Property Value Prediction Model Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "240a0298",
   "metadata": {},
   "source": [
    "# Property Value Prediction Model Analysis \n",
    "---\n",
    "## Introduction\n",
    "\n",
    "Permits are required in most jurisdictions whenever developers or homeowners want to alter or remodel their homes, construct new buildings, and/or demolish existing structures.  The type of permit required depends on both the local jurisdiction and the scope of work performed. In Philadelphia, Pennsylvania, permits are issued by The Department of Licenses & Inspections and the data for those permits are publicly available at https://www.opendataphilly.org/.  For this analysis, two files downloaded from [the Licenses and Inspections Building and Zoning Permits page](https://www.opendataphilly.org/dataset/licenses-and-inspections-building-permits) of this website will be used, listing information for permits from 2007 to the present.  \n",
    "\n",
    "The other data to be utilized in this analysis will be housing price data made available by Zillow at https://www.zillow.com/research/data/.  The data from Zillow is based on the Zillow Home Value Index (ZHVI) when represents the “typical home value for the region” as defined in Zillow's [methodology guide](https://www.zillow.com/research/zhvi-methodology-2019-highlights-26221/).  \n",
    "\n",
    "The goal of this analysis is to determine whether the number of permits issued in a given zip code within prior years is correlated to property value and changes of property values within the zip code.  The assumption going in is that building permits that better the buildings within a zip code should have some correlation with the future housing prices and change in housing prices.  Thus the theory is that if we're designing a machine learning model, the inclusion of permits would improve the model. \n",
    "\n",
    "The limitations for this analysis include the following:\n",
    "- The permit data available ranges back from 2007, which only gives 15 years of analysis.  \n",
    "- The housing market crash of 2008 occured during the short timeframe available within our data.  This crash would be assumed to have affected housing price data and may overshadow any correlations between housing prices and permit issuance. \n",
    "- Not all renovation work on a building requires a permit, which would limit the data available within our permit dataset.  In addition, some work is occasionally performed without proper permitting, which would again limit data available in our dataset.\n",
    "\n",
    "With all of that understood, we can begin exploring the data and performing the analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17cfd4a5",
   "metadata": {},
   "source": [
    "---\n",
    "## Data Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1559ee7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "matplotlib.rcParams['figure.figsize'] = (12,8)\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf80a117",
   "metadata": {},
   "source": [
    "### Zillow Data\n",
    "\n",
    "First we'll read the Zillow Home Data into a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3dd7fed5",
   "metadata": {},
   "outputs": [
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       "      <td>881363.0</td>\n",
       "      <td>884951.0</td>\n",
       "      <td>877356.0</td>\n",
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       "      <td>888335.0</td>\n",
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       "      <td>950893.0</td>\n",
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       "      <td>729930.0</td>\n",
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       "      <td>730100.0</td>\n",
       "      <td>731231.0</td>\n",
       "      <td>737921.0</td>\n",
       "      <td>742149.0</td>\n",
       "      <td>742312.0</td>\n",
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       "      <td>950593.0</td>\n",
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       "      <td>1038463.0</td>\n",
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       "      <td>1083079.0</td>\n",
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       "      <td>1093927.0</td>\n",
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       "      <td>1103741.0</td>\n",
       "      <td>1097791.0</td>\n",
       "      <td>1072287.0</td>\n",
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       "      <td>1021446.0</td>\n",
       "      <td>1011086.0</td>\n",
       "      <td>1010052.0</td>\n",
       "      <td>1019858.0</td>\n",
       "      <td>1027420.0</td>\n",
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       "      <td>1120979.0</td>\n",
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       "      <td>1102248.0</td>\n",
       "      <td>1109411.0</td>\n",
       "      <td>1116393.0</td>\n",
       "      <td>1120588.0</td>\n",
       "      <td>1129421.0</td>\n",
       "      <td>1138407.0</td>\n",
       "      <td>1148910.0</td>\n",
       "      <td>1153364.0</td>\n",
       "      <td>1152736.0</td>\n",
       "      <td>1153314.0</td>\n",
       "      <td>1159292.0</td>\n",
       "      <td>1171216.0</td>\n",
       "      <td>1190200.0</td>\n",
       "      <td>1207107.0</td>\n",
       "      <td>1221417.0</td>\n",
       "      <td>1227148.0</td>\n",
       "      <td>1234232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84654</td>\n",
       "      <td>1</td>\n",
       "      <td>60657</td>\n",
       "      <td>Zip</td>\n",
       "      <td>IL</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Chicago-Naperville-Elgin</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>311718.0</td>\n",
       "      <td>313166.0</td>\n",
       "      <td>315132.0</td>\n",
       "      <td>318910.0</td>\n",
       "      <td>322771.0</td>\n",
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       "      <td>338659.0</td>\n",
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       "      <td>382372.0</td>\n",
       "      <td>385978.0</td>\n",
       "      <td>388522.0</td>\n",
       "      <td>390712.0</td>\n",
       "      <td>393086.0</td>\n",
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       "      <td>398194.0</td>\n",
       "      <td>399791.0</td>\n",
       "      <td>400270.0</td>\n",
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       "      <td>494691.0</td>\n",
       "      <td>495376.0</td>\n",
       "      <td>494663.0</td>\n",
       "      <td>493369.0</td>\n",
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       "      <td>484895.0</td>\n",
       "      <td>484369.0</td>\n",
       "      <td>479106.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>469244.0</td>\n",
       "      <td>472688.0</td>\n",
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       "      <td>490811.0</td>\n",
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       "      <td>530092.0</td>\n",
       "      <td>532758.0</td>\n",
       "      <td>534840.0</td>\n",
       "      <td>539859.0</td>\n",
       "      <td>543658.0</td>\n",
       "      <td>546709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61637</td>\n",
       "      <td>2</td>\n",
       "      <td>10023</td>\n",
       "      <td>Zip</td>\n",
       "      <td>NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York-Newark-Jersey City</td>\n",
       "      <td>New York County</td>\n",
       "      <td>510209.0</td>\n",
       "      <td>514133.0</td>\n",
       "      <td>516889.0</td>\n",
       "      <td>523704.0</td>\n",
       "      <td>531799.0</td>\n",
       "      <td>542753.0</td>\n",
       "      <td>553379.0</td>\n",
       "      <td>562121.0</td>\n",
       "      <td>568005.0</td>\n",
       "      <td>575088.0</td>\n",
       "      <td>583480.0</td>\n",
       "      <td>593451.0</td>\n",
       "      <td>603077.0</td>\n",
       "      <td>614072.0</td>\n",
       "      <td>625572.0</td>\n",
       "      <td>634523.0</td>\n",
       "      <td>641547.0</td>\n",
       "      <td>647951.0</td>\n",
       "      <td>655617.0</td>\n",
       "      <td>660548.0</td>\n",
       "      <td>665768.0</td>\n",
       "      <td>671012.0</td>\n",
       "      <td>678942.0</td>\n",
       "      <td>685606.0</td>\n",
       "      <td>693091.0</td>\n",
       "      <td>699825.0</td>\n",
       "      <td>706912.0</td>\n",
       "      <td>713815.0</td>\n",
       "      <td>721207.0</td>\n",
       "      <td>728606.0</td>\n",
       "      <td>735000.0</td>\n",
       "      <td>740477.0</td>\n",
       "      <td>745985.0</td>\n",
       "      <td>751663.0</td>\n",
       "      <td>757610.0</td>\n",
       "      <td>762989.0</td>\n",
       "      <td>769288.0</td>\n",
       "      <td>775506.0</td>\n",
       "      <td>781926.0</td>\n",
       "      <td>787957.0</td>\n",
       "      <td>799169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>837506.0</td>\n",
       "      <td>851133.0</td>\n",
       "      <td>855268.0</td>\n",
       "      <td>863978.0</td>\n",
       "      <td>871893.0</td>\n",
       "      <td>877991.0</td>\n",
       "      <td>884163.0</td>\n",
       "      <td>889607.0</td>\n",
       "      <td>895128.0</td>\n",
       "      <td>901876.0</td>\n",
       "      <td>907521.0</td>\n",
       "      <td>915286.0</td>\n",
       "      <td>927793.0</td>\n",
       "      <td>943590.0</td>\n",
       "      <td>962434.0</td>\n",
       "      <td>978338.0</td>\n",
       "      <td>989548.0</td>\n",
       "      <td>1000374.0</td>\n",
       "      <td>1016299.0</td>\n",
       "      <td>1030574.0</td>\n",
       "      <td>1051915.0</td>\n",
       "      <td>1060490.0</td>\n",
       "      <td>1077662.0</td>\n",
       "      <td>1090444.0</td>\n",
       "      <td>1106242.0</td>\n",
       "      <td>1116679.0</td>\n",
       "      <td>1122126.0</td>\n",
       "      <td>1141974.0</td>\n",
       "      <td>1169320.0</td>\n",
       "      <td>1199484.0</td>\n",
       "      <td>1229607.0</td>\n",
       "      <td>1248395.0</td>\n",
       "      <td>1261318.0</td>\n",
       "      <td>1258709.0</td>\n",
       "      <td>1263763.0</td>\n",
       "      <td>1266705.0</td>\n",
       "      <td>1269734.0</td>\n",
