DC_Housing_Correlations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scipy.stats\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\", color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2698: DtypeWarning: Columns (18,19,20,21,22,23,24,33,34,35,37,43,45) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>BATHRM</th>\n",
       "      <th>HF_BATHRM</th>\n",
       "      <th>HEAT</th>\n",
       "      <th>AC</th>\n",
       "      <th>NUM_UNITS</th>\n",
       "      <th>ROOMS</th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>AYB</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>ASSESSMENT_NBHD</th>\n",
       "      <th>ASSESSMENT_SUBNBHD</th>\n",
       "      <th>CENSUS_TRACT</th>\n",
       "      <th>CENSUS_BLOCK</th>\n",
       "      <th>WARD</th>\n",
       "      <th>SQUARE</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>QUADRANT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Warm Cool</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.040832</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Warm Cool</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.040764</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Hot Water Rad</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.040678</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Hot Water Rad</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.040629</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Warm Cool</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.039361</td>\n",
       "      <td>Old City 2</td>\n",
       "      <td>040 D Old City 2</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>152</td>\n",
       "      <td>-77.040429</td>\n",
       "      <td>38.914881</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  BATHRM  HF_BATHRM           HEAT AC  NUM_UNITS  ROOMS  BEDRM  \\\n",
       "0           0       4          0      Warm Cool  Y        2.0      8      4   \n",
       "1           1       3          1      Warm Cool  Y        2.0     11      5   \n",
       "2           2       3          1  Hot Water Rad  Y        2.0      9      5   \n",
       "3           3       3          1  Hot Water Rad  Y        2.0      8      5   \n",
       "4           4       2          1      Warm Cool  Y        1.0     11      3   \n",
       "\n",
       "      AYB  YR_RMDL   ...     LONGITUDE  ASSESSMENT_NBHD ASSESSMENT_SUBNBHD  \\\n",
       "0  1910.0   1988.0   ...    -77.040832       Old City 2   040 D Old City 2   \n",
       "1  1898.0   2007.0   ...    -77.040764       Old City 2   040 D Old City 2   \n",
       "2  1910.0   2009.0   ...    -77.040678       Old City 2   040 D Old City 2   \n",
       "3  1900.0   2003.0   ...    -77.040629       Old City 2   040 D Old City 2   \n",
       "4  1913.0   2012.0   ...    -77.039361       Old City 2   040 D Old City 2   \n",
       "\n",
       "   CENSUS_TRACT CENSUS_BLOCK    WARD  SQUARE          X          Y QUADRANT  \n",
       "0        4201.0  004201 2006  Ward 2     152 -77.040429  38.914881       NW  \n",
       "1        4201.0  004201 2006  Ward 2     152 -77.040429  38.914881       NW  \n",
       "2        4201.0  004201 2006  Ward 2     152 -77.040429  38.914881       NW  \n",
       "3        4201.0  004201 2006  Ward 2     152 -77.040429  38.914881       NW  \n",
       "4        4201.0  004201 2006  Ward 2     152 -77.040429  38.914881       NW  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/DC_Properties.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this command will throw away ~33% of the data (maybe we will keep the data and do two seperate tests, one where we will throw away the rows where the data is missing and one where we will throw away these columns)\n",
    "# data = data[data[\"STRUCT\"].notnull() | data[\"STYLE\"].notnull() | data[\"GRADE\"].notnull() | data[\"CNDTN\"].notnull() | data[\"EXTWALL\"].notnull() | data[\"ROOF\"].notnull() | data[\"INTWALL\"].notnull()]\n",
    "\n",
    "# since our many concern is the price of the building, rows without the price have only a small to none value\n",
    "data = data[data[\"PRICE\"].notnull()]\n",
    "\n",
    "# remove rows with strange prices\n",
    "data = data.loc[(data[\"PRICE\"] <= 25000000) & (data[\"PRICE\"] >= 60000)]\n",
    "\n",
    "# in these very few rows (~200) there are values missing\n",
    "data = data[data[\"X\"].notnull() & data[\"Y\"].notnull() & data[\"QUADRANT\"].notnull() & data[\"AYB\"].notnull() & data[\"WARD\"].notnull() & data[\"ASSESSMENT_NBHD\"].notnull() & data[\"CENSUS_TRACT\"].notnull() & data[\"LONGITUDE\"].notnull() & data[\"LATITUDE\"].notnull() & data[\"ZIPCODE\"].notnull()]\n",
    "data = data[data.ROOMS != 0]\n",
    "data = data[data.AC != \"0\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set the missing sale date to the year it was build\n",
    "data[\"SALEDATE\"] = np.where(data[\"SALEDATE\"].isnull(), data[\"AYB\"], data[\"SALEDATE\"])\n",
    "\n",
    "# add a column wich says wether a building was remodeled and insert missing values in to the YR_RMDL column\n",
    "data[\"WAS_REMODELED\"] = np.where(data[\"YR_RMDL\"].isnull(), 0, 1)\n",
    "data[\"YR_RMDL\"] = np.where(data[\"YR_RMDL\"].isnull(), -1, data[\"YR_RMDL\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#convert saledate to datetime\n",
    "data['SALEDATE'] = pd.to_datetime(data['SALEDATE'])\n",
    "#Calculating the difference in years between Last Sale Date and Year Built\n",
    "data['SalevYB']=data['SALEDATE'].dt.year - data['AYB']\n",
    "#Calculating the difference in years between Last Sale Date and Year Improved\n",
    "data['SalevYI']=data['SALEDATE'].dt.year - data['EYB']\n",
    "\n",
    "data['SQUARE'] = data['SQUARE'].astype(str)\n",
    "\n",
    "data = data.drop([\"SALEDATE\", \"EYB\", \"AYB\", \"Unnamed: 0\", \"LIVING_GBA\", \"GIS_LAST_MOD_DTTM\", \"SOURCE\", \"CITY\", \"STATE\"], axis=1)\n",
    "data = data[data[\"STRUCT\"].notnull() & data[\"STYLE\"].notnull() & data[\"GRADE\"].notnull() & data[\"CNDTN\"].notnull() & data[\"EXTWALL\"].notnull() & data[\"ROOF\"].notnull() & data[\"INTWALL\"].notnull()  & data[\"STORIES\"].notnull() & data[\"KITCHENS\"].notnull() & data[\"ASSESSMENT_SUBNBHD\"].notnull()  & data[\"CENSUS_BLOCK\"].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['BATHRM', 'HF_BATHRM', 'HEAT', 'AC', 'NUM_UNITS', 'ROOMS', 'BEDRM',\n",
       "       'YR_RMDL', 'STORIES', 'PRICE', 'QUALIFIED', 'SALE_NUM', 'GBA',\n",
       "       'BLDG_NUM', 'STYLE', 'STRUCT', 'GRADE', 'CNDTN', 'EXTWALL', 'ROOF',\n",
       "       'INTWALL', 'KITCHENS', 'FIREPLACES', 'USECODE', 'LANDAREA', 'CMPLX_NUM',\n",
       "       'FULLADDRESS', 'ZIPCODE', 'NATIONALGRID', 'LATITUDE', 'LONGITUDE',\n",
       "       'ASSESSMENT_NBHD', 'ASSESSMENT_SUBNBHD', 'CENSUS_TRACT', 'CENSUS_BLOCK',\n",
       "       'WARD', 'SQUARE', 'X', 'Y', 'QUADRANT', 'WAS_REMODELED', 'SalevYB',\n",
       "       'SalevYI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this are the remaining columns\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def chi2_test(row, col):\n",
    "    table = pd.crosstab(row, col)\n",
    "    chi2, p, df, exp_obs = scipy.stats.chi2_contingency(table)\n",
    "    print(p)\n",
    "    print(table)\n",
    "    print(exp_obs)\n",
    "    '''return chi2, p, exp_obs, df'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def contingency_coefficent(row, col, return_C = False, return_mean_chi2 = False):\n",
    "    if row.size != col.size:\n",
    "        return None\n",
    "    n = row.size\n",
    "    c_table = pd.crosstab(row, col)\n",
    "    exp_table = scipy.stats.contingency.expected_freq(c_table)\n",
    "    chi2 = (pow(c_table - exp_table, 2) / exp_table).sum().sum()\n",
    "    #print(chi2/n)\n",
    "    C = pow(chi2 / (chi2 + n), 1/2)\n",
    "    #print(C)\n",
    "    Ckorr = C * pow(min(c_table.shape) / (min(c_table.shape) - 1), 1/2)\n",
    "    if return_C:\n",
    "        if return_mean_chi2:\n",
    "            return Ckorr, C, chi2/(n * (min(c_table.shape) - 1))\n",
    "        else:\n",
    "            return Ckorr, C\n",
    "    elif return_mean_chi2:\n",
    "        return Ckorr, chi2/(n * (min(c_table.shape) - 1))\n",
    "    else:\n",
    "        return Ckorr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Chi²-Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999998"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_coefficent(data.SQUARE, data.QUADRANT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_table_SQUARE_QUADRANT = pd.crosstab(data.SQUARE, data.QUADRANT)\n",
    "chi2, p, df, exp_obs = scipy.stats.chi2_contingency(c_table_SQUARE_QUADRANT)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9998854642052089"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.LONGITUDE.corr(data.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.723597658221731e-196"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_table_LONGITUDE_X = pd.crosstab(data.LONGITUDE, data.X)\n",
    "chi2, p, df, exp_obs = scipy.stats.chi2_contingency(c_table_LONGITUDE_X)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999104626920347"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.LATITUDE.corr(data.Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_table_LATITUDE_Y = pd.crosstab(data.LATITUDE, data.Y)\n",
