data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# PDF Text extracttion\n",
    "import tabula\n",
    "import re\n",
    "\n",
    "# Matching\n",
    "from fuzzywuzzy import fuzz \n",
    "from fuzzywuzzy import process\n",
    "\n",
    "# Interactive visualisations\n",
    "import os\n",
    "import plotly.express as px\n",
    "from plotly import graph_objects as go\n",
    "import chart_studio\n",
    "import chart_studio.plotly as py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_one = pd.read_csv('flats_berlin_1.csv')\n",
    "# data_two = pd.read_csv('flats_berlin_2.csv')\n",
    "# data = data_one.append(data_two, sort=True).drop_duplicates(subset='Url')\n",
    "# data.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-6208d269f320>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for duplicated rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "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>Adreese</th>\n",
       "      <th>Anbieter</th>\n",
       "      <th>Ausstattung</th>\n",
       "      <th>Badezimmer</th>\n",
       "      <th>Balkon/ Terrasse</th>\n",
       "      <th>Baujahr</th>\n",
       "      <th>Bezugsfrei ab</th>\n",
       "      <th>Einbauküche</th>\n",
       "      <th>Endenergiebedarf</th>\n",
       "      <th>Energieausweis</th>\n",
       "      <th>Energieausweistyp</th>\n",
       "      <th>Energieeffizienzklasse</th>\n",
       "      <th>Energieverbrauchskennwert</th>\n",
       "      <th>Etage</th>\n",
       "      <th>Gesamtmiete</th>\n",
       "      <th>Heizkosten</th>\n",
       "      <th>Heizungsart</th>\n",
       "      <th>Kaltmiete</th>\n",
       "      <th>Kaution o. Genossenschaftsanteile</th>\n",
       "      <th>Modernisierung/ Sanierung</th>\n",
       "      <th>Nebenkosten</th>\n",
       "      <th>Objektzustand</th>\n",
       "      <th>Personenaufzug</th>\n",
       "      <th>Schlafzimmer</th>\n",
       "      <th>Titel</th>\n",
       "      <th>Url</th>\n",
       "      <th>Wesentliche Energieträger</th>\n",
       "      <th>Wohnfläche ca.</th>\n",
       "      <th>Zimmer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Adreese, Anbieter, Ausstattung, Badezimmer, Balkon/ Terrasse, Baujahr, Bezugsfrei ab, Einbauküche, Endenergiebedarf, Energieausweis, Energieausweistyp, Energieeffizienzklasse, Energieverbrauchskennwert, Etage, Gesamtmiete, Heizkosten, Heizungsart, Kaltmiete, Kaution o. Genossenschaftsanteile, Modernisierung/ Sanierung, Nebenkosten, Objektzustand, Personenaufzug, Schlafzimmer, Titel, Url, Wesentliche Energieträger, Wohnfläche ca., Zimmer]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check percentage of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adreese                              0.000000\n",
       "Anbieter                             0.132150\n",
       "Ausstattung                          0.853304\n",
       "Badezimmer                           0.287968\n",
       "Balkon/ Terrasse                     0.804980\n",
       "Baujahr                              0.125986\n",
       "Bezugsfrei ab                        0.743836\n",
       "Einbauküche                          0.532791\n",
       "Endenergiebedarf                     0.915434\n",
       "Energieausweis                       0.308679\n",
       "Energieausweistyp                    0.315828\n",
       "Energieeffizienzklasse               0.918639\n",
       "Energieverbrauchskennwert            0.884615\n",
       "Etage                                0.111686\n",
       "Gesamtmiete                          0.000000\n",
       "Heizkosten                           0.000000\n",
       "Heizungsart                          0.160750\n",
       "Kaltmiete                            0.000000\n",
       "Kaution o. Genossenschaftsanteile    0.072239\n",
       "Modernisierung/ Sanierung            0.780079\n",
       "Nebenkosten                          0.000000\n",
       "Objektzustand                        0.241124\n",
       "Personenaufzug                       0.617604\n",
       "Schlafzimmer                         0.454635\n",
       "Titel                                0.000000\n",
       "Url                                  0.000000\n",
       "Wesentliche Energieträger            0.236933\n",
       "Wohnfläche ca.                       0.000000\n",
       "Zimmer                               0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4056 entries, 0 to 4055\n",
      "Data columns (total 29 columns):\n",
      "Adreese                              4056 non-null object\n",
      "Anbieter                             3520 non-null object\n",
      "Ausstattung                          595 non-null object\n",
      "Badezimmer                           2888 non-null float64\n",
      "Balkon/ Terrasse                     791 non-null object\n",
      "Baujahr                              3545 non-null object\n",
      "Bezugsfrei ab                        1039 non-null object\n",
      "Einbauküche                          1895 non-null object\n",
      "Endenergiebedarf                     343 non-null object\n",
      "Energieausweis                       2804 non-null object\n",
      "Energieausweistyp                    2775 non-null object\n",
      "Energieeffizienzklasse               330 non-null object\n",
      "Energieverbrauchskennwert            468 non-null object\n",
      "Etage                                3603 non-null object\n",
      "Gesamtmiete                          4056 non-null object\n",
      "Heizkosten                           4056 non-null object\n",
      "Heizungsart                          3404 non-null object\n",
      "Kaltmiete                            4056 non-null object\n",
      "Kaution o. Genossenschaftsanteile    3763 non-null object\n",
      "Modernisierung/ Sanierung            892 non-null object\n",
      "Nebenkosten                          4056 non-null object\n",
      "Objektzustand                        3078 non-null object\n",
      "Personenaufzug                       1551 non-null object\n",
      "Schlafzimmer                         2212 non-null float64\n",
      "Titel                                4056 non-null object\n",
      "Url                                  4056 non-null object\n",
      "Wesentliche Energieträger            3095 non-null object\n",
      "Wohnfläche ca.                       4056 non-null object\n",
      "Zimmer                               4056 non-null object\n",
      "dtypes: float64(2), object(27)\n",
      "memory usage: 919.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation\n",
    "\n",
    "Showcasing of different methods how data can be manipulated/cleaned up:\n",
    "\n",
    "- str.replace\n",
    "- .replace with regex\n",
    "- loc selection and assignment\n",
    "- custom functions\n",
    "- lambda functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Kaltmiete'] = pd.to_numeric(data['Kaltmiete'].str.replace('.','').str.replace(',','.').str.replace(' €',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Nebenkosten'] = data['Nebenkosten'].str.replace('.','').str.replace(',','.').str.replace(' €','')\n",
    "data.loc[data['Nebenkosten'] == 'keine Angabe', 'Nebenkosten' ] = np.nan\n",
    "data['Nebenkosten'] = pd.to_numeric(data['Nebenkosten'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_heizkosten(x):\n",
    "    x = x.replace('.','').replace(',','.').replace(' €','')\n",
    "    if x == 'in Nebenkosten enthalten' or 'inkl' in x:\n",
    "        x = 0\n",
    "    elif x == 'nicht in Nebenkosten enthalten' or x == 'keine Angabe':\n",
    "        x = np.nan\n",
    "    else:\n",
    "        x\n",
    "    return x\n",
    "\n",
    "data['Heizkosten'] = pd.to_numeric(data['Heizkosten'].apply(transform_heizkosten))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Gesamtmiete'] = data['Gesamtmiete'].replace({r\" €\":\"\", r\"\\.\":\"\"},regex=True).replace({r\"\\,\":\".\"},regex=True)\n",
    "data['Gesamtmiete'] = pd.to_numeric(data['Gesamtmiete'].replace(to_replace=[r\" \\(zzgl Nebenkosten & Heizkosten\\)\",\n",
    "                                                     r\" \\(zzgl Heizkosten\\)\",\n",
    "                                                     r\" \\(zzgl Nebenkosten\\)\"],value='',regex=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Wohnfläche ca.'] = pd.to_numeric(\n",
    "    data['Wohnfläche ca.'].apply(lambda string: string.replace(' m²','').replace('.','').replace(',','.')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Kaution o. Genossenschaftsanteile'] = data['Kaution o. Genossenschaftsanteile'].replace(to_replace= [r\" EUR\",r\" \\€\", r\"\\.\",r\"\\€\"],\n",
    "                                                  value= '',\n",
    "                                                  regex=True).replace().replace('\\,','.',regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2747    zwei Nettokaltmieten\n",
       "1792                    nein\n",
