Pandas Journey final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "711013000"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RPSALE = pd.read_csv('data/EXTR_RPSale.csv', low_memory=False)\n",
    "RPSALE[\"SalePrice\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>ExciseTaxNbr</th>\n",
       "      <th>Major</th>\n",
       "      <th>Minor</th>\n",
       "      <th>DocumentDate</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>RecordingNbr</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Page</th>\n",
       "      <th>PlatNbr</th>\n",
       "      <th>PlatType</th>\n",
       "      <th>...</th>\n",
       "      <th>PropertyType</th>\n",
       "      <th>PrincipalUse</th>\n",
       "      <th>SaleInstrument</th>\n",
       "      <th>AFForestLand</th>\n",
       "      <th>AFCurrentUseLand</th>\n",
       "      <th>AFNonProfitUse</th>\n",
       "      <th>AFHistoricProperty</th>\n",
       "      <th>SaleReason</th>\n",
       "      <th>PropertyClass</th>\n",
       "      <th>SaleWarning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2642952</td>\n",
       "      <td>720319</td>\n",
       "      <td>0520</td>\n",
       "      <td>11/20/2013</td>\n",
       "      <td>699950</td>\n",
       "      <td>20131126001594</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1381073</td>\n",
       "      <td>154580</td>\n",
       "      <td>1220</td>\n",
       "      <td>06/16/1994</td>\n",
       "      <td>125000</td>\n",
       "      <td>199406211626</td>\n",
       "      <td>089</td>\n",
       "      <td>011</td>\n",
       "      <td>154580</td>\n",
       "      <td>P</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2496482</td>\n",
       "      <td>356730</td>\n",
       "      <td>0045</td>\n",
       "      <td>06/09/2011</td>\n",
       "      <td>542200</td>\n",
       "      <td>20110617001045</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970507</td>\n",
       "      <td>252505</td>\n",
       "      <td>9206</td>\n",
       "      <td>06/25/2003</td>\n",
       "      <td>443000</td>\n",
       "      <td>20030702002332</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1701215</td>\n",
       "      <td>235930</td>\n",
       "      <td>0072</td>\n",
       "      <td>07/08/1999</td>\n",
       "      <td>225000</td>\n",
       "      <td>19990730000822</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ExciseTaxNbr   Major Minor DocumentDate  SalePrice    RecordingNbr Volume  \\\n",
       "0       2642952  720319  0520   11/20/2013     699950  20131126001594          \n",
       "1       1381073  154580  1220   06/16/1994     125000  199406211626      089   \n",
       "2       2496482  356730  0045   06/09/2011     542200  20110617001045          \n",
       "3       1970507  252505  9206   06/25/2003     443000  20030702002332          \n",
       "4       1701215  235930  0072   07/08/1999     225000  19990730000822          \n",
       "\n",
       "  Page PlatNbr PlatType     ...     PropertyType PrincipalUse SaleInstrument  \\\n",
       "0                           ...                2            6              3   \n",
       "1  011  154580        P     ...                3            6              2   \n",
       "2                           ...                3            6              3   \n",
       "3                           ...                3            6              3   \n",
       "4                           ...                3            6              3   \n",
       "\n",
       "  AFForestLand  AFCurrentUseLand  AFNonProfitUse  AFHistoricProperty  \\\n",
       "0            N                 N               N                   N   \n",
       "1            N                 N               N                   N   \n",
       "2                                                                      \n",
       "3            N                 N               N                   N   \n",
       "4            N                 N               N                   N   \n",
       "\n",
       "  SaleReason PropertyClass SaleWarning  \n",
       "0          1             8              \n",
       "1          1             8              \n",
       "2          1             8              \n",
       "3          1             8              \n",
       "4          1             8              \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RPSALE.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RPSALE['MergeID'] = RPSALE['Major']+RPSALE['Minor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1928494"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(RPSALE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RPSALE_TEST = RPSALE.drop_duplicates(subset='MergeID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "662074"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(RPSALE_TEST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "COMBUILD = pd.read_csv('data/EXTR_CommBldg.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Major</th>\n",
       "      <th>Minor</th>\n",
       "      <th>BldgNbr</th>\n",
       "      <th>NbrBldgs</th>\n",
       "      <th>Address</th>\n",
       "      <th>BuildingNumber</th>\n",
       "      <th>Fraction</th>\n",
       "      <th>DirectionPrefix</th>\n",
       "      <th>StreetName</th>\n",
       "      <th>StreetType</th>\n",
       "      <th>...</th>\n",
       "      <th>BldgQuality</th>\n",
       "      <th>BldgDescr</th>\n",
       "      <th>BldgGrossSqFt</th>\n",
       "      <th>BldgNetSqFt</th>\n",
       "      <th>YrBuilt</th>\n",
       "      <th>EffYr</th>\n",
       "      <th>PcntComplete</th>\n",
       "      <th>HeatingSystem</th>\n",
       "      <th>Sprinklers</th>\n",
       "      <th>Elevators</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192105</td>\n",
       "      <td>9114</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1407         AUBURN                    WAY  S ...</td>\n",
       "      <td>1407</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>AUBURN</td>\n",
       "      <td>WAY</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>RETAIL                                        ...</td>\n",
       "      <td>21296</td>\n",
       "      <td>21296</td>\n",
       "      <td>1967</td>\n",
       "      <td>1980</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>N</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>212206</td>\n",
       "      <td>9057</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>24409        WITTE                     RD   SE...</td>\n",
       "      <td>24409</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>WITTE</td>\n",
       "      <td>RD</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>Duplex                                        ...</td>\n",
       "      <td>1248</td>\n",
       "      <td>1248</td>\n",
