practice-project-2_G3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Practice Project No.1</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>2019 March</b>\n",
    "<p>Rama, Thomas, Cameron</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i> <b>NOTE: Project No.2 Starts lower down, and is marked.</b>\n",
    "<p>Code commentary is #commented out# in the script boxes, and project commentary is included in italicized text such as this. (Expository)</p>\n",
    "</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>The Beginning</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import Panda and Numpy\n",
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#defining the location for data and attempting to read it with panda\n",
    "location = \"datasets/DC_Properties.csv\"\n",
    "dcl_df = pd.read_csv(location, low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>Looking at the data we just loaded! Let's hope that worked! Without the command modifier 'low_memory=False' we get a memory error. The error does not prevent work from being done, but it is annoying to get an error in any case, so we have elected to run the command as such to minimize error messages.\n",
    "</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0152</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>0152</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>0152</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>0152</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>0152</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    0152 -77.040429  38.914881       NW  \n",
       "1        4201.0  004201 2006  Ward 2    0152 -77.040429  38.914881       NW  \n",
       "2        4201.0  004201 2006  Ward 2    0152 -77.040429  38.914881       NW  \n",
       "3        4201.0  004201 2006  Ward 2    0152 -77.040429  38.914881       NW  \n",
       "4        4201.0  004201 2006  Ward 2    0152 -77.040429  38.914881       NW  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcl_df.head()\n",
    "#looking at the head of our data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>158956</th>\n",
       "      <td>158956</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Warm Cool</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-77.01823</td>\n",
       "      <td>Southwest Waterfront</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ward 6</td>\n",
       "      <td>0504</td>\n",
       "      <td>-77.018232</td>\n",
       "      <td>38.872961</td>\n",
       "      <td>SW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  BATHRM  HF_BATHRM       HEAT AC  NUM_UNITS  ROOMS  BEDRM  \\\n",
       "158956      158956       1          0  Warm Cool  Y        NaN      2      0   \n",
       "\n",
       "           AYB  YR_RMDL   ...     LONGITUDE       ASSESSMENT_NBHD  \\\n",
       "158956  1965.0      NaN   ...     -77.01823  Southwest Waterfront   \n",
       "\n",
       "       ASSESSMENT_SUBNBHD  CENSUS_TRACT CENSUS_BLOCK    WARD  SQUARE  \\\n",
       "158956                NaN       11000.0          NaN  Ward 6    0504   \n",
       "\n",
       "                X          Y QUADRANT  \n",
       "158956 -77.018232  38.872961       SW  \n",
       "\n",
       "[1 rows x 49 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at the end of the file\n",
    "dcl_df.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Tepid Initial Analysis</h3>\n",
    "<p><h4> Getting A Feel</h4></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, BATHRM, HF_BATHRM, HEAT, AC, NUM_UNITS, ROOMS, BEDRM, AYB, YR_RMDL, EYB, STORIES, SALEDATE, PRICE, QUALIFIED, SALE_NUM, GBA, BLDG_NUM, STYLE, STRUCT, GRADE, CNDTN, EXTWALL, ROOF, INTWALL, KITCHENS, FIREPLACES, USECODE, LANDAREA, GIS_LAST_MOD_DTTM, SOURCE, CMPLX_NUM, LIVING_GBA, FULLADDRESS, CITY, STATE, ZIPCODE, NATIONALGRID, LATITUDE, LONGITUDE, ASSESSMENT_NBHD, ASSESSMENT_SUBNBHD, CENSUS_TRACT, CENSUS_BLOCK, WARD, SQUARE, X, Y, QUADRANT]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 49 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking for duplicates in the data\n",
    "dupe = dcl_df.duplicated()\n",
    "dcl_df[dupe]\n",
    "#there appear to be no duplicates in this data\n",
    "#it is already remarkably clean\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>There are no values that duplicate across all 49 columns. This is good, it makes our initial job (maybe) that much easier...\n",
    "</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0              int64\n",
       "BATHRM                  int64\n",
       "HF_BATHRM               int64\n",
       "HEAT                   object\n",
       "AC                     object\n",
       "NUM_UNITS             float64\n",
       "ROOMS                   int64\n",
       "BEDRM                   int64\n",
       "AYB                   float64\n",
       "YR_RMDL               float64\n",
       "EYB                     int64\n",
       "STORIES               float64\n",
       "SALEDATE               object\n",
       "PRICE                 float64\n",
       "QUALIFIED              object\n",
       "SALE_NUM                int64\n",
       "GBA                   float64\n",
       "BLDG_NUM                int64\n",
       "STYLE                  object\n",
       "STRUCT                 object\n",
       "GRADE                  object\n",
       "CNDTN                  object\n",
       "EXTWALL                object\n",
       "ROOF                   object\n",
       "INTWALL                object\n",
       "KITCHENS              float64\n",
       "FIREPLACES              int64\n",
       "USECODE                 int64\n",
       "LANDAREA                int64\n",
       "GIS_LAST_MOD_DTTM      object\n",
       "SOURCE                 object\n",
       "CMPLX_NUM             float64\n",
       "LIVING_GBA            float64\n",
       "FULLADDRESS            object\n",
       "CITY                   object\n",
       "STATE                  object\n",
       "ZIPCODE               float64\n",
