data_overview_cleaned.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "#import ptitprince as pt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('housing.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## look into RAW data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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>...</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",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>house_id</th>\n",
       "      <th>id.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>5650.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>7129300520</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>7242.0</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>...</td>\n",
       "      <td>19910.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>7639.0</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>6414100192</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770.0</td>\n",
       "      <td>10000.0</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>8062.0</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>5631500400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>5000.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>2487200875</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>8080.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>7503.0</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>1954400510</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7237550310</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5420.0</td>\n",
       "      <td>101930.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6561</td>\n",
       "      <td>-122.005</td>\n",
       "      <td>4760.0</td>\n",
       "      <td>101930.0</td>\n",
       "      <td>2014-05-12</td>\n",
       "      <td>1230000.0</td>\n",
       "      <td>7237550310</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1321400060</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>6819.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98003</td>\n",
       "      <td>47.3097</td>\n",
       "      <td>-122.327</td>\n",
       "      <td>2238.0</td>\n",
       "      <td>6819.0</td>\n",
       "      <td>2014-06-27</td>\n",
       "      <td>257500.0</td>\n",
       "      <td>1321400060</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008000270</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>9711.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98198</td>\n",
       "      <td>47.4095</td>\n",
       "      <td>-122.315</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>9711.0</td>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>291850.0</td>\n",
       "      <td>2008000270</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2414600126</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>7470.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5123</td>\n",
       "      <td>-122.337</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>8113.0</td>\n",
       "      <td>2015-04-15</td>\n",
       "      <td>229500.0</td>\n",
       "      <td>2414600126</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3793500160</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>6560.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98038</td>\n",
       "      <td>47.3684</td>\n",
       "      <td>-122.031</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>7570.0</td>\n",
       "      <td>2015-03-12</td>\n",
       "      <td>323000.0</td>\n",
       "      <td>3793500160</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1736800520</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3560.0</td>\n",
       "      <td>9796.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98007</td>\n",
       "      <td>47.6007</td>\n",
       "      <td>-122.145</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>8925.0</td>\n",
       "      <td>2015-04-03</td>\n",
       "      <td>662500.0</td>\n",
       "      <td>1736800520</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9212900260</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98115</td>\n",
       "      <td>47.6900</td>\n",
       "      <td>-122.292</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2014-05-27</td>\n",
       "      <td>468000.0</td>\n",
       "      <td>9212900260</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>114101516</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>19901.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7558</td>\n",
       "      <td>-122.229</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>12697.0</td>\n",
       "      <td>2014-05-28</td>\n",
       "      <td>310000.0</td>\n",
       "      <td>114101516</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6054650070</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1370.0</td>\n",
       "      <td>9680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6127</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1370.0</td>\n",
       "      <td>10208.0</td>\n",
       "      <td>2014-10-07</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>6054650070</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1175000570</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98107</td>\n",
       "      <td>47.6700</td>\n",
       "      <td>-122.394</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>2015-03-12</td>\n",
       "      <td>530000.0</td>\n",
       "      <td>1175000570</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "0   7129300520       3.0       1.00       1180.0    5650.0     1.0   \n",
       "1   6414100192       3.0       2.25       2570.0    7242.0     2.0   \n",
       "2   5631500400       2.0       1.00        770.0   10000.0     1.0   \n",
       "3   2487200875       4.0       3.00       1960.0    5000.0     1.0   \n",
       "4   1954400510       3.0       2.00       1680.0    8080.0     1.0   \n",
       "5   7237550310       4.0       4.50       5420.0  101930.0     1.0   \n",
       "6   1321400060       3.0       2.25       1715.0    6819.0     2.0   \n",
       "7   2008000270       3.0       1.50       1060.0    9711.0     1.0   \n",
       "8   2414600126       3.0       1.00       1780.0    7470.0     1.0   \n",
       "9   3793500160       3.0       2.50       1890.0    6560.0     2.0   \n",
       "10  1736800520       3.0       2.50       3560.0    9796.0     1.0   \n",
       "11  9212900260       2.0       1.00       1160.0    6000.0     1.0   \n",
       "12   114101516       3.0       1.00       1430.0   19901.0     1.5   \n",
       "13  6054650070       3.0       1.75       1370.0    9680.0     1.0   \n",
       "14  1175000570       5.0       2.00       1810.0    4850.0     1.5   \n",
       "\n",
       "    waterfront  view  condition  grade  ...  yr_renovated  zipcode      lat  \\\n",
       "0          NaN   0.0          3      7  ...           0.0    98178  47.5112   \n",
       "1          0.0   0.0          3      7  ...       19910.0    98125  47.7210   \n",
       "2          0.0   0.0          3      6  ...           NaN    98028  47.7379   \n",
       "3          0.0   0.0          5      7  ...           0.0    98136  47.5208   \n",
       "4          0.0   0.0          3      8  ...           0.0    98074  47.6168   \n",
       "5          0.0   0.0          3     11  ...           0.0    98053  47.6561   \n",
       "6          0.0   0.0          3      7  ...           0.0    98003  47.3097   \n",
       "7          0.0   NaN          3      7  ...           0.0    98198  47.4095   \n",
       "8          0.0   0.0          3      7  ...           0.0    98146  47.5123   \n",
       "9          0.0   0.0          3      7  ...           0.0    98038  47.3684   \n",
