Data Wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kings County Housing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Problem "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagine that we are a group of data scientists working for a real estate development company. Our goal is to construct a predictive model for stakeholders to determine what features (i.e., renovations) are worth investing resources in to increase the sale price of their properties. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For wrangling purposes, we used the Python library usaddress in order to calculate `zipcode` based on `address`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: usaddress in c:\\users\\kevin\\anaconda3\\envs\\learn-env\\lib\\site-packages (0.5.10)\n",
      "Requirement already satisfied: probableparsing in c:\\users\\kevin\\anaconda3\\envs\\learn-env\\lib\\site-packages (from usaddress) (0.0.1)\n",
      "Requirement already satisfied: future>=0.14 in c:\\users\\kevin\\anaconda3\\envs\\learn-env\\lib\\site-packages (from usaddress) (0.18.2)\n",
      "Requirement already satisfied: python-crfsuite>=0.7 in c:\\users\\kevin\\anaconda3\\envs\\learn-env\\lib\\site-packages (from usaddress) (0.9.9)\n"
     ]
    }
   ],
   "source": [
    "# run this cell to verify that usaddress is installed and if not, to install it \n",
    "!pip install usaddress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import usaddress\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `zip_codes` dataset contains all zipcodes within King County, WA as well the city they correspond to. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# opening zip_codes dataset \n",
    "zip_codes = pd.read_csv('KC_Data/King_County_zipcode.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping unnecessary columns\n",
    "zip_codes.drop(zip_codes.columns[2], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming columns in zipcode dataset \n",
    "zip_codes = zip_codes.rename(columns = {'ZIP':'zipcode', 'Primary City':'city'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our `kc_house_data` contains data on properties sold within King County, WA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30155 entries, 0 to 30154\n",
      "Data columns (total 25 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             30155 non-null  int64  \n",
      " 1   date           30155 non-null  object \n",
      " 2   price          30155 non-null  float64\n",
      " 3   bedrooms       30155 non-null  int64  \n",
      " 4   bathrooms      30155 non-null  float64\n",
      " 5   sqft_living    30155 non-null  int64  \n",
      " 6   sqft_lot       30155 non-null  int64  \n",
      " 7   floors         30155 non-null  float64\n",
      " 8   waterfront     30155 non-null  object \n",
      " 9   greenbelt      30155 non-null  object \n",
      " 10  nuisance       30155 non-null  object \n",
      " 11  view           30155 non-null  object \n",
      " 12  condition      30155 non-null  object \n",
      " 13  grade          30155 non-null  object \n",
      " 14  heat_source    30123 non-null  object \n",
      " 15  sewer_system   30141 non-null  object \n",
      " 16  sqft_above     30155 non-null  int64  \n",
      " 17  sqft_basement  30155 non-null  int64  \n",
      " 18  sqft_garage    30155 non-null  int64  \n",
      " 19  sqft_patio     30155 non-null  int64  \n",
      " 20  yr_built       30155 non-null  int64  \n",
      " 21  yr_renovated   30155 non-null  int64  \n",
      " 22  address        30155 non-null  object \n",
      " 23  lat            30155 non-null  float64\n",
      " 24  long           30155 non-null  float64\n",
      "dtypes: float64(5), int64(10), object(10)\n",
      "memory usage: 5.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# opening housing sales dataset and reading info \n",
    "pd.set_option('display.max_columns', None)\n",
    "housing_data = pd.read_csv('KC_Data/kc_house_data.csv')\n",
    "housing_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
       "       'sqft_lot', 'floors', 'waterfront', 'greenbelt', 'nuisance', 'view',\n",
       "       'condition', 'grade', 'heat_source', 'sewer_system', 'sqft_above',\n",
       "       'sqft_basement', 'sqft_garage', 'sqft_patio', 'yr_built',\n",
       "       'yr_renovated', 'address', 'lat', 'long'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting info on columns \n",
    "housing_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>greenbelt</th>\n",
       "      <th>nuisance</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>heat_source</th>\n",
       "      <th>sewer_system</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>sqft_garage</th>\n",
       "      <th>sqft_patio</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>address</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7399300360</td>\n",
       "      <td>5/24/2022</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180</td>\n",
       "      <td>7140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>Gas</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>2102 Southeast 21st Court, Renton, Washington ...</td>\n",
       "      <td>47.461975</td>\n",
       "      <td>-122.19052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8910500230</td>\n",
       "      <td>12/13/2021</td>\n",
       "      <td>920000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2770</td>\n",
       "      <td>6703</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>AVERAGE</td>\n",
       "      <td>Average</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>Oil</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>1570</td>\n",
       "      <td>1570</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>11231 Greenwood Avenue North, Seattle, Washing...</td>\n",
       "      <td>47.711525</td>\n",
       "      <td>-122.35591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1180000275</td>\n",
       "      <td>9/29/2021</td>\n",
       "      <td>311000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2880</td>\n",
       "      <td>6156</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>AVERAGE</td>\n",
       "      <td>Average</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>Gas</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>1580</td>\n",
       "      <td>1580</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1956</td>\n",
       "      <td>0</td>\n",
       "      <td>8504 South 113th Street, Seattle, Washington 9...</td>\n",
       "      <td>47.502045</td>\n",
       "      <td>-122.22520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7399300360   5/24/2022  675000.0         4        1.0         1180   \n",
       "1  8910500230  12/13/2021  920000.0         5        2.5         2770   \n",
       "2  1180000275   9/29/2021  311000.0         6        2.0         2880   \n",
       "\n",
       "   sqft_lot  floors waterfront greenbelt nuisance     view condition  \\\n",
       "0      7140     1.0         NO        NO       NO     NONE      Good   \n",
       "1      6703     1.0         NO        NO      YES  AVERAGE   Average   \n",
       "2      6156     1.0         NO        NO       NO  AVERAGE   Average   \n",
       "\n",
       "       grade heat_source sewer_system  sqft_above  sqft_basement  sqft_garage  \\\n",
       "0  7 Average         Gas       PUBLIC        1180              0            0   \n",
       "1  7 Average         Oil       PUBLIC        1570           1570            0   \n",
       "2  7 Average         Gas       PUBLIC        1580           1580            0   \n",
       "\n",
       "   sqft_patio  yr_built  yr_renovated  \\\n",
       "0          40      1969             0   \n",
       "1         240      1950             0   \n",
       "2           0      1956             0   \n",
       "\n",
       "                                             address        lat       long  \n",
       "0  2102 Southeast 21st Court, Renton, Washington ...  47.461975 -122.19052  \n",
       "1  11231 Greenwood Avenue North, Seattle, Washing...  47.711525 -122.35591  \n",
       "2  8504 South 113th Street, Seattle, Washington 9...  47.502045 -122.22520  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing first three records\n",
    "housing_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>greenbelt</th>\n",
       "      <th>nuisance</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>heat_source</th>\n",