       "      <td>1960</td>\n",
       "      <td>1990</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>384610</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>927       N  100TH                     ST     ...</td>\n",
       "      <td>927</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>100TH</td>\n",
       "      <td>ST</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>SEATTLE HOUSING AUTH                          ...</td>\n",
       "      <td>7892</td>\n",
       "      <td>7892</td>\n",
       "      <td>1986</td>\n",
       "      <td>1991</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>881640</td>\n",
       "      <td>755</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5229         UNIVERSITY                WAY  NE...</td>\n",
       "      <td>5229</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>UNIVERSITY</td>\n",
       "      <td>WAY</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>OFFICE &amp; APTS                                 ...</td>\n",
       "      <td>2274</td>\n",
       "      <td>2274</td>\n",
       "      <td>1963</td>\n",
       "      <td>1974</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30351</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1519         WEST VALLEY               HWY  N ...</td>\n",
       "      <td>1519</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>WEST VALLEY</td>\n",
       "      <td>HWY</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>OFFICE BLDG                                   ...</td>\n",
       "      <td>20428</td>\n",
       "      <td>16288</td>\n",
       "      <td>1993</td>\n",
       "      <td>1999</td>\n",
       "      <td>100</td>\n",
       "      <td>11</td>\n",
       "      <td>Y</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Major  Minor  BldgNbr  NbrBldgs  \\\n",
       "0  192105   9114        1         1   \n",
       "1  212206   9057        3         1   \n",
       "2  384610     10        1         2   \n",
       "3  881640    755        1         1   \n",
       "4   30351     60        1         1   \n",
       "\n",
       "                                             Address BuildingNumber Fraction  \\\n",
       "0  1407         AUBURN                    WAY  S ...          1407             \n",
       "1  24409        WITTE                     RD   SE...          24409            \n",
       "2  927       N  100TH                     ST     ...          927              \n",
       "3  5229         UNIVERSITY                WAY  NE...          5229             \n",
       "4  1519         WEST VALLEY               HWY  N ...          1519             \n",
       "\n",
       "  DirectionPrefix                 StreetName StreetType    ...      \\\n",
       "0                  AUBURN                          WAY     ...       \n",
       "1                  WITTE                           RD      ...       \n",
       "2              N   100TH                           ST      ...       \n",
       "3                  UNIVERSITY                      WAY     ...       \n",
       "4                  WEST VALLEY                     HWY     ...       \n",
       "\n",
       "  BldgQuality                                          BldgDescr  \\\n",
       "0           4  RETAIL                                        ...   \n",
       "1           4  Duplex                                        ...   \n",
       "2           4  SEATTLE HOUSING AUTH                          ...   \n",
       "3           4  OFFICE & APTS                                 ...   \n",
       "4           4  OFFICE BLDG                                   ...   \n",
       "\n",
       "   BldgGrossSqFt  BldgNetSqFt  YrBuilt  EffYr  PcntComplete HeatingSystem  \\\n",
       "0          21296        21296     1967   1980           100             6   \n",
       "1           1248         1248     1960   1990           100             1   \n",
       "2           7892         7892     1986   1991           100             2   \n",
       "3           2274         2274     1963   1974           100             1   \n",
       "4          20428        16288     1993   1999           100            11   \n",
       "\n",
       "   Sprinklers  Elevators  \n",
       "0           N             \n",
       "1                         \n",
       "2           N          N  \n",
       "3           N             \n",
       "4           Y             \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COMBUILD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "COMBUILD['MergeID'] = COMBUILD['Major'].map(str)+COMBUILD['Minor'].map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "COMBUILD['Comm_or_Res'] = 'Comm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "COMBUILD = COMBUILD.drop_duplicates(subset='MergeID',keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "first_merge = pd.merge(RPSALE, COMBUILD, how='left', on='MergeID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1928494 1928494\n"
     ]
    }
   ],
   "source": [
    "print len(RPSALE), len(first_merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RESBUILD = pd.read_csv('data/EXTR_ResBldg.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Major</th>\n",
       "      <th>Minor</th>\n",
       "      <th>BldgNbr</th>\n",
       "      <th>NbrLivingUnits</th>\n",
       "      <th>Address</th>\n",
       "      <th>BuildingNumber</th>\n",
       "      <th>Fraction</th>\n",
       "      <th>DirectionPrefix</th>\n",
       "      <th>StreetName</th>\n",
       "      <th>StreetType</th>\n",
       "      <th>...</th>\n",
       "      <th>FpAdditional</th>\n",
       "      <th>YrBuilt</th>\n",
       "      <th>YrRenovated</th>\n",
       "      <th>PcntComplete</th>\n",
       "      <th>Obsolescence</th>\n",
       "      <th>PcntNetCondition</th>\n",
       "      <th>Condition</th>\n",
       "      <th>AddnlCost</th>\n",
       "      <th>MergeID</th>\n",
       "      <th>Comm_or_Res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26910   86TH AVE S  98030</td>\n",
       "      <td>26910</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>86TH</td>\n",
       "      <td>AVE</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1915</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>401</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>922   22ND ST NE  98002</td>\n",
       "      <td>922</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>22ND</td>\n",
       "      <td>ST</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1952</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10045</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>923   28TH ST NE  98002</td>\n",
       "      <td>923</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>28TH</td>\n",