       "NATIONALGRID           object\n",
       "LATITUDE              float64\n",
       "LONGITUDE             float64\n",
       "ASSESSMENT_NBHD        object\n",
       "ASSESSMENT_SUBNBHD     object\n",
       "CENSUS_TRACT          float64\n",
       "CENSUS_BLOCK           object\n",
       "WARD                   object\n",
       "SQUARE                 object\n",
       "X                     float64\n",
       "Y                     float64\n",
       "QUADRANT               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at the data names and column types\n",
    "#there are 49 unique columns present in the CSV\n",
    "#The first 'Unnamed: 0' is a count-up from 0 to 158956\n",
    "dcl_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>AYB</th>\n",
       "      <th>YR_RMDL</th>\n",
       "      <th>EYB</th>\n",
       "      <th>STORIES</th>\n",
       "      <th>...</th>\n",
       "      <th>USECODE</th>\n",
       "      <th>LANDAREA</th>\n",
       "      <th>CMPLX_NUM</th>\n",
       "      <th>LIVING_GBA</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>CENSUS_TRACT</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>158957.000000</td>\n",
       "      <td>158957.000000</td>\n",
       "      <td>158957.000000</td>\n",
       "      <td>106696.000000</td>\n",
       "      <td>158957.000000</td>\n",
       "      <td>158957.000000</td>\n",
       "      <td>158686.000000</td>\n",
       "      <td>80928.000000</td>\n",
       "      <td>158957.000000</td>\n",
       "      <td>106652.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>158957.000000</td>\n",
       "      <td>158957.000000</td>\n",
       "      <td>52261.000000</td>\n",
       "      <td>52261.000000</td>\n",
       "      <td>158956.000000</td>\n",
       "      <td>158956.000000</td>\n",
       "      <td>158956.000000</td>\n",
       "      <td>158956.000000</td>\n",
       "      <td>158720.000000</td>\n",
       "      <td>158720.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>79478.000000</td>\n",
       "      <td>1.810678</td>\n",
       "      <td>0.458237</td>\n",
       "      <td>1.198039</td>\n",
       "      <td>6.187736</td>\n",
       "      <td>2.732506</td>\n",
       "      <td>1941.987579</td>\n",
       "      <td>1998.243537</td>\n",
       "      <td>1963.718024</td>\n",
       "      <td>2.091793</td>\n",
       "      <td>...</td>\n",
       "      <td>14.252999</td>\n",
       "      <td>2473.282158</td>\n",
       "      <td>2371.544249</td>\n",
       "      <td>888.834542</td>\n",
       "      <td>20012.694563</td>\n",
       "      <td>38.914854</td>\n",
       "      <td>-77.016676</td>\n",
       "      <td>5348.216324</td>\n",
       "      <td>-77.016712</td>\n",
       "      <td>38.914846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45887.077707</td>\n",
       "      <td>0.976396</td>\n",
       "      <td>0.587571</td>\n",
       "      <td>0.596924</td>\n",
       "      <td>2.618165</td>\n",
       "      <td>1.358864</td>\n",
       "      <td>33.640234</td>\n",
       "      <td>16.575786</td>\n",
       "      <td>24.923150</td>\n",
       "      <td>2.933323</td>\n",
       "      <td>...</td>\n",
       "      <td>3.725736</td>\n",
       "      <td>5059.046023</td>\n",
       "      <td>1114.272364</td>\n",
       "      <td>420.185822</td>\n",
       "      <td>15.627084</td>\n",
       "      <td>0.031723</td>\n",
       "      <td>0.040938</td>\n",
       "      <td>3369.645953</td>\n",
       "      <td>0.040933</td>\n",
       "      <td>0.031682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1754.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20001.000000</td>\n",
       "      <td>38.819731</td>\n",
       "      <td>-77.113909</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>-77.113135</td>\n",
       "      <td>38.820576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39739.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>697.000000</td>\n",
       "      <td>1501.000000</td>\n",
       "      <td>616.000000</td>\n",
       "      <td>20007.000000</td>\n",
       "      <td>38.895425</td>\n",
       "      <td>-77.042892</td>\n",
       "      <td>2102.000000</td>\n",
       "      <td>-77.042894</td>\n",
       "      <td>38.895432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>79478.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1937.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>1963.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>2265.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>20011.000000</td>\n",
       "      <td>38.915337</td>\n",
       "      <td>-77.019596</td>\n",
       "      <td>5201.000000</td>\n",
       "      <td>-77.019581</td>\n",
       "      <td>38.915229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>119217.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>2910.000000</td>\n",
       "      <td>1060.000000</td>\n",
       "      <td>20018.000000</td>\n",
       "      <td>38.936075</td>\n",
       "      <td>-76.988626</td>\n",
       "      <td>8302.000000</td>\n",
       "      <td>-76.988842</td>\n",
       "      <td>38.936077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>158956.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>826.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>942632.000000</td>\n",
       "      <td>5621.000000</td>\n",
       "      <td>8553.000000</td>\n",
       "      <td>20392.000000</td>\n",
       "      <td>38.995540</td>\n",
       "      <td>-76.909758</td>\n",
       "      <td>11100.000000</td>\n",
       "      <td>-76.910511</td>\n",
       "      <td>38.993646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0         BATHRM      HF_BATHRM      NUM_UNITS  \\\n",
       "count  158957.000000  158957.000000  158957.000000  106696.000000   \n",
       "mean    79478.000000       1.810678       0.458237       1.198039   \n",
       "std     45887.077707       0.976396       0.587571       0.596924   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%     39739.000000       1.000000       0.000000       1.000000   \n",