       "      <td>1239132.0</td>\n",
       "      <td>1199239.0</td>\n",
       "      <td>1157117.0</td>\n",
       "      <td>1162377.0</td>\n",
       "      <td>1192700.0</td>\n",
       "      <td>1211333.0</td>\n",
       "      <td>1215511.0</td>\n",
       "      <td>1207549.0</td>\n",
       "      <td>1214244.0</td>\n",
       "      <td>1217777.0</td>\n",
       "      <td>1205860.0</td>\n",
       "      <td>1198926.0</td>\n",
       "      <td>1188133.0</td>\n",
       "      <td>1224780.0</td>\n",
       "      <td>1260701.0</td>\n",
       "      <td>1301830.0</td>\n",
       "      <td>1312130.0</td>\n",
       "      <td>1319793.0</td>\n",
       "      <td>1332163.0</td>\n",
       "      <td>1336922.0</td>\n",
       "      <td>1346675.0</td>\n",
       "      <td>1327718.0</td>\n",
       "      <td>1316727.0</td>\n",
       "      <td>1274812.0</td>\n",
       "      <td>1265797.0</td>\n",
       "      <td>1268876.0</td>\n",
       "      <td>1273369.0</td>\n",
       "      <td>1273805.0</td>\n",
       "      <td>1216226.0</td>\n",
       "      <td>1160583.0</td>\n",
       "      <td>1093058.0</td>\n",
       "      <td>1077608.0</td>\n",
       "      <td>1060120.0</td>\n",
       "      <td>1067412.0</td>\n",
       "      <td>1053431.0</td>\n",
       "      <td>1053003.0</td>\n",
       "      <td>1023036.0</td>\n",
       "      <td>999321.0</td>\n",
       "      <td>978504.0</td>\n",
       "      <td>967766.0</td>\n",
       "      <td>952899.0</td>\n",
       "      <td>942671.0</td>\n",
       "      <td>934121.0</td>\n",
       "      <td>932375.0</td>\n",
       "      <td>935154.0</td>\n",
       "      <td>935312.0</td>\n",
       "      <td>939655.0</td>\n",
       "      <td>954976.0</td>\n",
       "      <td>970888.0</td>\n",
       "      <td>984151.0</td>\n",
       "      <td>987649.0</td>\n",
       "      <td>980527.0</td>\n",
       "      <td>978385.0</td>\n",
       "      <td>980834.0</td>\n",
       "      <td>995897.0</td>\n",
       "      <td>1007811.0</td>\n",
       "      <td>1014301.0</td>\n",
       "      <td>1016180.0</td>\n",
       "      <td>1017319.0</td>\n",
       "      <td>1015232.0</td>\n",
       "      <td>1021085.0</td>\n",
       "      <td>1025990.0</td>\n",
       "      <td>1032674.0</td>\n",
       "      <td>1031719.0</td>\n",
       "      <td>1032305.0</td>\n",
       "      <td>1029341.0</td>\n",
       "      <td>1032510.0</td>\n",
       "      <td>1033542.0</td>\n",
       "      <td>1041709.0</td>\n",
       "      <td>1049247.0</td>\n",
       "      <td>1056032.0</td>\n",
       "      <td>1060269.0</td>\n",
       "      <td>1063563.0</td>\n",
       "      <td>1069021.0</td>\n",
       "      <td>1073993.0</td>\n",
       "      <td>1070434.0</td>\n",
       "      <td>1069737.0</td>\n",
       "      <td>1071741.0</td>\n",
       "      <td>1079454.0</td>\n",
       "      <td>1086566.0</td>\n",
       "      <td>1095377.0</td>\n",
       "      <td>1103497.0</td>\n",
       "      <td>1112336.0</td>\n",
       "      <td>1121599.0</td>\n",
       "      <td>1140822.0</td>\n",
       "      <td>1158140.0</td>\n",
       "      <td>1169622.0</td>\n",
       "      <td>1173822.0</td>\n",
       "      <td>1181127.0</td>\n",
       "      <td>1193715.0</td>\n",
       "      <td>1206294.0</td>\n",
       "      <td>1220299.0</td>\n",
       "      <td>1231951.0</td>\n",
       "      <td>1248363.0</td>\n",
       "      <td>1264509.0</td>\n",
       "      <td>1278656.0</td>\n",
       "      <td>1292954.0</td>\n",
       "      <td>1304027.0</td>\n",
       "      <td>1317037.0</td>\n",
       "      <td>1320040.0</td>\n",
       "      <td>1319073.0</td>\n",
       "      <td>1314473.0</td>\n",
       "      <td>1316516.0</td>\n",
       "      <td>1318633.0</td>\n",
       "      <td>1324817.0</td>\n",
       "      <td>1327595.0</td>\n",
       "      <td>1338454.0</td>\n",
       "      <td>1347963.0</td>\n",
       "      <td>1361435.0</td>\n",
       "      <td>1366644.0</td>\n",
       "      <td>1372058.0</td>\n",
       "      <td>1378862.0</td>\n",
       "      <td>1390919.0</td>\n",
       "      <td>1398892.0</td>\n",
       "      <td>1402145.0</td>\n",
       "      <td>1406398.0</td>\n",
       "      <td>1414555.0</td>\n",
       "      <td>1421313.0</td>\n",
       "      <td>1424330.0</td>\n",
       "      <td>1426079.0</td>\n",
       "      <td>1427442.0</td>\n",
       "      <td>1425619.0</td>\n",
       "      <td>1422691.0</td>\n",
       "      <td>1421046.0</td>\n",
       "      <td>1425048.0</td>\n",
       "      <td>1422326.0</td>\n",
       "      <td>1417591.0</td>\n",
       "      <td>1414039.0</td>\n",
       "      <td>1420303.0</td>\n",
       "      <td>1447421.0</td>\n",
       "      <td>1471937.0</td>\n",
       "      <td>1485133.0</td>\n",
       "      <td>1465273.0</td>\n",
       "      <td>1450056.0</td>\n",
       "      <td>1450643.0</td>\n",
       "      <td>1466802.0</td>\n",
       "      <td>1478549.0</td>\n",
       "      <td>1480446.0</td>\n",
       "      <td>1506312.0</td>\n",
       "      <td>1527181.0</td>\n",
       "      <td>1543052.0</td>\n",
       "      <td>1527355.0</td>\n",
       "      <td>1520237.0</td>\n",
       "      <td>1522152.0</td>\n",
       "      <td>1539341.0</td>\n",
       "      <td>1554226.0</td>\n",
       "      <td>1568089.0</td>\n",
       "      <td>1568309.0</td>\n",
       "      <td>1543818.0</td>\n",
       "      <td>1505737.0</td>\n",
       "      <td>1466770.0</td>\n",
       "      <td>1464510.0</td>\n",
       "      <td>1453948.0</td>\n",
       "      <td>1451273.0</td>\n",
       "      <td>1445332.0</td>\n",
       "      <td>1449648.0</td>\n",
       "      <td>1442862.0</td>\n",
       "      <td>1411517.0</td>\n",
       "      <td>1398488.0</td>\n",
       "      <td>1413425.0</td>\n",
       "      <td>1453401.0</td>\n",
       "      <td>1480472.0</td>\n",
       "      <td>1487370.0</td>\n",
       "      <td>1483302.0</td>\n",
       "      <td>1492388.0</td>\n",
       "      <td>1493083.0</td>\n",
       "      <td>1492055.0</td>\n",
       "      <td>1494986.0</td>\n",
       "      <td>1504496.0</td>\n",
       "      <td>1517227.0</td>\n",
       "      <td>1514634.0</td>\n",
       "      <td>1514417.0</td>\n",
       "      <td>1513075.0</td>\n",
       "      <td>1487184.0</td>\n",
       "      <td>1478452.0</td>\n",
       "      <td>1464876.0</td>\n",
       "      <td>1483107.0</td>\n",
       "      <td>1477774.0</td>\n",
       "      <td>1476095.0</td>\n",
       "      <td>1477965.0</td>\n",
       "      <td>1492502.0</td>\n",
       "      <td>1508431.0</td>\n",
       "      <td>1517150.0</td>\n",
       "      <td>1521442.0</td>\n",
       "      <td>1521759.0</td>\n",
       "      <td>1532449.0</td>\n",
       "      <td>1542269.0</td>\n",
       "      <td>1559390.0</td>\n",
       "      <td>1572653.0</td>\n",
       "      <td>1591368.0</td>\n",
       "      <td>1600569.0</td>\n",
       "      <td>1607770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91982</td>\n",
       "      <td>3</td>\n",
       "      <td>77494</td>\n",
       "      <td>Zip</td>\n",
       "      <td>TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>Katy</td>\n",
       "      <td>Houston-The Woodlands-Sugar Land</td>\n",
       "      <td>Harris County</td>\n",
       "      <td>224385.0</td>\n",
       "      <td>224703.0</td>\n",
       "      <td>225427.0</td>\n",
       "      <td>226652.0</td>\n",
       "      <td>226215.0</td>\n",
       "      <td>225880.0</td>\n",
       "      <td>224761.0</td>\n",
       "      <td>225103.0</td>\n",
       "      <td>224705.0</td>\n",
       "      <td>225378.0</td>\n",
       "      <td>226549.0</td>\n",
       "      <td>227972.0</td>\n",
       "      <td>228593.0</td>\n",
       "      <td>227897.0</td>\n",
       "      <td>227028.0</td>\n",
       "      <td>226175.0</td>\n",
       "      <td>227495.0</td>\n",
       "      <td>229014.0</td>\n",
       "      <td>230192.0</td>\n",
       "      <td>230503.0</td>\n",
       "      <td>230070.0</td>\n",
       "      <td>230132.0</td>\n",
       "      <td>229664.0</td>\n",
       "      <td>228708.0</td>\n",
       "      <td>228122.0</td>\n",
       "      <td>227805.0</td>\n",
       "      <td>228698.0</td>\n",
       "      <td>229369.0</td>\n",
       "      <td>230476.0</td>\n",
       "      <td>231227.0</td>\n",
       "      <td>231090.0</td>\n",
       "      <td>230486.0</td>\n",
       "      <td>229107.0</td>\n",
       "      <td>228542.0</td>\n",
       "      <td>227739.0</td>\n",
       "      <td>228610.0</td>\n",
       "      <td>229964.0</td>\n",
       "      <td>231531.0</td>\n",
       "      <td>232316.0</td>\n",
       "      <td>232613.0</td>\n",
       "      <td>232816.0</td>\n",
       "      <td>233393.0</td>\n",
       "      <td>233957.0</td>\n",
       "      <td>234721.0</td>\n",
       "      <td>234599.0</td>\n",
       "      <td>234928.0</td>\n",
       "      <td>234922.0</td>\n",
       "      <td>235731.0</td>\n",
       "      <td>236811.0</td>\n",
       "      <td>238166.0</td>\n",
       "      <td>239875.0</td>\n",
       "      <td>240454.0</td>\n",
       "      <td>241032.0</td>\n",
       "      <td>241576.0</td>\n",
       "      <td>242473.0</td>\n",
       "      <td>243657.0</td>\n",
       "      <td>244509.0</td>\n",
       "      <td>245610.0</td>\n",
       "      <td>246686.0</td>\n",
       "      <td>247452.0</td>\n",
       "      <td>248590.0</td>\n",
       "      <td>249835.0</td>\n",
       "      <td>250738.0</td>\n",
       "      <td>250747.0</td>\n",
       "      <td>250033.0</td>\n",
       "      <td>249455.0</td>\n",
       "      <td>249265.0</td>\n",
       "      <td>248759.0</td>\n",
       "      <td>248163.0</td>\n",
       "      <td>247663.0</td>\n",
       "      <td>247377.0</td>\n",
       "      <td>247928.0</td>\n",
       "      <td>248672.0</td>\n",
       "      <td>249527.0</td>\n",
       "      <td>250275.0</td>\n",
       "      <td>250702.0</td>\n",
       "      <td>251353.0</td>\n",
       "      <td>251859.0</td>\n",
       "      <td>252538.0</td>\n",
       "      <td>253866.0</td>\n",
       "      <td>254904.0</td>\n",
       "      <td>255596.0</td>\n",