    "chi2, p, df, exp_obs = scipy.stats.chi2_contingency(c_table_LATITUDE_Y)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9995132725347345"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_coefficent(data.ASSESSMENT_NBHD, data.ASSESSMENT_SUBNBHD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_table_NBHD_SUBNBHD = pd.crosstab(data.ASSESSMENT_NBHD, data.ASSESSMENT_SUBNBHD)\n",
    "chi2, p, df, exp_obs = scipy.stats.chi2_contingency(c_table_NBHD_SUBNBHD)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_coefficent(data.CENSUS_BLOCK, data.CENSUS_TRACT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_table_BLOCK_TRACT = pd.crosstab(data.CENSUS_BLOCK, data.CENSUS_TRACT)\n",
    "chi2, p, df, exp_obs = scipy.stats.chi2_contingency(c_table_BLOCK_TRACT)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All correlation and chi² calculations show a high correlation and significance between the selected columns. (Small p value means that the null hypthesis that row and column are independent can be rejected.) They are all more or less geographical data about the specific property, so a high correlaltion can be expected. Because they have such high dependets, it is useful to drop some of the columns and only keep a few (one) that gives information about the location. Otherwise, the model would use independent variables that already have some relationship. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all categorical values we calculate the paired contingency coefficient for every two values. The other values are at least interval scaled so we can calulate the correlation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "categorical_cols = [\"HEAT\", \"AC\", \"QUALIFIED\", \"STYLE\", \"STRUCT\", \"GRADE\", \"CNDTN\", \"EXTWALL\", \"ROOF\", \n",
    "                    \"INTWALL\", \"ZIPCODE\", \"ASSESSMENT_NBHD\", \"ASSESSMENT_SUBNBHD\", \"CENSUS_TRACT\", \"CENSUS_BLOCK\", \n",
    "                    \"WARD\", \"SQUARE\", \"QUADRANT\", \"PRICE\"]\n",
    "numeric_cols = [\"BATHRM\", \"HF_BATHRM\", \"NUM_UNITS\", \"ROOMS\", \"BEDRM\", \"YR_RMDL\", \"STORIES\", \"PRICE\", \"SALE_NUM\", \"GBA\", \n",
    "               \"KITCHENS\", \"FIREPLACES\", \"LANDAREA\"\n",
    "                ,\"LATITUDE\", \"LONGITUDE\", \"X\", \"Y\"\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def paired_contincency(data):\n",
    "    names = data.columns.values.tolist()\n",
    "    result_df = pd.DataFrame(index=names, columns=names)\n",
    "    while len(names) > 0:\n",
    "        i = names.pop(0)\n",
    "        result_df[i][i] = 1\n",
    "        for ii in names:\n",
    "            try:\n",
    "                result_df[i][ii] = contingency_coefficent(data[i], data[ii])\n",
    "            except (TypeError, ZeroDivisionError):\n",
    "                print(i, ii)\n",
    "    return result_df\n",
    "\n",
    "def paired_corr(data):\n",
    "    names = data.columns.values.tolist()\n",
    "    result_df = pd.DataFrame(index=names, columns=names)\n",
    "    while len(names) > 0:\n",
    "        i = names.pop(0)\n",
    "        result_df[i][i] = 1\n",
    "        for ii in names:\n",
    "            result_df[i][ii] =  data[i].corr(data[ii])\n",
    "    return result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HEAT</th>\n",
       "      <th>AC</th>\n",
       "      <th>QUALIFIED</th>\n",
       "      <th>STYLE</th>\n",
       "      <th>STRUCT</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>CNDTN</th>\n",
       "      <th>EXTWALL</th>\n",
       "      <th>ROOF</th>\n",
       "      <th>INTWALL</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>ASSESSMENT_NBHD</th>\n",
       "      <th>ASSESSMENT_SUBNBHD</th>\n",
       "      <th>CENSUS_TRACT</th>\n",
       "      <th>CENSUS_BLOCK</th>\n",
       "      <th>WARD</th>\n",
       "      <th>SQUARE</th>\n",
       "      <th>QUADRANT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HEAT</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC</th>\n",
       "      <td>0.744567</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QUALIFIED</th>\n",
       "      <td>0.151159</td>\n",
       "      <td>0.237362</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STYLE</th>\n",
       "      <td>0.220895</td>\n",
       "      <td>0.205695</td>\n",
       "      <td>0.0824314</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRUCT</th>\n",
       "      <td>0.257921</td>\n",
       "      <td>0.282082</td>\n",
       "      <td>0.13953</td>\n",
       "      <td>0.49262</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRADE</th>\n",
       "      <td>0.358609</td>\n",
       "      <td>0.304519</td>\n",
       "      <td>0.156444</td>\n",
       "      <td>0.47498</td>\n",
       "      <td>0.408101</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNDTN</th>\n",
       "      <td>0.416794</td>\n",
       "      <td>0.616269</td>\n",
       "      <td>0.302916</td>\n",
       "      <td>0.249982</td>\n",
       "      <td>0.185076</td>\n",
       "      <td>0.323053</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXTWALL</th>\n",
       "      <td>0.285521</td>\n",
       "      <td>0.210951</td>\n",
       "      <td>0.0686165</td>\n",
       "      <td>0.336191</td>\n",
       "      <td>0.4531</td>\n",
       "      <td>0.335088</td>\n",
       "      <td>0.310085</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROOF</th>\n",
       "      <td>0.333252</td>\n",
       "      <td>0.246095</td>\n",
       "      <td>0.0739491</td>\n",
       "      <td>0.456407</td>\n",
       "      <td>0.652887</td>\n",
       "      <td>0.410321</td>\n",
       "      <td>0.201412</td>\n",
       "      <td>0.511211</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTWALL</th>\n",
       "      <td>0.308738</td>\n",
       "      <td>0.187224</td>\n",
       "      <td>0.0831081</td>\n",
       "      <td>0.143592</td>\n",
       "      <td>0.219508</td>\n",
       "      <td>0.201106</td>\n",
       "      <td>0.204097</td>\n",
       "      <td>0.350849</td>\n",
       "      <td>0.337022</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIPCODE</th>\n",
       "      <td>0.478851</td>\n",
       "      <td>0.360939</td>\n",
       "      <td>0.17704</td>\n",
       "      <td>0.545051</td>\n",
       "      <td>0.680024</td>\n",
       "      <td>0.754158</td>\n",
       "      <td>0.283672</td>\n",
       "      <td>0.518553</td>\n",
       "      <td>0.693162</td>\n",
       "      <td>0.374939</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSESSMENT_NBHD</th>\n",
       "      <td>0.506757</td>\n",
       "      <td>0.395094</td>\n",
       "      <td>0.202651</td>\n",
       "      <td>0.578394</td>\n",
       "      <td>0.726801</td>\n",
       "      <td>0.817309</td>\n",
       "      <td>0.356654</td>\n",
       "      <td>0.600292</td>\n",
       "      <td>0.746201</td>\n",
       "      <td>0.490191</td>\n",
       "      <td>0.981242</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSESSMENT_SUBNBHD</th>\n",
       "      <td>0.564663</td>\n",
       "      <td>0.449133</td>\n",
       "      <td>0.234034</td>\n",
       "      <td>0.669979</td>\n",
       "      <td>0.795508</td>\n",
       "      <td>0.870642</td>\n",
       "      <td>0.59821</td>\n",
       "      <td>0.747726</td>\n",
       "      <td>0.788025</td>\n",
       "      <td>0.590872</td>\n",
       "      <td>0.992245</td>\n",
       "      <td>0.999513</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CENSUS_TRACT</th>\n",
       "      <td>0.570975</td>\n",
       "      <td>0.460669</td>\n",
       "      <td>0.247569</td>\n",
       "      <td>0.67936</td>\n",
       "      <td>0.803682</td>\n",
       "      <td>0.853366</td>\n",
       "      <td>0.604119</td>\n",
       "      <td>0.722785</td>\n",
       "      <td>0.78653</td>\n",
       "      <td>0.596559</td>\n",
       "      <td>0.995494</td>\n",
       "      <td>0.99798</td>\n",
       "      <td>0.997531</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CENSUS_BLOCK</th>\n",
       "      <td>0.814858</td>\n",
       "      <td>0.602741</td>\n",
       "      <td>0.455427</td>\n",
       "      <td>0.907149</td>\n",
       "      <td>0.9066</td>\n",
       "      <td>0.939569</td>\n",
       "      <td>0.81027</td>\n",
       "      <td>0.922882</td>\n",
       "      <td>0.908797</td>\n",
       "      <td>0.816707</td>\n",
       "      <td>0.999329</td>\n",
       "      <td>0.999962</td>\n",
       "      <td>0.999959</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WARD</th>\n",
       "      <td>0.442295</td>\n",
       "      <td>0.308139</td>\n",
       "      <td>0.169701</td>\n",
       "      <td>0.487184</td>\n",
       "      <td>0.605098</td>\n",
       "      <td>0.692297</td>\n",
       "      <td>0.247543</td>\n",
       "      <td>0.442574</td>\n",
       "      <td>0.660388</td>\n",
       "      <td>0.372466</td>\n",
       "      <td>0.977029</td>\n",
       "      <td>0.989771</td>\n",
       "      <td>0.997949</td>\n",
       "      <td>0.998611</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQUARE</th>\n",
       "      <td>0.796312</td>\n",
       "      <td>0.591029</td>\n",
       "      <td>0.435164</td>\n",
       "      <td>0.897158</td>\n",
       "      <td>0.902432</td>\n",
       "      <td>0.936388</td>\n",