       "10         NaN   0.0          3      8  ...           0.0    98007  47.6007   \n",
       "11         0.0   0.0          4      7  ...           0.0    98115  47.6900   \n",
       "12         0.0   0.0          4      7  ...           NaN    98028  47.7558   \n",
       "13         0.0   0.0          4      7  ...           0.0    98074  47.6127   \n",
       "14         0.0   0.0          3      7  ...           0.0    98107  47.6700   \n",
       "\n",
       "       long  sqft_living15  sqft_lot15        date      price    house_id id.1  \n",
       "0  -122.257         1340.0      5650.0  2014-10-13   221900.0  7129300520    1  \n",
       "1  -122.319         1690.0      7639.0  2014-12-09   538000.0  6414100192    2  \n",
       "2  -122.233         2720.0      8062.0  2015-02-25   180000.0  5631500400    3  \n",
       "3  -122.393         1360.0      5000.0  2014-12-09   604000.0  2487200875    4  \n",
       "4  -122.045         1800.0      7503.0  2015-02-18   510000.0  1954400510    5  \n",
       "5  -122.005         4760.0    101930.0  2014-05-12  1230000.0  7237550310    6  \n",
       "6  -122.327         2238.0      6819.0  2014-06-27   257500.0  1321400060    7  \n",
       "7  -122.315         1650.0      9711.0  2015-01-15   291850.0  2008000270    8  \n",
       "8  -122.337         1780.0      8113.0  2015-04-15   229500.0  2414600126    9  \n",
       "9  -122.031         2390.0      7570.0  2015-03-12   323000.0  3793500160   10  \n",
       "10 -122.145         2210.0      8925.0  2015-04-03   662500.0  1736800520   11  \n",
       "11 -122.292         1330.0      6000.0  2014-05-27   468000.0  9212900260   12  \n",
       "12 -122.229         1780.0     12697.0  2014-05-28   310000.0   114101516   13  \n",
       "13 -122.045         1370.0     10208.0  2014-10-07   400000.0  6054650070   14  \n",
       "14 -122.394         1360.0      4850.0  2015-03-12   530000.0  1175000570   15  \n",
       "\n",
       "[15 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some notes about the culumns\n",
    "\n",
    "**id** - unique identified for a house\n",
    "\n",
    "**dateDate** - house was sold\n",
    "\n",
    "**pricePrice** - is prediction target\n",
    "\n",
    "**bedroomsNumber** - # of bedrooms\n",
    "\n",
    "**bathroomsNumber** - # of bathrooms\n",
    "\n",
    "**sqft_livingsquare** - footage of the home\n",
    "\n",
    "**sqft_lotsquare** - footage of the lot\n",
    "\n",
    "**floorsTotal** - floors (levels) in house\n",
    "\n",
    "**waterfront** - House which has a view to a waterfront\n",
    "\n",
    "**view** - Has been viewed\n",
    "\n",
    "**condition** - How good the condition is ( Overall )\n",
    "\n",
    "**grade** - overall grade given to the housing unit, based on King County grading system\n",
    "Classification by construction quality which refers to the types of materials used and the quality of workmanship. Buildings of better quality (higher grade) cost more to build per unit of measure and command higher value. \n",
    "(See Glossary in Area Report for Residential Building Grades in use by the King County Department of Assessments.) \n",
    "\n",
    "**sqft_above** - square footage of house apart from basement\n",
    "\n",
    "**sqft_basement** - square footage of the basement\n",
    "\n",
    "\n",
    "**yr_built** - Built Year\n",
    "\n",
    "**yr_renovated** - Year when house was renovated\n",
    "\n",
    "**zipcode** - zip\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**lat** - Latitude coordinate\n",
    "\n",
    "**long** - Longitude coordinate\n",
    "\n",
    "\n",
    "**sqft_living15** - The square footage of interior housing living space for the nearest 15 neighbors\n",
    "Die Quadratmeterzahl der Innenwohnfläche für die nächsten 15 Nachbarn\n",
    "\n",
    "- **sqft_lot15** - The square footage of the land lots of the nearest 15 neighbors\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deeper into every column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bedrooms\n",
       "3.0     9824\n",
       "4.0     6882\n",
       "2.0     2760\n",
       "5.0     1601\n",
       "6.0      272\n",
       "1.0      196\n",
       "7.0       38\n",
       "8.0       13\n",
       "9.0        6\n",
       "10.0       3\n",
       "11.0       1\n",
       "33.0       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXAMPLE Table of all data and their frequency\n",
    "df.value_counts('bedrooms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXAMPLE Unique values\n",
    "len(df['yr_renovated'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(3848)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXAMPLE: Count NA values\n",
    "df['yr_renovated'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EXAMPLE Histogram\n",
    "plt.hist(df['sqft_living15'], bins=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXAMPLE Minimum\n",
    "df['floors'].min(skipna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(3.5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXAMPLE Maximum\n",
    "df['floors'].max(skipna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21597 entries, 0 to 21596\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             21597 non-null  int64  \n",
      " 1   bedrooms       21597 non-null  float64\n",
      " 2   bathrooms      21597 non-null  float64\n",
      " 3   sqft_living    21597 non-null  float64\n",
      " 4   sqft_lot       21597 non-null  float64\n",
      " 5   floors         21597 non-null  float64\n",
      " 6   waterfront     19206 non-null  float64\n",
      " 7   view           21534 non-null  float64\n",
      " 8   condition      21597 non-null  int64  \n",
      " 9   grade          21597 non-null  int64  \n",
      " 10  sqft_above     21597 non-null  float64\n",
      " 11  sqft_basement  21145 non-null  float64\n",
      " 12  yr_built       21597 non-null  int64  \n",
      " 13  yr_renovated   17749 non-null  float64\n",
      " 14  zipcode        21597 non-null  int64  \n",
      " 15  lat            21597 non-null  float64\n",
      " 16  long           21597 non-null  float64\n",
      " 17  sqft_living15  21597 non-null  float64\n",
      " 18  sqft_lot15     21597 non-null  float64\n",
      " 19  date           21597 non-null  object \n",
      " 20  price          21597 non-null  float64\n",
      " 21  house_id       21597 non-null  int64  \n",
      " 22  id.1           21597 non-null  int64  \n",
      "dtypes: float64(15), int64(7), object(1)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Find out the type for every column\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning \n",
    "Information gathered from aboves lines resulted in some needs for cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['id.1', 'house_id'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7x/ksvhcqhj0rddxlhkfh5nyfk80000gn/T/ipykernel_19930/3532331397.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df.waterfront.fillna(0, inplace=True)\n",
      "/var/folders/7x/ksvhcqhj0rddxlhkfh5nyfk80000gn/T/ipykernel_19930/3532331397.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df.view.fillna(-1, inplace=True)\n",
      "/var/folders/7x/ksvhcqhj0rddxlhkfh5nyfk80000gn/T/ipykernel_19930/3532331397.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df.sqft_basement.fillna(-1, inplace=True)\n",
      "/var/folders/7x/ksvhcqhj0rddxlhkfh5nyfk80000gn/T/ipykernel_19930/3532331397.py:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df.yr_renovated.fillna(0, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# fill NAs\n",
    "df.waterfront.fillna(0, inplace=True)\n",
    "df.view.fillna(-1, inplace=True)\n",