       "      <td>ST</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10090</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>911   14TH ST NE  98002</td>\n",
       "      <td>911</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>14TH</td>\n",
       "      <td>ST</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1914</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10095</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17760   MARINE VIEW DR SW  98166</td>\n",
       "      <td>17760</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>MARINE VIEW</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1942</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1207</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Major  Minor  BldgNbr  NbrLivingUnits                           Address  \\\n",
       "0     40      1        1               1         26910   86TH AVE S  98030   \n",
       "1    100     45        1               1           922   22ND ST NE  98002   \n",
       "2    100     90        1               2           923   28TH ST NE  98002   \n",
       "3    100     95        1               1           911   14TH ST NE  98002   \n",
       "4    120      7        1               1  17760   MARINE VIEW DR SW  98166   \n",
       "\n",
       "  BuildingNumber Fraction DirectionPrefix                 StreetName  \\\n",
       "0          26910                           86TH                        \n",
       "1          922                             22ND                        \n",
       "2          923                             28TH                        \n",
       "3          911                             14TH                        \n",
       "4          17760                           MARINE VIEW                 \n",
       "\n",
       "  StreetType     ...      FpAdditional YrBuilt  YrRenovated  PcntComplete  \\\n",
       "0     AVE        ...                 0    1915            0             0   \n",
       "1     ST         ...                 0    1952            0             0   \n",
       "2     ST         ...                 0    1977            0             0   \n",
       "3     ST         ...                 0    1914            0             0   \n",
       "4     DR         ...                 0    1942            0             0   \n",
       "\n",
       "   Obsolescence  PcntNetCondition  Condition  AddnlCost  MergeID  Comm_or_Res  \n",
       "0             0                 0          3          0      401          Res  \n",
       "1             0                 0          4          0    10045          Res  \n",
       "2             0                 0          4          0    10090          Res  \n",
       "3             0                 0          4          0    10095          Res  \n",
       "4             0                 0          4          0     1207          Res  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RESBUILD['MergeID'] = RESBUILD['Major'].map(str)+RESBUILD['Minor'].map(str)\n",
    "RESBUILD['Comm_or_Res'] = 'Res'\n",
    "RESBUILD = RESBUILD.drop_duplicates(subset='MergeID',keep='first')\n",
    "RESBUILD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501840"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(RESBUILD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comm_and_res = pd.merge(RESBUILD, COMBUILD, how='inner', on='MergeID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\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>Major_x</th>\n",
       "      <th>Minor_x</th>\n",
       "      <th>BldgNbr_x</th>\n",
       "      <th>NbrLivingUnits</th>\n",
       "      <th>Address_x</th>\n",
       "      <th>BuildingNumber_x</th>\n",
       "      <th>Fraction_x</th>\n",
       "      <th>DirectionPrefix_x</th>\n",
       "      <th>StreetName_x</th>\n",
       "      <th>StreetType_x</th>\n",
       "      <th>...</th>\n",
       "      <th>BldgDescr</th>\n",
       "      <th>BldgGrossSqFt</th>\n",
       "      <th>BldgNetSqFt</th>\n",
       "      <th>YrBuilt_y</th>\n",
       "      <th>EffYr</th>\n",
       "      <th>PcntComplete_y</th>\n",
       "      <th>HeatingSystem</th>\n",
       "      <th>Sprinklers</th>\n",
       "      <th>Elevators</th>\n",
       "      <th>Comm_or_Res_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>300</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13535   53RD AVE S  98168</td>\n",
       "      <td>13535</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>53RD</td>\n",
       "      <td>AVE</td>\n",
       "      <td>...</td>\n",
       "      <td>2 COMMERCIAL SPACES OFFICE/RETAIL             ...</td>\n",
       "      <td>2389</td>\n",
       "      <td>2389</td>\n",
       "      <td>2002</td>\n",
       "      <td>2002</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Comm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4100</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14829   42ND AVE S  98168</td>\n",
       "      <td>14829</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>42ND</td>\n",
       "      <td>AVE</td>\n",
       "      <td>...</td>\n",
       "      <td>Lake Vista                                    ...</td>\n",
       "      <td>4653</td>\n",
       "      <td>4653</td>\n",
       "      <td>1980</td>\n",
       "      <td>1988</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Comm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6600</td>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6031   38TH AVE SW  98126</td>\n",
       "      <td>6031</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>38TH</td>\n",
       "      <td>AVE</td>\n",
       "      <td>...</td>\n",
       "      <td>21 RESIDENTIAL UNITS 2 COML UNIT              ...</td>\n",
       "      <td>4867</td>\n",
       "      <td>4867</td>\n",
       "      <td>1997</td>\n",
       "      <td>1997</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>Y</td>\n",
       "      <td>Comm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>952810</td>\n",
       "      <td>1240</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>5 Unit Apartment                              ...</td>\n",
       "      <td>6172</td>\n",
       "      <td>5536</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Comm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6600</td>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6016   38TH AVE SW  98126</td>\n",
       "      <td>6016</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>38TH</td>\n",
       "      <td>AVE</td>\n",
       "      <td>...</td>\n",
       "      <td>PACIFIC PLACE SHOPPING MALL &amp; PKG GAR.        ...</td>\n",
       "      <td>779969</td>\n",
       "      <td>339784</td>\n",
       "      <td>1999</td>\n",
       "      <td>2000</td>\n",
       "      <td>100</td>\n",