       "1323          nach Absprache\n",
       "4027                      ja\n",
       "4026                      ja\n",
       "Name: Kaution o. Genossenschaftsanteile, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To be further transformed if necessary\n",
    "data['Kaution o. Genossenschaftsanteile'].sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Baujahr'] = pd.to_numeric(data['Baujahr'].replace(\"unbekannt\",''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Modernisierung/ Sanierung'] = pd.to_numeric(data['Modernisierung/ Sanierung'].replace(r\"zuletzt \",'',regex=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Energieverbrauchskennwert\"] = pd.to_numeric(data[\"Energieverbrauchskennwert\"].replace(r\" kWh/\\(m\\²\\*a\\)\",\"\",regex=True).replace(r\"\\.\",\"\",regex=True).replace(r\"\\,\",\".\",regex=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correcting small spelling mistake\n",
    "data.rename(columns={'Adreese':'Adresse'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_address(x):\n",
    "    address_list = x.split(',')\n",
    "    length = len(address_list)\n",
    "    street = np.nan\n",
    "    zip_code = np.nan\n",
    "    kiez = np.nan\n",
    "    if length == 3:\n",
    "        street = address_list[0].strip()\n",
    "        zip_code = address_list[1].strip()\n",
    "        kiez = address_list[2].strip()\n",
    "        #return street, zip_code, kiez\n",
    "    if length == 2:\n",
    "        street = np.nan\n",
    "        zip_code = address_list[0].strip()\n",
    "        kiez = address_list[1].strip()\n",
    "        kiez = kiez.replace(' Die vollständige Adresse der Immobilie erhalten Sie vom Anbieter.','').strip()\n",
    "    \n",
    "    return street, zip_code, kiez\n",
    "\n",
    "address = pd.DataFrame.from_records(data['Adresse'].apply(split_address)).rename(columns={0:'Straße',1:'PLZ',2:'Kiez'})\n",
    "data = data.join(address)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_street_house_number(string):\n",
    "    street = np.nan\n",
    "    number = np.nan\n",
    "    \n",
    "    if isinstance(string, str):\n",
    "        match = re.search('\\d{1,5}\\s?\\w*',string)\n",
    "        if match:\n",
    "            street = string[:match.span()[0]].strip()\n",
    "            street = street.replace('Str.','Straße').replace('str.','straße')\n",
    "            number = string[match.span()[0]:match.span()[1]].strip().replace(' ','')\n",
    "    \n",
    "    return pd.Series({'Straße':street, 'Housenumber':number})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[['Straße','Hausnummer']] = data['Straße'].apply(split_street_house_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Hausnummer'] = data['Hausnummer'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Bezirk'] = data['Kiez'].str.split('(',expand=True)[0].str.strip(')').str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Ortsteil'] = data['Kiez'].str.split('(',expand=True)[1].str.strip(')').str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns = 'Kiez',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15212031558185404"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Straße'].isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Kaltmiete pro m²'] = data['Kaltmiete']/data['Wohnfläche ca.']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Location Rating\n",
    "\n",
    "PDF Source: https://www.stadtentwicklung.berlin.de/wohnen/mietspiegel/de/download/Strassenverzeichnis2019.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PDF Extraction with tabula (Part 1: Bezrik, Ortsteil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting main street information left tables\n",
    "top = 120\n",
    "left = 44\n",
    "height = 666\n",
    "width = 164\n",
    "\n",
    "y1 = top\n",
    "x1 = left\n",
    "y2 = top + height\n",
    "x2 = left + width\n",
    "\n",
    "area=(y1,x1,y2,x2)\n",
    "columns = [44,73,164]\n",
    "\n",
    "abbrevations_one = tabula.read_pdf(\"Strassenverzeichnis2019.pdf\", \n",
    "                     pages='2', \n",
    "                     area=area,\n",
    "                     columns=columns,\n",
    "                     pandas_options={'columns':['Unnamed: 0','Bezirk','Ortsteil','Ortsteil Abkürzung']},\n",
    "                     guess=False)[0]\n",
    "\n",
    "top = 120\n",
    "left = 216\n",
    "height = 666\n",
    "width = 164\n",
    "\n",
    "y1 = top\n",
    "x1 = left\n",
    "y2 = top + height\n",
    "x2 = left + width\n",
    "\n",
    "area=(y1,x1,y2,x2)\n",
    "columns = [216,245,336]\n",
    "\n",
    "abbrevations_two = tabula.read_pdf(\"Strassenverzeichnis2019.pdf\", \n",
    "                     pages='2', \n",
    "                     area=area,\n",
    "                     columns=columns,\n",
    "                     pandas_options={'columns':['Unnamed: 0','Bezirk','Ortsteil','Ortsteil Abkürzung']},\n",
    "                     guess=False)[0]\n",
    "\n",
    "top = 120\n",
    "left = 388\n",
    "height = 6666\n",
    "width = 164\n",
    "\n",
    "y1 = top\n",
    "x1 = left\n",
    "y2 = top + height\n",
    "x2 = left + width\n",
    "\n",
    "area=(y1,x1,y2,x2)\n",
    "columns = [388,418,509]\n",
    "\n",
    "abbrevations_three = tabula.read_pdf(\"Strassenverzeichnis2019.pdf\", \n",
    "                     pages='2', \n",
    "                     area=area,\n",
    "                     columns=columns,\n",
    "                     pandas_options={'columns':['Unnamed: 0','Bezirk','Ortsteil','Ortsteil Abkürzung']},\n",
    "                     guess=False)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "abbreviations = abbrevations_one.append(abbrevations_two).append(abbrevations_three).drop(columns='Unnamed: 0')\n",
    "\n",
    "#File was exported and quickly cleaned with GSheets\n",
    "abbreviations.to_csv('to_be_cleaned_abreviations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-ab062b2e3f36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Adjust one Ortsteil with other naming convention\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ortsteil'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Treptow'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Ortsteil'\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Alt-Treptow'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Add Ortsteil info to existing data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# After quick cleaning with GSheets reading in data again\n",
    "abbreviations = pd.read_csv('cleaned_abreviations.csv')\n",
    "abbreviations.drop_duplicates(subset='Ortsteil',keep='last',inplace=True)\n",
    "abbreviations.rename(columns={'Bezirk':'Bezirk Abkürzung'},inplace=True)\n",
    "\n",
    "# Adjust one Ortsteil with other naming convention\n",
    "data.loc[data['Ortsteil'] == 'Treptow', 'Ortsteil' ] = 'Alt-Treptow'\n",
    "\n",
    "# Add Ortsteil info to existing data\n",
    "data = pd.merge(data, abbreviations, on='Ortsteil', how='left')\n",
    "\n",
    "# Drop records where no Ortsteil could be matched\n",
    "data.dropna(subset=['Ortsteil'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PDF Extraction with tabula (Part 2: Location quality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting left tables\n",
    "top = 69\n",
    "left = 39\n",
    "height = 723\n",
    "width = 253\n",
    "\n",
    "y1 = top\n",
    "x1 = left\n",
    "y2 = top + height\n",
    "x2 = left + width\n",
    "\n",
    "area=(y1,x1,y2,x2)\n",
    "columns = [39,164.23,186.03,193.35,244.96,256.63,286.21]\n",
    "\n",
    "dfs_1 = tabula.read_pdf(\"Strassenverzeichnis2019.pdf\", \n",
    "                     pages='3-262', \n",
    "                     area=area,\n",
    "                     columns=columns,\n",
    "                     pandas_options={'columns':['Unnamed: 0',\n",
    "                                                'Straße',\n",
    "                                                'Bezirk Abkürzung',\n",
    "                                                'Gebietsstand',\n",
    "                                                'Hausnummer',\n",
    "                                                'Buchstabe',\n",
    "                                               'Wohnlage Einstufung',\n",
    "                                               'Lage im Stadtgebiet']},\n",
    "                     guess=False)\n",
    "\n",
    "# Extracting right tables\n",
    "top = 69\n",
    "left = 303\n",
    "height = 723\n",
    "width = 253\n",
    "\n",
    "y1 = top\n",
    "x1 = left\n",
    "y2 = top + height\n",
    "x2 = left + width\n",
    "\n",
    "area=(y1,x1,y2,x2)\n",
    "columns = [303,428, 450, 457,509, 520,550]\n",
    "\n",
    "dfs_2 = tabula.read_pdf(\"Strassenverzeichnis2019.pdf\", \n",
    "                     pages='3-262', \n",
    "                     area=area,\n",
    "                     columns=columns,\n",