       "      <td>0.79688</td>\n",
       "      <td>0.917391</td>\n",
       "      <td>0.901913</td>\n",
       "      <td>0.803329</td>\n",
       "      <td>0.999028</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999991</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>0.999987</td>\n",
       "      <td>0.999942</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QUADRANT</th>\n",
       "      <td>0.277132</td>\n",
       "      <td>0.0671344</td>\n",
       "      <td>0.0889259</td>\n",
       "      <td>0.278968</td>\n",
       "      <td>0.41866</td>\n",
       "      <td>0.506247</td>\n",
       "      <td>0.139586</td>\n",
       "      <td>0.296454</td>\n",
       "      <td>0.396057</td>\n",
       "      <td>0.265992</td>\n",
       "      <td>0.951575</td>\n",
       "      <td>0.92201</td>\n",
       "      <td>0.990963</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.859432</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRICE</th>\n",
       "      <td>0.849019</td>\n",
       "      <td>0.639757</td>\n",
       "      <td>0.656506</td>\n",
       "      <td>0.89066</td>\n",
       "      <td>0.871358</td>\n",
       "      <td>0.919618</td>\n",
       "      <td>0.809582</td>\n",
       "      <td>0.920212</td>\n",
       "      <td>0.857108</td>\n",
       "      <td>0.877975</td>\n",
       "      <td>0.909185</td>\n",
       "      <td>0.939235</td>\n",
       "      <td>0.981179</td>\n",
       "      <td>0.984665</td>\n",
       "      <td>0.999136</td>\n",
       "      <td>0.844697</td>\n",
       "      <td>0.998963</td>\n",
       "      <td>0.7385</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        HEAT         AC  QUALIFIED     STYLE    STRUCT  \\\n",
       "HEAT                       1        NaN        NaN       NaN       NaN   \n",
       "AC                  0.744567          1        NaN       NaN       NaN   \n",
       "QUALIFIED           0.151159   0.237362          1       NaN       NaN   \n",
       "STYLE               0.220895   0.205695  0.0824314         1       NaN   \n",
       "STRUCT              0.257921   0.282082    0.13953   0.49262         1   \n",
       "GRADE               0.358609   0.304519   0.156444   0.47498  0.408101   \n",
       "CNDTN               0.416794   0.616269   0.302916  0.249982  0.185076   \n",
       "EXTWALL             0.285521   0.210951  0.0686165  0.336191    0.4531   \n",
       "ROOF                0.333252   0.246095  0.0739491  0.456407  0.652887   \n",
       "INTWALL             0.308738   0.187224  0.0831081  0.143592  0.219508   \n",
       "ZIPCODE             0.478851   0.360939    0.17704  0.545051  0.680024   \n",
       "ASSESSMENT_NBHD     0.506757   0.395094   0.202651  0.578394  0.726801   \n",
       "ASSESSMENT_SUBNBHD  0.564663   0.449133   0.234034  0.669979  0.795508   \n",
       "CENSUS_TRACT        0.570975   0.460669   0.247569   0.67936  0.803682   \n",
       "CENSUS_BLOCK        0.814858   0.602741   0.455427  0.907149    0.9066   \n",
       "WARD                0.442295   0.308139   0.169701  0.487184  0.605098   \n",
       "SQUARE              0.796312   0.591029   0.435164  0.897158  0.902432   \n",
       "QUADRANT            0.277132  0.0671344  0.0889259  0.278968   0.41866   \n",
       "PRICE               0.849019   0.639757   0.656506   0.89066  0.871358   \n",
       "\n",
       "                       GRADE     CNDTN   EXTWALL      ROOF   INTWALL  \\\n",
       "HEAT                     NaN       NaN       NaN       NaN       NaN   \n",
       "AC                       NaN       NaN       NaN       NaN       NaN   \n",
       "QUALIFIED                NaN       NaN       NaN       NaN       NaN   \n",
       "STYLE                    NaN       NaN       NaN       NaN       NaN   \n",
       "STRUCT                   NaN       NaN       NaN       NaN       NaN   \n",
       "GRADE                      1       NaN       NaN       NaN       NaN   \n",
       "CNDTN               0.323053         1       NaN       NaN       NaN   \n",
       "EXTWALL             0.335088  0.310085         1       NaN       NaN   \n",
       "ROOF                0.410321  0.201412  0.511211         1       NaN   \n",
       "INTWALL             0.201106  0.204097  0.350849  0.337022         1   \n",
       "ZIPCODE             0.754158  0.283672  0.518553  0.693162  0.374939   \n",
       "ASSESSMENT_NBHD     0.817309  0.356654  0.600292  0.746201  0.490191   \n",
       "ASSESSMENT_SUBNBHD  0.870642   0.59821  0.747726  0.788025  0.590872   \n",
       "CENSUS_TRACT        0.853366  0.604119  0.722785   0.78653  0.596559   \n",
       "CENSUS_BLOCK        0.939569   0.81027  0.922882  0.908797  0.816707   \n",
       "WARD                0.692297  0.247543  0.442574  0.660388  0.372466   \n",
       "SQUARE              0.936388   0.79688  0.917391  0.901913  0.803329   \n",
       "QUADRANT            0.506247  0.139586  0.296454  0.396057  0.265992   \n",
       "PRICE               0.919618  0.809582  0.920212  0.857108  0.877975   \n",
       "\n",
       "                     ZIPCODE ASSESSMENT_NBHD ASSESSMENT_SUBNBHD CENSUS_TRACT  \\\n",
       "HEAT                     NaN             NaN                NaN          NaN   \n",
       "AC                       NaN             NaN                NaN          NaN   \n",
       "QUALIFIED                NaN             NaN                NaN          NaN   \n",
       "STYLE                    NaN             NaN                NaN          NaN   \n",
       "STRUCT                   NaN             NaN                NaN          NaN   \n",
       "GRADE                    NaN             NaN                NaN          NaN   \n",
       "CNDTN                    NaN             NaN                NaN          NaN   \n",
       "EXTWALL                  NaN             NaN                NaN          NaN   \n",
       "ROOF                     NaN             NaN                NaN          NaN   \n",
       "INTWALL                  NaN             NaN                NaN          NaN   \n",
       "ZIPCODE                    1             NaN                NaN          NaN   \n",
       "ASSESSMENT_NBHD     0.981242               1                NaN          NaN   \n",
       "ASSESSMENT_SUBNBHD  0.992245        0.999513                  1          NaN   \n",
       "CENSUS_TRACT        0.995494         0.99798           0.997531            1   \n",
       "CENSUS_BLOCK        0.999329        0.999962           0.999959            1   \n",
       "WARD                0.977029        0.989771           0.997949     0.998611   \n",
       "SQUARE              0.999028        0.999999           0.999991     0.999968   \n",
       "QUADRANT            0.951575         0.92201           0.990963     0.999995   \n",
       "PRICE               0.909185        0.939235           0.981179     0.984665   \n",
       "\n",
       "                   CENSUS_BLOCK      WARD    SQUARE QUADRANT PRICE  \n",
       "HEAT                        NaN       NaN       NaN      NaN   NaN  \n",
       "AC                          NaN       NaN       NaN      NaN   NaN  \n",
       "QUALIFIED                   NaN       NaN       NaN      NaN   NaN  \n",
       "STYLE                       NaN       NaN       NaN      NaN   NaN  \n",
       "STRUCT                      NaN       NaN       NaN      NaN   NaN  \n",
       "GRADE                       NaN       NaN       NaN      NaN   NaN  \n",
       "CNDTN                       NaN       NaN       NaN      NaN   NaN  \n",
       "EXTWALL                     NaN       NaN       NaN      NaN   NaN  \n",
       "ROOF                        NaN       NaN       NaN      NaN   NaN  \n",
       "INTWALL                     NaN       NaN       NaN      NaN   NaN  \n",
       "ZIPCODE                     NaN       NaN       NaN      NaN   NaN  \n",
       "ASSESSMENT_NBHD             NaN       NaN       NaN      NaN   NaN  \n",
       "ASSESSMENT_SUBNBHD          NaN       NaN       NaN      NaN   NaN  \n",
       "CENSUS_TRACT                NaN       NaN       NaN      NaN   NaN  \n",
       "CENSUS_BLOCK                  1       NaN       NaN      NaN   NaN  \n",
       "WARD                          1         1       NaN      NaN   NaN  \n",
       "SQUARE                 0.999987  0.999942         1      NaN   NaN  \n",
       "QUADRANT               0.999996  0.859432         1        1   NaN  \n",
       "PRICE                  0.999136  0.844697  0.998963   0.7385     1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat = paired_contincency(data[categorical_cols])\n",
    "df_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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44YUX6NChA4GBgYSEhNC+fXtWrFhBWFgY9+7do2HDhrz//vtlXlOCbw1Q2C2s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      "text/plain": [
       "<matplotlib.figure.Figure at 0x296ab8a22b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(df_cat.astype(float), cmap = \"viridis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BATHRM</th>\n",
       "      <th>HF_BATHRM</th>\n",
       "      <th>NUM_UNITS</th>\n",
       "      <th>ROOMS</th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>STORIES</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>SALE_NUM</th>\n",
       "      <th>GBA</th>\n",