       "50%     79478.000000       2.000000       0.000000       1.000000   \n",
       "75%    119217.000000       2.000000       1.000000       1.000000   \n",
       "max    158956.000000      14.000000      11.000000       6.000000   \n",
       "\n",
       "               ROOMS          BEDRM            AYB       YR_RMDL  \\\n",
       "count  158957.000000  158957.000000  158686.000000  80928.000000   \n",
       "mean        6.187736       2.732506    1941.987579   1998.243537   \n",
       "std         2.618165       1.358864      33.640234     16.575786   \n",
       "min         0.000000       0.000000    1754.000000     20.000000   \n",
       "25%         4.000000       2.000000    1918.000000   1985.000000   \n",
       "50%         6.000000       3.000000    1937.000000   2004.000000   \n",
       "75%         7.000000       3.000000    1960.000000   2010.000000   \n",
       "max        48.000000      24.000000    2019.000000   2019.000000   \n",
       "\n",
       "                 EYB        STORIES      ...              USECODE  \\\n",
       "count  158957.000000  106652.000000      ...        158957.000000   \n",
       "mean     1963.718024       2.091793      ...            14.252999   \n",
       "std        24.923150       2.933323      ...             3.725736   \n",
       "min      1800.000000       0.000000      ...            11.000000   \n",
       "25%      1954.000000       2.000000      ...            11.000000   \n",
       "50%      1963.000000       2.000000      ...            13.000000   \n",
       "75%      1975.000000       2.000000      ...            17.000000   \n",
       "max      2018.000000     826.000000      ...           117.000000   \n",
       "\n",
       "            LANDAREA     CMPLX_NUM    LIVING_GBA        ZIPCODE  \\\n",
       "count  158957.000000  52261.000000  52261.000000  158956.000000   \n",
       "mean     2473.282158   2371.544249    888.834542   20012.694563   \n",
       "std      5059.046023   1114.272364    420.185822      15.627084   \n",
       "min         0.000000   1001.000000      0.000000   20001.000000   \n",
       "25%       697.000000   1501.000000    616.000000   20007.000000   \n",
       "50%      1649.000000   2265.000000    783.000000   20011.000000   \n",
       "75%      3000.000000   2910.000000   1060.000000   20018.000000   \n",
       "max    942632.000000   5621.000000   8553.000000   20392.000000   \n",
       "\n",
       "            LATITUDE      LONGITUDE   CENSUS_TRACT              X  \\\n",
       "count  158956.000000  158956.000000  158956.000000  158720.000000   \n",
       "mean       38.914854     -77.016676    5348.216324     -77.016712   \n",
       "std         0.031723       0.040938    3369.645953       0.040933   \n",
       "min        38.819731     -77.113909     100.000000     -77.113135   \n",
       "25%        38.895425     -77.042892    2102.000000     -77.042894   \n",
       "50%        38.915337     -77.019596    5201.000000     -77.019581   \n",
       "75%        38.936075     -76.988626    8302.000000     -76.988842   \n",
       "max        38.995540     -76.909758   11100.000000     -76.910511   \n",
       "\n",
       "                   Y  \n",
       "count  158720.000000  \n",
       "mean       38.914846  \n",
       "std         0.031682  \n",
       "min        38.820576  \n",
       "25%        38.895432  \n",
       "50%        38.915229  \n",
       "75%        38.936077  \n",
       "max        38.993646  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the data, looking at min/max etc\n",
    "dcl_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Ward Breakdown: What Where?</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ward 6    23973\n",
       "Ward 3    23688\n",
       "Ward 4    22202\n",
       "Ward 2    22167\n",
       "Ward 5    21359\n",
       "Ward 1    17455\n",
       "Ward 7    17206\n",
       "Ward 8    10906\n",
       "Name: WARD, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#trying to figure out how many homes per which ward are present\n",
    "#that is, count of presence of \"Ward X\" in column WARD\n",
    "\n",
    "dcl_df['WARD'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i> It would appear the Wards have very comparable sales rates. Surprisingly, Ward 6 tops the chart--possiblye due to it's larger size? Not surprising is the low volume of sales in Ward 1 (which would be very expensive) and Ward 8, which is somewhat neglected.\n",
    "</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD\n",
       "Ward 1     895.214028\n",
       "Ward 2     860.502595\n",
       "Ward 3     932.902108\n",
       "Ward 4     860.575610\n",
       "Ward 5    1010.261691\n",
       "Ward 6     883.013510\n",
       "Ward 7     793.582723\n",
       "Ward 8     840.932631\n",
       "Name: LIVING_GBA, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcl_df['LIVING_GBA'].groupby(dcl_df['WARD']).mean()\n",
    "#attempting to display the average LIVING_GBA by ward\n",
    "#does one area have more living space than an other?\n",
    "#we predict a 'yes'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>\n",
    "</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD\n",
       "Ward 1    831.0\n",
       "Ward 2    752.0\n",
       "Ward 3    810.0\n",
       "Ward 4    740.0\n",
       "Ward 5    900.0\n",
       "Ward 6    781.0\n",
       "Ward 7    708.0\n",
       "Ward 8    766.0\n",
       "Name: LIVING_GBA, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcl_df['LIVING_GBA'].groupby(dcl_df['WARD']).median()\n",
    "#mean may be skewed by a larger farmhouse in Ward 5\n",
    "#so we now look at the median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD\n",
       "Ward 1    20\n",
       "Ward 2    19\n",
       "Ward 3    16\n",
       "Ward 4    24\n",
       "Ward 5    14\n",
       "Ward 6    12\n",
       "Ward 7    12\n",
       "Ward 8    13\n",