    "                     pandas_options={'columns':['Unnamed: 0',\n",
    "                                                'Straße',\n",
    "                                                'Bezirk Abkürzung',\n",
    "                                                'Gebietsstand',\n",
    "                                                'Hausnummer',\n",
    "                                                'Buchstabe',\n",
    "                                               'Wohnlage Einstufung',\n",
    "                                               'Lage im Stadtgebiet']},\n",
    "                     guess=False)\n",
    "\n",
    "dfs_1.append(dfs_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new empty final dataframe\n",
    "location_info = pd.DataFrame()\n",
    "tables = dfs_1\n",
    "for table in tables:\n",
    "    location_info = location_info.append(table)\n",
    "    \n",
    "location_info.drop(columns='Unnamed: 0',inplace=True)\n",
    "\n",
    "# Filter out letters, e.g. A, B ...\n",
    "location_info = location_info[location_info['Straße'].apply(lambda x: len(x)) != 1]\n",
    "\n",
    "location_info['Ortsteil Abkürzung'] = location_info['Straße'].str.split('(',expand=True)[1].str.strip(')').str.strip('')\n",
    "location_info['Straße'] = location_info['Straße'].str.split('(',expand=True)[0].str.strip('')\n",
    "\n",
    "\n",
    "location_info = location_info.applymap(lambda x: x.strip() if isinstance(x, str) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "abbr = pd.read_csv('cleaned_abreviations.csv')\n",
    "abbr_ortsteil = abbr[abbr['Ortsteil Abkürzung'].notnull()].set_index('Ortsteil Abkürzung')['Ortsteil'].to_dict()\n",
    "abbr_bezirk = abbr[abbr['Ortsteil Abkürzung'].isnull()].set_index('Bezirk')['Ortsteil'].to_dict()\n",
    "abbr_bezirk['Pank'] = 'Pankow'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "location_info['Ortsteil'] = location_info['Ortsteil Abkürzung'].apply(lambda x : abbr_ortsteil.get(x))\n",
    "location_info['Bezirk'] = location_info['Bezirk Abkürzung'].apply(lambda x: abbr_bezirk.get(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_housenumbers(df):\n",
    "    if isinstance(df['Hausnummer'], str):\n",
    "        # Split house numbers into start and end\n",
    "        start = df['Hausnummer'].split('-')[0].strip()\n",
    "        end = df['Hausnummer'].split('-')[1].strip()\n",
    "        \n",
    "        # Check for letters e.g. 5A \n",
    "        start_letter = re.search('[A-Z]', start)\n",
    "        end_letter = re.search('[A-Z]', end)\n",
    "        \n",
    "        # Find actual start and ending housenumber\n",
    "        start = int(re.search('\\d*', start).group())\n",
    "        end = int(re.search('\\d*', end).group())\n",
    "        \n",
    "        # Continuing streets, e.g. 1, 2, 3, 4, 5\n",
    "        if df['Buchstabe'] == 'F':\n",
    "            house_number_list = list(range(start,end+1))\n",
    "        \n",
    "        # Even or uneven streets, e.g. 1, 3, 5 or 2, 4, 6\n",
    "        elif df['Buchstabe'] == 'G' or df['Buchstabe'] == 'U':\n",
    "            house_number_list = list(range(start,end+1,2))\n",
    "        \n",
    "        house_number_list = [str(e) for e in house_number_list]\n",
    "        if start_letter:\n",
    "            start_letter = start_letter.group().strip()\n",
    "            house_number_list[0] += start_letter\n",
    "        elif end_letter:\n",
    "            end_letter = end_letter.group().strip()\n",
    "            house_number_list[-1] += end_letter\n",
    "        return house_number_list\n",
    "    else:\n",
    "        return df['Hausnummer'] \n",
    "\n",
    "location_info['Hausnummer'] = location_info.apply(transform_housenumbers, axis=1)\n",
    "\n",
    "location_info = location_info.reset_index(drop=True).explode('Hausnummer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wohnlage_preis = {\n",
    "    'mittel': -0.09,\n",
    "    'mittel *': -0.09,\n",
    "    'mittel*': -0.09,\n",
    "    'gut*': 0.74,\n",
    "    'gut *': 0.74,\n",
    "    'gut': 0.74,\n",
    "    'einfach': -0.28,\n",
    "    'einfach*': -0.28,\n",
    "    'einfach *': -0.28,\n",
    "    }\n",
    "\n",
    "location_info['Wohnlage Preis'] = location_info['Wohnlage Einstufung'].apply(lambda x: wohnlage_preis.get(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "location_info['Adresse'] = location_info['Straße'] + ' ' + location_info['Hausnummer'].fillna('') + ' - ' + location_info['Bezirk'] + ' ' + location_info['Ortsteil'].fillna('') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "source": [
    "location_info[['Straße','Hausnummer','Wohnlage Einstufung','Wohnlage Preis','Bezirk', 'Ortsteil']].to_csv('wohnlagenkarte_berlin.csv',index=False)"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 277,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                Straße Bezirk Abkürzung Gebietsstand Hausnummer Buchstabe  \\\n",
       "0      Aachener Straße             ChWi            W          1         F   \n",
       "0      Aachener Straße             ChWi            W          2         F   \n",
       "0      Aachener Straße             ChWi            W          3         F   \n",
       "0      Aachener Straße             ChWi            W          4         F   \n",
       "0      Aachener Straße             ChWi            W          5         F   \n",
       "...                ...              ...          ...        ...       ...   \n",
       "40942    Zur Ringallee             Pank            O          1         F   \n",
       "40943    Zur Ringallee             Pank            O          2         F   \n",
       "40943    Zur Ringallee             Pank            O         3A         F   \n",
       "40944    Zur Ringallee             Pank            O          4         F   \n",
       "40944    Zur Ringallee             Pank            O          5         F   \n",
       "\n",
       "      Wohnlage Einstufung Lage im Stadtgebiet Ortsteil Abkürzung Ortsteil  \\\n",
       "0                  mittel                   Z               None     None   \n",
       "0                  mittel                   Z               None     None   \n",
       "0                  mittel                   Z               None     None   \n",
       "0                  mittel                   Z               None     None   \n",
       "0                  mittel                   Z               None     None   \n",
       "...                   ...                 ...                ...      ...   \n",
       "40942           einfach *                   D               None     None   \n",
       "40943             einfach                   D               None     None   \n",
       "40943             einfach                   D               None     None   \n",
       "40944           einfach *                   D               None     None   \n",
       "40944           einfach *                   D               None     None   \n",
       "\n",
       "                           Bezirk  \\\n",
       "0      Charlottenburg-Wilmersdorf   \n",
       "0      Charlottenburg-Wilmersdorf   \n",
       "0      Charlottenburg-Wilmersdorf   \n",
       "0      Charlottenburg-Wilmersdorf   \n",
       "0      Charlottenburg-Wilmersdorf   \n",
       "...                           ...   \n",
       "40942                      Pankow   \n",
       "40943                      Pankow   \n",
       "40943                      Pankow   \n",
       "40944                      Pankow   \n",
       "40944                      Pankow   \n",
       "\n",
       "                                               Adresse  Wohnlage Preis  \n",
       "0      Aachener Straße 1 - Charlottenburg-Wilmersdorf            -0.09  \n",
       "0      Aachener Straße 2 - Charlottenburg-Wilmersdorf            -0.09  \n",
       "0      Aachener Straße 3 - Charlottenburg-Wilmersdorf            -0.09  \n",
       "0      Aachener Straße 4 - Charlottenburg-Wilmersdorf            -0.09  \n",
       "0      Aachener Straße 5 - Charlottenburg-Wilmersdorf            -0.09  \n",
       "...                                                ...             ...  \n",
       "40942                        Zur Ringallee 1 - Pankow            -0.28  \n",
       "40943                        Zur Ringallee 2 - Pankow            -0.28  \n",
       "40943                       Zur Ringallee 3A - Pankow            -0.28  \n",
       "40944                        Zur Ringallee 4 - Pankow            -0.28  \n",
       "40944                        Zur Ringallee 5 - Pankow            -0.28  \n",
       "\n",
       "[270068 rows x 12 columns]"
      ],