       "      <td>255572.0</td>\n",
       "      <td>256205.0</td>\n",
       "      <td>257048.0</td>\n",
       "      <td>257708.0</td>\n",
       "      <td>257874.0</td>\n",
       "      <td>257953.0</td>\n",
       "      <td>258300.0</td>\n",
       "      <td>258564.0</td>\n",
       "      <td>259299.0</td>\n",
       "      <td>260591.0</td>\n",
       "      <td>262013.0</td>\n",
       "      <td>263244.0</td>\n",
       "      <td>263790.0</td>\n",
       "      <td>264871.0</td>\n",
       "      <td>264967.0</td>\n",
       "      <td>265031.0</td>\n",
       "      <td>264109.0</td>\n",
       "      <td>263601.0</td>\n",
       "      <td>263502.0</td>\n",
       "      <td>263986.0</td>\n",
       "      <td>264749.0</td>\n",
       "      <td>264844.0</td>\n",
       "      <td>264252.0</td>\n",
       "      <td>262647.0</td>\n",
       "      <td>261125.0</td>\n",
       "      <td>260127.0</td>\n",
       "      <td>259836.0</td>\n",
       "      <td>260752.0</td>\n",
       "      <td>261831.0</td>\n",
       "      <td>263208.0</td>\n",
       "      <td>263349.0</td>\n",
       "      <td>263666.0</td>\n",
       "      <td>264238.0</td>\n",
       "      <td>264528.0</td>\n",
       "      <td>264246.0</td>\n",
       "      <td>264598.0</td>\n",
       "      <td>265602.0</td>\n",
       "      <td>266784.0</td>\n",
       "      <td>267713.0</td>\n",
       "      <td>269050.0</td>\n",
       "      <td>271309.0</td>\n",
       "      <td>272891.0</td>\n",
       "      <td>274101.0</td>\n",
       "      <td>274372.0</td>\n",
       "      <td>273544.0</td>\n",
       "      <td>272240.0</td>\n",
       "      <td>269185.0</td>\n",
       "      <td>266887.0</td>\n",
       "      <td>264805.0</td>\n",
       "      <td>264306.0</td>\n",
       "      <td>264583.0</td>\n",
       "      <td>264744.0</td>\n",
       "      <td>264406.0</td>\n",
       "      <td>263727.0</td>\n",
       "      <td>263037.0</td>\n",
       "      <td>263120.0</td>\n",
       "      <td>262886.0</td>\n",
       "      <td>262061.0</td>\n",
       "      <td>261438.0</td>\n",
       "      <td>260905.0</td>\n",
       "      <td>261287.0</td>\n",
       "      <td>261029.0</td>\n",
       "      <td>262131.0</td>\n",
       "      <td>263371.0</td>\n",
       "      <td>265132.0</td>\n",
       "      <td>266682.0</td>\n",
       "      <td>267962.0</td>\n",
       "      <td>268856.0</td>\n",
       "      <td>269243.0</td>\n",
       "      <td>269341.0</td>\n",
       "      <td>269093.0</td>\n",
       "      <td>268584.0</td>\n",
       "      <td>268505.0</td>\n",
       "      <td>268962.0</td>\n",
       "      <td>269904.0</td>\n",
       "      <td>271735.0</td>\n",
       "      <td>274251.0</td>\n",
       "      <td>277605.0</td>\n",
       "      <td>280900.0</td>\n",
       "      <td>284574.0</td>\n",
       "      <td>287570.0</td>\n",
       "      <td>290781.0</td>\n",
       "      <td>293898.0</td>\n",
       "      <td>296883.0</td>\n",
       "      <td>299930.0</td>\n",
       "      <td>301536.0</td>\n",
       "      <td>305181.0</td>\n",
       "      <td>308647.0</td>\n",
       "      <td>312621.0</td>\n",
       "      <td>313870.0</td>\n",
       "      <td>314748.0</td>\n",
       "      <td>316057.0</td>\n",
       "      <td>318279.0</td>\n",
       "      <td>320258.0</td>\n",
       "      <td>322847.0</td>\n",
       "      <td>325170.0</td>\n",
       "      <td>327731.0</td>\n",
       "      <td>330146.0</td>\n",
       "      <td>332204.0</td>\n",
       "      <td>334043.0</td>\n",
       "      <td>334940.0</td>\n",
       "      <td>336370.0</td>\n",
       "      <td>338169.0</td>\n",
       "      <td>339534.0</td>\n",
       "      <td>340069.0</td>\n",
       "      <td>340048.0</td>\n",
       "      <td>340557.0</td>\n",
       "      <td>341180.0</td>\n",
       "      <td>341368.0</td>\n",
       "      <td>341769.0</td>\n",
       "      <td>340928.0</td>\n",
       "      <td>339484.0</td>\n",
       "      <td>338113.0</td>\n",
       "      <td>337719.0</td>\n",
       "      <td>337991.0</td>\n",
       "      <td>336589.0</td>\n",
       "      <td>335016.0</td>\n",
       "      <td>333534.0</td>\n",
       "      <td>333285.0</td>\n",
       "      <td>333229.0</td>\n",
       "      <td>333665.0</td>\n",
       "      <td>333843.0</td>\n",
       "      <td>333623.0</td>\n",
       "      <td>333902.0</td>\n",
       "      <td>334011.0</td>\n",
       "      <td>333799.0</td>\n",
       "      <td>332823.0</td>\n",
       "      <td>332192.0</td>\n",
       "      <td>332138.0</td>\n",
       "      <td>332045.0</td>\n",
       "      <td>332439.0</td>\n",
       "      <td>332882.0</td>\n",
       "      <td>333391.0</td>\n",
       "      <td>334482.0</td>\n",
       "      <td>335520.0</td>\n",
       "      <td>335473.0</td>\n",
       "      <td>334859.0</td>\n",
       "      <td>334998.0</td>\n",
       "      <td>335899.0</td>\n",
       "      <td>336346.0</td>\n",
       "      <td>336179.0</td>\n",
       "      <td>336554.0</td>\n",
       "      <td>337640.0</td>\n",
       "      <td>338535.0</td>\n",
       "      <td>339463.0</td>\n",
       "      <td>339214.0</td>\n",
       "      <td>339497.0</td>\n",
       "      <td>339032.0</td>\n",
       "      <td>338889.0</td>\n",
       "      <td>337883.0</td>\n",
       "      <td>337197.0</td>\n",
       "      <td>336637.0</td>\n",
       "      <td>336883.0</td>\n",
       "      <td>337000.0</td>\n",
       "      <td>337791.0</td>\n",
       "      <td>338198.0</td>\n",
       "      <td>338534.0</td>\n",
       "      <td>338251.0</td>\n",
       "      <td>337882.0</td>\n",
       "      <td>337490.0</td>\n",
       "      <td>337782.0</td>\n",
       "      <td>338649.0</td>\n",
       "      <td>339604.0</td>\n",
       "      <td>340044.0</td>\n",
       "      <td>340183.0</td>\n",
       "      <td>341482.0</td>\n",
       "      <td>343070.0</td>\n",
       "      <td>345714.0</td>\n",
       "      <td>348971.0</td>\n",
       "      <td>353459.0</td>\n",
       "      <td>357493.0</td>\n",
       "      <td>362064.0</td>\n",
       "      <td>367477.0</td>\n",
       "      <td>374020.0</td>\n",
       "      <td>380980.0</td>\n",
       "      <td>389731.0</td>\n",
       "      <td>400469.0</td>\n",
       "      <td>411347.0</td>\n",
       "      <td>420464.0</td>\n",
       "      <td>428419.0</td>\n",
       "      <td>435498.0</td>\n",
       "      <td>441231.0</td>\n",
       "      <td>447322.0</td>\n",
       "      <td>456848.0</td>\n",
       "      <td>468519.0</td>\n",
       "      <td>482272.0</td>\n",
       "      <td>493709.0</td>\n",
       "      <td>501230.0</td>\n",
       "      <td>503505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84616</td>\n",
       "      <td>4</td>\n",
       "      <td>60614</td>\n",
       "      <td>Zip</td>\n",
       "      <td>IL</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Chicago-Naperville-Elgin</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>407594.0</td>\n",
       "      <td>408740.0</td>\n",
       "      <td>410905.0</td>\n",
       "      <td>414687.0</td>\n",
       "      <td>419260.0</td>\n",
       "      <td>423642.0</td>\n",
       "      <td>428258.0</td>\n",
       "      <td>433160.0</td>\n",
       "      <td>437338.0</td>\n",
       "      <td>442329.0</td>\n",
       "      <td>447665.0</td>\n",
       "      <td>453421.0</td>\n",
       "      <td>458393.0</td>\n",
       "      <td>462111.0</td>\n",
       "      <td>466794.0</td>\n",
       "      <td>471075.0</td>\n",
       "      <td>474958.0</td>\n",
       "      <td>478001.0</td>\n",
       "      <td>480441.0</td>\n",
       "      <td>483832.0</td>\n",
       "      <td>487138.0</td>\n",
       "      <td>490904.0</td>\n",
       "      <td>494097.0</td>\n",
       "      <td>496721.0</td>\n",
       "      <td>499526.0</td>\n",
       "      <td>502454.0</td>\n",
       "      <td>506150.0</td>\n",
       "      <td>508075.0</td>\n",
       "      <td>509796.0</td>\n",
       "      <td>510234.0</td>\n",
       "      <td>512287.0</td>\n",
       "      <td>514709.0</td>\n",
       "      <td>517788.0</td>\n",
       "      <td>520436.0</td>\n",
       "      <td>522800.0</td>\n",
       "      <td>526058.0</td>\n",
       "      <td>529706.0</td>\n",
       "      <td>533385.0</td>\n",
       "      <td>535791.0</td>\n",
       "      <td>537925.0</td>\n",
       "      <td>538378.0</td>\n",
       "      <td>538344.0</td>\n",
       "      <td>537421.0</td>\n",
       "      <td>537431.0</td>\n",
       "      <td>537931.0</td>\n",
       "      <td>539259.0</td>\n",
       "      <td>540637.0</td>\n",
       "      <td>542391.0</td>\n",
       "      <td>544988.0</td>\n",
       "      <td>547100.0</td>\n",
       "      <td>549166.0</td>\n",
       "      <td>551820.0</td>\n",
       "      <td>554953.0</td>\n",
       "      <td>558426.0</td>\n",
       "      <td>560164.0</td>\n",
       "      <td>561923.0</td>\n",
       "      <td>562718.0</td>\n",
       "      <td>564592.0</td>\n",
       "      <td>567320.0</td>\n",
       "      <td>570747.0</td>\n",
       "      <td>573826.0</td>\n",
       "      <td>576780.0</td>\n",
       "      <td>579635.0</td>\n",
       "      <td>583075.0</td>\n",
       "      <td>586903.0</td>\n",
       "      <td>590403.0</td>\n",
       "      <td>593511.0</td>\n",
       "      <td>596359.0</td>\n",
       "      <td>600626.0</td>\n",
       "      <td>604532.0</td>\n",
       "      <td>608597.0</td>\n",
       "      <td>612117.0</td>\n",
       "      <td>615883.0</td>\n",
       "      <td>619316.0</td>\n",
       "      <td>623200.0</td>\n",
       "      <td>625993.0</td>\n",
       "      <td>627782.0</td>\n",
       "      <td>627583.0</td>\n",
       "      <td>627596.0</td>\n",
       "      <td>626954.0</td>\n",
       "      <td>626444.0</td>\n",
       "      <td>625554.0</td>\n",
       "      <td>625977.0</td>\n",
       "      <td>627072.0</td>\n",
       "      <td>629502.0</td>\n",
       "      <td>631587.0</td>\n",
       "      <td>632954.0</td>\n",
       "      <td>632211.0</td>\n",
       "      <td>630996.0</td>\n",
       "      <td>629393.0</td>\n",
       "      <td>628204.0</td>\n",
