Project 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project 2\n",
    "\n",
    "In Project, we calculated information about the most expensive property, when the highest price was sold, and the average prices per WARD in Washington DC. \n",
    "In this project, we are going to visualize the data, and show where the relevant data and outliers are located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Step\n",
    "Create Data Frame from DC Properties CSV data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2698: DtypeWarning: Columns (33,43) 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",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SALEDATE</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>FULLADDRESS</th>\n",
       "      <th>CENSUS_BLOCK</th>\n",
       "      <th>WARD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-11-25 00:00:00</td>\n",
       "      <td>1095000.0</td>\n",
       "      <td>1748 SWANN STREET NW</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-08-17 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1746 SWANN STREET NW</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-21 00:00:00</td>\n",
       "      <td>2100000.0</td>\n",
       "      <td>1744 SWANN STREET NW</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-07-12 00:00:00</td>\n",
       "      <td>1602000.0</td>\n",
       "      <td>1742 SWANN STREET NW</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1804 NEW HAMPSHIRE AVENUE NW</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010-02-26 00:00:00</td>\n",
       "      <td>1950000.0</td>\n",
       "      <td>1709 S STREET NW</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-05-02 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1769 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-09-29 00:00:00</td>\n",
       "      <td>1050000.0</td>\n",
       "      <td>1746 1/2 T STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-05-03 00:00:00</td>\n",
       "      <td>1430000.0</td>\n",
       "      <td>1727 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008-12-05 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1733 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1755 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2009-03-12 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1734 T STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1717 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1719 1/2 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011-09-30 00:00:00</td>\n",
       "      <td>1325000.0</td>\n",
       "      <td>1721 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1723 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2010-11-04 00:00:00</td>\n",
       "      <td>1240000.0</td>\n",
       "      <td>1725 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2008-05-08 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1729 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-08-18 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1731 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2001-07-23 00:00:00</td>\n",
       "      <td>592250.0</td>\n",
       "      <td>1735 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016-01-12 00:00:00</td>\n",
       "      <td>33232.0</td>\n",
       "      <td>1441 Q STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-04-30 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1443 Q STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2006-01-09 00:00:00</td>\n",
       "      <td>907400.0</td>\n",
       "      <td>1445 Q STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2012-08-21 00:00:00</td>\n",
       "      <td>1065000.0</td>\n",
       "      <td>1447 Q STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2012-07-25 00:00:00</td>\n",
       "      <td>1100000.0</td>\n",
       "      <td>1449 Q STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1996-04-02 00:00:00</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1451 Q STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1458 CORCORAN STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2005-04-06 00:00:00</td>\n",
       "      <td>703700.0</td>\n",
       "      <td>1456 CORCORAN STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2015-02-06 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1454 CORCORAN STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013-12-11 00:00:00</td>\n",
       "      <td>1210000.0</td>\n",
       "      <td>1452 CORCORAN STREET NW</td>\n",
       "      <td>005201 4003</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>2007-03-08 00:00:00</td>\n",
       "      <td>1080000.0</td>\n",