      "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>Straße</th>\n      <th>Bezirk Abkürzung</th>\n      <th>Gebietsstand</th>\n      <th>Hausnummer</th>\n      <th>Buchstabe</th>\n      <th>Wohnlage Einstufung</th>\n      <th>Lage im Stadtgebiet</th>\n      <th>Ortsteil Abkürzung</th>\n      <th>Ortsteil</th>\n      <th>Bezirk</th>\n      <th>Adresse</th>\n      <th>Wohnlage Preis</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Aachener Straße</td>\n      <td>ChWi</td>\n      <td>W</td>\n      <td>1</td>\n      <td>F</td>\n      <td>mittel</td>\n      <td>Z</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Charlottenburg-Wilmersdorf</td>\n      <td>Aachener Straße 1 - Charlottenburg-Wilmersdorf</td>\n      <td>-0.09</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Aachener Straße</td>\n      <td>ChWi</td>\n      <td>W</td>\n      <td>2</td>\n      <td>F</td>\n      <td>mittel</td>\n      <td>Z</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Charlottenburg-Wilmersdorf</td>\n      <td>Aachener Straße 2 - Charlottenburg-Wilmersdorf</td>\n      <td>-0.09</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Aachener Straße</td>\n      <td>ChWi</td>\n      <td>W</td>\n      <td>3</td>\n      <td>F</td>\n      <td>mittel</td>\n      <td>Z</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Charlottenburg-Wilmersdorf</td>\n      <td>Aachener Straße 3 - Charlottenburg-Wilmersdorf</td>\n      <td>-0.09</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Aachener Straße</td>\n      <td>ChWi</td>\n      <td>W</td>\n      <td>4</td>\n      <td>F</td>\n      <td>mittel</td>\n      <td>Z</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Charlottenburg-Wilmersdorf</td>\n      <td>Aachener Straße 4 - Charlottenburg-Wilmersdorf</td>\n      <td>-0.09</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Aachener Straße</td>\n      <td>ChWi</td>\n      <td>W</td>\n      <td>5</td>\n      <td>F</td>\n      <td>mittel</td>\n      <td>Z</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Charlottenburg-Wilmersdorf</td>\n      <td>Aachener Straße 5 - Charlottenburg-Wilmersdorf</td>\n      <td>-0.09</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>40942</th>\n      <td>Zur Ringallee</td>\n      <td>Pank</td>\n      <td>O</td>\n      <td>1</td>\n      <td>F</td>\n      <td>einfach *</td>\n      <td>D</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Pankow</td>\n      <td>Zur Ringallee 1 - Pankow</td>\n      <td>-0.28</td>\n    </tr>\n    <tr>\n      <th>40943</th>\n      <td>Zur Ringallee</td>\n      <td>Pank</td>\n      <td>O</td>\n      <td>2</td>\n      <td>F</td>\n      <td>einfach</td>\n      <td>D</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Pankow</td>\n      <td>Zur Ringallee 2 - Pankow</td>\n      <td>-0.28</td>\n    </tr>\n    <tr>\n      <th>40943</th>\n      <td>Zur Ringallee</td>\n      <td>Pank</td>\n      <td>O</td>\n      <td>3A</td>\n      <td>F</td>\n      <td>einfach</td>\n      <td>D</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Pankow</td>\n      <td>Zur Ringallee 3A - Pankow</td>\n      <td>-0.28</td>\n    </tr>\n    <tr>\n      <th>40944</th>\n      <td>Zur Ringallee</td>\n      <td>Pank</td>\n      <td>O</td>\n      <td>4</td>\n      <td>F</td>\n      <td>einfach *</td>\n      <td>D</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Pankow</td>\n      <td>Zur Ringallee 4 - Pankow</td>\n      <td>-0.28</td>\n    </tr>\n    <tr>\n      <th>40944</th>\n      <td>Zur Ringallee</td>\n      <td>Pank</td>\n      <td>O</td>\n      <td>5</td>\n      <td>F</td>\n      <td>einfach *</td>\n      <td>D</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Pankow</td>\n      <td>Zur Ringallee 5 - Pankow</td>\n      <td>-0.28</td>\n    </tr>\n  </tbody>\n</table>\n<p>270068 rows × 12 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 278
    }
   ],
   "source": [
    "location_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Adresse'] = df['Straße'] + ' ' + df['Hausnummer'].fillna('')\n",
    "wohnlage_einstufung = df[\n",
    "    (df['Straße'].str.contains('Friedastraße')) & \n",
    "    (df['Hausnummer'] == '11')\n",
    "].set_index('Wohnlage Einstufung')['Adresse'].to_dict()\n",
    "if len(wohnlage_einstufung) == 0:\n",
    "    wohnlage_einstufung = df[df['Straße'].str.contains('Friedastraße')].set_index('Wohnlage Einstufung')['Adresse'].to_dict()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "execution_count": 118
    }
   ],
   "source": [
    "len(wohnlage_einstufung)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'Friedastraße ': 'mittel'}"
      ]
     },
     "metadata": {},
     "execution_count": 119
    }
   ],
   "source": [
    "wohnlage_einstufung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      index       Straße Bezirk Abkürzung Gebietsstand Hausnummer Buchstabe  \\\n",
       "1305     42  Antonstraße             Mitt            W          5         F   \n",
       "\n",
       "     Wohnlage Einstufung Lage im Stadtgebiet Ortsteil Abkürzung  \n",
       "1305             einfach                   Z               None  "
      ],
      "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>index</th>\n      <th>Straße</th>\n      <th>Bezirk Abkürzung</th>\n      <th>Gebietsstand</th>\n      <th>Hausnummer</th>\n      <th>Buchstabe</th>\n      <th>Wohnlage Einstufung</th>\n      <th>Lage im Stadtgebiet</th>\n      <th>Ortsteil Abkürzung</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1305</th>\n      <td>42</td>\n      <td>Antonstraße</td>\n      <td>Mitt</td>\n      <td>W</td>\n      <td>5</td>\n      <td>F</td>\n      <td>einfach</td>\n      <td>Z</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 72
    }
   ],
   "source": [
    "straßen[straßen['Hausnummer'] == '5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "['Wohnlage Einstufung'].to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Location Rating features to Scraped Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14752475247524752"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of data without an address which means we cannot assess location quality\n",
    "data['Adresse'].apply(lambda x: True if 'Die vollständige Adresse der Immobilie erhalten Sie vom Anbieter.' in x else False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19min 46s, sys: 6.11 s, total: 19min 52s\n",
      "Wall time: 20min 1s\n"
     ]
    }
   ],
   "source": [
    "def match_street(x):\n",
    "    if isinstance(x, str):\n",
    "        match = process.extractOne(x, list(location_info['Straße'].unique()))\n",
    "        if match[1] > 90:\n",
    "            return match[0]\n",
    "        else:\n",
    "            return x\n",
    "    else:\n",
    "        return x\n",
    "    \n",
    "#Computationally quite expensive (only do this once if you have new data)\n",
    "%time data['Straße'] = data['Straße'].apply(match_street)\n",
    "\n",
    "#Results temporarily saved to iterate over data faster\n",
    "data.to_csv('temp_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optionally saved df to iterate over problems much faster\n",
    "data = pd.read_csv('temp_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6029698149951315"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Strip one more time all strings of whitespace to guarantee correct merge\n",
    "data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "location_info = location_info.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "\n",
    "# First merge attempt on three criterias\n",
    "matching_one = pd.merge(data, location_info,on=['Straße','Bezirk Abkürzung','Hausnummer'],how='left')\n",
    "\n",
    "# Percentage of data for which we could add location rating\n",
    "matching_one.dropna(subset=['Url'])['Wohnlage Einstufung'].notnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12756489493201484"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In the pdf there are location info for entire streets (marked with letter K), allowing us not to merge with the exact housenumber\n",
    "matching_two = pd.merge(data, location_info[location_info['Buchstabe'] == 'K'], on=['Straße','Bezirk Abkürzung'], how='left')\n",
    "\n",
    "# Drop duplicated columns\n",
    "matching_two = matching_two.drop(columns='Hausnummer_y').rename(columns={'Hausnummer_x':'Hausnummer'})\n",
    "\n",
    "# Percentage of data for which we could add location rating\n",
    "matching_two.dropna(subset=['Url'])['Wohnlage Einstufung'].notnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine both merges from above to create final_data_frame\n",
    "matching = matching_one.append(matching_two)\n",
    "\n",
    "# Drop all records for which we could not find a location rating\n",
    "matching.dropna(subset=['Wohnlage Einstufung'], inplace=True)\n",
    "\n",
    "# Some streets are so common that they are duplicate, resulting in duplicated values\n",
    "# In the PDF only those streets had a location abbreviation. I filter out now those records to add them later back in the df\n",
    "matching_three = matching[matching['Ortsteil Abkürzung_x'] == matching['Ortsteil Abkürzung_y']]\n",
    "\n",
    "# Get rid of all duplicated records in the file\n",