       "      <td>627551.0</td>\n",
       "      <td>625986.0</td>\n",
       "      <td>624850.0</td>\n",
       "      <td>623238.0</td>\n",
       "      <td>621076.0</td>\n",
       "      <td>620279.0</td>\n",
       "      <td>612602.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>599472.0</td>\n",
       "      <td>604319.0</td>\n",
       "      <td>599395.0</td>\n",
       "      <td>600172.0</td>\n",
       "      <td>592745.0</td>\n",
       "      <td>588586.0</td>\n",
       "      <td>579808.0</td>\n",
       "      <td>570083.0</td>\n",
       "      <td>563902.0</td>\n",
       "      <td>556611.0</td>\n",
       "      <td>557503.0</td>\n",
       "      <td>554593.0</td>\n",
       "      <td>556677.0</td>\n",
       "      <td>556654.0</td>\n",
       "      <td>558713.0</td>\n",
       "      <td>555103.0</td>\n",
       "      <td>552678.0</td>\n",
       "      <td>547620.0</td>\n",
       "      <td>549184.0</td>\n",
       "      <td>547017.0</td>\n",
       "      <td>548906.0</td>\n",
       "      <td>544722.0</td>\n",
       "      <td>548766.0</td>\n",
       "      <td>546779.0</td>\n",
       "      <td>550641.0</td>\n",
       "      <td>542682.0</td>\n",
       "      <td>539090.0</td>\n",
       "      <td>530450.0</td>\n",
       "      <td>525864.0</td>\n",
       "      <td>518814.0</td>\n",
       "      <td>512538.0</td>\n",
       "      <td>508449.0</td>\n",
       "      <td>504751.0</td>\n",
       "      <td>505283.0</td>\n",
       "      <td>504549.0</td>\n",
       "      <td>507549.0</td>\n",
       "      <td>507049.0</td>\n",
       "      <td>509725.0</td>\n",
       "      <td>508816.0</td>\n",
       "      <td>507957.0</td>\n",
       "      <td>501474.0</td>\n",
       "      <td>500224.0</td>\n",
       "      <td>494789.0</td>\n",
       "      <td>493098.0</td>\n",
       "      <td>484291.0</td>\n",
       "      <td>487045.0</td>\n",
       "      <td>488444.0</td>\n",
       "      <td>493284.0</td>\n",
       "      <td>493093.0</td>\n",
       "      <td>490078.0</td>\n",
       "      <td>489616.0</td>\n",
       "      <td>487764.0</td>\n",
       "      <td>492045.0</td>\n",
       "      <td>493704.0</td>\n",
       "      <td>495899.0</td>\n",
       "      <td>501196.0</td>\n",
       "      <td>507343.0</td>\n",
       "      <td>513588.0</td>\n",
       "      <td>518818.0</td>\n",
       "      <td>524988.0</td>\n",
       "      <td>533519.0</td>\n",
       "      <td>539395.0</td>\n",
       "      <td>544615.0</td>\n",
       "      <td>547048.0</td>\n",
       "      <td>554906.0</td>\n",
       "      <td>557250.0</td>\n",
       "      <td>562579.0</td>\n",
       "      <td>564063.0</td>\n",
       "      <td>571147.0</td>\n",
       "      <td>574263.0</td>\n",
       "      <td>574299.0</td>\n",
       "      <td>575084.0</td>\n",
       "      <td>577670.0</td>\n",
       "      <td>578691.0</td>\n",
       "      <td>576287.0</td>\n",
       "      <td>576551.0</td>\n",
       "      <td>577811.0</td>\n",
       "      <td>579789.0</td>\n",
       "      <td>581896.0</td>\n",
       "      <td>583886.0</td>\n",
       "      <td>587735.0</td>\n",
       "      <td>591247.0</td>\n",
       "      <td>596962.0</td>\n",
       "      <td>600508.0</td>\n",
       "      <td>600594.0</td>\n",
       "      <td>600169.0</td>\n",
       "      <td>599703.0</td>\n",
       "      <td>599435.0</td>\n",
       "      <td>598322.0</td>\n",
       "      <td>597724.0</td>\n",
       "      <td>600269.0</td>\n",
       "      <td>604432.0</td>\n",
       "      <td>612988.0</td>\n",
       "      <td>618359.0</td>\n",
       "      <td>620994.0</td>\n",
       "      <td>618964.0</td>\n",
       "      <td>617440.0</td>\n",
       "      <td>617591.0</td>\n",
       "      <td>617990.0</td>\n",
       "      <td>618040.0</td>\n",
       "      <td>618426.0</td>\n",
       "      <td>621559.0</td>\n",
       "      <td>623452.0</td>\n",
       "      <td>626750.0</td>\n",
       "      <td>629218.0</td>\n",
       "      <td>634534.0</td>\n",
       "      <td>637689.0</td>\n",
       "      <td>638655.0</td>\n",
       "      <td>636913.0</td>\n",
       "      <td>635263.0</td>\n",
       "      <td>634031.0</td>\n",
       "      <td>634932.0</td>\n",
       "      <td>632082.0</td>\n",
       "      <td>630177.0</td>\n",
       "      <td>630638.0</td>\n",
       "      <td>637149.0</td>\n",
       "      <td>643295.0</td>\n",
       "      <td>644370.0</td>\n",
       "      <td>642966.0</td>\n",
       "      <td>641112.0</td>\n",
       "      <td>640284.0</td>\n",
       "      <td>639152.0</td>\n",
       "      <td>638058.0</td>\n",
       "      <td>638822.0</td>\n",
       "      <td>639541.0</td>\n",
       "      <td>641959.0</td>\n",
       "      <td>643303.0</td>\n",
       "      <td>640585.0</td>\n",
       "      <td>638476.0</td>\n",
       "      <td>636263.0</td>\n",
       "      <td>636989.0</td>\n",
       "      <td>635524.0</td>\n",
       "      <td>633687.0</td>\n",
       "      <td>632669.0</td>\n",
       "      <td>631669.0</td>\n",
       "      <td>630945.0</td>\n",
       "      <td>629884.0</td>\n",
       "      <td>629135.0</td>\n",
       "      <td>628300.0</td>\n",
       "      <td>628177.0</td>\n",
       "      <td>628618.0</td>\n",
       "      <td>628391.0</td>\n",
       "      <td>627595.0</td>\n",
       "      <td>626126.0</td>\n",
       "      <td>624940.0</td>\n",
       "      <td>626400.0</td>\n",
       "      <td>627960.0</td>\n",
       "      <td>633179.0</td>\n",
       "      <td>637071.0</td>\n",
       "      <td>642179.0</td>\n",
       "      <td>645638.0</td>\n",
       "      <td>646620.0</td>\n",
       "      <td>648239.0</td>\n",
       "      <td>648445.0</td>\n",
       "      <td>651047.0</td>\n",
       "      <td>651197.0</td>\n",
       "      <td>653755.0</td>\n",
       "      <td>657520.0</td>\n",
       "      <td>662553.0</td>\n",
       "      <td>665737.0</td>\n",
       "      <td>669648.0</td>\n",
       "      <td>674293.0</td>\n",
       "      <td>677215.0</td>\n",
       "      <td>679126.0</td>\n",
       "      <td>678889.0</td>\n",
       "      <td>681982.0</td>\n",
       "      <td>684421.0</td>\n",
       "      <td>690214.0</td>\n",
       "      <td>695114.0</td>\n",
       "      <td>698644.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionID  SizeRank  RegionName RegionType StateName State      City  \\\n",
       "0     61639         0       10025        Zip        NY    NY  New York   \n",
       "1     84654         1       60657        Zip        IL    IL   Chicago   \n",
       "2     61637         2       10023        Zip        NY    NY  New York   \n",
       "3     91982         3       77494        Zip        TX    TX      Katy   \n",
       "4     84616         4       60614        Zip        IL    IL   Chicago   \n",
       "\n",
       "                              Metro       CountyName  2000-01-31  2000-02-29  \\\n",
       "0       New York-Newark-Jersey City  New York County    329164.0    334102.0   \n",
       "1          Chicago-Naperville-Elgin      Cook County    311718.0    313166.0   \n",
       "2       New York-Newark-Jersey City  New York County    510209.0    514133.0   \n",
       "3  Houston-The Woodlands-Sugar Land    Harris County    224385.0    224703.0   \n",
       "4          Chicago-Naperville-Elgin      Cook County    407594.0    408740.0   \n",
       "\n",
       "   2000-03-31  2000-04-30  2000-05-31  2000-06-30  2000-07-31  2000-08-31  \\\n",
       "0    338063.0    346702.0    354503.0    361964.0    369817.0    376161.0   \n",
       "1    315132.0    318910.0    322771.0    326546.0    330445.0    334836.0   \n",
       "2    516889.0    523704.0    531799.0    542753.0    553379.0    562121.0   \n",
       "3    225427.0    226652.0    226215.0    225880.0    224761.0    225103.0   \n",
       "4    410905.0    414687.0    419260.0    423642.0    428258.0    433160.0   \n",
       "\n",
       "   2000-09-30  2000-10-31  2000-11-30  2000-12-31  2001-01-31  2001-02-28  \\\n",
       "0    381722.0    386636.0    394205.0    402739.0    412059.0    420596.0   \n",
       "1    338659.0    342459.0    346378.0    350691.0    354849.0    358166.0   \n",
       "2    568005.0    575088.0    583480.0    593451.0    603077.0    614072.0   \n",
       "3    224705.0    225378.0    226549.0    227972.0    228593.0    227897.0   \n",
       "4    437338.0    442329.0    447665.0    453421.0    458393.0    462111.0   \n",
       "\n",
       "   2001-03-31  2001-04-30  2001-05-31  2001-06-30  2001-07-31  2001-08-31  \\\n",
       "0    429298.0    436348.0    442197.0    447713.0    452784.0    457515.0   \n",
       "1    361967.0    364873.0    367820.0    370230.0    372627.0    375289.0   \n",
       "2    625572.0    634523.0    641547.0    647951.0    655617.0    660548.0   \n",
       "3    227028.0    226175.0    227495.0    229014.0    230192.0    230503.0   \n",
       "4    466794.0    471075.0    474958.0    478001.0    480441.0    483832.0   \n",
       "\n",
       "   2001-09-30  2001-10-31  2001-11-30  2001-12-31  2002-01-31  2002-02-28  \\\n",
       "0    461080.0    465719.0    469872.0    475329.0    478975.0    482527.0   \n",
       "1    378274.0    382372.0    385978.0    388522.0    390712.0    393086.0   \n",
       "2    665768.0    671012.0    678942.0    685606.0    693091.0    699825.0   \n",
       "3    230070.0    230132.0    229664.0    228708.0    228122.0    227805.0   \n",
       "4    487138.0    490904.0    494097.0    496721.0    499526.0    502454.0   \n",
       "\n",
       "   2002-03-31  2002-04-30  2002-05-31  2002-06-30  2002-07-31  2002-08-31  \\\n",
       "0    484545.0    488323.0    493191.0    499230.0    503783.0    507998.0   \n",
       "1    396377.0    398194.0    399791.0    400270.0    401570.0    403390.0   \n",
       "2    706912.0    713815.0    721207.0    728606.0    735000.0    740477.0   \n",
       "3    228698.0    229369.0    230476.0    231227.0    231090.0    230486.0   \n",
       "4    506150.0    508075.0    509796.0    510234.0    512287.0    514709.0   \n",
       "\n",
       "   2002-09-30  2002-10-31  2002-11-30  2002-12-31  2003-01-31  2003-02-28  \\\n",