       "      <td>2124 O STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>2010-01-14 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2138 O STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>2014-04-11 00:00:00</td>\n",
       "      <td>280745.0</td>\n",
       "      <td>2136 O STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>2016-10-27 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2134 O STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>2009-06-05 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2132 O STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>2013-03-22 00:00:00</td>\n",
       "      <td>1387000.0</td>\n",
       "      <td>2130 O STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>2015-12-09 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2128 O STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>2016-02-19 00:00:00</td>\n",
       "      <td>1120160.0</td>\n",
       "      <td>1331 22ND STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>2003-09-22 00:00:00</td>\n",
       "      <td>785000.0</td>\n",
       "      <td>2111 N STREET NW</td>\n",
       "      <td>005500 4002</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2134 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4002</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>2006-12-22 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2153 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2145 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2016-02-25 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1335 22ND STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>2005-01-14 00:00:00</td>\n",
       "      <td>725000.0</td>\n",
       "      <td>2117 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2115 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>2015-07-15 00:00:00</td>\n",
       "      <td>1050000.0</td>\n",
       "      <td>2132 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4002</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>2008-05-15 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2130 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4002</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>2011-08-04 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2128 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4002</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>2010-12-02 00:00:00</td>\n",
       "      <td>499000.0</td>\n",
       "      <td>2151 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2149 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2017-09-12 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2147 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1333 22ND STREET NW</td>\n",
       "      <td>005500 4001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>2012-08-20 00:00:00</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>2120 NEWPORT PLACE NW</td>\n",
       "      <td>005500 4002</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>2017-04-21 00:00:00</td>\n",
       "      <td>1037250.0</td>\n",
       "      <td>2128 N STREET NW</td>\n",
       "      <td>005500 3001</td>\n",
       "      <td>Ward 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1719 6TH STREET NW</td>\n",
       "      <td>004801 1007</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1721 6TH STREET NW</td>\n",
       "      <td>004801 1007</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2006-11-21 00:00:00</td>\n",
       "      <td>575000.0</td>\n",
       "      <td>1707 6TH STREET NW</td>\n",
       "      <td>004801 1007</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2014-06-04 00:00:00</td>\n",
       "      <td>737500.0</td>\n",
       "      <td>1709 6TH STREET NW</td>\n",
       "      <td>004801 1007</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2015-03-25 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>517 RHODE ISLAND AVENUE NW</td>\n",
       "      <td>004801 1007</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1999-05-04 00:00:00</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>519 RHODE ISLAND AVENUE NW</td>\n",
       "      <td>004801 1007</td>\n",
       "      <td>Ward 6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                SALEDATE      PRICE                   FULLADDRESS  \\\n",
       "0    2003-11-25 00:00:00  1095000.0          1748 SWANN STREET NW   \n",
       "1    2000-08-17 00:00:00        NaN          1746 SWANN STREET NW   \n",
       "2    2016-06-21 00:00:00  2100000.0          1744 SWANN STREET NW   \n",
       "3    2006-07-12 00:00:00  1602000.0          1742 SWANN STREET NW   \n",
       "4                    NaN        NaN  1804 NEW HAMPSHIRE AVENUE NW   \n",
       "5    2010-02-26 00:00:00  1950000.0              1709 S STREET NW   \n",