    "matching.drop_duplicates(subset=['Url'], keep=False, inplace=True)\n",
    "\n",
    "# Add back the records which had many streets with the same name\n",
    "matching = matching.append(matching_three)\n",
    "\n",
    "# Get rid of all duplicated records in the file once more\n",
    "matching.drop_duplicates(subset='Url',inplace=True)\n",
    "\n",
    "matching.reset_index(inplace=True, drop=True)\n",
    "\n",
    "matching.drop(columns='Ortsteil Abkürzung_y',inplace=True)\n",
    "matching.rename(columns={'Ortsteil Abkürzung_x': 'Ortsteil Abkürzung'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation of Rental Price Ceiling\n",
    "\n",
    "The main infromation for the calculation can be found here: https://stadtentwicklung.berlin.de/wohnen/wohnraum/mietendeckel/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Base Table (year of building, bathroom, central heating)\n",
    "The following base table takes the year the building was built, central heating and the existance of a bathroom to come up with a base price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"img/mietentabelle.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### English Translation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>min_year</th>\n",
       "      <th>max_year</th>\n",
       "      <th>central_heating</th>\n",
       "      <th>condition</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>price_per_square_meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1918</td>\n",
       "      <td>1918</td>\n",
       "      <td>True</td>\n",
       "      <td>and</td>\n",
       "      <td>True</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1918</td>\n",
       "      <td>1918</td>\n",
       "      <td>True</td>\n",
       "      <td>or</td>\n",
       "      <td>True</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1918</td>\n",
       "      <td>1918</td>\n",
       "      <td>False</td>\n",
       "      <td>and</td>\n",
       "      <td>False</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1919</td>\n",
       "      <td>1949</td>\n",
       "      <td>True</td>\n",
       "      <td>and</td>\n",
       "      <td>True</td>\n",
       "      <td>6.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1919</td>\n",
       "      <td>1949</td>\n",
       "      <td>True</td>\n",
       "      <td>or</td>\n",
       "      <td>True</td>\n",
       "      <td>5.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1919</td>\n",
       "      <td>1949</td>\n",
       "      <td>False</td>\n",
       "      <td>and</td>\n",
       "      <td>False</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1950</td>\n",
       "      <td>1964</td>\n",
       "      <td>True</td>\n",
       "      <td>and</td>\n",
       "      <td>True</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1950</td>\n",
       "      <td>1964</td>\n",
       "      <td>True</td>\n",
       "      <td>or</td>\n",
       "      <td>True</td>\n",
       "      <td>5.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1965</td>\n",
       "      <td>1972</td>\n",
       "      <td>True</td>\n",
       "      <td>and</td>\n",
       "      <td>True</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1973</td>\n",
       "      <td>1990</td>\n",
       "      <td>True</td>\n",
       "      <td>and</td>\n",
       "      <td>True</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1991</td>\n",
       "      <td>2002</td>\n",
       "      <td>True</td>\n",
       "      <td>and</td>\n",
       "      <td>True</td>\n",
       "      <td>8.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2003</td>\n",
       "      <td>2013</td>\n",
       "      <td>True</td>\n",
       "      <td>and</td>\n",
       "      <td>True</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min_year  max_year  central_heating condition  bathroom  \\\n",
       "0       1918      1918             True       and      True   \n",
       "1       1918      1918             True        or      True   \n",
       "2       1918      1918            False       and     False   \n",
       "3       1919      1949             True       and      True   \n",
       "4       1919      1949             True        or      True   \n",
       "5       1919      1949            False       and     False   \n",
       "6       1950      1964             True       and      True   \n",
       "7       1950      1964             True        or      True   \n",
       "8       1965      1972             True       and      True   \n",
       "9       1973      1990             True       and      True   \n",
       "10      1991      2002             True       and      True   \n",
       "11      2003      2013             True       and      True   \n",
       "\n",
       "    price_per_square_meter  \n",
       "0                     6.45  \n",
       "1                     5.00  \n",
       "2                     3.92  \n",
       "3                     6.27  \n",
       "4                     5.22  \n",
       "5                     4.59  \n",
       "6                     6.08  \n",
       "7                     5.62  \n",
       "8                     5.95  \n",
       "9                     6.04  \n",
       "10                    8.13  \n",
       "11                    9.80  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_ceiling_info = {\n",
    "    'min_year': [1918, 1918, 1918, 1919, 1919, 1919, 1950, 1950, 1965, 1973, 1991, 2003 ],\n",
    "    'max_year': [1918, 1918, 1918, 1949, 1949, 1949, 1964, 1964, 1972, 1990, 2002, 2013 ],\n",
    "    'central_heating' : [True, True, False, True, True, False, True, True, True, True, True, True],\n",
    "    'condition': ['and', 'or', 'and', 'and', 'or', 'and', 'and', 'or', 'and', 'and', 'and', 'and'],\n",
    "    'bathroom' : [True, True, False, True, True, False, True, True, True, True, True, True],\n",
    "    'price_per_square_meter' : [6.45, 5.00, 3.92, 6.27, 5.22, 4.59, 6.08, 5.62, 5.95, 6.04, 8.13, 9.8]\n",
    "}\n",
    "\n",
    "rental_ceiling_info = pd.DataFrame.from_dict(rental_ceiling_info)\n",
    "rental_ceiling_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Modern equipment\n",
    "\n",
    "Furthermore if at least three of the following criteria are met, one euro is added to the cold rent per squaremeter to adjust for modern equipment:\n",
    "\n",
    "*Für Wohnungen mit moderner Ausstattung erhöht sich der Wert um 1,00 Euro. Eine moderne Ausstattung liegt vor, wenn mindestens drei der folgenden Merkmale vorhanden sind:* \n",
    "\n",
    "- *schwellenlos erreichbarer Aufzug,*\n",
    "- *Einbauküche,*\n",
    "- *hochwertige Sanitärausstattung,*\n",
    "- *hochwertiger Bodenbelag in der überwiegenden Zahl der Wohnräume,*\n",
    "- *Energieverbrauchskennwert von weniger als 120 kWh/(m² a)*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Location Rating\n",
    "\n",
    "Next there is an additional premium or dedcution based on the location of the flat. The exact location criteria are not published yet but the PDF above gives the best potential idea of the to be published location rating:\n",
    "\n",
    "*Für Wohnungen in einfacher Wohnlage ist bei der Berechnung der Mietobergrenze ein Abschlag beim maßgeblichen Mietpreis in der Mietentabelle von 0,28 Euro zu berücksichtigen, für Wohnungen in mittlerer Wohnlage werden 0,09 Euro abgezogen und für Wohnungen in guter Wohnlage ist ein Zuschlag von 0,74 Euro zu berücksichtigen. (Die Lageeinordnung wird demnächst veröffentlicht.)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Single or double family house\n",
    "\n",
    "Since I scraped only flats this criteria can be negelected and I will not need to take into consideration an additional increase of 10% to the price.\n",
    "\n",
    "*9. Was ist mit Ein- und Zweifamilienhäusern? Auch für sie gilt das Gesetz. Liegt der Wohnraum in Gebäuden mit nicht mehr als zwei Wohnungen, erhöht sich jedoch die Mietobergrenze um einen Zuschlag von zehn Prozent.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assumptions\n",
    "\n",
    "With the mentioned criteria above, I took the following conservative assumptions to calculate the price rental ceiling per square meter:\n",
    "\n",
    "- maximum price from the base table (1) for the respective year of construction, which means that it is assumed that all the flats have central heating and a bath room\n",
    "- all the flats fufill at least three criteria, meaning that it is assumed that they all have modern equipment\n",
    "- the rent index PDF document published in 2019 is a good approximation for the location rating (2)\n",
    "- all the flats are non single family houses (scraped data only for apartments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation\n",
    "\n",
    "max_price_per_square_meter = (base_price + location factor + modern_equipment_factor) * (1 + 20%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max rental price ceiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_base_price(df):\n",