       "      <td>17</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Comm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Major_x  Minor_x  BldgNbr_x  NbrLivingUnits                  Address_x  \\\n",
       "0      300      100          1               1  13535   53RD AVE S  98168   \n",
       "1     4100      200          1               1  14829   42ND AVE S  98168   \n",
       "2     6600      280          1               1  6031   38TH AVE SW  98126   \n",
       "3   952810     1240          1               5                              \n",
       "4     6600      470          1               1  6016   38TH AVE SW  98126   \n",
       "\n",
       "  BuildingNumber_x Fraction_x DirectionPrefix_x               StreetName_x  \\\n",
       "0            13535                               53RD                        \n",
       "1            14829                               42ND                        \n",
       "2            6031                                38TH                        \n",
       "3                                           NaN                              \n",
       "4            6016                                38TH                        \n",
       "\n",
       "  StreetType_x      ...        \\\n",
       "0       AVE         ...         \n",
       "1       AVE         ...         \n",
       "2       AVE         ...         \n",
       "3                   ...         \n",
       "4       AVE         ...         \n",
       "\n",
       "                                           BldgDescr BldgGrossSqFt  \\\n",
       "0  2 COMMERCIAL SPACES OFFICE/RETAIL             ...          2389   \n",
       "1  Lake Vista                                    ...          4653   \n",
       "2  21 RESIDENTIAL UNITS 2 COML UNIT              ...          4867   \n",
       "3  5 Unit Apartment                              ...          6172   \n",
       "4  PACIFIC PLACE SHOPPING MALL & PKG GAR.        ...        779969   \n",
       "\n",
       "   BldgNetSqFt  YrBuilt_y  EffYr  PcntComplete_y  HeatingSystem  Sprinklers  \\\n",
       "0         2389       2002   2002             100              3               \n",
       "1         4653       1980   1988             100              2               \n",
       "2         4867       1997   1997             100              0               \n",
       "3         5536       1998   1998             100              2               \n",
       "4       339784       1999   2000             100             17           Y   \n",
       "\n",
       "   Elevators  Comm_or_Res_y  \n",
       "0                      Comm  \n",
       "1                      Comm  \n",
       "2          Y           Comm  \n",
       "3                      Comm  \n",
       "4          Y           Comm  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comm_and_res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(comm_and_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comm_and_res_ids = comm_and_res['MergeID'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sale_comm_res = pd.merge(first_merge, RESBUILD, how='left', on='MergeID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ExciseTaxNbr</th>\n",
       "      <th>Major_x</th>\n",
       "      <th>Minor_x</th>\n",
       "      <th>DocumentDate</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>RecordingNbr</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Page</th>\n",
       "      <th>PlatNbr</th>\n",
       "      <th>PlatType</th>\n",
       "      <th>...</th>\n",
       "      <th>FpFreestanding</th>\n",
       "      <th>FpAdditional</th>\n",
       "      <th>YrBuilt_y</th>\n",
       "      <th>YrRenovated</th>\n",
       "      <th>PcntComplete_y</th>\n",
       "      <th>Obsolescence</th>\n",
       "      <th>PcntNetCondition</th>\n",
       "      <th>Condition</th>\n",
       "      <th>AddnlCost</th>\n",
       "      <th>Comm_or_Res_y</th>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1381073</td>\n",
       "      <td>154580</td>\n",
       "      <td>1220</td>\n",
       "      <td>06/16/1994</td>\n",
       "      <td>125000</td>\n",
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       "      <td>154580</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970507</td>\n",
       "      <td>252505</td>\n",
       "      <td>9206</td>\n",
       "      <td>06/25/2003</td>\n",
       "      <td>443000</td>\n",
       "      <td>20030702002332</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1701215</td>\n",
       "      <td>235930</td>\n",
       "      <td>0072</td>\n",
       "      <td>07/08/1999</td>\n",
       "      <td>225000</td>\n",
       "      <td>19990730000822</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ExciseTaxNbr Major_x Minor_x DocumentDate  SalePrice    RecordingNbr  \\\n",
       "0       2642952  720319    0520   11/20/2013     699950  20131126001594   \n",
       "1       1381073  154580    1220   06/16/1994     125000  199406211626     \n",
       "2       2496482  356730    0045   06/09/2011     542200  20110617001045   \n",
       "3       1970507  252505    9206   06/25/2003     443000  20030702002332   \n",
       "4       1701215  235930    0072   07/08/1999     225000  19990730000822   \n",
       "\n",
       "  Volume Page PlatNbr PlatType      ...      FpFreestanding FpAdditional  \\\n",
       "0                                   ...                 NaN          NaN   \n",
       "1    089  011  154580        P      ...                 0.0          0.0   \n",
       "2                                   ...                 NaN          NaN   \n",
       "3                                   ...                 0.0          0.0   \n",
       "4                                   ...                 NaN          NaN   \n",
       "\n",
       "  YrBuilt_y YrRenovated  PcntComplete_y  Obsolescence  PcntNetCondition  \\\n",
       "0       NaN         NaN             NaN           NaN               NaN   \n",
       "1    1985.0         0.0             0.0           0.0               0.0   \n",
       "2       NaN         NaN             NaN           NaN               NaN   \n",
       "3    2001.0         0.0             0.0           0.0               0.0   \n",
       "4       NaN         NaN             NaN           NaN               NaN   \n",
       "\n",
       "  Condition AddnlCost Comm_or_Res_y  \n",
       "0       NaN       NaN           NaN  \n",
       "1       3.0       0.0           Res  \n",
       "2       NaN       NaN           NaN  \n",
       "3       3.0       0.0           Res  \n",
       "4       NaN       NaN           NaN  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sale_comm_res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1928494 1928494 1928494\n"
     ]
    }
   ],
   "source": [
    "print len(RPSALE), len(first_merge), len(sale_comm_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>ExciseTaxNbr</th>\n",