       "      <th>KITCHENS</th>\n",
       "      <th>FIREPLACES</th>\n",
       "      <th>LANDAREA</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BATHRM</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HF_BATHRM</th>\n",
       "      <td>0.108451</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NUM_UNITS</th>\n",
       "      <td>0.412446</td>\n",
       "      <td>-0.173995</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROOMS</th>\n",
       "      <td>0.63592</td>\n",
       "      <td>0.0852035</td>\n",
       "      <td>0.566168</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEDRM</th>\n",
       "      <td>0.62785</td>\n",
       "      <td>0.118604</td>\n",
       "      <td>0.367978</td>\n",
       "      <td>0.678659</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YR_RMDL</th>\n",
       "      <td>0.180149</td>\n",
       "      <td>0.0770022</td>\n",
       "      <td>0.0346291</td>\n",
       "      <td>0.0458601</td>\n",
       "      <td>0.0883877</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORIES</th>\n",
       "      <td>0.0262276</td>\n",
       "      <td>0.0325544</td>\n",
       "      <td>0.00886321</td>\n",
       "      <td>0.0236264</td>\n",
       "      <td>0.0240735</td>\n",
       "      <td>0.00848088</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRICE</th>\n",
       "      <td>0.493812</td>\n",
       "      <td>0.277297</td>\n",
       "      <td>0.00718951</td>\n",
       "      <td>0.318091</td>\n",
       "      <td>0.348007</td>\n",
       "      <td>0.207572</td>\n",
       "      <td>0.0456391</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALE_NUM</th>\n",
       "      <td>0.0704513</td>\n",
       "      <td>0.00782113</td>\n",
       "      <td>-0.00851176</td>\n",
       "      <td>-0.00605563</td>\n",
       "      <td>0.00684011</td>\n",
       "      <td>0.154843</td>\n",
       "      <td>0.010226</td>\n",
       "      <td>0.196723</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBA</th>\n",
       "      <td>0.688622</td>\n",
       "      <td>0.240594</td>\n",
       "      <td>0.32641</td>\n",
       "      <td>0.676385</td>\n",
       "      <td>0.641371</td>\n",
       "      <td>0.0647707</td>\n",
       "      <td>0.0407315</td>\n",
       "      <td>0.58814</td>\n",
       "      <td>-0.0491853</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KITCHENS</th>\n",
       "      <td>0.42257</td>\n",
       "      <td>-0.151578</td>\n",
       "      <td>0.912394</td>\n",
       "      <td>0.550888</td>\n",
       "      <td>0.373511</td>\n",
       "      <td>0.054662</td>\n",
       "      <td>0.01042</td>\n",
       "      <td>0.0470724</td>\n",
       "      <td>0.00562513</td>\n",
       "      <td>0.330376</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIREPLACES</th>\n",
       "      <td>0.338055</td>\n",
       "      <td>0.251967</td>\n",
       "      <td>-0.0288448</td>\n",
       "      <td>0.23428</td>\n",
       "      <td>0.249016</td>\n",
       "      <td>0.142563</td>\n",
       "      <td>0.0283609</td>\n",
       "      <td>0.506515</td>\n",
       "      <td>-0.0338708</td>\n",
       "      <td>0.478448</td>\n",
       "      <td>-0.00633004</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA</th>\n",
       "      <td>0.280118</td>\n",
       "      <td>0.133005</td>\n",
       "      <td>-0.0403307</td>\n",
       "      <td>0.268252</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>-0.0329886</td>\n",
       "      <td>-0.0141883</td>\n",
       "      <td>0.314802</td>\n",
       "      <td>-0.051157</td>\n",
       "      <td>0.450779</td>\n",
       "      <td>-0.0266986</td>\n",
       "      <td>0.279383</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LATITUDE</th>\n",
       "      <td>0.137453</td>\n",
       "      <td>0.0881356</td>\n",
       "      <td>-0.0951178</td>\n",
       "      <td>0.0944301</td>\n",
       "      <td>0.167919</td>\n",
       "      <td>0.0503195</td>\n",
       "      <td>-0.000139553</td>\n",
       "      <td>0.18551</td>\n",
       "      <td>-0.0167296</td>\n",
       "      <td>0.188002</td>\n",
       "      <td>-0.0767503</td>\n",
       "      <td>0.204107</td>\n",
       "      <td>0.17676</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LONGITUDE</th>\n",
       "      <td>-0.308645</td>\n",
       "      <td>-0.203552</td>\n",
       "      <td>0.0486786</td>\n",
       "      <td>-0.206588</td>\n",
       "      <td>-0.237405</td>\n",
       "      <td>-0.091954</td>\n",
       "      <td>-0.0238297</td>\n",
       "      <td>-0.460447</td>\n",
       "      <td>0.0447031</td>\n",
       "      <td>-0.406013</td>\n",
       "      <td>0.0258419</td>\n",
       "      <td>-0.435182</td>\n",
       "      <td>-0.216953</td>\n",
       "      <td>-0.566088</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>-0.308671</td>\n",
       "      <td>-0.203539</td>\n",
       "      <td>0.0486506</td>\n",
       "      <td>-0.206529</td>\n",
       "      <td>-0.237421</td>\n",
       "      <td>-0.0920916</td>\n",
       "      <td>-0.0239018</td>\n",
       "      <td>-0.460436</td>\n",
       "      <td>0.0446572</td>\n",
       "      <td>-0.405941</td>\n",
       "      <td>0.0258054</td>\n",
       "      <td>-0.435326</td>\n",
       "      <td>-0.21725</td>\n",
       "      <td>-0.566246</td>\n",
       "      <td>0.999885</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0.137605</td>\n",
       "      <td>0.0881503</td>\n",
       "      <td>-0.0951824</td>\n",
       "      <td>0.0944011</td>\n",
       "      <td>0.168065</td>\n",
       "      <td>0.0501034</td>\n",
       "      <td>-0.000110716</td>\n",
       "      <td>0.185626</td>\n",
       "      <td>-0.0168109</td>\n",
       "      <td>0.188162</td>\n",
       "      <td>-0.0768129</td>\n",
       "      <td>0.204327</td>\n",
       "      <td>0.176789</td>\n",
       "      <td>0.99991</td>\n",
       "      <td>-0.566166</td>\n",
       "      <td>-0.566329</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BATHRM   HF_BATHRM   NUM_UNITS       ROOMS       BEDRM  \\\n",
       "BATHRM              1         NaN         NaN         NaN         NaN   \n",
       "HF_BATHRM    0.108451           1         NaN         NaN         NaN   \n",
       "NUM_UNITS    0.412446   -0.173995           1         NaN         NaN   \n",
       "ROOMS         0.63592   0.0852035    0.566168           1         NaN   \n",
       "BEDRM         0.62785    0.118604    0.367978    0.678659           1   \n",
       "YR_RMDL      0.180149   0.0770022   0.0346291   0.0458601   0.0883877   \n",
       "STORIES     0.0262276   0.0325544  0.00886321   0.0236264   0.0240735   \n",
       "PRICE        0.493812    0.277297  0.00718951    0.318091    0.348007   \n",
       "SALE_NUM    0.0704513  0.00782113 -0.00851176 -0.00605563  0.00684011   \n",
       "GBA          0.688622    0.240594     0.32641    0.676385    0.641371   \n",
       "KITCHENS      0.42257   -0.151578    0.912394    0.550888    0.373511   \n",
       "FIREPLACES   0.338055    0.251967  -0.0288448     0.23428    0.249016   \n",
       "LANDAREA     0.280118    0.133005  -0.0403307    0.268252    0.276777   \n",
       "LATITUDE     0.137453   0.0881356  -0.0951178   0.0944301    0.167919   \n",
       "LONGITUDE   -0.308645   -0.203552   0.0486786   -0.206588   -0.237405   \n",
       "X           -0.308671   -0.203539   0.0486506   -0.206529   -0.237421   \n",
       "Y            0.137605   0.0881503  -0.0951824   0.0944011    0.168065   \n",
       "\n",
       "               YR_RMDL      STORIES      PRICE    SALE_NUM       GBA  \\\n",
       "BATHRM             NaN          NaN        NaN         NaN       NaN   \n",
       "HF_BATHRM          NaN          NaN        NaN         NaN       NaN   \n",
       "NUM_UNITS          NaN          NaN        NaN         NaN       NaN   \n",
       "ROOMS              NaN          NaN        NaN         NaN       NaN   \n",
       "BEDRM              NaN          NaN        NaN         NaN       NaN   \n",
       "YR_RMDL              1          NaN        NaN         NaN       NaN   \n",
       "STORIES     0.00848088            1        NaN         NaN       NaN   \n",
       "PRICE         0.207572    0.0456391          1         NaN       NaN   \n",
       "SALE_NUM      0.154843     0.010226   0.196723           1       NaN   \n",
       "GBA          0.0647707    0.0407315    0.58814  -0.0491853         1   \n",
       "KITCHENS      0.054662      0.01042  0.0470724  0.00562513  0.330376   \n",
       "FIREPLACES    0.142563    0.0283609   0.506515  -0.0338708  0.478448   \n",
       "LANDAREA    -0.0329886   -0.0141883   0.314802   -0.051157  0.450779   \n",
       "LATITUDE     0.0503195 -0.000139553    0.18551  -0.0167296  0.188002   \n",
       "LONGITUDE    -0.091954   -0.0238297  -0.460447   0.0447031 -0.406013   \n",
       "X           -0.0920916   -0.0239018  -0.460436   0.0446572 -0.405941   \n",
       "Y            0.0501034 -0.000110716   0.185626  -0.0168109  0.188162   \n",
       "\n",
       "              KITCHENS FIREPLACES  LANDAREA  LATITUDE LONGITUDE         X    Y  \n",