       "      <th>sewer_system</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>sqft_garage</th>\n",
       "      <th>sqft_patio</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>address</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4846</th>\n",
       "      <td>1233100736</td>\n",
       "      <td>9/28/2021</td>\n",
       "      <td>2600000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3500</td>\n",
       "      <td>8455</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>9 Better</td>\n",
       "      <td>Gas</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>2590</td>\n",
       "      <td>1410</td>\n",
       "      <td>500</td>\n",
       "      <td>290</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>12611 Northeast 87th Place, Kirkland, Washingt...</td>\n",
       "      <td>47.68069</td>\n",
       "      <td>-122.172135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
       "4846  1233100736  9/28/2021  2600000.0         3        4.0         3500   \n",
       "\n",
       "      sqft_lot  floors waterfront greenbelt nuisance  view condition  \\\n",
       "4846      8455     2.0         NO        NO       NO  NONE   Average   \n",
       "\n",
       "         grade heat_source sewer_system  sqft_above  sqft_basement  \\\n",
       "4846  9 Better         Gas       PUBLIC        2590           1410   \n",
       "\n",
       "      sqft_garage  sqft_patio  yr_built  yr_renovated  \\\n",
       "4846          500         290      2019             0   \n",
       "\n",
       "                                                address       lat        long  \n",
       "4846  12611 Northeast 87th Place, Kirkland, Washingt...  47.68069 -122.172135  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing duplicate rows\n",
    "housing_data[housing_data.duplicated(subset = 'id', keep = 'first')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the identical row \n",
    "housing_data = housing_data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We no longer need `id`, let's drop it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping unnecessary columns column\n",
    "housing_data.drop(columns = ['id'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Houses with No Bed/Bath\n",
    "Some properties within our dataset, when sold, contained no `bedrooms` nor `bathrooms`. We find this to be unusual data. It could be possible that these properties were considered uninhabitable because of such features. For our purposes, we decided to drop such properties from our analysis since it is difficult to ascertain the circumstances behind its selling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " There are 25 houses that have neither bed/bath at time of listing\n"
     ]
    }
   ],
   "source": [
    "# seeing what records have no bedrooms / bathrooms at time of listing\n",
    "len(housing_data[(housing_data['bedrooms'] == 0) & (housing_data['bathrooms'] == 0)])\n",
    "print(f\" There are {len(housing_data[(housing_data['bedrooms'] == 0) & (housing_data['bathrooms'] == 0)])} houses that have neither bed/bath at time of listing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping all records that have no bedroom or house for cleaner analysis \n",
    "housing_data = housing_data[(housing_data['bedrooms'] != 0) & (housing_data['bathrooms'] != 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>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>greenbelt</th>\n",
       "      <th>nuisance</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>heat_source</th>\n",
       "      <th>sewer_system</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>sqft_garage</th>\n",
       "      <th>sqft_patio</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>address</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [date, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, greenbelt, nuisance, view, condition, grade, heat_source, sewer_system, sqft_above, sqft_basement, sqft_garage, sqft_patio, yr_built, yr_renovated, address, lat, long]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying that records were dropped\n",
    "housing_data[(housing_data['bedrooms'] == 0) & (housing_data['bathrooms'] == 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Waterfront Properties\n",
    "Waterfront properties can be considered luxury properties. For our sake, including these properties may skew our data since the price distribution for waterfront properties may exceed the distribution of non-waterfront properties. We thus consider waterfront properties to be outliers in our dataset and decide to remove them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "waterfront\n",
       "NO     29591\n",
       "YES      513\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the amount of waterfront properties vs non-waterfront properties\n",
    "housing_data['waterfront'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " There are 29591 that do not have a waterfront view\n",
      " There are 513 that do not have a waterfront view\n"
     ]
    }
   ],
   "source": [
    "# evaluating how many properties have a waterfront view \n",
    "print(f\" There are {housing_data['waterfront'].value_counts()[0]} that do not have a waterfront view\")\n",
    "print(f\" There are {housing_data['waterfront'].value_counts()[1]} that do not have a waterfront view\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='waterfront', ylabel='price'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the price distribution for waterfront properties\n",
    "fig, ax = plt.subplots(figsize = (8,6))\n",
    "ax.set_xlabel(\"Waterfront Properties\")\n",
    "ax.set_ylabel(\"Price\")\n",
    "sns.boxplot(x = housing_data['waterfront'], y = 'price', data = housing_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Waterfront properties generally have a much higher median property sale price compared to those that are not. For a more robust analysis, we chose to exclude them from our final dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering out waterfront properties\n",
    "housing_data = housing_data[housing_data['waterfront'] ==\"NO\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NaN Values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date              0\n",
       "price             0\n",
       "bedrooms          0\n",
       "bathrooms         0\n",
       "sqft_living       0\n",
       "sqft_lot          0\n",
       "floors            0\n",
       "waterfront        0\n",
       "greenbelt         0\n",
       "nuisance          0\n",
       "view              0\n",
       "condition         0\n",
       "grade             0\n",
       "heat_source      21\n",
       "sewer_system     11\n",
       "sqft_above        0\n",
       "sqft_basement     0\n",
       "sqft_garage       0\n",
       "sqft_patio        0\n",
       "yr_built          0\n",
       "yr_renovated      0\n",
       "address           0\n",
       "lat               0\n",
       "long              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing nan values\n",
    "housing_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset contains `NaN` values! It looks like `heat_source` and `sewer_system` contain them. Without additional data to cross-reference, we can't necessarily impute or replace `Nan` values with some other value so let's drop the records where such `NaN` values exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping nan values \n",
    "housing_data.dropna(axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             0\n",
       "price            0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "greenbelt        0\n",
       "nuisance         0\n",
       "view             0\n",
       "condition        0\n",
       "grade            0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "sqft_garage      0\n",
       "sqft_patio       0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "address          0\n",
       "lat              0\n",
       "long             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking to see if NaN values were dropped\n",
    "housing_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Zipcode From Full Address"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to perform a `pd.merge` later using `zipcode` as our join key, we need to find the `zipcode` corresponding to each property's `address`. This is where we will need the `usaddress` library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import usaddress\n",
