notebook.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zachary Brooks: Seller, Invests in historical houses, best neighborhoods, high profits, best timing within a year, should renovate?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "What is historical?\n",
    "- mean of set?\n",
    "- oldest?\n",
    "- more than 80 years?\n",
    "-> lower 30% until 1935/40!\n",
    "- check unique values of years_built, renovated, to be sure there arent only \n",
    "- maybe replace built year with yr_renovated if there is no values\n",
    "\n",
    "\n",
    "Best neighborhoods? -> most expensive/desired ones\n",
    "- most potential price wise\n",
    "- most expensive ones, highest average rating\n",
    "- least time between **datedate and **view or **datedate\n",
    "- check **view as an indicator for popularity? - connection  \n",
    "\n",
    "\n",
    "Best timing for selling/ buying in those areas\n",
    "- check for distinctive patterns in **datedate\n",
    "- \n",
    "\n",
    "\n",
    "Should renovate?\n",
    "- How much does value differ if renovated?\n",
    "- time between **datedate and **year_renovated?\n",
    "- set duration since last renovation (if higher -> renovate)\n",
    "**condition\n",
    "**grade\n",
    "**view\n",
    "\n",
    "Depending on area?\n",
    "- whats prices and conditions in other comparable houses of same areas?\n",
    "- when are prices lowest, when highest? (1 year)\n",
    "- \n",
    "\n",
    "Customers with a lot of money do prefer ready to move in houses?!\n",
    "- do top **condition **grade houses sell fast? Whats the mean?\n",
    "\n",
    "\n",
    "Hypothesis:\n",
    "\n",
    "general:\n",
    "- older houses in best neighborhoods are expensive and mostly in good condition (no renovation needed/ not worth it because of monument protection reasons)\n",
    "- houses get sold more often during autumn and spring time (check for news that might cause value increase/decrease ..)\n",
    "\n",
    "Customer:\n",
    "\n",
    "(- if houses condition is not good, the investment is probably not worth it?!(higher prices after renovation?) (high material costs since covid))\n",
    "- Historical houses are an expensive niche, rich people prefer new ones. (compare **price and **built_year, **date)\n",
    "(- there are not that many old houses (older than x, 80 years probably))\n",
    "\n",
    "-> old houses are built in a style that differs from modern houses. Other materials, architecture that was common during their built period. **grade and **built_year \n",
    "\n",
    "\n",
    "______\n",
    "- Check connections between price and different variables\n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jr/p21bhxh16zs45yz56c_h0pbw0000gn/T/ipykernel_17853/163725736.py:7: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\n",
      "  pd.set_option('display.max_colwidth', -1)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>10/13/2014</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
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       "      <td>1180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>12/9/2014</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
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       "      <th>2</th>\n",
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       "      <td>180000.0</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>12/9/2014</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2/18/2015</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
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       "      <td>-122.045</td>\n",
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       "      <td>2.50</td>\n",
       "      <td>2310</td>\n",
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       "      <td>2014</td>\n",
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       "      <th>21594</th>\n",
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       "      <td>98027</td>\n",
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       "<p>21597 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "               id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0      7129300520  10/13/2014  221900.0  3         1.00       1180          \n",
       "1      6414100192  12/9/2014   538000.0  3         2.25       2570          \n",
       "2      5631500400  2/25/2015   180000.0  2         1.00       770           \n",
       "3      2487200875  12/9/2014   604000.0  4         3.00       1960          \n",
       "4      1954400510  2/18/2015   510000.0  3         2.00       1680          \n",
       "...           ...        ...        ... ..          ...        ...          \n",
       "21592  263000018   5/21/2014   360000.0  3         2.50       1530          \n",
       "21593  6600060120  2/23/2015   400000.0  4         2.50       2310          \n",
       "21594  1523300141  6/23/2014   402101.0  2         0.75       1020          \n",
       "21595  291310100   1/16/2015   400000.0  3         2.50       1600          \n",
       "21596  1523300157  10/15/2014  325000.0  2         0.75       1020          \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  condition  grade  sqft_above  \\\n",
       "0      5650      1.0    NaN          0.0   3          7      1180         \n",
       "1      7242      2.0     0.0         0.0   3          7      2170         \n",
       "2      10000     1.0     0.0         0.0   3          6      770          \n",
       "3      5000      1.0     0.0         0.0   5          7      1050         \n",
       "4      8080      1.0     0.0         0.0   3          8      1680         \n",
       "...     ...      ...     ...         ...  ..         ..       ...         \n",
       "21592  1131      3.0     0.0         0.0   3          8      1530         \n",
       "21593  5813      2.0     0.0         0.0   3          8      2310         \n",
       "21594  1350      2.0     0.0         0.0   3          7      1020         \n",
       "21595  2388      2.0    NaN          0.0   3          8      1600         \n",
       "21596  1076      2.0     0.0         0.0   3          7      1020         \n",
       "\n",
       "      sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "0      0.0           1955      0.0           98178    47.5112 -122.257   \n",
       "1      400.0         1951      1991.0        98125    47.7210 -122.319   \n",
       "2      0.0           1933     NaN            98028    47.7379 -122.233   \n",
       "3      910.0         1965      0.0           98136    47.5208 -122.393   \n",
       "4      0.0           1987      0.0           98074    47.6168 -122.045   \n",
       "...    ...            ...      ...             ...        ...      ...   \n",
       "21592  0.0           2009      0.0           98103    47.6993 -122.346   \n",
       "21593  0.0           2014      0.0           98146    47.5107 -122.362   \n",
       "21594  0.0           2009      0.0           98144    47.5944 -122.299   \n",
       "21595  0.0           2004      0.0           98027    47.5345 -122.069   \n",
       "21596  0.0           2008      0.0           98144    47.5941 -122.299   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "0      1340           5650        \n",
       "1      1690           7639        \n",
       "2      2720           8062        \n",
       "3      1360           5000        \n",
       "4      1800           7503        \n",
       "...     ...            ...        \n",
       "21592  1530           1509        \n",
       "21593  1830           7200        \n",
       "21594  1020           2007        \n",
       "21595  1410           1287        \n",
       "21596  1020           1357        \n",
       "\n",
       "[21597 rows x 21 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data\n",
    "df = pd.read_csv(\"data/King_County_House_prices_dataset.csv\")\n",
    "\n",
    "# as column 'condition' is truncated in the display, disable column truncation\n",
    "pd.set_option('display.max_columns', None) \n",
    "pd.set_option('display.width', None) \n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns that are not useful\n",
    "df = df.drop([\"lat\", \"long\" ],axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[160], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m df_unique_yr_renovated \u001b[39m=\u001b[39m df[\u001b[39m\"\u001b[39m\u001b[39myr_renovated\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mnunique()\n\u001b[0;32m----> 2\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39msorted\u001b[39;49m(df_unique_yr_renovated))\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "df_unique_yr_renovated = df[\"yr_renovated\"].nunique()\n",
    "print(sorted(df_unique_yr_renovated))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
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       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.159700e+04</td>\n",
       "      <td>2.159700e+04</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>2.159700e+04</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>19221.000000</td>\n",
       "      <td>21534.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "      <td>21597.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.580474e+09</td>\n",
       "      <td>5.402966e+05</td>\n",