       "      <th>Major_x</th>\n",
       "      <th>Minor_x</th>\n",
       "      <th>DocumentDate</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>RecordingNbr</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Page</th>\n",
       "      <th>PlatNbr</th>\n",
       "      <th>PlatType</th>\n",
       "      <th>...</th>\n",
       "      <th>FpFreestanding</th>\n",
       "      <th>FpAdditional</th>\n",
       "      <th>YrBuilt_y</th>\n",
       "      <th>YrRenovated</th>\n",
       "      <th>PcntComplete_y</th>\n",
       "      <th>Obsolescence</th>\n",
       "      <th>PcntNetCondition</th>\n",
       "      <th>Condition</th>\n",
       "      <th>AddnlCost</th>\n",
       "      <th>Comm_or_Res_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103921</th>\n",
       "      <td>2858041</td>\n",
       "      <td>952810</td>\n",
       "      <td>1240</td>\n",
       "      <td>04/07/2017</td>\n",
       "      <td>20000</td>\n",
       "      <td>20170411000680</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784415</th>\n",
       "      <td>2206600</td>\n",
       "      <td>952810</td>\n",
       "      <td>1240</td>\n",
       "      <td>05/11/2006</td>\n",
       "      <td>0</td>\n",
       "      <td>20060515000683</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ExciseTaxNbr Major_x Minor_x DocumentDate  SalePrice    RecordingNbr  \\\n",
       "103921       2858041  952810    1240   04/07/2017      20000  20170411000680   \n",
       "784415       2206600  952810    1240   05/11/2006          0  20060515000683   \n",
       "\n",
       "       Volume Page PlatNbr PlatType      ...      FpFreestanding FpAdditional  \\\n",
       "103921                                   ...                 0.0          0.0   \n",
       "784415                                   ...                 0.0          0.0   \n",
       "\n",
       "       YrBuilt_y YrRenovated  PcntComplete_y  Obsolescence  PcntNetCondition  \\\n",
       "103921    1996.0         0.0             0.0           0.0               0.0   \n",
       "784415    1996.0         0.0             0.0           0.0               0.0   \n",
       "\n",
       "       Condition AddnlCost Comm_or_Res_y  \n",
       "103921       3.0       0.0           Res  \n",
       "784415       3.0       0.0           Res  \n",
       "\n",
       "[2 rows x 103 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sale_comm_res[sale_comm_res['MergeID'].isin(comm_and_res_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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>ExciseTaxNbr</th>\n",
       "      <th>Major_x</th>\n",
       "      <th>Minor_x</th>\n",
       "      <th>DocumentDate</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>RecordingNbr</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Page</th>\n",
       "      <th>PlatNbr</th>\n",
       "      <th>PlatType</th>\n",
       "      <th>...</th>\n",
       "      <th>FpFreestanding</th>\n",
       "      <th>FpAdditional</th>\n",
       "      <th>YrBuilt_y</th>\n",
       "      <th>YrRenovated</th>\n",
       "      <th>PcntComplete_y</th>\n",
       "      <th>Obsolescence</th>\n",
       "      <th>PcntNetCondition</th>\n",
       "      <th>Condition</th>\n",
       "      <th>AddnlCost</th>\n",
       "      <th>Comm_or_Res_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1307121</td>\n",
       "      <td>440070</td>\n",
       "      <td>0200</td>\n",
       "      <td>05/05/1993</td>\n",
       "      <td>0</td>\n",
       "      <td>199305112239</td>\n",
       "      <td>062</td>\n",
       "      <td>076</td>\n",
       "      <td>440070</td>\n",
       "      <td>P</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2826129</td>\n",
       "      <td>445872</td>\n",
       "      <td>0260</td>\n",
       "      <td>10/03/2016</td>\n",
       "      <td>0</td>\n",
       "      <td>20161004000511</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2860712</td>\n",
       "      <td>408330</td>\n",
       "      <td>4150</td>\n",
       "      <td>03/15/2017</td>\n",
       "      <td>0</td>\n",
       "      <td>20170426000922</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2644191</td>\n",
       "      <td>312107</td>\n",
       "      <td>9036</td>\n",
       "      <td>12/05/2013</td>\n",
       "      <td>0</td>\n",
       "      <td>20131206000531</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1729614</td>\n",
       "      <td>172204</td>\n",
       "      <td>9157</td>\n",
       "      <td>12/21/1999</td>\n",
       "      <td>0</td>\n",
       "      <td>19991229001498</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ExciseTaxNbr Major_x Minor_x DocumentDate  SalePrice    RecordingNbr  \\\n",
       "8        1307121  440070    0200   05/05/1993          0  199305112239     \n",
       "10       2826129  445872    0260   10/03/2016          0  20161004000511   \n",
       "20       2860712  408330    4150   03/15/2017          0  20170426000922   \n",
       "21       2644191  312107    9036   12/05/2013          0  20131206000531   \n",
       "24       1729614  172204    9157   12/21/1999          0  19991229001498   \n",
       "\n",
       "   Volume Page PlatNbr PlatType      ...      FpFreestanding FpAdditional  \\\n",
       "8     062  076  440070        P      ...                 NaN          NaN   \n",
       "10                                   ...                 NaN          NaN   \n",
       "20                                   ...                 0.0          0.0   \n",
       "21                                   ...                 0.0          0.0   \n",
       "24                                   ...                 NaN          NaN   \n",
       "\n",
       "   YrBuilt_y YrRenovated  PcntComplete_y  Obsolescence  PcntNetCondition  \\\n",
       "8        NaN         NaN             NaN           NaN               NaN   \n",
       "10       NaN         NaN             NaN           NaN               NaN   \n",
       "20    1906.0         0.0             0.0           0.0               0.0   \n",
       "21    1994.0         0.0             0.0           0.0               0.0   \n",
       "24       NaN         NaN             NaN           NaN               NaN   \n",
       "\n",
       "   Condition AddnlCost Comm_or_Res_y  \n",
       "8        NaN       NaN           NaN  \n",
       "10       NaN       NaN           NaN  \n",
       "20       4.0       0.0           Res  \n",
       "21       3.0       0.0           Res  \n",
       "24       NaN       NaN           NaN  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sale_comm_res[sale_comm_res['SalePrice'] == 0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ExciseTaxNbr',\n",
       " 'Major_x',\n",
       " 'Minor_x',\n",
       " 'DocumentDate',\n",
       " 'SalePrice',\n",