       "6    2011-05-02 00:00:00        NaN          1769 SWANN STREET NW   \n",
       "7    2011-09-29 00:00:00  1050000.0          1746 1/2 T STREET NW   \n",
       "8    2018-05-03 00:00:00  1430000.0          1727 SWANN STREET NW   \n",
       "9    2008-12-05 00:00:00        NaN          1733 SWANN STREET NW   \n",
       "10                   NaN        NaN          1755 SWANN STREET NW   \n",
       "11   2009-03-12 00:00:00        NaN              1734 T STREET NW   \n",
       "12                   NaN        NaN          1717 SWANN STREET NW   \n",
       "13                   NaN        NaN      1719 1/2 SWANN STREET NW   \n",
       "14   2011-09-30 00:00:00  1325000.0          1721 SWANN STREET NW   \n",
       "15                   NaN        NaN          1723 SWANN STREET NW   \n",
       "16   2010-11-04 00:00:00  1240000.0          1725 SWANN STREET NW   \n",
       "17   2008-05-08 00:00:00        NaN          1729 SWANN STREET NW   \n",
       "18   2016-08-18 00:00:00        NaN          1731 SWANN STREET NW   \n",
       "19   2001-07-23 00:00:00   592250.0          1735 SWANN STREET NW   \n",
       "20   2016-01-12 00:00:00    33232.0              1441 Q STREET NW   \n",
       "21   2014-04-30 00:00:00        NaN              1443 Q STREET NW   \n",
       "22   2006-01-09 00:00:00   907400.0              1445 Q STREET NW   \n",
       "23   2012-08-21 00:00:00  1065000.0              1447 Q STREET NW   \n",
       "24   2012-07-25 00:00:00  1100000.0              1449 Q STREET NW   \n",
       "25   1996-04-02 00:00:00    20000.0              1451 Q STREET NW   \n",
       "26                   NaN        NaN       1458 CORCORAN STREET NW   \n",
       "27   2005-04-06 00:00:00   703700.0       1456 CORCORAN STREET NW   \n",
       "28   2015-02-06 00:00:00        NaN       1454 CORCORAN STREET NW   \n",
       "29   2013-12-11 00:00:00  1210000.0       1452 CORCORAN STREET NW   \n",
       "..                   ...        ...                           ...   \n",
       "970  2007-03-08 00:00:00  1080000.0              2124 O STREET NW   \n",
       "971  2010-01-14 00:00:00        NaN              2138 O STREET NW   \n",
       "972  2014-04-11 00:00:00   280745.0              2136 O STREET NW   \n",
       "973  2016-10-27 00:00:00        NaN              2134 O STREET NW   \n",
       "974  2009-06-05 00:00:00        NaN              2132 O STREET NW   \n",
       "975  2013-03-22 00:00:00  1387000.0              2130 O STREET NW   \n",
       "976  2015-12-09 00:00:00        NaN              2128 O STREET NW   \n",
       "977  2016-02-19 00:00:00  1120160.0           1331 22ND STREET NW   \n",
       "978  2003-09-22 00:00:00   785000.0              2111 N STREET NW   \n",
       "979                  NaN        NaN         2134 NEWPORT PLACE NW   \n",
       "980  2006-12-22 00:00:00        NaN         2153 NEWPORT PLACE NW   \n",
       "981                  NaN        NaN         2145 NEWPORT PLACE NW   \n",
       "982  2016-02-25 00:00:00        NaN           1335 22ND STREET NW   \n",
       "983  2005-01-14 00:00:00   725000.0         2117 NEWPORT PLACE NW   \n",
       "984                  NaN        NaN         2115 NEWPORT PLACE NW   \n",
       "985  2015-07-15 00:00:00  1050000.0         2132 NEWPORT PLACE NW   \n",
       "986  2008-05-15 00:00:00        NaN         2130 NEWPORT PLACE NW   \n",
       "987  2011-08-04 00:00:00        NaN         2128 NEWPORT PLACE NW   \n",
       "988  2010-12-02 00:00:00   499000.0         2151 NEWPORT PLACE NW   \n",
       "989                  NaN        NaN         2149 NEWPORT PLACE NW   \n",
       "990  2017-09-12 00:00:00        NaN         2147 NEWPORT PLACE NW   \n",
       "991                  NaN        NaN           1333 22ND STREET NW   \n",
       "992  2012-08-20 00:00:00   850000.0         2120 NEWPORT PLACE NW   \n",
       "993  2017-04-21 00:00:00  1037250.0              2128 N STREET NW   \n",
       "994                  NaN        NaN            1719 6TH STREET NW   \n",
       "995                  NaN        NaN            1721 6TH STREET NW   \n",
       "996  2006-11-21 00:00:00   575000.0            1707 6TH STREET NW   \n",
       "997  2014-06-04 00:00:00   737500.0            1709 6TH STREET NW   \n",
       "998  2015-03-25 00:00:00        NaN    517 RHODE ISLAND AVENUE NW   \n",
       "999  1999-05-04 00:00:00   114000.0    519 RHODE ISLAND AVENUE NW   \n",
       "\n",
       "    CENSUS_BLOCK    WARD  \n",
       "0    004201 2006  Ward 2  \n",
       "1    004201 2006  Ward 2  \n",
       "2    004201 2006  Ward 2  \n",
       "3    004201 2006  Ward 2  \n",
       "4    004201 2006  Ward 2  \n",
       "5    004201 2006  Ward 2  \n",