       "      <td>3.373200</td>\n",
       "      <td>2.115826</td>\n",
       "      <td>2080.321850</td>\n",
       "      <td>1.509941e+04</td>\n",
       "      <td>1.494096</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>0.233863</td>\n",
       "      <td>3.409825</td>\n",
       "      <td>7.657915</td>\n",
       "      <td>1788.596842</td>\n",
       "      <td>98077.951845</td>\n",
       "      <td>1986.620318</td>\n",
       "      <td>12758.283512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.876736e+09</td>\n",
       "      <td>3.673681e+05</td>\n",
       "      <td>0.926299</td>\n",
       "      <td>0.768984</td>\n",
       "      <td>918.106125</td>\n",
       "      <td>4.141264e+04</td>\n",
       "      <td>0.539683</td>\n",
       "      <td>0.086825</td>\n",
       "      <td>0.765686</td>\n",
       "      <td>0.650546</td>\n",
       "      <td>1.173200</td>\n",
       "      <td>827.759761</td>\n",
       "      <td>53.513072</td>\n",
       "      <td>685.230472</td>\n",
       "      <td>27274.441950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000102e+06</td>\n",
       "      <td>7.800000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.123049e+09</td>\n",
       "      <td>3.220000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1430.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.904930e+09</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.308900e+09</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068500e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.900000e+09</td>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
       "count  2.159700e+04  2.159700e+04  21597.000000  21597.000000  21597.000000   \n",
       "mean   4.580474e+09  5.402966e+05  3.373200      2.115826      2080.321850    \n",
       "std    2.876736e+09  3.673681e+05  0.926299      0.768984      918.106125     \n",
       "min    1.000102e+06  7.800000e+04  1.000000      0.500000      370.000000     \n",
       "25%    2.123049e+09  3.220000e+05  3.000000      1.750000      1430.000000    \n",
       "50%    3.904930e+09  4.500000e+05  3.000000      2.250000      1910.000000    \n",
       "75%    7.308900e+09  6.450000e+05  4.000000      2.500000      2550.000000    \n",
       "max    9.900000e+09  7.700000e+06  33.000000     8.000000      13540.000000   \n",
       "\n",
       "           sqft_lot        floors    waterfront          view     condition  \\\n",
       "count  2.159700e+04  21597.000000  19221.000000  21534.000000  21597.000000   \n",
       "mean   1.509941e+04  1.494096      0.007596      0.233863      3.409825       \n",
       "std    4.141264e+04  0.539683      0.086825      0.765686      0.650546       \n",
       "min    5.200000e+02  1.000000      0.000000      0.000000      1.000000       \n",
       "25%    5.040000e+03  1.000000      0.000000      0.000000      3.000000       \n",
       "50%    7.618000e+03  1.500000      0.000000      0.000000      3.000000       \n",
       "75%    1.068500e+04  2.000000      0.000000      0.000000      4.000000       \n",
       "max    1.651359e+06  3.500000      1.000000      4.000000      5.000000       \n",
       "\n",
       "              grade    sqft_above       zipcode  sqft_living15     sqft_lot15  \n",
       "count  21597.000000  21597.000000  21597.000000  21597.000000   21597.000000   \n",
       "mean   7.657915      1788.596842   98077.951845  1986.620318    12758.283512   \n",
       "std    1.173200      827.759761    53.513072     685.230472     27274.441950   \n",
       "min    3.000000      370.000000    98001.000000  399.000000     651.000000     \n",
       "25%    7.000000      1190.000000   98033.000000  1490.000000    5100.000000    \n",
       "50%    7.000000      1560.000000   98065.000000  1840.000000    7620.000000    \n",
       "75%    8.000000      2210.000000   98118.000000  2360.000000    10083.000000   \n",
       "max    13.000000     9410.000000   98199.000000  6210.000000    871200.000000  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21597 entries, 0 to 21596\n",
      "Data columns (total 19 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             21597 non-null  int64  \n",
      " 1   date           21597 non-null  object \n",
      " 2   price          21597 non-null  float64\n",
      " 3   bedrooms       21597 non-null  int64  \n",
      " 4   bathrooms      21597 non-null  float64\n",
      " 5   sqft_living    21597 non-null  int64  \n",
      " 6   sqft_lot       21597 non-null  int64  \n",
      " 7   floors         21597 non-null  float64\n",
      " 8   waterfront     19221 non-null  float64\n",
      " 9   view           21534 non-null  float64\n",
      " 10  condition      21597 non-null  int64  \n",
      " 11  grade          21597 non-null  int64  \n",
      " 12  sqft_above     21597 non-null  int64  \n",
      " 13  sqft_basement  21597 non-null  object \n",
      " 14  yr_built       21597 non-null  int64  \n",
      " 15  yr_renovated   17755 non-null  float64\n",
      " 16  zipcode        21597 non-null  int64  \n",
      " 17  sqft_living15  21597 non-null  int64  \n",
      " 18  sqft_lot15     21597 non-null  int64  \n",
      "dtypes: float64(6), int64(11), object(2)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    21420\n",
       "True     177  \n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicate rows in id column, dont drop now. Probably a house with several units\n",
    "df[\"id\"].duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert trending_date and publish_time into datetime and extract date part from publish_time\n",
    "# format specifies the present form of our argument we pass into the function\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df['yr_built'] = pd.to_datetime(df['yr_built'], format=\"%Y\")\n",
    "df['yr_renovated'] = pd.to_datetime(df['yr_renovated'], format=\"%y-%m-%d\")\n",
    "# use .year .month function\n",
    "# df.date.dt.month\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21597 entries, 0 to 21596\n",
      "Data columns (total 19 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   id             21597 non-null  int64         \n",
      " 1   date           21597 non-null  datetime64[ns]\n",
      " 2   price          21597 non-null  float64       \n",
      " 3   bedrooms       21597 non-null  int64         \n",
      " 4   bathrooms      21597 non-null  float64       \n",
      " 5   sqft_living    21597 non-null  int64         \n",
      " 6   sqft_lot       21597 non-null  int64         \n",
      " 7   floors         21597 non-null  float64       \n",
      " 8   waterfront     19221 non-null  float64       \n",
      " 9   view           21534 non-null  float64       \n",
      " 10  condition      21597 non-null  int64         \n",
      " 11  grade          21597 non-null  int64         \n",
      " 12  sqft_above     21597 non-null  int64         \n",
      " 13  sqft_basement  21597 non-null  object        \n",
      " 14  yr_built       21597 non-null  datetime64[ns]\n",
      " 15  yr_renovated   17755 non-null  datetime64[ns]\n",
      " 16  zipcode        21597 non-null  int64         \n",
      " 17  sqft_living15  21597 non-null  int64         \n",
      " 18  sqft_lot15     21597 non-null  int64         \n",
      "dtypes: datetime64[ns](3), float64(5), int64(10), object(1)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['yr_renovated'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1955-01-01</td>\n",
       "      <td>1970-01-01 00:00:00.000000000</td>\n",
       "      <td>98178</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1951-01-01</td>\n",
       "      <td>1970-01-01 00:00:00.000001991</td>\n",
       "      <td>98125</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1933-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>98028</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1965-01-01</td>\n",
       "      <td>1970-01-01 00:00:00.000000000</td>\n",
       "      <td>98136</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>1970-01-01 00:00:00.000000000</td>\n",
       "      <td>98074</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520 2014-10-13  221900.0  3         1.00       1180          \n",
       "1  6414100192 2014-12-09  538000.0  3         2.25       2570          \n",
       "2  5631500400 2015-02-25  180000.0  2         1.00       770           \n",
       "3  2487200875 2014-12-09  604000.0  4         3.00       1960          \n",
       "4  1954400510 2015-02-18  510000.0  3         2.00       1680          \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  condition  grade  sqft_above  \\\n",
       "0  5650      1.0    NaN          0.0   3          7      1180         \n",
       "1  7242      2.0     0.0         0.0   3          7      2170         \n",
       "2  10000     1.0     0.0         0.0   3          6      770          \n",
       "3  5000      1.0     0.0         0.0   5          7      1050         \n",
       "4  8080      1.0     0.0         0.0   3          8      1680         \n",
       "\n",
       "  sqft_basement   yr_built                  yr_renovated  zipcode  \\\n",
       "0  0.0          1955-01-01 1970-01-01 00:00:00.000000000  98178     \n",
       "1  400.0        1951-01-01 1970-01-01 00:00:00.000001991  98125     \n",
       "2  0.0          1933-01-01 NaT                            98028     \n",