       "Name: BEDRM, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorting ward by most rooms of a property in that area\n",
    "dcl_df['BEDRM'].groupby(dcl_df['WARD']).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD\n",
       "Ward 1    461500.0\n",
       "Ward 2    475000.0\n",
       "Ward 3    600000.0\n",
       "Ward 4    428000.0\n",
       "Ward 5    360710.0\n",
       "Ward 6    469900.0\n",
       "Ward 7    200000.0\n",
       "Ward 8    194900.0\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorting ward by median price\n",
    "dcl_df['PRICE'].groupby(dcl_df['WARD']).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD\n",
       "Ward 1     12780000.0\n",
       "Ward 2     53969391.0\n",
       "Ward 3    137427545.0\n",
       "Ward 4      5000000.0\n",
       "Ward 5      5250000.0\n",
       "Ward 6     11000000.0\n",
       "Ward 7     11000000.0\n",
       "Ward 8     25100000.0\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ward by maximum price (and minimum)\n",
    "\n",
    "dcl_df['PRICE'].groupby(dcl_df['WARD']).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD\n",
       "Ward 1     10.0\n",
       "Ward 2     10.0\n",
       "Ward 3      1.0\n",
       "Ward 4    250.0\n",
       "Ward 5      1.0\n",
       "Ward 6      1.0\n",
       "Ward 7     10.0\n",
       "Ward 8      1.0\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#minimum price\n",
    "dcl_df['PRICE'].groupby(dcl_df['WARD']).min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Structural Affairs</h3><p><h4>The Shape of Sales</h4></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row Inside       40593\n",
       "Single           32063\n",
       "Semi-Detached    16756\n",
       "Row End          12225\n",
       "Multi             4726\n",
       "Town Inside        218\n",
       "Town End            85\n",
       "Default             26\n",
       "Vacant Land          4\n",
       "Name: STRUCT, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#which style of building is most common?\n",
    "#we assume\n",
    "dcl_df['STRUCT'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i> It appears that D.C.'s famous row houses are indeed the best-setlling model on the market. What, exactly, \"Default\" means is anyone's guess, we might assume it to be a standard, standalone single-family dwelling based on the presence of \"Town\" and \"Row\" in the other titles.\n",
    "Vacant land--always hard to come by in a city in the first place, by definition--only sold four times. Some questions: Was this the same land, sold four times? What were the sizes of the lot(s)?\n",
    "</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> The Highest [Bank]Notes</h3>\n",
    "<p><h4>Cost and Space Among the Upper Decile</h4></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##found on stackexchange:\n",
    "#look into using pandas.qcut\n",
    "#pandas documentation\n",
    "#https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html\n",
    "\n",
    "#>>> pd.qcut(range(5), 4)\n",
    "# doctest: +ELLIPSIS\n",
    "#[(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]]\n",
    "#Categories (4, interval[float64]): [(-0.001, 1.0] < (1.0, 2.0] ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##COPIED FROM EXERCISE 3 AS A GUIDE\n",
    "##\n",
    "##\n",
    "#Interquartile Range Method\n",
    "#q1 = dcl_df['PRICE'].decile(.10)\n",
    "#q9 = dcl_df['PRICE'].decile(.90)\n",
    "#iqr = q9-q1\n",
    "#toprange = q3 + iqr * 1.5\n",
    "#botrange = q1 - iqr * 1.5\n",
    "\n",
    "#copydf = df.copy()\n",
    "#copydf = copydf.drop(copydf[copydf['grade'] > toprange].index)\n",
    "#copydf = copydf.drop(copydf[copydf['grade'] < botrange].index)\n",
    "\n",
    "#copydf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p><h1>Project No. 2</h1></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#bringing in the statsmodel\n",
    "import statsmodels.formula.api as smf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#bringing in seaborn\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "#so that we can see charts in the document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#defining the location for data and attempting to read it with panda\n",
    "location = \"datasets/DC_Properties.csv\"\n",
    "dcl_df = pd.read_csv(location, low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i> These may be excessive, but the number of libraries here allows me to try stuff from Module 4, 5, and 6 without worrying about dependencies in the moment. Please forgive our overnumerousness.\n",
    "</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#hoping this works! attempting to see if WARD and BEDRM are related to price\n",
    "#I would assume these are related\n",
    "first = smf.ols('PRICE ~ WARD + BEDRM', data=dcl_df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>PRICE</td>      <th>  R-squared:         </th>  <td>   0.019</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.019</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   235.6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 13 Mar 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:45:04</td>     <th>  Log-Likelihood:    </th> <td>-1.6873e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 98216</td>      <th>  AIC:               </th>  <td>3.375e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 98207</td>      <th>  BIC:               </th>  <td>3.375e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td> 1.097e+06</td> <td> 7.42e+04</td> <td>   14.780</td> <td> 0.000</td> <td> 9.52e+05</td> <td> 1.24e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 2]</th> <td>  3.94e+05</td> <td> 8.47e+04</td> <td>    4.654</td> <td> 0.000</td> <td> 2.28e+05</td> <td>  5.6e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 3]</th> <td> 2.596e+06</td> <td> 8.59e+04</td> <td>   30.225</td> <td> 0.000</td> <td> 2.43e+06</td> <td> 2.76e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 4]</th> <td> 1.314e+05</td> <td> 9.09e+04</td> <td>    1.445</td> <td> 0.148</td> <td>-4.68e+04</td> <td>  3.1e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 5]</th> <td>-1.807e+04</td> <td> 9.01e+04</td> <td>   -0.201</td> <td> 0.841</td> <td>-1.95e+05</td> <td> 1.58e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 6]</th> <td>-2.992e+04</td> <td> 8.32e+04</td> <td>   -0.359</td> <td> 0.719</td> <td>-1.93e+05</td> <td> 1.33e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 7]</th> <td>-2.352e+05</td> <td> 9.64e+04</td> <td>   -2.441</td> <td> 0.015</td> <td>-4.24e+05</td> <td>-4.