       "6    004201 2005  Ward 2  \n",
       "7    004201 2005  Ward 2  \n",
       "8    004201 2005  Ward 2  \n",
       "9    004201 2005  Ward 2  \n",
       "10   004201 2005  Ward 2  \n",
       "11   004201 2005  Ward 2  \n",
       "12   004201 2005  Ward 2  \n",
       "13   004201 2005  Ward 2  \n",
       "14   004201 2005  Ward 2  \n",
       "15   004201 2005  Ward 2  \n",
       "16   004201 2005  Ward 2  \n",
       "17   004201 2005  Ward 2  \n",
       "18   004201 2005  Ward 2  \n",
       "19   004201 2005  Ward 2  \n",
       "20   005201 4003  Ward 2  \n",
       "21   005201 4003  Ward 2  \n",
       "22   005201 4003  Ward 2  \n",
       "23   005201 4003  Ward 2  \n",
       "24   005201 4003  Ward 2  \n",
       "25   005201 4003  Ward 2  \n",
       "26   005201 4003  Ward 2  \n",
       "27   005201 4003  Ward 2  \n",
       "28   005201 4003  Ward 2  \n",
       "29   005201 4003  Ward 2  \n",
       "..           ...     ...  \n",
       "970  005500 4001  Ward 2  \n",
       "971  005500 4001  Ward 2  \n",
       "972  005500 4001  Ward 2  \n",
       "973  005500 4001  Ward 2  \n",
       "974  005500 4001  Ward 2  \n",
       "975  005500 4001  Ward 2  \n",
       "976  005500 4001  Ward 2  \n",
       "977  005500 4001  Ward 2  \n",
       "978  005500 4002  Ward 2  \n",
       "979  005500 4002  Ward 2  \n",
       "980  005500 4001  Ward 2  \n",
       "981  005500 4001  Ward 2  \n",
       "982  005500 4001  Ward 2  \n",
       "983  005500 4001  Ward 2  \n",
       "984  005500 4001  Ward 2  \n",
       "985  005500 4002  Ward 2  \n",
       "986  005500 4002  Ward 2  \n",
       "987  005500 4002  Ward 2  \n",
       "988  005500 4001  Ward 2  \n",
       "989  005500 4001  Ward 2  \n",
       "990  005500 4001  Ward 2  \n",
       "991  005500 4001  Ward 2  \n",
       "992  005500 4002  Ward 2  \n",
       "993  005500 3001  Ward 2  \n",
       "994  004801 1007  Ward 6  \n",
       "995  004801 1007  Ward 6  \n",
       "996  004801 1007  Ward 6  \n",
       "997  004801 1007  Ward 6  \n",
       "998  004801 1007  Ward 6  \n",
       "999  004801 1007  Ward 6  \n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location = \"DC_Properties.csv\"\n",
    "df = pd.read_csv(location, \n",
    "                 usecols=[\"SALEDATE\", \"PRICE\", \"WARD\", \"CENSUS_BLOCK\", \"FULLADDRESS\"])\n",
    "df\n",
    "df.dtypes \n",
    "df.head(1000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Question\n",
    "\n",
    "Which neighborhood has the most expensive property?\n",
    "\n",
    "To accomplish this step, we eliminated nulls from PRICE and FULLADDRESS columns.\n",
    "Then we sorted by PRICE and FULLADDRESS and found the max price:\n",
    "\n",
    "2327 GOOD HOPE COURT SE          25100000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x260d12dab70>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x260c3645b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_price=df[df['FULLADDRESS'].notnull() & df['PRICE'].notnull()]\n",
    "sorted_by_price = df_price.groupby(['PRICE', 'FULLADDRESS'], sort=True) ['PRICE'].max()\n",
    "sorted_by_price\n",
    "df_price['FULLADDRESS']=df_price['FULLADDRESS'].apply(lambda x: x.split(None,1)[1])\n",
    "df_price\n",
    "# The violin plot was good in the sense on looking at the magnitude of the data, but \n",
    "# not good at displaying details of the data.\n",
    "plt.subplots(figsize=(10,6))\n",
    "sns.set(style=\"whitegrid\")\n",
    "#sns.violinplot(x='FULLADDRESS', y='PRICE', data=df_price)\n",
    "sns.boxplot(data=df_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to plot Neighborhood price information to show where the expensive homes are located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second Question\n",
    "When houses sold at the highest price?\n",
    "\n",
    "To accomplish this step, we eliminated nulls from PRICE, SALEDATE, and FULLADDRESS columns.\n",
    "Then we sorted by PRICE and SALEDATE and found the max price:\n",
    "\n",
    "2018-02-15 00:00:00    25100000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n"
     ]
    },
    {
     "data": {
      "image/png": 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6dUs1NTUvnL906ZLKy8tVWVmp1tZW7z/kJsh3333nfvDBB67ruu7Nmzfdd955J3atv7/f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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x260d012cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_date=df[df['FULLADDRESS'].notnull() & df['PRICE'].notnull() & df['SALEDATE'].notnull()]\n",
    "sorted_by_date = df_price.groupby(['PRICE','SALEDATE'], sort=True) ['PRICE'].max()\n",
    "sorted_by_date\n",
    "df_date\n",
    "sns.lmplot(x='PRICE', y='SALEDATE', data=df_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Third Question\n",