       "0    511134.0    514347.0    518319.0    522555.0    527420.0    531353.0   \n",
       "1    405580.0    407556.0    408861.0    411094.0    413958.0    417348.0   \n",
       "2    745985.0    751663.0    757610.0    762989.0    769288.0    775506.0   \n",
       "3    229107.0    228542.0    227739.0    228610.0    229964.0    231531.0   \n",
       "4    517788.0    520436.0    522800.0    526058.0    529706.0    533385.0   \n",
       "\n",
       "   2003-03-31  2003-04-30  2003-05-31  2003-06-30  2003-07-31  2003-08-31  \\\n",
       "0    535386.0    539419.0    549521.0         NaN         NaN         NaN   \n",
       "1    419923.0    422009.0    421993.0    421667.0    421085.0    421489.0   \n",
       "2    781926.0    787957.0    799169.0         NaN    837506.0    851133.0   \n",
       "3    232316.0    232613.0    232816.0    233393.0    233957.0    234721.0   \n",
       "4    535791.0    537925.0    538378.0    538344.0    537421.0    537431.0   \n",
       "\n",
       "   2003-09-30  2003-10-31  2003-11-30  2003-12-31  2004-01-31  2004-02-29  \\\n",
       "0    613322.0    617190.0    619629.0    625469.0    631337.0    636172.0   \n",
       "1    422233.0    423507.0    425191.0    426864.0    428669.0    430347.0   \n",
       "2    855268.0    863978.0    871893.0    877991.0    884163.0    889607.0   \n",
       "3    234599.0    234928.0    234922.0    235731.0    236811.0    238166.0   \n",
       "4    537931.0    539259.0    540637.0    542391.0    544988.0    547100.0   \n",
       "\n",
       "   2004-03-31  2004-04-30  2004-05-31  2004-06-30  2004-07-31  2004-08-31  \\\n",
       "0    639825.0    643390.0    647277.0    652399.0    660047.0    669929.0   \n",
       "1    432065.0    434454.0    436964.0    439454.0    440722.0    442398.0   \n",
       "2    895128.0    901876.0    907521.0    915286.0    927793.0    943590.0   \n",
       "3    239875.0    240454.0    241032.0    241576.0    242473.0    243657.0   \n",
       "4    549166.0    551820.0    554953.0    558426.0    560164.0    561923.0   \n",
       "\n",
       "   2004-09-30  2004-10-31  2004-11-30  2004-12-31  2005-01-31  2005-02-28  \\\n",
       "0    684061.0    696896.0    708835.0    716890.0    728938.0    739607.0   \n",
       "1    443693.0    445721.0    448113.0    450907.0    453757.0    456142.0   \n",
       "2    962434.0    978338.0    989548.0   1000374.0   1016299.0   1030574.0   \n",
       "3    244509.0    245610.0    246686.0    247452.0    248590.0    249835.0   \n",
       "4    562718.0    564592.0    567320.0    570747.0    573826.0    576780.0   \n",
       "\n",
       "   2005-03-31  2005-04-30  2005-05-31  2005-06-30  2005-07-31  2005-08-31  \\\n",
       "0    759498.0    771672.0    786081.0    795663.0    805169.0    814693.0   \n",
       "1    458287.0    460229.0    462798.0    465149.0    467605.0    469560.0   \n",
       "2   1051915.0   1060490.0   1077662.0   1090444.0   1106242.0   1116679.0   \n",
       "3    250738.0    250747.0    250033.0    249455.0    249265.0    248759.0   \n",
       "4    579635.0    583075.0    586903.0    590403.0    593511.0    596359.0   \n",
       "\n",
       "   2005-09-30  2005-10-31  2005-11-30  2005-12-31  2006-01-31  2006-02-28  \\\n",
       "0    817139.0    832872.0    851044.0    876625.0    902028.0    921298.0   \n",
       "1    472691.0    475648.0    478791.0    481612.0    484739.0    487675.0   \n",
       "2   1122126.0   1141974.0   1169320.0   1199484.0   1229607.0   1248395.0   \n",
       "3    248163.0    247663.0    247377.0    247928.0    248672.0    249527.0   \n",
       "4    600626.0    604532.0    608597.0    612117.0    615883.0    619316.0   \n",
       "\n",
       "   2006-03-31  2006-04-30  2006-05-31  2006-06-30  2006-07-31  2006-08-31  \\\n",
       "0    927990.0    922371.0    924596.0    931732.0    934267.0    909405.0   \n",
       "1    490723.0    492680.0    494325.0    494258.0    494016.0    493077.0   \n",
       "2   1261318.0   1258709.0   1263763.0   1266705.0   1269734.0   1239132.0   \n",
       "3    250275.0    250702.0    251353.0    251859.0    252538.0    253866.0   \n",
       "4    623200.0    625993.0    627782.0    627583.0    627596.0    626954.0   \n",
       "\n",
       "   2006-09-30  2006-10-31  2006-11-30  2006-12-31  2007-01-31  2007-02-28  \\\n",
       "0    874217.0    840322.0    841363.0    864142.0    878202.0    881070.0   \n",
       "1    492410.0    491640.0    491594.0    492028.0    493426.0    494691.0   \n",
       "2   1199239.0   1157117.0   1162377.0   1192700.0   1211333.0   1215511.0   \n",
       "3    254904.0    255596.0    255572.0    256205.0    257048.0    257708.0   \n",
       "4    626444.0    625554.0    625977.0    627072.0    629502.0    631587.0   \n",
       "\n",
       "   2007-03-31  2007-04-30  2007-05-31  2007-06-30  2007-07-31  2007-08-31  \\\n",
       "0    875319.0    881363.0    884951.0    877356.0    873008.0    865182.0   \n",
       "1    495376.0    494663.0    493369.0    491717.0    490421.0    490029.0   \n",
       "2   1207549.0   1214244.0   1217777.0   1205860.0   1198926.0   1188133.0   \n",
       "3    257874.0    257953.0    258300.0    258564.0    259299.0    260591.0   \n",
       "4    632954.0    632211.0    630996.0    629393.0    628204.0    627551.0   \n",
       "\n",
       "   2007-09-30  2007-10-31  2007-11-30  2007-12-31  2008-01-31  2008-02-29  \\\n",
       "0    888335.0    909176.0    932812.0    932759.0    933902.0    929507.0   \n",
       "1    489022.0    488199.0    486785.0    484895.0    484369.0    479106.0   \n",
       "2   1224780.0   1260701.0   1301830.0   1312130.0   1319793.0   1332163.0   \n",
       "3    262013.0    263244.0    263790.0    264871.0    264967.0    265031.0   \n",
       "4    625986.0    624850.0    623238.0    621076.0    620279.0    612602.0   \n",
       "\n",
       "   2008-03-31  2008-04-30  2008-05-31  2008-06-30  2008-07-31  2008-08-31  \\\n",
       "0    935364.0    949135.0    950893.0    940593.0    901735.0    887786.0   \n",
       "1         NaN         NaN    476772.0    469244.0    472688.0    468219.0   \n",
       "2   1336922.0   1346675.0   1327718.0   1316727.0   1274812.0   1265797.0   \n",
       "3    264109.0    263601.0    263502.0    263986.0    264749.0    264844.0   \n",
       "4         NaN         NaN         NaN    599472.0    604319.0    599395.0   \n",
       "\n",
       "   2008-09-30  2008-10-31  2008-11-30  2008-12-31  2009-01-31  2009-02-28  \\\n",
       "0    894699.0    898514.0    891202.0    855084.0    823978.0    793195.0   \n",
       "1    468101.0    462056.0    458791.0    451672.0    444246.0    439634.0   \n",
       "2   1268876.0   1273369.0   1273805.0   1216226.0   1160583.0   1093058.0   \n",
       "3    264252.0    262647.0    261125.0    260127.0    259836.0    260752.0   \n",
       "4    600172.0    592745.0    588586.0    579808.0    570083.0    563902.0   \n",
       "\n",
       "   2009-03-31  2009-04-30  2009-05-31  2009-06-30  2009-07-31  2009-08-31  \\\n",
       "0    777880.0    766624.0    772643.0    770881.0    773405.0    756423.0   \n",
       "1    434531.0    435255.0    432749.0    434551.0    434381.0    436385.0   \n",
       "2   1077608.0   1060120.0   1067412.0   1053431.0   1053003.0   1023036.0   \n",
       "3    261831.0    263208.0    263349.0    263666.0    264238.0    264528.0   \n",
       "4    556611.0    557503.0    554593.0    556677.0    556654.0    558713.0   \n",
       "\n",
       "   2009-09-30  2009-10-31  2009-11-30  2009-12-31  2010-01-31  2010-02-28  \\\n",
       "0    737748.0    722304.0    713371.0    704042.0    697134.0    692875.0   \n",
       "1    433510.0    431112.0    426500.0    427327.0    426322.0    428885.0   \n",
       "2    999321.0    978504.0    967766.0    952899.0    942671.0    934121.0   \n",
       "3    264246.0    264598.0    265602.0    266784.0    267713.0    269050.0   \n",
       "4    555103.0    552678.0    547620.0    549184.0    547017.0    548906.0   \n",
       "\n",
       "   2010-03-31  2010-04-30  2010-05-31  2010-06-30  2010-07-31  2010-08-31  \\\n",
       "0    693179.0    697997.0    697718.0    700520.0    708058.0    717643.0   \n",
       "1    426691.0    430187.0    429040.0    431967.0    426372.0    423602.0   \n",
       "2    932375.0    935154.0    935312.0    939655.0    954976.0    970888.0   \n",
       "3    271309.0    272891.0    274101.0    274372.0    273544.0    272240.0   \n",
       "4    544722.0    548766.0    546779.0    550641.0    542682.0    539090.0   \n",
       "\n",
       "   2010-09-30  2010-10-31  2010-11-30  2010-12-31  2011-01-31  2011-02-28  \\\n",
       "0    725515.0    729930.0    729046.0    730100.0    731231.0    737921.0   \n",
       "1    417494.0    413825.0    408376.0    404291.0    401940.0    400445.0   \n",
       "2    984151.0    987649.0    980527.0    978385.0    980834.0    995897.0   \n",
       "3    269185.0    266887.0    264805.0    264306.0    264583.0    264744.0   \n",
       "4    530450.0    525864.0    518814.0    512538.0    508449.0    504751.0   \n",
       "\n",
       "   2011-03-31  2011-04-30  2011-05-31  2011-06-30  2011-07-31  2011-08-31  \\\n",
       "0    742149.0    742312.0    740256.0    739861.0    741127.0    744211.0   \n",
       "1    400812.0    399871.0    400939.0    400030.0    401729.0    400454.0   \n",
       "2   1007811.0   1014301.0   1016180.0   1017319.0   1015232.0   1021085.0   \n",
       "3    264406.0    263727.0    263037.0    263120.0    262886.0    262061.0   \n",
       "4    505283.0    504549.0    507549.0    507049.0    509725.0    508816.0   \n",
       "\n",
       "   2011-09-30  2011-10-31  2011-11-30  2011-12-31  2012-01-31  2012-02-29  \\\n",
       "0    745225.0    742735.0    740765.0    738708.0    740565.0    740751.0   \n",
       "1    399304.0    394538.0    394097.0    389842.0    388041.0    381000.0   \n",
       "2   1025990.0   1032674.0   1031719.0   1032305.0   1029341.0   1032510.0   \n",
       "3    261438.0    260905.0    261287.0    261029.0    262131.0    263371.0   \n",
       "4    507957.0    501474.0    500224.0    494789.0    493098.0    484291.0   \n",
       "\n",
       "   2012-03-31  2012-04-30  2012-05-31  2012-06-30  2012-07-31  2012-08-31  \\\n",