       "3  910.0        1965-01-01 1970-01-01 00:00:00.000000000  98136     \n",
       "4  0.0          1987-01-01 1970-01-01 00:00:00.000000000  98074     \n",
       "\n",
       "   sqft_living15  sqft_lot15  \n",
       "0  1340           5650        \n",
       "1  1690           7639        \n",
       "2  2720           8062        \n",
       "3  1360           5000        \n",
       "4  1800           7503        "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1900-01-01 00:00:00')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"yr_built\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0       2014-10-13\n",
       "1       2014-12-09\n",
       "2       2015-02-25\n",
       "3       2014-12-09\n",
       "4       2015-02-18\n",
       "           ...    \n",
       "21592   2014-05-21\n",
       "21593   2015-02-23\n",
       "21594   2014-06-23\n",
       "21595   2015-01-16\n",
       "21596   2014-10-15\n",
       "Name: date, Length: 21597, dtype: datetime64[ns]>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"date\"].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21597, 19)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id               0   \n",
       "date             0   \n",
       "price            0   \n",
       "bedrooms         0   \n",
       "bathrooms        0   \n",
       "sqft_living      0   \n",
       "sqft_lot         0   \n",
       "floors           0   \n",
       "waterfront       2376\n",
       "view             63  \n",
       "condition        0   \n",
       "grade            0   \n",
       "sqft_above       0   \n",
       "sqft_basement    0   \n",
       "yr_built         0   \n",
       "yr_renovated     3842\n",
       "zipcode          0   \n",
       "sqft_living15    0   \n",
       "sqft_lot15       0   \n",
       "dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data cleaning\n",
    "# check for missing values\n",
    "df.isnull().sum()\n",
    "# waterfront 0 = False, 1 = True -> look for nan and delete or not\n",
    "# view: 0 to 4 on how good the view of property was -> nulls are ok\n",
    "# yr_renovated: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# renovation column is useless\n",
    "df.hist(column=\"yr_renovated\", bins=8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'yr_built'}>]], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column=\"yr_built\", bins=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'price'}>]], dtype=object)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column=\"price\", bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='yr_built', ylabel='price'>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation time/price?!\n",
    "df.plot.scatter(x=\"yr_built\", y=\"price\", s=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='yr_built', ylabel='grade'>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y1P+xd1Ve5SgpwaPtT12hAdPeU2l5lZKTEvT1zNG1+u+Tky9HkkfS7llZteqrOf4HXx0M6suszdw2x7pq7iptKvQrI82nZQ9dVuv4obM+VGHxcaWltNUnOcNrnW8y+zPPN9ei5lrVtGdWVq11NNfFbV3N80Odi8k83rwuNesZcLo3aB3M2tzW3RzLrXa3626Ob/bXd0q+qhwpwSO1SUyodx1D3TbXqeZYu3JrH1/fPVJX//Vdd7d7yq2WULfN2iPZv9tcwt02P78i2X+0a2/KbZvmEm94JqiRiksrgtq8gp0qLC5VXsHOOrfzNxWp0jnZNkR5lRPUlpZXBbVm/86P51W3Zn01xzf7Mmszt82xNhX6g1rz+MLi40Gteb7J7M8831yLUJjr4rauplDnYjKPN69LzXrMdTBrc1t3cyy32t2uuzm+2V/15ahy3NcxVOY61RyrLqHeI+HU61ZLqMzaI91/NJmfX0C8IQQ1UkpyYlCbndlPaSnJys7sV+d2VkaqWnlOtg2RlOAJapOTEoJas3/Pj+dVt2Z9Ncc3+zJrM7fNsTLSfEGteXxaStug1jzfZPZnnm+uRSjMdXFbV1OoczGZx5vXpWY95jqYtbmtuzmWW+1u190c3+yv+nIkeNzXMVTmOtUcqy6h3iPh1OtWS6jM2iPdfzSZn19AvPE4jtOiQ3xJSYl8Pp/8fr+8Xm+sywEAAA3QFN+/eSYIAABYiRAEAACsRAgCAABWIgQBAAArEYIAAICVCEEAAMBKhCAAAGAlQhAAALASIQgAAFiJEAQAAKxECAIAAFYiBAEAACsRggAAgJUaHYL+67/+S0OHDlVqaqr27t0rSZozZ47+9re/Raw4AACAaGlUCMrLy9PkyZN1xRVXqLi4WJWVlZKklJQUzZkzJ5L1AQAAREWjQtDcuXP10ksv6Q9/+INatWoVePyiiy7S5s2bI1YcAABAtDQqBO3evVuDBg2q9XibNm109OjRsIsCAACItkaFoD59+mjjxo21Hl++fLkGDBgQbk0AAABRl9iYkyZPnqzx48fr+PHjchxHn332mRYvXqzc3Fy9/PLLka4RAAAg4hoVgu6++24lJydr6tSpOnbsmG6++Walpqbqueee00033RTpGgEAACLO4ziOE04Hx44d05EjR9S1a9dI1RRRJSUl8vl88vv98nq9sS4HAAA0QFN8/27UM0E1tWvXTu3atYtELQAAAE2mwSFo0KBB8ng8DTp2/fr1jS4IAACgKTQ4BI0dOzbw7+PHj+s//uM/dPbZZ2vw4MGSpDVr1mjLli164IEHIl4kAABApDU4BE2fPj3w77vvvlsTJkzQzJkzax2zf//+yFUHAAAQJY16Y7TP59O6devUv3//oMe3b9+uiy66SH6/P2IFhos3RgMAEH+a4vt3o35ZYnJysj755JNaj3/yySdq27Zt2EVVq6ys1LRp09SnTx8lJyerX79+mjlzpsL8gTYAAIDG/XTYxIkTlZ2drfXr1+viiy+WJK1du1avvPKKpk2bFrHinn76aeXl5WnBggU655xztG7dOt15553y+XyaMGFCxMYBAAD2aVQIysnJUd++ffXcc89p0aJFkqQBAwZo3rx5uuGGGyJW3KeffqoxY8YoKytLktS7d28tXrxYn332WcTGAAAAdmr07wm64YYbIhp46jJkyBD95S9/0bZt2/TTn/5U//jHP7Rq1So9++yzUR0XAAC0fGH/ssRoysnJUUlJic466yy1atVKlZWVevLJJ3XLLbec8pyysjKVlZUFtktKSqJSW++c/MC/98zKUv/H3lV5laOkBI+2P3WF+k7JV5UjJXikXbm195vbZn/R3K6pMX2Zc4t27fWtVbhzCXfu5z/+vopLK5SSnKiN00dp6KwPVVh8XGkpbfVJznBdNXeVNhX6lZHm07KHLqt1fM3+k5MSVFpepeSkBH09c3TYtZq1mNv11dLU91Rz2mYuzWM7nmu3eS7xplFvjK6srNQzzzyjiy++WN27d1enTp2CPiLljTfe0F//+le9+uqrWr9+vRYsWKBnnnlGCxYsOOU5ubm58vl8gY/09PSI1VOf8ionqP2xCbTmfrONJ+bcoq05rZU59+LSiqC2sPh4ULup0B/UmsfXVFpeFdSGy6zFbOurBQBs0KgQ9Pjjj+vZZ5/VjTfeKL/fr8mTJ+uaa65RQkKCZsyYEbHiHn30UeXk5Oimm27SwIEDddttt2nSpEnKzc095TlTpkyR3+8PfDTV7y1KSvAEtT82gdbcb7bxxJxbtDWntTLnnpKcGNSmpbQNajPSfEGteXxNyUkJQW24zFrMtr5aAMAGjfo9Qf369dPzzz+vrKwsdezYURs3bgw8tmbNGr366qsRKa5z58564oknlJ2dHXgsNzdX8+bN07Zt2xrUB78nCACA+NNsf0/QwYMHNXDgQElShw4dAr8c8V//9V+Vn1/3a4eNceWVV+rJJ59Ufn6+9uzZoyVLlujZZ5/V1VdfHbExAACAnRoVgnr06KEDBw5IOvms0N///ndJ0ueff642bdpErLi5c+fquuuu0wMPPKABAwbokUce0X333Vfrz3UAAACEqlEvh+Xk5Mjr9eqxxx7T66+/rltvvVW9e/fWvn37NGnSJM2aNSsatTYKL4cBABB/muL7d6NCkGnNmjX69NNP1b9/f1155ZWRqCtiCEEAAMSfpvj+HfKPhZSXl+u+++4L/E0vSbr00kt16aWXRrw4AACAaAn5PUFJSUl66623olELAABAk2nUG6PHjh2rpUuXRrgUAACAptOo35LWv39//du//Zs++eQTXXjhhWrfvn3Qfv7COwAAaO4a9cbo6vcC1dmhx6Ndu3aFVVQk8cZoAADiT7N8Y7Qk7d69O9J1AAAANKlGhaDJkyfX+bjH41Hbtm11xhlnaMyYMRH9Y6oAAACR1KiXw4YNG6b169ersrJSZ555piRp27ZtatWqlc466yxt3bpVHo9Hq1at0tlnnx3xokPBy2EAAMSfZvu3w8aMGaMRI0aoqKhIX3zxhb744gt98803+tWvfqVx48apsLBQv/jFLzRp0qRI1wsAABARjXomKC0tTR988EGtZ3m2bNmikSNHqrCwUOvXr9fIkSP13XffRazYxuCZIAAA4k+zfSbI7/fr0KFDtR7/v//7P5WUlEiSUlJSdOLEifCqAwAAiJJGvxz2m9/8RkuWLNE333yjb775RkuWLNFdd92lsWPHSpI+++wz/fSnP41krQAAABHTqJfDjhw5okmTJmnhwoWqqKiQJCUmJuqOO+7Q7Nmz1b59e23cuFGSdP7550ey3pDxchgAAPGn2f8V+SNHjgR+MWLfvn3VoUOHiBUWKYQgAADiT7P9ZYnVOnTooIyMjEjVAgAA0GQa9Z4gAACAeEcIAgAAViIEAQAAKxGCAACAlQhBAADASoQgAABgJUIQAACwEiEIAABYiRAEAACsRAgCAABWCuvPZtisd05+4N97ZmXF1XZNsa6luc+l/2PvqrzKUVKCR9ufukIDpr2n0vIqJScl6OuZo3X+4++ruLRCKcmJ2jh9lPpOyVeVIyV4pF25WbWOb6nXJZ5rZy7Nczuea7dpLvGOZ4KAepRXOUFtaXlVUFtcWhHU/nhYoDWPBwA0H4QgoB5JCZ6gNjkpIahNSU4Man88LNCaxwMAmg+P4zhOrIuIppKSEvl8Pvn9fnm93liXAwAAGqApvn/z31MAAGAlQhAAALASIQgAAFiJEAQAAKxECAIAAFYiBAEAACsRggAAgJUIQQAAwEqEIAAAYCVCEAAAsBIhCAAAWIkQBAAArEQIAgAAViIEAQAAKxGCAACAlQhBAADASoQgAABgJUIQAACwEiEIAABYiRAEAACsRAgCAABWIgQBAAArEYIAAICVCEEAAMBKhCAAAGAlQhAAALASIQgAAFip2YegwsJC3XrrrercubOSk5M1cOBArVu3LtZlAQCAOJcY6wLq88MPP2jo0KEaNmyY3nvvPXXp0kXbt2/XaaedFuvSAABAnGvWIejpp59Wenq65s2bF3isT58+MawIAAC0FM06BC1btkyjRo3S9ddfr48++khpaWl64IEHdM8995zynLKyMpWVlQW2S0pKolJb75z8wL/3zMrSgGnvqbS8SslJCfp65mhdNXeVNhX6lZHm07KHLqt1fCy3a4p1LfE+lz45+XIkeSTtruM+6DslX1WOlOCRduU277nE83WI5lz6P/auyqscJSV4tP2pK2p9bpv3QLi1mOOZ95S5f9Gavcor2KnszH669dJeQfdcK48n6Fi3sc5//H0Vl1YoJTlRG6ePqnX/mnN1+zo3dNaHKiw+rrSUtvokZ7jr+eZca5qweIPyNxUpKyNVz48bVOtct223801u+83+TOZ1qY+5Tua2eV1iyZx3KPNsjpr1e4J27dqlvLw89e/fX++//76ys7M1YcIELViw4JTn5ObmyufzBT7S09ObpNbS8qqgdlOhP6hFy+QYrXkfVP24o7pF/Cn/8eJVt+bntnkPRHo8854y9+cV7FRhcanyCnZKCr7nzGPdxiourQhqzfvXnKvb17nC4uNBrdv55lxryt9UpErnZFvXuW6t2/kmt/1mfybzutTHXCezNa9LLJnzDmWezVGzDkFVVVW64IIL9NRTT2nQoEG69957dc899+jPf/7zKc+ZMmWK/H5/4GP//v1NUmtyUkJQm5HmC2rRMnmM1rwPEn7cUd0i/iT9ePGqW/Nz27wHIj2eeU+Z+7Mz+yktJVnZmf0kBd9z5rFuY6UkJwa15v1rztXt61xaStug1u18c641ZWWkqpXnZFvXuW6t2/kmt/1mfybzutTHXCezNa9LLJnzDmWezZHHcZxm+3/UXr166Ve/+pVefvnlwGN5eXl64oknVFhY2KA+SkpK5PP55Pf75fV6o1UqAACIoKb4/t2snwkaOnSotm7dGvTYtm3b1KtX/L3uCAAAmpdmHYImTZqkNWvW6KmnntKOHTv06quv6i9/+YvGjx8f69IAAECca9Yh6Gc/+5mWLFmixYsX69xzz9XMmTM1Z84c3XLLLbEuDQAAxLlm/Z6gSOA9QQAAxB/r3xMEAAAQLYQgAABgJUIQAACwEiEIAABYiRAEAACsRAgCAABWIgQBAAArEYIAAICVCEEAAMBKhCAAAGAlQhAAALASIQgAAFiJEAQAAKxECAIAAFYiBAEAACsRggAAgJUIQQAAwEqEIAAAYCVCEAAAsBIhCAAAWIkQBAAArEQIAgAAViIEAQAAKxGCAACAlQhBAADASoQgAABgpcRYFxCvBkx7T6XlVUpOStDXM0dr0Zq9yivYqezMfrr10l61jp+weIPyNxUpKyNVz48b5Nrf+Y+/r+LSCqUkJ2rj9FG1zh8660MVFh9XWkpbfZIzvNb49Y131dxV2lToV0aaT8seusz13FBrcTvenKup/2PvqrzKUVKCR9ufuqLW8TXHy848I6h2t7mYtbqNba6Vud/tOpj7zXpqbkuqd53MWsy+e+fkB+axZ1ZWrW2362r213dKvqocKcEj7crNqrVd8/jvj5445TX6JGd4rbnU11dd18Xt8yuU+9/t89FtLDfm+eH2F45ojx3uXMOpL5brGul6mnLdEIxnghqptLwqqM0r2KnC4lLlFeys8/j8TUWqdE62DemvuLQiqDXPLyw+HtSa49c33qZCf1Drdm6otbgdb87VVF7lBLXm8TXHM2t3m4tZq9vY5lqZ+92ug7nfrKfmtts6mbW4zcXkdl3N/n6c4inbmsfXd43qmkt9fdXF7fMrlPvfbb/bWG7c7smmFO2xw51rOPXFcl3r0pRzaW5zj2eEoEZKTkoIarMz+yktJVnZmf3qPD4rI1WtPAr8j9+tv5TkxKDWPD8tpW1Qa45f33gZab6g1u3cUGtxO96cqykpwRPUmsfXHM+s3W0uZq1uY5trZe53uw7mfrOemttu62TW4jYXk9t1Nfv7cYqnbGseX981qmsu9fVVF7fPr1Duf7f9bmO5cbsnm1K0xw53ruHUF8t1rUtTzqW5zT2eeRzHcWJdRDSVlJTI5/PJ7/fL6/XGuhwAANAATfH9m2eCAACAlQhBAADASoQgAABgJUIQAACwEiEIAABYiRAEAACsRAgCAABWIgQBAAArEYIAAICVCEEAAMBKhCAAAGAlQhAAALASIQgAAFiJEAQAAKxECAIAAFYiBAEAACsRggAAgJUIQQAAwEqEIAAAYCVCEAAAsBIhCAAAWIkQBAAArEQIAgAAViIEAQAAKxGCAACAlQhBAADASnEVgmbNmiWPx6OJEyfGuhQAABDn4iYEff755/rP//xPZWRkxLoUAADQAsRFCDpy5IhuueUWvfTSSzrttNNiXQ4AAGgB4iIEjR8/XllZWRoxYoTrsWVlZSopKQn6iIZFa/Zq6KwVWrRmb1T6j6YJizeo35R8TVi8oc794c7N7fxo9h/p62L2F+7cQu0vnFrdXDV3lXrn5