Analyze_flo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "unzip_path = \"./dataset/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset extracted to \"C:\\WS\\ml_hackathon\\dataset\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wohngebiet</th>\n",
       "      <th>Nachbarschaft</th>\n",
       "      <th>Bedingung1</th>\n",
       "      <th>Bedingung2</th>\n",
       "      <th>Wohnungsklasse</th>\n",
       "      <th>Gebauedetyp</th>\n",
       "      <th>Wohnungsstil</th>\n",
       "      <th>Baujahr</th>\n",
       "      <th>Umbaujahr</th>\n",
       "      <th>Hausqualitaet</th>\n",
       "      <th>...</th>\n",
       "      <th>Garagenqualitaet</th>\n",
       "      <th>Garagenzustand</th>\n",
       "      <th>EinfahrtGepflastert</th>\n",
       "      <th>Sondermerkmal</th>\n",
       "      <th>SondermerkmalWert</th>\n",
       "      <th>Verkaufsmonat</th>\n",
       "      <th>Verkaufsjahr</th>\n",
       "      <th>Verkaufstyp</th>\n",
       "      <th>Verkaufsbedingung</th>\n",
       "      <th>Verkaufspreis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WN</td>\n",
       "      <td>Gallus</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>20</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Sto</td>\n",
       "      <td>1962</td>\n",
       "      <td>1962</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>Du</td>\n",
       "      <td>Ar</td>\n",
       "      <td>J</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2019</td>\n",
       "      <td>GarK</td>\n",
       "      <td>Normal</td>\n",
       "      <td>69700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WN</td>\n",
       "      <td>Gallus</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>20</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Sto</td>\n",
       "      <td>2015</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>J</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>GarK</td>\n",
       "      <td>Normal</td>\n",
       "      <td>133450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WN</td>\n",
       "      <td>Eckenhe</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>50</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Sto</td>\n",
       "      <td>1930</td>\n",
       "      <td>1960</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>GarK</td>\n",
       "      <td>Normal</td>\n",
       "      <td>165750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WN</td>\n",
       "      <td>Ginnhei</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>60</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Sto</td>\n",
       "      <td>2009</td>\n",
       "      <td>2010</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>J</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>GarK</td>\n",
       "      <td>Normal</td>\n",
       "      <td>157250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WN</td>\n",
       "      <td>Eckenhe</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>70</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Sto</td>\n",
       "      <td>1939</td>\n",
       "      <td>1960</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>J</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>GarK</td>\n",
       "      <td>Normal</td>\n",
       "      <td>195500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Wohngebiet Nachbarschaft Bedingung1 Bedingung2  Wohnungsklasse Gebauedetyp  \\\n",
       "Id                                                                              \n",
       "1          WN        Gallus       Norm       Norm              20        1Fam   \n",
       "2          WN        Gallus       Norm       Norm              20        1Fam   \n",
       "3          WN       Eckenhe       Norm       Norm              50        1Fam   \n",
       "4          WN       Ginnhei       Norm       Norm              60        1Fam   \n",
       "5          WN       Eckenhe       Norm       Norm              70        1Fam   \n",
       "\n",
       "   Wohnungsstil  Baujahr  Umbaujahr  Hausqualitaet  ...  Garagenqualitaet  \\\n",
       "Id                                                  ...                     \n",
       "1          1Sto     1962       1962              3  ...                Du   \n",
       "2          1Sto     2015       2016              5  ...                Du   \n",
       "3          2Sto     1930       1960              6  ...                Du   \n",
       "4          2Sto     2009       2010              6  ...                Du   \n",
       "5          2Sto     1939       1960              8  ...                Du   \n",
       "\n",
       "   Garagenzustand  EinfahrtGepflastert Sondermerkmal SondermerkmalWert  \\\n",
       "Id                                                                       \n",
       "1              Ar                    J           NaN               0.0   \n",
       "2              Du                    J           NaN               0.0   \n",
       "3              Du                    P           NaN               0.0   \n",
       "4              Du                    J           NaN               0.0   \n",
       "5              Du                    J           NaN               0.0   \n",
       "\n",
       "   Verkaufsmonat Verkaufsjahr  Verkaufstyp Verkaufsbedingung Verkaufspreis  \n",
       "Id                                                                          \n",
       "1              7         2019         GarK            Normal       69700.0  \n",
       "2              6         2017         GarK            Normal      133450.0  \n",
       "3              9         2016         GarK            Normal      165750.0  \n",
       "4              5         2017         GarK            Normal      157250.0  \n",
       "5              6         2016         GarK            Normal      195500.0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from io import BytesIO\n",
    "from urllib.request import urlopen\n",
    "from zipfile import ZipFile\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# data_url = \"https://dshare.mathematik.uni-marburg.de/index.php/s/GrPtczNmDZQcn9f/download/training_dataset.zip\"\n",
    "\n",
    "# with urlopen(data_url) as zipresp:\n",
    "#     with ZipFile(BytesIO(zipresp.read())) as zfile:\n",
    "#         zfile.extractall(unzip_path)\n",
    "        \n",
    "print('Dataset extracted to \"{:s}\"'.format(os.path.realpath(unzip_path)))\n",
    "df = pd.read_csv('dataset/train.csv', index_col='Id')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df.columns[:-1]\n",
    "features_20_1, features_20_2, features_20_3, features_19_4 = features[:20], features[20:40], features[40:60], features[60:]\n",
    "\n",
    "df_20_1 = pd.concat([df[features_20_1], df['Verkaufspreis']], axis=1)\n",
    "df_20_2 = pd.concat([df[features_20_2], df['Verkaufspreis']], axis=1)\n",
    "df_20_3 = pd.concat([df[features_20_3], df['Verkaufspreis']], axis=1)\n",
    "df_20_4 = pd.concat([df[features_19_4], df['Verkaufspreis']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Fundament</th>\n",
       "      <th>Mauerwerktyp</th>\n",
       "      <th>Mauerwerkflaeche</th>\n",
       "      <th>Verkleidung1</th>\n",
       "      <th>Verkleidung2</th>\n",
       "      <th>Dachtyp</th>\n",
       "      <th>Dachmaterial</th>\n",
       "      <th>Aussenmaterialqualitaet</th>\n",
       "      <th>Aussenmaterialzustand</th>\n",
       "      <th>Kellerhoehe</th>\n",
       "      <th>Kellerzustand</th>\n",
       "      <th>Kellerbelichtung</th>\n",
       "      <th>Kellerbereich1</th>\n",
       "      <th>Kellerbereichgroesse1</th>\n",
       "      <th>Kellerbereich2</th>\n",
       "      <th>Kellerbereichgroesse2</th>\n",
       "      <th>KellerbereichgroesseNAu</th>\n",
       "      <th>KellerbereichgroesseGes</th>\n",
       "      <th>KellerVollbadezimmer</th>\n",
       "      <th>KellerHalbbadezimmer</th>\n",
       "      <th>Verkaufspreis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000</td>\n",
       "      <td>1986</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1935</td>\n",
       "      <td>1935</td>\n",
       "      <td>1934</td>\n",
       "      <td>1937</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1937</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>2000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>Kein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>GWR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>906</td>\n",
       "      <td>1190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>699</td>\n",
       "      <td>696</td>\n",
       "      <td>1579</td>\n",
       "      <td>1973</td>\n",
       "      <td>1220</td>\n",
       "      <td>1739</td>\n",
       "      <td>861</td>\n",
       "      <td>1769</td>\n",
       "      <td>1296</td>\n",
       "      <td>593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1707</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.060906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.641016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.287899</td>\n",
       "      <td>49.829655</td>\n",
       "      <td>93.758569</td>\n",
       "      <td>0.421922</td>\n",
       "      <td>0.056557</td>\n",
       "      <td>153708.42060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.776509</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.240276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.681256</td>\n",
       "      <td>38.799414</td>\n",
       "      <td>40.553955</td>\n",
       "      <td>0.519678</td>\n",
       "      <td>0.233208</td>\n",
       "      <td>68822.30926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10870.65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.070000</td>\n",
       "      <td>70.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109650.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.390000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.400000</td>\n",
       "      <td>88.920000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>136000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.760000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.385000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.280000</td>\n",
       "      <td>115.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>181475.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124.020000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>507.960000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125.370000</td>\n",
       "      <td>210.240000</td>\n",
       "      <td>549.900000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>641750.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fundament Mauerwerktyp  Mauerwerkflaeche Verkleidung1 Verkleidung2  \\\n",
       "count       2000         1986       1986.000000         2000         2000   \n",
       "unique         6            4               NaN           14           15   \n",
       "top     BetonGeg         Kein               NaN        Vinyl        Vinyl   \n",
       "freq         906         1190               NaN          699          696   \n",
       "mean         NaN          NaN          9.060906          NaN          NaN   \n",
       "std          NaN          NaN         15.776509          NaN          NaN   \n",
       "min          NaN          NaN          0.000000          NaN          NaN   \n",
       "25%          NaN          NaN          0.000000          NaN          NaN   \n",
       "50%          NaN          NaN          0.000000          NaN          NaN   \n",
       "75%          NaN          NaN         14.760000          NaN          NaN   \n",
       "max          NaN          NaN        124.020000          NaN          NaN   \n",
       "\n",
       "       Dachtyp Dachmaterial Aussenmaterialqualitaet Aussenmaterialzustand  \\\n",
       "count     2000         2000                    2000                  2000   \n",
       "unique       6            6                       4                     5   \n",
       "top     Giebel      VerbSch                      Du                    Du   \n",
       "freq      1579         1973                    1220                  1739   \n",
       "mean       NaN          NaN                     NaN                   NaN   \n",
       "std        NaN          NaN                     NaN                   NaN   \n",
       "min        NaN          NaN                     NaN                   NaN   \n",
       "25%        NaN          NaN                     NaN                   NaN   \n",
       "50%        NaN          NaN                     NaN                   NaN   \n",
       "75%        NaN          NaN                     NaN                   NaN   \n",
       "max        NaN          NaN                     NaN                   NaN   \n",
       "\n",
       "       Kellerhoehe Kellerzustand Kellerbelichtung Kellerbereich1  \\\n",
       "count         1935          1935             1934           1937   \n",
       "unique           4             4                4              6   \n",
       "top             Ty            Ty               Ke            GWR   \n",
       "freq           861          1769             1296            593   \n",
       "mean           NaN           NaN              NaN            NaN   \n",
       "std            NaN           NaN              NaN            NaN   \n",
       "min            NaN           NaN              NaN            NaN   \n",
       "25%            NaN           NaN              NaN            NaN   \n",
       "50%            NaN           NaN              NaN            NaN   \n",
       "75%            NaN           NaN              NaN            NaN   \n",
       "max            NaN           NaN              NaN            NaN   \n",
       "\n",
       "        Kellerbereichgroesse1 Kellerbereich2  Kellerbereichgroesse2  \\\n",
       "count             1999.000000           1937            1999.000000   \n",
       "unique                    NaN              6                    NaN   \n",
       "top                       NaN            NAu                    NaN   \n",
       "freq                      NaN           1707                    NaN   \n",
       "mean                39.641016            NaN               4.287899   \n",
       "std                 41.240276            NaN              14.681256   \n",
       "min                  0.000000            NaN               0.000000   \n",
       "25%                  0.000000            NaN               0.000000   \n",
       "50%                 33.390000            NaN               0.000000   \n",
       "75%                 65.385000            NaN               0.000000   \n",
       "max                507.960000            NaN             125.370000   \n",
       "\n",
       "        KellerbereichgroesseNAu  KellerbereichgroesseGes  \\\n",
       "count               1999.000000              1999.000000   \n",
       "unique                      NaN                      NaN   \n",
       "top                         NaN                      NaN   \n",
       "freq                        NaN                      NaN   \n",
       "mean                  49.829655                93.758569   \n",
       "std                   38.799414                40.553955   \n",
       "min                    0.000000                 0.000000   \n",
       "25%                   20.070000                70.200000   \n",
       "50%                   41.400000                88.920000   \n",
       "75%                   71.280000               115.200000   \n",
       "max                  210.240000               549.900000   \n",
       "\n",
       "        KellerVollbadezimmer  KellerHalbbadezimmer  Verkaufspreis  \n",
       "count            1998.000000           1998.000000     2000.00000  \n",
       "unique                   NaN                   NaN            NaN  \n",
       "top                      NaN                   NaN            NaN  \n",
       "freq                     NaN                   NaN            NaN  \n",
       "mean                0.421922              0.056557   153708.42060  \n",
       "std                 0.519678              0.233208    68822.30926  \n",
       "min                 0.000000              0.000000    10870.65000  \n",
       "25%                 0.000000              0.000000   109650.00000  \n",
       "50%                 0.000000              0.000000   136000.00000  \n",
       "75%                 1.000000              0.000000   181475.00000  \n",
       "max                 3.000000              2.000000   641750.00000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 85\n",
    "pd.options.display.max_columns = 85\n",
    "df_20_2.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Flow\n",
    "\n",
    "### Keller:\n",
    "- Kellerhöhe,Kellerzustand,Kellerbelichtung, Kellerbereich1, Kellerbereich2 -> fehlende Werte zu NA ändern\n",
    "\n",
    "- Kellerbereichgroesse1,Kellerbereichgroesse2,KellerbereichgroesseGes,KellerbereichgroesseNau,KellerVollbadezimmer,KellerHalbbadezimmer -> fehlende Werte mit 0.0 auffüllen\n",
    "\n",
    "- Kellerbereichgroesse2 -> viele Werte 0 da kein zweiter Keller und Korrelation 0.025 -> löschen?\n",
    "\n",
    "### Mauerwerktyp und Mauerwerkfläche\n",
    "- bei 14 Datensätze fehlen beide Werte -> zu \"Kein\" und 0 ändern\n",
    "-\n",
    "\n",
    "### Dachmaterial:\n",
    "- evtl löschen, da 1973 mal selber wert\n",
    "- Außerdem hat das Feature keine Korrelation mit irgendeinem anderen Feature, und auch keine der Werte mit dem Verkaufspreis\n",
    "\n",
    "### Aussenmaterialzustand\n",
    "\n",
    "- 1739 Werte sind DU (durchschnittlich) -> trozdem beibehalten\n",
    "\n",
    "## Ausreißer\n",
    "\n",
    "- wenige, viele bei Keller2, da viele 0 Werte..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_feature(df, feature):\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.scatter(df[feature], df['Verkaufspreis'])\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Verkaufspreis')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_all_features(df):\n",
    "    plt.figure(figsize=(20, 20))\n",
    "    for i, feature in enumerate(df.columns):\n",
    "        # delete empty values\n",
    "        df = df[df[feature].notnull()]\n",