       " 'RecordingNbr',\n",
       " 'Volume',\n",
       " 'Page',\n",
       " 'PlatNbr',\n",
       " 'PlatType',\n",
       " 'PlatLot',\n",
       " 'PlatBlock',\n",
       " 'SellerName',\n",
       " 'BuyerName',\n",
       " 'PropertyType',\n",
       " 'PrincipalUse',\n",
       " 'SaleInstrument',\n",
       " 'AFForestLand',\n",
       " 'AFCurrentUseLand',\n",
       " 'AFNonProfitUse',\n",
       " 'AFHistoricProperty',\n",
       " 'SaleReason',\n",
       " 'PropertyClass',\n",
       " 'SaleWarning',\n",
       " 'MergeID',\n",
       " 'Major_y',\n",
       " 'Minor_y',\n",
       " 'BldgNbr_x',\n",
       " 'NbrBldgs',\n",
       " 'Address_x',\n",
       " 'BuildingNumber_x',\n",
       " 'Fraction_x',\n",
       " 'DirectionPrefix_x',\n",
       " 'StreetName_x',\n",
       " 'StreetType_x',\n",
       " 'DirectionSuffix_x',\n",
       " 'ZipCode_x',\n",
       " 'NbrStories',\n",
       " 'PredominantUse',\n",
       " 'Shape',\n",
       " 'ConstrClass',\n",
       " 'BldgQuality',\n",
       " 'BldgDescr',\n",
       " 'BldgGrossSqFt',\n",
       " 'BldgNetSqFt',\n",
       " 'YrBuilt_x',\n",
       " 'EffYr',\n",
       " 'PcntComplete_x',\n",
       " 'HeatingSystem',\n",
       " 'Sprinklers',\n",
       " 'Elevators',\n",
       " 'Comm_or_Res_x',\n",
       " 'Major',\n",
       " 'Minor',\n",
       " 'BldgNbr_y',\n",
       " 'NbrLivingUnits',\n",
       " 'Address_y',\n",
       " 'BuildingNumber_y',\n",
       " 'Fraction_y',\n",
       " 'DirectionPrefix_y',\n",
       " 'StreetName_y',\n",
       " 'StreetType_y',\n",
       " 'DirectionSuffix_y',\n",
       " 'ZipCode_y',\n",
       " 'Stories',\n",
       " 'BldgGrade',\n",
       " 'BldgGradeVar',\n",
       " 'SqFt1stFloor',\n",
       " 'SqFtHalfFloor',\n",
       " 'SqFt2ndFloor',\n",
       " 'SqFtUpperFloor',\n",
       " 'SqFtUnfinFull',\n",
       " 'SqFtUnfinHalf',\n",
       " 'SqFtTotLiving',\n",
       " 'SqFtTotBasement',\n",
       " 'SqFtFinBasement',\n",
       " 'FinBasementGrade',\n",
       " 'SqFtGarageBasement',\n",
       " 'SqFtGarageAttached',\n",
       " 'DaylightBasement',\n",
       " 'SqFtOpenPorch',\n",
       " 'SqFtEnclosedPorch',\n",
       " 'SqFtDeck',\n",
       " 'HeatSystem',\n",
       " 'HeatSource',\n",
       " 'BrickStone',\n",
       " 'ViewUtilization',\n",
       " 'Bedrooms',\n",
       " 'BathHalfCount',\n",
       " 'Bath3qtrCount',\n",
       " 'BathFullCount',\n",
       " 'FpSingleStory',\n",
       " 'FpMultiStory',\n",
       " 'FpFreestanding',\n",
       " 'FpAdditional',\n",
       " 'YrBuilt_y',\n",
       " 'YrRenovated',\n",
       " 'PcntComplete_y',\n",
       " 'Obsolescence',\n",
       " 'PcntNetCondition',\n",
       " 'Condition',\n",
       " 'AddnlCost',\n",
       " 'Comm_or_Res_y']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(sale_comm_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sale_comm_res['Comm_or_Res'] = sale_comm_res['Comm_or_Res_x'] + sale_comm_res['Comm_or_Res_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1928494 501840 32481\n"
     ]
    }
   ],
   "source": [
    "print len(sale_comm_res), len(RESBUILD), len(COMBUILD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>MergeID</th>\n",
       "      <th>Comm_or_Res</th>\n",
       "      <th>Comm_or_Res_x</th>\n",
       "      <th>Comm_or_Res_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7203190520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1545801220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3567300045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2525059206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2359300072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8946770240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9825700015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2470200060</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4400700200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6441800110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4458720260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9358400210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9358400260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9358400180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9358400300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9358400290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9358400220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9358400280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9358400270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9358400250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4083304150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3121079036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2722059197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2841500010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1722049157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1722049005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1923049020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comm</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8857300120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1657300360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1989201430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comm</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928464</th>\n",
       "      <td>3303800170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928465</th>\n",
       "      <td>6743700299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928466</th>\n",
       "      <td>1545801860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928467</th>\n",
       "      <td>3211530280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928468</th>\n",
       "      <td>2896400600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928469</th>\n",
       "      <td>3530210010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928470</th>\n",
       "      <td>6648750220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928471</th>\n",
       "      <td>1423600030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928472</th>\n",
       "      <td>5050250080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928473</th>\n",
       "      <td>2473390160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928474</th>\n",
       "      <td>7697910030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928475</th>\n",
       "      <td>4092300885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928476</th>\n",
       "      <td>7696810060</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928477</th>\n",
       "      <td>6781200047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928478</th>\n",
       "      <td>9262800161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928479</th>\n",
