   15.44361           24        0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalTable = TableGrazHome_merged.merge(GrazTable, on='Borough').drop(\n",
    "    columns={'Price per sqm (Scout)', 'Price per sqm (Immo)', 'Postal Code'})\n",
    "# Add venues labels to the table\n",
    "finalTable = finalTable.merge(GrazVenues_grouped, on='Borough')\n",
    "finalTable = finalTable.rename(columns={'Venue': 'Venue Count'})\n",
    "# Add cluster labels to the table\n",
    "finalTable['Cluster'] = kMeansGraz_realty.labels_\n",
    "\n",
    "finalTable.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Results <a name=\"results\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clustering provides 5 clusters with 7, 1, 3, 3 and 3 boroughs, respectively.\n",
    "\n",
    "* The largest cluster provides moderate rent and a lively neighbourhood.\n",
    "* The smallest cluster is the inner city and expensive.\n",
    "* Cluster 2 consists of 3 boroughs, shows low rent, high realty prices and low vibrancy in the environment.\n",
    "* Cluster 3 consists of 3 boroughs and is cheap but very densely populated.\n",
    "* Cluster 4 consists of 3 boroughs, shows rather high rent, moderate realty prices and a rather vibrant neighbourhood.\n",
    "\n",
    "**Note:** Cluster numbers may change when you re-run the cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Rent per sqm</th>\n",
       "      <th>Average Price per sqm</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Venue Count</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Geidorf</td>\n",
       "      <td>10.56</td>\n",
       "      <td>2944.624120</td>\n",
       "      <td>5.50</td>\n",
       "      <td>4452</td>\n",
       "      <td>47.08481</td>\n",
       "      <td>15.44361</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gries</td>\n",
       "      <td>10.36</td>\n",
       "      <td>2621.380675</td>\n",
       "      <td>5.05</td>\n",
       "      <td>5386</td>\n",
       "      <td>47.05672</td>\n",
       "      <td>15.42688</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Liebenau</td>\n",
       "      <td>10.41</td>\n",
       "      <td>2632.527983</td>\n",
       "      <td>7.99</td>\n",
       "      <td>1723</td>\n",
       "      <td>47.03230</td>\n",
       "      <td>15.46449</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Waltendorf</td>\n",
       "      <td>11.88</td>\n",
       "      <td>2270.054059</td>\n",
       "      <td>4.48</td>\n",
       "      <td>2648</td>\n",
       "      <td>47.07256</td>\n",
       "      <td>15.48158</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Eggenberg</td>\n",
       "      <td>12.33</td>\n",
       "      <td>2402.925951</td>\n",
       "      <td>7.79</td>\n",
       "      <td>2509</td>\n",
       "      <td>47.07111</td>\n",
       "      <td>15.39883</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Wetzelsdorf</td>\n",
       "      <td>11.08</td>\n",
       "      <td>2408.188601</td>\n",
       "      <td>5.77</td>\n",
       "      <td>2599</td>\n",
       "      <td>47.05190</td>\n",
       "      <td>15.40077</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Puntigam</td>\n",
       "      <td>9.96</td>\n",
       "      <td>2683.333333</td>\n",
       "      <td>6.18</td>\n",
       "      <td>1272</td>\n",
       "      <td>47.02872</td>\n",
       "      <td>15.43471</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Rent per sqm  Average Price per sqm  Area  \\\n",
       "2       Geidorf         10.56            2944.624120  5.50   \n",
       "4         Gries         10.36            2621.380675  5.05   \n",
       "6      Liebenau         10.41            2632.527983  7.99   \n",
       "8    Waltendorf         11.88            2270.054059  4.48   \n",
       "13    Eggenberg         12.33            2402.925951  7.79   \n",
       "14  Wetzelsdorf         11.08            2408.188601  5.77   \n",
       "16     Puntigam          9.96            2683.333333  6.18   \n",
       "\n",
       "    Population Density  Latitude  Longitude  Venue Count  Cluster  \n",
       "2                 4452  47.08481   15.44361           24        0  \n",
       "4                 5386  47.05672   15.42688           20        0  \n",
       "6                 1723  47.03230   15.46449           24        0  \n",
       "8                 2648  47.07256   15.48158            8        0  \n",
       "13                2509  47.07111   15.39883           20        0  \n",
       "14                2599  47.05190   15.40077            8        0  \n",
       "16                1272  47.02872   15.43471           23        0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalTable[finalTable['Cluster']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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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 per sqm</th>\n",