    "df.sqft_basement.fillna(-1, inplace=True)\n",
    "df.yr_renovated.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['yr_renovated'] = df['yr_renovated']/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7x/ksvhcqhj0rddxlhkfh5nyfk80000gn/T/ipykernel_19930/979454551.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['yr_renovated'].replace(0, np.nan, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df['yr_renovated'].replace(0, np.nan, inplace=True)\n",
    "# Konvertiere in datetime und lasse NaN unangetastet\n",
    "df['yr_renovated'] = pd.to_datetime(df['yr_renovated'], format='%Y', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['yr_renovated'] = pd.to_datetime(df['yr_renovated'], format='%Y', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['bedrooms', 'sqft_living', 'sqft_lot', 'view', 'grade', 'sqft_above', 'sqft_basement']] = df[['bedrooms', 'sqft_living', 'sqft_lot', 'view', 'grade', 'sqft_above', 'sqft_basement']].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['waterfront'] = df['waterfront'].map({0.0: 'No_Waterfront', 1.0: 'Waterfront'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['zipcode', 'condition']] = df[['zipcode', 'condition']].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "waterfront\n",
       "No_Waterfront    21451\n",
       "Waterfront         146\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.value_counts('waterfront')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis Testing\n",
    "\n",
    "- H0: waterfront houses are more expensive \n",
    "- H1: Waterfront houses are viewed more often on average than non-waterfront houses.\n",
    "- H2: The square footage of land is smaller for waterfront houses in Kings County.\n",
    "- H3: There is a positive correlation between the condition of a house and the King County Building Grades."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# H0 plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='waterfront', ylabel='price'>"
      ]
     },
     "execution_count": 216,
     "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": [
    "sns.violinplot(x=df['waterfront'], y=df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='Density'>"
      ]
     },
     "execution_count": 209,
     "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": [
    "sns.kdeplot(data=df, x=\"price\", hue=\"waterfront\", multiple=\"stack\")\n",
    "#there are close to no houses on the waterfront"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# H2 Plot\n",
    "Waterfront houses are viewed more often on average than non-waterfront houses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "view\n",
       " 0    19422\n",
       " 2      957\n",
       " 3      508\n",
       " 1      330\n",
       " 4      317\n",
       "-1       63\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1UqUUaBhqiIgooEiShDvvvBOXX345dPWlMBZv7vh9HU0wFa6FXqfBo48+in79+nVipRRoGGqIiCjgaDQaLFq0CL169YahbA80jRUdup+xeCPgtGH27NkYMmRI5xZJAYehhoiIApLBYMD8+fMACIQUrQeE+4zba+uOQ19ZiIyMDPzsZz/rmiIpoDDUEBFRwBo8eDAmTpwIrbUS+vJ97W8o3Ag5/D9oNJ4TU2o0/Hrrjvi/TkREAW3WrFnQ6/XQl+1p91QK2roSaJprcc011yA9Pb2LK6RAwVBDREQBLTIyEpdccgm0TTXQWKva3EZfeQAAMHHixK4sjQIMQw0REQW8q6++GsDJ8OLD5YC++jBSUlIwcODALq6MAglDDRERBbyRI0fCYgmDrqb4tNu09SWA24krrriCp0Do5hhqiIgo4Ol0OqSn94XGVn/amby1TTUAwLNuE0MNqZ9oZ2AhEQWX3r17AxDQnHKyS01zLQCgV69eClRFgYShhlSPzdFE6uAJNYCmucZnvaapBjqdDomJiQpURYGEoYaIiIKCxWLxLJx6Lii3E2azBVqttuuLooDCUEOqx+4nInVoDS3SqTMLCzc0Wn6dEUMNdQPsfiJSB3mW4FN+qEhCQMdWGgJDDRERBQmns6XbSfL96hKS5uRt1K0x1JDqud1nPgkeEQWHujrPUU9CZ/RZL3RG1NXVsauZGGpI/dj9RKQOtbWeQ7fbCjUulwuNjY1KlEUBhKGGVM/hcChdAhH5QU1NDQBA6EJ81rdeb72dui+GGlK91iZrIgpu5eXlAAC3weyzXhhMPrdT98VQQ6rkcp2cRv348eMKVkJE/lJWVgahMwBavc96t8Ei307dG0MNqVJFRYW8fOzYMQUrISJ/OXGiDG69+bT1oqXl5sSJE11dEgUYhhpSpUOHDsnLBw8eVLASIvIHq9WKhoZ6OcB4a13H7idiqCFV2r59OwBAaLQoLi5GVVWVwhUR0flobX09dTyN9zqGGmKoIVXatm0bIGlgT8gGAOzYsUPZgojovLQGlrZaaqDVQ+gMDDXEUEPqY7VaUVBQAKc5Fs6IngBaQg4RBa3KykoAgFtvavN2t96Ecq+xdNQ9BU2oee6555CdnY3w8HCEh4dj1KhR+PTTT5UuiwLQ9u3b4XK54ApPgtscA6E1YMuWLUqXRUTn4eTEeyFt3i50Iaivr+cM4t1c0ISanj17YunSpdiyZQs2b96Myy+/HJMnT8bu3buVLo0CTGuAcYUnAZIGzrAEFBcX83BPoiBWX1/vWThlNuFWQmuEcLs5q3A3FzShZtKkSZg4cSL69euH/v37Y8mSJbBYLPj++++VLo0CzO7duwGNFi5zHADAFZYIANi1a5eSZRHReTh53idDm7e3njpBDj/ULemULuBcuFwuvPPOO2hsbMSoUaOULocCTOmJE3AZLIBGCwBwh4QD4MRcRMHMbrcDAISmna+tlvc7T4vSvQVVqNm5cydGjRqF5uZmWCwWvP/++8jMzGx3e5vNBpvNJl/ndPnq53Q6UVNdDWFJkNdxCnWi4HdyrEw7J6htOXGt92zi1P0ETfcTAAwYMADbtm3Dhg0bcMcdd2D69On48ccf290+Ly8PERER8iUlJaULqyUl1NXVQQjhM5iwdbl1oCERBR85rEjtfW0x1FCQhRqDwYD09HQMHz4ceXl5GDx4MJ566ql2t1+8eDFqa2vlS3FxcRdWS0oIDw+HVquF5LDK6yS7Zzk6OlqpsojoPGk0LV9Xor2jm4TvdtQtBVX306ncbrdP99KpjEYjjMa2R8qTOul0OsTHx+NYeY28TmPzdDsmJycrVBURna/Q0FAAgOR2tMSXU7g8Y2lMprbnsaHuIWhCzeLFizFhwgT06tUL9fX1ePPNN5Gfn4/Vq1crXRoFmOTkZM+ZuZ02QGeEptnT7ZSUlKRwZUR0rszmlpmEXW0PBJZa1reGH+qegibUlJWV4aabbkJJSQkiIiKQnZ2N1atX46qrrlK6NAowWVlZ2LRpE3T1pXBG9Ya2rgQajQYZGRlKl0ZE56i1BUZy2du8vXU9W2q6t6AJNa+88orSJVCQGD58OF599VVoa4/BGZ4IXWMZMjIzYbFYlC6NiM5RbGwsgJNj5E4lOawIDw+HwdD2PDbUPXBEFanOgAEDYLZYoKs7Dm19KSAERowYoXRZRHQe4uI8k2lqHG2HGo3dKm9D3RdDDamOTqdDdlYWNLY66KuKAABDhgxRtCYiOj+tgUWyN5x+o8sByWVnqCGGGlKn7OxsAIC+8gC0Wi3H0xAFuR49ekCSJGhsp58GofUIx8TExK4uiwIMQw2p0qBBg+Tlfv36ISSk7TP7ElFwMBqNiI+Ph6b59JnhW49w5ASrxFBDqpSamiovp6WlKVcIEflNSkoKNPbG0w7rbg06DDXEUEOqFBYWJi8nJCScYUsiChatoeXU1hpNk6elpmfPnl1eEwUWhhpSvfj4eKVLICI/aG2B1TRV+6zXNFcjJDSU73ViqCH1Cw8PV7oEIvKDk6Gm5uRK4Ya2uRZpqak87xMx1JD68YOOSB1ax8dpm0+21Ei2esDt8hlHR90XP+1J9YRo8/R3RBRkwsLCEB0d49NSo21ZZqghgKGGugG21BCpR2pqb89cNW4nAEDTXAMA6N27t4JVUaDgpz0REQWN1vDSesRTa6sNQw0BDDXUDbD7iUg9evXqBeDkhHua5loYDAYe+UQAGGqoG5AkSekSiMhPkpOTAUA+XYLGVo+kpCR2MxMAhhrqBthSQ6QeJ0NNHeC0QXLa5HVEDDWkegw1ROoRHx8PjUYDyVYvt9bwRJbUiqGGVI/dT0TqodPpEB0dDY3dCsluBeA5gzcRwFBDRERBJi4uDhqHFRpHIwAgNjZW4YooUDDUEBFRUImJiQHcTvmwboYaasVQQ0REQSUyMhLAycO6IyIiFKyGAglDDRERBRWz2QwAkOwNAACLxaJkORRAGGqIiCiotIYYjY2hhnwx1BARUVAxmUwAAEm4oNFoYDQaFa6IAgVDDRERBRWdTue1rFewEgo0DDVERBRUtFqtvKzTac+wJXU3DDVERBRUvFtqvAMOEUMNEREFFbfbfXKZp0EhLww1REQUVJxOp7zs8lomYqgh1eMJLYnUxeVyyctOhhrywlBDqscTWhKpi81mk5edTqdPyKHujaGGiIiCSlNTEwDArTP6XCdiqCHVY/cTkbq0hhihN/lcJ2KoIdVj9xORujQ2NgI4GWqsVquS5VAAYagh1WOoIVKX+vp6AIA7JMznOhFDDame95wWRBT85FBj8ISauro6JcuhAMJQQ6rHMTVE6lJXVwehM0LoQwCwpYZOYqgh1WvtfycidaitrYXQGiFajn5iSw21YqghVfJunSktLVWwEiLyJyGEJ9ToQiB0npaa2tpahauiQMFQQ6rkHWT279+vYCVE5E9NTU1wOBwQ+pMtNQw11IqhhlSpoKBAXt63b5+ClRCRP7UGGLbUUFsYakiVvvvuOwCAW2/CiRMnUFhYqHBFROQPrYOChdYIaA0AJI6pIRlDDalOfX09vlq7Fu6QCDSnjgEAfPTRRwpXRUT+0BpghM4ISBKEzsBQQzKGGlKd1atXw2G3wx43AK6IZAiDBatXf85ZR4lUwCfUwNNiw1BDrRhqSFVsNhvefvtfgEYHR2w6IGlgjxuApiYrPvjgA6XLI6Lz1PrjRGgN8t/GRv5gIQ+GGlKVVatWoaKiHLb4TKBlEKE9PgNCF4I33niTk3QRBTn5vE9avfy3qcnKmcMJQBCFmry8PIwcORJhYWHo0aMHpkyZwqNayEdjYyNe/+c/AZ0R9oSskzdoDbAlZqOxsQFvvfWWcgUS0XmTu5G9Qg3AM3WTR9CEmnXr1mH27Nn4/vvv8cUXX8DhcODqq6/mbLEk++KLL1BfVwdb/CCgpb+9laPHQAiDCatWrYLNZlOoQiI6X83NzQAAodF5VrT8bV1P3ZtO6QI66rPPPvO5vnLlSvTo0QNbtmzBZZddplBVFEi++OILABIccf1Ov1Gjgz06HY2lO7Bhwwa+ZoiClNPp9CxIWs9fjdZ3PXVrQdNSc6rWyZaio6MVroQCQUlJCXbv3g1neBKE3tTmNs7YvgCAL7/8sitLIyI/stvtAACh8Xx9Ccnz1+FwKFYTBY6gaanx5na7MXfuXIwZMwaDBg1qdzubzebT1cDD/tTrxx9/BAA4I3q2u407NApugwW7du/uqrKIyM9cLpdnoSXMtP5lSw0BQdpSM3v2bOzatQtvv/32GbfLy8tDRESEfElJSemiCqmrhYeHAwAk95k/2CS3E+FhYV1REhF1AkmSPAvySWuF73rq1oIu1MyZMwcff/wx1q5di5492/9VDgCLFy9GbW2tfCkuLu6iKqmrRUVFAQAk5xmOgHC7ITmb2WVJFMQ0mlO+tkQ766lbCpruJyEE7rzzTrz//vvIz89HWlraT97HaDTCaDT+5HYU/Hr06AFJkqBtKPP8gmvjV5u2sRwAEB8f39XlEZGfyOGFLTXUhqCJtrNnz8Y///lPvPnmmwgLC0NpaSlKS0s5NwEB8HQ/XXrppdA2VkDbcKLNbfSlOwEAEydO7MrSiMiP9HrPvDSS8IytkdyevwaDQbGaKHAETah57rnnUFtbi9zcXCQmJsqXf/3rX0qXRgHihhtuAAAYSnacdpumqRr6miMYNGgQsrKyTrudiIJDSIhnpnC0jp9r+RsaGqpQRRRIgqr7iehMMjIyMGTIEGzbtg2axgq4zbHyba1BZ+rUqUqVR0R+0BpqJJfnEG7J7fnLoQYEBFFLDVFHTJ8+HQBgPP6DvE7TVAN95UGkp/fDqFGjlCqNiPygtUWmNczA5YRWq5W7pah7Y6ghVRk6dCiGDBkCXU0xNA2egcGG