    "        try:\n",
    "            plt.subplot(5, 4, i+1)\n",
    "            plt.scatter(df[feature], df['Verkaufspreis'])\n",
    "            plt.xlabel(feature)\n",
    "            plt.ylabel('Verkaufspreis')\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num must be an integer with 1 <= num <= 20, not 21\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_features(df_20_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawbox_all(df):\n",
    "    plt.figure(figsize=(20, 20))\n",
    "    # get only numeric features\n",
    "    df = df.select_dtypes(include=['float64', 'int64'])\n",
    "    for i, feature in enumerate(df.columns):\n",
    "        # delete empty values\n",
    "        df = df[df[feature].notnull()]\n",
    "        try:\n",
    "            plt.subplot(5, 4, i+1)\n",
    "            plt.boxplot(df[feature])\n",
    "            plt.xlabel(feature)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'str' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\WS\\ml_hackathon\\Analyze_flo.ipynb Cell 10\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/WS/ml_hackathon/Analyze_flo.ipynb#X12sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m Q1 \u001b[39m=\u001b[39m df_20_2\u001b[39m.\u001b[39;49mquantile(\u001b[39m0.25\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/WS/ml_hackathon/Analyze_flo.ipynb#X12sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m Q3 \u001b[39m=\u001b[39m df_20_2\u001b[39m.\u001b[39mquantile(\u001b[39m0.75\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/WS/ml_hackathon/Analyze_flo.ipynb#X12sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m IQR \u001b[39m=\u001b[39m Q3 \u001b[39m-\u001b[39m Q1\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\frame.py:11786\u001b[0m, in \u001b[0;36mDataFrame.quantile\u001b[1;34m(self, q, axis, numeric_only, interpolation, method)\u001b[0m\n\u001b[0;32m  11779\u001b[0m axis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_axis_number(axis)\n\u001b[0;32m  11781\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m is_list_like(q):\n\u001b[0;32m  11782\u001b[0m     \u001b[39m# BlockManager.quantile expects listlike, so we wrap and unwrap here\u001b[39;00m\n\u001b[0;32m  11783\u001b[0m     \u001b[39m# error: List item 0 has incompatible type \"Union[float, Union[Union[\u001b[39;00m\n\u001b[0;32m  11784\u001b[0m     \u001b[39m# ExtensionArray, ndarray[Any, Any]], Index, Series], Sequence[float]]\";\u001b[39;00m\n\u001b[0;32m  11785\u001b[0m     \u001b[39m# expected \"float\"\u001b[39;00m\n\u001b[1;32m> 11786\u001b[0m     res_df \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mquantile(  \u001b[39m# type: ignore[call-overload]\u001b[39;49;00m\n\u001b[0;32m  11787\u001b[0m         [q],\n\u001b[0;32m  11788\u001b[0m         axis\u001b[39m=\u001b[39;49maxis,\n\u001b[0;32m  11789\u001b[0m         numeric_only\u001b[39m=\u001b[39;49mnumeric_only,\n\u001b[0;32m  11790\u001b[0m         interpolation\u001b[39m=\u001b[39;49minterpolation,\n\u001b[0;32m  11791\u001b[0m         method\u001b[39m=\u001b[39;49mmethod,\n\u001b[0;32m  11792\u001b[0m     )\n\u001b[0;32m  11793\u001b[0m     \u001b[39mif\u001b[39;00m method \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39msingle\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m  11794\u001b[0m         res \u001b[39m=\u001b[39m res_df\u001b[39m.\u001b[39miloc[\u001b[39m0\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\frame.py:11831\u001b[0m, in \u001b[0;36mDataFrame.quantile\u001b[1;34m(self, q, axis, numeric_only, interpolation, method)\u001b[0m\n\u001b[0;32m  11827\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m  11828\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mInvalid method: \u001b[39m\u001b[39m{\u001b[39;00mmethod\u001b[39m}\u001b[39;00m\u001b[39m. Method must be in \u001b[39m\u001b[39m{\u001b[39;00mvalid_method\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m  11829\u001b[0m     )\n\u001b[0;32m  11830\u001b[0m \u001b[39mif\u001b[39;00m method \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39msingle\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m> 11831\u001b[0m     res \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39;49m_mgr\u001b[39m.\u001b[39;49mquantile(qs\u001b[39m=\u001b[39;49mq, interpolation\u001b[39m=\u001b[39;49minterpolation)\n\u001b[0;32m  11832\u001b[0m \u001b[39melif\u001b[39;00m method \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mtable\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m  11833\u001b[0m     valid_interpolation \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39mnearest\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mlower\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mhigher\u001b[39m\u001b[39m\"\u001b[39m}\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1508\u001b[0m, in \u001b[0;36mBlockManager.quantile\u001b[1;34m(self, qs, interpolation)\u001b[0m\n\u001b[0;32m   1504\u001b[0m new_axes \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes)\n\u001b[0;32m   1505\u001b[0m new_axes[\u001b[39m1\u001b[39m] \u001b[39m=\u001b[39m Index(qs, dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39mfloat64)\n\u001b[0;32m   1507\u001b[0m blocks \u001b[39m=\u001b[39m [\n\u001b[1;32m-> 1508\u001b[0m     blk\u001b[39m.\u001b[39;49mquantile(qs\u001b[39m=\u001b[39;49mqs, interpolation\u001b[39m=\u001b[39;49minterpolation) \u001b[39mfor\u001b[39;00m blk \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mblocks\n\u001b[0;32m   1509\u001b[0m ]\n\u001b[0;32m   1511\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)(blocks, new_axes)\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:1587\u001b[0m, in \u001b[0;36mBlock.quantile\u001b[1;34m(self, qs, interpolation)\u001b[0m\n\u001b[0;32m   1584\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m2\u001b[39m\n\u001b[0;32m   1585\u001b[0m \u001b[39massert\u001b[39;00m is_list_like(qs)  \u001b[39m# caller is responsible for this\u001b[39;00m\n\u001b[1;32m-> 1587\u001b[0m result \u001b[39m=\u001b[39m quantile_compat(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mvalues, np\u001b[39m.\u001b[39;49masarray(qs\u001b[39m.\u001b[39;49m_values), interpolation)\n\u001b[0;32m   1588\u001b[0m \u001b[39m# ensure_block_shape needed for cases where we start with EA and result\u001b[39;00m\n\u001b[0;32m   1589\u001b[0m \u001b[39m#  is ndarray, e.g. IntegerArray, SparseArray\u001b[39;00m\n\u001b[0;32m   1590\u001b[0m result \u001b[39m=\u001b[39m ensure_block_shape(result, ndim\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\array_algos\\quantile.py:39\u001b[0m, in \u001b[0;36mquantile_compat\u001b[1;34m(values, qs, interpolation)\u001b[0m\n\u001b[0;32m     37\u001b[0m     fill_value \u001b[39m=\u001b[39m na_value_for_dtype(values\u001b[39m.\u001b[39mdtype, compat\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m     38\u001b[0m     mask \u001b[39m=\u001b[39m isna(values)\n\u001b[1;32m---> 39\u001b[0m     \u001b[39mreturn\u001b[39;00m quantile_with_mask(values, mask, fill_value, qs, interpolation)\n\u001b[0;32m     40\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     41\u001b[0m     \u001b[39mreturn\u001b[39;00m values\u001b[39m.\u001b[39m_quantile(qs, interpolation)\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\array_algos\\quantile.py:97\u001b[0m, in \u001b[0;36mquantile_with_mask\u001b[1;34m(values, mask, fill_value, qs, interpolation)\u001b[0m\n\u001b[0;32m     95\u001b[0m     result \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrepeat(flat, \u001b[39mlen\u001b[39m(values))\u001b[39m.\u001b[39mreshape(\u001b[39mlen\u001b[39m(values), \u001b[39mlen\u001b[39m(qs))\n\u001b[0;32m     96\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 97\u001b[0m     result \u001b[39m=\u001b[39m _nanpercentile(\n\u001b[0;32m     98\u001b[0m         values,\n\u001b[0;32m     99\u001b[0m         qs \u001b[39m*\u001b[39;49m \u001b[39m100.0\u001b[39;49m,\n\u001b[0;32m    100\u001b[0m         na_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[0;32m    101\u001b[0m         mask\u001b[39m=\u001b[39;49mmask,\n\u001b[0;32m    102\u001b[0m         interpolation\u001b[39m=\u001b[39;49minterpolation,\n\u001b[0;32m    103\u001b[0m     )\n\u001b[0;32m    105\u001b[0m     result \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(result, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m    106\u001b[0m     result \u001b[39m=\u001b[39m result\u001b[39m.\u001b[39mT\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\array_algos\\quantile.py:199\u001b[0m, in \u001b[0;36m_nanpercentile\u001b[1;34m(values, qs, na_value, mask, interpolation)\u001b[0m\n\u001b[0;32m    195\u001b[0m \u001b[39mif\u001b[39;00m mask\u001b[39m.\u001b[39many():\n\u001b[0;32m    196\u001b[0m     \u001b[39m# Caller is responsible for ensuring mask shape match\u001b[39;00m\n\u001b[0;32m    197\u001b[0m     \u001b[39massert\u001b[39;00m mask\u001b[39m.\u001b[39mshape \u001b[39m==\u001b[39m values\u001b[39m.\u001b[39mshape\n\u001b[0;32m    198\u001b[0m     result \u001b[39m=\u001b[39m [\n\u001b[1;32m--> 199\u001b[0m         _nanpercentile_1d(val, m, qs, na_value, interpolation\u001b[39m=\u001b[39;49minterpolation)\n\u001b[0;32m    200\u001b[0m         \u001b[39mfor\u001b[39;00m (val, m) \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(\u001b[39mlist\u001b[39m(values), \u001b[39mlist\u001b[39m(mask))\n\u001b[0;32m    201\u001b[0m     ]\n\u001b[0;32m    202\u001b[0m     \u001b[39mif\u001b[39;00m values\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mkind \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m    203\u001b[0m         \u001b[39m# preserve itemsize\u001b[39;00m\n\u001b[0;32m    204\u001b[0m         result \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(result, dtype\u001b[39m=\u001b[39mvalues\u001b[39m.\u001b[39mdtype, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\u001b[39m.\u001b[39mT\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\array_algos\\quantile.py:145\u001b[0m, in \u001b[0;36m_nanpercentile_1d\u001b[1;34m(values, mask, qs, na_value, interpolation)\u001b[0m\n\u001b[0;32m    139\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(values) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m    140\u001b[0m     \u001b[39m# Can't pass dtype=values.dtype here bc we might have na_value=np.nan\u001b[39;00m\n\u001b[0;32m    141\u001b[0m     \u001b[39m#  with values.dtype=int64 see test_quantile_empty\u001b[39;00m\n\u001b[0;32m    142\u001b[0m     \u001b[39m# equiv: 'np.array([na_value] * len(qs))' but much faster\u001b[39;00m\n\u001b[0;32m    143\u001b[0m     \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39mfull(\u001b[39mlen\u001b[39m(qs), na_value)\n\u001b[1;32m--> 145\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39;49mpercentile(\n\u001b[0;32m    146\u001b[0m     values,\n\u001b[0;32m    147\u001b[0m     qs,\n\u001b[0;32m    148\u001b[0m     \u001b[39m# error: No overload variant of \"percentile\" matches argument\u001b[39;49;00m\n\u001b[0;32m    149\u001b[0m     \u001b[39m# types \"ndarray[Any, Any]\", \"ndarray[Any, dtype[floating[_64Bit]]]\"\u001b[39;49;00m\n\u001b[0;32m    150\u001b[0m     \u001b[39m# , \"Dict[str, str]\"  [call-overload]\u001b[39;49;00m\n\u001b[0;32m    151\u001b[0m     method\u001b[39m=\u001b[39;49minterpolation,  \u001b[39m# type: ignore[call-overload]\u001b[39;49;00m\n\u001b[0;32m    152\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\lib\\function_base.py:4283\u001b[0m, in \u001b[0;36mpercentile\u001b[1;34m(a, q, axis, out, overwrite_input, method, keepdims, interpolation)\u001b[0m\n\u001b[0;32m   4281\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m _quantile_is_valid(q):\n\u001b[0;32m   4282\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mPercentiles must be in the range [0, 100]\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m-> 4283\u001b[0m \u001b[39mreturn\u001b[39;00m _quantile_unchecked(\n\u001b[0;32m   4284\u001b[0m     a, q, axis, out, overwrite_input, method, keepdims)\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\lib\\function_base.py:4555\u001b[0m, in \u001b[0;36m_quantile_unchecked\u001b[1;34m(a, q, axis, out, overwrite_input, method, keepdims)\u001b[0m\n\u001b[0;32m   4547\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_quantile_unchecked\u001b[39m(a,\n\u001b[0;32m   4548\u001b[0m                         q,\n\u001b[0;32m   4549\u001b[0m                         axis\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4552\u001b[0m                         method\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mlinear\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   4553\u001b[0m                         keepdims\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m   4554\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Assumes that q is in [0, 1], and is an ndarray\"\"\"\u001b[39;00m\n\u001b[1;32m-> 4555\u001b[0m     \u001b[39mreturn\u001b[39;00m _ureduce(a,\n\u001b[0;32m   4556\u001b[0m                     func\u001b[39m=\u001b[39;49m_quantile_ureduce_func,\n\u001b[0;32m   4557\u001b[0m                     q\u001b[39m=\u001b[39;49mq,\n\u001b[0;32m   4558\u001b[0m                     keepdims\u001b[39m=\u001b[39;49mkeepdims,\n\u001b[0;32m   4559\u001b[0m                     axis\u001b[39m=\u001b[39;49maxis,\n\u001b[0;32m   4560\u001b[0m                     out\u001b[39m=\u001b[39;49mout,\n\u001b[0;32m   4561\u001b[0m                     overwrite_input\u001b[39m=\u001b[39;49moverwrite_input,\n\u001b[0;32m   4562\u001b[0m                     method\u001b[39m=\u001b[39;49mmethod)\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\lib\\function_base.py:3823\u001b[0m, in \u001b[0;36m_ureduce\u001b[1;34m(a, func, keepdims, **kwargs)\u001b[0m\n\u001b[0;32m   3820\u001b[0m             index_out \u001b[39m=\u001b[39m (\u001b[39m0\u001b[39m, ) \u001b[39m*\u001b[39m nd\n\u001b[0;32m   3821\u001b[0m             kwargs[\u001b[39m'\u001b[39m\u001b[39mout\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m out[(\u001b[39mEllipsis\u001b[39m, ) \u001b[39m+\u001b[39m index_out]\n\u001b[1;32m-> 3823\u001b[0m r \u001b[39m=\u001b[39m func(a, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m   3825\u001b[0m \u001b[39mif\u001b[39;00m out \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   3826\u001b[0m     \u001b[39mreturn\u001b[39;00m out\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\lib\\function_base.py:4721\u001b[0m, in \u001b[0;36m_quantile_ureduce_func\u001b[1;34m(a, q, axis, out, overwrite_input, method)\u001b[0m\n\u001b[0;32m   4719\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   4720\u001b[0m         arr \u001b[39m=\u001b[39m a\u001b[39m.\u001b[39mcopy()\n\u001b[1;32m-> 4721\u001b[0m result \u001b[39m=\u001b[39m _quantile(arr,\n\u001b[0;32m   4722\u001b[0m                    quantiles\u001b[39m=\u001b[39;49mq,\n\u001b[0;32m   4723\u001b[0m                    axis\u001b[39m=\u001b[39;49maxis,\n\u001b[0;32m   4724\u001b[0m                    method\u001b[39m=\u001b[39;49mmethod,\n\u001b[0;32m   4725\u001b[0m                    out\u001b[39m=\u001b[39;49mout)\n\u001b[0;32m   4726\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\lib\\function_base.py:4840\u001b[0m, in \u001b[0;36m_quantile\u001b[1;34m(arr, quantiles, axis, method, out)\u001b[0m\n\u001b[0;32m   4838\u001b[0m     result_shape \u001b[39m=\u001b[39m virtual_indexes\u001b[39m.\u001b[39mshape \u001b[39m+\u001b[39m (\u001b[39m1\u001b[39m,) \u001b[39m*\u001b[39m (arr\u001b[39m.\u001b[39mndim \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m)\n\u001b[0;32m   4839\u001b[0m     gamma \u001b[39m=\u001b[39m gamma\u001b[39m.\u001b[39mreshape(result_shape)\n\u001b[1;32m-> 4840\u001b[0m     result \u001b[39m=\u001b[39m _lerp(previous,\n\u001b[0;32m   4841\u001b[0m                    \u001b[39mnext\u001b[39;49m,\n\u001b[0;32m   4842\u001b[0m                    gamma,\n\u001b[0;32m   4843\u001b[0m                    out\u001b[39m=\u001b[39;49mout)\n\u001b[0;32m   4844\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39many(slices_having_nans):\n\u001b[0;32m   4845\u001b[0m     \u001b[39mif\u001b[39;00m result\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mand\u001b[39;00m out \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   4846\u001b[0m         \u001b[39m# can't write to a scalar, but indexing will be correct\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\weidn\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655\u001b[0m, in \u001b[0;36m_lerp\u001b[1;34m(a, b, t, out)\u001b[0m\n\u001b[0;32m   4641\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_lerp\u001b[39m(a, b, t, out\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m   4642\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   4643\u001b[0m \u001b[39m    Compute the linear interpolation weighted by gamma on each point of\u001b[39;00m\n\u001b[0;32m   4644\u001b[0m \u001b[39m    two same shape array.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4653\u001b[0m \u001b[39m        Output array.\u001b[39;00m\n\u001b[0;32m   4654\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4655\u001b[0m     diff_b_a \u001b[39m=\u001b[39m subtract(b, a)\n\u001b[0;32m   4656\u001b[0m     \u001b[39m# asanyarray is a stop-gap until gh-13105\u001b[39;00m\n\u001b[0;32m   4657\u001b[0m     lerp_interpolation \u001b[39m=\u001b[39m asanyarray(add(a, diff_b_a \u001b[39m*\u001b[39m t, out\u001b[39m=\u001b[39mout))\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'str' and 'str'"
     ]
    }
   ],
   "source": [
    "Q1 = df_20_2.quantile(0.25)\n",
    "Q3 = df_20_2.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Ausreißer sind Werte, die unter Q1-1.5*IQR oder über Q3+1.5*IQR liegen\n",
    "outliers = (df_20_2 < (Q1 - 1.5 * IQR)) | (df_20_2 > (Q3 + 1.5 * IQR))\n",
    "\n",
    "print(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x2000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawbox_all(df_20_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_correlation(df):\n",
    "    df = df.select_dtypes(include=['float64', 'int64'])\n",
    "    corr = df.corr()\n",
    "    corr.style.background_gradient(cmap='coolwarm')\n",
    "    return corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mauerwerkflaeche</th>\n",
       "      <th>Kellerbereichgroesse1</th>\n",
       "      <th>Kellerbereichgroesse2</th>\n",
       "      <th>KellerbereichgroesseNAu</th>\n",
       "      <th>KellerbereichgroesseGes</th>\n",
       "      <th>KellerVollbadezimmer</th>\n",
       "      <th>KellerHalbbadezimmer</th>\n",
       "      <th>Verkaufspreis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mauerwerkflaeche</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.290041</td>\n",
       "      <td>0.001515</td>\n",
       "      <td>0.104695</td>\n",
       "      <td>0.395093</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.015106</td>\n",
       "      <td>0.513073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kellerbereichgroesse1</th>\n",
       "      <td>0.290041</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.039868</td>\n",
       "      <td>-0.457726</td>\n",
       "      <td>0.564568</td>\n",
       "      <td>0.632484</td>\n",
       "      <td>0.096986</td>\n",
       "      <td>0.443108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kellerbereichgroesse2</th>\n",
       "      <td>0.001515</td>\n",
       "      <td>-0.039868</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.231715</td>\n",
       "      <td>0.099785</td>\n",
       "      <td>0.165763</td>\n",
       "      <td>0.080442</td>\n",
       "      <td>0.025036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KellerbereichgroesseNAu</th>\n",
       "      <td>0.104695</td>\n",
       "      <td>-0.457726</td>\n",
       "      <td>-0.231715</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.407378</td>\n",
       "      <td>-0.383817</td>\n",
       "      <td>-0.095483</td>\n",
       "      <td>0.175611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KellerbereichgroesseGes</th>\n",
       "      <td>0.395093</td>\n",
       "      <td>0.564568</td>\n",
       "      <td>0.099785</td>\n",
       "      <td>0.407378</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.336438</td>\n",
       "      <td>0.036461</td>\n",
       "      <td>0.627685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KellerVollbadezimmer</th>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.632484</td>\n",
       "      <td>0.165763</td>\n",
       "      <td>-0.383817</td>\n",
       "      <td>0.336438</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.135017</td>\n",
       "      <td>0.282349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KellerHalbbadezimmer</th>\n",
       "      <td>0.015106</td>\n",
       "      <td>0.096986</td>\n",
       "      <td>0.080442</td>\n",
       "      <td>-0.095483</td>\n",
       "      <td>0.036461</td>\n",
       "      <td>-0.135017</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Verkaufspreis</th>\n",
       "      <td>0.513073</td>\n",
       "      <td>0.443108</td>\n",
       "      <td>0.025036</td>\n",
       "      <td>0.175611</td>\n",
       "      <td>0.627685</td>\n",
       "      <td>0.282349</td>\n",
       "      <td>-0.010584</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Mauerwerkflaeche  Kellerbereichgroesse1  \\\n",
       "Mauerwerkflaeche                 1.000000               0.290041   \n",
       "Kellerbereichgroesse1            0.290041               1.000000   \n",
       "Kellerbereichgroesse2            0.001515              -0.039868   \n",
       "KellerbereichgroesseNAu          0.104695              -0.457726   \n",
       "KellerbereichgroesseGes          0.395093               0.564568   \n",
       "KellerVollbadezimmer             0.147556               0.632484   \n",
       "KellerHalbbadezimmer             0.015106               0.096986   \n",
       "Verkaufspreis                    0.513073               0.443108   \n",
       "\n",
       "                         Kellerbereichgroesse2  KellerbereichgroesseNAu  \\\n",
       "Mauerwerkflaeche                      0.001515                 0.104695   \n",
       "Kellerbereichgroesse1                -0.039868                -0.457726   \n",
       "Kellerbereichgroesse2                 1.000000                -0.231715   \n",
       "KellerbereichgroesseNAu              -0.231715                 1.000000   \n",
       "KellerbereichgroesseGes               0.099785                 0.407378   \n",
       "KellerVollbadezimmer                  0.165763                -0.383817   \n",
       "KellerHalbbadezimmer                  0.080442                -0.095483   \n",
       "Verkaufspreis                         0.025036                 0.175611   \n",
       "\n",
       "                         KellerbereichgroesseGes  KellerVollbadezimmer  \\\n",
       "Mauerwerkflaeche                        0.395093              0.147556   \n",
       "Kellerbereichgroesse1                   0.564568              0.632484   \n",
       "Kellerbereichgroesse2                   0.099785              0.165763   \n",
       "KellerbereichgroesseNAu                 0.407378             -0.383817   \n",
       "KellerbereichgroesseGes                 1.000000              0.336438   \n",
       "KellerVollbadezimmer                    0.336438              1.000000   \n",
       "KellerHalbbadezimmer                    0.036461             -0.135017   \n",
       "Verkaufspreis                           0.627685              0.282349   \n",
       "\n",
       "                         KellerHalbbadezimmer  Verkaufspreis  \n",
       "Mauerwerkflaeche                     0.015106       0.513073  \n",
       "Kellerbereichgroesse1                0.096986       0.443108  \n",
       "Kellerbereichgroesse2                0.080442       0.025036  \n",
       "KellerbereichgroesseNAu             -0.095483       0.175611  \n",
       "KellerbereichgroesseGes              0.036461       0.627685  \n",
       "KellerVollbadezimmer                -0.135017       0.282349  \n",
       "KellerHalbbadezimmer                 1.000000      -0.010584  \n",
       "Verkaufspreis                       -0.010584       1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_correlation(df_20_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fundament</th>\n",
       "      <th>Mauerwerktyp</th>\n",
       "      <th>Verkleidung1</th>\n",
       "      <th>Verkleidung2</th>\n",
       "      <th>Dachtyp</th>\n",
       "      <th>Dachmaterial</th>\n",
       "      <th>Aussenmaterialqualitaet</th>\n",
       "      <th>Aussenmaterialzustand</th>\n",
       "      <th>Kellerhoehe</th>\n",
       "      <th>Kellerzustand</th>\n",
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       "      <th>Kellerbereich1</th>\n",
       "      <th>Kellerbereich2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fundament</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.257425</td>\n",
       "      <td>-0.405769</td>\n",
       "      <td>-0.417279</td>\n",
       "      <td>-0.066451</td>\n",
       "      <td>-0.018187</td>\n",
       "      <td>-0.473779</td>\n",
       "      <td>0.058517</td>\n",
       "      <td>0.586439</td>\n",
       "      <td>-0.099027</td>\n",
       "      <td>0.190542</td>\n",
       "      <td>0.028123</td>\n",
       "      <td>0.025716</td>\n",
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       "    <tr>\n",
       "      <th>Mauerwerktyp</th>\n",
       "      <td>-0.257425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005959</td>\n",
       "      <td>0.025619</td>\n",
       "      <td>0.227407</td>\n",
       "      <td>-0.025964</td>\n",
       "      <td>0.171469</td>\n",
       "      <td>-0.030250</td>\n",
       "      <td>-0.198898</td>\n",
       "      <td>0.080215</td>\n",
       "      <td>-0.170946</td>\n",
       "      <td>-0.089832</td>\n",
       "      <td>0.023059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Verkleidung1</th>\n",
       "      <td>-0.405769</td>\n",
       "      <td>0.005959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.870599</td>\n",
       "      <td>-0.003374</td>\n",
       "      <td>0.002562</td>\n",
       "      <td>0.315502</td>\n",
       "      <td>0.022968</td>\n",
       "      <td>-0.344138</td>\n",
       "      <td>0.044167</td>\n",
       "      <td>-0.110432</td>\n",
       "      <td>-0.013498</td>\n",
       "      <td>-0.055126</td>\n",
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       "    <tr>\n",
       "      <th>Verkleidung2</th>\n",
       "      <td>-0.417279</td>\n",
       "      <td>0.025619</td>\n",
       "      <td>0.870599</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001753</td>\n",
       "      <td>0.018578</td>\n",
       "      <td>0.321304</td>\n",
       "      <td>0.028162</td>\n",
       "      <td>-0.365419</td>\n",
       "      <td>0.035983</td>\n",
       "      <td>-0.115840</td>\n",
       "      <td>-0.035044</td>\n",
       "      <td>-0.041326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dachtyp</th>\n",
       "      <td>-0.066451</td>\n",
       "      <td>0.227407</td>\n",
       "      <td>-0.003374</td>\n",
       "      <td>0.001753</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061487</td>\n",
       "      <td>-0.068158</td>\n",
       "      <td>-0.013429</td>\n",
       "      <td>-0.063585</td>\n",
       "      <td>0.022109</td>\n",
       "      <td>-0.058596</td>\n",
       "      <td>-0.035828</td>\n",
       "      <td>-0.033271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dachmaterial</th>\n",
       "      <td>-0.018187</td>\n",
       "      <td>-0.025964</td>\n",
       "      <td>0.002562</td>\n",
       "      <td>0.018578</td>\n",
       "      <td>0.061487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005987</td>\n",
       "      <td>-0.012868</td>\n",
       "      <td>0.025863</td>\n",
       "      <td>0.025694</td>\n",
       "      <td>0.116507</td>\n",
       "      <td>-0.011512</td>\n",
       "      <td>0.086845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aussenmaterialqualitaet</th>\n",
       "      <td>-0.473779</td>\n",
       "      <td>0.171469</td>\n",
       "      <td>0.315502</td>\n",
       "      <td>0.321304</td>\n",
       "      <td>-0.068158</td>\n",
       "      <td>-0.005987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032990</td>\n",
       "      <td>-0.371512</td>\n",
       "      <td>0.060334</td>\n",
       "      <td>-0.113357</td>\n",
       "      <td>-0.003750</td>\n",
       "      <td>0.000529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aussenmaterialzustand</th>\n",
       "      <td>0.058517</td>\n",
       "      <td>-0.030250</td>\n",
       "      <td>0.022968</td>\n",
       "      <td>0.028162</td>\n",
       "      <td>-0.013429</td>\n",
       "      <td>-0.012868</td>\n",
       "      <td>0.032990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071005</td>\n",
       "      <td>-0.054028</td>\n",
       "      <td>0.025005</td>\n",
       "      <td>-0.036006</td>\n",
       "      <td>0.003114</td>\n",
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       "    <tr>\n",
       "      <th>Kellerhoehe</th>\n",
       "      <td>0.586439</td>\n",
       "      <td>-0.198898</td>\n",
       "      <td>-0.344138</td>\n",
       "      <td>-0.365419</td>\n",
       "      <td>-0.063585</td>\n",
       "      <td>0.025863</td>\n",
       "      <td>-0.371512</td>\n",
       "      <td>0.071005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009277</td>\n",
       "      <td>0.219560</td>\n",
       "      <td>0.038760</td>\n",
       "      <td>0.015729</td>\n",
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       "    <tr>\n",
       "      <th>Kellerzustand</th>\n",
       "      <td>-0.099027</td>\n",
       "      <td>0.080215</td>\n",
       "      <td>0.044167</td>\n",
       "      <td>0.035983</td>\n",
       "      <td>0.022109</td>\n",
       "      <td>0.025694</td>\n",
       "      <td>0.060334</td>\n",
       "      <td>-0.054028</td>\n",
       "      <td>0.009277</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028130</td>\n",
       "      <td>-0.087670</td>\n",
       "      <td>0.022679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kellerbelichtung</th>\n",
       "      <td>0.190542</td>\n",
       "      <td>-0.170946</td>\n",
       "      <td>-0.110432</td>\n",
       "      <td>-0.115840</td>\n",
       "      <td>-0.058596</td>\n",
       "      <td>0.116507</td>\n",
       "      <td>-0.113357</td>\n",
       "      <td>0.025005</td>\n",
       "      <td>0.219560</td>\n",
       "      <td>-0.028130</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068850</td>\n",
       "      <td>0.018127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kellerbereich1</th>\n",
       "      <td>0.028123</td>\n",
       "      <td>-0.089832</td>\n",
       "      <td>-0.013498</td>\n",
       "      <td>-0.035044</td>\n",
       "      <td>-0.035828</td>\n",
       "      <td>-0.011512</td>\n",
       "      <td>-0.003750</td>\n",
       "      <td>-0.036006</td>\n",
       "      <td>0.038760</td>\n",
       "      <td>-0.087670</td>\n",
       "      <td>0.068850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.144144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kellerbereich2</th>\n",
       "      <td>0.025716</td>\n",
       "      <td>0.023059</td>\n",
       "      <td>-0.055126</td>\n",
       "      <td>-0.041326</td>\n",
       "      <td>-0.033271</td>\n",
       "      <td>0.086845</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.003114</td>\n",
       "      <td>0.015729</td>\n",
       "      <td>0.022679</td>\n",
       "      <td>0.018127</td>\n",
       "      <td>-0.144144</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                         Fundament  Mauerwerktyp  Verkleidung1  Verkleidung2  \\\n",
       "Fundament                 1.000000     -0.257425     -0.405769     -0.417279   \n",
       "Mauerwerktyp             -0.257425      1.000000      0.005959      0.025619   \n",
       "Verkleidung1             -0.405769      0.005959      1.000000      0.870599   \n",
       "Verkleidung2             -0.417279      0.025619      0.870599      1.000000   \n",
       "Dachtyp                  -0.066451      0.227407     -0.003374      0.001753   \n",
       "Dachmaterial             -0.018187     -0.025964      0.002562      0.018578   \n",
       "Aussenmaterialqualitaet  -0.473779      0.171469      0.315502      0.321304   \n",
       "Aussenmaterialzustand     0.058517     -0.030250      0.022968      0.028162   \n",
       "Kellerhoehe               0.586439     -0.198898     -0.344138     -0.365419   \n",
       "Kellerzustand            -0.099027      0.080215      0.044167      0.035983   \n",
       "Kellerbelichtung          0.190542     -0.170946     -0.110432     -0.115840   \n",
       "Kellerbereich1            0.028123     -0.089832     -0.013498     -0.035044   \n",
       "Kellerbereich2            0.025716      0.023059     -0.055126     -0.041326   \n",
       "\n",
       "                          Dachtyp  Dachmaterial  Aussenmaterialqualitaet  \\\n",
       "Fundament               -0.066451     -0.018187                -0.473779   \n",
       "Mauerwerktyp             0.227407     -0.025964                 0.171469   \n",
       "Verkleidung1            -0.003374      0.002562                 0.315502   \n",
       "Verkleidung2             0.001753      0.018578                 0.321304   \n",
       "Dachtyp                  1.000000      0.061487                -0.068158   \n",
       "Dachmaterial             0.061487      1.000000                -0.005987   \n",
       "Aussenmaterialqualitaet -0.068158     -0.005987                 1.000000   \n",
       "Aussenmaterialzustand   -0.013429     -0.012868                 0.032990   \n",
       "Kellerhoehe             -0.063585      0.025863                -0.371512   \n",
       "Kellerzustand            0.022109      0.025694                 0.060334   \n",
       "Kellerbelichtung        -0.058596      0.116507                -0.113357   \n",
       "Kellerbereich1          -0.035828     -0.011512                -0.003750   \n",
       "Kellerbereich2          -0.033271      0.086845                 0.000529   \n",
       "\n",
       "                         Aussenmaterialzustand  Kellerhoehe  Kellerzustand  \\\n",
       "Fundament                             0.058517     0.586439      -0.099027   \n",
       "Mauerwerktyp                         -0.030250    -0.198898       0.080215   \n",
       "Verkleidung1                          0.022968    -0.344138       0.044167   \n",
       "Verkleidung2                          0.028162    -0.365419       0.035983   \n",
       "Dachtyp                              -0.013429    -0.063585       0.022109   \n",
       "Dachmaterial                         -0.012868     0.025863       0.025694   \n",
       "Aussenmaterialqualitaet               0.032990    -0.371512       0.060334   \n",
       "Aussenmaterialzustand                 1.000000     0.071005      -0.054028   \n",
       "Kellerhoehe                           0.071005     1.000000       0.009277   \n",
       "Kellerzustand                        -0.054028     0.009277       1.000000   \n",
       "Kellerbelichtung                      0.025005     0.219560      -0.028130   \n",
       "Kellerbereich1                       -0.036006     0.038760      -0.087670   \n",
       "Kellerbereich2                        0.003114     0.015729       0.022679   \n",
       "\n",
       "                         Kellerbelichtung  Kellerbereich1  Kellerbereich2  \n",
       "Fundament                        0.190542        0.028123        0.025716  \n",
       "Mauerwerktyp                    -0.170946       -0.089832        0.023059  \n",
       "Verkleidung1                    -0.110432       -0.013498       -0.055126  \n",
       "Verkleidung2                    -0.115840       -0.035044       -0.041326  \n",
       "Dachtyp                         -0.058596       -0.035828       -0.033271  \n",
       "Dachmaterial                     0.116507       -0.011512        0.086845  \n",
       "Aussenmaterialqualitaet         -0.113357       -0.003750        0.000529  \n",
       "Aussenmaterialzustand            0.025005       -0.036006        0.003114  \n",
       "Kellerhoehe                      0.219560        0.038760        0.015729  \n",
       "Kellerzustand                   -0.028130       -0.087670        0.022679  \n",
       "Kellerbelichtung                 1.000000        0.068850        0.018127  \n",