       "0    741038.0    744713.0    747159.0    751777.0    753229.0    758277.0   \n",
       "1    382501.0    382858.0    385650.0    385230.0    382913.0    382690.0   \n",
       "2   1033542.0   1041709.0   1049247.0   1056032.0   1060269.0   1063563.0   \n",
       "3    265132.0    266682.0    267962.0    268856.0    269243.0    269341.0   \n",
       "4    487045.0    488444.0    493284.0    493093.0    490078.0    489616.0   \n",
       "\n",
       "   2012-09-30  2012-10-31  2012-11-30  2012-12-31  2013-01-31  2013-02-28  \\\n",
       "0    761989.0    767112.0    766164.0    766678.0    767999.0    775158.0   \n",
       "1    381670.0    385166.0    386669.0    387874.0    391388.0    395839.0   \n",
       "2   1069021.0   1073993.0   1070434.0   1069737.0   1071741.0   1079454.0   \n",
       "3    269093.0    268584.0    268505.0    268962.0    269904.0    271735.0   \n",
       "4    487764.0    492045.0    493704.0    495899.0    501196.0    507343.0   \n",
       "\n",
       "   2013-03-31  2013-04-30  2013-05-31  2013-06-30  2013-07-31  2013-08-31  \\\n",
       "0    780229.0    787212.0    790741.0    797377.0    803579.0    815114.0   \n",
       "1    400588.0    404746.0    409041.0    414859.0    418971.0    423114.0   \n",
       "2   1086566.0   1095377.0   1103497.0   1112336.0   1121599.0   1140822.0   \n",
       "3    274251.0    277605.0    280900.0    284574.0    287570.0    290781.0   \n",
       "4    513588.0    518818.0    524988.0    533519.0    539395.0    544615.0   \n",
       "\n",
       "   2013-09-30  2013-10-31  2013-11-30  2013-12-31  2014-01-31  2014-02-28  \\\n",
       "0    822646.0    829676.0    833671.0    841421.0    851097.0    864190.0   \n",
       "1    425234.0    432040.0    434536.0    439529.0    440543.0    445645.0   \n",
       "2   1158140.0   1169622.0   1173822.0   1181127.0   1193715.0   1206294.0   \n",
       "3    293898.0    296883.0    299930.0    301536.0    305181.0    308647.0   \n",
       "4    547048.0    554906.0    557250.0    562579.0    564063.0    571147.0   \n",
       "\n",
       "   2014-03-31  2014-04-30  2014-05-31  2014-06-30  2014-07-31  2014-08-31  \\\n",
       "0    878417.0    892522.0    901841.0    912531.0    920909.0    934043.0   \n",
       "1    447714.0    447931.0    448309.0    449461.0    449101.0    446550.0   \n",
       "2   1220299.0   1231951.0   1248363.0   1264509.0   1278656.0   1292954.0   \n",
       "3    312621.0    313870.0    314748.0    316057.0    318279.0    320258.0   \n",
       "4    574263.0    574299.0    575084.0    577670.0    578691.0    576287.0   \n",
       "\n",
       "   2014-09-30  2014-10-31  2014-11-30  2014-12-31  2015-01-31  2015-02-28  \\\n",
       "0    942793.0    950593.0    954327.0    957626.0    959696.0    964338.0   \n",
       "1    446655.0    447539.0    448901.0    449947.0    451054.0    453342.0   \n",
       "2   1304027.0   1317037.0   1320040.0   1319073.0   1314473.0   1316516.0   \n",
       "3    322847.0    325170.0    327731.0    330146.0    332204.0    334043.0   \n",
       "4    576551.0    577811.0    579789.0    581896.0    583886.0    587735.0   \n",
       "\n",
       "   2015-03-31  2015-04-30  2015-05-31  2015-06-30  2015-07-31  2015-08-31  \\\n",
       "0    965856.0    968816.0    967257.0    973973.0    979385.0    989376.0   \n",
       "1    455488.0    458836.0    460814.0    460103.0    459018.0    457867.0   \n",
       "2   1318633.0   1324817.0   1327595.0   1338454.0   1347963.0   1361435.0   \n",
       "3    334940.0    336370.0    338169.0    339534.0    340069.0    340048.0   \n",
       "4    591247.0    596962.0    600508.0    600594.0    600169.0    599703.0   \n",
       "\n",
       "   2015-09-30  2015-10-31  2015-11-30  2015-12-31  2016-01-31  2016-02-29  \\\n",
       "0    992569.0    999094.0   1006136.0   1016084.0   1024410.0   1031883.0   \n",
       "1    456767.0    455122.0    453817.0    455674.0    458919.0    465972.0   \n",
       "2   1366644.0   1372058.0   1378862.0   1390919.0   1398892.0   1402145.0   \n",
       "3    340557.0    341180.0    341368.0    341769.0    340928.0    339484.0   \n",
       "4    599435.0    598322.0    597724.0    600269.0    604432.0    612988.0   \n",
       "\n",
       "   2016-03-31  2016-04-30  2016-05-31  2016-06-30  2016-07-31  2016-08-31  \\\n",
       "0   1036781.0   1039276.0   1038463.0   1039533.0   1042028.0   1043708.0   \n",
       "1    470369.0    472719.0    471867.0    471134.0    472317.0    473077.0   \n",
       "2   1406398.0   1414555.0   1421313.0   1424330.0   1426079.0   1427442.0   \n",
       "3    338113.0    337719.0    337991.0    336589.0    335016.0    333534.0   \n",
       "4    618359.0    620994.0    618964.0    617440.0    617591.0    617990.0   \n",
       "\n",
       "   2016-09-30  2016-10-31  2016-11-30  2016-12-31  2017-01-31  2017-02-28  \\\n",
       "0   1044105.0   1043140.0   1042973.0   1047933.0   1047098.0   1047696.0   \n",
       "1    473770.0    474184.0    476617.0    478077.0    480669.0    482402.0   \n",
       "2   1425619.0   1422691.0   1421046.0   1425048.0   1422326.0   1417591.0   \n",
       "3    333285.0    333229.0    333665.0    333843.0    333623.0    333902.0   \n",
       "4    618040.0    618426.0    621559.0    623452.0    626750.0    629218.0   \n",
       "\n",
       "   2017-03-31  2017-04-30  2017-05-31  2017-06-30  2017-07-31  2017-08-31  \\\n",
       "0   1032615.0   1023510.0   1028427.0   1042353.0   1053176.0   1048683.0   \n",
       "1    486381.0    488002.0    488328.0    486562.0    485273.0    484805.0   \n",
       "2   1414039.0   1420303.0   1447421.0   1471937.0   1485133.0   1465273.0   \n",
       "3    334011.0    333799.0    332823.0    332192.0    332138.0    332045.0   \n",
       "4    634534.0    637689.0    638655.0    636913.0    635263.0    634031.0   \n",
       "\n",
       "   2017-09-30  2017-10-31  2017-11-30  2017-12-31  2018-01-31  2018-02-28  \\\n",
       "0   1046694.0   1041332.0   1033301.0   1026504.0   1021199.0   1031661.0   \n",
       "1    485689.0    484150.0    483141.0    483973.0    489278.0    493737.0   \n",
       "2   1450056.0   1450643.0   1466802.0   1478549.0   1480446.0   1506312.0   \n",
       "3    332439.0    332882.0    333391.0    334482.0    335520.0    335473.0   \n",
       "4    634932.0    632082.0    630177.0    630638.0    637149.0    643295.0   \n",
       "\n",
       "   2018-03-31  2018-04-30  2018-05-31  2018-06-30  2018-07-31  2018-08-31  \\\n",
       "0   1039454.0   1054816.0   1056888.0   1066356.0   1061627.0   1065767.0   \n",
       "1    494477.0    493197.0    492120.0    491699.0    490811.0    489892.0   \n",
       "2   1527181.0   1543052.0   1527355.0   1520237.0   1522152.0   1539341.0   \n",
       "3    334859.0    334998.0    335899.0    336346.0    336179.0    336554.0   \n",
       "4    644370.0    642966.0    641112.0    640284.0    639152.0    638058.0   \n",
       "\n",
       "   2018-09-30  2018-10-31  2018-11-30  2018-12-31  2019-01-31  2019-02-28  \\\n",
       "0   1060728.0   1069695.0   1077440.0   1083079.0   1101547.0   1107046.0   \n",
       "1    490294.0    490815.0    492716.0    493777.0    492277.0    490459.0   \n",
       "2   1554226.0   1568089.0   1568309.0   1543818.0   1505737.0   1466770.0   \n",
       "3    337640.0    338535.0    339463.0    339214.0    339497.0    339032.0   \n",
       "4    638822.0    639541.0    641959.0    643303.0    640585.0    638476.0   \n",
       "\n",
       "   2019-03-31  2019-04-30  2019-05-31  2019-06-30  2019-07-31  2019-08-31  \\\n",
       "0   1118206.0   1109296.0   1105068.0   1093927.0   1073984.0   1042854.0   \n",
       "1    488601.0    488506.0    487519.0    486469.0    486229.0    485262.0   \n",
       "2   1464510.0   1453948.0   1451273.0   1445332.0   1449648.0   1442862.0   \n",
       "3    338889.0    337883.0    337197.0    336637.0    336883.0    337000.0   \n",
       "4    636263.0    636989.0    635524.0    633687.0    632669.0    631669.0   \n",
       "\n",
       "   2019-09-30  2019-10-31  2019-11-30  2019-12-31  2020-01-31  2020-02-29  \\\n",
       "0   1040516.0   1056679.0   1091548.0   1103741.0   1097791.0   1072287.0   \n",
       "1    484470.0    482996.0    482345.0    481868.0    482014.0    483630.0   \n",
       "2   1411517.0   1398488.0   1413425.0   1453401.0   1480472.0   1487370.0   \n",
       "3    337791.0    338198.0    338534.0    338251.0    337882.0    337490.0   \n",
       "4    630945.0    629884.0    629135.0    628300.0    628177.0    628618.0   \n",
       "\n",
       "   2020-03-31  2020-04-30  2020-05-31  2020-06-30  2020-07-31  2020-08-31  \\\n",
       "0   1037630.0   1021446.0   1011086.0   1010052.0   1019858.0   1027420.0   \n",
       "1    484999.0    486131.0    485226.0    484958.0    486531.0    488670.0   \n",
       "2   1483302.0   1492388.0   1493083.0   1492055.0   1494986.0   1504496.0   \n",
       "3    337782.0    338649.0    339604.0    340044.0    340183.0    341482.0   \n",
       "4    628391.0    627595.0    626126.0    624940.0    626400.0    627960.0   \n",
       "\n",
       "   2020-09-30  2020-10-31  2020-11-30  2020-12-31  2021-01-31  2021-02-28  \\\n",
       "0   1039959.0   1058372.0   1093179.0   1127518.0   1120979.0   1113384.0   \n",
       "1    492859.0    495963.0    499887.0    502511.0    503635.0    504988.0   \n",
       "2   1517227.0   1514634.0   1514417.0   1513075.0   1487184.0   1478452.0   \n",
       "3    343070.0    345714.0    348971.0    353459.0    357493.0    362064.0   \n",
       "4    633179.0    637071.0    642179.0    645638.0    646620.0    648239.0   \n",
       "\n",
       "   2021-03-31  2021-04-30  2021-05-31  2021-06-30  2021-07-31  2021-08-31  \\\n",
       "0   1102248.0   1109411.0   1116393.0   1120588.0   1129421.0   1138407.0   \n",
       "1    505005.0    506952.0    507483.0    510301.0    514104.0    518577.0   \n",
       "2   1464876.0   1483107.0   1477774.0   1476095.0   1477965.0   1492502.0   \n",
       "3    367477.0    374020.0    380980.0    389731.0    400469.0    411347.0   \n",
       "4    648445.0    651047.0    651197.0    653755.0    657520.0    662553.0   \n",
       "\n",