4z8AELjllps5mJAoyIW1Tsng9EzCJ7lssFjC+N4mAAw1pEI333wzAMBwYjckeyP0VYcwYMAAttIQqYDFYgEASC5PqNE47QgP59xT5MFQQ6qTnZ2NpORk6GuLoas6BMBzxBN/yREFv9aWGsnpmS1ectnloEPEUEOqI0kSci67DHA5EFK8EZIk4ZJLLlG6LCLyg4iICACA5GwGXA7A7ZTXETHUkCp5h5jMzEzExMQoWA0R+cvJ2cObPcHGax0RQw2pUt++feXl9PR0BSshIn+yWCzQarWQHM2QHJ5QExkZqWxRFDAYakiV5Am6ACQlJSlYCRH5kyRJiIiI9GmpYfcTtTrrUPPVV1+hubm5M2oh6hQJCQlKl0BEfhQVFQkNu5+oDWc9o/DPfvYzOJ1OjBw5Erm5ucjJycGYMWM4RTUFLM40SqQuUVFRkAoLobE3AmD3E5101i011dXVWLNmDSZMmICNGzfi5z//OSIjIzFmzBjcd999nVEjERGRrDXEaJpqfK4TnXWo0ev1GDNmDP7v//4Pq1evxvfff48bbrgBGzduRF5eXmfUSHReeN4wInVpHUOjaa4BwFBDJ51191NBQQHy8/ORn5+PdevWwWaz4dJLL8Xjjz+O3NzcTiiR6Pxw0j0idTnZUlPrc53orEPNwIEDERcXh7vvvhuLFi1CVlYWvzQooPH1SaQu8gR8wgWj0chxcyQ76+6nu+66C8nJyXj44Ydx++23495778Xnn38Oq9XaGfURnTd2PxGpS3h4+MllHs5NXs461Cxfvhxbt25FaWkpFi9eDLvdjnvvvRexsbEYM2ZMZ9RIdF7YUkOkLt7negoP48ks6aRznnzP5XLB4XDAZrOhubkZNpsN+/bt82dtREREpwnzCjI8mSV5O6fup+zsbMTHx+O2227D8ePHMXPmTPzwww8oLy/vjBqJzgu7n4jUxTvIMNSQt7MeKFxSUoJZs2YhNzcXgwYN6oyaiIiI2mUymdpcJjrrUPPOO+90Rh1EREQdwlBD7TmnMTWvv/46xowZg6SkJBw+fBiAZwDxqlWr/FocERHRqQwGg7zMUEPezjrUPPfcc5g3bx4mTpyImpoauFwuAJ7Jj5YvX+7v+oiIiHx4H9HIOWrI21mHmqeffhovvfQS7r33Xmi1Wnn9iBEjsHPnTr8WR0REdCberTZEZx1qDh06hKFDh5623mg0orGx0S9FERERdQRbasjbWYeatLQ0bNu27bT1n332GTIyMvxRExERUYfo9XqlS6AActZHP82bNw+zZ89Gc3MzhBDYuHEj3nrrLeTl5eHll1/ujBqJiIjapNOd9dcYqdhZvxp+97vfITQ0FPfddx+sViumTp2KpKQkPPXUU/jNb37TGTUSERG1iaGGvJ3Tq2HatGmYNm0arFYrGhoa0KNHD3/XRURE9JO8D1ghOq+IazKZOEcAEREphiesJW8dCjXDhg3DmjVrEBUVhaFDh57xRbR161a/FUfkDzz3E5F6aTTnfF5mUqEOhZrJkyfLh81NmTKlM+sh8jv+kiMi6h46FGoeeOABebm4uBjTpk3D2LFjO60oIiKijuCPFvJ21u125eXlmDBhAlJSUvDHP/4R27dv74y6iPyG3U9E6sX3N3k761CzatUqlJSU4E9/+hM2btyIYcOG4YILLsCjjz6KoqKiTiiR6PzwlxwRUfdwTiOsoqKiMGvWLOTn5+Pw4cOYMWMGXn/9daSnp/u7PiIionaxpYa8ndewcYfDgc2bN2PDhg0oKipCfHy8v+oiIiL6SQw15O2cQs3atWsxc+ZMxMfHY8aMGQgPD8fHH3+Mo0eP+rs+ovPGDz0i9eL7m7yd9eR7ycnJqKqqwvjx4/Hiiy9i0qRJPEsqEREpwu12K10CBZCzDjUPPvggrrvuOkRGRnZCOUT+53A4lC6BiDoJW2rI21mHmpkzZ3ZGHUSdpra2VukSiKiTsKWGvHF+aVIl719vZWVlClZCRJ2JoYa8MdSQKtXX18vLDDVE6sVQQ94YakiViouL5eVjx44pWAkRdSaOqSFvDDWkSocOHZKXDx48yA8+IpViSw15Y6ghVTp48CAAwBUSCavVihMnTihcERF1Bv5gIW9BFWq+/vprTJo0CUlJSZAkCR988IHSJVGAaj0PmTM61ec6EakLQw15C6pQ09jYiMGDB+PZZ59VuhQKcEVFh+E2hsFtigEAHD58WOGKiKgzMNSQt7Oep0ZJEyZMwIQJE5QugwJcfX09qqoq4Y5IgSskEgBDDZFacUwNeQuqlhqijqisrAQAuI0WCKMFAFBeXq5kSURE1AWCqqXmbNlsNthsNvl6XV2dgtVQV7Hb7Z4FjRaQPLmdp0ogIlI/VbfU5OXlISIiQr6kpKQoXRJ1gdZQIyQtIEmARncy6BARkWqpOtQsXrwYtbW18sV7QjZSL42m5WUt3IAQgHCfXEdERKql6u4no9EIo9GodBnUxeLj4wEAGnsDJIcVEG55HRGpiyRJSpdAASSoQk1DQwMOHDggXz906BC2bduG6Oho9OrVS8HKKJBERUVBr9fDZWuAZGsAAIYaIpViqCFvQdUmv3nzZgwdOhRDhw4FAMybNw9Dhw7F/fffr3BlFEg0Gg0SExOhsdVBY/MMDk9KSlK4KiIi6mxB1VKTm5vLiZaoQ/r27YsjR45AV1MsXyci9eF4OfLGVwOpUmuI0VcXQZI0SEtLU7giIuoMDDXkja8GUqU+ffrIy8nJSQgJCVGwGiLqLBxTQ94YakiVkpOT5eWePXsqWAkRdSa21JA3vhpIlRITE+Vl74BDROrCUEPe+GogVTIYDPJyQkKCgpUQUWdiqCFvfDWQ6kVERChdAhF1EoYa8sZXA6mexWJRugQi6iQMNeSNrwZSPR4dQaReWq1W6RIogDDUkOrxlxyRevH9Td74aiAioqDFlhryxlBDqsdTaxCpF1tqyBtfDaR6HFNDpF4MNeSNrwZSPbbUEKkX39/kjaGGVI8fekTqxZZY8sZQQ6rHDz0i9eKPFvLGUENEREGLoYa8MdQQEVHQYksseWOoISIiIlVgqCEiIiJVYKghIqKgxe4n8sZQQ0RERKrAUENERESqwFBDRERBi4d0kzeGGlI99rkTqRff3+SNoYaIiIhUgaGGVI/N00Tqxfc3eWOoISIiIlVgqCEiIiJVYKgh1XO73UqXQEREXYChhoiIghbH1JA3hhpSPZvNpnQJRNRJeEg3eWOoIVVyuVzycklJiYKVEFFnYqghbww1pErffvutvPzRRx/B6XQqWA0RdRZ2P5E3hhpSHSEE3nrrLQCAMzwZJSUl+OabbxSuiog6A1tqyBtDDalKeXk5/vKXv2Dv3r1wRKWiufcoABL+9vTTWL16NY+EIiJSMYYaUgWr1YpXX30V06b9Fp9++ilcoVGw9RwBERKO5pQLUV1Th7y8PMyaNQtbtmxRulwi8hN2P5E3ndIFEJ0Pp9OJ//73v3h1xQrUVFdDGMxoTrsQzpi+gOTJ7I6EC+CMToXx2FYcOLAf8+fPx0UXXYTbbrsNffr0UfgZEBGRvzDUUNAQQuD48ePYt28fCgoK5L+NjY2AVg9b8nDY4y8AtKe/rD1h51LY4zNhLN6EDRs2YMOGDUju2RMD+vfHgAED0L9/f/Tv3x9ms1mBZ0dEROeLoYYCkhACJSUlcnjZt28f9hUUoLGhwWsrCa6QCLjiL4A9MRtCH/qT+3WbYtA0YDy0tUdhOPEjjpaW49jRo/jqq6/kbXr27ImBAweif0vY6devH0wmUyc8SyIi8ieGGlKcEAKlpaU+LTD79hWgoaHeZztXSCTcMX3hMsXCbY6FyxQNaPXn9JiuiJ5oiugJCAHJ3ghtYwU01gpoGytQXFqOo0eP4ssvvwTgOboiJSVFDjkDBgxAeno6gw5RAOCYGvLGUEOdSgiB2tpalJeXn/HS3Nzscz93SARc0X3hMsfCbY6ByxRzzgHmjCQJwmiB02gBolNbi4Zkb/AEncZKaK0VOHz8BI4cOeITdKKiohAXF4e4uDjExsbKy97rQkJC/F8zERG1iaGGzpnT6URVVRUqKip8Asqp18808Z3Qh8KtN8MdndQSYGI7L8B0lCRBGMPgNIYB0WkthQpItnporZXQNFZAa61EhbUBVfsLsW/fvnZ3FRYWhh49epwWeryvm81mzrVBROQHDDXUJrvdjoqKitMCivelsqoKor15XyQJbr0JwhgNd5gJwmCG22CC0Jtbls2eMTAabdc+sXMlSRAh4XCGhJ8MOgAgBOCyQ2O3QnI0ev7aGyHZG6FxNKLGbkXd4aMoLCxsd9choaGIa6Olx/sSERHB4ENE9BMYaroRIQTq6upQVVV1xktlVRXqamvb35FG62ldMfeA0LeEFUNLWNG3LOtD5EOqVU2SAJ0Rbp0RQBRc7W3ncvqEHo29EZLDs+yyN6KptALFxcXtPoxOp0NMTAyio6N/8mI0GjvjmRIFJIZ98sZQowJNTU0/GVSqqqpQVV0N10+cA0noQuDWh0CEJ58MK3qTp2WlpbUFWqPny5w6TquD0EbAFRLR/jZuFyRHkyfwtISe1mWX3YqSmkacKK8A3O1GJwCA2WzuUPiJjIyEVhskLWVERB0QdKHm2WefxWOPPYbS0lIMHjwYTz/9NC688EKly/I7p9OJmpoaVFZW/mSrSnNT05l3ptHBrQ+FOyQaQm+C0Ie2XExwy8uhELpQQNMNWlcClUYLYbTAZbS0v40QgNvhCT+OJk9rj6PJ63oT6hxNaCitRHHxUQDtHxkiaTSIjIjoUACyWCz8RUxEAS+oQs2//vUvzJs3D88//zwuuugiLF++HOPGjcO+ffvQo0cPpcs7Jw6HA0VFRThw4AAOHDiAwsJCFBUVoba29syHKkoaCH0I3DoTRESsbzhpubhbAoyig27JvyQJ0BogtIYzt/oAgHBDctrk0CM5rHLwaQ1DFU1NqDpcDOkMY34AQK/XIz4+Hunp6UhPT0ffvn2Rnp6O2NhYhh0iChhBFWqeeOIJzJw5EzfffDMA4Pnnn8cnn3yCV199FYsWLVK4up9WW1uLwsJCFBYW4sCBA9i/fz8OHz4Ml8u7O0GCKyQcIiwBQmdqCSehPq0rnlYVdgHRT5A08uvmJ7mdkBzNpwSfkwHI5WhCcWkFjh49ivz8fPlu4RER6OcVctLT09GrVy/odEH10UJEKhE0nzx2ux1btmzB4sWL5XUajQZXXnkl/ve//ylY2encbjdKSkrk1hfPpRDl5WW+G2r1cIbGwG2KgdsUDVdoNNymKEATNP8tpBYaHYTRAmG0oN3zmAvh6eayVkJrrYLGWoWapkps2bLF5yShOp0OaWlp6Nevn9yq07dvX1gsZ+hWIyLyg6D59qyoqIDL5UJ8fLzP+vj4eOzdu7fN+9hsNthsNvl6XV1dp9YIeLrIVq58DU1NVp/1boMZ7sgUuEwxcIdGw2WKhjCGsbWlE6ycM6HN9TOe+bSLK1EZSYIwmOAymOCKTDm53uWApqm6JehUwmWtwv7CQ9i/f7/P3Xv37o1ly5YhISGhiwsnou4iaELNucjLy8NDDz3UpY/pdDrhcNh91rkNZjgjU+CyJMAVFg9h4AkTSUW0ergtPeA2xUJjrYS7oRSiLgTauqOQvMaFNdtsnNKeiDpV0ISa2NhYaLVanDhxwmf9iRMn2v3lt3jxYsybN0++XldXh5SUlDa39Zdp06bhV7/6Ffbs2YMdO3Zgx44d2LVrF5rL9gJlnhYltzEMrrAEOC3xcIUlsMXGz9gi00XcTmgbyqFtOAFtfSl0DWWA++SUAcnJycjOzpYvSUlJHFRMRJ0qaEKNwWDA8OHDsWbNGkyZMgWAZ+zKmjVrMGfOnDbvYzQaFZmIzGg0YsiQIRgyZAgAT+vNgQMHsGPHDmzfvh07d+5EXcV+6Cs8zfNCHwpXSITXYdZeh10bPNehNTD4UNdpmTPHc6i41eeIqda5dLRN1YDwjMCRJAlpaWnIzs7G4MGDkZWVhdjYWIWfBBF1N0ETagBg3rx5mD59OkaMGIELL7wQy5cvR2Njo3w0VKDS6XQYOHAgBg4ciF//+tdwu904fPgwduzYgZ07d2LHjh0oryhr/5QDACBpWo6CMsl/T4Ye76OiONcMtcN7jhu79ZRDva2+IcZpO+OuDEYj0jMGyq0wWVlZCAsL66InQkTUtqAKNddffz3Ky8tx//33o7S0FEOGDMFnn3122uDhQKfRaJCWloa0tDRMnjwZgKc1p7a2Vp5sr7Ky0mfiPe/19sbyM+7fMyvwyaDjOXVB6GktQZy/RiWEgORsPtmSYj99PhpNy6Ha3t1DbQkPD0dMQhKio6MRExPjc2oG7+smk4ldSUQUcIIq1ADAnDlz2u1uCmat5/aJiYk543ZCCDQ0NLQZdnyXq9BQd+zMD6rVw60LbTnRZOjJ8zjpTXK3lzCYeIi5UoRomTyv8WRQaT19gsN6cp2zydMK0w6tVtsSSpLlcHLq35iYGERFRUGvZ9AlouDFb6sgI0kSwsLCEBYWht69e59xW5vNhurq6tNafE4NQtXVZXCfoetL6IwtLT9mn7Ajt/q0hKJucQJLf3E5WkKJ9eQ5nuSgcvJv65iVtoSEhiI2vv3WlNa/4eHh0LBLkoi6AYYaFTMajUhISPjJeUFcLhdqampQUVGByspKlJeXo7KyEhUVFT7r6s7Y8iPJsx+7W06CeVoA6g4nw5QH2LYdVCSHFVqHFXA52t2FVqdDbEwM4uJSERMTg9jYWMTGxiImJgZxcXHyOpPJ1IVPjIgo8DHUELRabYe6vmw2G6qqqn4y/DTXVLa/E43WE3z0JoiQCLhCo+A2RcMdGg2hD/HzM+skwg1Ncx001ipomqqgbaqGZGtsGWDb3O7dJElCZGQkYlNS5aDSVlhhywoR0blhqKEOMxqNSExMRGJiYrvbCCFgtVpPCzre4ad1vauhDN4jOITeBFdoFFym6JagEwV3SASg0Xb+k2uH5GiGpqkKGms1tE2eUwNom2sAt8tnO7PZjNjk+NPCivf16OhonhOJyM84oSN54ycs+ZUkSTCbzTCbzWcc8+NyuXD8+HEUFhbi4MGDnhN9HjyI0pJj0Hl3c0kauEIi4TZFtZwby3Pp0Ekaz4bbDU1zbUuAqYK2qQpaazUkh+/pLgxGI/r074c+ffogPT0dffr0QZ8+fRAeHu7feoiI6Kwx1JAitFotUlJSkJKSgtzcXHl9Q0MDDh06JAedgwcP4kBhIZorq6BHobyd0IfCFRoFZ3QaHDHp59SaIzXXwnDiR2jrT3haX04ZlJuQkIC+fYeib9++6NOnD/r27YukpCRotcq1HBERUfsYaiigWCwWZGVlISsrS17ndrtRWlp6WqvO8WPHoKs7DuOxH2CLvwCOHgM7NPeOprEChpId0FcXAfAcRdQ3M0M+m3SfPn2QlpbGs0oTEQUZhhoKeBqNBklJSUhKSsKll14qr6+qqsK7776L999/H9LRTQgp3Q5bXAYc8Zmnd08JAW19KQwlO+TurYEDB2LatGkYM2YMB+YSEakAQw0FrejoaMycORM33HADPvzwQ/z7nXdQU7IdxhO7YYvPhD15OCBJkJprEXrwa2hbZmIeMWIEpk2bhiFDhnBWXKIgx/cweWOooaBnsVgwdepU/PKXv8Rnn32GN996CydKdsBtioUzqjdCDn0DbWM5cnJyMHXqVAwYMEDpkomIqBMw1JBqGI1GTJ48GSNGjMD06dNhPLoJcDuhayhDbm4uHnzwQaVLJCI/4