       "Kellerbereich1                   0.068850        1.000000       -0.144144  \n",
       "Kellerbereich2                   0.018127       -0.144144        1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_correlation_non_numeric(df):\n",
    "    df = df.select_dtypes(exclude=['float64', 'int64'])\n",
    "    # Convert non-numeric columns to ranks\n",
    "    for col in df.columns:\n",
    "        df[col] = df[col].rank(method='min')\n",
    "    corr = df.corr(method='spearman')\n",
    "    return corr\n",
    "\n",
    "calculate_correlation_non_numeric(df_20_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>HolzSchd</th>\n",
       "      <th>HolzScht</th>\n",
       "      <th>KiesTeer</th>\n",
       "      <th>Metall</th>\n",
       "      <th>TonFli</th>\n",
       "      <th>VerbSch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      HolzSchd  HolzScht  KiesTeer  Metall  TonFli  VerbSch\n",
       "Id                                                         \n",
       "1        False     False      True   False   False    False\n",
       "2        False     False     False   False   False     True\n",
       "3        False     False     False   False   False     True\n",
       "4        False     False     False   False   False     True\n",
       "5        False     False     False   False   False     True\n",
       "...        ...       ...       ...     ...     ...      ...\n",
       "1996     False     False     False   False   False     True\n",
       "1997     False     False     False   False   False     True\n",
       "1998     False     False     False   False   False     True\n",
       "1999     False     False     False   False   False     True\n",
       "2000     False     False     False   False   False     True\n",
       "\n",
       "[2000 rows x 6 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['Dachmaterial'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation of Verkaufspreis with Ag: 0.03605479544597006\n",
      "Correlation of Verkaufspreis with Ar: -0.13437022522684297\n",
      "Correlation of Verkaufspreis with Du: 0.10065227416242445\n",
      "Correlation of Verkaufspreis with Gu: -0.0455555202220945\n",
      "Correlation of Verkaufspreis with Sc: -0.05316933265399493\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Convert the categorical column to numerical\n",
    "dummies = pd.get_dummies(df_20_2['Aussenmaterialzustand'])\n",
    "\n",
    "# Add the dummy variables to your DataFrame\n",
    "df_20_2 = pd.concat([df_20_2, dummies], axis=1)\n",
    "\n",
    "# Now calculate the correlation for each dummy variable\n",
    "for column in dummies.columns:\n",
    "    correlation = df_20_2['Verkaufspreis'].corr(df_20_2[column])\n",
    "    print(f\"Correlation of Verkaufspreis with {column}: {correlation}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Wert: 7.298783149376925\n",
      "P-Wert: 8.697536832224511e-07\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# Erstellen Sie eine Liste von 'Verkaufspreis'-Werten für jede Kategorie\n",
    "values = [df_20_2.loc[df_20_2['Dachmaterial'] == category, 'Verkaufspreis'] for category in df_20_2['Dachmaterial'].unique()]\n",
    "\n",
    "# Führen Sie die ANOVA durch\n",
    "f_val, p_val = stats.f_oneway(*values)\n",
    "\n",
    "print(f\"F-Wert: {f_val}\")\n",
    "print(f\"P-Wert: {p_val}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kellerbereichgroesse2\n",
       "0.00     1769\n",
       "26.46       4\n",
       "33.66       3\n",
       "15.12       3\n",
       "53.64       2\n",
       "         ... \n",
       "25.56       1\n",
       "23.76       1\n",
       "22.23       1\n",
       "20.88       1\n",
       "33.93       1\n",
       "Name: count, Length: 198, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_20_2['Kellerbereichgroesse2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kellerbereich2\n",
       "NAu    1707\n",
       "DAR      73\n",
       "SQ       58\n",
       "UWR      46\n",
       "DWR      30\n",
       "GWR      23\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_20_2['Kellerbereich2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fundament                  62\n",
       "Mauerwerktyp               62\n",
       "Mauerwerkflaeche           62\n",
       "Verkleidung1               62\n",
       "Verkleidung2               62\n",
       "Dachtyp                    62\n",
       "Dachmaterial               62\n",
       "Aussenmaterialqualitaet    62\n",
       "Aussenmaterialzustand      62\n",
       "Kellerhoehe                 0\n",
       "Kellerzustand               0\n",
       "Kellerbelichtung            0\n",
       "Kellerbereich1              0\n",
       "Kellerbereichgroesse1      62\n",
       "Kellerbereich2              0\n",
       "Kellerbereichgroesse2      62\n",
       "KellerbereichgroesseNAu    62\n",
       "KellerbereichgroesseGes    62\n",
       "KellerVollbadezimmer       61\n",
       "KellerHalbbadezimmer       61\n",
       "Verkaufspreis              62\n",
       "HolzSchd                   62\n",
       "HolzScht                   62\n",
       "KiesTeer                   62\n",
       "Metall                     62\n",
       "TonFli                     62\n",
       "VerbSch                    62\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count where Kellerbereich2 is NAu and Kellerbereichgroesse2 is zero\n",
    "df_20_2[(df_20_2['Kellerbereich2'] != \"NAu\") & (df_20_2['Kellerbereichgroesse2'] == 0)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Fundament</th>\n",
       "      <th>Mauerwerktyp</th>\n",
       "      <th>Mauerwerkflaeche</th>\n",
       "      <th>Verkleidung1</th>\n",
       "      <th>Verkleidung2</th>\n",
       "      <th>Dachtyp</th>\n",
       "      <th>Dachmaterial</th>\n",
       "      <th>Aussenmaterialqualitaet</th>\n",
       "      <th>Aussenmaterialzustand</th>\n",
       "      <th>Kellerhoehe</th>\n",
       "      <th>Kellerzustand</th>\n",
       "      <th>Kellerbelichtung</th>\n",
       "      <th>Kellerbereich1</th>\n",
       "      <th>Kellerbereichgroesse1</th>\n",
       "      <th>Kellerbereich2</th>\n",
       "      <th>Kellerbereichgroesse2</th>\n",
       "      <th>KellerbereichgroesseNAu</th>\n",
       "      <th>KellerbereichgroesseGes</th>\n",
       "      <th>KellerVollbadezimmer</th>\n",
       "      <th>KellerHalbbadezimmer</th>\n",
       "      <th>Verkaufspreis</th>\n",
       "      <th>HolzSchd</th>\n",
       "      <th>HolzScht</th>\n",
       "      <th>KiesTeer</th>\n",
       "      <th>Metall</th>\n",
       "      <th>TonFli</th>\n",
       "      <th>VerbSch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Du</td>\n",
       "      <td>GWR</td>\n",
       "      <td>42.75</td>\n",
       "      <td>DWR</td>\n",
       "      <td>26.73</td>\n",
       "      <td>12.78</td>\n",
       "      <td>82.26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99662.50</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63.63</td>\n",
       "      <td>63.63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149600.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zement</td>\n",
       "      <td>ZementPl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>73.17</td>\n",
       "      <td>73.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>175057.50</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Ag</td>\n",
       "      <td>Du</td>\n",
       "      <td>Ag</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Gu</td>\n",
       "      <td>GWR</td>\n",
       "      <td>124.74</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>62.10</td>\n",
       "      <td>186.84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>395250.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>GWR</td>\n",
       "      <td>56.25</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.15</td>\n",
       "      <td>77.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>184025.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>137.52</td>\n",
       "      <td>137.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200494.60</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Ag</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>129.24</td>\n",
       "      <td>129.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191250.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.60</td>\n",
       "      <td>75.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168725.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Ag</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ke</td>\n",
       "      <td>GWR</td>\n",
       "      <td>140.58</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.72</td>\n",
       "      <td>150.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>286297.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zement</td>\n",
       "      <td>ZementPl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>128.52</td>\n",
       "      <td>128.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154700.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>69.12</td>\n",
       "      <td>69.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154491.75</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HolzSch</td>\n",
       "      <td>HlzSch</td>\n",
       "      <td>Walm</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Ag</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Du</td>\n",
       "      <td>GWR</td>\n",
       "      <td>67.05</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.90</td>\n",
       "      <td>85.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263511.05</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Walm</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Du</td>\n",
       "      <td>GWR</td>\n",
       "      <td>52.47</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>86.67</td>\n",
       "      <td>139.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>227205.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>Ke</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>139.50</td>\n",
       "      <td>139.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>176375.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fundament Mauerwerktyp  Mauerwerkflaeche Verkleidung1 Verkleidung2  \\\n",
       "Id                                                                        \n",
       "18    BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "219   BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "340   BetonGeg          NaN               NaN       Zement     ZementPl   \n",
       "390   BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "453   BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "746   BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "853   BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "856   BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "1321  BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "1517  BetonGeg          NaN               NaN       Zement     ZementPl   \n",
       "1622  BetonSte          NaN               NaN        Vinyl        Vinyl   \n",
       "1684  BetonGeg          NaN               NaN      HolzSch       HlzSch   \n",
       "1934  BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "1948  BetonGeg          NaN               NaN        Vinyl        Vinyl   \n",
       "\n",
       "     Dachtyp Dachmaterial Aussenmaterialqualitaet Aussenmaterialzustand  \\\n",
       "Id                                                                        \n",
       "18    Giebel      VerbSch                      Du                    Du   \n",
       "219   Giebel      VerbSch                      Gu                    Du   \n",
       "340   Giebel      VerbSch                      Gu                    Du   \n",
       "390   Giebel      VerbSch                      Ag                    Du   \n",
       "453   Giebel      VerbSch                      Gu                    Du   \n",
       "746   Giebel      VerbSch                      Gu                    Du   \n",
       "853   Giebel      VerbSch                      Gu                    Du   \n",
       "856   Giebel      VerbSch                      Gu                    Du   \n",
       "1321  Giebel      VerbSch                      Gu                    Du   \n",
       "1517  Giebel      VerbSch                      Gu                    Du   \n",
       "1622  Giebel      VerbSch                      Gu                    Du   \n",
       "1684    Walm      VerbSch                      Gu                    Du   \n",
       "1934    Walm      VerbSch                      Gu                    Du   \n",
       "1948  Giebel      VerbSch                      Gu                    Du   \n",
       "\n",
       "     Kellerhoehe Kellerzustand Kellerbelichtung Kellerbereich1  \\\n",
       "Id                                                               \n",
       "18            Gu            Ty               Du            GWR   \n",
       "219           Gu            Ty               Ke            NAu   \n",
       "340           Gu            Ty               Ke            NAu   \n",
       "390           Ag            Gu               Gu            GWR   \n",
       "453           Gu            Ty               Ke            GWR   \n",
       "746           Gu            Ty               Ke            NAu   \n",
       "853           Ag            Ty               Ke            NAu   \n",
       "856           Gu            Ty               Ke            NAu   \n",
       "1321          Ag            Gu               Ke            GWR   \n",
       "1517          Gu            Ty               Ke            NAu   \n",
       "1622          Gu            Ty               Ke            NAu   \n",
       "1684          Ag            Ty               Du            GWR   \n",
       "1934          Gu            Ty               Du            GWR   \n",
       "1948          Gu            Ty               Ke            NAu   \n",
       "\n",
       "      Kellerbereichgroesse1 Kellerbereich2  Kellerbereichgroesse2  \\\n",
       "Id                                                                  \n",
       "18                    42.75            DWR                  26.73   \n",
       "219                    0.00            NAu                   0.00   \n",
       "340                    0.00            NAu                   0.00   \n",
       "390                  124.74            NAu                   0.00   \n",
       "453                   56.25            NAu                   0.00   \n",
       "746                    0.00            NAu                   0.00   \n",
       "853                    0.00            NAu                   0.00   \n",
       "856                    0.00            NAu                   0.00   \n",
       "1321                 140.58            NAu                   0.00   \n",
       "1517                   0.00            NAu                   0.00   \n",
       "1622                   0.00            NAu                   0.00   \n",
       "1684                  67.05            NAu                   0.00   \n",
       "1934                  52.47            NAu                   0.00   \n",
       "1948                   0.00            NAu                   0.00   \n",
       "\n",
       "      KellerbereichgroesseNAu  KellerbereichgroesseGes  KellerVollbadezimmer  \\\n",
       "Id                                                                             \n",
       "18                      12.78                    82.26                   1.0   \n",
       "219                     63.63                    63.63                   0.0   \n",
       "340                     73.17                    73.17                   0.0   \n",
       "390                     62.10                   186.84                   1.0   \n",
       "453                     21.15                    77.40                   1.0   \n",
       "746                    137.52                   137.52                   0.0   \n",
       "853                    129.24                   129.24                   0.0   \n",
       "856                     75.60                    75.60                   0.0   \n",
       "1321                     9.72                   150.30                   1.0   \n",
       "1517                   128.52                   128.52                   0.0   \n",
       "1622                    69.12                    69.12                   0.0   \n",
       "1684                    18.90                    85.95                   1.0   \n",
       "1934                    86.67                   139.14                   0.0   \n",
       "1948                   139.50                   139.50                   0.0   \n",
       "\n",
       "      KellerHalbbadezimmer  Verkaufspreis  HolzSchd  HolzScht  KiesTeer  \\\n",
       "Id                                                                        \n",
       "18                     0.0       99662.50     False     False     False   \n",
       "219                    0.0      149600.00     False     False     False   \n",
       "340                    0.0      175057.50     False     False     False   \n",
       "390                    0.0      395250.00     False     False     False   \n",
       "453                    0.0      184025.00     False     False     False   \n",
       "746                    0.0      200494.60     False     False     False   \n",
       "853                    0.0      191250.00     False     False     False   \n",
       "856                    0.0      168725.00     False     False     False   \n",
       "1321                   0.0      286297.00     False     False     False   \n",
       "1517                   0.0      154700.00     False     False     False   \n",
       "1622                   0.0      154491.75     False     False     False   \n",
       "1684                   0.0      263511.05     False     False     False   \n",
       "1934                   0.0      227205.00     False     False     False   \n",
       "1948                   0.0      176375.00     False     False     False   \n",
       "\n",
       "      Metall  TonFli  VerbSch  \n",
       "Id                             \n",