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 8]</th> <td>-1.882e+05</td> <td> 1.09e+05</td> <td>   -1.733</td> <td> 0.083</td> <td>-4.01e+05</td> <td> 2.46e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BEDRM</th>          <td>-2.233e+05</td> <td>  1.7e+04</td> <td>  -13.112</td> <td> 0.000</td> <td>-2.57e+05</td> <td> -1.9e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>202097.260</td> <th>  Durbin-Watson:     </th>   <td>   0.786</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>457092454.646</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td>17.822</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>335.302</td>  <th>  Cond. No.          </th>   <td>    26.1</td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  PRICE   R-squared:                       0.019\n",
       "Model:                            OLS   Adj. R-squared:                  0.019\n",
       "Method:                 Least Squares   F-statistic:                     235.6\n",
       "Date:                Wed, 13 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                        14:45:04   Log-Likelihood:            -1.6873e+06\n",
       "No. Observations:               98216   AIC:                         3.375e+06\n",
       "Df Residuals:                   98207   BIC:                         3.375e+06\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept       1.097e+06   7.42e+04     14.780      0.000    9.52e+05    1.24e+06\n",
       "WARD[T.Ward 2]   3.94e+05   8.47e+04      4.654      0.000    2.28e+05     5.6e+05\n",
       "WARD[T.Ward 3]  2.596e+06   8.59e+04     30.225      0.000    2.43e+06    2.76e+06\n",
       "WARD[T.Ward 4]  1.314e+05   9.09e+04      1.445      0.148   -4.68e+04     3.1e+05\n",
       "WARD[T.Ward 5] -1.807e+04   9.01e+04     -0.201      0.841   -1.95e+05    1.58e+05\n",
       "WARD[T.Ward 6] -2.992e+04   8.32e+04     -0.359      0.719   -1.93e+05    1.33e+05\n",
       "WARD[T.Ward 7] -2.352e+05   9.64e+04     -2.441      0.015   -4.24e+05   -4.64e+04\n",
       "WARD[T.Ward 8] -1.882e+05   1.09e+05     -1.733      0.083   -4.01e+05    2.46e+04\n",
       "BEDRM          -2.233e+05    1.7e+04    -13.112      0.000   -2.57e+05    -1.9e+05\n",
       "==============================================================================\n",
       "Omnibus:                   202097.260   Durbin-Watson:                   0.786\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        457092454.646\n",
       "Skew:                          17.822   Prob(JB):                         0.00\n",
       "Kurtosis:                     335.302   Cond. No.                         26.1\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>It would appear that WARDs are related, albeit far more positively (more expensive?) in WARD 2 and 3 and somewhat in 4. WARD 1 is not showing for some reason that I cannot determine. The R-squared is despairingly low...0.019, which I sure hope isn't permanent!\n",
    "<p> I will next attempt to combine the WARDS into a single variable...</p>\n",
    "</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3025f1828>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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WSciStEjSlZJ2KTY0c4Qx5llJX7TWhhNfIgAAAADgZHQ1ycrdkjIlDbPWTrXWTpE0XLFg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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3025f1160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#making a scatterplot of ward and bedrooms...\n",
    "plt.subplots(figsize=(15,9))\n",
    "sns.violinplot(x='WARD', y='BEDRM', data=dcl_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.subplots(figsize=(15,9))\n",
    "#sns.violinplot(x='ZIPCODE', y='PRICE', data=dcl_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1b300712198>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B3hTLpvoUzfH+KPQH3xCaG6I3gCnxTJxUKhUHPaTiL96j8LeBN4GUxY8Hv5gf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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b30334bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='BEDRM', y='PRICE', data=dcl_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>According to this, the more bedrooms you have the cheaper they get! Or, as I am more inclined to believe, I have done something horribly horribly wrong.\n",
    "<p> I have heard that this may be in part due to nearly ~800 entries of 2-bedroom sales happening for $1.37million dollars. Which seems somewhat excessive, to say the least.\n",
    "</p></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#commented out because this is not very useful data and is cluttering\n",
    "#up my view\n",
    "#dcl_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#What about year remodelled and price? Maybe newere(feature) homes\n",
    "#are more expensive...\n",