yHd5I0DCUh1kpOT8ctf/hIaWz1CD30NnU6H2267TemyiKgTsPuJvDHUkCrdeOON8vK11157xgkDiYhIHRhqSJW8D8fu27evgpUQUWdi9xN5Y6gh1YuKilK6BCLqJOx+Im8MNaR6nIOGiKh74Kc9qR5/yRERdQ8MNUREFLQ4poa8MdQQEVHQYksseWOoIdXjhx6RevH9Td4Yakj1+KFHpF7sfiJvDDWkevzQI1Iv/mghbww1REREpAoMNUREFLTYEkveGGqIiChosfuJvDHUEBERkSow1BAREZEqMNQQEVHQ4pga8sZQQ0RERKrAUENERESqwFBDRERBi91P5I2hhoiIghYP6SZvDDVERESkCgw1pHpsniZSL76/yRtDDakem6eJ1Iuhhrwx1BARUdByuVxKl0ABhKGGVI+/5IjUi6GGvDHUEBFR0HK73UqXQAGEoYaIiIIWQw15Y6gh1WP3E5F6MdSQN4YaUj0e/USkXhxTQ94Yakj1GGqI1IstseSNoYZUjx96ROrF7ifyxlBDqsdQQ6ReDDXkjaGGVM9utytdAhF1Ev5oIW9BE2qWLFmC0aNHw2QyITIyUulyKIgw1BCpF0MNeQuaUGO323HdddfhjjvuULoUCjIMNUTqxe4n8qZTuoCOeuihhwAAK1euVLYQCjoOh0PpEoiok7ClhrwFTUsN0bliqCEi6h6CpqXmXNhsNthsNvl6XV2dgtWQUvhLjoioe1C0pWbRokWQJOmMl717957z/vPy8hARESFfUlJS/Fg9ERERBRJFW2rmz5+PGTNmnHGbPn36nPP+Fy9ejHnz5snX6+rqGGyIiFSELbHkTdFQExcXh7i4uE7bv9FohNFo7LT9U3DQ6VTdy0rUrfE0KOQtaD7tjxw5gqqqKhw5cgQulwvbtm0DAKSnp8NisShbHAU0vV6vdAlE1EkYashb0ISa+++/H6+99pp8fejQoQCAtWvXIjc3V6GqKBgw1BARdQ9Bc0j3ypUrIYQ47cJAQz/FYDAoXQIRdRKNJmi+xqgL8NVAqsdxVUTqxVBD3vhqINXTarVKl0BEnYRjasgbQw2pHn/JEakX39/kja8GUj3OY0GkXgw15I2vBlI9Nk8TqRe7l8kbQw2p1v3334/BgwcjKytL6VKIqJOwpYa8Bc08NURn6/LLL8fll1+udBlE1IkYasgbXw1ERBS0GGrIG18NREQUtBhqyBtfDUREFLQ4UJi8MdQQEVHQYksNeeOrgYiIghanbCBvDDVERBS0OLkmeWOoISKioMWWGvLGUENERESqwFBDRERBi91P5I2hhoiIgha7n8gbQw0RERGpAkMNEREFLXY/kTeGGiIiClrsfiJvDDVERESkCgw1REREpAoMNUREFHSuu+46SJKEtLQ0pUuhACKJbjTKqq6uDhEREaitrUV4eLjS5RAR0TkSQqChoQFhYWFKl0JdoKPf32ypISKioCNJEgMNnYahhoiIiFSBoYaIiIhUgaGGiIiIVIGhhoiIiFSBoYaIiIhUgaGGiIiIVIGhhoiIiFSBoYaIiIhUgaGGiIiIVIGhhoiIiFSBoYaIiIhUgaGGiIiIVIGhhoiIiFRBp3QBXUkIAcBzCnMiIiIKDq3f263f4+3pVqGmvr4eAJCSkqJwJURERHS26uvrERER0e7tkvip2KMibrcbx48fR1hYGCRJUroc6mR1dXVISUlBcXExwsPDlS6HiPyI7+/uRQiB+vp6JCUlQaNpf+RMt2qp0Wg06Nmzp9JlUBcLDw/nhx6RSvH93X2cqYWmFQcKExERkSow1BAREZEqMNSQahmNRjzwwAMwGo1Kl0JEfsb3N7WlWw0UJiIiIvViSw0RERGpAkMNERERqQJDDVEHPPjgg4iPj4ckSfjggw+ULoeI2vHiiy8iJSUFGo0Gy5cvV7oc6mIMNdSuGTNmQJIkLF261Gf9Bx984JfJCxctWoSBAwf6rNu7dy8kScKMGTN81q9cuRJGoxFNTU0/ud+VK1ciMjLyvOtrtWfPHjz00EN44YUXUFJSggkTJvht321JTU3lhzEFnOeffx5hYWFwOp3yuoaGBuj1euTm5vpsm5+fD0mSUFhYeMZ9tm5XU1Pjlxrr6uowZ84c3HPPPTh27BhmzZrll/22Jzc3F3Pnzu3Ux6Czw1BDZxQSEoJly5ahurra7/seO3Ys9u3bh9LSUnnd2rVrkZKSgvz8fJ9t165di4svvhihoaF+r6M9LpcLbrdb/mCePHkyEhIS2jzawm63d1ldREoYO3YsGhoasHnzZnndN998g4SEBGzYsAHNzc3y+rVr16JXr17o27dvl9QmhIDT6cSRI0fgcDhwzTXXIDExESaT6bRt+V5VN4YaOqMrr7wSCQkJyMvLa3ebd999FxdccAGMRiNSU1Px17/+tUP7vuSSS6DX630CTH5+PmbPno2qqioUFRX5rB87diwA4IknnkBWVhbMZjNSUlLw+9//Hg0NDfJ2N998M2prayFJEiRJwoMPPggAsNlsWLBgAZKTk2E2m3HRRRf5PHZrC8+HH36IzMxMGI1G3HLLLZg0aRIAz4zUrS1UM2bMwJQpU7BkyRIkJSVhwIABAICdO3fi8ssvR2hoKGJiYjBr1iy5Nu/7Pf7440hMTERMTAxmz54Nh8MBwPPL7/Dhw/jDH/4g108UCAYMGIDExMTT3q+TJ09GWloavv/+e5/1Y8eOxeuvv44RI0YgLCwMCQkJmDp1KsrKygAARUVF8ns6KirKp4XW7XYjLy8PaWlpCA0NxeDBg/Gf//zHZ/+SJOHTTz/F8OHDYTQa8c9//hNZWVkAgD59+kCSJBQVFeHBBx/EkCFD8PLLLyMtLQ0hISEAgCNHjmDy5MmwWCwIDw/Hr3/9a5w4cUJ+jNb7vf7660hNTUVERAR+85vfyOcQnDFjBtatW4ennnpKfq96f2aRQgRRO6ZPny4mT54s3nvvPRESEiKKi4uFEEK8//77ovWls3nzZqHRaMTDDz8s9u3bJ1asWCFCQ0PFihUrOvQYo0ePFrNmzZKv9+jRQ2zatEmMHz9evPrqq0IIIQoLCwUAkZ+fL4QQ4sknnxRfffWVOHTokFizZo0YMGCAuOOOO4QQQthsNrF8+XIRHh4uSkpKRElJiaivrxdCCPG73/1OjB49Wnz99dfiwIED4rHHHhNGo1EUFBQIIYRYsWKF0Ov1YvTo0WL9+vVi7969ora2VqxYsUIAkPfX+m9jsVjEjTfeKHbt2iV27dolGhoaRGJiovjFL34hdu7cKdasWSPS0tLE9OnTff5Nw8PDxe233y727NkjPvroI2EymcSLL74ohBCisrJS9OzZUzz88MM+j0cUCKZOnSquvvpq+frIkSPFO++8I26//XZx//33CyGEsFqtwmg0ipUrV4pXXnlF/Pe//xWFhYXif//7nxg1apSYMGGCEEIIp9Mp3n33XQFA7Nu3T5SUlIiamhohhBB//vOfxcCBA8Vnn30mCgsLxYoVK4TRaJQ/A9auXSsAiOzsbPH555+LAwcOiKNHj4ovv/xSABAbN24UJSUlwul0igceeECYzWYxfvx4sXXrVrF9+3bhcrnEkCFDxCWXXCI2b94svv/+ezF8+HCRk5MjP7cHHnhAWCwW+f389ddfi4SEBPF///d/QgghampqxKhRo8TMmTPl96rT6eyK/wY6A4YaaldrqBFCiIsvvljccsstQgjfUDN16lRx1VVX+dxv4cKFIjMzs0OPce+994r+/fsLIYTYvXu3CA8PF06nUzz66KPipptuEkII8corr4iQkBDR3Nzc5j7eeecdERMTI19fsWKFiIiI8Nnm8OHDQqvVimPHjvmsv+KKK8TixYvl+wEQ27Zt89nG+/m2mj59uoiPjxc2m01e9+KLL4qoqCjR0NAgr/vkk0+ERqMRpaWl8v169+7t8+F33XXXieuvv16+3rt3b/Hkk0+2+VyJlPTSSy8Js9ksHA6HqKurEzqdTpSVlYk333xTXHbZZUIIIdasWSMAiMOHD592/02bNgkA8g+N1nBSXV0tb9Pc3CxMJpP47rvvfO576623ihtuuMHnfh988IHPNj/88IMAIA4dOiSve+CBB4RerxdlZWXyus8//1xotVpx5MgRed3u3bvlQNR6P5PJJOrq6uRtFi5cKC666CL5ek5Ojrj77rs78k9HXYTdT9Qhy5Ytw2uvvYY9e/b4rN+zZw/GjBnjs27MmDHYv38/XC7XT+43NzcXBQUFKCkpQX5+Pi655BJotVrk5OTIzdz5+fkYPXq0PJblyy+/xBVXXIHk5GSEhYXhxhtvRGVlJaxWa7uPs3PnTrhcLvTv3x8Wi0W+rFu3zmcwo8FgQHZ2dof+TbKysmAwGHz+LQYPHgyz2ezzb+F2u7Fv3z553QUXXACtVitfT0xMlJvkiQJZbm4uGhsbsWnTJnzzzTfo378/4uLikJOTI4+ryc/PR58+fdCrVy9s2bIFkyZNQq9evRAWFoacnBwAnq6f9hw4cABWqxVXXXWVz3v1H//4x2kDj0eMGNGhunv37o24uDj5+p49e5CSkoKUlBR5XWZmJiIjI30+41JTUxEWFiZf53s18HWrs3TTubvsssswbtw4LF68+LQjk87HmDFjYDAYsHbtWqxdu1b+0Bs5ciQqKipw8OBB5Ofn47bbbgPg6Ye/9tprcccdd2DJkiWIjo7Gt99+i1tvvRV2u73NgYGA5ygNrVaLLVu2+AQKALBYLPJyaGhoh8exeIeXs6HX632uS5IEt9t9Tvsi6krp6eno2bMn1q5di+rqavn9mpSUhJSUFHz33XdYu3YtLr/8cjQ2NmLcuHEYN24c3njjDcTFxeHIkSMYN27cGQfrto5B++STT5CcnOxz26mD9Dv6HuR7tftgqKEOW7p0KYYMGSIPigWAjIwMrF+/3me79evXo3///qeFh7aEhobKA3bXrVuHhQsXAvB8mFx88cV45ZVXUFxcLA8o3LJlC9xuN/76179Co/E0NP773//22afBYDitlWjo0KFwuVwoKyvDpZdeevZPvgMyMjKwcuVKNDY2yh+i69evh0aj8fk3+ylt1U8UKMaOHYv8/HxUV1fL71fA88Pn008/xcaNG3HHHXdg7969qKysxNKlS+UWEe8jpwDILZ3er/fWQfpHjhyRQ5O/ZWRkoLi4GMXFxXJtP/74I2pqapCZmdnh/fC9GnjY/UQdlpWVhWnTpuFvf/ubvG7+/PlYs2YNHnnkERQUFOC1117DM888gwULFnR4v2PHjsXbb7+N5uZmDBs2TF6fk5ODp59+GmazGSNHjgTg+aXocDjw9NNP4+DBg3j99dfx/PPP++wvNTUVDQ0NWLNmDSoqKmC1WtG/f39MmzYNN910E9577z0cOnQIGzduRF5eHj755JPz/JfxmDZtGkJCQjB9+nTs2rULa9euxZ133okbb7wR8fHxHd5Pamoqvv76axw7dgwVFRV+qY3IX8aOHYtvv/0W27Zt8wkdOTk5eOGFF2C32zF27Fj06tULBoNBfq9++OGHeOSRR3z21bt3b0iShI8//hjl5eVoaGhAWFgYFixYgD/84Q947bXXUFhYiK1bt+Lpp5/Ga6+95pfncOWVV8qfZ1u3bsXGjRtx0003IScnp8NdWoDnvbphwwYUFRWhoqKCrTgBgKGGzsrDDz/s88YdNmwY/v3vf+Ptt9/GoEGDcP/99+Phhx8+qy6qsWPHor6+HmPGjIFOd7LxMCcnB/X19fKh3wAwePBgPPHEE1i2bBkGDRqEN95447TDzUePHo3bb78d119/PeLi4vCXv/wFALBixQrcdNNNmD9/PgYMGIApU6Zg06ZN6NWr13n8i5xkMpmwevVqVFVVYeTIkfjVr36FK664As8888xZ7efhhx9GUVER+vbt6zMOgCgQjB07Fk1NTUhPT/cJ663v19ZDv+Pi4rBy5Uq88847yMzMxNKlS/H444/77Cs5ORkPPfQQFi1ahPj4eMyZMwcA8Mgjj+BPf/oT8vLykJGRgfHjx+OTTz5BWlqaX56DJElYtWoVoqKicNlll+HKK69Enz598K9//eus9rNgwQJotVpkZmbK3WukLJ6lm4iIiFSBLTVERESkCgw11Gm++eYbn0MyT70QERH5E7ufqNM0NTXh2LFj7d6enp7ehdUQEZHaMdQQERGRKrD7iYiIiFSBoYaIiIhUgaGGiIiIVIGhhoiIiFSBoYaIVKu0tBRXXXUVzGYzIiMjlS6HiDoZQw0RBYzc3FzMnTvXb/t78sknUVJSgm3btqGgoMBv+21Lfn4+JElCTU1Npz4OEbWPZ+kmItWx2+0wGAwoLCzE8OHD0a9fv3a3dTgc8rnFiCi4saWGiM7Zxx9/jMjISLhcLgDAtm3bIEkSFi1aJG/zu9/9Dr/97W9RWVmJG264AcnJyTCZTMjKysJbb70lbzdjxgysW7cOTz31FCRJgiRJKCoqAgDs2rULEyZMgMViQXx8PG688UafM5jn5uZizpw5mDt3LmJjYzFu3Dikpqbi3XffxT/+8Q9IkiSfZFWSJDz33HP42c9+BrPZjCVLlgAAnnvuOfTt2xcGgwEDBgzA66+/7vNcJUnCyy+/jJ///OcwmUzo168fPvzwQwBAUVERxo4dCwCIioryeTwi6kKCiOgc1dTUCI1GIzZt2iSEEGL58uUiNjZWXHTRRfI26enp4qWXXhJHjx4Vjz32mPjhhx9EYWGh+Nvf/ia0Wq3YsGGDvK9Ro0aJmTNnipKSElFSUiKcTqeorq4WcXFxYvHixWLPnj1i69at4qqrrhJjx46VHyMnJ0dYLBaxcOFCsXfvXrF3715RVlYmxo8fL37961+LkpISUVNTI4QQAoDo0aOHePXVV0VhYaE4fPiweO+994RerxfPPvus2Ldvn/jrX/8qtFqt+Oqrr+THACB69uwp3nzzTbF//35x1113CYvFIiorK4XT6RTvvvuuACD27dvn83hE1HUYaojovAwbNkw89thjQgghpkyZIpYsWSIMBoOor68XR48eFQBEQUFBm/e95pprxPz58+XrOTk54u677/bZ5pFHHhFXX321z7ri4mI5QLTeb+jQoaftf/LkyWL69Ok+6wCIuXPn+qwbPXq0mDlzps+66667TkycONHnfvfdd598vaGhQQAQn376qRBCiLVr1woAorq6us3nSkSdj91PRHRecnJykJ+fDyEEvvnmG/ziF79ARkYGvv32W6xbtw5JSUno168fXC4XHnnkEWRlZSE6OhoWiwWrV6/GkSNHzrj/7du3Y+3atT4nQx04cCAAoLCwUN5u+PDhHa55xIgRPtf37NmDMWPG+KwbM2YM9uzZ47MuOztbXjabzQgPD0dZWVmHH5eIOhcHChPRecnNzcWrr76K7du3Q6/XY+DAgcjNzUV+fj6qq6uRk5MDAHjsscfw1FNPYfny5cjKyoLZbMbcuXNht9vPuP+GhgZMmjQJy5YtO+22xMREedlsNne45rPZ1tupA4olSYLb7T6nfRGR/7GlhojOy6WXXor6+no8+eSTcoBpDTX5+fnIzc0FAKxfvx6TJ0/Gb3/7WwwePBh9+vQ57TBrg8EgDzpuNWzYMOzevRupqalIT0/3uZxrODlVRkYG1q9f77Nu/fr1yMzM7PA+DAYDAJxWPxF1HYYaIjovUVFRyM7OxhtvvCEHmMsuuwxbt25FQUGBHHT69euHL774At999x327NmD2267DSdOnPDZV2pqKjZs2ICioiJUVFTA7XZj9uzZqKqqwg033IBNmzahsLAQq1evxs033+y3ALFw4UKsXLkSzz33HPbv348nnngC7733HhYsWNDhffTu3RuSJOHjjz9GeXk5Ghoa/FIbEXUcQw0RnbecnBy4XC451ERHRyMzMxMJCQkYMGAAAOC+++7DsGHDMG7cOOTm5iIhIQFTpkzx2c+CBQug1WqRmZmJuLg4HDlyBElJSVi/fj1cLheuvvpqZGVlYe7cuYiMjIRG45+PsClTpuCpp57C448/jgsuuAAvvPACVqxYIT+fjkhOTsZDDz2ERYsWIT4+HnPmzPFLbUTUcZIQQihdBBEREdH5YksNERERqQJDDREREakCQw0RERGpAkMNERERqQJDDREREakCQw0RERGpAkMNERERqQJDDREREakCQw0RERGpAkMNERERqQJDDREREakCQw0RERGpwv8DI8Mod9RmbjwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=df['waterfront'], y=df['view'])\n",