       "18     False   False     True  \n",
       "219    False   False     True  \n",
       "340    False   False     True  \n",
       "390    False   False     True  \n",
       "453    False   False     True  \n",
       "746    False   False     True  \n",
       "853    False   False     True  \n",
       "856    False   False     True  \n",
       "1321   False   False     True  \n",
       "1517   False   False     True  \n",
       "1622   False   False     True  \n",
       "1684   False   False     True  \n",
       "1934   False   False     True  \n",
       "1948   False   False     True  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_20_2[df_20_2['Mauerwerktyp'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fundament</th>\n",
       "      <th>Mauerwerktyp</th>\n",
       "      <th>Mauerwerkflaeche</th>\n",
       "      <th>Verkleidung1</th>\n",
       "      <th>Verkleidung2</th>\n",
       "      <th>Dachtyp</th>\n",
       "      <th>Dachmaterial</th>\n",
       "      <th>Aussenmaterialqualitaet</th>\n",
       "      <th>Aussenmaterialzustand</th>\n",
       "      <th>Kellerhoehe</th>\n",
       "      <th>Kellerzustand</th>\n",
       "      <th>Kellerbelichtung</th>\n",
       "      <th>Kellerbereich1</th>\n",
       "      <th>Kellerbereichgroesse1</th>\n",
       "      <th>Kellerbereich2</th>\n",
       "      <th>Kellerbereichgroesse2</th>\n",
       "      <th>KellerbereichgroesseNAu</th>\n",
       "      <th>KellerbereichgroesseGes</th>\n",
       "      <th>KellerVollbadezimmer</th>\n",
       "      <th>KellerHalbbadezimmer</th>\n",
       "      <th>Verkaufspreis</th>\n",
       "      <th>HolzSchd</th>\n",
       "      <th>HolzScht</th>\n",
       "      <th>KiesTeer</th>\n",
       "      <th>Metall</th>\n",
       "      <th>TonFli</th>\n",
       "      <th>VerbSch</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ZiegAlg</td>\n",
       "      <td>ZiegAll</td>\n",
       "      <td>Flach</td>\n",
       "      <td>KiesTeer</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69700.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>HolzSch</td>\n",
       "      <td>HlzSch</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83300.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69105.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130475.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Ar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101119.40</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ZiegMau</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93415.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Platten</td>\n",
       "      <td>ZiegGla</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168725.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>AsphSch</td>\n",
       "      <td>AsphSch</td>\n",
       "      <td>Walm</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85000.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101029.30</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143650.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91375.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106250.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>ZiegGla</td>\n",
       "      <td>8.82</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Walm</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102000.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90525.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>BetonSt</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Ar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69275.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Walm</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93075.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>ZiegGla</td>\n",
       "      <td>12.96</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.24</td>\n",
       "      <td>84.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>163625.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ZiegMau</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Walm</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118660.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>AsbeSch</td>\n",
       "      <td>AsbeSch</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68425.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111350.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72165.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ZiegMau</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84150.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Walm</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83555.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>AsbeSch</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Sc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11135.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>ZiegFli</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85000.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>AsbeSch</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Ar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69700.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110500.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>ZiegAlg</td>\n",
       "      <td>11.52</td>\n",
       "      <td>AsbeSch</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52275.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ZiegMau</td>\n",
       "      <td>Stein</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76500.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ZiegMau</td>\n",
       "      <td>ZiegGla</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82450.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85000.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>HolzSch</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114267.20</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>BetonSt</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67150.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96663.70</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81600.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79050.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76500.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1039</th>\n",
       "      <td>Platten</td>\n",
       "      <td>ZiegAlg</td>\n",
       "      <td>5.22</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Walm</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133875.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>ZiegFli</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57888.40</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Holz</td>\n",
       "      <td>HlzSch</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136000.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>ZiegFli</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38250.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ZiegMau</td>\n",
       "      <td>Stein</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69190.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125785.55</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101029.30</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>ZiegFli</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90312.50</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Metall</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76415.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241995.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Stuck</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93500.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95200.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107100.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Stuck</td>\n",
       "      <td>Stein</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79475.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111350.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.25</td>\n",
       "      <td>65.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180292.65</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>AsbeSch</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69700.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86530.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>Platten</td>\n",
       "      <td>ZiegGla</td>\n",
       "      <td>30.60</td>\n",
       "      <td>Stuck</td>\n",
       "      <td>Stuck</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74375.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119850.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91800.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121510.05</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103700.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>Platten</td>\n",
       "      <td>ZiegGla</td>\n",
       "      <td>12.60</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Walm</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87465.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td>BetonGeg</td>\n",
       "      <td>ZiegGla</td>\n",
       "      <td>20.34</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Vinyl</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Du</td>\n",
       "      <td>Gu</td>\n",
       "      <td>Ty</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NAu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143.55</td>\n",
       "      <td>143.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>188275.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>Platten</td>\n",
       "      <td>ZiegGla</td>\n",
       "      <td>20.88</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114750.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>Platten</td>\n",
       "      <td>ZiegGla</td>\n",
       "      <td>28.80</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Sperrho</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>122400.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>BetonSte</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Holz</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Ar</td>\n",
       "      <td>Ar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48981.25</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>Platten</td>\n",
       "      <td>Kein</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Hartfas</td>\n",
       "      <td>Giebel</td>\n",
       "      <td>VerbSch</td>\n",
       "      <td>Du</td>\n",
       "      <td>Du</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83300.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fundament Mauerwerktyp  Mauerwerkflaeche Verkleidung1 Verkleidung2  \\\n",
       "Id                                                                        \n",
       "1      Platten         Kein              0.00      ZiegAlg      ZiegAll   \n",
       "28     Platten         Kein              0.00      HolzSch       HlzSch   \n",
       "47    BetonSte         Kein              0.00      Sperrho      Sperrho   \n",
       "78    BetonSte         Kein              0.00         Holz         Holz   \n",
       "106    Platten         Kein              0.00      Hartfas      Hartfas   \n",
       "117    Platten         Kein              0.00      ZiegMau         Holz   \n",
       "177    Platten      ZiegGla             45.00      Hartfas      Hartfas   \n",
       "212   BetonSte         Kein              0.00      AsphSch      AsphSch   \n",
       "221    Platten         Kein              0.00      Hartfas      Hartfas   \n",
       "224    Platten         Kein              0.00      Sperrho      Sperrho   \n",
       "231    Platten         Kein              0.00        Vinyl        Vinyl   \n",
       "244    Platten         Kein              0.00         Holz         Holz   \n",
       "248   BetonGeg      ZiegGla              8.82         Holz         Holz   \n",
       "265    Platten         Kein              0.00       Metall       Metall   \n",
       "281   BetonSte         Kein              0.00      BetonSt        Vinyl   \n",
       "295   BetonSte         Kein              0.00         Holz         Holz   \n",
       "321   BetonGeg      ZiegGla             12.96        Vinyl        Vinyl   \n",
       "372    Platten         Kein              0.00      ZiegMau         Holz   \n",
       "401    Platten         Kein              0.00      AsbeSch      AsbeSch   \n",
       "440    Platten         Kein              0.00         Holz       Metall   \n",
       "500   BetonSte         Kein              0.00       Metall       Metall   \n",
       "524    Platten         Kein              0.00      ZiegMau         Holz   \n",
       "529   BetonSte         Kein              0.00       Metall       Metall   \n",
       "542    Platten         Kein              0.00      AsbeSch        Vinyl   \n",
       "559    ZiegFli         Kein              0.00       Metall       Metall   \n",
       "610   BetonGeg         Kein              0.00      AsbeSch      Sperrho   \n",
       "650   BetonSte         Kein              0.00       Metall       Metall   \n",
       "699   BetonGeg      ZiegAlg             11.52      AsbeSch      Sperrho   \n",
       "713    Platten         Kein              0.00      ZiegMau        Stein   \n",
       "773   BetonGeg         Kein              0.00      ZiegMau      ZiegGla   \n",
       "798   BetonSte         Kein              0.00      Sperrho      Sperrho   \n",
       "816   BetonSte         Kein              0.00      HolzSch      Sperrho   \n",
       "845   BetonGeg         Kein              0.00       Metall      BetonSt   \n",
       "866    Platten         Kein              0.00      Hartfas      Hartfas   \n",
       "908    Platten         Kein              0.00       Metall       Metall   \n",
       "964    Platten         Kein              0.00         Holz         Holz   \n",
       "996    Platten         Kein              0.00       Metall       Metall   \n",
       "1039   Platten      ZiegAlg              5.22      Sperrho      Sperrho   \n",
       "1058   ZiegFli         Kein              0.00       Metall       Metall   \n",
       "1069   Platten         Kein              0.00         Holz       HlzSch   \n",
       "1086   ZiegFli         Kein              0.00       Metall       Metall   \n",
       "1102   Platten         Kein              0.00      ZiegMau        Stein   \n",
       "1115   Platten         Kein              0.00      Hartfas      Hartfas   \n",
       "1136   Platten         Kein              0.00      Sperrho      Sperrho   \n",
       "1159   ZiegFli         Kein              0.00       Metall       Metall   \n",
       "1180   Platten         Kein              0.00       Metall       Metall   \n",
       "1191   Platten         Kein              0.00      Sperrho      Sperrho   \n",
       "1270   Platten         Kein              0.00        Stuck         Holz   \n",
       "1272   Platten         Kein              0.00        Vinyl        Vinyl   \n",
       "1294  BetonSte         Kein              0.00      Sperrho      Sperrho   \n",
       "1365  BetonSte         Kein              0.00        Stuck        Stein   \n",
       "1425  BetonGeg         Kein              0.00      Hartfas      Hartfas   \n",
       "1444  BetonSte         Kein              0.00        Vinyl        Vinyl   \n",
       "1448  BetonGeg         Kein              0.00      AsbeSch      Sperrho   \n",
       "1479   Platten         Kein              0.00      Hartfas      Hartfas   \n",
       "1567   Platten      ZiegGla             30.60        Stuck        Stuck   \n",
       "1570   Platten         Kein              0.00      Sperrho      Sperrho   \n",
       "1624  BetonSte         Kein              0.00        Vinyl        Vinyl   \n",
       "1675   Platten         Kein              0.00      Sperrho      Sperrho   \n",
       "1745  BetonSte         Kein              0.00        Vinyl        Vinyl   \n",
       "1754   Platten      ZiegGla             12.60         Holz         Holz   \n",
       "1768  BetonGeg      ZiegGla             20.34        Vinyl        Vinyl   \n",
       "1846   Platten      ZiegGla             20.88         Holz         Holz   \n",
       "1922   Platten      ZiegGla             28.80      Sperrho      Sperrho   \n",
       "1944  BetonSte         Kein              0.00         Holz         Holz   \n",
       "1949   Platten         Kein              0.00      Hartfas      Hartfas   \n",
       "\n",
       "     Dachtyp Dachmaterial Aussenmaterialqualitaet Aussenmaterialzustand  \\\n",
       "Id                                                                        \n",
       "1      Flach     KiesTeer                      Du                    Du   \n",
       "28    Giebel      VerbSch                      Du                    Du   \n",
       "47    Giebel      VerbSch                      Du                    Gu   \n",
       "78    Giebel      VerbSch                      Du                    Du   \n",
       "106   Giebel      VerbSch                      Du                    Ar   \n",
       "117   Giebel      VerbSch                      Du                    Du   \n",
       "177   Giebel      VerbSch                      Gu                    Du   \n",
       "212     Walm      VerbSch                      Du                    Du   \n",
       "221   Giebel      VerbSch                      Du                    Du   \n",
       "224   Giebel      VerbSch                      Du                    Du   \n",
       "231   Giebel      VerbSch                      Du                    Du   \n",
       "244   Giebel      VerbSch                      Du                    Du   \n",
       "248     Walm      VerbSch                      Du                    Du   \n",
       "265   Giebel      VerbSch                      Du                    Du   \n",
       "281   Giebel      VerbSch                      Du                    Ar   \n",
       "295     Walm      VerbSch                      Du                    Du   \n",
       "321   Giebel      VerbSch                      Gu                    Du   \n",
       "372     Walm      VerbSch                      Du                    Du   \n",
       "401   Giebel      VerbSch                      Du                    Du   \n",