OuquavqPN+8T+rb73ZPhVtrPGnJcwNammYfgl577TWtX79eubm5DTo+NzdXPp8v8JGenh6VuvIKdqqwuFR5BTuj0n805W8qUqVzsq1LuHNzOz+a/Uf6upj9hTu3UPsLp1Y3mwr9Qa15vnmf1Lff7Z4Kt9Z40pLnBrQ0zToE7d+/Xw8//LD++te/qm3btg06Z8qUKfL7/YGP/fv3R6W27Mx+SktJVnZmv6j0H01ZGalq5TnZ1iXcubmdH83+I31dzP7CnVuo/YVTq5uMNF9Qa55v3if17Xe7p8KtNZ605LkBLY3HcRwn1kWcytKlS3X11VerVatWgccqKyvl8XiUkJCgsrKyoH11KSkpkc/nk9/vl9frjXbJAAAgApri+3diVHqNkOHDh2vz5s1Bj915550666yz9Pvf/941AAEAAJxKsw5BHTt21Lnnnhv0WPv27dW5c+dajwMAAISiWb8nCAAAIFqa9TNBdSkoKIh1CQAAoAXgmSAAAGAlQhAAALASIQgAAFiJEAQAAKxECAIAAFYiBAEAACsRggAAgJUIQQAAwEqEIAAAYCVCEAAAsBIhCAAAWIkQBAAArEQIAgAAViIEAQAAKxGCAACAlQhBAADASoQgAABgJUIQAACwEiEIAABYiRAEAACsRAgCAABWIgQBAAArEYIAAICVCEEAAMBKhCAAAGAlQhAAALASIQhNbtGavRo6a4UWrdkb61IAABYjBKHJ5RXsVGFxqfIKdsa6FACAxQhBaHLZmf2UlpKs7Mx+sS4FAGAxj+M4TqyLiKaSkhL5fD75/X55vd5YlwMAABqgKb5/80wQAACwEiEIAABYiRAEAACsRAgCAABWIgQBAAArEYIAAICVCEEAAMBKhCAAAGAlQhAAALASIQgAAFiJEAQAAKxECAIAAFYiBAEAACslxrqAaHMcR9LJv0YLAADiQ/X37erv49HQ4kPQ4cOHJUnp6ekxrgQAAITq8OHD8vl8Uenb40QzYjUDVVVVKioqUseOHeXxeCLWb0lJidLT07V//355vd6I9WsD1q5xWLfGY+0ah3VrPNaucWquW8eOHXX48GGlpqYqISE6795p8c8EJSQkqEePHlHr3+v1coM3EmvXOKxb47F2jcO6NR5r1zjV6xatZ4Cq8cZoAABgJUIQAACwEiGokdq0aaPp06erTZs2sS4l7rB2jcO6NR5r1zisW+Oxdo3T1OvW4t8YDQAAUBeeCQIAAFYiBAEAACsRggAAgJWsDkEff/yxrrzySqWmpsrj8Wjp0qVB+7/99lv9+te/Vmpqqtq1a6fLL79c27dvDzrm+PHjGj9+vDp37qwOHTro2muv1bfffht0zL59+5SVlaV27dqpa9euevTRR1VRURHt6UVVuGv3/fff66GHHtKZZ56p5ORk9ezZUxMmTJDf7w/qp6WtXSTuuWqO42j06NF19tPS1k2K3NqtXr1av/zlL9W+fXt5vV794he/UGlpaWD/999/r1tuuUVer1cpKSm66667dOTIkWhPL2oisW4HDx7Ubbfdpu7du6t9+/a64IIL9NZbbwUd09LWLTc3Vz/72c/UsWNHde3aVWPHjtXWrVuDjonU1/+CggJdcMEFatOmjc444wzNnz8/2tOLmkis2z/+8Q+NGzdO6enpSk5O1oABA/Tcc8/VGisS62Z1CDp69KjOO+88vfjii7X2OY6jsWPHateuXfrb3/6mDRs2qFevXhoxYoSOHj0aOG7SpEn67//+b7355pv66KOPVFRUpGuuuSawv7KyUllZWTpx4oQ+/fRTLViwQPPnz9cf//jHJpljtIS7dkVFRSoqKtIzzzyjL7/8UvPnz9fy5ct11113BfppiWsXiXuu2pw5c+r8Legtcd2kyKzd6tWrdfnll2vkyJH67LPP9Pnnn+vBBx8M+m20t9xyi7Zs2aIPPvhA77zzjj7++GPde++9TTLHaIjEut1+++3aunWrli1bps2bN+uaa67RDTfcoA0bNgSOaWnr9tFHH2n8+PFas2aNPvjgA5WXl2vkyJER//q/e/duZWVladiwYdq4caMmTpyou+++W++//36TzjdSIrFuX3zxhbp27apFixZpy5Yt+sMf/qApU6bohRdeCBwTsXVz4DiO40hylixZEtjeunWrI8n58ssvA49VVlY6Xbp0cV566SXHcRynuLjYSUpKct58883AMV9//bUjyVm9erXjOI7z7rvvOgkJCc7BgwcDx+Tl5Tler9cpKyuL8qyaRmPWri5vvPGG07p1a6e8vNxxnJa/duGs24YNG5y0tDTnwIEDtfpp6evmOI1fu0suucSZOnXqKfv96quvHEnO559/Hnjsvffeczwej1NYWBjZScRAY9etffv2zsKFC4P66tSpU+CYlr5ujuM4hw4dciQ5H330keM4kfv6/7vf/c4555xzgsa68cYbnVGjRkV7Sk2iMetWlwceeMAZNmxYYDtS62b1M0H1KSsrkyS1bds28FhCQoLatGmjVatWSTqZVsvLyzVixIjAMWeddZZ69uyp1atXSzr5P8+BAweqW7dugWNGjRqlkpISbdmypSmm0uQasnZ18fv98nq9Skw8+ddcbFu7hq7bsWPHdPPNN+vFF19U9+7da/Vj27pJDVu7Q4cOae3ateratauGDBmibt266V/+5V+C1nb16tVKSUnRRRddFHhsxIgRSkhI0Nq1a5toNk2noffckCFD9Prrr+v7779XVVWVXnvtNR0/flyZmZmS7Fi36pfqO3XqJClyX/9Xr14d1Ef1MdV9xLvGrNup+qnuQ4rcuhGCTqH6okyZMkU//PCDTpw4oaefflrffPONDhw4IOnk6+StW7dWSkpK0LndunXTwYMHA8fU/ASo3l+9ryVqyNqZvvvuO82cOTPo6XPb1q6h6zZp0iQNGTJEY8aMqbMf29ZNatja7dq1S5I0Y8YM3XPPPVq+fLkuuOACDR8+PPAemIMHD6pr165BfScmJqpTp04tcu0aes+98cYbKi8vV+fOndWmTRvdd999WrJkic444wxJLX/dqqqqNHHiRA0dOlTnnnuupMh9/T/VMSUlJUHvVYtHjV0306effqrXX3+9Qd8fQl03QtApJCUl6e2339a2bdvUqVMntWvXTitXrtTo0aOj9tdsW4pQ166kpERZWVk6++yzNWPGjKYvuJloyLotW7ZMK1as0Jw5c2JbbDPTkLWrqqqSJN1333268847NWjQIM2ePVtnnnmmXnnllViWHzMN/VydNm2aiouL9T//8z9at26dJk+erBtuuEGbN2+OYfVNZ/z48fryyy/12muvxbqUuBKJdfvyyy81ZswYTZ8+XSNHjoxgdSfx3bweF154oTZu3Kji4mIdOHBAy5cv1z//+U/17dtXktS9e3edOHFCxcXFQed9++23gZcpunfvXuunBaq363opo6VwW7tqhw8f1uWXX66OHTtqyZIlSkpKCuyzce3c1m3FihXauXOnUlJSlJiYGHjp8Nprrw28NGHjuknua3f66adLks4+++yg8wYMGKB9+/ZJOrk+hw4dCtpfUVGh77//vsWundu67dy5Uy+88IJeeeUVDR8+XOedd56mT5+uiy66KPBm65a8bg8++KDeeecdrVy5Uj169Ag8Hqmv/6c6xuv1Kjk5OdLTaTLhrFu1r776SsOHD9e9996rqVOnBu2L1LoRghrA5/OpS5cu2r59u9atWxd4GeLCCy9UUlKSPvzww8CxW7du1b59+zR48GBJ0uDBg7V58+agLxAffPCBvF5vrS/GLdGp1k46+QzQyJEj1bp1ay1btizofQmS3Wt3qnXLycnRpk2btHHjxsCHJM2ePVvz5s2TZPe6Sadeu969eys1NbXWj+tu27ZNvXr1knRy7YqLi/XFF18E9q9YsUJVVVW65JJLmm4SMXCqdTt27Jgk1XoWt1WrVoFn11riujmOowcffFBLlizRihUr1KdPn6D9kfr6P3jw4KA+qo+p7iPeRGLdJGnLli0aNmyY7rjjDj355JO1xonYuoX0NuoW5vDhw86GDRucDRs2OJKcZ5991tmwYYOzd+9ex3FO/rTSypUrnZ07dzpLly51evXq5VxzzTVBfdx///1Oz549nRUrVjjr1q1zBg8e7AwePDiwv6Kiwjn33HOdkSNHOhs3bnSWL1/udOnSxZkyZUqTzjXSwl07v9/vXHLJJc7AgQOdHTt2OAcOHAh8VFRUOI7TMtcuEvecScZP/LTEdXOcyKzd7NmzHa/X67z55pvO9u3bnalTpzpt27Z1duzYETjm8ssvdwYNGuSsXbvWWbVqldO/f39n3LhxTTrXSAp33U6cOOGcccYZzs9//nNn7dq1zo4dO5xnnnnG8Xg8Tn5+fuC4lrZu2dnZjs/ncwoKCoK+Ph07dixwTCS+/u/atctp166d8+ijjzpff/218+KLLzqtWrVyli9f3qTzjZRIrNvmzZudLl26OLfeemtQH4cOHQocE6l1szoErVy50pFU6+OOO+5wHMdxnnvuOadHjx5OUlKS07NnT2fq1Km1fsS4tLTUeeCBB5zTTjvNadeunXP11Vc7Bw4cCDpmz549zujRo53k5GTnJz/5ifPb3/428GPg8SrctTvV+ZKc3bt3B45raWsXiXvOZIYgx2l56+Y4kVu73Nxcp0ePHk67du2cwYMHO//7v/8btP+f//ynM27cOKdDhw6O1+t17rzzTufw4cNNMcWoiMS6bdu2zbnmmmucrl27Ou3atXMyMjJq/ch8S1u3U319mjdvXuCYSH39X7lypXP++ec7rVu3dvr27Rs0RryJxLpNnz69zj569eoVNFYk1o2/Ig8AAKzEe4IAAICVCEEAAMBKhCAAAGAlQhAAALASIQgAAFiJEAQAAKxECAIAAFYiBAEAACsRggDEjczMTE2cODGsPvbs2SOPxxP4u2sFBQXyeDy1/qAjgJaPEATAKunp6Tpw4IDOPffcOvfPnz9fKSkpTVsUgJggBAFoFk6cONEk47Rq1Urdu3dXYmJik4wHoPkiBAGIioULF6pz584qKysLenzs2LG67bbbNGPGDJ1//vl6+eWX1adPH7Vt27ZB/VZUVOjBBx+Uz+fTT37yE02bNk01/wSix+PR0qVLg85JSUnR/PnzJdV+OaymgoIC3XnnnfL7/fJ4PPJ4PJoxY0Yo0wYQRwhBAKLi+uuvV2VlpZYtWxZ47NChQ8rPz9dvfvMbSdKOHTv01ltv6e23364zlNRlwYIFSkxM1GeffabnnntOzz77rF5++eWI1DxkyBDNmTNHXq9XBw4c0IEDB/TII49EpG8AzQ/PBwOIiuTkZN18882aN2+err/+eknSokWL1LNnT2VmZuqjjz7SiRMntHDhQnXp0qXB/aanp2v27NnyeDw688wztXnzZs2ePVv33HNP2DW3bt1aPp9PHo9H3bt3D7s/AM0bzwQBiJp77rlHf//731VYWCjp5JuOf/3rX8vj8UiSevXqFVIAkqRLL700cL4kDR48WNu3b1dlZWXkCgdgBZ4JAhA1gwYN0nnnnaeFCxdq5MiR2rJli/Lz8wP727dvH/ExPR5P0HuEJKm8vDzi4wCIf4QgAFF19913a86cOSosLNSIESOUnp4eVn9r164N2l6zZo369++vVq1aSZK6dOmiAwcOBPZv375dx44da3D/rVu35lklwBK8HAYgqm6++WZ98803eumllwJviA7Hvn37NHnyZG3dulWLFy/W3Llz9fDDDwf2//KXv9QLL7ygDRs2aN26dbr//vuVlJTU4P579+6tI0eO6MMPP9R3330XUoACEF8IQQCiyufz6dprr1WHDh00duzYsPu7/fbbVVpaqosvvljjx4/Xww8/rHvvvTew/9///d+Vnp6un//857r55pv1yCOPqF27dg3uf8iQIbr//vt14403qkuXLvrTn/4Uds0AmiePY754DgARNnz4cJ1zzjl6/vnnY10KAAQQggBEzQ8//KCCggJdd911+uqrr3TmmWfGuiQACOCN0QCiZtCgQfrhhx/09NNPuwagffv26eyzzz7l/q+++ko9e/aMdIkALMYzQQCahYqKCu3Zs+eU+3v37s3f+wIQUYQgAABgJX46DAAAWIkQBAAArEQIAgAAViIEAQAAKxGCAACAlQhBAADASoQgAABgJUIQAACw0v8D231NA8+XsVMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# year built/condition/grade\n",
    "df.plot.scatter(x=\"yr_built\", y=\"grade\", s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='condition', ylabel='yr_built'>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shitty one\n",
    "df.plot.scatter(x=\"condition\", y=\"yr_built\", s=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate corr between grade/price and condition/price grade seems to be correlated, condition not so much. But renovation\n",
    "# is more connected to condition?!\n",
    "df.plot.scatter(x=\"grade\", y=\"price\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='condition', ylabel='price'>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"condition\", y=\"price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='yr_built', ylabel='grade'>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#shit one\n",
    "df.plot.scatter(x=\"yr_built\", y=\"grade\", s=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='zipcode', ylabel='price'>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XYfXq1cjNzXVzt4iIiMhDXA1W3n77bTefnoiIiHzAUzkrRERERJEYrBAREZGnMVghIiIiT2OwQkRERJ7GYIWIiIg8zfWFDKn5Kimr4EKIRESkicEKuWLGklK8uGJX8O+7R3TFpDGFLu4RERF5FYeByHElZRVhgQoAvLhiF0rKKlzaIyIi8jIGK+S43cdqDbUTEVHzxmCFHNelTbqhdiIiat4YrJDjigpycPeIrmFt94zoyiRbIiJSxARbcsWkMYUY3bc9ZwMREZEmBivkmqKCHAYpRESkicGKCawPQkRkPR5bSYbBikGsD0JEZD0eW0kNE2wNYH0QIiLr8dhKWhisGMD6IERE1uOxlbQwWDGA9UGIiKzHYytpYbBiAOuDEBFZj8dW0hInhBBu74RZ1dXVyMrKQlVVFTIzMx17XmasExFZj8fW5sPo+ZvBChERETnK6Pmbw0BERETkaQxWiIiIyNMYrBAREZGnMVghIiIiT2OwQkRERJ7GtYEswOl2RERE9mGwEiUuvkVERGQvDgNFgYtvERER2c90sPLGG2/g0ksvRV5eHvbu3QsAeO655/D+++9btnNex8W3iIiI7GcqWJk9ezYmTpyIH/3oR6isrERDQwMAIDs7G88995yV++dpXHyLiIjIfqaClRdeeAEvv/wy/vCHPyAhISHYftFFF2Hz5s2W7ZzXcfEtIiIi+5lKsN29ezeKioqatCcnJ6O2tnkNgUwaU4jRfdtzNhAREZFNTAUrXbp0wcaNG9GpU6ew9g8//BCFhc1vJkxRQQ6DFCIiIpuYGgaaOHEiiouLMX/+fAghsGbNGkybNg2TJ0/Ggw8+aGpHZsyYgbi4OPzqV78ydX8iIiKKTaZ6Vn75y18iNTUV//Vf/4VTp07hZz/7GfLy8vD888/jpz/9qeHHW7t2Lf7yl7+gf//+ZnaHfIQF9IiIyCjTReFuvfVW3HrrrTh16hROnjyJtm3bmnqckydP4tZbb8XLL7+Mxx9/3OzukA+wgB4REZlhahho9+7d2L59OwAgLS0tGKhs374de/bsMfRYxcXFuPbaa3HVVVdpbltXV4fq6uqwf+QPLKBHRERmmQpWbr/9dqxatapJ+1dffYXbb79d9+O8/fbb2LBhA6ZPn65r++nTpyMrKyv4Lz8/X/dzkbtYQI+IiMwyFayUlJTg0ksvbdJ+ySWXYOPGjboeo7y8HA888ADefPNNpKSk6LrP5MmTUVVVFfxXXl5uZLfJRSygR0REZpkKVuLi4lBTU9OkvaqqKljNVsv69etx5MgRDBo0CImJiUhMTMSKFSvwpz/9CYmJiYqPk5ycjMzMzLB/5A8soEdERGbFCSGE0Ttdd911SE1NxVtvvRWsYNvQ0IBx48ahtrYWS5Ys0XyMmpqa4JpCAXfccQd69+6Nhx56CBdeeKHmY1RXVyMrKwtVVVUMXHyCs4GIiMjo+dvUbKAnn3wSV1xxBXr16oXLL78cAPCvf/0L1dXV+PTTT3U9RkZGRpOAJD09Ha1bt9YVqJA/hRbQY+BCRER6mApW+vTpg6+//hp//vOfsWnTJqSmpuK2227Dfffdh1atWlm9jxSDOI2ZiIj0MjUM5BVuDQOxRyA6JWUVuHFW09lki+4dzveTiGICzxPqbBsG+vrrr3HhhRciPj4eX3/9teq2sVyJlj0C0VObxswfNRH5Hc8T1tMdrAwcOBCHDh1C27ZtMXDgQMTFxUGpUyYuLk73jCC/kRU2G923PU+yBnAaMxHFKp4n7KE7WNm9ezdyc3OD/2+O2CNgjcA05tAfNKcxE1Es4HnCHrqDlU6dOgEA6uvrMXXqVDz88MPo0qWLbTvmRewRiF5gHHd03/YY3bc9x3SJKKbwPGEPw0XhWrRogYULF9qxL57HwmbRmbGkFDfOWoWJCzbhxlmrsHTrIYwd1JHvHxHFDJ4n7GFqNtD48eMxcOBA/PrXv7Zjn3RzazbQ/LVl2FReiQH52Rg3pMCx5/UzzgAiu3H2BXkJv4/qHCkK16NHDzz22GP44osvMHjwYKSnh3dvTZgwwczD+kJolve8NeXYfayWWd46cByX7MTZF+Q1oQUwKXqmgpVXX30V2dnZWL9+PdavXx92W1xcXMwGK8zyNo/juGQX/i6JYp+pYCV0NlBgFCkuLs6aPfIw9g6Y993hpgtfBtr53lE0+Lt0Boc1yE2mVl0GzveuXHjhhUhJSUFKSgouvPBCvPLKK1bum+cY6R0oKavAuxv2oaSswu7d8oVN5ZWG2on0Yq+d/SKT42csKXV7l6iZMRWsPPLII3jggQdw3XXX4Z133sE777yD6667Dr/+9a/xyCOPWL2PnqE3y5s/7KYG5GcbaifSi7Mv7CUbZuOFGDnJ1Gyg3Nxc/OlPf8Itt9wS1v7WW2/h/vvvx7FjxyzbQTVeXBuIs17kbpi5EhvLq4J/F+VnYVHxZS7uEcUSDlPY490N+zBxwaYm7c/cPABjB3V0YY8oFjgyG6i+vh4XXXRRk/bBgwfj3LlzZh7SV9SyvDl+Lvde8WWc9k224ewLe3CYjbzA1DDQL37xC8yePbtJ+0svvYRbb7016p3yM/6w1Y0bUoAnxvZnoELkExxmIy8w1bMCnE+w/eijj3DJJZcAAL766iuUlZXhtttuw8SJE4PbPfPMM9HvpY9w3RsiijWTxhRyeQxylamclVGjRul78Lg4fPrpp4Z3Si+3clb04Pg5ERGRMqPnb1PBild4OVhpDswEZKH3AcCAjjyBFxdEznIkwZbITHnzyPuEYnl0cgtL9RN5n+micNR8mam7oHQfI/cnsgNriBD5A4MVE5p7dVq16dlG72N0GyIrmfkuE5HzOAxkELuMzU3P1jN1m9O7yWksNUDkD+xZMUDWZTx/bVmTnpb5a8vw+3e/xvy1ZU7vpu3M1F1Quo+R+xPZgTVEiPyBPSsGyLqGH1q4Ofj/u0d0xepdx4Nl5eetKcdba8rwXoyVlTdTdyHyPgBnA5H3+HZ6JFEM49RlA2Tr/ujx5E39WLWVyGO4lheRO4yevzkMZIDWUIaaZaWHLd4bIooWE2yJ/IHDQAZNGlOIhPg4bNhbgbzsVCzcsF/X/XLSkmzeM2pOWMTMnMj3jQm2ROq8cqxhsGLQDTNXBvNRACA3IwlHa84G/x5blId3Sw40ud9Ph3IIiKzBGWnmyN43ruVFpMxLxxoGKwbMX1sWFqgAwNGasyge1Q3dclsGI8+2mSk8+JEtZDPSRvdtz++YCrX3jYv0ETXltWMNgxUDNpVXKrZX1J7F2NEdg3/z4Ed2Ucux4PdMTut9C/wjovO8dqxhsGJATrpy3olSe3M6+HllTLM5YI6FOXzfiIzx2m+Gs4EM6Jbb0lB7czBjSSlunLUKExdswo2zVmHGklLd943lwnl2YREzc/i+ERnjtd8M66wYwJoM4aJ5PyITlQfmZ8Vc4Tw7BHqx6hsa0SIhnr1ZBtnVC8jeRYpVdn23jZ6/OQxEppkd01RKVN5YXoX5a8tYOE+FUmb+2EEdVe5BkewYnvXSjAkiq3klpcHVYaDZs2ejf//+yMzMRGZmJoYNG4YlS5a4uUuqWEAqXH1Do6H2AFmisqyd5Jn5zXXlb6/g50LkDFeDlY4dO2LGjBlYv3491q1bhx/84Ae4/vrrsXXrVjd3S8prCUdua5Gg/PWRtQcMyM821O6UkrKKJgtSegUDZXuZ/ez5uRA5w9VhoOuuuy7s72nTpmH27NlYvXo1+vbt69JeyRUV5CC1RTxO13/fc5Da4vyJ+d0N+5rdeLXZ4G3ckAK8tSZ8KKgoP8vVISCvd+UzULZPNJ89PxciZ3gmZ6WhoQHvvPMOamtrMWzYMMVt6urqUFdXF/y7urraqd0DADy19NuwQAUATtc3hiWZeu0k5zWBZK1Hr+uLT0oPY8PeCgzqlIPfje7t6j55qfiRkkBmPosNykUmAupJDIz2s+fnQrHOK8njrgcrmzdvxrBhw3DmzBm0bNkSixYtQp8+fRS3nT59OqZOnerwHn5vw17tLmKvneTsZDTBNvIKNuDLXSfQ0ChcC/K8VvxIhsUG5SK/WwPzs8J67mQXEVZ89vxcKFZ5qcfZ9TorvXr1wsaNG/HVV1/hnnvuwfjx4/HNN98objt58mRUVVUF/5WXlzu6r4M66TsINZfxaiNd4EpXsKHcTEr0U1d+UUEOxg7qyBNiCKXvVuRsM9n3y6rPnp8LxRqvJY+7HqwkJSWhe/fuGDx4MKZPn44BAwbg+eefV9w2OTk5OHMo8M9JvxvdG7kZ2qsne/EkZwcjRYP0BHBuBXleK35Exuj93uw+VtskkbaoIAcD87PCthuYn6X7s/dyUja5y+9FL72WPO76MFCkxsbGsLwUr7lpUEfV7ubmdpLT2wWuJ4BzM8hjV75/6f3e/Gv7UUxcsCn4990jumJ03/aKNX9Kyio0vwNe6iInbwktejlvTTneWlPmu6KXXutxdjVYmTx5MsaMGYOCggLU1NRg3rx5+Oyzz7B06VI3d0tK1t1cPKobKmrPYkB+drMsamZF0SAvBHleKX5ExigluUZeRIwtysO7JQfC7vfiil3YceSk4mO+vaZM9bvgh6RsckesFL30WvK4q8HKkSNHcNttt+HgwYPIyspC//79sXTpUvzwhz90c7ekZN1fM5fvBHA+gt59rJZXVwpk793PhubjJxfl8wBPUVHqGQudxbD7WG2TYAUADlWdUXy8ilNnVZ/PL0nZ5Dy1opd+ClYAb/U4uxqsvPrqq24+vWF6ur94daVM9t4xUCGrRPaM6ekpG9ErF1sONC2BcGVhO9X7ea2LnLxjQH425q1pOvnD7aKXfud6gq2fKCViKmkus4GMYBIruUn2/fvd6N5NEmz1FCjk95lkxg0pMPWd8qIZS0px46xVmLhgE26ctQozlpS6ti9cddmE+WvLsKm8EjnpScEhoFDNdRVmPQLvXXPN77GKVwo1+Y3sfTP7veTnQDJ+P9aVlFWEFTwNsOr8ZvT8zWDFIK3iU/eM6IqHmLOiiLMnrMH3kdzAwKx5eXfDvrDZcwHP3DzAktXejZ6/PTd12ctks4GevKkfWiTEh/2I/fTDNruvRq4cZLMnkhPjMbJXW8+/R17BWSjkBgbIzY/X8rIYrBggy0VpkRAfFmn66Ydtdl+N1hH4bNsRxfbnl+3A88t2ePo98hLOQiGnMUBunrw2dZkJtgboiTS9VqJYjdl9VasjYJZX3yOv8drVDsU+r1UyJedMGlOIRfcOxzM3D8Cie4e7muLAYMUAPTMA/PTDNruvanUEZPKyU03vD32Ps1C8KZbL7jNAbt68su4Vh4GiFJmd7Kcfttl9NVNHoEWCdlzsxffIi7xUqIn8NexrhteGA6h5YrBigJ6xWz/9sM3ua892GYbaAe1AxKvvkVdxaQBvaC75HAyQyW0MVgzQm9zo5x+2nnnsZpI8lQIjo89LZBezM+KaU8IzA2RyE4MVA4wMm/jhh232qtCOoa5YvBolZ5ktwhXNMI6fhn2J/IwJthb47nCNL5PrnEwGVgqMnHheah5umLkSDy3cjHlryvHQws24YeZKXfeLdvYeE56JnMGeFQNktUIeWrg5+H8/JdeZvSo00/WtJxDh1SiZoTaVXquHZean26Xtr9w+VNfz+3nY1wg/FbpUEyuvo7lhsGIxPw1nmE2wNRPkMMGWrKB0ollWelhx24Xr9zWpLB3pUPUZQ+0yfhj2jUaszHiKldfRHDFYMUBPrRDgfA+MXw5cZq4K7ZjxxARb0iI70TQ2Kn971uypwJo9FWHbRhrRqy22HKhRbKfzYmXGU6y8juaKOSsG6KkVAgCHqoxdlbnNTNEfo5UNtYaBWMGW1KjllvS9IEvz/rLv11WF7RS3l7U3R34qdKkmVl5Hc8VgxYD6hkZd27XPSrF5T7zBSJCz8+hJzW1kOUFEaieakTp7QZQeQ/ad43fxe7Ljnt7joVc4PXMrlqsau4HDQAbo7VnRe/D0OyOJahW1Zx3aq9gWeM/rGxo18zFiidqJRquGj9ZjWOGppd9iw94KDOqUg9+N7m3b87hBdtzTezz0CicLdjI3xnoMVgzQcyWRm5HULE4eRn+MshL9oZpLkGdW5Hse0BwOhEUFORiYnxU262dgflbwtxaZe7V06yFdJ6WRvdri+WU7FNv1GjLtYxytOR+Mf7nrBBasK8faP/xQ9/29LpZqyTgxc4u5MfbwV2jssgOVpzW3OVpzNqrVh/3ATG2KcUMKMDBfO7eAlKnVqWkO+T4lZRWK05NDX3fosKRTq8U+tfTbYKAScLTmLJ5a+q0tz+eGWKslY/fCfMyNsQd7VmywqbzSUAVNvzFbYvy94sswf20Z3vhyL7YcqG5yu59mUTlN60AXi+XdQ5ld4kHrPYm2XP6GvcpBoqzdr5pLLRkrxFJPlJewZ8UAvV3DOelJMZ1cJUuW1ZNEO25IAfrmZSre5rdZVE7SOtDF+oHQrhOAnsdV+y0P6qR80pa1+5ndPRKxItZ6oryCPSsGfHe4aT0GJd8cqMbM5TuDf8daToEsWVZPEm1JWQX2HHevO9Sv1SvVkkibw4HQruRIrVwYrdys343ujRc/24mGkFIvCXGIuSRbMoY9UdZjsGLA61/u0bXd8m1Hw/6OteQqWbLsgPxs1fvJEkSd4vcM/dADYHObDQScf/1d2qSbWqxQRisXRitRcv7asrBABQAaBHSV+vcbvwb6bon1qsZOY7BiwOmzDZrbFLRKRdmJpom4Xs4pMHoQGjekAG+tCV+PpSg/S/XgrGchQzvr0+jJ0PfDwbg5HwBDg815a8qx+1ht1MGmmWTI0N/ypvJKxW28lLdmxffa74G+G/xwPPETBisGFBXkYNexU6rbKAUqgHdzCpQOQnq6Ly/p2josWMlOO5+nI9teT5EtO6cuy54/cOLhwdjb7JoO+rfVe6XtD/9bH8XbQn/LZnsZnWLF95pTcY3j8cR6TLA14NLubXRtN6pXbtjfXs0pkB2Ebpy1ChMXbMKNs1ZhxpJSXfdbvu2odHs9QvMErDZjSaliLQ3g/InHzFRscpaeHhAzSe1as4G0EiWVpuRr9TI6xarvNafiGsPjiT3Ys2KA3vLSE67sgQlX9vB8F6Ce3g6lKyi1g5TsiktrEchAnoDV75Xa8FPgxPPuhn2Kt3t56K650Zq1Y/ZKNiVR+Xot0K4nUTIwJd/KXBorqC0lYOR7zam4xkQ7Hd5rvDKcxWDFAD1F4UKvvPz4xVQS+SPTOkgp/Sj1vHd2/JhlB45xF3UMFgrjwdj71GYDRTNMUdA6DQer6xTbQ59b63HGDSnwTJBiNSfL1DvB7pNvLB1PvDScxWEgC1zVOxc/G5qPJ2/qZ1ulTDvozRGJ/JEpdY+rba+XHT9m2WPOX7cvOGTFugj+FJiEE80wRefWyt8PWbufyH7fZnLDnKoIbLcZS0o1h7mjFSvHE68NZzFYMWBfhXLvwCffHsW8NeV4aOFmW778dlH6UUWOv8t+ZJPGFKJ4VDfFx1WqR6N1gLTrx6wWWIX+8GLlYByr1A6c0VzJxnJRt1g5aVrFyZNvLBxPvLYiOYeBDNAzlOG3LHmlMXm93aSyInBK0zaVim/1aJuOe0Z2t30sdNKYQiQnxism2YYOPTXnacFep9Z7MnZQR9PDFLGyorCMVcXJvDQcYJbTuSQ8nliLwYoBgzrl4MtdJzS381siVeSPSu+PzMi0TaXiW9uPOJe0JVtd14/jyM2RVu+J2ZOy0V4ZM/kOavdxInkx2pNmrExdjqVcEidYsSK5lVy9fJg+fTqGDBmCjIwMtG3bFjfccAO2bdvm5i6pKmiVpr0Rms+X38i0TdlVzTvryh0ZA2WXuL/p+fzMrF0T6PELJZtGbybfQe0+TuRPWCFWpi7zGGCM194vV3tWVqxYgeLiYgwZMgTnzp3D73//e1x99dX45ptvkJ7uvRO+rFplqOb25dc7bVMWwM1bU455a8od6VZ2er0OO66aA4/ZXMvtW/35qZXbD318rd4Fpc9a7T6B/8sez0tiqUfCyWOAV6b8RsOOJS7McjVY+fDDD8P+njt3Ltq2bYv169fjiiuucGmv5GTDHqGE6q2xSc+0TbWF+ADnDtROjSPbMcYvW1vJj/kDZln9+enNY1DbbunWQ4qfdbSl/L0i1qYuO3EMiIUcH8CeJS7M8lQWWVXV+SucVq1aKd5eV1eH6urqsH9O0hNVNsdKhfPXluH3736N+WvLVLcLzCDKTlWOkWd+ul16XzPVSd1ix6wDteJ2zfE7ZxW9vQay7eobGk3NUpIVmNRbeNJpTs1u8dPvXMZrU37N8trr8Eyw0tjYiF/96le49NJLceGFFypuM336dGRlZQX/5efnO7qPv1mwUdd2fhvLjcYNM1fioYWbg1O3b5i5UnXbmct3ovL0OcXbD1WfUWy3cmzfiYOhHWP8WvdtTt85KylNs1dql43fy2YNaZXrj/VZSGb4JYdHS6zk+HjtdXhmNlBxcTG2bNmClSvlJ7vJkydj4sSJwb+rq6sdDViWbj2kazs/juWaMX9tmeJ4//y1ZU16oZS2jZSUmNCkzcqZCE51zdoxxq9138AaR34fI3fastLD0vbI77Bsmr8SrVlKfssDsfu3EyszjgD/fbYyXnsdngjj77vvPnzwwQdYvnw5OnbsKN0uOTkZmZmZYf+cVFffoLmNn8dyjZId6JUSkfUkJycrrNNiVXTvZJemHVn0asXt7hnRFUu3HoqJq1KnNTYqZ5mFtof2xkXOODL7WXttpoUaJ347Tl7F29276qfP1k9c7VkRQuD+++/HokWL8Nlnn6FLly5u7o6mrLQkHDupXAgt4Mtdxx3aG2OsvuqesaQUH32jXMlQqc5K3TntsXilqqFWRfdOF4RyYtbBqF65mHBlDwDAjbNWhd3m16vSaJhZTLB1y2TVdj09Cmqftdr9nZ6dZpYTvx2nruKd6l31y2erxmsLMroarBQXF2PevHl4//33kZGRgUOHzg+zZGVlITVVfZVeN5zV0bMiGwZxk9U/ULVkT1mdlX0VpzQfV6mOjVUzEdzo0rRy1oHSe75821Fcc2F7zbyJ5uCGmSuDw4zz1pTjrTVleK/4sibbGQnajQxNKH3Weu7vhyqnO4+eNNRuhhMzjpweavLDZ6vGa0ngrgYrs2fPBgCMHDkyrH3OnDm4/fbbnd8hDbVntYMVQLncvFvM/EC1rlDVVjJ+8scDFG+rOaOcVBtKKU8AsOYqxe/TL2Xv+UMLN+PGojzF2/w2Rm6W3twppaC9fVaK4mO2z0qJ+srSa1emZsmW1ZC1W8XqMhCx8nk4xWtJ4K4PA/lJelICquu0AxY9Qx5O0fMDDb3anPr3rZpXqLKT4E+HygO0fhdk4ZuDyjMvAmT5A4A1VyleKnBklFrgsajkAHq0Tcf2I99/1rIqrLFIlg8VetEgC9pli3HmZadG3RvntStTs4wsq2GWE70eXksY9TqvvV+eSLD1i6FdW+vaTilR1C1aX7jI6YKyK9RoqQUyAbL8AavMWFIaNs3aSBKq2/Uf1BJsAYQFKsD3VVibA9lJM7RdFrTLegdaJMRHnSjptStTs4wsq2GWEwm2THw1xshSFE7wzNRlPyjZq+/g76WeFbXhD7Xck1CRw1pqS4dH80U+frLO9H21RHPl5pVqlIGeoYcWbta1fXPp3h43pABvrQkfCoo8mcqC9oNVyrV9vthxDGMHdYyqN85rV6bR0LushllO9UKx3L5+epeicAqDFQMqz9Tr2s5LPSuA/Aeq96rFiu5ePc8VHx8X9fPImA2wvFb/YdyQAuw+Vhu2T2OL8vBuyYEm2/rxpGjWe8WX4TcLNgZPpk/fPFDX/Y7WKAcrBypPA4iu3Ljf86Qi9WyXEVyPympO9kKx3L4+dl2UmsVgxQidQf7BqjOei6qVfqB6DjpK3b1mlg7Xc4V0ZWE7zW3MOiS5gtbixaQ8peCzbWZKzJwU9Yj8fYXOBtpxtBY7j54My7WSfY4NkjyplBYJ0kA1OTEeI3u1NfX++itL73t2n3xjqRdK9r3p0ibdV3lyXsNgxYDExDg0nNM+3Gw7VBNW90LvD9vpACcwJhna1TcwPwu3DC2wvLvXzXH6GUtKMX/dPsXb1AIswHsH0dDvyNhB3xdQjIW6DnqUlFXgT8u2Y/m2o8G2ET3baM4Gkn1emaktFNtbJMRJA5znl+3A88t2aP6u9fTKee2iRokTvYux1Asl65F4aOFmVxcCNMrMRamdGKwYoDZbJdSBiKt4PT9sN7oNZWOSj16XoRqkvL1GOeH27TVl0teop2fluY+/s/zKQy0vR0+ymJcOolrfEb/XddAiW3V6xXfHFLcPzbWSrQFUdly5/k/5iVOaAanW71qrG92p33y0AZFbwwF+7YVS0xyLNVrFW8kVHhfNTGu1nA0nylkrrYxsNgO/4pSk7oKkHdDXs3LMhroNaq9F74wZp1acVaPnO+L2jCU76U0GDxWa6C6b3nxGkgyfEB+nOQMLMD9bxanlH/yyOKCTy2HY/TvJy1YvaOqXBQ29tpAhgxUD0pKaLrSnl9pVmt1fCtnKyGaHOHq0yzDUrucxgejeX7PPq/c9jlwTxmla3xG/nJTMMvNbqAlJiJcliQ+UtI/Q2dVd39AoPfHJTlp52amOnAisCgBk3f5WDgc4dWJ04neidWHmlzwcr9UJYrBigN68i/yc8KqYWsMGduZFqFX3VLpy/EHvXM3H3HZIuUt9ze4T5ncUwFU2JNhqXR3HwoHDyatSt6h9Tr3bt1Rsz0lLCv6/pySQnnBlDyRETEJLiAN+N7q3Zm9ObkYSHlq4WXriU5vh4kQulFUBgBP1SZx4P5z6najts5/ycAIz4vS2243BigFVp/VNXT4RMZzh5uKGatU9ge+HOEb1Oh+kfPrtUc0rDtnMmrV7KqT3k417h9L7/hoVeI2RgVjkgUOte9iNIZbQ55Sd+A5UnvZcd60dlApUFbRKxaJ7h+OOS5UXQA1dGFP2Xry9pgwNEcO7DeJ8kK/1/h2tCf+dR5741E7AfgsA7B4KdeL9cOp3ovhdzUl1bQg5VjDB1gC9KSu1Z8OvgrUWN7QzgS0nPUlXe+jsCkA9ESw3Q15pNpoEsm8OVGlvZFJRQQ7+evtQabKhWrKjG8nPkc8pW//n+WU7msXaQErJ4GUnTuO7wzW6rgBl78Xn3x1VbH/xs514ZtxAw/tpZEq73TO4rE4OtzuB2+73w6mZfYrf1Qp3eiOi4bXZQOxZcYish8NuiZJCa6Htstk9siuOFpH95jrup+cLfqLWnp4VLWrdw24MsSg95yKFom+ht0UGLH7qbtZDbSHHpVsOKd4W2gNYVJCD3IzwAD03IwmVkt68Q9VnFK+QtYSuRKznSt7uXCgvJIcb4XZumBXULj7JPPasGBDZXWyEWhVYOyNY2ZBNoD20mFYk2RWH1mqrSvkVsqmjWvezkqyHRO2ksve480XhzHRLX94jF7cN6+z5mh1mqV39fnv4pGJ76PIN89eWNRm2OVpzFulJytdr6UkJilfIWmYu34kvdhzDe8WXeSZB0aoeETvL7TvBiwUevcxr7xd7VgzQ6FAI6tE2/MCqteiXneO17bNSpO1Kybd6nj8uTv2NUMqv0NOzJOsFAqLPGVHrIZGdCP+1/ahiEAnYO8Ri5rHrGxpj4qpURs804kihyzfIvn+dJe/1uKEFmkHjPSO64qZBFzRpDwz7xspChoB8RqGfODUMpDYLzE+8VhDTf78aF6XrnFrbqVUanrypH342NB9P3tQPi0LKfsvY1V2rNu1QdgAf2jkHPdplSAMDrdp4Sl9mPesLtUxW7uizYrqh1lVC5IlwbFGe6tCLnWT7o8aPJ0C7hS7fIPv+9W6fqdhe0CpN2gMy7qKOWHTvcAgACzfsV9xm4fp9nulZiZbajEI/cWrV5VgJUr22SjWHgQxISIgH0KC53cZ9VXjl9qGGu0rtSGCTldQvKsjBd4drMG9NeZP7rNlTgTV7zgcqSsmk+yuVq36qGTekAH/8aFuTrvhQcQo9K1aV+padIAKBVWRy3+5jtYqLAwbY3RVqdH/8dgI0ykxRuLIT339Pxw0pwJTFW3G6/vv3KbVFPPZVKH+X392wT/r7vbhrawBQ3Z+4uNg5aS0rPSxt99twkBPLUnitRyIaqyNmsro5s9VfvxqXnao7p2u7c5KqmG5QW+Z73JACzQRCpWTS02fVAzalhN2SsgrVQAVQ7lmxYrrhjCWleGjh5ibtkVcJocMoWgcWJw48ofsTmripZMPe2KmposRMcmLoezJ/bVlYoAIAp+sbpeX2a86cUz3paH3/OuakxcxJS7bMSGOj8GXV5FgeLrWS13rUGKwYIFuhNVJOepJnfsRaJ/v3ii9D8ahuGNa1Fa6SFISLfIwOGmOvSmX39QQX3XObHsSjPeCrXZGrfZpqORJOdYWGfoe0kpp3H6v1zHfODlsPVBu+T2idlXc3KC9kWXNG+QIkLSlR8TswsmcbvLOuHH/fpD5EWHOm3nPd6Gb1vUD5gqbiVH1MV002K1ZmA2nV6HIah4EMSIgHzmmPAuF0fYOpVZftoHWyly0Op/YY/S/IQulB+ewepbL7eoYpWrdsWr8l2loRakGS1nBSaJdxfUNjsPKoXSeb0BowS7ceCnvNI3q2Ub1v9Zl6z3zn7KCSey0VWhFZtq5XoiRrvlX6+dWYF0YEOZ9JFk2MFFo9N1Tkbvhh1WXZbMX1ZZVhf3ORvtgyID9bMU1AT/6hHRisGKCzYwWHquvC/nbzR6x2steTB6AUGOzSGJLoltu0/LmeYYqvJOX6oxln1rM2kNrjObWSsVbQKFtZOODbQ+GfSaydOK4sbIePvlG+Mk2KB84qxMIzl+/AK+OHAABuGtwxmIcVKrlFAnC6ae/K3uOn8NTSbzWHLmUGdVL+fYV+Ln5ZdVlP2YEATgOOjdlAJWUVmLtqj+JtH2455EquEoMVA6KpszLz0+145fah1u2MAbKTvZ6hGaWXvFsyzh8Q2YsyY0kp5q9T7oYPpZa4azZoUArWQulNTLXzCthM8qgeVlRA9opxQwrw1hrlqfZKgQoQHlTL1gY6K8kvqzh1Nqo8oBYJ8ZrDAVYkjmuxIiAy0u3vt3wcO/g9sVrrwmljuTvDzP549zyihZm+6P9zqFq5OJtTlJLKvtih3aWtmGBbrz4WFvqjNHIibrQpLzkyoz2UngOI3Su1+m0s2y3vFV+GLq3TdG/fOmRJCVlg3iAJVuPi4sJyXozSOmk7kddgVfVl2ZIdfmV3bpefE6v1HK/jNeps2YXBigGtWpr/0epdct5qaj9MvatnRh7os1JaqG4f+qM0MmunRaL1X0e1wneA9gHEzysau7WGh11mLCnV7NULlRWSNyLrQauVzGyrrD0b1Srgn5Qedn04wKqF+5SGdY0+p1fYfeEByIfNjAynuUXP59chy53hLAYrBtQojG3rFc2BzyytH6beK8fIA32v9spd6kqMXE00RDPOJiGrERGgdQBx4gpYb1ARWRk5VO/24ScUP846UWNmqGxVSM+hLDCXfeXONgg88Y9vDD1fqEUb9qsOB6gVa7SKVVf4Rrb3cu+BUxceC9crD3nL2r1Ez+en9yLXagxWDIim8JbTXf16fph6A6jIg26tRr2ZyIXa9JZJb5RN2YiCbFZGgNZ4/CrJUJms3Qy979H2I/KrnjMRNUSsfyfdJVtsU82ZkHwU2RpZsg7txHjggOQ++gjVCrZOTGu26jmUFoFsm5HUpEZToNikV1nV06RFNkri0uiJIXqORTVn3Flwlgm2BrRIiEddg465yx6gZxEqvQFUZLRde1Y9WIk8SE8aU4gubdIVC7OFsqFjBYM65agm92qNx8vyc7TydgBjSbmhSdB7j9dK1ySS2RMxPBJrs4GUavdoEQL4/btfY0B+tnSNrMR4oF4hpkhqkYBLuraWltPXMqxbG+kVaKA98Luwc3FAKyq2Ki0CeaTmLI5EtAWKTXr1O+dULknHnDR8tbtpb03HHP35Vl6WZMNwvR7sWTEgI8V8bOd0/oCeH6bsajOU0pVYh0zlA3+AUve3WxVWtZ5320H1YmOyXCOtHCQzY+OBJGirvitezx8w4koTw6gCCC6898/NBw3d99y5Rgzt0srwcwaUHa/VXPE8UFk5sI9eLaqmNZQaKtrvnJ3Jr04V6ZP1PLjVI2GEnuHWXIV6WE5gz4oBWsMfMm7kD0RTTO2q3rn4Uf886ZXY9qPqBySzU5ftsOe4+r5+o1LcDpAHdGqBXrTrGVmViOfl/AGnyYbQlHpVgPM5K69J6kzosef4KXRVSUy1as0rLVZMXZaV21cSzVC5E3VnnFgbSG15Aq/TE2xWRZG7GQ32rBhwWk/5WgVufUUnjSkMW/05ciVnWdc4AKzbc0J60jxxsk6xPSC0+9uuGiJ6dW6tfsKu0/hMP//uqGL7VyrToaMdG3ernLWXufGe7K8wn0jYumUyjkt+J8dP1vlq6rKs3L4Ss7VEnJx1Z/faQLL3y8j76BY9FzgpLTgM5HmJ8ebeLremump1M8umUH7y7dHgfW6YubLJ7Upl8WWMdAvbkX/206HqeQBZqfJp2DOWlOLoSeVciZYq07ejHRu3qq5FLA0DOV3iOykhDi0kpfj1GN23HWokPbGydqtZlVBqZKq12Z4Vp5JfgfM5OL9/92vbFuRzYqaXXfQk2PbNy3Rob8IxWDEgmoOXF2cD6ZmCZmaVzdAfpZGhiNbp6vVb7JAdMlsodLxcq0eoY478AP6JZIxf1h4pMYrig6H0FP3zCz0rhKvJTElQbJf9pFNbJCA3Qz03S01edqq0V69z63RpAHDOwqECtdlIRhiZqmq2Z8Wp5NcbZq4Mu4BTuhizix/qrADne+QX3TscF3dR7nkycrFqJQYrBlhxHHFqZVzZFYmZoCmyC/5glfzglZuRZLp7tY3KycHs+6Y15bXy/2aZRCbE/mnZdtX7VZ+WJ8utkLzHsvZIehKf9XCrHoJdLuna2tD2vdq1xLCurVA8qhuGdjaWLCsgkN/KfPGrDXsrpL16Px1aID2pz1y+07JEW63ZSHoZ+T6aDS6cSH5VKhBp5mJMi+zY6+Uk6khFBTnS3JRN++RFNu3EBFsD6iXriOgxsldbxxYuA+QHjeeX7UDduUZMGlMoXU01UmQX/Nlz8qjtaM1ZzF9bFpyGaaQb96hkSYJo3rct+9V/WNWnzyn2oizfppyrEpCpMnzUPjMFWw40vYpqrzGLymopLZR7E/zITO7TtsPn1wb6ctcJdJaU6ZddgJxtaMQxyRCgHnuOny8RMDA/K+wEqacWiV+mnbdKa4ETp74P2qOts2J38qss72lTeaWl08bVeq788tkCwM4jygvWytrt5mrPyueff47rrrsOeXl5iIuLw3vvvefm7mhKMJmzApzvArQ6gUytt0Ft7DHwvErFnpQY/SGHHhSMXGlVKfRWRJt4d0pSTj0gKTFOGlD9oHeu9H5VKnU/fti3vaF2u5zSqIfjJ9EOo0bWoQlIliQLtkxqgcNRrOfVuXU6SsoqFK/kA789tdwAK3I1rCr3L0sUDg1UgO9fWzTsTH6V5T1ZnQ+lVS7BL7lkDZJIXtZuN1eDldraWgwYMAAzZ850czd0i6YWjqxWgdkvrp46HpPGFOKBK7tLn1ep2JOS3yzYGPa3VkpF6I/fWAXbpm3RzppIS1LvXchKbSENqO7/QQ9kSWrrHKiUn8jMrroaCD5DVwuORs2Z2AlWvtghn30VDSE58Nada4hq4dJBnXKiShq1IldDdtI0WvPooIFhIC8vyilbeVvWbpZWuQS/lBSQFX9rlkXhxowZg8cffxw33nijm7uhW30UEaWs7LuZL66R3gZZBnqXNul4d4O+2ieRqxYbfRsCCVtqPRUAFKcDmalzEkorWMlMbSEdL1+69RCqJCd8tQJPZpIFQ4PPtXsrVfdZL6cWzHPCiVr16fJmnZWUTa4924B2KlP7tRyoPK2a4OrElH5Z1V+j1YC1eif9wonp4oB6uQQ/rdklC9XdWjXAVwm2dXV1qK6uDvvnlJKyCpyWVZDS4adDCyxLILNqmp/epXgyI3oXtO4m60X69Fv1PBClH4GsFoxajZhQWnkHsgPLoeozqieTaIYEI9l14nJriqEdkqO8msvPUf6+yL7LQkRXxGvVzuOqPRtaJ0grhgp6SHoMZO0yqRoBfygvT8/dKslfk7WbtV2S09GrXcsmta68qqSsAvWNyuc7O9Zw08NXCbbTp0/H1KlTHX/eyARPowLTI3u2y8CTN/VDi4R4xQSy+WvLdK0TYmRKotrVxJAurbBmj3aXcP+O2ZrbhFLqRdJz8FW6yJUlAes9KB6pUe+BOVJTpxgsLCo5oHq/9lny6Xt61mXSs320rJwGa4SRNZH0iuZCAQDKK4zlnyQlxEWVYLv7WK30CtSpYaBjNcq9UbJ2mbzsVM1Kz34gW5gyugUrm9pXoZwfZWZ9Kzdone/cylnxVbAyefJkTJw4Mfh3dXU18vPzbX1OK656GwRw46xVwb/vHtEVYwd1DNvmhpkrg8l489aU4601ZXiv+DLFx7NqSuKa3Sd0bff1vsqwvxPi1BcdVDpJRlOGOxpnNZ535faj6N/R2sqSRoeB7HpvPvv2CH43urctjy1j14y3ilpnD/RnGwSOGjyphzpT39CkRzJA1m41ywqtGbiS/mzbkagCVDsC3QDZ2m7RrPmmRDYLzw+z8/Sc71Qmg9rKV8NAycnJyMzMDPtnNzuueiPzS+yc/69WTXGPztdWHZG3kaBRHC8yaApU0jUj2gOu1uLIahfsgztlS29Tu+qWFX+StZstpKUlmtksZthaMt3hgXIBfStry5w91ygtnqWnqJYVxx2rgiUjCbbR1Agys/inERdJau3I2s2S5fj4IffHyzOVfBWsuMGuzO3QYmVq8/+VGCnnrFZsSe/CjO0i6oPEaUTWJ0Kugo32TEVOxbaqCqca2fv5X9f2kdbnaNNSPuVblrMja7frO+Z01r6dCYyyBHW7xEO991CLEPJ1pWTtoaz4TkQTLIUyMnyhN5cskhNrA8kqQ1tVMTrgjCTINbsQrpP0fO/SmuPaQCdPnsTGjRuxceNGAMDu3buxceNGlJXZs2aDEYFppAB0T701Yv66fcFSz0bn/+up9hhagyUwG+eZmwdg0b3Dg0ledTqL3PVuH56QV6dxFD9S/X33udETVeRVldlpwFaRJReqrQ0kO7HK2o1M7zbipMNTl6OduaWme1v5Csa2iPLr1SIxDkckw0hHauo0T+pWlGaPHL7VapepN9DvbzbBVlZpWqsCtRF2fj9DJUmOTafrGz1fwVbPsSg50Z3hLFdzVtatW4dRo0YF/w7ko4wfPx5z5851aa+Ux90X3Ts8LO/ECoGhnnFDCvDHj7aF1Txpm5GkmmSrVu1Raf9HKxQk05snZbRokrAgWzxQ6dGJNUNkB8TJ736Nbw8pZ/bLCmUB52d+zV/XdFq41qKKVquuc7bbOdqZW6ocnoEgmQihmxDyXRZCe1r566v2RF1Vdd8J5Rw2WbvMqXr7g15ZbRKtmiVG2Pr9DKE2W8YPFWwnjSnE0Zo6LNywX/H2CpWlRuzkarAycuRIS05sVpJ1R9rVVb+s9DB6tstoUpztSM3ZYKVLmaKCnCa3y/ZfKenRyDsfmvimpUP29z/+aGak7D5Wi52SAmmflB627AcvG6eVBSoA0Cpd3pUeqAwc+pmqrZnkRM0NJ0Q7c0vN1gPOlSmwwun6RiTGKwc98fHayfBG842UZhLWNSgHq7J2GSOHaNmMNy2dW6fjq91Nh3zUapYYZVVFXy3nNHqeo01CttuQaR/rKhbqNF/NBnKC2iJUdshJSzI81VWNngSpQHSv19TFW3HKwNTRXSGlzaOZxdGlTTr+ulL5JL5im3UzXcwEzJd2ly+qp1QZOHLNpFBervrpFdEku7pFSH4ysvZQLQx0tctmEibGxeMsmj5ZYpyxMS4jOR1mc8mc6I10aki5QeN4YvWwk5WeWvqtJwMVgAm2TThdCnlQpxx8seOY4m2y9gCltYH07r+RsWAjgQoA1IVsb3bdjdQW8SgqyEGqZLqfrN2MeBMJdmpXY7LKwLJ2Lx+8jLAzwTaaAm2uUSkBqnU1n5Mmz4kKpTaTUFaB12hlXq2eglBeXunb7HG2OdG7MrwbGKxEsCvZUeZA5WlpxrtaJny00/ycmqJmdt2N0/WNeGrpt+iaq5xYKWs3w0xX88dbD0lv23lE+b2VtR93uIaIXexMYExwKKE6wIrQSK1c+TsKvQhhz6+zt09tJmFnyYWLrF3GyDIjZj9rJxJszRxnzUg3UPHXa6y8CLQah4EUhCav1jc02jYEBJz/casl4imR5aWM7tte91WsnReqmcnff+GjOdhs2Fsh/eGrJbga1SbD2FROQH1hvVrJasey9mqXEtb8JCMlERWn/PU+ySbRnBPAXo3EUb0XEznpkpln6Un4x9cHFW8zupChkbwzozONArZISt7L2s2oknx/ZO1mndU5y9IKeque6zW8exvL1iazGoMViUDyqtVRd6SdR2vRLTcduxWWsO+Wq3wFZEVlSotLC4QJPbjprZKrpO5co/REbqRQlZb5JgKq0yoHJNnBStbuwwEORbIA0orA0upaGG7Tej16pwurldSXTV03OqXdyPfT7BIF0jWaTD2asvMVZJseT6yuLKtVNduq2UdGqp7r5eXFTzkMpMHu4ZIDlacMF29Sm86r98tWY2OBotAfq9rqxFqiXbxOrxO11l5ZyYb4Ze2xch6W5f6YyQmK5LFJg5q0XnGNRsCQqFElOkDtwsWqAEDnrgAw10sJAP0uUF7yQtZuRmaqpKKvpN0sre+qFbPj7Kp67lT9KjO8u2cum7+2DL9/92vp1FmrmCmwE5gaGyowNVZvF2+1jV3qLUKObvVRlAHNy05FmmQYSNZuiolzqZU/nFgZBrqysJ2hdiPcWVnKPK1v/UmN0usZqfoSbHdIVvjdceSk9D0z/F4a+X2YjCq/3KU8rCprN6PqtHKAKGs3S6tophUF/4xWPdfL6QkmRjBYUXDDzJV4aOFmzFtTjpnLd9r6XGa6INWmxurtCcrSOdvAjNBVObW6RNVc2r2NtFs5mhVxIxm5cgyw8uRZbfHB0i1lJ5RXm5W1G7HPgsfwE6EzT+SUJA9K1m6GkZ/woSpzQ37lkkJ1snYz7BymNEK27IYRRque61VUkIOB+dYu7GoVBisRlLrX7HS05gx2HpXMHpG0v75qj7Rdb2Q8qnf0XZEyoReN0Uw53Xn0JOrOSQpbSdrNMDgz23LHa509WNpFNu3RiumQ0azT40e1OuvKyEq7y9rtFh/XdH0vPZzIWZH18tY3CMUyEHaxYp2rcUMKmgQVRflZUSfZlpRVOHr+M4LBSgQrol4jjtfW44TkZCVr31+hfLWxv+K07iJKssQ8q2kVSFKz/XANLpDk4Mja/UitPLefOFETp7mo1zmjRLZGVcuUFpCtN2fnOnTHT9WbKqeQItkpWbsZsjwgAdi62nMks3k9kS7pGl6Y8uKu8kKVenm5QCWDlQhOr+4qAJyWjF/L2mVTCc81CnyiM9has8f4LJ3CDhkoHtXN0H3izCSE/J/GRiEts+638utqMlQWRfST4d3bGGo3orkdqPT2JMmS0JMT4yGLd5yaWWtk1eQzku5NWbsZshlYkW+11as9R4qmqneAXatUb7VwqrjVmtsxQNOgTs6v2SArJS5tVwluFkmqpEYyE0KUHqwxnMMTzdpAO47W4rTkYCVrJ/fYufZKCzOJRc2A2rHDiaEVLV66UjdSidvOWaCy2jhGWFG+QomVJSGsxmAlwtwvdjv+nLJy1rJ22Wh2A4CzOi/JnFpIK5oD45HqOum03liZ7gtYP3XaLXO/2GOo3Yh4HqkUHapWHs6VtZM+ds6KsaJmkF2r0Z/wcDVtHgIi7FUozmY32eJfZhYF0zuL4JsD3u3uC6hvaDBc3dePYuWl7KtQ/u3I2o2oq4+Vd6l5saKmiNMG5mfZejFnxfITsuF+vWkAMqc0ptS7icFKBDdmHcjm5WvN11eiN7zZJ0nS9ZKUFgnW1Yog2zVIAmVZuxEMVfznnhFdHevBtdLG8ipHZgVFY8W2o4ba9YpmQoTdGKxEaGz092mwLsopj17i5SifmrIzv8hE7URymXdPe9rszFmxoq6L2iSLaCTEeXd83ftnLIf5PW9TbxJZtF9qJzS32hp+Z2dCZ8fsNAsexTnePeQ7x+5ZNXayM2clmvXSArIkSwTI2vWytDK4xRiseIAbSaRaa5MQecmP+ndw9PmiLe/BYOU8L80G0iuwdIldquui7zHOSFYOSmTteiV7uCYSgxUPkHVy+KDzIyqL7h2OZ24e4PZukA/YVcQwJVE5rBiQH93JSu9ChLHOimRSpwWWLmmODlV5N5eRwUoz5YWpv0UFORg7qKPbuxGzkmLohPnJN/ZUlm4lqXnRLTe6YYAOWbFTYTka7bNS3N4FU5yuZG6U0SVa9DpzzrtXyAxWmqkEHyTYUnRaRtkl7CXRLIipJkuyunG0SZAtk73bne4kP+TGKfnusPJq1l6xR1JiQ9YeC3jGaq48PEWNrFFbFzt5ST3aZVj+mEX5WciUBCs1Ub536TEUKEbj7a/8OZzi9ZO+F6oTO43BSjMVK4vnkZyZOj1edXmP6NcXCvXkTf2wqPgy2xIVbxrM4U0AqDwdG9WZ6XvJLg0vM1iJQpqdy5farLnEKn7+jPSIpbwUJ40bcn518r4XZCneLmvXq2e7DOmMIH5i5Gey3ki7xfaR3Gay8W4j3DrZJDaTnBUrPiMvG9AxupOqX9hVtl32uCN7tY0qqNh9rFb6225OAWYMde7pFusf7xU9c1153uZxxrKJbCaBEVasSGuGhwsVWqp1y+g/Iy8b3r0NCtu3VLxN1u5HRQU5uHtEV8seLzA1VelxA2Xio1k8sUubdOnxwYrjhh7s2XFHz/bW51d5RWqLeDx980BXnpvBShRapkSfRJeelIBMycwBWbsVB6GkxHjp40fLS1eOvdpnOv6cspcva4/mYxjZqy2u7tte8TZZu19NGlNo2WM9tHAzbpi5Mvi4gZo/i+4djof+73l6tjV30gkEO/0kvV6ydqu1y0w21C7jnV+zP/SPcgjRq24adAFK/3uMa8/PYCUKVtQIufCCLGnVQFl7qzTloQ1Zu5LstCRLgi0lPdp654r+0u7WJmbq0VtyZSVr75Kr76R4Y1Fe2N+Bk6LaUEasSZP8JmTtajaWV6kW/3pibD/DjwkgGOzIZjD1UMlnsdIISXe9rF0mndOwDfFrbRktbvWoBDBYicK4IQUYmB8eRRflZzVpU/PToQWGu4u7SYIBWbuShDggz6b1Vm4b3tmyx5Llx+rNm7VrjY90lTU0UpOUg0BZu968k4JWaYo9AGpDGbHm4q6tDLVr2VReiRlLSnHjrFWYuGATbpy1CjOWlEazi0HdciW/09yWaOPQUJAVekYxbbw5nmBi8SLBC1gMIErvFV+G+WvLsKm8EgPys4OzDAJtb60pl859D5xQjteeVbxd1m7FHPuuuS3RNy8T6/Zau9BYUX5W8D2wQnZaEo6ebPo+ZKfpO9gHTuQvrthl2T4BQOfW8kDPzrWeigpyFIOQSWMKMbpve+w+VosubdJjMlABgDl3DEW3yf8IS9xMiDvf3nnSPww/Xk56EmYu3xnW9uKKXcH3MhqyQLlLm3Rc0SsXCzfsj+rxgfNDOoermxawa5eZLL3CN3rl37t9BjaUVZrZPaTo7PFKToxDnUL11GTJcgheZtcxp7lrjoGv5cYNKcATY/uHnaQDbbKu/86t0oJXxicUTsZq7TmS4R5Zu5LiUd0tvwK4adAFWFR8WVhbtPk1sqBALViINGlMIR64srvu7fW4UGVcWu0kpUTvyUPr8wosXxCrgUrAzunXYlSvXLTNSMKoXrnYOf1aU49TlJ8l7f0IBH3RUOvx0hqe7NxKX+L9V7+/Stpu1fDggPxsQ9uH6t1BX69M3zzJFHJJuxlOrtFnZX4VncdgxSS9b9zVfdsptl8fmn9g8IyuNhau5yBn1xDBwg37m3Shj+ylPD4ua4/0+2v7GGqXsTow++lQee+R7DZZu55988OwjtHE4mjMuWMo1vzhh5hzx1DD901tER8sCqcWWFoxA0mWvKsVCN0zSl9wXVKm3DNaUlZh2fCg0nC3XnqL+f38kk6G2s0wmzDtRbL6UbFcVyp2X5lJeqvzDe6UrWs7PVc3soRUWbvaWPj1RReo7k88vk8AVOvm7qTzyi7Siyt2hR1A+0vyMULbZSVfEuKty8dQepy0JHNff63nN7rPsu2VTnJedkGO8ndG1m41vb/dS7u3CfaCan1WgWDjZ0PzdT32oILsJm1KPV5agdCBSn2r38p+w4F2WbBk1HvFl+HJm/rhZ0Pz8eRN+hOP9a66bLQ30gxZzphdjM7yNMKumWaJkkOirN1JnshZmTlzJp566ikcOnQIAwYMwAsvvIChQ41fMVkhI7UF6iTDL6H0XtkrjV9Gnrhm3NQfN85a1eS+M27qr/iYaj/sLm3S8fyyHdL9aZXeImx7JU/e1A8922Uo7pMeu4/VGgomerZtidJDTRcO6/l/wZpV+RiRjzP9n6VYs8dYzs7VfdrqOuAb3WfZ9l7vTQn1q6t6YuKCTYrtTmiTkYz9ldonx7554dPZtT6rwN/z1pRrPraRnoBJYwpRUXsW89ft032fSHpO8rI8J6PGDSkIBnmTF26GnqUl9Q5x6jlORiv02KfFis7AS7q1xkffHFFsj1bn1un4anfTY1fn1tEFd4M75Sg+7uBO7h+HXI+X5s+fj4kTJ+LRRx/Fhg0bMGDAAIwePRpHjjT9kJ0wboj2FZTRH5HW1Y3W1VEktatBrSu2gSHjz7LHGTekIKou8NADpZ6epSfGKgdloe1W5WOEPo5s/ZZe7VpKrx6vLFQe1tN6Lju29xonro7VXNJV30lA6Tup9d7r/T0Yfa3RDA3K9suJIcMf9NY3jGtk+NWqXiAZI7/dJAsSe2XPZ2Q/ZIwONeslK8dhRZmOaMUJ4e4qMRdffDGGDBmCP//5zwCAxsZG5Ofn4/7778ekSZNU71tdXY2srCxUVVUhM9O64l9Dpn2MozVNe1d+0DsX9/+gh+UHgpKyCsVejEX3Dld9rpKyCunV4Py1ZXho4WZdj6n2OIHbdh492WTWBAD0btcS34Ysp37PiK5NDjIzlpQ2uWIys40dbpi5EhvLq4J/F+VnBZOE1W4jZW59jgGFDy/B6frvr/kT4sJLvke7P4HfQ31DI5ZuPYRPvz0a9WNb8Z6p/YbtIDtmhXL6s9cj8jedkZyAmrqGJtuN6pVrKh9K6/msPIbY9Vtz6rhn9PztarBy9uxZpKWl4X//939xww03BNvHjx+PyspKvP/++2Hb19XVoa7u+2l61dXVyM/PtzxYAYCnln6LDXsrkJediku7t7H9IGDHF8/qx5R9ifUcKK3axg5KU8/13EbK3PocA36zYGPwM3v65oG27o9Vj+32e2aG0vHlah9Mn4/8TStNhTc7w0zP81nJru+NE8c9XwUrBw4cwAUXXIBVq1Zh2LBhwfYHH3wQK1aswFdffRW2/ZQpUzB16tQmj2NHsOIGO754Vj8mT95EFODHIEvJHXPWYOuBKvTNy7KkR4W0xXSw4mTPChEREdnDaLDi6mygNm3aICEhAYcPHw5rP3z4MNq3b7oIW3JyMpKTjS3CRURERP7m6mygpKQkDB48GMuWLQu2NTY2YtmyZWE9LURERNR8uV5nZeLEiRg/fjwuuugiDB06FM899xxqa2txxx13uL1rRERE5AGuByvjxo3D0aNH8cgjj+DQoUMYOHAgPvzwQ7RrF/1cdCIiIvI/1+usRMOuOitERERkH6Pnb9cr2BIRERGpYbBCREREnsZghYiIiDyNwQoRERF5GoMVIiIi8jQGK0RERORprtdZiUZg1nV1dbXLe0JERER6Bc7bequn+DpYqampAQDk5+e7vCdERERkVE1NDbKysjS383VRuMbGRhw4cAAZGRmIi4uL+vECqziXl5fHbJE5vsbYwNcYG/gaYwNfo3FCCNTU1CAvLw/x8doZKb7uWYmPj0fHjh0tf9zMzMyY/cIF8DXGBr7G2MDXGBv4Go3R06MSwARbIiIi8jQGK0RERORpDFZCJCcn49FHH0VycrLbu2IbvsbYwNcYG/gaYwNfo/18nWBLREREsY89K0RERORpDFaIiIjI0xisEBERkaf5PlipqanBr371K3Tq1AmpqakYPnw41q5dG7z95MmTuO+++9CxY0ekpqaiT58+ePHFF8Me48yZMyguLkbr1q3RsmVL3HTTTTh8+HDYNmVlZbj22muRlpaGtm3b4ne/+x3OnTsXts1nn32GQYMGITk5Gd27d8fcuXN98xo3bdqEW265Bfn5+UhNTUVhYSGef/75Jq8vLi6uyb9Dhw754jUCUNz/t99+u8nr9OvnOHfuXMXXGBcXhyNHjgRfn5c/x5deegkjR45EZmYm4uLiUFlZ2eR5Tpw4gVtvvRWZmZnIzs7GXXfdhZMnT4Zt8/XXX+Pyyy9HSkoK8vPz8T//8z9Rvz6nXuOePXtw1113oUuXLkhNTUW3bt3w6KOP4uzZs2HbKH2Oq1ev9sVrBIDOnTs32f8ZM2aEbePnz1H2W4uLiws+l5c/xxMnTuD+++9Hr169kJqaioKCAkyYMAFVVVVhz+PI+VH43M033yz69OkjVqxYIbZv3y4effRRkZmZKfbt2yeEEOI//uM/RLdu3cTy5cvF7t27xV/+8heRkJAg3n///eBj3H333SI/P18sW7ZMrFu3TlxyySVi+PDhwdvPnTsnLrzwQnHVVVeJkpIS8c9//lO0adNGTJ48ObjNrl27RFpampg4caL45ptvxAsvvCASEhLEhx9+6IvX+Oqrr4oJEyaIzz77TOzcuVO88cYbIjU1VbzwwgvBbZYvXy4AiG3btomDBw8G/zU0NPjiNQohBAAxZ86csP0/ffp08Ha/f46nTp0Ke20HDx4Uo0ePFiNGjAhu4/XP8dlnnxXTp08X06dPFwBERUVFk+e55pprxIABA8Tq1avFv/71L9G9e3dxyy23BG+vqqoS7dq1E7feeqvYsmWLeOutt0Rqaqr4y1/+4ovXuGTJEnH77beLpUuXip07d4r3339ftG3bVvzmN78JbrN7924BQHzyySdhn+PZs2d98RqFEKJTp07iscceC9v/kydPBm/3++dYV1fX5Pf4y1/+UnTp0kU0NjYKIbz9OW7evFmMHTtWLF68WOzYsUMsW7ZM9OjRQ9x0003B53Dq/OjrYOXUqVMiISFBfPDBB2HtgwYNEn/4wx+EEEL07dtXPPbYY9LbKysrRYsWLcQ777wTvL20tFQAEF9++aUQQoh//vOfIj4+Xhw6dCi4zezZs0VmZqaoq6sTQgjx4IMPir59+4Y9z7hx48To0aN98RqV3HvvvWLUqFHBvwMnOaWDTjScfI0AxKJFi6T7Emuf45EjR0SLFi3E66+/Hmzz8ucYSraf33zzjQAg1q5dG2xbsmSJiIuLE/v37xdCCDFr1iyRk5MT/H0KIcRDDz0kevXq5YvXqOR//ud/RJcuXYJ/B05yJSUlxl+ICidfY6dOncSzzz4r3ZdY+xzPnj0rcnNzwx7XL59jwIIFC0RSUpKor68XQjh3fvT1MNC5c+fQ0NCAlJSUsPbU1FSsXLkSADB8+HAsXrwY+/fvhxACy5cvx3fffYerr74aALB+/XrU19fjqquuCt6/d+/eKCgowJdffgkA+PLLL9GvXz+0a9cuuM3o0aNRXV2NrVu3BrcJfYzANoHH8PprVFJVVYVWrVo1aR84cCA6dOiAH/7wh/jiiy+ien1uvMbi4mK0adMGQ4cOxV//+tewVT9j7XN8/fXXkZaWhh//+MdNbvPi56jHl19+iezsbFx00UXBtquuugrx8fH46quvgttcccUVSEpKCm4zevRobNu2DRUVFZ5/jUpkv8d///d/R9u2bXHZZZdh8eLFUT0H4PxrnDFjBlq3bo2ioiI89dRTYcMHsfY5Ll68GMePH8cdd9zR5Da/fI5VVVXIzMxEYuL51XocOz/qDms8atiwYWLEiBFi//794ty5c+KNN94Q8fHxomfPnkIIIc6cOSNuu+02AUAkJiaKpKQk8dprrwXv/+abb4qkpKQmjztkyBDx4IMPCiHOd5VdffXVYbfX1tYKAOKf//ynEEKIHj16iCeeeCJsm3/84x8CgDh16pTnX2OkL774QiQmJoqlS5cG27799lvx4osvinXr1okvvvhC3HHHHSIxMVGsX78+qtfn5Gt87LHHxMqVK8WGDRvEjBkzRHJysnj++eeDt8fa51hYWCjuueeesDYvf46hZFer06ZNCz5eqNzcXDFr1iwhhBA//OEPxX/+53+G3b5161YBQHzzzTeef42Rtm/fLjIzM8VLL70UbDt69Kh4+umnxerVq8WaNWvEQw89JOLi4sKGKbz+Gp9++mmxfPlysWnTJjF79myRnZ0tfv3rXwdvj7XPccyYMWLMmDFhbX75HAP7WlBQIH7/+98H25w6P/p6IUMAeOONN3DnnXfiggsuQEJCAgYNGoRbbrkF69evBwC88MILWL16NRYvXoxOnTrh888/R3FxMfLy8ppEel7l9GvcsmULrr/+ejz66KNhEXavXr3Qq1ev4N/Dhw/Hzp078eyzz+KNN97wxWt8+OGHg/8vKipCbW0tnnrqKUyYMCGq/dfD6c/xyy+/RGlpaZPPJhY+Rzc5/Rr379+Pa665Bj/5yU/wH//xH8H2Nm3aYOLEicG/hwwZggMHDuCpp57Cv//7v/viNYbuf//+/ZGUlIT/9//+H6ZPn257pVSnP8d9+/Zh6dKlWLBgQVi7Xz7H6upqXHvttejTpw+mTJkS1X6Zoiuk8YGTJ0+KAwcOCCHOJxX96Ec/EqdOnRItWrRoMmZ31113BcfKli1bphgRFxQUiGeeeUYIIcTDDz8sBgwYEHb7rl27BACxYcMGIYQQl19+uXjggQfCtvnrX/8qMjMzLXqF9r7GgK1bt4q2bduGRc5qfvvb34pLLrnE5CtqyonXGOqDDz4QAMSZM2eEELHzOQohxJ133ikGDhyoa5+88jmGkl2tvvrqqyI7Ozusrb6+XiQkJIh3331XCCHEL37xC3H99deHbfPpp58KAOLEiRNRvrrz7HyNAfv37xc9evQQv/jFL3QlQP/5z38W7du3N/5iJJx4jaG2bNkiAIhvv/1WCBE7n6MQ53t1c3NzdSXOeu1zrK6uFsOGDRNXXnll2IQEIZw7P/o6ZyVUeno6OnTogIqKCixduhTXX3896uvrUV9fj/j48JeZkJCAxsZGAMDgwYPRokULLFu2LHj7tm3bUFZWhmHDhgEAhg0bhs2bNwenfgLAxx9/jMzMTPTp0ye4TehjBLYJPIbXXyMAbN26FaNGjcL48eMxbdo0Xfu0ceNGdOjQwYJXd57dr1Fp/3NycoJXcbHwOQLnpyQuWLAAd911l6598srnqMewYcNQWVkZvDoEgE8//RSNjY24+OKLg9t8/vnnqK+vD27z8ccfo1evXsjJyYny1Z1n52sEzveojBw5EoMHD8acOXOaPKYSP32OSjZu3Ij4+Hi0bdsWQGx8jgAghMCcOXNw2223oUWLFprbe+lzrK6uxtVXX42kpCQsXry4SQ6MY+dH3WGNR3344YdiyZIlYteuXeKjjz4SAwYMEBdffHEweh0xYoTo27evWL58udi1a5eYM2eOSElJCY5tC3F+OmhBQYH49NNPxbp168SwYcPEsGHDgrcHpmZdffXVYuPGjeLDDz8Uubm5ilOzfve734nS0lIxc+ZMy6a8OvEaN2/eLHJzc8XPf/7zsOlzR44cCW7z7LPPivfee09s375dbN68WTzwwAMiPj5efPLJJ754jYsXLxYvv/yy2Lx5s9i+fbuYNWuWSEtLE4888khwG79/jgGvvPKKSElJUbzS8/rnePDgQVFSUiJefvllAUB8/vnnoqSkRBw/fjy4zTXXXCOKiorEV199JVauXCl69OgRNnW5srJStGvXTvziF78QW7ZsEW+//bZIS0uzZMqrE69x3759onv37uLKK68U+/btC/tNBsydO1fMmzdPlJaWitLSUjFt2jQRHx8v/vrXv/riNa5atUo8++yzYuPGjWLnzp3ib3/7m8jNzRW33XZb8DH8/jkGfPLJJwKAKC0tbbIfXv4cq6qqxMUXXyz69esnduzYEfY9PHfunBDCufOj74OV+fPni65du4qkpCTRvn17UVxcLCorK4O3Hzx4UNx+++0iLy9PpKSkiF69eomnn346OMddCCFOnz4t7r33XpGTkyPS0tLEjTfeGHZQEEKIPXv2iDFjxojU1FTRpk0b8Zvf/CY4dStg+fLlYuDAgSIpKUl07dpVzJkzxzev8dFHHxUAmvzr1KlTcJsnn3xSdOvWTaSkpIhWrVqJkSNHik8//dQ3r3HJkiVi4MCBomXLliI9PV0MGDBAvPjii0261/38OQYMGzZM/OxnP1PcD69/jrLvYujncPz4cXHLLbeIli1biszMTHHHHXeImpqasH3ZtGmTuOyyy0RycrK44IILxIwZM3zzGufMmaN4e+j15dy5c0VhYaFIS0sTmZmZYujQoWHT2r3+GtevXy8uvvhikZWVJVJSUkRhYaF44okngkOyAX7+HANuueWWJjWfArz8OQaGt5T+7d69O/g4TpwfueoyEREReVrM5KwQERFRbGKwQkRERJ7GYIWIiIg8jcEKEREReRqDFSIiIvI0BitERETkaQxWiIiIyNMYrBAREZGnMVghIkvNnTsX2dnZbu8GpkyZgoEDB7q9G0RkAQYrRGSpcePG4bvvvnN7N4gohiS6vQNEFFtSU1ORmprq9m4QUQxhzwoRGbZnzx7ExcU1+Tdy5Mgmw0CB4Zi//OUvyM/PR1paGm6++WZUVVWFPeZf//pX9O3bF8nJyejQoQPuu+++4G1lZWW4/vrr0bJlS2RmZuLmm2/G4cOHw+4/Y8YMtGvXDhkZGbjrrrtw5syZJvv9yiuvoLCwECkpKejduzdmzZpl7RtDRLZgsEJEhuXn5+PgwYPBfyUlJWjdujWuuOIKxe137NiBBQsW4O9//zs+/PBDlJSU4N577w3ePnv2bBQXF+M///M/sXnzZixevBjdu3cHADQ2NuL666/HiRMnsGLFCnz88cfYtWsXxo0bF7z/ggULMGXKFDzxxBNYt24dOnTo0CQQefPNN/HII49g2rRpKC0txRNPPIGHH34Yr732mg3vEBFZyuTK00REQgghTp8+LS6++GLxb//2b6KhoUHMmTNHZGVlBW9/9NFHRUJCgti3b1+wbcmSJSI+Pl4cPHhQCCFEXl6e+MMf/qD4+B999JFISEgQZWVlwbatW7cKAGLNmjVCCCGGDRsm7r333rD7XXzxxWLAgAHBv7t16ybmzZsXts1///d/i2HDhpl63UTkHPasEFFU7rzzTtTU1GDevHmIj1c+pBQUFOCCCy4I/j1s2DA0NjZi27ZtOHLkCA4cOIArr7xS8b6lpaXIz89Hfn5+sK1Pnz7Izs5GaWlpcJuLL7447H7Dhg0L/r+2thY7d+7EXXfdhZYtWwb/Pf7449i5c6fp105EzmCCLRGZ9vjjj2Pp0qVYs2YNMjIyTD2GE8m4J0+eBAC8/PLLTYKahIQE25+fiKLDnhUiMmXhwoV47LHHsGDBAnTr1k1127KyMhw4cCD49+rVqxEfH49evXohIyMDnTt3xrJlyxTvW1hYiPLycpSXlwfbvvnmG1RWVqJPnz7Bbb766quw+61evTr4/3bt2iEvLw+7du1C9+7dw/516dLF8GsnImexZ4WIDNuyZQtuu+02PPTQQ+jbty8OHToEAEhKSlLcPiUlBePHj8cf//hHVFdXY8KECbj55pvRvn17AOdnDN19991o27YtxowZg5qaGnzxxRe4//77cdVVV6Ffv3649dZb8dxzz+HcuXO49957MWLECFx00UUAgAceeAC33347LrroIlx66aV48803sXXrVnTt2jW4D1OnTsWECROQlZWFa665BnV1dVi3bh0qKiowceJEm98xIooGe1aIyLB169bh1KlTePzxx9GhQ4fgv7Fjxypu3717d4wdOxY/+tGPcPXVV6N///5hs3XGjx+P5557DrNmzULfvn3xb//2b9i+fTsAIC4uDu+//z5ycnJwxRVX4KqrrkLXrl0xf/784P3HjRuHhx9+GA8++CAGDx6MvXv34p577gnbh1/+8pd45ZVXMGfOHPTr1w8jRozA3Llz2bNC5ANxQgjh9k4QUeyaMmUK3nvvPWzcuNHtXSEin2LPChEREXkagxUiIiLyNA4DERERkaexZ4WIiIg8jcEKEREReRqDFSIiIvI0BitERETkaQxWiIiIyNMYrBAREZGnMVghIiIiT2OwQkRERJ7GYIWIiIg87f8DAOJc6LBCvQYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"zipcode\", y=\"price\", s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'date'}>]], dtype=object)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column=\"date\", bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['yr_built'], dtype='object')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[149], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m box_pl \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39;49mboxplot(column\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39myr_built\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      2\u001b[0m box_pl\u001b[39m.\u001b[39mplot()\n",