    "def extract_zipcode(address):\n",
    "    parsed_address = usaddress.parse(address)\n",
    "    for component in parsed_address:\n",
    "        if component[1] == 'ZipCode':\n",
    "            return component[0]\n",
    "    return None\n",
    "# applying function to the 'address' column to extract the zip code\n",
    "housing_data['zipcode'] = housing_data['address'].apply(extract_zipcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaning zip code column\n",
    "housing_data['zipcode'] = housing_data['zipcode'].str.replace(',','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting zip codes to strings\n",
    "zip_codes['zipcode'] = zip_codes['zipcode'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28652"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking that records match with correct zip codes in King County\n",
    "housing_data['zipcode'].isin(zip_codes['zipcode']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering properties whos address contains 'Washington'\n",
    "housing_data = housing_data[housing_data['address'].str.contains('Washington') == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# further cleaning data by dropping unnecessary columns\n",
    "housing_data.drop(columns = ['address'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging housing_data with zip_code using zipcode to add city onto final dataframe\n",
    "housing_data = pd.merge(housing_data, zip_codes, on = 'zipcode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             0\n",
       "price            0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "greenbelt        0\n",
       "nuisance         0\n",
       "view             0\n",
       "condition        0\n",
       "grade            0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "sqft_garage      0\n",
       "sqft_patio       0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "lat              0\n",
       "long             0\n",
       "zipcode          0\n",
       "city             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking to see if merge produced any NaN values \n",
    "housing_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a merged dataset containing our columns of interest, we chose to wrangle our data further by filtering based on the distribution of properties sold by city as well as performing minor calculations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cities with Few Properties Sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Mean Price</th>\n",
       "      <th>Count_city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Seattle</th>\n",
       "      <td>$995,538.69</td>\n",
       "      <td>11838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duvall</th>\n",
       "      <td>$975,077.43</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ravensdale</th>\n",
       "      <td>$964,111.46</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carnation</th>\n",
       "      <td>$941,285.84</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vashon</th>\n",
       "      <td>$876,493.64</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renton</th>\n",
       "      <td>$867,534.22</td>\n",
       "      <td>2094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black Diamond</th>\n",
       "      <td>$803,996.30</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maple Valley</th>\n",
       "      <td>$797,169.46</td>\n",
       "      <td>831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kent</th>\n",
       "      <td>$647,053.57</td>\n",
       "      <td>2064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enumclaw</th>\n",
       "      <td>$645,524.01</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auburn</th>\n",
       "      <td>$620,536.47</td>\n",
       "      <td>1568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Federal Way</th>\n",
       "      <td>$604,963.72</td>\n",
       "      <td>1111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milton</th>\n",
       "      <td>$574,159.26</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific</th>\n",
       "      <td>$492,587.12</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skykomish</th>\n",
       "      <td>$442,664.29</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medina</th>\n",
       "      <td>$4,551,076.92</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baring</th>\n",
       "      <td>$241,500.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mercer Island</th>\n",
       "      <td>$2,447,696.97</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellevue</th>\n",
       "      <td>$2,022,034.76</td>\n",
       "      <td>1536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sammamish</th>\n",
       "      <td>$1,765,743.34</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kirkland</th>\n",
       "      <td>$1,608,416.33</td>\n",
       "      <td>1284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Preston</th>\n",
       "      <td>$1,600,500.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fall City</th>\n",
       "      <td>$1,559,884.13</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redmond</th>\n",
       "      <td>$1,557,528.57</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woodinville</th>\n",
       "      <td>$1,525,987.50</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Issaquah</th>\n",
       "      <td>$1,450,952.25</td>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bothell</th>\n",
       "      <td>$1,207,399.33</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snoqualmie</th>\n",
       "      <td>$1,182,494.13</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenmore</th>\n",
       "      <td>$1,070,672.29</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Bend</th>\n",
       "      <td>$1,043,571.94</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Mean Price  Count_city\n",
       "city                                    \n",
       "Seattle          $995,538.69       11838\n",
       "Duvall           $975,077.43         243\n",
       "Ravensdale       $964,111.46          65\n",
       "Carnation        $941,285.84         148\n",
       "Vashon           $876,493.64         141\n",
       "Renton           $867,534.22        2094\n",
       "Black Diamond    $803,996.30         293\n",
       "Maple Valley     $797,169.46         831\n",
       "Kent             $647,053.57        2064\n",
       "Enumclaw         $645,524.01         451\n",
       "Auburn           $620,536.47        1568\n",
       "Federal Way      $604,963.72        1111\n",
       "Milton           $574,159.26          23\n",
       "Pacific          $492,587.12          77\n",
       "Skykomish        $442,664.29          14\n",
       "Medina         $4,551,076.92          52\n",
       "Baring           $241,500.00           4\n",
       "Mercer Island  $2,447,696.97         322\n",
       "Bellevue       $2,022,034.76        1536\n",
       "Sammamish      $1,765,743.34         820\n",
       "Kirkland       $1,608,416.33        1284\n",
       "Preston        $1,600,500.00           2\n",
       "Fall City      $1,559,884.13         106\n",
       "Redmond        $1,557,528.57         953\n",
       "Woodinville    $1,525,987.50         580\n",
       "Issaquah       $1,450,952.25         697\n",
       "Bothell        $1,207,399.33         259\n",
       "Snoqualmie     $1,182,494.13         285\n",
       "Kenmore        $1,070,672.29         346\n",
       "North Bend     $1,043,571.94         445"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a groupby DataFrame that groups property by city and calculates count and mean price\n",
    "mean_prices = housing_data.groupby('city')['price'].mean().map('${:,.2f}'.format)\n",
    "city_count_values = housing_data.groupby('city')['price'].count()\n",
    "# print(\"Mean Prices by City:\")\n",
    "# print(mean_prices)\n",
    "# print(\"\\nCount Values by City:\")\n",
    "# print(city_count_values)\n",
    "# print(\"\\nMean bedrooms:\")\n",
    "# print(bedroom_mean)\n",
    "combined_df = pd.concat([mean_prices, city_count_values], axis=1)\n",
    "combined_df.columns = ['Mean Price', 'Count_city']\n",