       "440   Giebel      VerbSch                      Du                    Du   \n",
       "500   Giebel      VerbSch                      Du                    Du   \n",
       "524   Giebel      VerbSch                      Du                    Du   \n",
       "529     Walm      VerbSch                      Du                    Du   \n",
       "542   Giebel      VerbSch                      Ar                    Sc   \n",
       "559   Giebel      VerbSch                      Du                    Du   \n",
       "610   Giebel      VerbSch                      Du                    Ar   \n",
       "650   Giebel      VerbSch                      Du                    Du   \n",
       "699   Giebel      VerbSch                      Du                    Du   \n",
       "713   Giebel      VerbSch                      Du                    Du   \n",
       "773   Giebel      VerbSch                      Du                    Du   \n",
       "798   Giebel      VerbSch                      Du                    Du   \n",
       "816   Giebel      VerbSch                      Du                    Du   \n",
       "845   Giebel      VerbSch                      Du                    Du   \n",
       "866   Giebel      VerbSch                      Du                    Du   \n",
       "908   Giebel      VerbSch                      Du                    Gu   \n",
       "964   Giebel      VerbSch                      Du                    Du   \n",
       "996   Giebel      VerbSch                      Du                    Du   \n",
       "1039    Walm      VerbSch                      Du                    Du   \n",
       "1058  Giebel      VerbSch                      Du                    Du   \n",
       "1069  Giebel      VerbSch                      Du                    Du   \n",
       "1086  Giebel      VerbSch                      Du                    Gu   \n",
       "1102  Giebel      VerbSch                      Du                    Du   \n",
       "1115  Giebel      VerbSch                      Du                    Du   \n",
       "1136  Giebel      VerbSch                      Du                    Du   \n",
       "1159  Giebel      VerbSch                      Du                    Du   \n",
       "1180  Giebel      VerbSch                      Du                    Du   \n",
       "1191  Giebel      VerbSch                      Du                    Du   \n",
       "1270  Giebel      VerbSch                      Du                    Du   \n",
       "1272  Giebel      VerbSch                      Du                    Du   \n",
       "1294  Giebel      VerbSch                      Du                    Du   \n",
       "1365  Giebel      VerbSch                      Du                    Du   \n",
       "1425  Giebel      VerbSch                      Gu                    Du   \n",
       "1444  Giebel      VerbSch                      Gu                    Du   \n",
       "1448  Giebel      VerbSch                      Du                    Du   \n",
       "1479  Giebel      VerbSch                      Du                    Du   \n",
       "1567  Giebel      VerbSch                      Du                    Du   \n",
       "1570  Giebel      VerbSch                      Du                    Du   \n",
       "1624  Giebel      VerbSch                      Du                    Du   \n",
       "1675  Giebel      VerbSch                      Du                    Du   \n",
       "1745  Giebel      VerbSch                      Du                    Du   \n",
       "1754    Walm      VerbSch                      Du                    Du   \n",
       "1768  Giebel      VerbSch                      Gu                    Du   \n",
       "1846  Giebel      VerbSch                      Du                    Du   \n",
       "1922  Giebel      VerbSch                      Du                    Du   \n",
       "1944  Giebel      VerbSch                      Ar                    Ar   \n",
       "1949  Giebel      VerbSch                      Du                    Du   \n",
       "\n",
       "     Kellerhoehe Kellerzustand Kellerbelichtung Kellerbereich1  \\\n",
       "Id                                                               \n",
       "1            NaN           NaN              NaN            NaN   \n",
       "28           NaN           NaN              NaN            NaN   \n",
       "47           NaN           NaN              NaN            NaN   \n",
       "78           NaN           NaN              NaN            NaN   \n",
       "106          NaN           NaN              NaN            NaN   \n",
       "117          NaN           NaN              NaN            NaN   \n",
       "177          NaN           NaN              NaN            NaN   \n",
       "212          NaN           NaN              NaN            NaN   \n",
       "221          NaN           NaN              NaN            NaN   \n",
       "224          NaN           NaN              NaN            NaN   \n",
       "231          NaN           NaN              NaN            NaN   \n",
       "244          NaN           NaN              NaN            NaN   \n",
       "248          NaN           NaN              NaN            NaN   \n",
       "265          NaN           NaN              NaN            NaN   \n",
       "281          NaN           NaN              NaN            NaN   \n",
       "295          NaN           NaN              NaN            NaN   \n",
       "321           Gu            Ty              NaN            NAu   \n",
       "372          NaN           NaN              NaN            NaN   \n",
       "401          NaN           NaN              NaN            NaN   \n",
       "440          NaN           NaN              NaN            NaN   \n",
       "500          NaN           NaN              NaN            NaN   \n",
       "524          NaN           NaN              NaN            NaN   \n",
       "529          NaN           NaN              NaN            NaN   \n",
       "542          NaN           NaN              NaN            NaN   \n",
       "559          NaN           NaN              NaN            NaN   \n",
       "610          NaN           NaN              NaN            NaN   \n",
       "650          NaN           NaN              NaN            NaN   \n",
       "699          NaN           NaN              NaN            NaN   \n",
       "713          NaN           NaN              NaN            NaN   \n",
       "773          NaN           NaN              NaN            NaN   \n",
       "798          NaN           NaN              NaN            NaN   \n",
       "816          NaN           NaN              NaN            NaN   \n",
       "845          NaN           NaN              NaN            NaN   \n",
       "866          NaN           NaN              NaN            NaN   \n",
       "908          NaN           NaN              NaN            NaN   \n",
       "964          NaN           NaN              NaN            NaN   \n",
       "996          NaN           NaN              NaN            NaN   \n",
       "1039         NaN           NaN              NaN            NaN   \n",
       "1058         NaN           NaN              NaN            NaN   \n",
       "1069         NaN           NaN              NaN            NaN   \n",
       "1086         NaN           NaN              NaN            NaN   \n",
       "1102         NaN           NaN              NaN            NaN   \n",
       "1115         NaN           NaN              NaN            NaN   \n",
       "1136         NaN           NaN              NaN            NaN   \n",
       "1159         NaN           NaN              NaN            NaN   \n",
       "1180         NaN           NaN              NaN            NaN   \n",
       "1191         NaN           NaN              NaN            NaN   \n",
       "1270         NaN           NaN              NaN            NaN   \n",
       "1272         NaN           NaN              NaN            NaN   \n",
       "1294         NaN           NaN              NaN            NaN   \n",
       "1365         NaN           NaN              NaN            NaN   \n",
       "1425         NaN           NaN              NaN            NaN   \n",
       "1444          Gu            Ty              NaN            NAu   \n",
       "1448         NaN           NaN              NaN            NaN   \n",
       "1479         NaN           NaN              NaN            NaN   \n",
       "1567         NaN           NaN              NaN            NaN   \n",
       "1570         NaN           NaN              NaN            NaN   \n",
       "1624         NaN           NaN              NaN            NaN   \n",
       "1675         NaN           NaN              NaN            NaN   \n",
       "1745         NaN           NaN              NaN            NaN   \n",
       "1754         NaN           NaN              NaN            NaN   \n",
       "1768          Gu            Ty              NaN            NAu   \n",
       "1846         NaN           NaN              NaN            NaN   \n",
       "1922         NaN           NaN              NaN            NaN   \n",
       "1944         NaN           NaN              NaN            NaN   \n",
       "1949         NaN           NaN              NaN            NaN   \n",
       "\n",
       "      Kellerbereichgroesse1 Kellerbereich2  Kellerbereichgroesse2  \\\n",
       "Id                                                                  \n",
       "1                       0.0            NaN                    0.0   \n",
       "28                      0.0            NaN                    0.0   \n",
       "47                      0.0            NaN                    0.0   \n",
       "78                      0.0            NaN                    0.0   \n",
       "106                     0.0            NaN                    0.0   \n",
       "117                     0.0            NaN                    0.0   \n",
       "177                     0.0            NaN                    0.0   \n",
       "212                     0.0            NaN                    0.0   \n",
       "221                     0.0            NaN                    0.0   \n",
       "224                     0.0            NaN                    0.0   \n",
       "231                     0.0            NaN                    0.0   \n",
       "244                     0.0            NaN                    0.0   \n",
       "248                     0.0            NaN                    0.0   \n",
       "265                     0.0            NaN                    0.0   \n",
       "281                     0.0            NaN                    0.0   \n",
       "295                     0.0            NaN                    0.0   \n",
       "321                     0.0            NAu                    0.0   \n",
       "372                     0.0            NaN                    0.0   \n",
       "401                     0.0            NaN                    0.0   \n",
       "440                     0.0            NaN                    0.0   \n",
       "500                     0.0            NaN                    0.0   \n",
       "524                     0.0            NaN                    0.0   \n",
       "529                     0.0            NaN                    0.0   \n",
       "542                     0.0            NaN                    0.0   \n",
       "559                     0.0            NaN                    0.0   \n",
       "610                     0.0            NaN                    0.0   \n",
       "650                     0.0            NaN                    0.0   \n",
       "699                     0.0            NaN                    0.0   \n",
       "713                     0.0            NaN                    0.0   \n",
       "773                     0.0            NaN                    0.0   \n",
       "798                     0.0            NaN                    0.0   \n",
       "816                     0.0            NaN                    0.0   \n",
       "845                     NaN            NaN                    NaN   \n",
       "866                     0.0            NaN                    0.0   \n",
       "908                     0.0            NaN                    0.0   \n",
       "964                     0.0            NaN                    0.0   \n",
       "996                     0.0            NaN                    0.0   \n",
       "1039                    0.0            NaN                    0.0   \n",
       "1058                    0.0            NaN                    0.0   \n",
       "1069                    0.0            NaN                    0.0   \n",
       "1086                    0.0            NaN                    0.0   \n",
       "1102                    0.0            NaN                    0.0   \n",
       "1115                    0.0            NaN                    0.0   \n",
       "1136                    0.0            NaN                    0.0   \n",
       "1159                    0.0            NaN                    0.0   \n",
       "1180                    0.0            NaN                    0.0   \n",
       "1191                    0.0            NaN                    0.0   \n",
       "1270                    0.0            NaN                    0.0   \n",
       "1272                    0.0            NaN                    0.0   \n",
       "1294                    0.0            NaN                    0.0   \n",
       "1365                    0.0            NaN                    0.0   \n",
       "1425                    0.0            NaN                    0.0   \n",
       "1444                    0.0            NAu                    0.0   \n",
       "1448                    0.0            NaN                    0.0   \n",
       "1479                    0.0            NaN                    0.0   \n",
       "1567                    0.0            NaN                    0.0   \n",
       "1570                    0.0            NaN                    0.0   \n",
       "1624                    0.0            NaN                    0.0   \n",
       "1675                    0.0            NaN                    0.0   \n",
       "1745                    0.0            NaN                    0.0   \n",
       "1754                    0.0            NaN                    0.0   \n",
       "1768                    0.0            NAu                    0.0   \n",
       "1846                    0.0            NaN                    0.0   \n",
       "1922                    0.0            NaN                    0.0   \n",
       "1944                    0.0            NaN                    0.0   \n",
       "1949                    0.0            NaN                    0.0   \n",
       "\n",
       "      KellerbereichgroesseNAu  KellerbereichgroesseGes  KellerVollbadezimmer  \\\n",
       "Id                                                                             \n",
       "1                        0.00                     0.00                   0.0   \n",
       "28                       0.00                     0.00                   0.0   \n",
       "47                       0.00                     0.00                   0.0   \n",
       "78                       0.00                     0.00                   0.0   \n",
       "106                      0.00                     0.00                   0.0   \n",
       "117                      0.00                     0.00                   0.0   \n",
       "177                      0.00                     0.00                   0.0   \n",
       "212                      0.00                     0.00                   0.0   \n",
       "221                      0.00                     0.00                   0.0   \n",
       "224                      0.00                     0.00                   0.0   \n",
       "231                      0.00                     0.00                   0.0   \n",
       "244                      0.00                     0.00                   0.0   \n",
       "248                      0.00                     0.00                   0.0   \n",
       "265                      0.00                     0.00                   0.0   \n",
       "281                      0.00                     0.00                   0.0   \n",
       "295                      0.00                     0.00                   0.0   \n",
       "321                     84.24                    84.24                   0.0   \n",
       "372                      0.00                     0.00                   0.0   \n",
       "401                      0.00                     0.00                   0.0   \n",
       "440                      0.00                     0.00                   0.0   \n",
       "500                      0.00                     0.00                   0.0   \n",
       "524                      0.00                     0.00                   0.0   \n",
       "529                      0.00                     0.00                   0.0   \n",
       "542                      0.00                     0.00                   0.0   \n",
       "559                      0.00                     0.00                   0.0   \n",
       "610                      0.00                     0.00                   0.0   \n",
       "650                      0.00                     0.00                   0.0   \n",
       "699                      0.00                     0.00                   0.0   \n",
       "713                      0.00                     0.00                   0.0   \n",
       "773                      0.00                     0.00                   0.0   \n",
       "798                      0.00                     0.00                   0.0   \n",
       "816                      0.00                     0.00                   0.0   \n",
       "845                       NaN                      NaN                   NaN   \n",
       "866                      0.00                     0.00                   0.0   \n",
       "908                      0.00                     0.00                   0.0   \n",
       "964                      0.00                     0.00                   0.0   \n",
       "996                      0.00                     0.00                   0.0   \n",