       "   2021-09-30  2021-10-31  2021-11-30  2021-12-31  2022-01-31  2022-02-28  \\\n",
       "0   1148910.0   1153364.0   1152736.0   1153314.0   1159292.0   1171216.0   \n",
       "1    521153.0    523727.0    526511.0    528499.0    529879.0    530092.0   \n",
       "2   1508431.0   1517150.0   1521442.0   1521759.0   1532449.0   1542269.0   \n",
       "3    420464.0    428419.0    435498.0    441231.0    447322.0    456848.0   \n",
       "4    665737.0    669648.0    674293.0    677215.0    679126.0    678889.0   \n",
       "\n",
       "   2022-03-31  2022-04-30  2022-05-31  2022-06-30  2022-07-31  \n",
       "0   1190200.0   1207107.0   1221417.0   1227148.0   1234232.0  \n",
       "1    532758.0    534840.0    539859.0    543658.0    546709.0  \n",
       "2   1559390.0   1572653.0   1591368.0   1600569.0   1607770.0  \n",
       "3    468519.0    482272.0    493709.0    501230.0    503505.0  \n",
       "4    681982.0    684421.0    690214.0    695114.0    698644.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow = pd.read_csv('data_files/ZillowHomeValues.csv')\n",
    "zillow.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f4d3f25",
   "metadata": {},
   "source": [
    "Next we'll remove any of the rows with locations that are not Philadelphia, Pennsylvania"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "486b12c1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>RegionID</th>\n",
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       "      <th>2006-07-31</th>\n",
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       "      <th>2006-09-30</th>\n",
       "      <th>2006-10-31</th>\n",
       "      <th>2006-11-30</th>\n",
       "      <th>2006-12-31</th>\n",
       "      <th>2007-01-31</th>\n",
       "      <th>2007-02-28</th>\n",
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       "      <th>2008-07-31</th>\n",
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       "      <th>2009-01-31</th>\n",
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       "      <th>2009-03-31</th>\n",
       "      <th>2009-04-30</th>\n",
       "      <th>2009-05-31</th>\n",
       "      <th>2009-06-30</th>\n",
       "      <th>2009-07-31</th>\n",
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       "      <th>2009-12-31</th>\n",
       "      <th>2010-01-31</th>\n",
       "      <th>2010-02-28</th>\n",
       "      <th>2010-03-31</th>\n",
       "      <th>2010-04-30</th>\n",
       "      <th>2010-05-31</th>\n",
       "      <th>2010-06-30</th>\n",
       "      <th>2010-07-31</th>\n",
       "      <th>2010-08-31</th>\n",
       "      <th>2010-09-30</th>\n",
       "      <th>2010-10-31</th>\n",
       "      <th>2010-11-30</th>\n",
       "      <th>2010-12-31</th>\n",
       "      <th>2011-01-31</th>\n",
       "      <th>2011-02-28</th>\n",
       "      <th>2011-03-31</th>\n",
       "      <th>2011-04-30</th>\n",
       "      <th>2011-05-31</th>\n",
       "      <th>2011-06-30</th>\n",
       "      <th>2011-07-31</th>\n",
       "      <th>2011-08-31</th>\n",
       "      <th>2011-09-30</th>\n",
       "      <th>2011-10-31</th>\n",
       "      <th>2011-11-30</th>\n",
       "      <th>2011-12-31</th>\n",
       "      <th>2012-01-31</th>\n",
       "      <th>2012-02-29</th>\n",
       "      <th>2012-03-31</th>\n",
       "      <th>2012-04-30</th>\n",
       "      <th>2012-05-31</th>\n",
       "      <th>2012-06-30</th>\n",
       "      <th>2012-07-31</th>\n",
       "      <th>2012-08-31</th>\n",
       "      <th>2012-09-30</th>\n",
       "      <th>2012-10-31</th>\n",
       "      <th>2012-11-30</th>\n",
       "      <th>2012-12-31</th>\n",
       "      <th>2013-01-31</th>\n",
       "      <th>2013-02-28</th>\n",
       "      <th>2013-03-31</th>\n",
       "      <th>2013-04-30</th>\n",
       "      <th>2013-05-31</th>\n",
       "      <th>2013-06-30</th>\n",
       "      <th>2013-07-31</th>\n",
       "      <th>2013-08-31</th>\n",
       "      <th>2013-09-30</th>\n",
       "      <th>2013-10-31</th>\n",
       "      <th>2013-11-30</th>\n",
       "      <th>2013-12-31</th>\n",
       "      <th>2014-01-31</th>\n",
       "      <th>2014-02-28</th>\n",
       "      <th>2014-03-31</th>\n",
       "      <th>2014-04-30</th>\n",
       "      <th>2014-05-31</th>\n",
       "      <th>2014-06-30</th>\n",
       "      <th>2014-07-31</th>\n",
       "      <th>2014-08-31</th>\n",
       "      <th>2014-09-30</th>\n",
       "      <th>2014-10-31</th>\n",
       "      <th>2014-11-30</th>\n",
       "      <th>2014-12-31</th>\n",
       "      <th>2015-01-31</th>\n",
       "      <th>2015-02-28</th>\n",
       "      <th>2015-03-31</th>\n",
       "      <th>2015-04-30</th>\n",
       "      <th>2015-05-31</th>\n",
       "      <th>2015-06-30</th>\n",
       "      <th>2015-07-31</th>\n",
       "      <th>2015-08-31</th>\n",
       "      <th>2015-09-30</th>\n",
       "      <th>2015-10-31</th>\n",
       "      <th>2015-11-30</th>\n",
       "      <th>2015-12-31</th>\n",
       "      <th>2016-01-31</th>\n",
       "      <th>2016-02-29</th>\n",
       "      <th>2016-03-31</th>\n",
       "      <th>2016-04-30</th>\n",
       "      <th>2016-05-31</th>\n",
       "      <th>2016-06-30</th>\n",
       "      <th>2016-07-31</th>\n",
       "      <th>2016-08-31</th>\n",
       "      <th>2016-09-30</th>\n",
       "      <th>2016-10-31</th>\n",
       "      <th>2016-11-30</th>\n",
       "      <th>2016-12-31</th>\n",
       "      <th>2017-01-31</th>\n",
       "      <th>2017-02-28</th>\n",
       "      <th>2017-03-31</th>\n",
       "      <th>2017-04-30</th>\n",
       "      <th>2017-05-31</th>\n",
       "      <th>2017-06-30</th>\n",
       "      <th>2017-07-31</th>\n",
       "      <th>2017-08-31</th>\n",
       "      <th>2017-09-30</th>\n",
       "      <th>2017-10-31</th>\n",
       "      <th>2017-11-30</th>\n",
       "      <th>2017-12-31</th>\n",
       "      <th>2018-01-31</th>\n",
       "      <th>2018-02-28</th>\n",
       "      <th>2018-03-31</th>\n",
       "      <th>2018-04-30</th>\n",
       "      <th>2018-05-31</th>\n",
       "      <th>2018-06-30</th>\n",
       "      <th>2018-07-31</th>\n",
       "      <th>2018-08-31</th>\n",
       "      <th>2018-09-30</th>\n",
       "      <th>2018-10-31</th>\n",
       "      <th>2018-11-30</th>\n",
       "      <th>2018-12-31</th>\n",
       "      <th>2019-01-31</th>\n",
       "      <th>2019-02-28</th>\n",
       "      <th>2019-03-31</th>\n",
       "      <th>2019-04-30</th>\n",
       "      <th>2019-05-31</th>\n",
       "      <th>2019-06-30</th>\n",
       "      <th>2019-07-31</th>\n",
       "      <th>2019-08-31</th>\n",
       "      <th>2019-09-30</th>\n",
       "      <th>2019-10-31</th>\n",
       "      <th>2019-11-30</th>\n",
       "      <th>2019-12-31</th>\n",
       "      <th>2020-01-31</th>\n",
       "      <th>2020-02-29</th>\n",
       "      <th>2020-03-31</th>\n",
       "      <th>2020-04-30</th>\n",
       "      <th>2020-05-31</th>\n",
       "      <th>2020-06-30</th>\n",
       "      <th>2020-07-31</th>\n",
       "      <th>2020-08-31</th>\n",
       "      <th>2020-09-30</th>\n",
       "      <th>2020-10-31</th>\n",
       "      <th>2020-11-30</th>\n",
       "      <th>2020-12-31</th>\n",
       "      <th>2021-01-31</th>\n",
       "      <th>2021-02-28</th>\n",
       "      <th>2021-03-31</th>\n",
       "      <th>2021-04-30</th>\n",
       "      <th>2021-05-31</th>\n",
       "      <th>2021-06-30</th>\n",
       "      <th>2021-07-31</th>\n",
       "      <th>2021-08-31</th>\n",
       "      <th>2021-09-30</th>\n",
       "      <th>2021-10-31</th>\n",
       "      <th>2021-11-30</th>\n",
       "      <th>2021-12-31</th>\n",
       "      <th>2022-01-31</th>\n",
       "      <th>2022-02-28</th>\n",
       "      <th>2022-03-31</th>\n",
       "      <th>2022-04-30</th>\n",
       "      <th>2022-05-31</th>\n",
       "      <th>2022-06-30</th>\n",
       "      <th>2022-07-31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>65810</td>\n",
       "      <td>126</td>\n",
       "      <td>19143</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>60701.0</td>\n",
       "      <td>61009.0</td>\n",
       "      <td>61132.0</td>\n",
       "      <td>61497.0</td>\n",
       "      <td>62089.0</td>\n",
       "      <td>62804.0</td>\n",
       "      <td>63377.0</td>\n",
       "      <td>63925.0</td>\n",
       "      <td>64313.0</td>\n",
       "      <td>64609.0</td>\n",
       "      <td>64756.0</td>\n",
       "      <td>65187.0</td>\n",
       "      <td>65676.0</td>\n",
       "      <td>65861.0</td>\n",
       "      <td>65823.0</td>\n",
       "      <td>66081.0</td>\n",
       "      <td>66273.0</td>\n",
       "      <td>66797.0</td>\n",
       "      <td>67099.0</td>\n",
       "      <td>67641.0</td>\n",
       "      <td>67686.0</td>\n",
       "      <td>67959.0</td>\n",
       "      <td>68203.0</td>\n",
       "      <td>68695.0</td>\n",
       "      <td>68447.0</td>\n",
       "      <td>68180.0</td>\n",
       "      <td>67802.0</td>\n",
       "      <td>67913.0</td>\n",
       "      <td>68143.0</td>\n",
       "      <td>68498.0</td>\n",
       "      <td>68586.0</td>\n",
       "      <td>68493.0</td>\n",
       "      <td>68414.0</td>\n",
       "      <td>68426.0</td>\n",
       "      <td>68588.0</td>\n",
       "      <td>68758.0</td>\n",
       "      <td>69396.0</td>\n",
       "      <td>70405.0</td>\n",
       "      <td>71333.0</td>\n",
       "      <td>72108.0</td>\n",
       "      <td>71545.0</td>\n",
       "      <td>70832.0</td>\n",
       "      <td>69969.0</td>\n",
       "      <td>69189.0</td>\n",
       "      <td>68933.0</td>\n",
       "      <td>68952.0</td>\n",
       "      <td>69436.0</td>\n",
       "      <td>69497.0</td>\n",
       "      <td>69166.0</td>\n",
       "      <td>68806.0</td>\n",
       "      <td>68433.0</td>\n",
       "      <td>67859.0</td>\n",
       "      <td>68503.0</td>\n",
       "      <td>69640.0</td>\n",
       "      <td>72312.0</td>\n",
       "      <td>74534.0</td>\n",
       "      <td>76922.0</td>\n",
       "      <td>77949.0</td>\n",
       "      <td>78439.0</td>\n",
       "      <td>78686.0</td>\n",
       "      <td>79661.0</td>\n",
       "      <td>81557.0</td>\n",
       "      <td>83961.0</td>\n",