    "combined_df.sort_values(by = 'Mean Price', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some cities contain less than 100 properties that were sold (e.g., Preston). These could be considered outliers in our distribution when looking at the distribution of property sale price for the entire county. For our purposes, we chose to remover records where the number of properties sold within a given city were less than 100. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = housing_data['city'].value_counts()\n",
    "city_filter = counts[counts < 100].index.tolist()\n",
    "housing_data = housing_data[~housing_data['city'].isin(city_filter)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>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>greenbelt</th>\n",
       "      <th>nuisance</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>sqft_garage</th>\n",
       "      <th>sqft_patio</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5/24/2022</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180</td>\n",
       "      <td>7140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>47.461975</td>\n",
       "      <td>-122.190520</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3/2/2022</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>7969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>930</td>\n",
       "      <td>930</td>\n",
       "      <td>240</td>\n",
       "      <td>90</td>\n",
       "      <td>1950</td>\n",
       "      <td>2008</td>\n",
       "      <td>47.466730</td>\n",
       "      <td>-122.214000</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/29/2022</td>\n",
       "      <td>728000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2170</td>\n",
       "      <td>7520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1240</td>\n",
       "      <td>1240</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>1973</td>\n",
       "      <td>0</td>\n",
       "      <td>47.463930</td>\n",
       "      <td>-122.189740</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/24/2022</td>\n",
       "      <td>565000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>10364</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>6 Low Average</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>1971</td>\n",
       "      <td>0</td>\n",
       "      <td>47.448450</td>\n",
       "      <td>-122.212430</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12/28/2021</td>\n",
       "      <td>645000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1520</td>\n",
       "      <td>8250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>8 Good</td>\n",
       "      <td>1190</td>\n",
       "      <td>590</td>\n",
       "      <td>420</td>\n",
       "      <td>200</td>\n",
       "      <td>1981</td>\n",
       "      <td>0</td>\n",
       "      <td>47.460870</td>\n",
       "      <td>-122.188690</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28539</th>\n",
       "      <td>12/16/2021</td>\n",
       "      <td>1826000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2070</td>\n",
       "      <td>71264</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>EXCELLENT</td>\n",
       "      <td>Average</td>\n",
       "      <td>9 Better</td>\n",
       "      <td>2070</td>\n",
       "      <td>1360</td>\n",
       "      <td>480</td>\n",
       "      <td>952</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>47.504070</td>\n",
       "      <td>-122.471550</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28540</th>\n",
       "      <td>4/14/2022</td>\n",
       "      <td>256000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1330</td>\n",
       "      <td>113815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1330</td>\n",
       "      <td>0</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>1911</td>\n",
       "      <td>1962</td>\n",
       "      <td>47.387960</td>\n",
       "      <td>-122.456440</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28541</th>\n",
       "      <td>7/14/2021</td>\n",
       "      <td>950000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1960</td>\n",
       "      <td>11406</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>AVERAGE</td>\n",
       "      <td>Average</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1960</td>\n",
       "      <td>840</td>\n",
       "      <td>420</td>\n",
       "      <td>560</td>\n",
       "      <td>1938</td>\n",
       "      <td>1985</td>\n",
       "      <td>47.386190</td>\n",
       "      <td>-122.387935</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28542</th>\n",
       "      <td>9/21/2021</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>840</td>\n",
       "      <td>138520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>6 Low Average</td>\n",
       "      <td>840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>47.458555</td>\n",
       "      <td>-122.475450</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28543</th>\n",
       "      <td>6/18/2021</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>760</td>\n",
       "      <td>148975</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2 Substandard</td>\n",
       "      <td>760</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>1983</td>\n",
       "      <td>0</td>\n",
       "      <td>47.415170</td>\n",
       "      <td>-122.505720</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28415 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date      price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "0       5/24/2022   675000.0         4        1.0         1180      7140   \n",
       "1        3/2/2022   750000.0         3        2.0         1830      7969   \n",
       "2       3/29/2022   728000.0         4        2.0         2170      7520   \n",
       "3       3/24/2022   565000.0         4        2.0         1400     10364   \n",
       "4      12/28/2021   645000.0         3        2.0         1520      8250   \n",
       "...           ...        ...       ...        ...          ...       ...   \n",
       "28539  12/16/2021  1826000.0         2        3.0         2070     71264   \n",
       "28540   4/14/2022   256000.0         2        1.0         1330    113815   \n",
       "28541   7/14/2021   950000.0         2        2.5         1960     11406   \n",
       "28542   9/21/2021   450000.0         2        2.0          840    138520   \n",
       "28543   6/18/2021   400000.0         1        1.0          760    148975   \n",
       "\n",
       "       floors waterfront greenbelt nuisance       view condition  \\\n",
       "0         1.0         NO        NO       NO       NONE      Good   \n",
       "1         1.0         NO        NO       NO       NONE   Average   \n",
       "2         1.0         NO        NO       NO       NONE   Average   \n",
       "3         1.5         NO        NO       NO       NONE      Good   \n",
       "4         1.0         NO        NO       NO       NONE   Average   \n",
       "...       ...        ...       ...      ...        ...       ...   \n",
       "28539     2.0         NO        NO       NO  EXCELLENT   Average   \n",
       "28540     1.0         NO        NO       NO       NONE      Good   \n",
       "28541     2.0         NO        NO       NO    AVERAGE   Average   \n",
       "28542     1.0         NO        NO       NO       NONE   Average   \n",
       "28543     2.0         NO        NO       NO       NONE      Poor   \n",
       "\n",
       "               grade  sqft_above  sqft_basement  sqft_garage  sqft_patio  \\\n",
       "0          7 Average        1180              0            0          40   \n",
       "1          7 Average         930            930          240          90   \n",
       "2          7 Average        1240           1240          490          60   \n",
       "3      6 Low Average        1400              0          330         330   \n",
       "4             8 Good        1190            590          420         200   \n",
       "...              ...         ...            ...          ...         ...   \n",
       "28539       9 Better        2070           1360          480         952   \n",
       "28540      7 Average        1330              0          280           0   \n",
       "28541      7 Average        1960            840          420         560   \n",
       "28542  6 Low Average         840              0            0         230   \n",
       "28543  2 Substandard         760              0            0         200   \n",