       "1039                     0.00                     0.00                   0.0   \n",
       "1058                     0.00                     0.00                   0.0   \n",
       "1069                     0.00                     0.00                   0.0   \n",
       "1086                     0.00                     0.00                   0.0   \n",
       "1102                     0.00                     0.00                   0.0   \n",
       "1115                     0.00                     0.00                   0.0   \n",
       "1136                     0.00                     0.00                   0.0   \n",
       "1159                     0.00                     0.00                   0.0   \n",
       "1180                     0.00                     0.00                   0.0   \n",
       "1191                     0.00                     0.00                   NaN   \n",
       "1270                     0.00                     0.00                   0.0   \n",
       "1272                     0.00                     0.00                   0.0   \n",
       "1294                     0.00                     0.00                   0.0   \n",
       "1365                     0.00                     0.00                   0.0   \n",
       "1425                     0.00                     0.00                   0.0   \n",
       "1444                    65.25                    65.25                   0.0   \n",
       "1448                     0.00                     0.00                   0.0   \n",
       "1479                     0.00                     0.00                   0.0   \n",
       "1567                     0.00                     0.00                   0.0   \n",
       "1570                     0.00                     0.00                   0.0   \n",
       "1624                     0.00                     0.00                   0.0   \n",
       "1675                     0.00                     0.00                   0.0   \n",
       "1745                     0.00                     0.00                   0.0   \n",
       "1754                     0.00                     0.00                   0.0   \n",
       "1768                   143.55                   143.55                   0.0   \n",
       "1846                     0.00                     0.00                   0.0   \n",
       "1922                     0.00                     0.00                   0.0   \n",
       "1944                     0.00                     0.00                   0.0   \n",
       "1949                     0.00                     0.00                   0.0   \n",
       "\n",
       "      KellerHalbbadezimmer  Verkaufspreis  HolzSchd  HolzScht  KiesTeer  \\\n",
       "Id                                                                        \n",
       "1                      0.0       69700.00     False     False      True   \n",
       "28                     0.0       83300.00     False     False     False   \n",
       "47                     0.0       69105.00     False     False     False   \n",
       "78                     0.0      130475.00     False     False     False   \n",
       "106                    0.0      101119.40     False     False     False   \n",
       "117                    0.0       93415.00     False     False     False   \n",
       "177                    0.0      168725.00     False     False     False   \n",
       "212                    0.0       85000.00     False     False     False   \n",
       "221                    0.0      101029.30     False     False     False   \n",
       "224                    0.0      143650.00     False     False     False   \n",
       "231                    0.0       91375.00     False     False     False   \n",
       "244                    0.0      106250.00     False     False     False   \n",
       "248                    0.0      102000.00     False     False     False   \n",
       "265                    0.0       90525.00     False     False     False   \n",
       "281                    0.0       69275.00     False     False     False   \n",
       "295                    0.0       93075.00     False     False     False   \n",
       "321                    0.0      163625.00     False     False     False   \n",
       "372                    0.0      118660.00     False     False     False   \n",
       "401                    0.0       68425.00     False     False     False   \n",
       "440                    0.0      111350.00     False     False     False   \n",
       "500                    0.0       72165.00     False     False     False   \n",
       "524                    0.0       84150.00     False     False     False   \n",
       "529                    0.0       83555.00     False     False     False   \n",
       "542                    0.0       11135.00     False     False     False   \n",
       "559                    0.0       85000.00     False     False     False   \n",
       "610                    0.0       69700.00     False     False     False   \n",
       "650                    0.0      110500.00     False     False     False   \n",
       "699                    0.0       52275.00     False     False     False   \n",
       "713                    0.0       76500.00     False     False     False   \n",
       "773                    0.0       82450.00     False     False     False   \n",
       "798                    0.0       85000.00     False     False     False   \n",
       "816                    0.0      114267.20     False     False     False   \n",
       "845                    NaN       67150.00     False     False     False   \n",
       "866                    0.0       96663.70     False     False     False   \n",
       "908                    0.0       81600.00     False     False     False   \n",
       "964                    0.0       79050.00     False     False     False   \n",
       "996                    0.0       76500.00     False     False     False   \n",
       "1039                   0.0      133875.00     False     False     False   \n",
       "1058                   0.0       57888.40     False     False     False   \n",
       "1069                   0.0      136000.00     False     False     False   \n",
       "1086                   0.0       38250.00     False     False     False   \n",
       "1102                   0.0       69190.00     False     False     False   \n",
       "1115                   0.0      125785.55     False     False     False   \n",
       "1136                   0.0      101029.30     False     False     False   \n",
       "1159                   0.0       90312.50     False     False     False   \n",
       "1180                   0.0       76415.00     False     False     False   \n",
       "1191                   NaN      241995.00     False     False     False   \n",
       "1270                   0.0       93500.00     False     False     False   \n",
       "1272                   0.0       95200.00     False     False     False   \n",
       "1294                   0.0      107100.00     False     False     False   \n",
       "1365                   0.0       79475.00     False     False     False   \n",
       "1425                   0.0      111350.00     False     False     False   \n",
       "1444                   0.0      180292.65     False     False     False   \n",
       "1448                   0.0       69700.00     False     False     False   \n",
       "1479                   0.0       86530.00     False     False     False   \n",
       "1567                   0.0       74375.00     False     False     False   \n",
       "1570                   0.0      119850.00     False     False     False   \n",
       "1624                   0.0       91800.00     False     False     False   \n",
       "1675                   0.0      121510.05     False     False     False   \n",
       "1745                   0.0      103700.00     False     False     False   \n",
       "1754                   0.0       87465.00     False     False     False   \n",
       "1768                   0.0      188275.00     False     False     False   \n",
       "1846                   0.0      114750.00     False     False     False   \n",
       "1922                   0.0      122400.00     False     False     False   \n",
       "1944                   0.0       48981.25     False     False     False   \n",
       "1949                   0.0       83300.00     False     False     False   \n",
       "\n",
       "      Metall  TonFli  VerbSch  \n",
       "Id                             \n",
       "1      False   False    False  \n",
       "28     False   False     True  \n",
       "47     False   False     True  \n",
       "78     False   False     True  \n",
       "106    False   False     True  \n",
       "117    False   False     True  \n",
       "177    False   False     True  \n",
       "212    False   False     True  \n",
       "221    False   False     True  \n",
       "224    False   False     True  \n",
       "231    False   False     True  \n",
       "244    False   False     True  \n",
       "248    False   False     True  \n",
       "265    False   False     True  \n",
       "281    False   False     True  \n",
       "295    False   False     True  \n",
       "321    False   False     True  \n",
       "372    False   False     True  \n",
       "401    False   False     True  \n",
       "440    False   False     True  \n",
       "500    False   False     True  \n",
       "524    False   False     True  \n",
       "529    False   False     True  \n",
       "542    False   False     True  \n",
       "559    False   False     True  \n",
       "610    False   False     True  \n",
       "650    False   False     True  \n",
       "699    False   False     True  \n",
       "713    False   False     True  \n",
       "773    False   False     True  \n",
       "798    False   False     True  \n",
       "816    False   False     True  \n",
       "845    False   False     True  \n",
       "866    False   False     True  \n",
       "908    False   False     True  \n",
       "964    False   False     True  \n",
       "996    False   False     True  \n",
       "1039   False   False     True  \n",
       "1058   False   False     True  \n",
       "1069   False   False     True  \n",
       "1086   False   False     True  \n",
       "1102   False   False     True  \n",
       "1115   False   False     True  \n",
       "1136   False   False     True  \n",
       "1159   False   False     True  \n",
       "1180   False   False     True  \n",
       "1191   False   False     True  \n",
       "1270   False   False     True  \n",
       "1272   False   False     True  \n",
       "1294   False   False     True  \n",
       "1365   False   False     True  \n",
       "1425   False   False     True  \n",
       "1444   False   False     True  \n",
       "1448   False   False     True  \n",
       "1479   False   False     True  \n",
       "1567   False   False     True  \n",
       "1570   False   False     True  \n",
       "1624   False   False     True  \n",
       "1675   False   False     True  \n",
       "1745   False   False     True  \n",
       "1754   False   False     True  \n",
       "1768   False   False     True  \n",
       "1846   False   False     True  \n",
       "1922   False   False     True  \n",
       "1944   False   False     True  \n",
       "1949   False   False     True  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_20_2[df_20_2[\"Kellerbelichtung\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 non-numeric values found in KellerHalbbadezimmer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KellerHalbbadezimmer\n",
       "0.0    1886\n",
       "1.0     111\n",
       "2.0       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "non_numeric = pd.to_numeric(df_20_2['KellerHalbbadezimmer'], errors='coerce').isna().sum()\n",
    "if non_numeric > 0:\n",
    "    print(f\"{non_numeric} non-numeric values found in KellerHalbbadezimmer\")\n",
    "else:\n",
    "    print(\"All values in KellerHalbbadezimmer are numeric\")\n",
    "# check if KellerHalbbadezimmer has not numeric values\n",
    "df_20_2[\"KellerHalbbadezimmer\"].value_counts() # -> all values are numeric\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OberirdischeWohnflaeche</th>\n",
       "      <th>1Stockflaeche</th>\n",
       "      <th>2Stockflaeche</th>\n",
       "      <th>GeringequalitaetFlaeche</th>\n",
       "      <th>OberirdischeRaueme</th>\n",
       "      <th>Funktionalitaet</th>\n",
       "      <th>OberirdischeVollbadezimmer</th>\n",
       "      <th>OberirdischeHalbbadezimmer</th>\n",
       "      <th>Schlafzimmer</th>\n",
       "      <th>Kuechen</th>\n",
       "      <th>KuechenQualitaet</th>\n",
       "      <th>Kamine</th>\n",
       "      <th>Kaminqualitaet</th>\n",
       "      <th>Heizung</th>\n",
       "      <th>Heizungsqualitaet</th>\n",
       "      <th>Klimalanlage</th>\n",
       "      <th>Elektrik</th>\n",
       "      <th>Holzdeckflaeche</th>\n",
       "      <th>OffeneVerandaflaeche</th>\n",
       "      <th>GeschlosseneVerandaflaeche</th>\n",
       "      <th>Verkaufspreis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84.96</td>\n",
       "      <td>84.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Ger1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Ar</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWD</td>\n",
       "      <td>Ar</td>\n",
       "      <td>N</td>\n",
       "      <td>SichAr</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.10</td>\n",
       "      <td>98.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Gu</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>78.12</td>\n",
       "      <td>78.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Gu</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>12.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>103.86</td>\n",
       "      <td>71.64</td>\n",
       "      <td>32.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Gu</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>144.99</td>\n",
       "      <td>70.56</td>\n",
       "      <td>74.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Ag</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>12.96</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166430.0</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",
       "      <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>1989</th>\n",
       "      <td>111.24</td>\n",
       "      <td>54.00</td>\n",
       "      <td>57.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Ag</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>78.48</td>\n",
       "      <td>78.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Du</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>88.92</td>\n",
       "      <td>88.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Du</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>80.91</td>\n",
       "      <td>80.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Ag</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>103.50</td>\n",
       "      <td>103.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Du</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>960 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      OberirdischeWohnflaeche  1Stockflaeche  2Stockflaeche  \\\n",
       "Id                                                            \n",
       "1                       84.96          84.96           0.00   \n",
       "2                       98.10          98.10           0.00   \n",
       "7                       78.12          78.12           0.00   \n",
       "10                     103.86          71.64          32.22   \n",
       "14                     144.99          70.56          74.43   \n",
       "...                       ...            ...            ...   \n",
       "1989                   111.24          54.00          57.24   \n",
       "1994                    78.48          78.48           0.00   \n",
       "1995                    88.92          88.92           0.00   \n",
       "1996                    80.91          80.91           0.00   \n",
       "1999                   103.50         103.50           0.00   \n",
       "\n",
       "      GeringequalitaetFlaeche  OberirdischeRaueme Funktionalitaet  \\\n",
       "Id                                                                  \n",
       "1                         0.0                   4            Ger1   \n",
       "2                         0.0                   5             Typ   \n",
       "7                         0.0                   6             Typ   \n",
       "10                        0.0                   7             Typ   \n",
       "14                        0.0                   6             Typ   \n",
       "...                       ...                 ...             ...   \n",
       "1989                      0.0                   4             Typ   \n",
       "1994                      0.0                   5             Typ   \n",
       "1995                      0.0                   6             Typ   \n",
       "1996                      0.0                   5             Typ   \n",
       "1999                      0.0                   6             Typ   \n",
       "\n",
       "      OberirdischeVollbadezimmer  OberirdischeHalbbadezimmer  Schlafzimmer  \\\n",
       "Id                                                                           \n",
       "1                              1                           0             2   \n",
       "2                              1                           1             2   \n",
       "7                              1                           0             2   \n",
       "10                             1                           0             3   \n",
       "14                             2                           1             3   \n",
       "...                          ...                         ...           ...   \n",
       "1989                           2                           1             2   \n",
       "1994                           1                           0             3   \n",