       "      <td>8561101030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928480</th>\n",
       "      <td>2223089075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928481</th>\n",
       "      <td>8084900195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928482</th>\n",
       "      <td>3352400195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928483</th>\n",
       "      <td>9211510400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928484</th>\n",
       "      <td>8562740640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928485</th>\n",
       "      <td>9297301700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928486</th>\n",
       "      <td>6131600255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928487</th>\n",
       "      <td>0303550320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928488</th>\n",
       "      <td>6145601200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Res</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928489</th>\n",
       "      <td>8141401080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928490</th>\n",
       "      <td>5437400170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928491</th>\n",
       "      <td>3362400025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928492</th>\n",
       "      <td>7967200340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928493</th>\n",
       "      <td>3205200650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1928494 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            MergeID Comm_or_Res Comm_or_Res_x Comm_or_Res_y\n",
       "0        7203190520         NaN           NaN           NaN\n",
       "1        1545801220         NaN           NaN           Res\n",
       "2        3567300045         NaN           NaN           NaN\n",
       "3        2525059206         NaN           NaN           Res\n",
       "4        2359300072         NaN           NaN           NaN\n",
       "5        8946770240         NaN           NaN           NaN\n",
       "6        9825700015         NaN           NaN           NaN\n",
       "7        2470200060         NaN           NaN           NaN\n",
       "8        4400700200         NaN           NaN           NaN\n",
       "9        6441800110         NaN           NaN           NaN\n",
       "10       4458720260         NaN           NaN           NaN\n",
       "11       9358400210         NaN           NaN           NaN\n",
       "12       9358400260         NaN           NaN           NaN\n",
       "13       9358400180         NaN           NaN           NaN\n",
       "14       9358400300         NaN           NaN           NaN\n",
       "15       9358400290         NaN           NaN           NaN\n",
       "16       9358400220         NaN           NaN           NaN\n",
       "17       9358400280         NaN           NaN           NaN\n",
       "18       9358400270         NaN           NaN           NaN\n",
       "19       9358400250         NaN           NaN           NaN\n",
       "20       4083304150         NaN           NaN           Res\n",
       "21       3121079036         NaN           NaN           Res\n",
       "22       2722059197         NaN           NaN           Res\n",
       "23       2841500010         NaN           NaN           NaN\n",
       "24       1722049157         NaN           NaN           NaN\n",
       "25       1722049005         NaN           NaN           Res\n",
       "26       1923049020         NaN          Comm           NaN\n",
       "27       8857300120         NaN           NaN           NaN\n",
       "28       1657300360         NaN           NaN           NaN\n",
       "29       1989201430         NaN          Comm           NaN\n",
       "...             ...         ...           ...           ...\n",
       "1928464  3303800170         NaN           NaN           NaN\n",
       "1928465  6743700299         NaN           NaN           NaN\n",
       "1928466  1545801860         NaN           NaN           Res\n",
       "1928467  3211530280         NaN           NaN           NaN\n",
       "1928468  2896400600         NaN           NaN           NaN\n",
       "1928469  3530210010         NaN           NaN           NaN\n",
       "1928470  6648750220         NaN           NaN           NaN\n",
       "1928471  1423600030         NaN           NaN           NaN\n",
       "1928472  5050250080         NaN           NaN           NaN\n",
       "1928473  2473390160         NaN           NaN           NaN\n",
       "1928474  7697910030         NaN           NaN           NaN\n",
       "1928475  4092300885         NaN           NaN           NaN\n",
       "1928476  7696810060         NaN           NaN           NaN\n",
       "1928477  6781200047         NaN           NaN           NaN\n",
       "1928478  9262800161         NaN           NaN           NaN\n",
       "1928479  8561101030         NaN           NaN           NaN\n",
       "1928480  2223089075         NaN           NaN           Res\n",
       "1928481  8084900195         NaN           NaN           NaN\n",
       "1928482  3352400195         NaN           NaN           NaN\n",
       "1928483  9211510400         NaN           NaN           NaN\n",
       "1928484  8562740640         NaN           NaN           NaN\n",
       "1928485  9297301700         NaN           NaN           Res\n",
       "1928486  6131600255         NaN           NaN           NaN\n",
       "1928487  0303550320         NaN           NaN           NaN\n",
       "1928488  6145601200         NaN           NaN           Res\n",
       "1928489  8141401080         NaN           NaN           NaN\n",
       "1928490  5437400170         NaN           NaN           NaN\n",
       "1928491  3362400025         NaN           NaN           NaN\n",
       "1928492  7967200340         NaN           NaN           NaN\n",
       "1928493  3205200650         NaN           NaN           NaN\n",
       "\n",
       "[1928494 rows x 4 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sale_comm_res[['MergeID','Comm_or_Res','Comm_or_Res_x','Comm_or_Res_y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "sale_res = sale_comm_res[sale_comm_res[\"Comm_or_Res_y\"]=='Res']\n",
    "sale_res = sale_res[sale_res[\"SalePrice\"] > 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186285"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sale_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x180823e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0061d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sale_com = sale_comm_res[sale_comm_res[\"Comm_or_Res_x\"]=='Comm']\n",
    "sale_com = sale_com[sale_com[\"SalePrice\"] >10000]\n",
    "matplotlib.pyplot.hist(sale_res[\"SalePrice\"], bins = 100, range = (0, 1000000))\n",
    "matplotlib.pyplot.show()\n",