       "\n",
       "       yr_built  yr_renovated        lat        long zipcode    city  \n",
       "0          1969             0  47.461975 -122.190520   98055  Renton  \n",
       "1          1950          2008  47.466730 -122.214000   98055  Renton  \n",
       "2          1973             0  47.463930 -122.189740   98055  Renton  \n",
       "3          1971             0  47.448450 -122.212430   98055  Renton  \n",
       "4          1981             0  47.460870 -122.188690   98055  Renton  \n",
       "...         ...           ...        ...         ...     ...     ...  \n",
       "28539      2001             0  47.504070 -122.471550   98070  Vashon  \n",
       "28540      1911          1962  47.387960 -122.456440   98070  Vashon  \n",
       "28541      1938          1985  47.386190 -122.387935   98070  Vashon  \n",
       "28542      2020             0  47.458555 -122.475450   98070  Vashon  \n",
       "28543      1983             0  47.415170 -122.505720   98070  Vashon  \n",
       "\n",
       "[28415 rows x 23 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Year & Month Sold "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For future EDA, it might be useful to consider the year and month the property sold as a potential factor in predicting sale price. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating month and year sold from date column\n",
    "housing_data['sold_year'] = pd.to_datetime(housing_data['date']).dt.year\n",
    "housing_data['sold_month'] = pd.to_datetime(housing_data['date']).dt.month\n",
    "\n",
    "# dropping date column since no longer needed\n",
    "housing_data.drop(columns = 'date', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Property Age When Sold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age of the property when it was sold, regardless of whether it had renovations done, could be useful in predicting sale price. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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>sold_year</th>\n",
       "      <th>yr_built</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>1969</td>\n",
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       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>1950</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28539</th>\n",
       "      <td>2021</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28540</th>\n",
       "      <td>2022</td>\n",
       "      <td>1911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28541</th>\n",
       "      <td>2021</td>\n",
       "      <td>1938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28542</th>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28543</th>\n",
       "      <td>2021</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28415 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sold_year  yr_built\n",
       "0           2022      1969\n",
       "1           2022      1950\n",
       "2           2022      1973\n",
       "3           2022      1971\n",
       "4           2021      1981\n",
       "...          ...       ...\n",
       "28539       2021      2001\n",
       "28540       2022      1911\n",
       "28541       2021      1938\n",
       "28542       2021      2020\n",
       "28543       2021      1983\n",
       "\n",
       "[28415 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to calculate property age when sold, need to know when property was built and sold\n",
    "housing_data[['sold_year', 'yr_built']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating property age from the difference between the year it was sold vs built \n",
    "housing_data['age_sold'] = housing_data['sold_year'] - housing_data['yr_built']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Houses That Have Been Renovated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our current dataset contains information on the year a property was renovated. It might be useful to not only consider the year a property was renovated, but whether a property was renovated or not in predicting sale price. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " There are 1244 houses that have been renovated\n"
     ]
    }
   ],
   "source": [
    "# calculating how many properties were renovated \n",
    "print(f\" There are {len(housing_data[housing_data['yr_renovated'] != 0])} houses that have been renovated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>greenbelt</th>\n",
       "      <th>nuisance</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>sqft_garage</th>\n",
       "      <th>sqft_patio</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>city</th>\n",
       "      <th>sold_year</th>\n",
       "      <th>sold_month</th>\n",
       "      <th>age_sold</th>\n",
       "      <th>renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>675000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180</td>\n",
       "      <td>7140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>47.461975</td>\n",
       "      <td>-122.190520</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>750000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1830</td>\n",
       "      <td>7969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>930</td>\n",
       "      <td>930</td>\n",
       "      <td>240</td>\n",
       "      <td>90</td>\n",
       "      <td>1950</td>\n",
       "      <td>2008</td>\n",
       "      <td>47.466730</td>\n",
       "      <td>-122.214000</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>728000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2170</td>\n",
       "      <td>7520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1240</td>\n",
       "      <td>1240</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>1973</td>\n",
       "      <td>0</td>\n",
       "      <td>47.463930</td>\n",
       "      <td>-122.189740</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>565000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>10364</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>6 Low Average</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>1971</td>\n",
       "      <td>0</td>\n",
       "      <td>47.448450</td>\n",
       "      <td>-122.212430</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>51</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>645000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1520</td>\n",
       "      <td>8250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>8 Good</td>\n",
       "      <td>1190</td>\n",
       "      <td>590</td>\n",
       "      <td>420</td>\n",
       "      <td>200</td>\n",
       "      <td>1981</td>\n",
       "      <td>0</td>\n",
       "      <td>47.460870</td>\n",
       "      <td>-122.188690</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>40</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28539</th>\n",
       "      <td>1826000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2070</td>\n",
       "      <td>71264</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>EXCELLENT</td>\n",
       "      <td>Average</td>\n",
       "      <td>9 Better</td>\n",
       "      <td>2070</td>\n",
       "      <td>1360</td>\n",
       "      <td>480</td>\n",
       "      <td>952</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>47.504070</td>\n",
       "      <td>-122.471550</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28540</th>\n",
       "      <td>256000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1330</td>\n",
       "      <td>113815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Good</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1330</td>\n",
       "      <td>0</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>1911</td>\n",
       "      <td>1962</td>\n",
       "      <td>47.387960</td>\n",
       "      <td>-122.456440</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>111</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28541</th>\n",
       "      <td>950000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1960</td>\n",
       "      <td>11406</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>AVERAGE</td>\n",
       "      <td>Average</td>\n",
       "      <td>7 Average</td>\n",
       "      <td>1960</td>\n",
       "      <td>840</td>\n",
       "      <td>420</td>\n",
       "      <td>560</td>\n",
       "      <td>1938</td>\n",
       "      <td>1985</td>\n",