    "    \"\"\"Takes in a dataframe and finds base price for the year the building was built.\n",
    "    \n",
    "    Args:\n",
    "    - df: Dataframe\n",
    "    \n",
    "    Returns:\n",
    "    - column (float): base price for the respective year the house was built\n",
    "    \n",
    "    \"\"\"\n",
    "    # Initialising criteria\n",
    "    \n",
    "    location_factor, base_price = (np.nan, np.nan)\n",
    "    \n",
    "    # Age of the building\n",
    "    if df['Baujahr'] <= 1918:\n",
    "        base_price = 6.45\n",
    "    elif df['Baujahr'] >= 1919 and df['Baujahr'] <= 1949:\n",
    "        base_price = 6.27\n",
    "    elif df['Baujahr'] >= 1950 and df['Baujahr'] <= 1964:\n",
    "        base_price = 6.08\n",
    "    elif df['Baujahr'] >= 1965 and df['Baujahr'] <= 1972:\n",
    "        base_price = 5.95\n",
    "    elif df['Baujahr'] >= 1973 and df['Baujahr'] <= 1990:\n",
    "        base_price = 6.04\n",
    "    elif df['Baujahr'] >= 1991 and df['Baujahr'] <= 2002:\n",
    "        base_price = 8.13\n",
    "    elif df['Baujahr'] >= 2003 and df['Baujahr'] <= 2013:\n",
    "        base_price = 9.80\n",
    "    \n",
    "    return base_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_location_factor(df):\n",
    "    \"\"\"Takes in a dataframe and finds the location factor\n",
    "    \n",
    "    Args:\n",
    "    - df: Dataframe\n",
    "    \n",
    "    Returns:\n",
    "    - column (float): location factor depending on the quality of the location\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # Location factor of the flat\n",
    "    if df['Wohnlage Einstufung'].strip(' *') == 'einfach':\n",
    "        location_factor = -0.28\n",
    "    elif df['Wohnlage Einstufung'].strip(' *') == 'mittel':\n",
    "        location_factor = -0.09\n",
    "    elif df['Wohnlage Einstufung'].strip(' *') == 'gut':\n",
    "        location_factor = 0.74\n",
    "    \n",
    "    return location_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_price = matching.apply(find_base_price, axis=1)\n",
    "location_factor = matching.apply(find_location_factor, axis=1)\n",
    "\n",
    "\n",
    "# Modern equipment factor of the flat\n",
    "modern_equipment_factor = 1\n",
    "\n",
    "# Maximum overpricing\n",
    "percent = 0.2\n",
    "    \n",
    "max_price_per_square_meter = (base_price + location_factor + modern_equipment_factor) * (1+percent)\n",
    "\n",
    "matching['Zulässige Mietobergrenze pro m²'] = max_price_per_square_meter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "illegal_pricing = matching[matching['Kaltmiete pro m²'] > matching['Zulässige Mietobergrenze pro m²']]\n",
    "legal_pricing = matching[matching['Kaltmiete pro m²'] < matching['Zulässige Mietobergrenze pro m²']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "username = os.getenv('PLOTLY_USERNAME')\n",
    "api_key = os.getenv('PLOTLY_API_KEY') # your api key - go to profile > settings > regenerate key\n",
    "\n",
    "chart_studio.tools.set_credentials_file(username=username, api_key=api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
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    "fig = go.Figure(go.Funnel(\n",
    "    y = ['All Scraped Listings', 'Listings with sufficient Information', 'Listings over rental cap'],\n",
    "    x = [len(data),len(legal_pricing) + len(illegal_pricing),len(illegal_pricing)],\n",
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    "fig.update_layout(\n",
    "    title=\"Overview of Analysed Listings\")\n",
    "\n",
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.plot(fig, filename = 'funnel_listings', auto_open=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average price before and after the law"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.116483831008976"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matching['Kaltmiete pro m²'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.209192649673984"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matching['Zulässige Mietobergrenze pro m²'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3907847385261087"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(matching['Kaltmiete pro m²'].mean() - matching['Zulässige Mietobergrenze pro m²'].mean())/ matching['Kaltmiete pro m²'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pricing differenct between districts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_rent_difference_per_district = matching.groupby('Ortsteil')[['Kaltmiete pro m²','Zulässige Mietobergrenze pro m²']].mean().stack().reset_index()\n",
    "\n",
    "average_rent_difference_per_district.rename(columns={\n",
    "   'level_1':'Comparison', 0 :'Average Rent' \n",
    "}, inplace=True)"
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   "source": [
    "fig = px.bar(average_rent_difference_per_district, \n",
    "             x='Ortsteil', \n",
    "             y='Average Rent',\n",
    "             color='Comparison',\n",
    "             barmode='group',\n",
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   "source": [
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    "percentage_rent_difference_per_district = matching.groupby('Ortsteil')[['Kaltmiete pro m²','Zulässige Mietobergrenze pro m²']].mean()\n",
    "\n",
    "percentage_rent_difference_per_district = pd.DataFrame((percentage_rent_difference_per_district['Kaltmiete pro m²'] - percentage_rent_difference_per_district['Zulässige Mietobergrenze pro m²']) / percentage_rent_difference_per_district['Kaltmiete pro m²']*100)\n",
    "\n",
    "percentage_rent_difference_per_district = percentage_rent_difference_per_district.rename(columns={0:'Percentage Change in Average Price'}).reset_index()\n",
    "\n",
    "percentage_rent_difference_per_district = percentage_rent_difference_per_district.sort_values(['Percentage Change in Average Price'],ascending=False)"
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   ],
   "source": [
    "fig = px.bar(percentage_rent_difference_per_district, \n",
    "             x='Ortsteil', \n",
    "             y='Percentage Change in Average Price',\n",
    "             title='Comparison of Price Change per District')\n",
    "fig.show()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.plot(fig, filename = 'percentage_rent_difference_per_district', auto_open=True)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exccess Rent paid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excess amount paid per month\n",
    "illegal_cold_rent = illegal_pricing['Kaltmiete pro m²'] * illegal_pricing['Wohnfläche ca.']\n",
    "illegal_rent_cap = illegal_pricing['Zulässige Mietobergrenze pro m²'] * illegal_pricing['Wohnfläche ca.']\n",
    "illegal_excess_rent = illegal_cold_rent.sum() - illegal_rent_cap.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "463059.78743999975"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "illegal_excess_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Titel</th>\n",
       "      <th>Anbieter</th>\n",
       "      <th>Url</th>\n",
       "      <th>Adresse</th>\n",
       "      <th>Baujahr</th>\n",
       "      <th>Wohnlage Einstufung</th>\n",
       "      <th>Wohnfläche ca.</th>\n",
       "      <th>Kaltmiete</th>\n",
       "      <th>Kaltmiete pro m²</th>\n",
       "      <th>Zulässige Mietobergrenze pro m²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nutzen Sie Ihre Chance - 2 Zimmerwohnung mit b...</td>\n",
       "      <td>German Prop Tech GmbH</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/114414026</td>\n",
       "      <td>Reinickendorfer Straße 4, 13447 Berlin, Weddin...</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>einfach *</td>\n",
       "      <td>78.00</td>\n",
       "      <td>979.4</td>\n",
       "      <td>12.556410</td>\n",
       "      <td>8.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lichtdurchflutetes Appartement - Tageslichtbad...</td>\n",
       "      <td>Stadthaus Verwaltungsgesellschaft mbH Franklin...</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/100591334</td>\n",
       "      <td>Schulenburgring 125, 12101 Berlin, Tempelhof (...</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>mittel</td>\n",
       "      <td>38.48</td>\n",
       "      <td>503.0</td>\n",
       "      <td>13.071726</td>\n",
       "      <td>8.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eine Perle mit modernem Grundriss</td>\n",
       "      <td>wfi- Wohlfühlimmobilien Thomas- Müntzer- Damm ...</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/115502827</td>\n",
       "      <td>Richard- Sorge- Straße 10, 10249 Berlin, Fried...</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>gut</td>\n",
       "      <td>91.89</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>14.038524</td>\n",