    "df['view'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# H2 Plot\n",
    "The square footage of land is smaller for waterfront houses in Kings County\n",
    "---> Wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='waterfront', ylabel='sqft_lot'>"
      ]
     },
     "execution_count": 218,
     "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_filtered = df[df['sqft_lot'] < 30000]\n",
    "\n",
    "sns.violinplot(x=df_filtered['waterfront'], y=df_filtered['sqft_lot'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# H3 - Plot: \n",
    "There is a positive correlation between the condition of a house and the King County Building Grades. --> wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 650.375x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filtered[['grade', 'condition']] =  df_filtered[['grade', 'condition']].astype('float64')\n",
    "\n",
    "noise_mean = 0\n",
    "noise_std = 0.1\n",
    "noise_cond = np.random.normal(loc=noise_mean, scale=noise_std, size=df.shape[0])\n",
    "\n",
    "noise_mean = 0\n",
    "noise_std = 0.2\n",
    "noise_grade = np.random.normal(loc=noise_mean, scale=noise_std, size=df.shape[0])\n",
    "\n",
    "df_filtered['condition_with_noise'] = df_filtered['condition'] + noise_cond\n",
    "df_filtered['grade_with_noise'] = df_filtered['grade'] + noise_grade\n",
    "\n",
    "\n",
    "\n",
    "sns.lmplot(\n",
    "    x=\"condition_with_noise\",\n",
    "    y=\"grade_with_noise\",\n",
    "    data=df_filtered,\n",
    "    hue=\"waterfront\",\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Client Erin Robinson\n",
    "## Unlimited Budget, 4+ bathrooms or smaller house nearby, big lot (tennis court & pool), golf, historic, no waterfront"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# finding golf \n",
    "\n",
    "from https://gismaps.kingcounty.gov/iMap/\n",
    "df['zipcode'].value_counts() \n",
    "\n",
    "### Interbay Golf Center\n",
    "2501 15th Ave W, Seattle, WA 98119, Vereinigte Staaten\n",
    "47.64364, -122.37834\n",
    " \n",
    "\n",
    "### Vashon Island Golf & Country Club\n",
    "24615 75th Ave SW, Vashon, WA 98070, Vereinigte Staaten\n",
    "47.38309, -122.42884\n",
    "auf einer Insel, mit pool \n",
    "kinder angebote\n",
    "\n",
    "\n",
    "### Broadmoor Golf Club\n",
    "2340 Broadmoor Dr E, Seattle, WA 98112, Vereinigte Staaten\n",
    "47.64049770129618, -122.28964444365356\n",
    "japanischer garten \n",
    "\n",
    "\n",
    "### Riverbend Golf Complex\n",
    "2019 W Meeker St, Kent, WA 98032, Vereinigte Staaten\n",
    "47.380891694211876, -122.2628972397312\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in der Postleitzahl \n",
    "98119\n",
    "98070\n",
    "98112\n",
    "98032\n",
    "jeweils die 15 nearest neigbors lot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = df\n",
    "desired_zipcodes = [98119, 98070, 98112, 98032]\n",
    "df_filtered = df[df['zipcode'].isin(desired_zipcodes)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17329af10>"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filtered.head(15)\n",
    "df_filtered = df_filtered[df_filtered['bathrooms']>3.5]\n",
    "\n",
    "sns.displot( data=df_filtered[\"bathrooms\"], kde=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered_sorted_multiple = df_filtered.sort_values(by=['sqft_lot', 'price'],ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "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>bathrooms</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>grade</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>price</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10543</th>\n",
       "      <td>4.25</td>\n",
       "      <td>308080</td>\n",
       "      <td>3300</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1976</td>\n",
       "      <td>NaT</td>\n",
       "      <td>90604.0</td>\n",
       "      <td>729000.0</td>\n",
       "      <td>47.3979</td>\n",
       "      <td>-122.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14172</th>\n",
       "      <td>4.00</td>\n",
       "      <td>30120</td>\n",
       "      <td>4200</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1933</td>\n",
       "      <td>NaT</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>2230000.0</td>\n",
       "      <td>47.6379</td>\n",
       "      <td>-122.311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18314</th>\n",
       "      <td>4.75</td>\n",
       "      <td>24085</td>\n",
       "      <td>4575</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1926</td>\n",
       "      <td>NaT</td>\n",
       "      <td>9687.0</td>\n",
       "      <td>2920000.0</td>\n",
       "      <td>47.6263</td>\n",
       "      <td>-122.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16110</th>\n",
       "      <td>3.75</td>\n",
       "      <td>16101</td>\n",
       "      <td>3600</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>NaT</td>\n",
       "      <td>9506.0</td>\n",
       "      <td>1930000.0</td>\n",
       "      <td>47.6308</td>\n",
       "      <td>-122.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3381</th>\n",
       "      <td>4.75</td>\n",
       "      <td>13431</td>\n",
       "      <td>5410</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1941</td>\n",
       "      <td>NaT</td>\n",
       "      <td>11596.0</td>\n",
       "      <td>2540000.0</td>\n",
       "      <td>47.6306</td>\n",
       "      <td>-122.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>4.75</td>\n",
       "      <td>13180</td>\n",
       "      <td>6390</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1940</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8137.0</td>\n",
       "      <td>2470000.0</td>\n",
       "      <td>47.6312</td>\n",
       "      <td>-122.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10435</th>\n",
       "      <td>4.00</td>\n",
       "      <td>11765</td>\n",
       "      <td>4260</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1939</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>10408.0</td>\n",
       "      <td>3400000.0</td>\n",
       "      <td>47.6380</td>\n",
       "      <td>-122.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18462</th>\n",
       "      <td>4.00</td>\n",
       "      <td>11750</td>\n",
       "      <td>7710</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1904</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8325.0</td>\n",
       "      <td>3300000.0</td>\n",
       "      <td>47.6263</td>\n",
       "      <td>-122.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11963</th>\n",
       "      <td>3.75</td>\n",
       "      <td>11265</td>\n",
       "      <td>4590</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1927</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8996.0</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>47.6389</td>\n",
       "      <td>-122.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14499</th>\n",
       "      <td>4.50</td>\n",
       "      <td>10800</td>\n",
       "      <td>5480</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>2400000.0</td>\n",