       "      <td>47.386190</td>\n",
       "      <td>-122.387935</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28542</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>840</td>\n",
       "      <td>138520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Average</td>\n",
       "      <td>6 Low Average</td>\n",
       "      <td>840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>47.458555</td>\n",
       "      <td>-122.475450</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28543</th>\n",
       "      <td>400000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>760</td>\n",
       "      <td>148975</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2 Substandard</td>\n",
       "      <td>760</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>1983</td>\n",
       "      <td>0</td>\n",
       "      <td>47.415170</td>\n",
       "      <td>-122.505720</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28415 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "0       675000.0         4        1.0         1180      7140     1.0   \n",
       "1       750000.0         3        2.0         1830      7969     1.0   \n",
       "2       728000.0         4        2.0         2170      7520     1.0   \n",
       "3       565000.0         4        2.0         1400     10364     1.5   \n",
       "4       645000.0         3        2.0         1520      8250     1.0   \n",
       "...          ...       ...        ...          ...       ...     ...   \n",
       "28539  1826000.0         2        3.0         2070     71264     2.0   \n",
       "28540   256000.0         2        1.0         1330    113815     1.0   \n",
       "28541   950000.0         2        2.5         1960     11406     2.0   \n",
       "28542   450000.0         2        2.0          840    138520     1.0   \n",
       "28543   400000.0         1        1.0          760    148975     2.0   \n",
       "\n",
       "      waterfront greenbelt nuisance       view condition          grade  \\\n",
       "0             NO        NO       NO       NONE      Good      7 Average   \n",
       "1             NO        NO       NO       NONE   Average      7 Average   \n",
       "2             NO        NO       NO       NONE   Average      7 Average   \n",
       "3             NO        NO       NO       NONE      Good  6 Low Average   \n",
       "4             NO        NO       NO       NONE   Average         8 Good   \n",
       "...          ...       ...      ...        ...       ...            ...   \n",
       "28539         NO        NO       NO  EXCELLENT   Average       9 Better   \n",
       "28540         NO        NO       NO       NONE      Good      7 Average   \n",
       "28541         NO        NO       NO    AVERAGE   Average      7 Average   \n",
       "28542         NO        NO       NO       NONE   Average  6 Low Average   \n",
       "28543         NO        NO       NO       NONE      Poor  2 Substandard   \n",
       "\n",
       "       sqft_above  sqft_basement  sqft_garage  sqft_patio  yr_built  \\\n",
       "0            1180              0            0          40      1969   \n",
       "1             930            930          240          90      1950   \n",
       "2            1240           1240          490          60      1973   \n",
       "3            1400              0          330         330      1971   \n",
       "4            1190            590          420         200      1981   \n",
       "...           ...            ...          ...         ...       ...   \n",
       "28539        2070           1360          480         952      2001   \n",
       "28540        1330              0          280           0      1911   \n",
       "28541        1960            840          420         560      1938   \n",
       "28542         840              0            0         230      2020   \n",
       "28543         760              0            0         200      1983   \n",
       "\n",
       "       yr_renovated        lat        long zipcode    city  sold_year  \\\n",
       "0                 0  47.461975 -122.190520   98055  Renton       2022   \n",
       "1              2008  47.466730 -122.214000   98055  Renton       2022   \n",
       "2                 0  47.463930 -122.189740   98055  Renton       2022   \n",
       "3                 0  47.448450 -122.212430   98055  Renton       2022   \n",
       "4                 0  47.460870 -122.188690   98055  Renton       2021   \n",
       "...             ...        ...         ...     ...     ...        ...   \n",
       "28539             0  47.504070 -122.471550   98070  Vashon       2021   \n",
       "28540          1962  47.387960 -122.456440   98070  Vashon       2022   \n",
       "28541          1985  47.386190 -122.387935   98070  Vashon       2021   \n",
       "28542             0  47.458555 -122.475450   98070  Vashon       2021   \n",
       "28543             0  47.415170 -122.505720   98070  Vashon       2021   \n",
       "\n",
       "       sold_month  age_sold  renovated  \n",
       "0               5        53      False  \n",
       "1               3        72       True  \n",
       "2               3        49      False  \n",
       "3               3        51      False  \n",
       "4              12        40      False  \n",
       "...           ...       ...        ...  \n",
       "28539          12        20      False  \n",
       "28540           4       111       True  \n",
       "28541           7        83       True  \n",
       "28542           9         1      False  \n",
       "28543           6        38      False  \n",
       "\n",
       "[28415 rows x 26 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a new column based on whether properties were renovated when sold \n",
    "housing_data['renovated'] =  housing_data['yr_renovated'] != 0\n",
    "housing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Grade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`grade` in our dataset contains information on the construction quality of improvements on a scale from 1-13. We'll need to clean the values in the column to extract only the integer value from the string. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n1-3 Falls short of minimum building standards. Normally cabin or inferior structure.\\n\\n4 Generally older, low quality construction. Does not meet code.\\n\\n5 Low construction costs and workmanship. Small, simple design.\\n\\n6 Lowest grade currently meeting building code. Low quality materials and simple designs.\\n\\n7 Average grade of construction and design. Commonly seen in plats and older sub-divisions.\\n\\n8 Just above average in construction and design. Usually better materials in both the exterior and interior finish work.\\n\\n9 Better architectural design with extra interior and exterior design and quality.\\n\\n10 Homes of this quality generally have high quality features. Finish work is better and more design quality is seen in the floor plans. Generally have a larger square footage.\\n\\n11 Custom design and higher quality finish work with added amenities of solid woods, bathroom fixtures and more luxurious options.\\n\\n12 Custom design and excellent builders. All materials are of the highest quality and all conveniences are present.\\n\\n13 Generally custom designed and built. Mansion level. Large amount of highest quality cabinet work, wood trim, marble, entry ways etc.\\n'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Represents the construction quality of improvements. Grades run from grade 1 to 13. Generally defined as:\n",
    "'''\n",
    "1-3 Falls short of minimum building standards. Normally cabin or inferior structure.\n",
    "\n",
    "4 Generally older, low quality construction. Does not meet code.\n",
    "\n",
    "5 Low construction costs and workmanship. Small, simple design.\n",
    "\n",
    "6 Lowest grade currently meeting building code. Low quality materials and simple designs.\n",
    "\n",
    "7 Average grade of construction and design. Commonly seen in plats and older sub-divisions.\n",
    "\n",
    "8 Just above average in construction and design. Usually better materials in both the exterior and interior finish work.\n",
    "\n",
    "9 Better architectural design with extra interior and exterior design and quality.\n",
    "\n",
    "10 Homes of this quality generally have high quality features. Finish work is better and more design quality is seen in the floor plans. Generally have a larger square footage.\n",