    "What are the average prices per WARD in Washington DC?\n",
    "\n",
    "For our dataset, we grouped by WARD and PRICE and then calculated the mean for each ward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD\n",
       "Ward 1    5.738363e+05\n",
       "Ward 2    1.098570e+06\n",
       "Ward 3    3.077716e+06\n",
       "Ward 4    4.899224e+05\n",
       "Ward 5    3.952432e+05\n",
       "Ward 6    5.268684e+05\n",
       "Ward 7    2.339222e+05\n",
       "Ward 8    2.715224e+05\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('WARD')['PRICE'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We are going to plot Ward price information to show where the expensive homes are located."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x260ae3a5eb8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AQeA7ZrbF3b9f8bZ3AyPu/rNmth74FPDrSdUkIrVVDgMlbbbZP3s2Xlsn7Guc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\niYhkrfecQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x260a7de6a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_price=df[df['FULLADDRESS'].notnull() & df['PRICE'].notnull()]\n",
    "df_price['WARD']=df_price['WARD'].apply(lambda x: x.replace('Ward ', '') )\n",
    "df_price['WARD'] = pd.to_numeric(df_price['WARD'])\n",
    "df_price.dtypes\n",
    "#ward_price_df = df.groupby('WARD')['PRICE'].mean()\n",
    "sns.lmplot(x='WARD', y='PRICE', data=df_price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x260aed0c400>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DyJytRNVTiipWypL2Ize4ev/8qp8C10XEqkYG1m66xoxiw7MvbVve8JuX6dp9VzatW7/d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x260aed01be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show correlation of stats via heatmap\n",
    "corr = df_price.corr()\n",
    "\n",
    "sns.heatmap(corr, vmin=-1, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x260af8896d8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0jL6fP3sGK955BodGSkzrymBmvPjSIX646UUG9xzk3a9/FWf29vDoc3tZ/fA2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\nIiLSNv8f3SGHqHWVb2QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x260af89b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a histogram of the distribution of attack power\n",
    "sns.distplot(df_price['PRICE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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>SALEDATE</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>FULLADDRESS</th>\n",
       "      <th>CENSUS_BLOCK</th>\n",
       "      <th>WARD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-11-25 00:00:00</td>\n",
       "      <td>1095000.0</td>\n",
       "      <td>1748 SWANN STREET NW</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-07-12 00:00:00</td>\n",
       "      <td>1602000.0</td>\n",
       "      <td>1742 SWANN STREET NW</td>\n",
       "      <td>004201 2006</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-09-29 00:00:00</td>\n",
       "      <td>1050000.0</td>\n",
       "      <td>1746 1/2 T STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-05-03 00:00:00</td>\n",
       "      <td>1430000.0</td>\n",
       "      <td>1727 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011-09-30 00:00:00</td>\n",
       "      <td>1325000.0</td>\n",
       "      <td>1721 SWANN STREET NW</td>\n",
       "      <td>004201 2005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SALEDATE      PRICE           FULLADDRESS CENSUS_BLOCK  WARD\n",
       "0   2003-11-25 00:00:00  1095000.0  1748 SWANN STREET NW  004201 2006     2\n",
       "3   2006-07-12 00:00:00  1602000.0  1742 SWANN STREET NW  004201 2006     2\n",
       "7   2011-09-29 00:00:00  1050000.0  1746 1/2 T STREET NW  004201 2005     2\n",
       "8   2018-05-03 00:00:00  1430000.0  1727 SWANN STREET NW  004201 2005     2\n",
       "14  2011-09-30 00:00:00  1325000.0  1721 SWANN STREET NW  004201 2005     2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meangrade = df_price['PRICE'].mean()\n",
    "stdgrade = df_price['PRICE'].std()\n",
    "\n",
    "toprange = meangrade + stdgrade * 1.96\n",
    "botrange = meangrade - stdgrade * 1.96\n",
    "\n",
    "copydf = df_price.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.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x260b170e0b8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NB/wuBwAAjBLCCKJSXUunJDEzMo7dvHiqLp+bp68+tUP7jjX7XQ4AABgFhBFE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x260b171c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a histogram of the distribution of attack power\n",
    "plt.figure(figsize=(13,7))\n",
    "sns.distplot(copydf['PRICE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}