    "remod = smf.ols('PRICE ~ YR_RMDL + WARD', data=dcl_df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>PRICE</td>      <th>  R-squared:         </th>  <td>   0.029</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.028</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   211.8</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 13 Mar 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:01:55</td>     <th>  Log-Likelihood:    </th> <td>-1.0057e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 57674</td>      <th>  AIC:               </th>  <td>2.011e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 57665</td>      <th>  BIC:               </th>  <td>2.011e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>-7.625e+07</td> <td> 4.93e+06</td> <td>  -15.472</td> <td> 0.000</td> <td>-8.59e+07</td> <td>-6.66e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 2]</th> <td> 1.197e+06</td> <td> 1.36e+05</td> <td>    8.785</td> <td> 0.000</td> <td>  9.3e+05</td> <td> 1.46e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 3]</th> <td> 4.281e+06</td> <td> 1.39e+05</td> <td>   30.772</td> <td> 0.000</td> <td> 4.01e+06</td> <td> 4.55e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 4]</th> <td>-1.123e+05</td> <td> 1.49e+05</td> <td>   -0.753</td> <td> 0.452</td> <td>-4.05e+05</td> <td>  1.8e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 5]</th> <td>-2.374e+05</td> <td> 1.51e+05</td> <td>   -1.577</td> <td> 0.115</td> <td>-5.33e+05</td> <td> 5.77e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 6]</th> <td> 7.099e+04</td> <td> 1.33e+05</td> <td>    0.532</td> <td> 0.595</td> <td>-1.91e+05</td> <td> 3.33e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 7]</th> <td>-3.181e+05</td> <td> 1.67e+05</td> <td>   -1.909</td> <td> 0.056</td> <td>-6.45e+05</td> <td> 8437.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WARD[T.Ward 8]</th> <td>-3.898e+05</td> <td> 1.99e+05</td> <td>   -1.961</td> <td> 0.050</td> <td>-7.79e+05</td> <td> -245.772</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>YR_RMDL</th>        <td> 3.834e+04</td> <td> 2460.656</td> <td>   15.580</td> <td> 0.000</td> <td> 3.35e+04</td> <td> 4.32e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>103980.415</td> <th>  Durbin-Watson:     </th>   <td>   0.790</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>90565281.824</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td>13.565</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>195.227</td>  <th>  Cond. No.          </th>   <td>2.62e+05</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  PRICE   R-squared:                       0.029\n",
       "Model:                            OLS   Adj. R-squared:                  0.028\n",
       "Method:                 Least Squares   F-statistic:                     211.8\n",
       "Date:                Wed, 13 Mar 2019   Prob (F-statistic):               0.00\n",
       "Time:                        14:01:55   Log-Likelihood:            -1.0057e+06\n",
       "No. Observations:               57674   AIC:                         2.011e+06\n",
       "Df Residuals:                   57665   BIC:                         2.011e+06\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept      -7.625e+07   4.93e+06    -15.472      0.000   -8.59e+07   -6.66e+07\n",
       "WARD[T.Ward 2]  1.197e+06   1.36e+05      8.785      0.000     9.3e+05    1.46e+06\n",
       "WARD[T.Ward 3]  4.281e+06   1.39e+05     30.772      0.000    4.01e+06    4.55e+06\n",
       "WARD[T.Ward 4] -1.123e+05   1.49e+05     -0.753      0.452   -4.05e+05     1.8e+05\n",
       "WARD[T.Ward 5] -2.374e+05   1.51e+05     -1.577      0.115   -5.33e+05    5.77e+04\n",
       "WARD[T.Ward 6]  7.099e+04   1.33e+05      0.532      0.595   -1.91e+05    3.33e+05\n",
       "WARD[T.Ward 7] -3.181e+05   1.67e+05     -1.909      0.056   -6.45e+05    8437.544\n",
       "WARD[T.Ward 8] -3.898e+05   1.99e+05     -1.961      0.050   -7.79e+05    -245.772\n",
       "YR_RMDL         3.834e+04   2460.656     15.580      0.000    3.35e+04    4.32e+04\n",
       "==============================================================================\n",
       "Omnibus:                   103980.415   Durbin-Watson:                   0.790\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         90565281.824\n",
       "Skew:                          13.565   Prob(JB):                         0.00\n",
       "Kurtosis:                     195.227   Cond. No.                     2.62e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.62e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remod.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3631b0208>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NysnOJkq6Q9LGi7PvQRAECxFBNduSQ4HbSCFs3gaOAX6pxAjgc0Al1/FjOeHa1sBFpDhp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\nMy50UHKFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b302764c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#running all the corr data through a heatmap.\n",
    "#It is quite fine, and not perhaps that obviously useful\n",
    "#but it remains interesting in potential\n",
    "fineheat = dcl_df.corr()\n",
    "\n",
    "sns.heatmap(fineheat, vmin=-1, annot=False)\n",
    "#plt.xticks(rotation=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>Obviously, the number of bathrooms and number of bedrooms is correlated. Larger homes usually have more of each! It appears there is also heavy correlation between longitude and latitude, which makes sense (the homes are not in the exact same spot, when one moves N, S, E, or W, it is likely we move a bit in the another direction as well).\n",
    "</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158957, 49)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcl_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158589, 49)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking for the standard deviation of price, to graph\n",
    "#already have the mean, media, mode from Pt.1, but maybe this is\n",
    "#the more elegant way to devise it?\n",
    "\n",
    "meanprice = dcl_df['PRICE'].mean()\n",
    "stdprice = dcl_df['PRICE'].std()\n",
    "toprange = meanprice + stdprice * 1.96\n",
    "botrange = meanprice - stdprice * 1.96\n",
    "\n",
    "copydf = dcl_df.copy() #to not mess up the original df\n",
    "copydf = copydf.drop(copydf[copydf['PRICE'] > toprange].index)\n",
    "copydf = copydf.drop(copydf[copydf['PRICE'] < botrange].index)\n",
    "\n",
    "copydf.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#on standard deviation PRICE and WARD?\n",
    "#What about year remodelled and price? Maybe newere(feature) homes\n",