    "\n",
    "11 Custom design and higher quality finish work with added amenities of solid woods, bathroom fixtures and more luxurious options.\n",
    "\n",
    "12 Custom design and excellent builders. All materials are of the highest quality and all conveniences are present.\n",
    "\n",
    "13 Generally custom designed and built. Mansion level. Large amount of highest quality cabinet work, wood trim, marble, entry ways etc.\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting only the integer value from the grade column\n",
    "housing_data['grade'] = housing_data['grade'].str.extract('(\\d+)').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "1         1\n",
       "2         1\n",
       "3         7\n",
       "4        37\n",
       "5       347\n",
       "6      2753\n",
       "7     11314\n",
       "8      8706\n",
       "9      3501\n",
       "10     1276\n",
       "11      353\n",
       "12      100\n",
       "13       19\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting values by grade category\n",
    "housing_data['grade'].value_counts().sort_index(ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Condition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`condition` in our dataset takes into consideration the building `age` as well as the `grade`. As is, `condition` is grouped into various categories which are objects. `condition`, however, can be considered ordinal data, so it will be useful for future analysis to consider the integer equivalent of such values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Good', 'Average', 'Very Good', 'Fair', 'Poor'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing the unique values for condition\n",
    "housing_data['condition'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# replacing the values within the column with integers to better represent its ordinal structure\n",
    "housing_data['condition'].replace({'Poor': 1, 'Fair': 2, 'Average': 3, 'Good': 4, 'Very Good': 5}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4\n",
       "1        3\n",
       "2        3\n",
       "3        4\n",
       "4        3\n",
       "        ..\n",
       "28539    3\n",
       "28540    4\n",
       "28541    3\n",
       "28542    3\n",
       "28543    1\n",
       "Name: condition, Length: 28415, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing the values after replacement\n",
    "housing_data['condition']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Property View\n",
    "`view` in our datast refers to the quality of view from house. This considers whether the property includes views of geographic landmarks or natural bodies of water, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NONE', 'AVERAGE', 'GOOD', 'FAIR', 'EXCELLENT'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data['view'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`View` can be considered ordinal data, so it is worth converting the values to integer for future analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_data['view'].replace({'NONE': 0, 'FAIR': 1, 'AVERAGE': 2, 'GOOD': 3, 'EXCELLENT': 4}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>greenbelt</th>\n",
       "      <th>nuisance</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>sqft_garage</th>\n",
       "      <th>sqft_patio</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>city</th>\n",
       "      <th>sold_year</th>\n",
       "      <th>sold_month</th>\n",
       "      <th>age_sold</th>\n",
       "      <th>renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>675000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180</td>\n",
       "      <td>7140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>47.461975</td>\n",
       "      <td>-122.19052</td>\n",
       "      <td>98055</td>\n",
       "      <td>Renton</td>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  bedrooms  bathrooms  sqft_living  sqft_lot  floors waterfront  \\\n",
       "0  675000.0         4        1.0         1180      7140     1.0         NO   \n",
       "\n",
       "  greenbelt nuisance  view  condition  grade  sqft_above  sqft_basement  \\\n",
       "0        NO       NO     0          4      7        1180              0   \n",
       "\n",
       "   sqft_garage  sqft_patio  yr_built  yr_renovated        lat       long  \\\n",
       "0            0          40      1969             0  47.461975 -122.19052   \n",
       "\n",
       "  zipcode    city  sold_year  sold_month  age_sold  renovated  \n",
       "0   98055  Renton       2022           5        53      False  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Price Bins \n",
    "For future EDA purposes, it might be useful to 'bin' so as to visualize the distribution of property sales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create intervals of housing prices for grouping purposes:\n",
    "price_bins = [0, 500000, 1000000, 1500000, 2000000, 3000000, 5000000, float('inf')]\n",
    "price_labels=['<500K', '500K-1M', '1M-1.5M','1.5M-2M','2M-3M', '3M-5M', '>5M']\n",
    "\n",
    "# Create a new column of price intervals:\n",
    "housing_data['price_group'] = pd.cut(housing_data['price'], bins = price_bins, labels = price_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price_group\n",
       "500K-1M    14509\n",
       "1M-1.5M     5854\n",
       "<500K       2961\n",
       "1.5M-2M     2718\n",
       "2M-3M       1720\n",
       "3M-5M        550\n",
       ">5M          103\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing distribution of properties based on sale price bins\n",
    "housing_data['price_group'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After binning, we can see that the majority of properties were sold at a range of `$500,000 - $1M USD`. Less than 1% of properties were sold at a range greater than `$2M USD`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rounding Bathroom "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rounding bathrooms for future modeling purposes\n",
    "housing_data['bathrooms'] = housing_data['bathrooms'].round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaned Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After cleaning, our new dataset has been narrowed to contain only non-waterfront properties. We've dealt with NaN values and duplicate rows. Additionally, we've used `address` to calculate property `zipcode` and merged dataframes to include `city`. We've performed minor calculations to determine the `age_sold` of properties, created a new variable `renovated` that converts `yr_renovated` to a `boolean` type, and finally, grouped properties based on `price_groups`. We've also filtered our dataframe to only include cities where more than 100 properties were sold. Our new dataset is ready to be exported and used to perform EDA on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_data.to_csv('KC_Data/cleaned_housing_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
       "      <td>500K-1M</td>\n",
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       "      <th>1</th>\n",
       "      <td>750000.0</td>\n",
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       "      <td>NO</td>\n",
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       "      <td>3</td>\n",
       "      <td>7</td>\n",
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       "      <td>90</td>\n",
       "      <td>1950</td>\n",
       "      <td>2008</td>\n",
       "      <td>47.466730</td>\n",
       "      <td>-122.214000</td>\n",
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       "      <td>3</td>\n",
       "      <td>72</td>\n",
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       "      <td>500K-1M</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>728000.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2170</td>\n",
       "      <td>7520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1240</td>\n",
       "      <td>1240</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>1973</td>\n",
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       "      <td>False</td>\n",
       "      <td>500K-1M</td>\n",
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       "      <th>3</th>\n",
       "      <td>565000.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>10364</td>\n",