       "      <td>13.848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hochwertiges DG im gepflegten Altbau! **Besich...</td>\n",
       "      <td>SchönLife Immobilien Wielandstraße 12 10629 Be...</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/115172918</td>\n",
       "      <td>Cauerstrasse 25, 10587 Berlin, Charlottenburg ...</td>\n",
       "      <td>1905.0</td>\n",
       "      <td>mittel *</td>\n",
       "      <td>119.00</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Praktische Familienwohnung mit Laminat - in He...</td>\n",
       "      <td>Immonexxt GmbH Am Borsigturm 53 13507 Berlin</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/114889465</td>\n",
       "      <td>Kastanienallee 86, 12627 Berlin, Hellersdorf (...</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>mittel</td>\n",
       "      <td>68.54</td>\n",
       "      <td>639.0</td>\n",
       "      <td>9.323023</td>\n",
       "      <td>8.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2834</th>\n",
       "      <td>NEUBAU ERSTBEZUG 200 m² - Exklusive Dachgescho...</td>\n",
       "      <td>Massih-Hausverwaltung Knesebeckstraße 55 10719...</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/115805257</td>\n",
       "      <td>Eisenacher Str. 64, 10823 Berlin, Schöneberg (...</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>mittel</td>\n",
       "      <td>200.00</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>8.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2836</th>\n",
       "      <td>Möblierte 1 Zimmer Studio in Berlin, Schöneber...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/108875055</td>\n",
       "      <td>Goltzstraße 39, 10781 Berlin, Schöneberg (Schö...</td>\n",
       "      <td>1908.0</td>\n",
       "      <td>gut</td>\n",
       "      <td>45.00</td>\n",
       "      <td>950.0</td>\n",
       "      <td>21.111111</td>\n",
       "      <td>9.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2841</th>\n",
       "      <td>Toller Altbau in begehrter Lage- Anfragen bitt...</td>\n",
       "      <td>Andrej Schafranek Immobilien</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/115773874</td>\n",
       "      <td>Buchholzer Str. 4, 10437 Berlin, Prenzlauer Be...</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>gut</td>\n",
       "      <td>94.61</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>12.673079</td>\n",
       "      <td>9.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2842</th>\n",
       "      <td>Helle 2 Zimmer Wohnung! Ihr neues zu Hause in ...</td>\n",
       "      <td>ALEPA Real Estate GmbH Tauentzienstraße 6 1078...</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/115760600</td>\n",
       "      <td>Mühlenstraße 30, 13187 Berlin, Pankow (Pankow)</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>mittel *</td>\n",
       "      <td>73.00</td>\n",
       "      <td>799.0</td>\n",
       "      <td>10.945205</td>\n",
       "      <td>8.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2844</th>\n",
       "      <td>Möbilierte Wohnung, close to Prenzlauer Allee/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.immobilienscout24.de/expose/115716846</td>\n",
       "      <td>Heinersdorfer Straße 35, 13086 Berlin, Weißens...</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>mittel</td>\n",
       "      <td>60.00</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>10.848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1272 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Titel  \\\n",
       "0     Nutzen Sie Ihre Chance - 2 Zimmerwohnung mit b...   \n",
       "2     Lichtdurchflutetes Appartement - Tageslichtbad...   \n",
       "3                     Eine Perle mit modernem Grundriss   \n",
       "4     Hochwertiges DG im gepflegten Altbau! **Besich...   \n",
       "6     Praktische Familienwohnung mit Laminat - in He...   \n",
       "...                                                 ...   \n",
       "2834  NEUBAU ERSTBEZUG 200 m² - Exklusive Dachgescho...   \n",
       "2836  Möblierte 1 Zimmer Studio in Berlin, Schöneber...   \n",
       "2841  Toller Altbau in begehrter Lage- Anfragen bitt...   \n",
       "2842  Helle 2 Zimmer Wohnung! Ihr neues zu Hause in ...   \n",
       "2844  Möbilierte Wohnung, close to Prenzlauer Allee/...   \n",
       "\n",
       "                                               Anbieter  \\\n",
       "0                                 German Prop Tech GmbH   \n",
       "2     Stadthaus Verwaltungsgesellschaft mbH Franklin...   \n",
       "3     wfi- Wohlfühlimmobilien Thomas- Müntzer- Damm ...   \n",
       "4     SchönLife Immobilien Wielandstraße 12 10629 Be...   \n",
       "6          Immonexxt GmbH Am Borsigturm 53 13507 Berlin   \n",
       "...                                                 ...   \n",
       "2834  Massih-Hausverwaltung Knesebeckstraße 55 10719...   \n",
       "2836                                                NaN   \n",
       "2841                       Andrej Schafranek Immobilien   \n",
       "2842  ALEPA Real Estate GmbH Tauentzienstraße 6 1078...   \n",
       "2844                                                NaN   \n",
       "\n",
       "                                                    Url  \\\n",
       "0     https://www.immobilienscout24.de/expose/114414026   \n",
       "2     https://www.immobilienscout24.de/expose/100591334   \n",
       "3     https://www.immobilienscout24.de/expose/115502827   \n",
       "4     https://www.immobilienscout24.de/expose/115172918   \n",
       "6     https://www.immobilienscout24.de/expose/114889465   \n",
       "...                                                 ...   \n",
       "2834  https://www.immobilienscout24.de/expose/115805257   \n",
       "2836  https://www.immobilienscout24.de/expose/108875055   \n",
       "2841  https://www.immobilienscout24.de/expose/115773874   \n",
       "2842  https://www.immobilienscout24.de/expose/115760600   \n",
       "2844  https://www.immobilienscout24.de/expose/115716846   \n",
       "\n",
       "                                                Adresse  Baujahr  \\\n",
       "0     Reinickendorfer Straße 4, 13447 Berlin, Weddin...   1972.0   \n",
       "2     Schulenburgring 125, 12101 Berlin, Tempelhof (...   1956.0   \n",
       "3     Richard- Sorge- Straße 10, 10249 Berlin, Fried...   2010.0   \n",
       "4     Cauerstrasse 25, 10587 Berlin, Charlottenburg ...   1905.0   \n",
       "6     Kastanienallee 86, 12627 Berlin, Hellersdorf (...   1985.0   \n",
       "...                                                 ...      ...   \n",
       "2834  Eisenacher Str. 64, 10823 Berlin, Schöneberg (...   1900.0   \n",
       "2836  Goltzstraße 39, 10781 Berlin, Schöneberg (Schö...   1908.0   \n",
       "2841  Buchholzer Str. 4, 10437 Berlin, Prenzlauer Be...   1900.0   \n",
       "2842     Mühlenstraße 30, 13187 Berlin, Pankow (Pankow)   1910.0   \n",
       "2844  Heinersdorfer Straße 35, 13086 Berlin, Weißens...   2002.0   \n",
       "\n",
       "     Wohnlage Einstufung  Wohnfläche ca.  Kaltmiete  Kaltmiete pro m²  \\\n",
       "0              einfach *           78.00      979.4         12.556410   \n",
       "2                 mittel           38.48      503.0         13.071726   \n",
       "3                    gut           91.89     1290.0         14.038524   \n",
       "4               mittel *          119.00     1785.0         15.000000   \n",
       "6                 mittel           68.54      639.0          9.323023   \n",
       "...                  ...             ...        ...               ...   \n",
       "2834              mittel          200.00     3400.0         17.000000   \n",
       "2836                 gut           45.00      950.0         21.111111   \n",
       "2841                 gut           94.61     1199.0         12.673079   \n",
       "2842            mittel *           73.00      799.0         10.945205   \n",
       "2844              mittel           60.00     1100.0         18.333333   \n",
       "\n",
       "      Zulässige Mietobergrenze pro m²  \n",
       "0                               8.004  \n",
       "2                               8.388  \n",
       "3                              13.848  \n",
       "4                               8.832  \n",
       "6                               8.340  \n",
       "...                               ...  \n",
       "2834                            8.832  \n",
       "2836                            9.828  \n",
       "2841                            9.828  \n",
       "2842                            8.832  \n",
       "2844                           10.848  \n",
       "\n",
       "[1272 rows x 10 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "illegal_pricing[['Titel', 'Anbieter' ,'Url', 'Adresse', 'Baujahr', 'Wohnlage Einstufung', 'Wohnfläche ca.', 'Kaltmiete', 'Kaltmiete pro m²', 'Zulässige Mietobergrenze pro m²']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_10_illegal_real_estate_comp = illegal_pricing.groupby('Anbieter')[['Url']].count().sort_values(by='Url',ascending=False).head(10)\n",