       "      <th>Average Price per sqm</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Venue Count</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inner City</td>\n",
       "      <td>12.9</td>\n",
       "      <td>4642.891689</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3287</td>\n",
       "      <td>47.07249</td>\n",
       "      <td>15.44114</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Borough  Rent per sqm  Average Price per sqm  Area  Population Density  \\\n",
       "0  Inner City          12.9            4642.891689  1.16                3287   \n",
       "\n",
       "   Latitude  Longitude  Venue Count  Cluster  \n",
       "0  47.07249   15.44114           96        1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalTable[finalTable['Cluster']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Rent per sqm</th>\n",
       "      <th>Average Price per sqm</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Venue Count</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Andritz</td>\n",
       "      <td>11.64</td>\n",
       "      <td>2588.170448</td>\n",
       "      <td>18.47</td>\n",
       "      <td>1023</td>\n",
       "      <td>47.11285</td>\n",
       "      <td>15.42132</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gösting</td>\n",
       "      <td>10.48</td>\n",
       "      <td>2156.959991</td>\n",
       "      <td>10.83</td>\n",
       "      <td>1003</td>\n",
       "      <td>47.10659</td>\n",
       "      <td>15.39359</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Straßgang</td>\n",
       "      <td>12.85</td>\n",
       "      <td>2344.702614</td>\n",
       "      <td>11.75</td>\n",
       "      <td>1265</td>\n",
       "      <td>47.03078</td>\n",
       "      <td>15.40277</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Borough  Rent per sqm  Average Price per sqm   Area  Population Density  \\\n",
       "11    Andritz         11.64            2588.170448  18.47                1023   \n",
       "12    Gösting         10.48            2156.959991  10.83                1003   \n",
       "15  Straßgang         12.85            2344.702614  11.75                1265   \n",
       "\n",
       "    Latitude  Longitude  Venue Count  Cluster  \n",
       "11  47.11285   15.42132           28        2  \n",
       "12  47.10659   15.39359           12        2  \n",
       "15  47.03078   15.40277           26        2  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalTable[finalTable['Cluster']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Rent per sqm</th>\n",
       "      <th>Average Price per sqm</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Venue Count</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>St. Leonhard</td>\n",
       "      <td>11.32</td>\n",
       "      <td>2221.927651</td>\n",
       "      <td>1.83</td>\n",
       "      <td>8650</td>\n",
       "      <td>47.06945</td>\n",
       "      <td>15.45639</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lend</td>\n",
       "      <td>9.70</td>\n",
       "      <td>2061.288869</td>\n",
       "      <td>3.70</td>\n",
       "      <td>8162</td>\n",
       "      <td>47.07944</td>\n",
       "      <td>15.41866</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jakomini</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2049.535035</td>\n",
       "      <td>4.06</td>\n",
       "      <td>7979</td>\n",
       "      <td>47.05714</td>\n",
       "      <td>15.45063</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Rent per sqm  Average Price per sqm  Area  \\\n",
       "1  St. Leonhard         11.32            2221.927651  1.83   \n",
       "3          Lend          9.70            2061.288869  3.70   \n",
       "5      Jakomini         10.50            2049.535035  4.06   \n",
       "\n",
       "   Population Density  Latitude  Longitude  Venue Count  Cluster  \n",
       "1                8650  47.06945   15.45639           20        3  \n",
       "3                8162  47.07944   15.41866            6        3  \n",
       "5                7979  47.05714   15.45063            7        3  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalTable[finalTable['Cluster']==3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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 per sqm</th>\n",
       "      <th>Average Price per sqm</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Venue Count</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>St. Peter</td>\n",
       "      <td>11.62</td>\n",
       "      <td>3388.475845</td>\n",
       "      <td>8.86</td>\n",
       "      <td>1655</td>\n",
       "      <td>47.05155</td>\n",
       "      <td>15.47759</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ries</td>\n",
       "      <td>10.15</td>\n",
       "      <td>2967.948923</td>\n",