       "BATHRM             NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "HF_BATHRM          NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "NUM_UNITS          NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "ROOMS              NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "BEDRM              NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "YR_RMDL            NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "STORIES            NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "PRICE              NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "SALE_NUM           NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "GBA                NaN        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "KITCHENS             1        NaN       NaN       NaN       NaN       NaN  NaN  \n",
       "FIREPLACES -0.00633004          1       NaN       NaN       NaN       NaN  NaN  \n",
       "LANDAREA    -0.0266986   0.279383         1       NaN       NaN       NaN  NaN  \n",
       "LATITUDE    -0.0767503   0.204107   0.17676         1       NaN       NaN  NaN  \n",
       "LONGITUDE    0.0258419  -0.435182 -0.216953 -0.566088         1       NaN  NaN  \n",
       "X            0.0258054  -0.435326  -0.21725 -0.566246  0.999885         1  NaN  \n",
       "Y           -0.0768129   0.204327  0.176789   0.99991 -0.566166 -0.566329    1  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num = paired_corr(data[numeric_cols])\n",
    "df_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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73//i4cOH8PPzg7a2Nn777TccOXIEoaGhCA4OZpKaXrx4EcuXL8eKFStw7NgxHD58GFVV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\ngUAgEAzK/wAq1PVCeYEDpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29696f0fa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(df_num.astype(float), cmap = \"viridis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BATHRM</th>\n",
       "      <th>HF_BATHRM</th>\n",
       "      <th>NUM_UNITS</th>\n",
       "      <th>ROOMS</th>\n",
       "      <th>BEDRM</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>STORIES</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>SALE_NUM</th>\n",
       "      <th>GBA</th>\n",
       "      <th>KITCHENS</th>\n",
       "      <th>FIREPLACES</th>\n",
       "      <th>LANDAREA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BATHRM</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HF_BATHRM</th>\n",
       "      <td>0.324846</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NUM_UNITS</th>\n",
       "      <td>0.590928</td>\n",
       "      <td>0.236189</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROOMS</th>\n",
       "      <td>0.764594</td>\n",
       "      <td>0.776381</td>\n",
       "      <td>0.683309</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEDRM</th>\n",
       "      <td>0.784384</td>\n",
       "      <td>0.25686</td>\n",
       "      <td>0.506675</td>\n",
       "      <td>0.858006</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YR_RMDL</th>\n",
       "      <td>0.303186</td>\n",
       "      <td>0.177123</td>\n",
       "      <td>0.196704</td>\n",
       "      <td>0.34473</td>\n",
       "      <td>0.298297</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORIES</th>\n",
       "      <td>0.399567</td>\n",
       "      <td>0.268762</td>\n",
       "      <td>0.251353</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>0.422548</td>\n",
       "      <td>0.233363</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRICE</th>\n",
       "      <td>0.919275</td>\n",
       "      <td>0.846176</td>\n",
       "      <td>0.854297</td>\n",
       "      <td>0.943431</td>\n",
       "      <td>0.888328</td>\n",
       "      <td>0.968125</td>\n",
       "      <td>0.94196</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALE_NUM</th>\n",
       "      <td>0.134721</td>\n",
       "      <td>0.0711719</td>\n",
       "      <td>0.0560062</td>\n",
       "      <td>0.0970068</td>\n",
       "      <td>0.0874508</td>\n",
       "      <td>0.427701</td>\n",
       "      <td>0.0936957</td>\n",
       "      <td>0.830918</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBA</th>\n",
       "      <td>0.965834</td>\n",
       "      <td>0.905432</td>\n",
       "      <td>0.868417</td>\n",
       "      <td>0.975461</td>\n",
       "      <td>0.959505</td>\n",
       "      <td>0.951681</td>\n",
       "      <td>0.940627</td>\n",
       "      <td>0.99941</td>\n",
       "      <td>0.665568</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KITCHENS</th>\n",
       "      <td>0.603866</td>\n",
       "      <td>0.23277</td>\n",
       "      <td>0.958996</td>\n",
       "      <td>0.677057</td>\n",
       "      <td>0.525476</td>\n",
       "      <td>0.210391</td>\n",
       "      <td>0.238121</td>\n",
       "      <td>0.825334</td>\n",
       "      <td>0.0505054</td>\n",
       "      <td>0.866338</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIREPLACES</th>\n",
       "      <td>0.56348</td>\n",
       "      <td>0.307168</td>\n",
       "      <td>0.204481</td>\n",
       "      <td>0.665919</td>\n",
       "      <td>0.379001</td>\n",
       "      <td>0.314146</td>\n",
       "      <td>0.466555</td>\n",
       "      <td>0.943109</td>\n",
       "      <td>0.0584913</td>\n",
       "      <td>0.973791</td>\n",
       "      <td>0.215145</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA</th>\n",
       "      <td>0.950548</td>\n",
       "      <td>0.839432</td>\n",
       "      <td>0.835578</td>\n",
       "      <td>0.967603</td>\n",
       "      <td>0.957997</td>\n",
       "      <td>0.975407</td>\n",
       "      <td>0.95055</td>\n",
       "      <td>0.999672</td>\n",
       "      <td>0.812403</td>\n",
       "      <td>0.999703</td>\n",
       "      <td>0.814797</td>\n",
       "      <td>0.95285</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              BATHRM  HF_BATHRM  NUM_UNITS      ROOMS      BEDRM   YR_RMDL  \\\n",
       "BATHRM             1        NaN        NaN        NaN        NaN       NaN   \n",
       "HF_BATHRM   0.324846          1        NaN        NaN        NaN       NaN   \n",
       "NUM_UNITS   0.590928   0.236189          1        NaN        NaN       NaN   \n",
       "ROOMS       0.764594   0.776381   0.683309          1        NaN       NaN   \n",
       "BEDRM       0.784384    0.25686   0.506675   0.858006          1       NaN   \n",
       "YR_RMDL     0.303186   0.177123   0.196704    0.34473   0.298297         1   \n",
       "STORIES     0.399567   0.268762   0.251353   0.430584   0.422548  0.233363   \n",
       "PRICE       0.919275   0.846176   0.854297   0.943431   0.888328  0.968125   \n",
       "SALE_NUM    0.134721  0.0711719  0.0560062  0.0970068  0.0874508  0.427701   \n",
       "GBA         0.965834   0.905432   0.868417   0.975461   0.959505  0.951681   \n",
       "KITCHENS    0.603866    0.23277   0.958996   0.677057   0.525476  0.210391   \n",
       "FIREPLACES   0.56348   0.307168   0.204481   0.665919   0.379001  0.314146   \n",
       "LANDAREA    0.950548   0.839432   0.835578   0.967603   0.957997  0.975407   \n",
       "\n",
       "              STORIES     PRICE   SALE_NUM       GBA  KITCHENS FIREPLACES  \\\n",
       "BATHRM            NaN       NaN        NaN       NaN       NaN        NaN   \n",
       "HF_BATHRM         NaN       NaN        NaN       NaN       NaN        NaN   \n",
       "NUM_UNITS         NaN       NaN        NaN       NaN       NaN        NaN   \n",
       "ROOMS             NaN       NaN        NaN       NaN       NaN        NaN   \n",
       "BEDRM             NaN       NaN        NaN       NaN       NaN        NaN   \n",
       "YR_RMDL           NaN       NaN        NaN       NaN       NaN        NaN   \n",
       "STORIES             1       NaN        NaN       NaN       NaN        NaN   \n",
       "PRICE         0.94196         1        NaN       NaN       NaN        NaN   \n",
       "SALE_NUM    0.0936957  0.830918          1       NaN       NaN        NaN   \n",
       "GBA          0.940627   0.99941   0.665568         1       NaN        NaN   \n",
       "KITCHENS     0.238121  0.825334  0.0505054  0.866338         1        NaN   \n",
       "FIREPLACES   0.466555  0.943109  0.0584913  0.973791  0.215145          1   \n",
       "LANDAREA      0.95055  0.999672   0.812403  0.999703  0.814797    0.95285   \n",
       "\n",
       "           LANDAREA  \n",
       "BATHRM          NaN  \n",
       "HF_BATHRM       NaN  \n",
       "NUM_UNITS       NaN  \n",
       "ROOMS           NaN  \n",
       "BEDRM           NaN  \n",
       "YR_RMDL         NaN  \n",
       "STORIES         NaN  \n",
       "PRICE           NaN  \n",
       "SALE_NUM        NaN  \n",
       "GBA             NaN  \n",
       "KITCHENS        NaN  \n",
       "FIREPLACES      NaN  \n",