    "\n",
    "average_illegal_current_rent = illegal_pricing[illegal_pricing['Anbieter'].isin(top_10_illegal_real_estate_comp.index)].groupby('Anbieter')[['Kaltmiete pro m²']].mean()\n",
    "\n",
    "average_illegal_rent_cap = illegal_pricing[illegal_pricing['Anbieter'].isin(top_10_illegal_real_estate_comp.index)].groupby('Anbieter')[['Zulässige Mietobergrenze pro m²']].mean()\n",
    "\n",
    "top_10_illegal_real_estate_comp = top_10_illegal_real_estate_comp.join(average_illegal_current_rent).join(average_illegal_rent_cap)\n",
    "\n",
    "top_10_illegal_real_estate_comp['Average excess rent'] = top_10_illegal_real_estate_comp['Kaltmiete pro m²'] - top_10_illegal_real_estate_comp['Zulässige Mietobergrenze pro m²']\n",
    "\n",
    "top_10_illegal_real_estate_comp.rename(columns = {'Zulässige Mietobergrenze pro m²':'Rent cap'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_10_illegal_real_estate_comp.sort_values('Average excess rent',ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_10_illegal_real_estate_comp = pd.DataFrame(top_10_illegal_real_estate_comp.drop(columns=['Kaltmiete pro m²','Url']).stack()).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_10_illegal_real_estate_comp.rename(columns={'level_1':'Explanation',0:'Average rent per m²'},inplace=True)"
   ]
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    "fig = px.bar(top_10_illegal_real_estate_comp,\n",
    "             x=\"Anbieter\", \n",
    "             y=\"Average rent per m²\", \n",
    "             color='Explanation',\n",
    "             title='Calculated average rent cap per landlord (blue) and current average excess rent asked (red)')\n",
    "fig.show()"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.plot(fig, filename = 'top_10_illegal_real_estate_comp', auto_open=True)"
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   "execution_count": 73,
   "metadata": {},
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   "source": [
    "schöneberg = illegal_pricing[illegal_pricing['Ortsteil'] == 'Schöneberg']\n",
    "(schöneberg['Kaltmiete pro m²'] - schöneberg['Zulässige Mietobergrenze pro m²']).sort_values(ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "example = illegal_pricing.loc[[1815]]"
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   "metadata": {},
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   "source": [
    "(example['Kaltmiete pro m²'] - example['Zulässige Mietobergrenze pro m²']) * example['Wohnfläche ca.']"
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  {
   "cell_type": "code",
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   "source": [
    "example['Kaltmiete pro m²']  * example['Wohnfläche ca.']"
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   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
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     "data": {
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       "1815    243.06552\n",
       "dtype: float64"
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     "execution_count": 77,
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   "source": [
    "example['Zulässige Mietobergrenze pro m²']  * example['Wohnfläche ca.']"
   ]
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  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "excess_dist = pd.DataFrame(illegal_pricing['Kaltmiete pro m²'] - illegal_pricing['Zulässige Mietobergrenze pro m²'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "excess_dist.rename(columns={0:'Excess rent per m² under new rental cap'},inplace=True)"
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  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
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    "                  title=\"Excess rent per m² under new rental cap\")\n",
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   ],
   "source": [
    "table_price = example.apply(find_base_price, axis=1).sum()\n",
    "location_factor = example.apply(find_location_factor,axis=1).sum()\n",
    "modern_equipment_factor = 1\n",
    "rent_cap = table_price + location_factor + modern_equipment\n",
    "cold_rent = example['Kaltmiete pro m²'].sum()\n",
    "additional_maximum = rent_cap * 0.20\n",
    "max_rent = additional_maximum + rent_cap\n",
    "excess_rent = cold_rent - max_rent\n",
    "\n",
    "x = [\"Table price\", \n",
    "         \"Location factor\",\n",
    "         \"Modern equipment\", \n",
    "         \"Rent cap\",\n",
    "         \"Maximum additional 20%\",\n",
    "         \"Maximum rent cap\",\n",
    "         \"Excess rent\",\n",
    "         \"Cold rent\"]\n",
    "\n",
    "y = [table_price, \n",
    "         location_factor, \n",
    "         modern_equipment_factor, \n",
    "         rent_cap, \n",
    "         additional_maximum,\n",
    "         max_rent,\n",
    "         excess_rent,\n",
    "         cold_rent]\n",
    "\n",
    "y = [round(e,2) for e in y]\n",
    "\n",
    "text = [str(round(e,2))+' €' for e in y]\n",
    "\n",
    "fig = go.Figure(go.Waterfall(\n",
    "    name = \"Rent price composition\", orientation = \"v\",\n",
    "    measure = [\"relative\",\"relative\",\"relative\",\"total\",\"relative\",\"total\",\"relative\",\"total\"],\n",
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    "    text = text,\n",
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    "))\n",
    "\n",
    "fig.update_layout(\n",
    "        title = \"Rent price compsition and comparison with cold rent\",\n",
    "        showlegend = True\n",
    ")\n",
    "\n",
    "fig.show()"
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  {
   "cell_type": "code",
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   "metadata": {},
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    {
     "data": {
      "text/plain": [
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      ]
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     "execution_count": 321,
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   "execution_count": 322,
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   "source": [
    "# Excess amount paid per month\n",
    "illegal_cold_rent = illegal_pricing['Kaltmiete pro m²'] * illegal_pricing['Wohnfläche ca.']\n",
    "illegal_rent_cap = illegal_pricing['Zulässige Mietobergrenze pro m²'] * illegal_pricing['Wohnfläche ca.']\n",
    "illegal_excess_rent = illegal_cold_rent.sum() - illegal_rent_cap.sum()"
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       "var gd = document.getElementById('809cfcb0-6cc6-4b36-950c-7a1a24fe7a9e');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_price = (illegal_pricing.apply(find_base_price, axis=1) * illegal_pricing['Wohnfläche ca.']).sum()\n",
    "location_factor = (illegal_pricing.apply(find_location_factor,axis=1) * illegal_pricing['Wohnfläche ca.']).sum()\n",
    "modern_equipment_factor = (1 * illegal_pricing['Wohnfläche ca.']).sum()\n",
    "rent_cap = table_price + location_factor + modern_equipment_factor\n",
    "cold_rent = (illegal_pricing['Kaltmiete pro m²'] * illegal_pricing['Wohnfläche ca.']).sum()\n",
    "additional_maximum = rent_cap * 0.20\n",
    "max_rent = additional_maximum + rent_cap\n",
    "excess_rent = cold_rent - max_rent\n",
    "\n",
    "x = [\"Table price\", \n",
    "         \"Location factor\",\n",
    "         \"Modern equipment\", \n",
    "         \"Rent cap\",\n",
    "         \"Maximum additional 20%\",\n",
    "         \"Maximum rent cap\",\n",
    "         \"Excess rent\",\n",
    "         \"Cold rent\"]\n",
    "\n",
    "y = [table_price, \n",
    "         location_factor, \n",
    "         modern_equipment_factor, \n",
    "         rent_cap, \n",
    "         additional_maximum,\n",
    "         max_rent,\n",
    "         excess_rent,\n",
    "         cold_rent]\n",
    "\n",
    "y = [round(e,2) for e in y]\n",
    "\n",
    "text = [str(round(e/1000)).replace('.0','K €') for e in y]\n",
    "\n",
    "fig = go.Figure(go.Waterfall(\n",
    "    name = \"Rent price composition\", orientation = \"v\",\n",
    "    measure = [\"relative\",\"relative\",\"relative\",\"total\",\"relative\",\"total\",\"relative\",\"total\"],\n",
    "    x = x,\n",
    "    textposition = \"outside\",\n",
    "    y = y,\n",
    "    text = text,\n",
    "    connector = {\"line\":{\"color\":\"rgb(63, 63, 63)\"}},\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "        title = \"Rent price compsition and comparison with cold rent for all illegal listings\",\n",
    "        showlegend = True\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://plot.ly/~felix.vemmer/59/'"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py.plot(fig, filename = 'rent_price_composition_dataset', auto_open=True)"
   ]
  }
 ],
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