       "      <td>47.6307</td>\n",
       "      <td>-122.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15539</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9778</td>\n",
       "      <td>3970</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1928</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8460.0</td>\n",
       "      <td>2300000.0</td>\n",
       "      <td>47.6312</td>\n",
       "      <td>-122.366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>4.25</td>\n",
       "      <td>9453</td>\n",
       "      <td>4860</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1905</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8557.0</td>\n",
       "      <td>2250000.0</td>\n",
       "      <td>47.6196</td>\n",
       "      <td>-122.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4697</th>\n",
       "      <td>4.25</td>\n",
       "      <td>9378</td>\n",
       "      <td>3100</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6334.0</td>\n",
       "      <td>1130000.0</td>\n",
       "      <td>47.6381</td>\n",
       "      <td>-122.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7408</th>\n",
       "      <td>4.50</td>\n",
       "      <td>9097</td>\n",
       "      <td>4800</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6037.0</td>\n",
       "      <td>1980000.0</td>\n",
       "      <td>47.6259</td>\n",
       "      <td>-122.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3255</th>\n",
       "      <td>3.75</td>\n",
       "      <td>8550</td>\n",
       "      <td>3150</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8550.0</td>\n",
       "      <td>1900000.0</td>\n",
       "      <td>47.6387</td>\n",
       "      <td>-122.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4855</th>\n",
       "      <td>4.00</td>\n",
       "      <td>8208</td>\n",
       "      <td>4690</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1926</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8001.0</td>\n",
       "      <td>2050000.0</td>\n",
       "      <td>47.6321</td>\n",
       "      <td>-122.304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17274</th>\n",
       "      <td>4.25</td>\n",
       "      <td>7800</td>\n",
       "      <td>5150</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1954</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>2250000.0</td>\n",
       "      <td>47.6288</td>\n",
       "      <td>-122.282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19912</th>\n",
       "      <td>4.25</td>\n",
       "      <td>7757</td>\n",
       "      <td>4645</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6970.0</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>47.6248</td>\n",
       "      <td>-122.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>3.75</td>\n",
       "      <td>7623</td>\n",
       "      <td>4490</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1941</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7653.0</td>\n",
       "      <td>1720000.0</td>\n",
       "      <td>47.6315</td>\n",
       "      <td>-122.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18394</th>\n",
       "      <td>4.75</td>\n",
       "      <td>7200</td>\n",
       "      <td>5540</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1909</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>2630000.0</td>\n",
       "      <td>47.6361</td>\n",
       "      <td>-122.366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15166</th>\n",
       "      <td>3.75</td>\n",
       "      <td>7200</td>\n",
       "      <td>3790</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1931</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>2200000.0</td>\n",
       "      <td>47.6264</td>\n",
       "      <td>-122.289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>4.25</td>\n",
       "      <td>7200</td>\n",
       "      <td>2820</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1930</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7522.0</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>47.6298</td>\n",
       "      <td>-122.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3809</th>\n",
       "      <td>4.00</td>\n",
       "      <td>7173</td>\n",
       "      <td>5780</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7994.0</td>\n",
       "      <td>2890000.0</td>\n",
       "      <td>47.6374</td>\n",
       "      <td>-122.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16706</th>\n",
       "      <td>4.00</td>\n",
       "      <td>7128</td>\n",
       "      <td>5110</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1906</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6480.0</td>\n",
       "      <td>2140000.0</td>\n",
       "      <td>47.6285</td>\n",
       "      <td>-122.304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>4.50</td>\n",
       "      <td>6420</td>\n",
       "      <td>4230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1916</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4085.0</td>\n",
       "      <td>1470000.0</td>\n",
       "      <td>47.6301</td>\n",
       "      <td>-122.369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3404</th>\n",
       "      <td>3.75</td>\n",
       "      <td>5850</td>\n",
       "      <td>3070</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1927</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5573.0</td>\n",
       "      <td>1570000.0</td>\n",
       "      <td>47.6330</td>\n",
       "      <td>-122.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19261</th>\n",
       "      <td>4.25</td>\n",
       "      <td>5328</td>\n",
       "      <td>3650</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1907</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>1520000.0</td>\n",
       "      <td>47.6380</td>\n",
       "      <td>-122.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20228</th>\n",
       "      <td>4.50</td>\n",
       "      <td>5126</td>\n",
       "      <td>3130</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7784.0</td>\n",
       "      <td>1330000.0</td>\n",
       "      <td>47.6239</td>\n",
       "      <td>-122.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15779</th>\n",
       "      <td>4.50</td>\n",
       "      <td>4800</td>\n",
       "      <td>3830</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1919</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>989000.0</td>\n",
       "      <td>47.6404</td>\n",
       "      <td>-122.361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10457</th>\n",
       "      <td>3.75</td>\n",
       "      <td>4500</td>\n",
       "      <td>2870</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6354.0</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>47.6291</td>\n",
       "      <td>-122.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>4.50</td>\n",
       "      <td>4426</td>\n",
       "      <td>3732</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>1230000.0</td>\n",
       "      <td>47.6189</td>\n",
       "      <td>-122.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5742</th>\n",
       "      <td>3.75</td>\n",
       "      <td>4000</td>\n",
       "      <td>2690</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1909</td>\n",
       "      <td>1989-01-01</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>800000.0</td>\n",
       "      <td>47.6418</td>\n",
       "      <td>-122.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12092</th>\n",
       "      <td>4.25</td>\n",
       "      <td>3600</td>\n",
       "      <td>3350</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>1430000.0</td>\n",