       "      <td>10.16</td>\n",
       "      <td>573</td>\n",
       "      <td>47.08078</td>\n",
       "      <td>15.48768</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mariatrost</td>\n",
       "      <td>9.83</td>\n",
       "      <td>3196.892857</td>\n",
       "      <td>13.99</td>\n",
       "      <td>678</td>\n",
       "      <td>47.10747</td>\n",
       "      <td>15.48955</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Rent per sqm  Average Price per sqm   Area  \\\n",
       "7    St. Peter         11.62            3388.475845   8.86   \n",
       "9         Ries         10.15            2967.948923  10.16   \n",
       "10  Mariatrost          9.83            3196.892857  13.99   \n",
       "\n",
       "    Population Density  Latitude  Longitude  Venue Count  Cluster  \n",
       "7                 1655  47.05155   15.47759           10        4  \n",
       "9                  573  47.08078   15.48768            7        4  \n",
       "10                 678  47.10747   15.48955            9        4  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalTable[finalTable['Cluster']==4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Compare Averages of Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Rent per sqm</th>\n",
       "      <th>Average Price per sqm</th>\n",
       "      <th>Area</th>\n",
       "      <th>Population Density</th>\n",
       "      <th>Venue Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</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>0</th>\n",
       "      <td>10.940000</td>\n",
       "      <td>2566.147817</td>\n",
       "      <td>6.108571</td>\n",
       "      <td>2941.285714</td>\n",
       "      <td>18.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.900000</td>\n",
       "      <td>4642.891689</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>3287.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.656667</td>\n",
       "      <td>2363.277684</td>\n",
       "      <td>13.683333</td>\n",
       "      <td>1097.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.506667</td>\n",
       "      <td>2110.917185</td>\n",
       "      <td>3.196667</td>\n",
       "      <td>8263.666667</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.533333</td>\n",
       "      <td>3184.439208</td>\n",
       "      <td>11.003333</td>\n",
       "      <td>968.666667</td>\n",
       "      <td>8.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Rent per sqm  Average Price per sqm       Area  Population Density  \\\n",
       "Cluster                                                                       \n",
       "0           10.940000            2566.147817   6.108571         2941.285714   \n",
       "1           12.900000            4642.891689   1.160000         3287.000000   \n",
       "2           11.656667            2363.277684  13.683333         1097.000000   \n",
       "3           10.506667            2110.917185   3.196667         8263.666667   \n",
       "4           10.533333            3184.439208  11.003333          968.666667   \n",
       "\n",
       "         Venue Count  \n",
       "Cluster               \n",
       "0          18.142857  \n",
       "1          96.000000  \n",
       "2          22.000000  \n",
       "3          11.000000  \n",
       "4           8.666667  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = finalTable[['Cluster','Rent per sqm', 'Average Price per sqm', 'Area', 'Population Density', 'Venue Count']]\n",
    "dfAvg = df[df['Cluster']==0].groupby('Cluster').mean()\n",
    "dfAvg = dfAvg.append(df[df['Cluster']==1].groupby('Cluster').mean())\n",
    "dfAvg = dfAvg.append(df[df['Cluster']==2].groupby('Cluster').mean())\n",
    "dfAvg = dfAvg.append(df[df['Cluster']==3].groupby('Cluster').mean())\n",
    "dfAvg = dfAvg.append(df[df['Cluster']==4].groupby('Cluster').mean())\n",
    "dfAvg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 Discussion and Conclusions <a name=\"conclusions\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Interpretation of the Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The clusters are interpreted as follows:\n",
    "\n",
    "* Buy here! Affordable prices, moderate entertainment (dark green).\n",
    "* Rent here! Affordable rent, lively environment (light green).\n",
    "* Cheap but densely populated (orange).\n",
    "* Cheap rent but little entertainment (light red).\n",
    "* Simply expensive (dark red)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalTable['Recommendation'] = finalTable['Cluster']\n",
    "\n",
    "# After checking our results, we can set our recommendations\n",
    "for cluster in range(0,numClust):\n",
    "    tmpAvg = finalTable[finalTable['Cluster']==cluster].groupby('Cluster').mean()\n",
    "    #\n",
    "    rent  = tmpAvg.loc[cluster,'Rent per sqm']\n",