       "LANDAREA          1  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = paired_contincency(data[numeric_cols])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qovi4Lt5pb2+P2NhYFBUVoXnz5jh58iRWrFjBrjcxMUFeXh4ePXqEzp0749dff4WVlVWj\ncRUq4WhqagrdhKmsBplz+0tfY1UFp/EAwO4Gt0OEl3od5Hx+HYB6TYSIg+taakZGRpg5cybGjx8P\nPp+PkSNHokePHggMDERISAisrKwQGRmJ7777DgzD4LPPPkNERESjcWlITQYCLn/DaTwNFe4Tzo3n\n7TmNd6nXQU7jVaOEQ0jjgq75i7Xdpj57ZNyShilUD4cQQojklKW0DSUcQghRcpJWEWgqlHAIIUTJ\nVdKMn4QQQuShQoKyNU1JoRJObm4uXFxc2MoB1aUTvL29ERISgjdv3mDt2rU4f/48mjdvDm1tbUyf\nPl2oksG5c+cQExOD0tJSqKiooH///pg5cyaaN2+O3NxcODk5YfTo0Vi+fDm7z927d+Ht7Y3IyEj4\n+Pjg3r17iIiIwMuXL1FZWYlevXph4cKFaNGihdzfE0IIaYyyDKkp3JkmQ0NDpKSkICUlBUePHsW+\nffuwfft2PHz4EEFBQWjWrBl++eUXHDlyBGFhYZg7dy47wdrFixexZMkSLF26FMePH8fhw4dRUVGB\nqVOnsuVw9PT0cO7cOVRWVrKvWXs+npkzZ2LmzJk4cuQIjh49CjU1NWzYsEG+bwQhhIhJ3NI2TU3h\nEk5t1fPT3L59G0+fPsWCBQugrq4OAOjevTumTJmC+Ph4AEB8fDyCg4PxxRdfAKiq/LxgwQL8+eef\nuHbtGgBAS0sLFhYWuHLlCvsaFy5cgL29Pfv8+fPnePfuHQBARUUFwcHBYle0JoQQeRMwKmI9mppC\nDakBVUXjvLy88P79exQXF8PKygpxcXG4f/8+LC0tRaYo+M9//oPo6GgAwK1bt7BkyRKh9c2aNUPv\n3r1x69YttGnTBkBVHbfU1FTY2dkhKysLZmZmqHk70oIFCzBlyhQYGhrC1tYWTk5OGDx4sGwPnBBC\npKQIvRdxNH3Kq6V6SO3YsWPw8vICwzDo378/eDye0DBYNT6fzyYhHo+HigrRmyTLy8uFEpWjoyPO\nnj0LgUBQ53w8Pj4+OH/+PObOnQs1NTWEhoayNYUIIUTRyGICNllQuIRTTUVFBfPmzUN+fj62bduG\nnj174vbt2+Dz+ULb3bhxA5aWlgCAHj16iBSPKy8vx507d9htgKphNXNzc1y7dg2XLl0SGk77+++/\nsXHjRmhra8PZ2RlLlizBvn37pJ5BlBBCZK1CoCLWo6k1fQsaoKamhnnz5iE+Ph4mJibo2rUrIiIi\n2KRz+/Zt/Pjjj5g6dSoAYPr06fjxxx+RnZ0NoKr3Ex4ejs6dO6NPnz5CsV1dXREdHQ1LS0uh6Q1a\nt26N3bt34+LFi+yyu3fvwsLCQtaHSwghUlGWiwYU7hxObQ4ODujduzc2bNiAuLg4rF+/Hu7u7lBV\nVYWuri7WrFkDW1tbAICNjQ1WrVqFlStX4tWrV6ioqICDgwPi4+NFzv18+eWXWLhwIWbMmCG0XEdH\nB1u2bMGaNWsQFhaGZs2awdTUFOvWrZPbMRNCiCQUIZmIg4p3ygAV7+QOFe8kpHGuZ2c0vhGA4w5N\ne3uHwvdwCCGENExZejiUcAghRMlRwvmEaam95zTenYU9OI0HAC+cuP3ot3c24jQeAEzUyed8Ujca\noiMfI0W4Ak0clHAIIUTJMdTDIYQQIg+KcFOnOCjhEEKIkqNzOIQQQuSChtSklJmZiaCgIHTs2BEM\nw4DP58PPzw8BAQHw9/dHXl6e0Lw0+vr62LZtG2JjY5GQkAB9fX0wDAOBQAA/Pz+MGzcOAITWA1Ul\nb9TU1LB06VL06dMHsbGxiIuLQ0JCAnr37s3GX7lyJXbv3o379+/L940ghBAxVdJFA9KztLTEnj17\nAAClpaUYPnw4+vfvDwAIDw9nKwvU5ufnh+nTpwMAioqKEBAQAA0NDYwaNUpkPQDs3LkTUVFRbJ20\nNm3aIDU1lU04DMMITWNACCGKSFmG1BQ+Lb5//x6qqqpo2bKlRPu1bt0aU6ZMwc8//1zneoFAgLy8\nPOjq6rLLnJyc8Ouvv7LPr169il69eknXcEIIkROGEe/R1BSyh3P79m14eXlBIBDg8ePHcHV1haGh\nIQAgLCxMaEjNxcUFU6ZMqTNOt27d8OjRI/Z5QkIC0tLSUFJSAoFAgMGDByMiIoJd36pVKxgbGyMr\nKws9evTAsWPH4Obmhn379snoSAkh5MPRVWofoPaQ2qRJk7BlyxYADQ+p1cbj8aCpqck+rx5SKyws\nREBAAHr16sUmsmrVk7N98cUXuH79OhYtWsTRURFCiGwoy0UDCj+kpq2tDVdXV/z+++8S73v//n10\n6dJFZLmBgQHCw8OxfPly5OTkCK0bMmQIfv31V1y+fBk2NjZQUVH4t4gQ8olTlukJFP7btLKyEpcv\nX0b37t0l2q+goACbNm3C2LFj61xvbW2NwYMHY82aNULLW7Vqhfbt22PDhg1wc3OTut2EECIvAgFP\nrEdTU8ghtepzONVTRpuZmSEwMBBXr14VOYcDgB1+qz5Hw+PxwDAMRo8ejeHDh9f7OrNmzYKbmxuu\nXr0qtNzFxQUbN24UujyaEEIUlbIMqdF8ODIw9fdxnMaTRfHOHI6Ldy7wPMxpPKCqeCfXqHgn+Rh9\nkbJUrO2yvcTbTlYUsodDCCFEfMrSbaCEQwghSk5ZhtQo4RBCiJJThCvQxEEJRwZurOL2YgOdZ684\njQcAXfZz+9FvujuC03gAsKOE23GCc7GbOZ/QDaDzQqTpUQ+HEEKIfNA5HEIIIfJAPRxCCCFyQVep\nEUIIkQtl6eEofGkbQgghDWMEPLEekjh69Cjc3NwwdOhQ7N27V2T9o0eP4O/vD09PT3zzzTd49arx\ni5tk2sNZvnw5nj9/jpiYGHbZ+fPnsWTJEujq6uL169dsmZrS0lIYGxtj7dq17KycdQkNDcWlS5fY\neWzevn0LPT09REZGokuXLggNDUVycjLOnDkDIyMjdr+pU6fi3r17SE9PR1JSEqKiotC2bVswDIPy\n8nK4u7tjypQpUFVVRWxsLAAITdZGCCEKi+Mhtfz8fKxfvx5JSUlQV1eHn58fbG1t0bVr16qXYxhM\nmTIFCxcuhIODA9auXYstW7Zg7ty5DcaVaQ9n9uzZuH37NjupWVlZGZYuXYqIiAhoaWkhPDwcKSkp\nSElJwalTp6CtrY0dO3Y0GjckJITd7+TJk+jZsyebJADAyMgIJ0+eZJ+Xlpbizp07QjEcHR2RkpKC\nI0eOICkpCVevXhWKQQghyoJheGI9SkpKkJubK/IoKSkRipeRkQE7Ozvo6emhRYsWGDZsGE6cOMGu\nz87ORosWLeDg4AAACAoKqrdQck0yTTjVSWXFihUoKytDTEwMHB0d65zPpqysDMXFxUIzcIqjvLwc\nhYWFQvsNHToUqamp7PO0tDQMHjy43hgtWrTArFmzsG/fPlBpOUKI0mHEe+zatQtOTk4ij127dgmF\nKygogIGBAfvc0NAQ+fn/1jZ8/Pgx9PX18f3332PEiBFYsmSJSFHlusj8ogF7e3sMGDAACxYswKNH\nj3DgwAF2XVhYGJo3b46ioiLo6urCzc0NEyZMaDRmTEwMdu7ciZcvX0JDQwNDhgzBtGnT2PUWFhY4\nf/48nj9/Dn19fRw/fhwTJ07E2bNn6435+eef4+XLlygqKvqg4yWEEHkT96KBgIAAjBghepO2jo6O\n0HOBQAAe79+YDMMIPa+oqMDly5fxf//3f7CyssIPP/yAqKgoREVFNfj6crloIDQ0FBcuXEBYWJjQ\nDJzh4eE4cuQIYmJi8OrVKzg7O0NdXb3ReCEhIThy5Ah27twJPp+PgQMHQltbW2iboUOH4uTJk3j1\n6hVKS0vRvn37BmNWv5kaGhpSHCEhhDQhMXs4Ojo66NChg8ijdsJp06YNCgsL2eeFhYVCsyMbGBjA\nxMQEVlZWAAB3d3dkZWU12ky5JBxtbW3o6OjU+6VvbW0Nf39/zJ49GxUVFWLH7dy5M+bMmYN58+bh\n9evXQuuqp4o+deoUnJ2dG411//59tGnTRiRxEUKIwmN44j3EZG9vj4sXL6KoqAhv377FyZMn2fM1\nANC7d28UFRXh3r17AID09HR88cUXjcZVmMuiv/76a7x58waJiYkS7efu7o727dsjPj5eaLm5uTme\nP3+O/fv3w8XFpcEYr1+/xoYNG8Q66UUIIQpHzB6OuIyMjDBz5kyMHz8e3t7ecHd3R48ePRAYGIhb\nt25BU1MTGzduRFhYGIYPH47MzEyEhoY2GldhbvxUV1fHd999h4iICHh6eqJly5Zi7ztv3jxMmDAB\nX331ldByZ2dnXL58GW3atEFubq7QuvT0dHZW0crKSgwdOhSBgYHs+s2bN2P79u3s82XLlsHT01PK\noyOEEBmSwY2fHh4e8PDwEFq2detW9t89e/bEwYMHJYpJM37KgP3oaE7j6dznvlq0QJ3b3xpFVtwP\nRWrIoFq0LFC1aNLUTLavFmu7fybOk3FLGqYwPZyaZs+ejT///FNkuaOjI2bMmNEELSKEEAWmJN0G\nhUw40dHc9hDkrbQ9t6fGmpWJP7woLh5fwGm8t4bcd+nf6nMb02r9FLS58o7TmKd+3kFz7JAmx5Ow\nbE1TUciEQ4gscJ1sCFEY1MMhhBAiF0pSLZoSDiGEKDvq4RBCCJELSjiEEELkghJO/U6cOIEtW7ag\noqICDMPAy8sLZmZmWLt2LYB/K5G2aNECHTp0wMaNG8Hn8xEXF4fjx49DQ0MDGhoamDhxItzc3AAA\nsbGxSEhIYOfSKS8vh5qaGpYuXYo+ffogKSkJly9fRlRUlMi21TZt2oS2bdti79692L9/P1uw7uuv\nv4a3t7d83yRCCBETXaVWj/z8fKxatQpJSUlo1aoV3rx5A39/f5iamiIlJQUA4O/vj+DgYKFpDBYt\nWoT3798jKSkJ2trayMnJQWBgIMrLy9lk4OfnJzRp2s6dOxEVFSVUobpa7W2r3bx5EwcOHEBiYiI0\nNTXx4sUL+Pr6wtzcHObm5ly/HYQQ8uGUpIcj91pqxcXF4PP5ePeu6hJVLS0tREVFsTPJ1SUnJwep\nqalYuXIlW1zT2NgYCxYsQFxcXJ37CAQC5OXlSTy/TmFhIRiGwdu3bwEAn332GWJiYtCqVSuJ4hBC\nCBEm9x6Oubk5nJycMGTIEFhYWMDW1hYeHh4wMTGpd5/bt2+jS5cuIhP82NjYICcnBy9fvgQAJCQk\nIC0tDSUlJRAIBBg8eDAiIiLqjFm9bbXqoTsHBwckJSVh4MCB6NWrF2xtbeHl5SU0XTUhhCgSHvVw\n6rds2TKkp6djzJgxePr0Kf773/8KTQldW3WBzdqqpzKonsvGz88PKSkp2L9/P7S0tNCrVy+hORxq\nqt62+rFx40YAVUVE4+Pj8csvv8DV1RXZ2dnw9PTEjRs3PvSwCSFENjienkBW5J5wTp8+jWPHjsHI\nyAi+vr5Yv349wsLCGqw62qNHD/z999949Uq4iOX169dhbGwsMmxmYGCA8PBwLF++HDk5ORK1Lzk5\nGRcvXoSJiQnGjh2LTZs2ISAggD2/RAghCofj6QlkRe4JR1NTE9HR0ex0AQzD4O7du7CwsKh3n3bt\n2sHDwwMLFy7EmzdvAFRdyRYZGYng4OA697G2tsbgwYOxZs0aidpXWVmJ6Ohodqrp8vJyPHjwAN27\nd5coDiGEyAtPIN6jqcn9HI6dnR2Cg4MRFBQEPp8PABg4cCCmTZvW4H5LlizB5s2bMXLkSKiqqkJd\nXR0zZsxgL4uuy6xZs+Dm5oarV6+KrKt9DgcA5s+fD19fXxQXF2PMmDFQUanKx8OHD8fIkSMlPVRC\nCJEPBei9iIPmw5GBHrPWcxqv1QPxp90WF9fVogt7q3MaDwDA8S8yWRTvPPXzDs5jAlQtmkimy9p1\nYm33cM4sGbekYVRpgBBClJyyXKVGCYcQQpSdAlyBJg5KODKwdUYMp/GuvDXlNB4ANOOJXmb+IQa3\neMBpPAC4x6/7knZp2c8o5DQeADzgc//T8vNm2pxP6kZDdB83RbggQByUcAghRNnRkBohhBB5oHM4\nhBBC5IMSDiGEELmghEMIIUQeaEhNBnJzc+Hi4oIuXbqAx+OBz+fD0NAQkZGRmDt3LvLy8tiK0qWl\npTA2NsbatWuhr6+P0NBQ9O3bFz4+PgCqaqbt2bMHFRUVEAgEGDVqFMaPHw+gaj6emrEAQF9fH9u2\nbZP/QRNCSGMo4ciGoaGhUCGwyFKWAAAgAElEQVTNqKgorF69GgAQHh7OTtomEAgQEhKCHTt2YO7c\nuUIxEhMTkZCQgM2bN8PQ0BAlJSWYOHEimjdvjlGjRonEIoQQRaYsPZwmmZ6AS7a2tnjwQPQekLKy\nMhQXF9c5AduPP/6IuXPnslMX6OjoYNWqVejWjdt7HwghRC6UpFq00vVwauLz+UhNTUWvXr3w999/\nIywsDM2bN0dRURF0dXXh5uaGCRMmCO1TVFSEZ8+eiVR/7tKli9DzsLAwoSE1FxcXTJkyRWbHQggh\nUlOAZCIOpUs4BQUF8PLyAlA1dUCPHj0we/ZsTJ8+nR0G+/333xESEgJnZ2eoqwsXlayuAK2hodHg\n69CQGiFEWSjLkJrSJZza53DqYm1tDX9/f8yePRuHDx+Gmtq/h6mnpwdjY2Pcvn0b//nPf9jlly9f\nxtmzZzFnzhyZtZ0QQmRCSRKO0p/Dqc/XX3+NN2/eIDExUWTdN998g6ioKBQWVtXWKioqQlRUFExM\nTOTdTEII+WA0AVsTU1dXx3fffYeIiAh4enoKrRszZgwqKiowceJE8Hg8MAyD0aNHs1eoAaLncABg\nz5490NHRkUv7CSFEbErSw6EJ2GQg8x9uqztTtWhu2GtyXy26qFI21aK5RtWiP25fLBBv0sfsyJky\nbknDPtoeDiGEfDKUpNtACYcQQpQdJZxP19LBIziNJ8jjfihIUF7OabzDrcw4jQcA4FdwGu77KVac\nxgMA4y13OI/peD6X03hzWj/kfEI3gIbpFIkiXBAgjo/2KjVCCPlU8BjxHpI4evQo3NzcMHToUOzd\nu7fe7U6fPg1HR0exYlIPhxBClB3HQ2r5+flYv349kpKSoK6uDj8/P9ja2qJr165C2z1//hyrVq0S\nOy71cAghRNmJWUutpKQEubm5Io+SkhKhcBkZGbCzs4Oenh5atGiBYcOG4cSJEyIvGxYWhuDgYLGb\nST0cQghRcjwxt9u1axfi4uJElgcHB2P69Ons84KCAhgYGLDPDQ0NkZWVJbTP7t270b17d/Ts2VPs\ndlLCIYQQZSfmkFpAQABGjBC9qKn2De0CgQA83r9pjGEYoed//PEHTp48iZ07dyIvL0/sZnI2pHbi\nxAn4+PjA09MTHh4e+Omnn4TW+/j4ICgoSGhZbGwsYmNjRWIlJSWhb9++8PLyEnrcvHmzwTaYmZnh\nwIEDQsv8/f2RmZnJrq/N0dGR7VaamZlh8eLFQuvv3r0LMzMzJCUlNfjahBDSVMQtbaOjo4MOHTqI\nPGonnDZt2rClvwCgsLCQnc4FqPq+LywshK+vL7799lsUFBTgq6++arSdnPRw8vPzsWrVKiQlJaFV\nq1Z48+YN/P39YWpqCicnJ9y7dw/q6uq4d+8enj17hrZt2zYa09HREVFRURK3JTo6GgMGDBDrNWrT\n09PDuXPnUFlZCVVVVQDAsWPH0Lp1a4ljEUKI3HB80YC9vT1iY2NRVFSE5s2b4+TJk1ixYgW7PiQk\nBCEhIQCqZmIeP348fv7550bjctLDKS4uBp/Px7t37wAAWlpaiIqKYq9oSEpKQv/+/eHk5IT9+/dz\n8ZL1CggIQFhYmFT7amlpwcLCAleuXGGXXbhwAfb29lw1jxBCOMf1ZdFGRkaYOXMmxo8fD29vb7i7\nu6NHjx4IDAzErVu3pG4nJz0cc3NzODk5YciQIbCwsICtrS08PDxgYmICPp+Po0ePYs+ePXj58iVm\nzpyJadOmCU0ZUJf09HR23hugqhhn7eGyugQGBuLUqVM4cOCAUDFOcbm6uiI1NRV2dnbIysqCmZkZ\nqNwcIUShyeArysPDAx4eHkLLtm7dKrJdhw4dkJ6eLlZMzi4aWLZsGaZOnYrz58/j/Pnz+O9//4u1\na9eCx+PBwMAAXbt2BcMwUFFRwW+//QZnZ+cG40k7pKampoaoqCiMHz8eAwYMEFpX86RXteo2CQQC\n9nV/+OEHCAQCHD9+HK6urjh27JjE7SCEEHn5pCZgO336NMrKyuDm5gZfX1/4+vpi//79OHjwIFRU\nVPDs2TP2TtTS0lIkJCQ0mnA+RLdu3eocWtPV1UVJSYnQCbLi4mLo6Ojg5cuXAKqG1czNzXHt2jVc\nunQJs2fPpoRDCFFsSpJwODmHo6mpiejoaOTmVtWAYhgGd+/eRdu2bZGRkYH//e9/SE9PR3p6OpKT\nk3Hp0iXk5ORw8dL1CgwMRHFxMa5fv84us7Ozw8GDB9nnycnJ+Pzzz6GtLVwO3tXVFdHR0bC0tGx0\n6I8QQpraJzUBm52dHYKDgxEUFAQ+nw8AGDhwINq2bYtBgwbByMiI3dbY2BiOjo5ITEyEhoYGNm/e\njO3bt7Prly1bBkD0HA5QNYunt7e3WG2qHlrz8fFhl4WFhWHp0qVISkoCwzBo27Yt1q1bJ7Lvl19+\niYULF2LGjBnivwmEENJUlKSHQxOwyYCr6SxO4ylDtWjVVrqcxgPAebXonE+4WrQsULVoxdHnW/Em\nYLu2hSZgE9vjx4+Fyi/UFB4eDisr7r9QCCFE4SlJt0GpEk7Hjh2RkpLS1M0ghBCFwlOSgSqlSjjK\nYsf5BE7j3eG35DQeADzht+I0nofWE07jAUAZU8lpvHfMcU7jAcC7adwXXDdvpsVpvN5XR3MaDwCu\n2yRyPqkbDdFJTxEuCBAHJRxCCFF2ytHBoYRDCCHK7pO68ZMQQkgTooRDCCFEHqiH08QqKiqwdetW\nHDlyBDweD5WVlRgxYgQmT56MBQsW4NKlS9DV1YVAIICmpiZWrFgBc3Nzdv8//vgDHh4eiImJwbBh\nw5rwSAghpBGUcJrWsmXL8Pz5cyQmJkJHRwelpaWYNm0aWrasuuIrJCSErUKQlpaGsLAwobI3hw4d\ngouLCxITEynhEEIUGk+gHBmH+2s6FUBeXh6OHDmCqKgotlCntrY2Fi9eDH19fZHtX79+LbS8ekqF\n7777DtnZ2Xj8+LHc2k4IIZLiej4cWfkoE05WVha6dOkCXV3hcitdunRheysxMTHw8vLC0KFDsWjR\nIowdO5bd7syZM2jXrh1MTU0xZMgQJCYmyrX9hBAiEUbMRxP7KBMOIDz3zYkTJ+Dl5QUPDw/4+voC\nqBpSS0lJwcmTJ7F7927MmDGDrWB96NAhuLu7AwDc3NyQlJSEco5rjxFCCFc+qWrRisbS0hIPHz5E\naWkptLW14eLiAhcXF3bu7dqsra3RsWNHZGdno0WLFjh37hyys7Oxe/duMAyDkpISnDp1CsOHD2+C\noyGEkEYoQO9FHB9lD6ddu3bw9PTE/PnzUVJSAqDqqrXTp