    "#are more expensive...\n",
    "#commented out to reduce clutter\n",
    "\n",
    "#stdev1 = smf.ols('PRICE ~ WARD', data=copydf).fit()\n",
    "#stdev1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#running the standard WARD and PRICE through a heatmap\n",
    "#It is quite fine, and not perhaps that obviously useful\n",
    "#but it remains interesting in potential\n",
    "\n",
    "#no clue how to remove all of the redundant data from a heatmap\n",
    "#I looked at the seaborn documentation, but there doesn't\n",
    "#appear to be a chart-level way to do this...and I have to figure out\n",
    "#which features I am trying to measure by...and how to get the data\n",
    "#to fit a weird shape...\n",
    "#PRICE by WARD (corr. in heatmap)hmm....\n",
    "\n",
    "\n",
    "\n",
    "#wardheat = copydf.corr()   #literally the same as above: useless\n",
    " \n",
    "#sns.heatmap(wardheat, vmin=-1, annot=False)\n",
    "#plt.xticks(rotation=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3034e08d0>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hjsQ5O289p/X22JeWhDASZ62/1vOr4Sfpb953niUWY4wxjorsvGKJxRhjwk6ET95bYjHG\nmHBjicUYY4yjIvsbXSyxGGNM2LHJe2OMMU5SSyzGGGMcZXMsxhhjHBXZecUSizHGhB0bCjPGGOMo\nSyzGGGMcZYnFGGOMoyyxGGOMcZJGdl6xxGKMMWHHeizGGGMcZZ9jMcE4PTOVO3OH4BJ4L6+AV3Yc\n8CuPcQkPjhvBiNREKt1NzFm1lfy6BkalJnHPqUMBEIQXtuWxJL80FE04bn/85c3MmjmeopJKJl1w\nX6jDCdo5A9J4eNpQokR4fXM+z67Z51d+w6n9+Pqo3jSrUlrXyP0LtnGwugGAFy7KZVx2T1YequC/\nP9gYivDbdVbfNO6fPIQoEd7akc/zG/b7lU/M6sl9k4cyIi2R+xZv4cO84taytddMY3t5DQCHahq4\nc8Gmbo29rTOyUrlr7BBcCPPzCvjLdv+2xLiEhyaMYGRKEpWNTTzyny3k1zW0lmcnxPHyeRN4YUse\nr+488lp0AX+aPo6iejf3rwhhG6MjO7FE+FedRQYX8L2xQ7h3xUauXbCGmX0zGZSU4FfnogHZVDU2\ncdXHq3lj10FuGZUDwK6qWm5aso4bF6/j3hUbuefUoURFyHPu5b8tYva1Pwt1GMfFJTDn7GHcMH8D\n//XaSi4ZlsmwtB5+dTYVV3Pp/67hojdW88GuYh6YOri17H/W7uf7H23p7rCPySXw4BlDue2jjcx+\nZxWzcjIZkuLfrkM1DTy8bCvv7y486viG5hYun7+Gy+evCXlScQF3nzqUez7dyDUfr+b8fpnkJPu/\nni4emE2Vu4krPlrF6zsPcOuYHL/yO3IHs6Kg7KhzXz60L3ura7sw+iCJBPcX1KnkQhHZKiI7ROSB\nDup9TURURCZ1NnxHE4t4LBWRWT77vi4i/xSRZhFZKyIbRORdEUnt4Dw5IlLnrb9JRF4SkRhv2Qxv\n42/0qT/eu+8e7/Y8EdktIutEZJv3+H4+9feISIaTbe/IqLRkDtTUc6i2gSZVPjpYxLTevfzqTOvd\ni3/u97ygFx0qZkJmCuB5QTd7f/061uUiqB/CDhPLPttCaXl1qMM4LqdlJbO3oo59VfU0tijzdxRx\nfk66X53lByuob2oBYG1BJb0T41rLPjlQTk1jc7fGHIyx6cnkVdWzv7qephblgz1FnDvA/zl4sKaB\nbeW1aJg/yUalJbO/pp6D3tfTvw8UMa23/2M0rU86H+zzvJ4WHixmYsaRy83ZvXtxsKae3VX+CSQz\nPpap2b14d29B1zfiWFwS3N8xiEgU8HtgFjAauFJERgeolwzcCaxwJHwnTnKYqipwCzBXROJFJBH4\nCXA7UKeq41Q1Fyj17uvITlUdB4wF+gNf9ylbD3zDZ/sKYF2b4+9V1dOAkcAaYIGIxJ5g0zolIz6W\nwjp363ZRvZvM+LgAdTxd9WaFmsYmUmI9I5WjUpN4ccZ4Xpgxnl99vrM10RjnZSfGcajmyJBJfk0D\n2YntP20uP6U3i/KOfucbbrJ6xJHv066CWjfZPeI6OMJfbJSL1748jr/MOo3zBqQf+4AulOnzWgEo\nqmsgMz623TrNCjVNntdTfJSLq4f354WteUed986xQ3hm4+6wSKwqEtRfEE4HdqjqLlV1A68BswPU\newz4BVDvRPyOD4Wp6gbgXeB+4EfAS6q6s021T4F+bY9t53zNwGdt6ucB8SKSLSICXAh80M7xqqq/\nBvLxZO1uF+jh1zZ9DwnwJDn8BN9cXs11C9dw85J1XDOsP7ERvmIknB3Pv+zs4VmMzUrmf9buO3bl\nEAt0DTqe6+eX/vczrnh/LQ8s2cp9k4fQPynesdiOVzBtCfiaU7jxlIG8sfMgdc0tfmVnZqdR3tDI\n1ooax+LsFFeQf8fWD/B9gu6nzbVXRMYDA1R1fmfDPqyrJu9/DKwG3IDfeJ23azYTeD6YE4lIPHAG\ncFebojeBy/H0RlYDDXRsNXAK8HaQ93sTcBPAs88+C33GBHNYQEX1brISjryjyoyPpbje7V+nroGs\nhDiK6t1ECSTGRFPZ2ORXZ291HXXNLQxOTmRrRWQNMUWK/JoG+vgMbfVOjKOgxn1UvTP7pXLbxIFc\n9fY63C1h8Bb3GApqGvyG7LJ7xFJYe6yXzBFF3h73/up6VuZXMKpXEvurHXlze9wK69xkJRxpS2ZC\n3FGvp8J6t//rKdrzehqdlsyMvhncOiaHpJhoVJWGlhYy42M5q3cvpmSnEetykRgdxcMTRvDY6m3d\n3TyPIN88+l6nvJ5T1ed8qwQ4rPUJKyIu4NfA9ccfZPu6JLGoao2IvA5Uq+rhZ2+CiKwFcoBVwIfH\nOM1Qb/3hwJuq+nmb8jeA1/Eki1eBM49xvuN6m+99cA4/QPqXd5cdz+F+tpRX0T8xgT7eJ/rMvpk8\nunqrX51lBaVc2D+LjWVVTO+TweriCgD6JMRRWN9As3pWsgxMSiC/LjQv6C+CzwuryElNoH9yPAU1\nDVw8LJPv/dt/Mn50RiKPTx/ODe+tp6SuMUSRHp8NJVUMSo6nX1IcBbVuZuVkcv+Srcc+EOgZG01d\nUzONLUpqXDTjsnry5437j31gF9lSXsWAxAT69IijqM7N+f0y+fGqNq+n/FJmDfC8nmb0zWB1cTkA\nty9d31rnhpEDqWtq5q3dhwB4dvNeAManp3DFsH6hSyoAUcF1R9pcpwLZDwzw2e4PHPTZTgZygYXe\nUZPewDsi8hVVXXk8IfvqyuXGLd6/w+pUdZyIpADz8cyx/LaD43d66/fB0+ivqOo7hwtVNV9EGoEL\n8PRmjpVYxgMfnUhDOqtZ4akNu3hyyhhcAu/vK2RPdR03jBzI1vJqlhWU8l5eAQ+OH8Ffz5tAlbuJ\nOd7EMza9J1cP609TSwsKzF2/kwp3U8d3GCZe/N0dnD11FBlpyexY8TSPzX2TF19fGOqwOtSs8OMl\nO5h3cS4uEd7cks/2slq+O3kQ64uq+GhPKQ9MHUJiTBS/+5JnDvRgdQM3e5cWv3bpaQxJTSAxJoql\n3zyDHyzcxpJ9oZ+DaVb46Wc7+eP5uUSJ8PcdBeysqOX20waxsaSKhftLGZOexG9mjCY5NprpA3px\n27iBXPbOaganJPCjKcNpUcUlwvMb9rGrInQrp5oV5n6+k7lTc1uX7++uquXGUwaypbyaZfmlzN+b\nz8MTRvLazIlUNjYxZ2X4rdTrkHOj3f8BhovIYOAAnvnoqw4XqmoF0LqQSUQWAvd0JqkAiHbRTJWI\nzMHTY3nSu12tqkne2+PxDEkNVdWj3vKJSA4w3zvRj4hcBtynqlNFZAaehl8sImcCWar6D9/7E5F5\n3uPf9M7B3OH9G6OqbhHZA0xS1eK2990OPacTPZZws/iSs1pvJwy8MoSROKsu71UAhj6zOMSROGfn\nree03h770pIQRuKs9deeDcC0t5eGOBJnLZ09DRxICzk/eC+oC/OeJy465n2JyJeBp4Ao4M+q+hMR\neRRY6ftm3Vt3IQ4klpB8QFJV14jIOjzZ8+UgDvkHMEdEzm5znk86OOaXIvIw0ANYDpzrXRVx2Oci\ncrhH9Yaq3h18C4wxpgs5uEBHVd8H3m+z75F26s5w4j67LLGo6pw220ltti/p4Ng9eMb9Dm8rcJpP\nlYUd3Z+qXn+M2HI6KjfGmJCyr3QxxhjjqEj5eo12hDSxiMhYjh4Ka1DVM0IRjzHGhIUI/6xaSBOL\nqq4HxoUyBmOMCTuWWIwxxjgpyK9rCVuWWIwxJtxE+PfOW2IxxphwYz0WY4wxjoqO7C6LJRZjjAk3\nkd1hscRijDHhRm1VmDHGGEfZHIsxxhhHWY/FGGOMk1xRoY6gcyyxGGNMmInwkTBLLMYYE24iPbF0\n2Q99nWTsH8kYE6xOp4WhzywO6pqz89ZzwjIFWY/FGGPCTKT3WCyxGGNMmLHE8gVxyYcnz++Nv3vB\nkV94Phl/Hz5h4JUhjsQ5dXmvtt4+591lIYzEWYsvOQuAsS+dPK8rgPXXnn3sSkGwVWHGGGMcFeEf\nY7HEYowx4caGwowxxjjKEosxxhhHSYRnFkssxhgTZiSyf47FEosxxoQblyUWY4wxTorwkTBLLMYY\nE25subExxhhHWY/FGGOMoyyxGGOMcZQrKrIziyUWY4wJM9ZjMcYY4yhLLMYYYxxlq8JMh24aOYSJ\nGb1oaG7hNxu3srOq5qg6Q5OT+O6YEcRGuVhVXMpzW3f5lV82qB83jBjC1Qs/pbKxiTMye3H10BwU\npVmVP23dxabyyu5qkp9zBqTx8LShRInw+uZ8nl2zz6/8hlP78fVRvWlWpbSukfsXbONgdQMAL1yU\ny7jsnqw8VMF/f7AxFOGfkD/+8mZmzRxPUUklky64L9ThdOj0zFTuzB2CS+C9vAJe2XHArzzGJTw4\nbgQjUhOpdDcxZ9VW8usamJSRws2jcohxCY0tyjOb9rC6pAKAmX0z+Obw/ihQXO/m8TXbqHA3haB1\nHmf1TeP+yUOIEuGtHfk8v2G/X/nErJ7cN3koI9ISuW/xFj7MK/YrT4yJ4u3ZE/k4r4SffrazO0Nv\nV6T3WEL6+U4RaRaRtSKyTkRWi8iZ3v05IlLnLTv8d623bI+IrPf+bRKRx0UkLsBxm0TkJRGJ8ZbN\nEBEVkRt97n+8d989XdG+iRlp9O2RwM3LVvL7zdu5ddSwgPVuGzWMpzdv5+ZlK+nbI4GJ6WmtZRlx\nsYzrlUZhXX3rvnWl5dy5fDV3LV/Dbzdu447Rw7si/GNyCcw5exg3zN/Af722kkuGZTIsrYdfnU3F\n1Vz6v2u46I3VfLCrmAemDm4t+5+1+/n+R1u6O+xOe/lvi5h97c9CHcYxuYDvjR3CvSs2cu2CNczs\nm8mgpAS/OhcNyKaqsYmrPl7NG7sOcsuoHAAq3E088Nlmrl+0lp+u3c6D4z3PsSiBO3MHc9enG/jW\norXsrKzhqzl9urllR7gEHjxjKLd9tJHZ76xiVk4mQ1L8n4OHahp4eNlW3t9dGPAc3xk3iFUFFd0R\nbtDEFdxfUOcSuVBEtorIDhF5IEB5nIi87i1fISI5nY0/1F8cUKeq41T1NOAHwBM+ZTu9ZYf/XvIp\nO1dVxwKnA0OA59oeB4wF+gNf9ylbD3zDZ/sKYJ2D7fEzJTOdjw95nsxbK6pIjI4mLTbGr05abAw9\noqPYWlEFwMeHCpmSld5a/u2RQ3lh+258fwC7vrml9XZcVBQa1K9jO++0rGT2VtSxr6qexhZl/o4i\nzs9J96uz/GAF9U2eeNcWVNI7Ma617JMD5dQ0NndrzE5Y9tkWSsurQx3GMY1KS+ZATT2HahtoUuWj\ng0VM693Lr8603r34537Pc3TRoWImZKYAsL2yhpIGNwC7q2qJjXIR4xJAEIT4KM8vUSVGR1Nc7+6+\nRrUxNj2ZvKp69lfX09SifLCniHMH+LfxYE0D28prA75ORvdKIj0+lk8OlnVTxMFxuSSov2MRkSjg\n98AsYDRwpYiMblPtRqBMVYcBvwZ+3un4O3sCB/UEjuvRVdVq4BbgUhHp1aasGfgM6OezOw+IF5Fs\n8Xx96IXAB52KugPpcbEU1ze0bpfUu0mPj/OvEx/nV6e4voH0uFgATs/sRUlDA3uqjx4+m5KZzjNn\nTuRH48fwm03buqgFHctOjONQzZHY82sayE6Mbbf+5af0ZlFeeL2AT2YZ8bEU1h256BfVu8ls8/zz\n1PE8hs0KNY1NpMT6j5BP75PO9ooaGls8Q6+/Wr+TedPH8fcLJpOTnMB7eQVd35h2ZPWII9/nOVhQ\n6ya7R1wHRxwhwD2TBvOrVbuOWbe7iQT3F4TTgR2quktV3cBrwOw2dWYDL3pvvwnMlE5+vXKoE0uC\nd9hqC/An4DGfsqFthsIC/uanqlYCuwG/8SARiQfOAP7Z5pA3gcuBM4HVQANd5ujHpu27pkCPngJx\nLhdfHzyAV3buDXjm5UUl3PrJKn6ydhPXDB3U+VBPwPE882YPz2JsVjL/s3bfsSsbRwR+bvk/AQNd\nP3yfozlJCdwyahBPfu6Ze4gS4dJBvblx8Tou+/A/7Kys5Zrh/Z0M+7gEuvwF24G/YmQflhwoo6A2\ndD2u9jiYWPoBvi+6/fi/2faro6pNQAWQTieEevK+zjtshYhMBV4SkVxv2eEhrWD4/hMPFZG1eBLN\nm6r6eZu6bwCvA6cAr+JJMEefUOQm4CaAZ599FgaPCiqQL/fvw3/17w3A9ooqMnzeIabHx1La4J/H\niusb/OpkxMdR2uCmd494shOAyRN+AAAgAElEQVTi+e2UCZ79cXE8dcZ47v5sLeXuxtb6G8sr6dMj\ngZ4x0VQ2du8Ean5NA318hrZ6J8ZRUHP0i/TMfqncNnEgV729DndLiMbtvoCK6t1kJRzpQWbGxx41\nbFVU10BWQhxF9W6iBBJ9nkeZ8bH8ZPIofrJmOwdrPXN8w3smArRuLzhYzNXD2l6nuk9BTYPf8Gp2\nj1gKa4N7r3haZk8mZPfkGyP70CM6ihiXUNvUzFOr93RRtMELtr/ge53yek5VfacG2nvvynHWOS6h\nTiytVPVTEckAMo/nOBFJBnKAbUAK3oQkIn2AhSLyFVV9x+d+8kWkEbgAuIt2Eov3wTn8AOm7Hy4J\nKp739x/i/f2HAJiUkcbFA/qyOL+IkSnJ1DY1U+aTFADK3I3UNTUzMiWZrRVVnNcni3f3HWRvdS3f\nXLSitd6fpk3m7hVrqGxsok9CPIe8k/lDkxOJFun2pALweWEVOakJ9E+Op6CmgYuHZfK9f/tPxo/O\nSOTx6cO54b31lNQ1tnMm0xW2lFfRPzGBPt7EMbNvJo+u3upXZ1lBKRf2z2JjWRXT+2SwutgziZ0U\nHcXPTx/Nc1v2sqGsqrV+Ub2bnOQepMRGU+FuYlJmKnur67q1Xb42lFQxKDmefklxFNS6mZWTyf1L\nth77QOCBpUfqzR6axZj05LBIKhD8cuM216lA9gMDfLb7AwfbqbNfRKLxXEdLg401kLBJLCJyChAF\nlAA9jlH98DFJwB+Af6hqmYikHC5T1UPeFRA/AN5pc+gjQJaqNnflL7WtLC5jUkYvnjtrkme5sc9c\nyG+mjOeu5WsA+MOWHZ7lxi4Xq4rLWFXc8TzEmdkZnNcniyZV3M0t/GJ9aFZWNSv8eMkO5l2ci0uE\nN7fks72slu9OHsT6oio+2lPKA1OHkBgTxe++5JkvPFjdwM3epcWvXXoaQ1ITSIyJYuk3z+AHC7ex\nZF/4z8G8+Ls7OHvqKDLSktmx4mkem/smL76+MNRhHaVZ4akNu3hyyhhcAu/vK2RPdR03jBzI1vJq\nlhWU8l5eAQ+OH8Ffz5tAlbuJOd7E89XBfeiXGM+1w/tzrXeo6/vLN1HS4OaFbft4+syxNKmSX9vA\nE2u3h7SNP/1sJ388P5coEf6+o4CdFbXcftogNpZUsXB/KWPSk/jNjNEkx0YzfUAvbhs3kMveWR2y\nmIPh4OdY/gMMF5HBwAE8C5aualPnHeA64FPga8DHqp1bEiSdPL5TRKQZz0ot8HTHfqiq73mXu20G\nfN96/FlVfysie4Aqb30X8HfgMVWt9x43X1VzvecXYC3wHTxJ6x5VvbhNDHOAalV9soNQ9ZIgeyyR\n4N0LjkxXDX1mcQgjcdbOW88BIGHglSGOxDl1ea+23j7n3WUhjMRZiy85C4CxL508ryuA9deeDcc3\n/RjQrH8tDerC/MGXph3zvkTky8BTeK6Bf1bVn4jIo8BKVX3HOx/9MjAeT0/lClXt1IqGkPZYVDWq\nnf17gIR2ynI6ON8eINdnW4HTfKosDHDMnCBCNcaYbuPkqipVfR94v82+R3xu1+NZ0OSYsBkKM8YY\n4+GSyF7kYonFGGPCjH1XmDHGGEeF+gOGnWWJxRhjwkyUy4bCjDHGOMiGwowxxjjKhsKMMcY4ylaF\nGWOMcZQNhRljjHGUDYUZY4xxVLStCjPGGOMkGwozxhjjKBsKM8YY4yhbFWaMMcZRNhRmjDHGUZZY\njDHGOCo6wofCQvoLkhHE/pGMMcHqdH/j7hUfB3XNmXvGeWHZt7EeizHGhBlbFfYFcdXCRaEOwTF/\nnTG99fbJ9Jvj3t8bPyl/Gx4gYeCVIYzEWXV5rwLwaeF7IY7EWVOzLnLkPDbHYowxxlES4XMslliM\nMSbMRFuPxRhjjJPsA5LGGGMcZXMsxhhjHGWJxRhjjKOiQh1AJ1liMcaYMGNzLMYYYxwVHeGfkLTE\nYowxYSbK5liMMcY4ySbvjTHGOMrmWIwxxjjKeizGGGMcZcuNjTHGOCraZUNhxhhjHGSrwky7VJV9\nr79O5Yb1uGJjybn+enoMHHRUvZq9e9kz7wW0sZGeuWMZ8I1vICLU7ttH3iuv0NxQT1x6BoNvvJGo\nhAQaiovZOOdHxGdnA5A4ZAiDrr6mu5sHwFl907h/8hCiRHhrRz7Pb9jvVz4xqyf3TR7KiLRE7lu8\nhQ/zilvL1l4zje3lNQAcqmngzgWbujV2X6dnpnJn7hBcAu/lFfDKjgN+5TEu4cFxIxiRmkilu4k5\nq7aSX9fApIwUbh6VQ4xLaGxRntm0h9UlFQDM7JvBN4f3R4HiejePr9lGhbspBK07tj/+8mZmzRxP\nUUklky64L9ThnDBV5ZXf/J3Pl28mNi6Wb//wSnJG9j+q3hN3/J6Kkkpi4mIAuHfuzfRMS+7ucNt1\n0s+xiEi1qiYF2H8TcLd3sxK4W1WXessWAkmqOsm7PQl4UlVneLdPB34B9AOqgEPAA6q6XkTmANXA\nYOAsINZ7e6v3vh4HvgPco6orvefLAearaq6IzADeBnYBPYAC4BeqOt9bdw7w30CRT3NmqGr5sf4t\njlflhg00FBYw5rHHqdm9m72vvMKoH/zwqHp5f32FQdd8k8QhQ9jxu99SuXEDKblj2fvyS/T/2tdI\nHjGS4mVLyf/Xv+g3ezYAcZmZjH74EadDPi4ugQfPGMpNH24gv7aB1748jgX7StlVUdta51BNAw8v\n28p1Y45+cTc0t3D5/DXdGXJALuB7Y4dw9/KNFNW5ee7s01iaX8re6rrWOhcNyKaqsYmrPl7NeX0z\nuGVUDnNWb6XC3cQDn22mpMHN4OQePHnGaP7fv1cSJXBn7mCuXbiGCncTt4waxFdz+vDCtn2ha2gH\nXv7bIv744v/xp1/fFupQOuXz5Zsp2F/Mz1/9ITs37eWlX73JI899N2Ddmx+5hsGnDOjmCIPTXYlF\nRHoBrwM5wB7g66pa1k7dnsBm4O+q+p2OzntCn+8UkYuBm4FpqnoKcAvwVxHp7VMtS0RmBTg2G3gD\n+KGqDlfVCcATwFDfeqp6u6qOA74M7FTVcd6/N4MIcYmqjlfVkcCdwNMiMtOn/Nc+5xvXFUkFoHzd\nWtKnTEVESBoyhOa6Ohor/O+qsaKc5ro6koYORURInzKV8rVrAagvKCBp+AgAeo4aTfma1V0R5gkb\nm55MXlU9+6vraWpRPthTxLkDevnVOVjTwLbyWjSMh4xHpSVzoKaeQ7UNNKny0cEipvX2b8e03r34\n5/5CABYdKmZCZgoA2ytrKGlwA7C7qpbYKBcxLgEEQYiP8kzDJkZHU1zv7r5GHadln22htLw61GF0\n2pqlGzjrwkmICMPG5FBbXUd5cWWowzpuLgnuzwEPAB+p6nDgI+92ex4Dgvop3RP94oD7gXtVtRhA\nVVcDLwK3+9T5JfBQgGO/A7yoqp8c3qGqS1X1HycYS4dUdS3wqPd+u1VjeTmxvdJat2NT03CX+ScW\nd1k5sWlH6sSkpdFY7qmT0Lc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kJtCnRxxFdW7O75fJj1dt9auzLL+UWQOy2FhWxYy+Gawu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\nMDgQC6gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b303476d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#trying to make a new dataframe to look at WARD and PRICE more rigorously\n",
    "#here we go:\n",
    "\n",
    "wardpr = dcl_df[['WARD','PRICE','YR_RMDL','BEDRM','LONGITUDE','LATITUDE']]\n",
    "\n",
    "warheat = wardpr.corr()\n",
    "\n",
    "sns.heatmap(warheat, vmin=-1, annot=True, linewidths=1.5, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>A somewhat basic heatmap, true. But really a quite useful task to learn about. I would imagine introducing more variables would look pretty. But many of these do not seem to be quite so useful. Although we can see that Longitude and Latitude are, again, highly correlated!\n",
    "</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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