      "File \u001b[0;32m~/neuefische/housing_project/.venv/lib/python3.9/site-packages/pandas/plotting/_core.py:511\u001b[0m, in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, backend, **kwargs)\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[39m@Substitution\u001b[39m(backend\u001b[39m=\u001b[39m_backend_doc)\n\u001b[1;32m    495\u001b[0m \u001b[39m@Appender\u001b[39m(_boxplot_doc)\n\u001b[1;32m    496\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mboxplot_frame\u001b[39m(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    508\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m    509\u001b[0m ):\n\u001b[1;32m    510\u001b[0m     plot_backend \u001b[39m=\u001b[39m _get_plot_backend(backend)\n\u001b[0;32m--> 511\u001b[0m     \u001b[39mreturn\u001b[39;00m plot_backend\u001b[39m.\u001b[39;49mboxplot_frame(\n\u001b[1;32m    512\u001b[0m         \u001b[39mself\u001b[39;49m,\n\u001b[1;32m    513\u001b[0m         column\u001b[39m=\u001b[39;49mcolumn,\n\u001b[1;32m    514\u001b[0m         by\u001b[39m=\u001b[39;49mby,\n\u001b[1;32m    515\u001b[0m         ax\u001b[39m=\u001b[39;49max,\n\u001b[1;32m    516\u001b[0m         fontsize\u001b[39m=\u001b[39;49mfontsize,\n\u001b[1;32m    517\u001b[0m         rot\u001b[39m=\u001b[39;49mrot,\n\u001b[1;32m    518\u001b[0m         grid\u001b[39m=\u001b[39;49mgrid,\n\u001b[1;32m    519\u001b[0m         figsize\u001b[39m=\u001b[39;49mfigsize,\n\u001b[1;32m    520\u001b[0m         layout\u001b[39m=\u001b[39;49mlayout,\n\u001b[1;32m    521\u001b[0m         return_type\u001b[39m=\u001b[39;49mreturn_type,\n\u001b[1;32m    522\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m    523\u001b[0m     )\n",
      "File \u001b[0;32m~/neuefische/housing_project/.venv/lib/python3.9/site-packages/pandas/plotting/_matplotlib/boxplot.py:394\u001b[0m, in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m    379\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mboxplot_frame\u001b[39m(\n\u001b[1;32m    380\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    381\u001b[0m     column\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds,\n\u001b[1;32m    391\u001b[0m ):\n\u001b[1;32m    392\u001b[0m     \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m--> 394\u001b[0m     ax \u001b[39m=\u001b[39m boxplot(\n\u001b[1;32m    395\u001b[0m         \u001b[39mself\u001b[39;49m,\n\u001b[1;32m    396\u001b[0m         column\u001b[39m=\u001b[39;49mcolumn,\n\u001b[1;32m    397\u001b[0m         by\u001b[39m=\u001b[39;49mby,\n\u001b[1;32m    398\u001b[0m         ax\u001b[39m=\u001b[39;49max,\n\u001b[1;32m    399\u001b[0m         fontsize\u001b[39m=\u001b[39;49mfontsize,\n\u001b[1;32m    400\u001b[0m         grid\u001b[39m=\u001b[39;49mgrid,\n\u001b[1;32m    401\u001b[0m         rot\u001b[39m=\u001b[39;49mrot,\n\u001b[1;32m    402\u001b[0m         figsize\u001b[39m=\u001b[39;49mfigsize,\n\u001b[1;32m    403\u001b[0m         layout\u001b[39m=\u001b[39;49mlayout,\n\u001b[1;32m    404\u001b[0m         return_type\u001b[39m=\u001b[39;49mreturn_type,\n\u001b[1;32m    405\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds,\n\u001b[1;32m    406\u001b[0m     )\n\u001b[1;32m    407\u001b[0m     plt\u001b[39m.\u001b[39mdraw_if_interactive()\n\u001b[1;32m    408\u001b[0m     \u001b[39mreturn\u001b[39;00m ax\n",
      "File \u001b[0;32m~/neuefische/housing_project/.venv/lib/python3.9/site-packages/pandas/plotting/_matplotlib/boxplot.py:371\u001b[0m, in \u001b[0;36mboxplot\u001b[0;34m(data, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m    369\u001b[0m     columns \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mcolumns\n\u001b[1;32m    370\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 371\u001b[0m     data \u001b[39m=\u001b[39m data[columns]\n\u001b[1;32m    373\u001b[0m result \u001b[39m=\u001b[39m plot_group(columns, data\u001b[39m.\u001b[39mvalues\u001b[39m.\u001b[39mT, ax)\n\u001b[1;32m    374\u001b[0m ax\u001b[39m.\u001b[39mgrid(grid)\n",
      "File \u001b[0;32m~/neuefische/housing_project/.venv/lib/python3.9/site-packages/pandas/core/frame.py:3464\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3462\u001b[0m     \u001b[39mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m   3463\u001b[0m         key \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(key)\n\u001b[0;32m-> 3464\u001b[0m     indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mloc\u001b[39m.\u001b[39;49m_get_listlike_indexer(key, axis\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)[\u001b[39m1\u001b[39m]\n\u001b[1;32m   3466\u001b[0m \u001b[39m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m   3467\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(indexer, \u001b[39m\"\u001b[39m\u001b[39mdtype\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m) \u001b[39m==\u001b[39m \u001b[39mbool\u001b[39m:\n",
      "File \u001b[0;32m~/neuefische/housing_project/.venv/lib/python3.9/site-packages/pandas/core/indexing.py:1314\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1311\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1312\u001b[0m     keyarr, indexer, new_indexer \u001b[39m=\u001b[39m ax\u001b[39m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 1314\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_read_indexer(keyarr, indexer, axis)\n\u001b[1;32m   1316\u001b[0m \u001b[39mif\u001b[39;00m needs_i8_conversion(ax\u001b[39m.\u001b[39mdtype) \u001b[39mor\u001b[39;00m \u001b[39misinstance\u001b[39m(\n\u001b[1;32m   1317\u001b[0m     ax, (IntervalIndex, CategoricalIndex)\n\u001b[1;32m   1318\u001b[0m ):\n\u001b[1;32m   1319\u001b[0m     \u001b[39m# For CategoricalIndex take instead of reindex to preserve dtype.\u001b[39;00m\n\u001b[1;32m   1320\u001b[0m     \u001b[39m#  For IntervalIndex this is to map integers to the Intervals they match to.\u001b[39;00m\n\u001b[1;32m   1321\u001b[0m     keyarr \u001b[39m=\u001b[39m ax\u001b[39m.\u001b[39mtake(indexer)\n",
      "File \u001b[0;32m~/neuefische/housing_project/.venv/lib/python3.9/site-packages/pandas/core/indexing.py:1374\u001b[0m, in \u001b[0;36m_LocIndexer._validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1372\u001b[0m     \u001b[39mif\u001b[39;00m use_interval_msg:\n\u001b[1;32m   1373\u001b[0m         key \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(key)\n\u001b[0;32m-> 1374\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNone of [\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m] are in the [\u001b[39m\u001b[39m{\u001b[39;00maxis_name\u001b[39m}\u001b[39;00m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   1376\u001b[0m not_found \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[39m.\u001b[39mnonzero()[\u001b[39m0\u001b[39m]]\u001b[39m.\u001b[39munique())\n\u001b[1;32m   1377\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mnot_found\u001b[39m}\u001b[39;00m\u001b[39m not in index\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['yr_built'], dtype='object')] are in the [columns]\""
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_pl = df.boxplot(column=\"yr_built\")\n",
    "box_pl.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# round floats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1970-01-01 00:00:00.000000000\n",
       "1       1970-01-01 00:00:00.000001991\n",
       "2       NaT                          \n",
       "3       1970-01-01 00:00:00.000000000\n",
       "4       1970-01-01 00:00:00.000000000\n",
       "                     ...             \n",
       "21592   1970-01-01 00:00:00.000000000\n",
       "21593   1970-01-01 00:00:00.000000000\n",
       "21594   1970-01-01 00:00:00.000000000\n",
       "21595   1970-01-01 00:00:00.000000000\n",
       "21596   1970-01-01 00:00:00.000000000\n",
       "Name: yr_renovated, Length: 21597, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"yr_renovated\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00.000001991'), NaT, Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00'), NaT, Timestamp('1970-01-01 00:00:00'), Timestamp('1970-01-01 00:00:00.000002002')]\n"
     ]
    }
   ],
   "source": [
    "df['yr_renovated'].nunique()\n",
    "#df_unique_yr_renovated = df[\"yr_renovated\"].head(70)\n",
    "print(sorted(df_unique_yr_renovated))\n",
    "# renovation does increase the price: replace all 0 with nan!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make column with month sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature engineering?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#days between renovating and selling? Price?"
   ]
  }
 ],
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