09DRUX0kJ88eYLc3FyYm5sjJSUFdnZ2\nOHv2LNLT0/Hbb78hKCgICQnclqshhBCu8ASMWI+m9lH2cABg6dKl2LFjB8aPH4/Kykq8efMGtra2\n2Lp1KzZv3oyYmBjs2rULqqqqeP/+PebPn49OnTrh8OHDmDlTuIT32LFj8dNPP+Hhw4fo0qVLEx0R\nIYTUTREuCBAHzYcjA3lP2nEaj4p3cuOdDP7U3zGfbvFOrlHxTukN8Fkr1nbnk+bIuCUN+2h7OIQQ\n8qlQlh4OJRxCCFF2SjJQRQlHBvqdrntWUml13sZpOADAzj0xnMZzujmR03gAUPygNafxuiS+5TQe\nAFRqcv9fyCnmPKfxDD3vcRoPAPbcF72B+kP5g9v5dYBPZ5iOejiEECIm/5bPm7oJSk0R7rERByUc\nQghRdgpwybM4KOEQQoiyU458QwmHEEKUHZ3DIYQQIh+f8lVqmZmZiIuLw549ewAApaWlmDhxIqyt\nrZGdnY3g4GDk5uZi9+7dAICHDx+iY8eOaNasGaytrbFkyRKUlpYiOjoaV65cgaqqKnR0dBAaGoov\nvviCrYmWnp4u9LpmZma4f/8+MjMzERQUhI4dOwqtDw4OhrOzM8zMzBAeHo5Ro0ax6/z9/REcHAxb\nW1tkZmZi3bp1ePv2LSorKzFo0CDMnj0bqqqqsni7CCHkg1AP5/978+YNJk2ahL59+2LOnDnw9/cH\nAPj6+rKVmx0dHbFlyxZ06NABACAQCBAYGAhbW1skJydDTU0Nly5dQmBgIH755RexXtfS0pJNeHWJ\njo7GgAED0LZtW6Hl5eXlmD17Nvbt2wdjY2OUl5cjJCQEe/furbPwJyGENDVFqJMmDpkW7ywrK8O3\n334LOzs7zJkjfkmFzMxMPHv2DCEhIVBTq8qJdnZ2iIyMhEDAzfV/AQEBCAsLE1n+9u1blJaW4u3b\nqns21NXVsXDhQvTt25eT1yWEEM4JxHw0MZn1cN6+fYvJkyfjjz/+wMaNGyXa986dOzA3Nxep7Dxo\n0CAAQG5uLgoKCuDl5VVvjNu3b4us37lzJ1q1qqohFhgYiFOnTuHAgQNCQ2u6urqYPHkyfHx8YGpq\nCltbW7i4uMDGxkaiYyCEEHnhfcrncADg1q1bmDFjBjp37oywsDDExcWJva+Kigo0NDQa3MbQ0BAp\nKSlCy8zMzNh/NzakpqamhqioKIwfPx4DBgwQWjdlyhSMHj0aGRkZuHDhAgIDAzFjxgxMmDBB7GMg\nhBC5UY58I7shtd69e2Pq1KkIDQ3FgwcPJJpPxtLSEnfu3EHtQtbr1q3DpUuXOGtjt27dRIbWbty4\ngb1796J169Zwd3dHZGQk4uLicODAAc5elxBCOMUw4j2amMwSTrNmzQAAzZs3x+rVq7F69Wr8+eef\nYu1rY2ODzz77DHFxcaisrCpRf+7cOSQlJaFr166ctjMwMBDFxcW4fv06gKohtbi4ONy792/9qezs\nbFhYWHD6uoQQwhWagK2Gnj17YsKECZg5cyaaN2/e6PY8Hg/x8fGIjIyEu7s71NTU0KpVK2zZsgX6\n+vrIzc1tNEZd53CGDx+Ob7/9VmhZ9dCaj48PAMDU1BRRUVH4/vvvUVpaCh6Phx49emDx4sUSHDEh\nhMiPLGqpHT16FD/++CMqKioQEBCAsWPHCq1PS0tDbGwsGIZBhw4dEBkZCV1d3YbbSROwcc90bySn\n8ZShWrR3FlWL5grX1aJPWzX+I09S4+5zO+GerIp3firVop37h4u13akLolfm1iU/Px9jxoxBUlIS\n1NXV4efnh3Xr1rEjTKWlpXBxccGhQ4dgZGSEDRs24PXr13Ve+VuTTC+LJoQQIgeMmA8xZWRkwM7O\nDnp6emjRogWGDRuGEydOsOv5fD6WLFkCIyMjAFUXbD179qzRuFTahhBClJy4l0WXlJSgpKREZLmO\njg50dHTY5wUFBTAwMGCfGxoaIisri33eqlUrODs7AwDevXuHLVu2sDf1N4QSjgwwZdy+rWVG3HdE\nvxk5ldN4FQvLOY0HADp/cHvcrztxP7RUbM79ZzNS53dO451RdeA0HgC4az3mNN6wkVM4jQcAqQd3\nQZD3iUzqJmbC2bVrV523qAQHB2P69H8njhQIBODxeDXCM0LPq71+/RrTpk2Dubk5RowY0ejrU8Ih\nhBAlx6sUL+EEBATUmRhq9m4AoE2bNrh69Sr7vLCwEIaGhkLbFBQU4JtvvoGdnR2+//57sV6fEg4h\nhCg7MXs4tYfO6mNvb4/Y2FgUFRWhefPmOHnyJFasWMGur6ysRFBQEFxdXTF1qvijJZRwCCFE2XF8\nsbGRkRFmzpyJ8ePHg8/nY+TIkejRowcCAwMREhKCvLw83LlzB5WVlUhNTQVQdcP+ypUrG4xLCYcQ\nQpSdDO7D8fDwgIeHh9CyrVu3AgCsrKyEbo4XFyUcQghRch9N8c7c3Fy4uLigS5cuQsuHDh2KnJwc\nREVFITY2FgkJCdDX1wdQNaeMmpoali5dij59+oisr7Zp0yY8fvyYnSyNx+Ph3bt36NatGyIiIqCt\nrc1OqlaXiooKDB48GMOGDcOiRYuE1t24cQPr169HcXExBAIBbGxsEBoaCk1NzQbb07ZtW+zduxf7\n9+9nr8z4+uuv4e3t3fi7SQghTeFjSThA3ZWZk5KSkJOTwz738/MTuqxu586diIqKYote1l5f7fHj\nxyKVnUNCQrB582bMnj27wXadOXMGVlZWOH78OObMmcOWzbl37x6Cg4OxceNG9OzZExUVFVixYgUW\nLVqENWvWNNiemzdv4sCBA0hMTISmpiZevHgBX19fmJubw9zcvLG3ihBC5I+jecJkTSaVBgQCAfLy\n8hqtq1Ofvn374sGDB41ul5SUBGdnZ/To0UNoJtBt27bB19cXPXv2BFBVL23u3LkYMmRIozELCwvB\nMAw7Adtnn32GmJgYdh4dQghROB/TBGy1Jzvz8PBA69bCda4SEhKQlpaGkpISCAQCDB48GBERESLr\nq3Xo0KHOidnKysqQnp7e6AybRUVFyMjIQEREBFRVVfF///d/GDlyJADg7t277F2w1bS1tTFs2LBG\n2+Pg4ICkpCQMHDgQvXr1gq2tLby8vNgSDoQQomg+mnM4QP1DajVVD1EVFhYiICAAvXr1ErpRqL4h\nLEC4snNFRQXs7Ozw9ddfN9imI0eOwM7ODrq6unBycsKiRYtw584ddO/eHTwer9EJ3Oprj7q6OuLj\n4/HPP//g/PnzOHfuHLZt24adO3eiV69eDcYkhJAm8TElHEkYGBggPDwc33zzDWxsbGBsbNzoPo3N\nzlmXpKQkFBQUwNHREUDVLKEJCQlYvnw5LC0tcevWLXZKaqCquumcOXMQE9NwleTk5GQYGRmhX79+\nMDExwdixY7F+/XqkpKRQwiGEKCYFmOtGHDI5h2NtbY3BgwezJ+i5dvv2beTl5eH06dNIT09Heno6\nNm/ejKNHj6K0tBQTJkzAvn372GJzfD4fUVFR0NbWhrq6eoOxKysrER0djaKiIgBVV9w9ePAA3bt3\nl8mxEELIBxMIxHs0MZndhzNr1iy4ubmx9XhqnzMBgPnz50NVVbXRWL1792b/3a5dO9ja2sLHxwea\nmprscltbW5iamuLo0aMYM2YM1qxZg5UrV+Lt27fg8/mwt7cXmquhvvb4+vqiuLgYY8aMgYpKVT4e\nPnw4e36IEEIUjpIMqdEEbDLQaSu3Pbu26dx3RFv+XcZpvHwZVIvmneB2AjbNYu5/4cmiWvSRCdz+\n/UzvzH216H3/cDtJnN9/ZVMtWhYUsVq0a+c5Ym13/NFaGbekYVRpgBBClB3T9MNl4qCEQwghyk5J\nBqoo4chAuus6TuOlDLDiNB4AVEJ0MqUPEaB7m9N4APCuN7e/2lryuP9zLxJUcB6zk1pLTuPZ/v6e\n03gA0EqlBafx+m+6zGk8AHhc8ZrzmB3VWnI+qRsnQ3RKcpUaJRxCCFF2CnAFmjgo4RBCiLKjITVC\nCCFyQT0cQgghcqEkPRxObiLIzMyEv79/nev++OMPmJmZsdOQVvP398ecOcLXjsfGxiI2NpZd7+zs\nDC8vL7i7u8PPzw8ZGRki8X18fBAUFCS0LCkpCX379oWXlxe7/9ChQ9kbPWuvr37cvHmz0XYTQojC\nYRjxHk1M5j2cQ4cOwcXFBYmJiULVmgHgxIkTcHFxqXfagPDwcNja2gIAbt26hUmTJmHv3r3o2rUr\ngKp5b9TV1XHv3j08e/YMbdu2Zfd1dHREVFQU+zwtLQ2LFy9mX6v2eknaTQghCkVJrlKTSS21anw+\nH0ePHsV3332H7OxsPH78WGj9lClTsGzZMrx8+bLRWFZWVnB1dWUndAOqeir9+/eHk5MT9u/f3+D+\nT548EXt+nsbaTQghioSprBTr0dRkmnDOnDmDdu3awdTUFEOGDEFiYqLQehsbG7i4uCA8PFyseJ9/\n/jkePXoE4N+k4OrqCldXVxw8eBAVFf/eE5Geng4vLy84OTmhf//+yM7ORnx8vMj66seoUaPEbjch\nhCgUJRlSk2nCOXToENzd3QEAbm5uSEpKQnm5cM2tWbNmISsrS6SQZl14PB5bsPP06dMwMDBA165d\n0adPH6ioqOC3335jt3V0dERKSgpSUlJgYmKCTp06wdTUtM71KSkpQj0ncdpNCCEK41OvFv3ixQuc\nO3cO2dnZ2L17NxiGQUlJCU6dOoXhw4ez2zVv3hwRERGYOXMmhg0b1uCw1/3799GlSxcAVUnh2bNn\n7Hw4paWlSEhIqHOmz1WrVsHDwwP9+vUTqjz9Ie0mhBCFoQC9F3HILOGkpKTAzs4OP/30E7ssNjYW\nCQkJIl/c1UNr+/btw+TJk+uMl5WVhdTUVBw8eBDPnz9HRkYGTp06xU79nJOTAxcXF+Tk5Ijsa2xs\njHHjxmHlypVCPZkPbTchhCgCRgF6L+LgLOFcvXpVqPdgYGCA0NBQoW3Gjh2Ln376CQ8fPhTZf9as\nWThz5ozQsrCwMLRo0YIdSlu/fj06dOiAbdu2YdCgQWyyAaqSiqOjIxITE9G5c2eR+JMnT8bBgwdx\n9OhRAP+ew6np66+/xuHDhzFz5sx6213dwyKEEIVRqRwJh+bDkYFHuW0b30gCKa8/0eKdHJdc/1SL\ndy4p/ILTeACwzCCb03jLn1twGg8AJuhxXxC0I8efDcBN8c5hmmPF2i713d4Pfq0PQZUGCCFEyTFK\nch8OJRxCCFF2NAEbIYQQeVCWHg6dwyGEECIXMr3xkxBCCKlGCYcQQohcUMIhhBAiF5RwCCGEyAUl\nHEIIIXJBCYcQQohcUMIhhBAiF5RwCCGEyAUlHEIIIXJBCYcQQohcUMIhhBAiF1S8U8klJyc3uN7b\n21vimPfv30fr1q1hYGCArKwspKSkwMLCAiNHjpS2mUrrf//7H9zd3SXeT57vYWlpKbS1tSXeTxZ/\nO/X566+/YGpqylm8DyWvz0fRjrupUfFOORk/fnyD63fv3i1VXHNzc3z22Wfo168fmjVrJrI+MjJS\nonjJycmIiYnBhg0boKenB09PT4wfPx5//vknunfvjmnTpkkU7+3bt4iJiYGrqyt69OiBiIgIHDhw\nAN27d8e6deuEZm0Vh7m5OXi8fyePq/nny+PxcPfuXYniNcba2hq///67RPtw/R4CwKRJk9hpzzdv\n3iw0FfuIESNw+PBhiWNy/bdTW0VFBU6ePIl9+/bh9u3buH79usQxBAIBfv75Z/Tt2xfdunXD7t27\nceDAAVhYWGDx4sVSJ1quP5+auDjujxZD5MLT05Pp168fs2zZMubMmTNMZmam0ENad+7cYdauXcuM\nGDGCWbBgAXPu3DmmsrJS6nje3t7MixcvGIZhmNjYWGby5MkMwzDM+/fvGTc3N4njhYWFMUuWLGGe\nP3/OnD59munXrx/z119/Mb/88gszdepUieOFh4cz3t7ezNKlS5nLly8zAoFA4hiS6NWrl8T7cP0e\nMgzDeHl5CcWvb50kuP7bqfb48WNm7dq1TL9+/ZgvvviCiY2NZd8PSa1evZoJCgpicnJymKtXrzLW\n1tbMhQsXmC1btjDz5s2TKqYsPh+G4fa4P1Y0pCYnKSkp+Ouvv3Ds2DHExsaiY8eOcHV1hYODA9TV\n1aWOa2FhAQsLC8yePRu3bt3CsWPHsG7dOlhaWmL48OGwtbWVKJ5AIEDr1q0BAJmZmXBzcwMAqdt4\n48YNHD16FADw66+/wtXVFZ06dUKnTp0QFxcncbyFCxcCAK5evYpjx44hMjISNjY2GD58OHr27ClV\nGxtSszclLq7fw9rtYGoNSkjTRoD7v51Tp04hISEB2dnZcHZ2xpo1a7Bo0SIEBwdL1T4AOHv2LA4f\nPgw1NTXs2rULw4YNg729Pezt7eHq6ipVTK4/H1kc98eKEo4cmZqaYtq0aZg2bRoePHiA48ePY/Pm\nzejSpQuioqI+OL6VlRWsrKxw9epVrF27FkePHpW4O8/j8VBeXo6ysjJcv34dERERAIDi4mJUVlZK\n3CYVlX+vS8nMzMTcuXPZ53w+X+J41WxsbGBjYwOBQIDMzExERkaioKAA6enpEsdqKPFJ00au38O6\n4nONi7+d6dOnw9XVFYmJiTAxMeGkrSoqKlBTq/qaunz5stBQokAg3SyXXH8+sjjujxUlnCZQWVmJ\nvLw85Ofno7i4WOQXq6QYhsGVK1dw4sQJnD17FhYWFvD398eXX34pcaxRo0Zh9OjRAIBBgwbB2NgY\nFy9exPr16/Hf//5X4nh6enrIyspCWVkZCgoKYG9vD6Aq+bRp00bieDXdvn0bqampSEtLQ7t27TB1\n6tQPileXml9w4mroPRw1apRU7Xjz5g2uXr0KgUCAsrIyXLlyhV1XVlYmVUyA27+dI0eOICkpCV99\n9RXat2+P4cOHf3CCbd68OZ4+fYo3b97g4cOH7N/PvXv3pDp/A3D/+cjiuD9WdNGAnPD5fFy4cAEn\nTpzA5cuXYWNjAxcXFwwYMOCDhlqWLFmCc+fOoXv37nB1dYWjoyOaN2/+QW3NyspCYWEhHBwc0KxZ\nMyQnJ0MgEMDHx0fiWPfu3cOsWbPw4sULLFiwAN7e3oiPj8eePXuwefNm9OjRQ6J4N2/exIkTJ5Ce\nno4OHTrA1dUVQ4YMgZ6ensRtq0tRURE0NTXRokWLD4rD5XsIAP7+/g2u37Nnj8QxZfG3A1SdND99\n+jSSkpJw9uxZ2NvbY+zYsRg0aJDEsTIzMzFnzhyUlpYiKCgIkydPxs8//4yNGzciMjISDg4OUrWR\n688HqPu4v/rqKwwePFjqmB8bSjhyYmNjg5YtW2Lo0KF1nrf5z3/+I1Vcc3Nz6OnpsV+Qtbvyv/76\nq0Txal8BJQv//PMPWrdujZYtW0q8r7m5Odq2bQtHR0e0atVKZL004+YMwyA2Nhb79u3Dy5cvAQBt\n2rTB2LFjMWnSJInj5efn13v13cWLF9GvXz+JY8oC1387dSkqKkJycjKSk5Nx5MgRqWKUl5fj3bt3\n0NHRAVD1o0NXVxedOnWSKp48Ph8ujvtjRAlHThr6hcrj8aS+LPrJkycNrm/fvr1E8aS9xLY+XN/r\nERsb2+D4uDQJJy4uDteuXcPMmTPRrVs38Hg83Lt3DzExMbC2tpb4Mtma7+H06dMRGxtb5zpJHTp0\nCJ9//jnbK1y3bh1MTFiwyYUAAB2oSURBVEzg6+srVTyu/3bqip+dnQ1zc3N07NhRqhg1hw7rIs0P\nNa4/n6ysrHp76snJyZzez6Ts6ByOnDQ05FFaWip13Pbt2+PRo0fQ0tIS+tX24sULbNiwAcuXL5c6\nNhdCQ0MbvNdD0v+M06dPr3edpPfLVDt27BiSkpKgqanJLuvZsyd++OEHjB07VuKEU/M3XE5OTr3r\nJLFnzx4cOXIEq1atYpcNGDAAq1atwvv37/HVV19JHLN24lZRUYGOjo7Uw4n3799HWFgY9PX1MW7c\nOISEhKBjx4548uQJ5s+fL1VijImJqbPdN27cAFD1ZS8prj+fJUuWsElq9OjRSExMZNft2rWLEk4N\nlHDkpKioCDt27ICenh4CAgKgpqYGgUCAhIQExMXFISMjQ6q4sbGx2L59OwBg48aNsLe3x08//YT4\n+Hj07t1b4nh3796FhYWFyHKGYaS6sfLw4cM4duwYLly4AHNzc7i5ucHe3l7o6jVJXL9+HVFRUdDV\n1UVERAT09fXx5MkTrF69GqdPn8bNmzcljtmsWTOhZFOtZcuWUFVVlThezS/y2l/q0l69dPDgQezd\nu1foRHnfvn2xdetWTJgwQaqEM27cOPB4PKEv2eLiYnTr1g3r1q2TuIezePFiTJ48Ga9fv8aUKVOw\na9cu9O7dG0+ePEFQUJBUCaf2D7W8vDwsXLgQn3/+udRXdnL9+dR8/96/f1/vOkIJR27mzJkDLS0t\nFBcXo7y8HM7Ozpg1axbevHmDBQsWSB03OTkZqampKCgoQExMDLZv3478/Hxs2LABAwcOlDieubl5\no8NgkuD6Xo8lS5bA19cXeXl52LhxI3r27Inly5fjyy+/xC+//CJVG6VNfvKkoqJS51VZrVu3lrr9\n9V1CnpycjGXLlmHLli0SxXv79i2GDBkCANi0aRP7g6d9+/Z19m4ldfDgQaxbtw4BAQGYNGmSVD8G\nZEEWPzA+VpRw5OTx48dIS0tDaWkp/Pz88PPPP8Pf3x8TJkz4oKvUtLS0YGhoCENDQ2RlZcHb2xub\nN29WmP+MNXFxr0dFRQUCAgLAMAy+/PJLXLlyBdu2bZOqN1ft6dOn9Sb9p0+fShyvsLCQvben5r+r\nn0tDVVUVL168wGeffSa0/Pnz55xfguvt7Y1t27ZJvF/Nv7naw3If8ks/Pz8f33//PV6+fIldu3bh\n888/lzoWIJvPh4iHEo6cVP861dbWxsuXLxEbG/tBX5LVav66bdWqFUJDQz8onouLy4c2SQSX93pU\nJ2cejwcVFRXs3LkT+vr6H9S+ht6zvn37ShzPz8+vzn/X9Vxc48aNQ2BgIObNm4fu3btDQ0MDt27d\nwqpVq6SOybWXL18iOTkZDMOw/waqPv9Xr15JFbNmryYwMJCT3ijXn0/NHyy1f7xI84PlY0YJR05q\ndq319fU5STa149Z1HkJSnp6eDf4nadeunUTxat/rMXfu3A+616Pm8erq6n5wsgGqrkzikixKmnh7\ne+P9