    "matplotlib.pyplot.hist(sale_com[\"SalePrice\"], bins = 100, range = (0, 10000000))\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "sale_com['DocumentDate'] = pd.to_datetime(sale_com['DocumentDate'])\n",
    "sale_res['DocumentDate'] = pd.to_datetime(sale_res['DocumentDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "sale_com['year'] = sale_com['DocumentDate'].dt.year\n",
    "sale_res['year'] = sale_res['DocumentDate'].dt.year\n",
    "sale_com = sale_com[sale_com['year'] >= 1982]\n",
    "sale_res = sale_res[sale_res['year'] >= 1982]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>ExciseTaxNbr</th>\n",
       "      <th>Major_x</th>\n",
       "      <th>Minor_x</th>\n",
       "      <th>DocumentDate</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>RecordingNbr</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Page</th>\n",
       "      <th>PlatNbr</th>\n",
       "      <th>PlatType</th>\n",
       "      <th>...</th>\n",
       "      <th>YrBuilt_y</th>\n",
       "      <th>YrRenovated</th>\n",
       "      <th>PcntComplete_y</th>\n",
       "      <th>Obsolescence</th>\n",
       "      <th>PcntNetCondition</th>\n",
       "      <th>Condition</th>\n",
       "      <th>AddnlCost</th>\n",
       "      <th>Comm_or_Res_y</th>\n",
       "      <th>Comm_or_Res</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1625698</td>\n",
       "      <td>252611</td>\n",
       "      <td>9029</td>\n",
       "      <td>1998-07-13</td>\n",
       "      <td>20000</td>\n",
       "      <td>199807161059</td>\n",
       "      <td>000</td>\n",
       "      <td>000</td>\n",
       "      <td>000000</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1415794</td>\n",
       "      <td>312604</td>\n",
       "      <td>9025</td>\n",
       "      <td>1995-02-06</td>\n",
       "      <td>260000</td>\n",
       "      <td>199502100116</td>\n",
       "      <td>000</td>\n",
       "      <td>000</td>\n",
       "      <td>000000</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2892617</td>\n",
       "      <td>952810</td>\n",
       "      <td>4530</td>\n",
       "      <td>2017-09-15</td>\n",
       "      <td>2019200</td>\n",
       "      <td>20170929001867</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>1350727</td>\n",
       "      <td>122505</td>\n",
       "      <td>9187</td>\n",
       "      <td>1993-11-30</td>\n",
       "      <td>300000</td>\n",
       "      <td>199312292467</td>\n",
       "      <td>000</td>\n",
       "      <td>000</td>\n",
       "      <td>000000</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>777030</td>\n",
       "      <td>239710</td>\n",
       "      <td>1505</td>\n",
       "      <td>1984-05-25</td>\n",
       "      <td>175000</td>\n",
       "      <td>198406040077</td>\n",
       "      <td>004</td>\n",
       "      <td>036</td>\n",
       "      <td>239710</td>\n",
       "      <td>P</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ExciseTaxNbr Major_x Minor_x DocumentDate  SalePrice    RecordingNbr  \\\n",
       "89        1625698  252611    9029   1998-07-13      20000  199807161059     \n",
       "95        1415794  312604    9025   1995-02-06     260000  199502100116     \n",
       "324       2892617  952810    4530   2017-09-15    2019200  20170929001867   \n",
       "684       1350727  122505    9187   1993-11-30     300000  199312292467     \n",
       "742        777030  239710    1505   1984-05-25     175000  198406040077     \n",
       "\n",
       "    Volume Page PlatNbr PlatType  ...  YrBuilt_y YrRenovated PcntComplete_y  \\\n",
       "89     000  000  000000           ...        NaN         NaN            NaN   \n",
       "95     000  000  000000           ...        NaN         NaN            NaN   \n",
       "324                               ...        NaN         NaN            NaN   \n",
       "684    000  000  000000           ...        NaN         NaN            NaN   \n",
       "742    004  036  239710        P  ...        NaN         NaN            NaN   \n",
       "\n",
       "    Obsolescence  PcntNetCondition  Condition  AddnlCost Comm_or_Res_y  \\\n",
       "89           NaN               NaN        NaN        NaN           NaN   \n",
       "95           NaN               NaN        NaN        NaN           NaN   \n",
       "324          NaN               NaN        NaN        NaN           NaN   \n",
       "684          NaN               NaN        NaN        NaN           NaN   \n",
       "742          NaN               NaN        NaN        NaN           NaN   \n",
       "\n",
       "    Comm_or_Res  year  \n",
       "89          NaN  1998  \n",
       "95          NaN  1995  \n",
       "324         NaN  2017  \n",
       "684         NaN  1993  \n",
       "742         NaN  1984  \n",
       "\n",
       "[5 rows x 105 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sale_com.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "year_avg = defaultdict(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "com_sale_by_year = sale_com.groupby([\"year\"])[\"SalePrice\"].median()\n",
    "res_sale_by_year = sale_res.groupby([\"year\"])[\"SalePrice\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1982     191666\n",
       "1983     248240\n",
       "1984     246000\n",
       "1985     189000\n",
       "1986     230000\n",
       "1987     228470\n",
       "1988     220000\n",
       "1989     315000\n",
       "1990     327500\n",
       "1991     411967\n",
       "1992     329500\n",
       "1993     419990\n",
       "1994     386000\n",
       "1995     350000\n",
       "1996     500000\n",
       "1997     610000\n",
       "1998     550000\n",
       "1999     665000\n",
       "2000     689975\n",
       "2001     700000\n",
       "2002     791512\n",
       "2003     660000\n",
       "2004     868750\n",
       "2005    1000000\n",
       "2006    1136000\n",
       "2007    1625000\n",
       "2008    1397041\n",
       "2009     976000\n",
       "2010     875000\n",
       "2011     905000\n",
       "2012    1243875\n",
       "2013    1361100\n",
       "2014    1400000\n",
       "2015    1797400\n",
       "2016    1855000\n",
       "2017    1910000\n",
       "Name: SalePrice, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com_sale_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1803c7f90>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LOmuwUkrVYMOeA0wZ2kUDSTVqDCYi8pKIpInIBq+0t0RkrbvtqFgbXkT6iUih17ZnvY4Z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\npolgEgRBEDTNP6QxLFdV2ZOlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17d932050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(com_sale_by_year)\n",
    "plt.plot(res_sale_by_year)\n",
    "plt.title(\"Median Real Estate Sale Prices in King County\")"
   ]
  },
  {
   "cell_type": "code",
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