       "      <td>47.6351</td>\n",
       "      <td>-122.364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11299</th>\n",
       "      <td>3.75</td>\n",
       "      <td>3600</td>\n",
       "      <td>2380</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1927</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3520.0</td>\n",
       "      <td>695000.0</td>\n",
       "      <td>47.6374</td>\n",
       "      <td>-122.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3254</th>\n",
       "      <td>3.75</td>\n",
       "      <td>3200</td>\n",
       "      <td>2930</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1925</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4320.0</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>47.6430</td>\n",
       "      <td>-122.359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17327</th>\n",
       "      <td>4.00</td>\n",
       "      <td>2800</td>\n",
       "      <td>2710</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>47.6346</td>\n",
       "      <td>-122.364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15272</th>\n",
       "      <td>4.00</td>\n",
       "      <td>1774</td>\n",
       "      <td>2530</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>1120000.0</td>\n",
       "      <td>47.6362</td>\n",
       "      <td>-122.369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bathrooms  sqft_lot  sqft_living  grade  yr_built yr_renovated  \\\n",
       "10543       4.25    308080         3300    9.0      1976          NaT   \n",
       "14172       4.00     30120         4200   11.0      1933          NaT   \n",
       "18314       4.75     24085         4575   10.0      1926          NaT   \n",
       "16110       3.75     16101         3600    9.0      1951          NaT   \n",
       "3381        4.75     13431         5410   10.0      1941          NaT   \n",
       "5697        4.75     13180         6390   10.0      1940          NaT   \n",
       "10435       4.00     11765         4260   11.0      1939   2010-01-01   \n",
       "18462       4.00     11750         7710   12.0      1904          NaT   \n",
       "11963       3.75     11265         4590   11.0      1927          NaT   \n",
       "14499       4.50     10800         5480    9.0      1999          NaT   \n",
       "15539       4.75      9778         3970   11.0      1928          NaT   \n",
       "779         4.25      9453         4860   10.0      1905          NaT   \n",
       "4697        4.25      9378         3100   11.0      1978          NaT   \n",
       "7408        4.50      9097         4800   10.0      2007          NaT   \n",
       "3255        3.75      8550         3150   10.0      2007          NaT   \n",
       "4855        4.00      8208         4690    9.0      1926          NaT   \n",
       "17274       4.25      7800         5150   11.0      1954          NaT   \n",
       "19912       4.25      7757         4645   10.0      2006          NaT   \n",
       "1942        3.75      7623         4490   10.0      1941          NaT   \n",
       "18394       4.75      7200         5540   11.0      1909          NaT   \n",
       "15166       3.75      7200         3790   10.0      1931          NaT   \n",
       "1985        4.25      7200         2820   10.0      1930          NaT   \n",
       "3809        4.00      7173         5780   11.0      2008          NaT   \n",
       "16706       4.00      7128         5110   11.0      1906          NaT   \n",
       "7661        4.50      6420         4230    8.0      1916          NaT   \n",
       "3404        3.75      5850         3070    9.0      1927          NaT   \n",
       "19261       4.25      5328         3650    9.0      1907   2014-01-01   \n",
       "20228       4.50      5126         3130   10.0      2014          NaT   \n",
       "15779       4.50      4800         3830    9.0      1919   2004-01-01   \n",
       "10457       3.75      4500         2870   10.0      2012          NaT   \n",
       "1245        4.50      4426         3732   10.0      2001          NaT   \n",
       "5742        3.75      4000         2690    9.0      1909   1989-01-01   \n",
       "12092       4.25      3600         3350   10.0      2014          NaT   \n",
       "11299       3.75      3600         2380    7.0      1927          NaT   \n",
       "3254        3.75      3200         2930    9.0      1925          NaT   \n",
       "17327       4.00      2800         2710   10.0      1974   2008-01-01   \n",
       "15272       4.00      1774         2530    9.0      2013          NaT   \n",
       "\n",
       "       sqft_lot15      price      lat     long  \n",
       "10543     90604.0   729000.0  47.3979 -122.416  \n",
       "14172     12200.0  2230000.0  47.6379 -122.311  \n",
       "18314      9687.0  2920000.0  47.6263 -122.284  \n",
       "16110      9506.0  1930000.0  47.6308 -122.287  \n",
       "3381      11596.0  2540000.0  47.6306 -122.288  \n",
       "5697       8137.0  2470000.0  47.6312 -122.291  \n",
       "10435     10408.0  3400000.0  47.6380 -122.288  \n",
       "18462      8325.0  3300000.0  47.6263 -122.314  \n",
       "11963      8996.0  3000000.0  47.6389 -122.288  \n",
       "14499      7200.0  2400000.0  47.6307 -122.367  \n",
       "15539      8460.0  2300000.0  47.6312 -122.366  \n",
       "779        8557.0  2250000.0  47.6196 -122.286  \n",
       "4697       6334.0  1130000.0  47.6381 -122.314  \n",
       "7408       6037.0  1980000.0  47.6259 -122.291  \n",
       "3255       8550.0  1900000.0  47.6387 -122.284  \n",
       "4855       8001.0  2050000.0  47.6321 -122.304  \n",
       "17274      7800.0  2250000.0  47.6288 -122.282  \n",
       "19912      6970.0  1600000.0  47.6248 -122.290  \n",
       "1942       7653.0  1720000.0  47.6315 -122.290  \n",
       "18394      7200.0  2630000.0  47.6361 -122.366  \n",
       "15166      7200.0  2200000.0  47.6264 -122.289  \n",
       "1985       7522.0  1600000.0  47.6298 -122.290  \n",
       "3809       7994.0  2890000.0  47.6374 -122.288  \n",
       "16706      6480.0  2140000.0  47.6285 -122.304  \n",
       "7661       4085.0  1470000.0  47.6301 -122.369  \n",
       "3404       5573.0  1570000.0  47.6330 -122.283  \n",
       "19261      3600.0  1520000.0  47.6380 -122.362  \n",
       "20228      7784.0  1330000.0  47.6239 -122.290  \n",
       "15779      4800.0   989000.0  47.6404 -122.361  \n",
       "10457      6354.0  2000000.0  47.6291 -122.363  \n",
       "1245       4000.0  1230000.0  47.6189 -122.288  \n",
       "5742       4000.0   800000.0  47.6418 -122.372  \n",
       "12092      3600.0  1430000.0  47.6351 -122.364  \n",
       "11299      3520.0   695000.0  47.6374 -122.307  \n",
       "3254       4320.0  1100000.0  47.6430 -122.359  \n",
       "17327      3600.0  1200000.0  47.6346 -122.364  \n",
       "15272      2400.0  1120000.0  47.6362 -122.369  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered_sorted_multiple.head(37)\n",
    "columns_of_interest = ['bathrooms','sqft_lot', 'sqft_living', 'grade', 'yr_built','yr_renovated','sqft_lot15', 'price', 'lat','long']\n",
    "df_filtered_selected_columns = df_filtered_sorted_multiple[columns_of_interest]\n",
    "df_filtered_selected_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Recommandations for this client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 18314\t4.75\t24085\t4575\t10.0\t1926\tNaT\t9687.0\t2920000.0\t47.6263\t-122.284\n",
    "# 10543\t4.25\t308080\t3300\t9.0\t1976\tNaT\t90604.0\t729000.0\t47.3979\t-122.416\n",
    "# 14172\t4.00\t30120\t4200\t11.0\t1933\tNaT\t12200.0\t2230000.0\t47.6379\t-122.311"
   ]
  }
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