+/dYsGABnj17Bh6PB2NjY0ycOJHThFNaWooDBw6wd8xLws7ODpmZmSL/BiDx0Gm1sLAwAMD6\n9evxww8/sMulPacI1P35VFRUsBUNJFXzB0vtHyjS/GD5mNFl0XIydOhQREREQCAQYNGiRVi5cqXQ\nMIO09+FYWlqyV6fVvL+g+j+kpPdSODo6ipxIriZNPK7v9RgwYAD7BZuQkCDyZSvNl72Tk1OD6yVt\nY2Pn5D60CnNxcTFUVFSgq6v7QXFqVt5mGIa9Sq1fv35YuHChVMl87969MDQ0hLOzM0aOHImioiKo\nqalh69atUiUxWXj//j0WL16MIUOGwNnZGQAQFBQEXV1drFix4oOGuGuTdnqLjxUlHDlRlvtwuMZ1\n+xor+ClNwpk6dSru3r2LwYMHw83NTaQXJ2kbG7uPQ5oelTznrpHWli1bcPHiRSxevBimpqbw9PTE\njz/+iN9++w137txha5ZJIi0tjb0Q4dWrV0JJduvWrQgMDJQ45vLlyyEQCDB37lxoaWkBqOrZrVq1\nChoaGmyvSly//vorFi9eDD09PcTHx8PExAQ3b95EeHg4njx5IvUVqB8jSjhKrrExYkmHwBq60Y7H\n48HGxkaieNWuXbuGW7dugcfjwcrKCtbW1lLFkZXS0lKkpaXhxIkTePPmDYYMGQIXFxeJ5+tpzO+/\n/y7Vscui18T1TZWurq44ePAg+yXu7e2N5ORkVFRUwMXFBWlpaRK3seaNmLVvypT2JloPDw+kpKSI\nnA/i8/nw9vaW+GrHYcOGYe7cuXj69Cnu3LmDTp06YfPmzRg3bhwmT54sdc23jxGdw5ETWfwyB4Tv\npSgsLISBgQG7TpohMK5vtHv37h2mTp2KP//8E7169QKfz8f27dvRtWtXbNy4UarzTocOHcLevXvx\n119/QUNDA127dsXYsWOlLlcPVF3M4e3tDW/v/9feuUdFVbVh/BlALbFEIygNXYa3csCwbC0CMkdI\nxBuri5q31FUEKpmGeAPLKE1iCQqiEJJWFmMyC8cSBBMpEqPCEhXITAvIEK8jDuEMs78/WDPfmWFA\nzm1u7N8/Duc47+xhmPOevff7Pk84VCoVioqKsGzZMri4uODzzz9nFUuMXqHOEgrXxmGhP2tnZ2dD\nsgGAqKgoAICLi4vRcTYw74dN74253is7OzubLT7o0aMHp/Ltnj17GmZhgYGBqKurw8GDB/HII49w\nGp8jQxOOFTC398AVZi+F/o6SD0I32qWkpGDIkCHIzMw0bMreuXMHH3zwAZKTk1n3IO3duxc5OTlY\nvHgxhg8fDqCtw33nzp24efMm79/rtWvXUFhYiIKCAjQ1NRnW+NkgRq+QGI3DQn/WOp3OyO564sSJ\nAIBbt24JUl0mVI+Lm5sbKisr4ePjY3S8srKS0w0Qsxz8nnvuQUZGBucE6/CIaO5G6QCuDo13w9QJ\nki9fffUV8ff3Jzt37iRarZZTjClTphCNRtPu+J07d8jEiRM5xbt27Vq74w0NDZzff0NDA/n888/J\n3LlzSVhYGElJSSFVVVWcYhFCyKRJkwghhOh0OjJu3DgyefJkUlFRwTkeIYQsXLiQLF26lMyZM4ek\np6eTc+fOkalTpxKZTEaUSiWv2IQI81mnp6eTxYsXk1u3bhmONTU1kaVLl5Ls7GxOMZmfqenny/Xz\nPnnyJAkICCApKSmkqKiIlJSUkLS0NBIQEECOHz8u6BgpxtAZjhUQq/uYCLQdJ2SjnU6nM1tuynX5\nwsnJyaxKtIeHB6fxAW2eKA899BCef/55Q+VWdXU1qqurAbDfkBejV0isxmEhP+uIiAi8++67CAoK\ngre3NyQSCf744w9Mnz4dCxcu5BTz4sWLmD9/frvHhBD89ddfnGI+8cQTyMrKQnZ2No4cOQKJRAKp\nVIrs7GzDrJkNnfXhAPyrEh0JmnAcCCESmdCNdq6urqiursbIkSONjldVVRnk5tkghgzN9OnTIZFI\noFKpUF5e3u4824QjRq+QGI3DQn/Wzs7OSEhIwNKlSw37P1KpFA8//DDnmBkZGbzG1BEjR45EYmKi\n0TG1Wg25XG4wZ+sqnfXhUGkbY2iVmoVg7q1s3boVy5YtMzrPtazVtJfC9DHbxjhmYmB+WbjG+/77\n7/Huu+8iOjoaPj4+aG1txa+//oodO3Zg8+bNrBvjmH04puTk5KC0tJRVvLtRV1fHevNXjF4hZkWW\nEHt1gPCftb1SXV2NnJwcKJVKDBkyBLm5uYLEraurw759+7BixQpB4jkCdIZjIZhd16Zd2AD3hKNf\n9hEKoeMFBQXh/fffR3p6OjZs2ACgbUkjKSkJTz75JOt4nRUFcC0YuHDhAlJTU+Hm5oa3334brq6u\naGpqQnp6Ovbu3cu6qqwz6RSuMC2mm5ub8fPPP/NuHO7KZ33mzBmMGjWKdWyhYN5QMeGbFFtaWvDN\nN98gJycHNTU1cHJyQkZGBm9lAJ1Oh6NHj0Iul6OsrAwymYxXPEeDznAsxLlz53iLDprD1ECKL6a9\nGRKJBH379oW3t7fNqyozK6TYMGvWLPj6+qKxsREDBgxAYGAgYmNjMXDgQMTGxvLqGVKpVJBIJJzc\nTZnMmzfPaPbKhE/j8N0Q2pCPD0LN7N5//30UFBTAx8fHYK09bdq0DtWzu0JDQwPkcjlyc3MhkUhw\n+/ZtKBQKeHl58R6vI0FnOBYiNjZWlC+uqYEUX8z1Zly9ehXNzc1IT08365VzN3766Sekp6cbGj+l\nUimWLFnCqYn0tddeQ1ZWFoD2dtjz5s3j9Du+fv061q5dizt37mDKlCnIz8/H6tWrMXnyZNax9CiV\nSqSmpqKurg4A4OXlhejoaEydOpVTvKSkJCQmJuL333+Hn58fYmJiOO2BscWW7keF2g8pKCiAr68v\nnn/+eYwfPx59+vThFTsqKgo1NTWQyWTYsmULxowZgwkTJtBkYwaacCyEWF9ctVrdbnmFCdullo6c\nScvLy7Fx48ZOnUvNUVZWhtjYWERFRWHt2rXQaDQ4efIkli9fjqSkJNaijleuXDE8LigoMEo4XH/H\nejHRnj17oqWlBbt378aQIUM4xQKA/Px87NixA3FxcRg7diy0Wi0qKirw4YcfokePHpwUudeuXYvh\nw4dj6tSpOHz4MDZt2mSR6idb2vQW6jtUUlKCkpISKBQKvPfee/D390dzczPu3LnDqeJPr2Ho5uaG\nfv36QSKR2NTvzZagCcdCXLp0qdMmR64Xj8bGRmzbtq1DsU2hllqefvpp3Lhxg/Xztm/fjszMTKOZ\n0eOPP47Ro0dj06ZN2Lt3L6t4ppvbHZ3jGrNfv368kg0AfPLJJ8jMzDS6w33uuefw6KOPYsWKFZwS\nTkNDg8GjJiAgwCa00yyNUBdxZ2dnyGQyyGQyXLt2DUqlEnV1dQgKCsILL7yAVatWsYqnUChQU1MD\nhUKBuXPnwsPDA01NTe2UPyg04ViM3r17iyJVPnjwYNHW703hcofZ1NRkdhlOKpVy9kjRI9QFiOnj\ncvPmzXb7BGwv7i0tLWaXUwYNGtTOgrirMHuWuPYw2SN69XKgLenqlb0JRzV0U/r3748FCxZgwYIF\nOH36NOc9ohEjRmDNmjVYuXIliouLkZubi+DgYIwbN87sMnV3hSYcC+Hm5ia474oYmBMDValUyM3N\nbScF0hXUarVZrxGtVgutVss6HrNaS61WG4ocCCFQq9Ws4wGd+7gA7BPOf//9h+bm5na+P2q1WjB3\nTkst2Vh7D4ftEi4fpFIpL7t3oE03LiQkBCEhIbhy5QqUSqVAo3MMaJWahZgxYwb27dsneNzS0lIE\nBgaaPXfhwgXWy0Omfjh6j5RnnnkGUVFRrDWi3nvvPfTs2dOoOa61tRUbN25Ejx49WDuUmto8mFZu\niXWBSk1NRXR0dJf+b3JyMurq6rBx40b06tULQJue2Lp16/DYY48ZRC3ZwPQ9Av6/b8DnTv/UqVPw\n9fU1e+7AgQOYPn06amtrrb75ffPmTbS2tqJ///4A2vYThw4davhZSMaMGYOKigrWzzt//jz279+P\nP//80yAo+/LLL/NqenVEaMKxMkI3h2m1WhQWFuLLL7/E6dOncfLkSUHiMikuLu6yPbRarUZkZCQu\nXboEqVSK1tZWVFZWYtiwYUhLS+O0SXv+/Hncd9998PDwQGZmJioqKjBq1Ci8/vrrgriemoNNebBW\nq0VcXByKiorg7e0NrVaLixcvYtq0aXjnnXc4zU7E8D1ivqeZM2dCLpebPWdNzp49i4iICGzcuBHP\nPvssgLaErlAo8PHHH7dTsOALl4RTVlaGt956C2FhYRg2bBgkEglqampQWFiIlJQU6vrJRHy5Noop\nra2tpKioiLz22mtk1KhRJDo6mnfMv//+myQlJRF/f38yatQokpqaSq5evSrAaNvDVqDwjz/+IAcO\nHCBZWVlk165d5KeffiKNjY0kPj6e9Wt/+umnZNy4cUQmk5HVq1eTmTNnkj179pAlS5aQlStXso7X\nVbgIrtbW1pL8/HxSUFBA/vnnHxFGxQ/mezJ9f2IJzLJl/vz55MSJE+2Of/fdd+TVV18V/PX8/PxY\nP2fWrFmksrKy3fHffvuNvPLKK0IMy2GgezgWxFxzWH5+Pq8li6KiIuTk5ODMmTMICQnBRx99hPj4\neM7+Ol2BsJgUp6amIjs7G0CbJ1BAQACysrLwxhtv4IknnmD92jk5OTh06BCam5sRHByM0tJSuLq6\nYs6cOaJWbrGZleibcR955BGb9kRhviehpP+FRqVSmS2dDwoKQlJSEqeYevUC/d+xOWkoNjQ1NUEq\nlbY77uvri+bmZk5jdFRowrEQYjWHRUdHY9KkSZDL5QbPeLEvFmzi5+Xl4fDhw7h8+TK2bduGTz75\nBA0NDUhJSUFQUBDr13ZxcUHv3r3Ru3dveHl5GfaUnJ2dzapSWwN9syeFP1qtFjqdrp3KhU6ng0aj\n4RRTaPkmW/m7swfob8pCiNUcplQqoVAoMHv2bAwcOBCTJ08WrBJKCFxdXeHh4QEPDw+cOnUK4eHh\nyMjIMDKtYgPzwsM1htjoK+k6mgly0T0Tg85k9e9mXW4pxo4di7S0NLz55ptGx9PT083OKrqCWq1G\nRkaGQbWBr8VDZ58318pJR4UmHAshVnPY8OHDsXr1asTExODYsWNQKBS4cuUKIiIiMGfOHIwbN07A\nd8EeZoLo168f66o0U8TwR+kMvT6bt7d3l59jqWZcvnQmq28rG90rVqxAREQE8vLyMHLkSPTq1Qtn\nz55F//79sWPHDk4x16xZA0IIAgMDcfToUVy+fBlxcXGcx+jp6YmtW7eaPcfHp8kRoVVqVkCr1eLY\nsWPIzc1FWVkZnn32WUGbw65du4a8vDzk5eWJ0gfARkSRWe0kROWTOb8aJlwulHr75r59+2LBggUG\n++Yvv/wS27dvZ23fLJTIpDX5+uuvMWXKFGsPA0DbzcSJEydQVVUFJycnSKVSTjp8esLCwnDo0CEA\nbTOQmTNn4uDBg0INl9IJdIZjQZjlvH/++ScIIQgPDxdU/qK+vh5nzpxBcHAwFi1axPr5pmrRpowd\nO9aofPZunDt3ztAdLkSnuBh33jExMXB1dcX169eh0WgQEhKCFStW4Pbt27wbAe2V9evX20TCSUhI\nQHx8PPz9/eHv7y9ITH1vFNCmAMJ3aTYtLa3DcxKJBEuWLOEV35GgCcdCfPrpp8jOzoazszOefvpp\nXLhwAWFhYSgvL8ft27c5x62pqUFcXBzc3d0xd+5cvPnmmxg0aBDq6+uxatUqvPjii6zizZs3Dw88\n8IBhCYk5AdYvBzG/sHfj8OHDrF7fGght3xwTE4Pi4mIMHToUXl5eOHLkCPbv34/HHnsMixcvtgtZ\nGltZ+ODShMkWMYpsbty4AblcjgEDBtCEw4AmHAshl8tFKeddv3493njjDdy6dQtRUVHYs2cP/Pz8\nUF9fj8jISNYJJy0tDfn5+fjrr78wfvx4hIWF8RKz5NKQaGmEtm+uqanBoUOHsHnzZlRXVyMmJgbr\n1q1DVVUVEhMTsW7dOqGGLhq2Uhat0Whw6dKlDhPggAEDWMdk7v2Z+5ntHptpC8K3336LDRs24JVX\nXqFunybQhGMhxCrn1ScwANi5c6fhQjlw4EBOd9LBwcEIDg5GS0sLiouLkZycjMuXL0MmkyEsLMym\n+0q4wry4uru780o2QJssjFwux7333oukpCTIZDK8/PLLIIQgLCyM73AFo7OlIK4lx0Jz8eJFzJ07\nt8MCDC6SPhkZGUIMrR0qlQoJCQk4deoUtmzZwmufyVGhCcdCiFXOy4zVu3dvo3N8lkV69eqF0NBQ\nhIaG4vz581i3bh2Sk5Md0udeaPtmiURiEO788ccfMXv2bMNxe4HpM2RNhg4dKngBxuDBg4106ZiU\nlZVxinn06FFs2LABoaGhOHDggGgSS/YOTTgWQqxyXqa0vv6xPi4f+f/6+noUFBSgsLAQGo0GoaGh\n+OijjzjHs2X69OljKGP28PAwKnHlUsbs7OwMlUoFtVqNqqoqBAQEAGj7ndpSk2BHahS1tbX46quv\nLDwayxEZGWmoljS1aE9MTGRdSbly5UocPnwYUVFReOqpp1BZWWl03lb6rmwB2/nrd3DEmsZ3Jq3P\n1k0TADIzM1FYWAidTofQ0FAkJSVZXS1YbFQqFdasWSPYhSEiIgLh4eHQarV46aWX4OHhgUOHDiE5\nOdlmN5B1Oh2OHj0KuVyOsrIyyGQyaw8JAIz2VoSCOXs1tWjnsirw77//YvTo0Th+/Hi7Enpb6ruy\nBWgfTjcgPj4eCQkJXfq/I0eOhKenJwYNGgSg/TKQI355SktLsWHDBgQHB2P58uW8us71NDQ04Pr1\n6wY145KSEtxzzz2cbgLExJy+n0KhsJmbjK6U6bOls94woVWyuViEODJ0htMNOH36dJf/ryMmlLsR\nGBgIpVKJrVu34qWXXsL69euNqp+4VEJ5enoa7RNYW/HBHGLp+wlJZw3RGo0GOTk5FhxN19BbhOTk\n5KCyslIUixB7hSYcihG2Imliae69914sW7YM//77L6KionD//fcLZmNsq4il7yckUqkUq1atanf8\n/PnzWL58OaeYjY2Nhgo95mP9z1ypra2FXC6HQqGASqVCZGQkUlJSOMdzRGjCoRihl27XI5FIDI6f\n69evh5ubmxVHJx7FxcVISEhAYGAgiouLDb05joxY+n5CUlFRgeTkZKPksm/fPiQmJmLBggWcYs6a\nNcvsY3M/dwVrWITYLRZx3aFYFbaGaaY0NjaS7du3k+XLlws0ItsiOjqaTJgwgRw/ftzaQ7EaGo2G\nFBUVkcjISDJ69GhBTAGF4NatW2TWrFlk27Zt5ObNm2Tp0qVk4sSJ5LfffhPl9X755RfWzxkxYgR5\n6623yMWLFw3HZDKZkMNyGJzunpIo9g7hWRfi7u6OxYsXo6amRqAR2RYPPvgglEqlYFpd9kRxcTFq\na2sN5do6nQ7h4eHw9fW18sja6NOnD7KysnDixAmEhITA3d0deXl5vMZ38uRJzJw5ExEREbhy5QqA\ntpL1ZcuWYeHChazjKZVKeHp6Yvbs2ZgxYwb27NljUxYhtgRNOHZOV5LJM888I8hr2YMGGBfi4+Pb\nNc12B3bt2oW0tDS0tLQYJHiCg4Ph5OSEhoYGaw/PgKurK7KysjBixAh4enrybqp85513EBYWBm9v\nb2zfvh15eXmYOnUqXFxc8M0337COp7cIKSkpQUREBH788UeDRcixY8d4jdXRoGXRdg6zjFNMSfnC\nwkJ88cUX2L17tyjxKZZn2rRpRhI8//zzD7Zs2WKQ4MnPz7f2EDFv3jzDnmJzczMqKyvh5+dnuPnh\nUlWptycghGD8+PHo06cPEhISeEsaMdFbhMjlcrsQsLUUtGjAzmHeL+zatYt3wpHJZO0qlZqamjB4\n8GAkJibyik2xLexBgic6OlrwmPo+K4lEAicnJ+zevRvu7u6Cvkb//v2xaNGiTvXquiM04dg5zIuD\nEJPVzz77zOjnS5cu4f777+8WVVvdDXuQ4BGjTJ/5nenbt6/gyYbSMbbxV0URBCHuTJlLGMD/jdIa\nGxuh0WgcUryzu2KPEjxC0FkfDtCxxhyFP3QPx87x8/ODj48PCCE4ffo0fHx8APw/UfBVDrh9+zY2\nb96M0tJSJCQkGO6CKY6BvUjwCMndlrnYJhzTmzQ9hBBUVFTgzJkzrOI5MjTh2Dnl5eWdzmz4CFKW\nlZUhLi4OAQEBiI2NpctqFIoZysvLOz3fXdU7zEETjp2jVwbQz2hM/+WyBKZWq/Hhhx/SWQ3FIVmz\nZk2n5zdt2mShkXQ/6B6OnVNdXW14HB4eztusijmrOXjwoMGZlEJxFLy8vPDwww9bexjdEppwHAgh\nigYWLlwIFxcXlJaW4ocffjAcJw4uZEnpPhQVFRl617Kzs7Fo0SIrj6j7QBOOAyHE6ihNKBRHh/k9\nOXjwIE04FoQmHAdCiBnOwIEDBRgJhWIf0C1sy0ITjp3DVAZoaGjAhAkTANAlMAqlI0ztNyiWg1ap\n2Tn19fWdnqczFgrFGKlUanBj1ZvQAfQmzRLQhEOhULoV9CbNetCEQ6FQKBSLQP1wKBQKhWIRaMKh\nUCgUikWgCYdCoVAoFoEmHAqFQqFYBJpwKBQKhWIR/gdcSZoCtAKd/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x296957657f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(df.astype(float), cmap = \"viridis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rpw/phUzTdDz76iFs2l6LaZOycP1lF0CWWOg5lvoqwvjGqnn44jWz8OnRJhysa8Wx023Y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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29695c7e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data.LONGITUDE, data.PRICE, kind='reg')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}