       "1995                           1                           0             3   \n",
       "1996                           1                           0             3   \n",
       "1999                           1                           0             3   \n",
       "\n",
       "      Kuechen KuechenQualitaet  Kamine Kaminqualitaet Heizung  \\\n",
       "Id                                                              \n",
       "1           1               Ar       0            NaN   GasWD   \n",
       "2           1               Du       0            NaN   GasWO   \n",
       "7           1               Du       0            NaN   GasWO   \n",
       "10          1               Gu       0            NaN   GasWO   \n",
       "14          1               Gu       0            NaN   GasWO   \n",
       "...       ...              ...     ...            ...     ...   \n",
       "1989        1               Gu       0            NaN   GasWO   \n",
       "1994        1               Du       0            NaN   GasWO   \n",
       "1995        1               Du       0            NaN   GasWO   \n",
       "1996        1               Gu       0            NaN   GasWO   \n",
       "1999        1               Du       0            NaN   GasWO   \n",
       "\n",
       "     Heizungsqualitaet Klimalanlage Elektrik  Holzdeckflaeche  \\\n",
       "Id                                                              \n",
       "1                   Ar            N   SichAr             0.00   \n",
       "2                   Gu            J    SLSRo             0.00   \n",
       "7                   Gu            J    SLSRo            12.42   \n",
       "10                  Gu            J    SLSRo             0.00   \n",
       "14                  Ag            J    SLSRo            12.96   \n",
       "...                ...          ...      ...              ...   \n",
       "1989                Ag            J    SLSRo             0.00   \n",
       "1994                Du            J    SLSRo             0.00   \n",
       "1995                Du            J    SLSRo             0.00   \n",
       "1996                Ag            J    SLSRo             5.76   \n",
       "1999                Du            J    SLSRo             0.00   \n",
       "\n",
       "      OffeneVerandaflaeche  GeschlosseneVerandaflaeche  Verkaufspreis  \n",
       "Id                                                                     \n",
       "1                     0.00                         0.0        69700.0  \n",
       "2                     4.41                         0.0       133450.0  \n",
       "7                     0.00                         0.0       101915.0  \n",
       "10                    8.64                         0.0       106675.0  \n",
       "14                    3.24                         0.0       166430.0  \n",
       "...                    ...                         ...            ...  \n",
       "1989                 14.94                         0.0       129200.0  \n",
       "1994                  0.00                         0.0       104550.0  \n",
       "1995                  7.20                         0.0       101150.0  \n",
       "1996                  0.00                         0.0       110500.0  \n",
       "1999                  0.00                         0.0       124100.0  \n",
       "\n",
       "[960 rows x 21 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_20_3[(df_20_3['Kamine'] == 0) & (df_20_3['Kaminqualitaet'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>OberirdischeWohnflaeche</th>\n",
       "      <th>1Stockflaeche</th>\n",
       "      <th>2Stockflaeche</th>\n",
       "      <th>GeringequalitaetFlaeche</th>\n",
       "      <th>OberirdischeRaueme</th>\n",
       "      <th>Funktionalitaet</th>\n",
       "      <th>OberirdischeVollbadezimmer</th>\n",
       "      <th>OberirdischeHalbbadezimmer</th>\n",
       "      <th>Schlafzimmer</th>\n",
       "      <th>Kuechen</th>\n",
       "      <th>KuechenQualitaet</th>\n",
       "      <th>Kamine</th>\n",
       "      <th>Kaminqualitaet</th>\n",
       "      <th>Heizung</th>\n",
       "      <th>Heizungsqualitaet</th>\n",
       "      <th>Klimalanlage</th>\n",
       "      <th>Elektrik</th>\n",
       "      <th>Holzdeckflaeche</th>\n",
       "      <th>OffeneVerandaflaeche</th>\n",
       "      <th>GeschlosseneVerandaflaeche</th>\n",
       "      <th>Verkaufspreis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84.96</td>\n",
       "      <td>84.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Ger1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Ar</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWD</td>\n",
       "      <td>Ar</td>\n",
       "      <td>N</td>\n",
       "      <td>SichAr</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>69700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.10</td>\n",
       "      <td>98.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Gu</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>133450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>139.50</td>\n",
       "      <td>84.60</td>\n",
       "      <td>54.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Ger2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Gu</td>\n",
       "      <td>J</td>\n",
       "      <td>SichAr</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.61</td>\n",
       "      <td>165750.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>147.96</td>\n",
       "      <td>86.49</td>\n",
       "      <td>61.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Ag</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>41.40</td>\n",
       "      <td>3.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>157250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>136.53</td>\n",
       "      <td>71.28</td>\n",
       "      <td>65.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>2</td>\n",
       "      <td>Gu</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Ar</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>195500.00</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",
       "      <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>1996</th>\n",
       "      <td>80.91</td>\n",
       "      <td>80.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Ag</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>110500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>141.03</td>\n",
       "      <td>141.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>2</td>\n",
       "      <td>Du</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Du</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>23.76</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>166600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>162.00</td>\n",
       "      <td>162.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Ag</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Ag</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>18.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>315321.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>103.50</td>\n",
       "      <td>103.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Du</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>124100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>129.60</td>\n",
       "      <td>129.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Ag</td>\n",
       "      <td>J</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.91</td>\n",
       "      <td>155465.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      OberirdischeWohnflaeche  1Stockflaeche  2Stockflaeche  \\\n",
       "Id                                                            \n",
       "1                       84.96          84.96           0.00   \n",
       "2                       98.10          98.10           0.00   \n",
       "3                      139.50          84.60          54.90   \n",
       "4                      147.96          86.49          61.47   \n",
       "5                      136.53          71.28          65.25   \n",
       "...                       ...            ...            ...   \n",
       "1996                    80.91          80.91           0.00   \n",
       "1997                   141.03         141.03           0.00   \n",
       "1998                   162.00         162.00           0.00   \n",
       "1999                   103.50         103.50           0.00   \n",
       "2000                   129.60         129.60           0.00   \n",
       "\n",
       "      GeringequalitaetFlaeche  OberirdischeRaueme Funktionalitaet  \\\n",
       "Id                                                                  \n",
       "1                         0.0                   4            Ger1   \n",
       "2                         0.0                   5             Typ   \n",
       "3                         0.0                   7            Ger2   \n",
       "4                         0.0                   7             Typ   \n",
       "5                         0.0                   7             Typ   \n",
       "...                       ...                 ...             ...   \n",
       "1996                      0.0                   5             Typ   \n",
       "1997                      0.0                   5             Typ   \n",
       "1998                      0.0                   6             Typ   \n",
       "1999                      0.0                   6             Typ   \n",
       "2000                      0.0                   7             Typ   \n",
       "\n",
       "      OberirdischeVollbadezimmer  OberirdischeHalbbadezimmer  Schlafzimmer  \\\n",
       "Id                                                                           \n",
       "1                              1                           0             2   \n",
       "2                              1                           1             2   \n",
       "3                              1                           1             3   \n",
       "4                              2                           1             3   \n",
       "5                              1                           0             3   \n",
       "...                          ...                         ...           ...   \n",
       "1996                           1                           0             3   \n",
       "1997                           2                           0             2   \n",
       "1998                           2                           0             2   \n",
       "1999                           1                           0             3   \n",
       "2000                           2                           0             3   \n",
       "\n",
       "      Kuechen KuechenQualitaet  Kamine Kaminqualitaet Heizung  \\\n",
       "Id                                                              \n",
       "1           1               Ar       0            NaN   GasWD   \n",
       "2           1               Du       0            NaN   GasWO   \n",
       "3           1               Du       1             Du   GasWO   \n",
       "4           1               Du       1             Du   GasWO   \n",
       "5           1               Gu       2             Gu   GasWO   \n",
       "...       ...              ...     ...            ...     ...   \n",
       "1996        1               Gu       0            NaN   GasWO   \n",
       "1997        1               Gu       2             Du   GasWO   \n",
       "1998        1               Ag       1             Gu   GasWO   \n",
       "1999        1               Du       0            NaN   GasWO   \n",
       "2000        1               Gu       1             Du   GasWO   \n",
       "\n",
       "     Heizungsqualitaet Klimalanlage Elektrik  Holzdeckflaeche  \\\n",
       "Id                                                              \n",
       "1                   Ar            N   SichAr             0.00   \n",
       "2                   Gu            J    SLSRo             0.00   \n",
       "3                   Gu            J   SichAr             0.00   \n",
       "4                   Ag            J    SLSRo            41.40   \n",
       "5                   Ar            J    SLSRo             0.00   \n",
       "...                ...          ...      ...              ...   \n",
       "1996                Ag            J    SLSRo             5.76   \n",
       "1997                Du            J    SLSRo            23.76   \n",
       "1998                Ag            J    SLSRo            18.90   \n",
       "1999                Du            J    SLSRo             0.00   \n",
       "2000                Ag            J    SLSRo             0.00   \n",
       "\n",
       "      OffeneVerandaflaeche  GeschlosseneVerandaflaeche  Verkaufspreis  \n",
       "Id                                                                     \n",
       "1                     0.00                        0.00       69700.00  \n",
       "2                     4.41                        0.00      133450.00  \n",
       "3                     0.00                       38.61      165750.00  \n",
       "4                     3.78                        0.00      157250.00  \n",
       "5                     0.00                        0.00      195500.00  \n",
       "...                    ...                         ...            ...  \n",
       "1996                  0.00                        0.00      110500.00  \n",
       "1997                  2.88                        0.00      166600.00  \n",
       "1998                  0.00                        0.00      315321.95  \n",
       "1999                  0.00                        0.00      124100.00  \n",
       "2000                  0.00                        8.91      155465.00  \n",
       "\n",
       "[2000 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_20_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OberirdischeWohnflaeche</th>\n",
       "      <th>1Stockflaeche</th>\n",
       "      <th>2Stockflaeche</th>\n",
       "      <th>GeringequalitaetFlaeche</th>\n",
       "      <th>OberirdischeRaueme</th>\n",
       "      <th>Funktionalitaet</th>\n",
       "      <th>OberirdischeVollbadezimmer</th>\n",
       "      <th>OberirdischeHalbbadezimmer</th>\n",
       "      <th>Schlafzimmer</th>\n",
       "      <th>Kuechen</th>\n",
       "      <th>KuechenQualitaet</th>\n",
       "      <th>Kamine</th>\n",
       "      <th>Kaminqualitaet</th>\n",
       "      <th>Heizung</th>\n",
       "      <th>Heizungsqualitaet</th>\n",
       "      <th>Klimalanlage</th>\n",
       "      <th>Elektrik</th>\n",
       "      <th>Holzdeckflaeche</th>\n",
       "      <th>OffeneVerandaflaeche</th>\n",
       "      <th>GeschlosseneVerandaflaeche</th>\n",
       "      <th>Verkaufspreis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>65.97</td>\n",
       "      <td>65.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Ar</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WandOf</td>\n",
       "      <td>Sc</td>\n",
       "      <td>N</td>\n",
       "      <td>SichDu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11135.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>74.88</td>\n",
       "      <td>74.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Du</td>\n",
       "      <td>1</td>\n",
       "      <td>Gu</td>\n",
       "      <td>GasWO</td>\n",
       "      <td>Du</td>\n",
       "      <td>N</td>\n",
       "      <td>SLSRo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10870.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      OberirdischeWohnflaeche  1Stockflaeche  2Stockflaeche  \\\n",
       "Id                                                            \n",
       "542                     65.97          65.97            0.0   \n",
       "1911                    74.88          74.88            0.0   \n",
       "\n",
       "      GeringequalitaetFlaeche  OberirdischeRaueme Funktionalitaet  \\\n",
       "Id                                                                  \n",
       "542                       0.0                   4             NaN   \n",
       "1911                      0.0                   5             Typ   \n",
       "\n",
       "      OberirdischeVollbadezimmer  OberirdischeHalbbadezimmer  Schlafzimmer  \\\n",
       "Id                                                                           \n",
       "542                            1                           0             2   \n",
       "1911                           1                           0             2   \n",
       "\n",
       "      Kuechen KuechenQualitaet  Kamine Kaminqualitaet Heizung  \\\n",
       "Id                                                              \n",
       "542         1               Ar       0            NaN  WandOf   \n",
       "1911        1               Du       1             Gu   GasWO   \n",
       "\n",
       "     Heizungsqualitaet Klimalanlage Elektrik  Holzdeckflaeche  \\\n",
       "Id                                                              \n",
       "542                 Sc            N   SichDu              0.0   \n",
       "1911                Du            N    SLSRo              0.0   \n",
       "\n",
       "      OffeneVerandaflaeche  GeschlosseneVerandaflaeche  Verkaufspreis  \n",
       "Id                                                                     \n",
       "542                    0.0                         0.0       11135.00  \n",
       "1911                   0.0                         0.0       10870.65  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = df_20_2.loc[542, 'Verkaufspreis']\n",
    "\n",
    "lower_bound = price - (price * 0.9)\n",
    "upper_bound = price + (price * 0.9)\n",
    "\n",
    "similar = df_20_3[(df_20_3['Verkaufspreis'] >= lower_bound) & (df_20_3['Verkaufspreis'] <= upper_bound)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Funktionalität: Typ\n"
     ]
    }
   ],
   "source": [
    "mode = similar['Funktionalitaet'].mode()[0]\n",
    "print(\"Mode of Funktionalität:\", mode)\n"
   ]
  }
 ],
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