    "    venue = tmpAvg.loc[cluster,'Venue Count']\n",
    "    price = tmpAvg.loc[cluster,'Average Price per sqm']\n",
    "    popul = tmpAvg.loc[cluster,'Population Density']\n",
    "            \n",
    "    if rent<11 and venue>17:\n",
    "        # Rent here!\n",
    "        finalTable.loc[finalTable['Cluster']==cluster, 'Recommendation'] = 1\n",
    "    elif price<2400 and venue>20:\n",
    "        # Buy here!\n",
    "        finalTable.loc[finalTable['Cluster']==cluster, 'Recommendation'] = 2\n",
    "    elif popul > 8000:\n",
    "        # Cheap but densely populated.\n",
    "        finalTable.loc[finalTable['Cluster']==cluster, 'Recommendation'] = 3\n",
    "    elif rent<10.6 and venue<9:\n",
    "        # Cheap rent but little entertainment.\n",
    "        finalTable.loc[finalTable['Cluster']==cluster, 'Recommendation'] = 4\n",
    "    elif rent > 12:\n",
    "        # Simply Expensive.\n",
    "        finalTable.loc[finalTable['Cluster']==cluster, 'Recommendation'] = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Cluster Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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        circle_marker_a42b5337805d476c9b334c67812a984e.bindPopup(popup_fc8911b691f2409c8b0f4e2158b537e2)
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            var marker_24a9262b6ad64987bcd7637d7d73e06d = L.marker(
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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 0x26cc42a5240>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import matplotlib.cm as cm\n",
    "#import matplotlib.colors as colors\n",
    "#colors_array = cm.rainbow(np.linspace(0, 1, len(set(finalTable['Cluster']))+2))\n",
    "#rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "\n",
    "mapGrazCluster = folium.Map(location=[locGraz.latitude, locGraz.longitude], zoom_start=12)\n",
    "\n",
    "for name, lat, lon, cluster, recomm in zip(finalTable['Borough'], finalTable['Latitude'], finalTable['Longitude'], finalTable['Cluster'], finalTable['Recommendation']):\n",
    "    label = folium.Popup(name, parse_html=True)\n",
    "    \n",
    "    # Add info with cluster interpretation\n",
    "    if recomm==1:\n",
    "        info = 'Rent here! Affordable rent, lively environment.'\n",
    "        infocolor = 'lightgreen'        \n",
    "    elif recomm==2:\n",
    "        info = 'Buy here! Affordable prices, moderate entertainment.'\n",
    "        infocolor = 'darkgreen'\n",
    "    elif recomm==3:\n",
    "        info = 'Cheap but densely populated.'\n",
    "        infocolor = 'orange'        \n",
    "    elif recomm==4:\n",
    "        info = 'Cheap rent but little entertainment.'\n",
    "        infocolor = 'red'\n",
    "    elif recomm==5:\n",
    "        info = 'Simply expensive.'\n",
    "        infocolor = 'darkred'\n",
    "    \n",
    "    # Mark the center of a borough\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        popup = label,\n",
    "        radius = 10,\n",
    "        color = infocolor,\n",
    "        fill_color = infocolor,\n",
    "        fill=True,\n",
    "        fill_opacity = 0.7,\n",
    "        parse_html = False\n",
    "    ).add_to(mapGrazCluster)\n",
    "        \n",
    "    folium.Marker(\n",
    "        [lat, lon],\n",
    "        popup = info,\n",
    "        icon=folium.Icon(color = infocolor)\n",
    "    ).add_to(mapGrazCluster)\n",
    "\n",
    "# Show the map\n",
    "mapGrazCluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final results look reasonable for a typical student city: the cluster \n",
    "of boroughs recommended for renting is the largest one consisting of \n",
    "7 boroughs.\n",
    "Furthermore, the inner city is not recommended for students, since it is\n",
    "too expensive and boroughs including or being close to transport hubs\n",
    "are cheap but densely populated.\n",
    "\n",
    "Cluster 2 is more expensive, less populated and only shows little vibrancy -- characteristics of the periphery. \n",
    "For cluster 4, on the other hand, realty prices are lower and it provides more entertainment -- a developing borough promising an increase in value.\n",
    "This shows that the choice of 5 clusters was necessary since a reduction to 4 clusters would group these two clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data analysis project showed that a recommendation system\n",
    "for renting and buying apartments in Graz is generally feasible.\n",
    "The results are derived from an objective analysis, i.e. a clustering\n",
    "of similar characteristics, look reasonable and are based on freely \n",
    "available data.\n",
    "It is furthermore noteworthy that this approach can simply be\n",
    "applied to any other location provided the required information\n",
    "is available."
   ]
  }
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