       "      <td>1.5</td>\n",
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       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>1400</td>\n",
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       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>1971</td>\n",
       "      <td>0</td>\n",
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       "      <td>-122.212430</td>\n",
       "      <td>98055</td>\n",
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       "      <td>2022</td>\n",
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       "      <td>51</td>\n",
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       "      <td>500K-1M</td>\n",
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       "      <th>4</th>\n",
       "      <td>645000.0</td>\n",
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       "      <td>8250</td>\n",
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       "      <td>Renton</td>\n",
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       "      <th>28539</th>\n",
       "      <td>1826000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2070</td>\n",
       "      <td>71264</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2070</td>\n",
       "      <td>1360</td>\n",
       "      <td>480</td>\n",
       "      <td>952</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>47.504070</td>\n",
       "      <td>-122.471550</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>1.5M-2M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28540</th>\n",
       "      <td>256000.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1330</td>\n",
       "      <td>113815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1330</td>\n",
       "      <td>0</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>1911</td>\n",
       "      <td>1962</td>\n",
       "      <td>47.387960</td>\n",
       "      <td>-122.456440</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>111</td>\n",
       "      <td>True</td>\n",
       "      <td>&lt;500K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28541</th>\n",
       "      <td>950000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1960</td>\n",
       "      <td>11406</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1960</td>\n",
       "      <td>840</td>\n",
       "      <td>420</td>\n",
       "      <td>560</td>\n",
       "      <td>1938</td>\n",
       "      <td>1985</td>\n",
       "      <td>47.386190</td>\n",
       "      <td>-122.387935</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>True</td>\n",
       "      <td>500K-1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28542</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>840</td>\n",
       "      <td>138520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>47.458555</td>\n",
       "      <td>-122.475450</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>&lt;500K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28543</th>\n",
       "      <td>400000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>760</td>\n",
       "      <td>148975</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>760</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>1983</td>\n",
       "      <td>0</td>\n",
       "      <td>47.415170</td>\n",
       "      <td>-122.505720</td>\n",
       "      <td>98070</td>\n",
       "      <td>Vashon</td>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>&lt;500K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28415 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "0       675000.0         4        1.0         1180      7140     1.0   \n",
       "1       750000.0         3        2.0         1830      7969     1.0   \n",
       "2       728000.0         4        2.0         2170      7520     1.0   \n",
       "3       565000.0         4        2.0         1400     10364     1.5   \n",
       "4       645000.0         3        2.0         1520      8250     1.0   \n",
       "...          ...       ...        ...          ...       ...     ...   \n",
       "28539  1826000.0         2        3.0         2070     71264     2.0   \n",
       "28540   256000.0         2        1.0         1330    113815     1.0   \n",
       "28541   950000.0         2        2.0         1960     11406     2.0   \n",
       "28542   450000.0         2        2.0          840    138520     1.0   \n",
       "28543   400000.0         1        1.0          760    148975     2.0   \n",
       "\n",
       "      waterfront greenbelt nuisance  view  condition  grade  sqft_above  \\\n",
       "0             NO        NO       NO     0          4      7        1180   \n",
       "1             NO        NO       NO     0          3      7         930   \n",
       "2             NO        NO       NO     0          3      7        1240   \n",
       "3             NO        NO       NO     0          4      6        1400   \n",
       "4             NO        NO       NO     0          3      8        1190   \n",
       "...          ...       ...      ...   ...        ...    ...         ...   \n",
       "28539         NO        NO       NO     4          3      9        2070   \n",
       "28540         NO        NO       NO     0          4      7        1330   \n",
       "28541         NO        NO       NO     2          3      7        1960   \n",
       "28542         NO        NO       NO     0          3      6         840   \n",
       "28543         NO        NO       NO     0          1      2         760   \n",
       "\n",
       "       sqft_basement  sqft_garage  sqft_patio  yr_built  yr_renovated  \\\n",
       "0                  0            0          40      1969             0   \n",
       "1                930          240          90      1950          2008   \n",
       "2               1240          490          60      1973             0   \n",
       "3                  0          330         330      1971             0   \n",
       "4                590          420         200      1981             0   \n",
       "...              ...          ...         ...       ...           ...   \n",
       "28539           1360          480         952      2001             0   \n",
       "28540              0          280           0      1911          1962   \n",
       "28541            840          420         560      1938          1985   \n",
       "28542              0            0         230      2020             0   \n",
       "28543              0            0         200      1983             0   \n",
       "\n",
       "             lat        long  zipcode    city  sold_year  sold_month  \\\n",
       "0      47.461975 -122.190520    98055  Renton       2022           5   \n",
       "1      47.466730 -122.214000    98055  Renton       2022           3   \n",
       "2      47.463930 -122.189740    98055  Renton       2022           3   \n",
       "3      47.448450 -122.212430    98055  Renton       2022           3   \n",
       "4      47.460870 -122.188690    98055  Renton       2021          12   \n",
       "...          ...         ...      ...     ...        ...         ...   \n",
       "28539  47.504070 -122.471550    98070  Vashon       2021          12   \n",
       "28540  47.387960 -122.456440    98070  Vashon       2022           4   \n",
       "28541  47.386190 -122.387935    98070  Vashon       2021           7   \n",
       "28542  47.458555 -122.475450    98070  Vashon       2021           9   \n",
       "28543  47.415170 -122.505720    98070  Vashon       2021           6   \n",
       "\n",
       "       age_sold  renovated price_group  \n",
       "0            53      False     500K-1M  \n",
       "1            72       True     500K-1M  \n",
       "2            49      False     500K-1M  \n",
       "3            51      False     500K-1M  \n",
       "4            40      False     500K-1M  \n",
       "...         ...        ...         ...  \n",
       "28539        20      False     1.5M-2M  \n",
       "28540       111       True       <500K  \n",
       "28541        83       True     500K-1M  \n",
       "28542         1      False       <500K  \n",
       "28543        38      False       <500K  \n",
       "\n",
       "[28415 rows x 27 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('KC_Data/cleaned_housing_data', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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