       "      <td>86307.0</td>\n",
       "      <td>88384.0</td>\n",
       "      <td>90337.0</td>\n",
       "      <td>92085.0</td>\n",
       "      <td>93843.0</td>\n",
       "      <td>95070.0</td>\n",
       "      <td>96082.0</td>\n",
       "      <td>96754.0</td>\n",
       "      <td>97226.0</td>\n",
       "      <td>97366.0</td>\n",
       "      <td>97250.0</td>\n",
       "      <td>97431.0</td>\n",
       "      <td>98005.0</td>\n",
       "      <td>98827.0</td>\n",
       "      <td>99968.0</td>\n",
       "      <td>101264.0</td>\n",
       "      <td>102415.0</td>\n",
       "      <td>102880.0</td>\n",
       "      <td>103031.0</td>\n",
       "      <td>103011.0</td>\n",
       "      <td>103479.0</td>\n",
       "      <td>104487.0</td>\n",
       "      <td>105486.0</td>\n",
       "      <td>106479.0</td>\n",
       "      <td>107086.0</td>\n",
       "      <td>107907.0</td>\n",
       "      <td>108633.0</td>\n",
       "      <td>109295.0</td>\n",
       "      <td>109709.0</td>\n",
       "      <td>109799.0</td>\n",
       "      <td>110175.0</td>\n",
       "      <td>110832.0</td>\n",
       "      <td>111722.0</td>\n",
       "      <td>111804.0</td>\n",
       "      <td>112139.0</td>\n",
       "      <td>111773.0</td>\n",
       "      <td>111921.0</td>\n",
       "      <td>111393.0</td>\n",
       "      <td>111219.0</td>\n",
       "      <td>110379.0</td>\n",
       "      <td>109495.0</td>\n",
       "      <td>108215.0</td>\n",
       "      <td>107367.0</td>\n",
       "      <td>106351.0</td>\n",
       "      <td>105522.0</td>\n",
       "      <td>104364.0</td>\n",
       "      <td>103805.0</td>\n",
       "      <td>103242.0</td>\n",
       "      <td>103065.0</td>\n",
       "      <td>102323.0</td>\n",
       "      <td>101444.0</td>\n",
       "      <td>101301.0</td>\n",
       "      <td>101305.0</td>\n",
       "      <td>101310.0</td>\n",
       "      <td>100671.0</td>\n",
       "      <td>101311.0</td>\n",
       "      <td>101380.0</td>\n",
       "      <td>101148.0</td>\n",
       "      <td>99991.0</td>\n",
       "      <td>99764.0</td>\n",
       "      <td>99667.0</td>\n",
       "      <td>100342.0</td>\n",
       "      <td>101206.0</td>\n",
       "      <td>101751.0</td>\n",
       "      <td>101410.0</td>\n",
       "      <td>99899.0</td>\n",
       "      <td>99054.0</td>\n",
       "      <td>98190.0</td>\n",
       "      <td>97760.0</td>\n",
       "      <td>97027.0</td>\n",
       "      <td>95989.0</td>\n",
       "      <td>95607.0</td>\n",
       "      <td>94610.0</td>\n",
       "      <td>93853.0</td>\n",
       "      <td>92438.0</td>\n",
       "      <td>91449.0</td>\n",
       "      <td>90053.0</td>\n",
       "      <td>89361.0</td>\n",
       "      <td>88056.0</td>\n",
       "      <td>86772.0</td>\n",
       "      <td>85735.0</td>\n",
       "      <td>84583.0</td>\n",
       "      <td>84007.0</td>\n",
       "      <td>82600.0</td>\n",
       "      <td>81821.0</td>\n",
       "      <td>80929.0</td>\n",
       "      <td>80279.0</td>\n",
       "      <td>79375.0</td>\n",
       "      <td>78214.0</td>\n",
       "      <td>76773.0</td>\n",
       "      <td>75875.0</td>\n",
       "      <td>75539.0</td>\n",
       "      <td>75270.0</td>\n",
       "      <td>75758.0</td>\n",
       "      <td>75887.0</td>\n",
       "      <td>76684.0</td>\n",
       "      <td>77167.0</td>\n",
       "      <td>77425.0</td>\n",
       "      <td>77025.0</td>\n",
       "      <td>76461.0</td>\n",
       "      <td>77015.0</td>\n",
       "      <td>78310.0</td>\n",
       "      <td>79731.0</td>\n",
       "      <td>80026.0</td>\n",
       "      <td>80722.0</td>\n",
       "      <td>80064.0</td>\n",
       "      <td>79452.0</td>\n",
       "      <td>78172.0</td>\n",
       "      <td>78217.0</td>\n",
       "      <td>78507.0</td>\n",
       "      <td>78747.0</td>\n",
       "      <td>78096.0</td>\n",
       "      <td>76899.0</td>\n",
       "      <td>76226.0</td>\n",
       "      <td>76352.0</td>\n",
       "      <td>77600.0</td>\n",
       "      <td>78201.0</td>\n",
       "      <td>78684.0</td>\n",
       "      <td>78404.0</td>\n",
       "      <td>78963.0</td>\n",
       "      <td>79121.0</td>\n",
       "      <td>78435.0</td>\n",
       "      <td>76924.0</td>\n",
       "      <td>76583.0</td>\n",
       "      <td>77810.0</td>\n",
       "      <td>79529.0</td>\n",
       "      <td>80410.0</td>\n",
       "      <td>80371.0</td>\n",
       "      <td>79269.0</td>\n",
       "      <td>78491.0</td>\n",
       "      <td>78287.0</td>\n",
       "      <td>78132.0</td>\n",
       "      <td>78159.0</td>\n",
       "      <td>77924.0</td>\n",
       "      <td>78513.0</td>\n",
       "      <td>78295.0</td>\n",
       "      <td>78311.0</td>\n",
       "      <td>78364.0</td>\n",
       "      <td>78514.0</td>\n",
       "      <td>79223.0</td>\n",
       "      <td>80707.0</td>\n",
       "      <td>81456.0</td>\n",
       "      <td>80844.0</td>\n",
       "      <td>79670.0</td>\n",
       "      <td>79979.0</td>\n",
       "      <td>81003.0</td>\n",
       "      <td>81645.0</td>\n",
       "      <td>81964.0</td>\n",
       "      <td>82782.0</td>\n",
       "      <td>84137.0</td>\n",
       "      <td>86169.0</td>\n",
       "      <td>87425.0</td>\n",
       "      <td>88374.0</td>\n",
       "      <td>90395.0</td>\n",
       "      <td>93178.0</td>\n",
       "      <td>97105.0</td>\n",
       "      <td>99056.0</td>\n",
       "      <td>101865.0</td>\n",
       "      <td>104178.0</td>\n",
       "      <td>107011.0</td>\n",
       "      <td>108961.0</td>\n",
       "      <td>110034.0</td>\n",
       "      <td>110958.0</td>\n",
       "      <td>112422.0</td>\n",
       "      <td>114015.0</td>\n",
       "      <td>115970.0</td>\n",
       "      <td>117291.0</td>\n",
       "      <td>118019.0</td>\n",
       "      <td>118858.0</td>\n",
       "      <td>120055.0</td>\n",
       "      <td>122956.0</td>\n",
       "      <td>124885.0</td>\n",
       "      <td>126050.0</td>\n",
       "      <td>126668.0</td>\n",
       "      <td>128030.0</td>\n",
       "      <td>129620.0</td>\n",
       "      <td>131469.0</td>\n",
       "      <td>134062.0</td>\n",
       "      <td>136766.0</td>\n",
       "      <td>138666.0</td>\n",
       "      <td>140376.0</td>\n",
       "      <td>141110.0</td>\n",
       "      <td>142564.0</td>\n",
       "      <td>144531.0</td>\n",
       "      <td>149060.0</td>\n",
       "      <td>152057.0</td>\n",
       "      <td>154865.0</td>\n",
       "      <td>157516.0</td>\n",
       "      <td>161147.0</td>\n",
       "      <td>164398.0</td>\n",
       "      <td>167188.0</td>\n",
       "      <td>169351.0</td>\n",
       "      <td>171125.0</td>\n",
       "      <td>172719.0</td>\n",
       "      <td>174880.0</td>\n",
       "      <td>176204.0</td>\n",
       "      <td>176015.0</td>\n",
       "      <td>174596.0</td>\n",
       "      <td>173114.0</td>\n",
       "      <td>172087.0</td>\n",
       "      <td>171445.0</td>\n",
       "      <td>171542.0</td>\n",
       "      <td>171680.0</td>\n",
       "      <td>171878.0</td>\n",
       "      <td>171607.0</td>\n",
       "      <td>171333.0</td>\n",
       "      <td>171771.0</td>\n",
       "      <td>172611.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>65779</td>\n",
       "      <td>249</td>\n",
       "      <td>19111</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>85062.0</td>\n",
       "      <td>85325.0</td>\n",
       "      <td>85511.0</td>\n",
       "      <td>85937.0</td>\n",
       "      <td>86553.0</td>\n",
       "      <td>87106.0</td>\n",
       "      <td>87591.0</td>\n",
       "      <td>87826.0</td>\n",
       "      <td>88161.0</td>\n",
       "      <td>88561.0</td>\n",
       "      <td>88958.0</td>\n",
       "      <td>89438.0</td>\n",
       "      <td>89950.0</td>\n",
       "      <td>90515.0</td>\n",
       "      <td>91088.0</td>\n",
       "      <td>91677.0</td>\n",
       "      <td>92113.0</td>\n",
       "      <td>92536.0</td>\n",
       "      <td>92992.0</td>\n",
       "      <td>93485.0</td>\n",
       "      <td>94003.0</td>\n",
       "      <td>94417.0</td>\n",
       "      <td>95089.0</td>\n",
       "      <td>95722.0</td>\n",
       "      <td>96345.0</td>\n",
       "      <td>96941.0</td>\n",
       "      <td>97579.0</td>\n",
       "      <td>98372.0</td>\n",
       "      <td>99076.0</td>\n",
       "      <td>99886.0</td>\n",
       "      <td>100862.0</td>\n",
       "      <td>101890.0</td>\n",
       "      <td>102984.0</td>\n",
       "      <td>104071.0</td>\n",
       "      <td>105134.0</td>\n",
       "      <td>106151.0</td>\n",
       "      <td>107061.0</td>\n",
       "      <td>108177.0</td>\n",
       "      <td>109247.0</td>\n",
       "      <td>110301.0</td>\n",
       "      <td>111277.0</td>\n",
       "      <td>112438.0</td>\n",
       "      <td>114178.0</td>\n",
       "      <td>115959.0</td>\n",
       "      <td>117757.0</td>\n",
       "      <td>119093.0</td>\n",
       "      <td>120471.0</td>\n",
       "      <td>121920.0</td>\n",
       "      <td>123332.0</td>\n",
       "      <td>124897.0</td>\n",
       "      <td>126029.0</td>\n",
       "      <td>127138.0</td>\n",
       "      <td>128446.0</td>\n",
       "      <td>130704.0</td>\n",
       "      <td>133641.0</td>\n",
       "      <td>136937.0</td>\n",
       "      <td>139741.0</td>\n",
       "      <td>141849.0</td>\n",
       "      <td>143846.0</td>\n",
       "      <td>146431.0</td>\n",
       "      <td>149653.0</td>\n",
       "      <td>151750.0</td>\n",
       "      <td>153916.0</td>\n",
       "      <td>155841.0</td>\n",
       "      <td>158797.0</td>\n",
       "      <td>161690.0</td>\n",
       "      <td>165007.0</td>\n",
       "      <td>168454.0</td>\n",
       "      <td>171486.0</td>\n",
       "      <td>173952.0</td>\n",
       "      <td>176189.0</td>\n",
       "      <td>178068.0</td>\n",
       "      <td>179278.0</td>\n",
       "      <td>180165.0</td>\n",
       "      <td>180564.0</td>\n",
       "      <td>181480.0</td>\n",
       "      <td>182426.0</td>\n",
       "      <td>183849.0</td>\n",
       "      <td>185148.0</td>\n",
       "      <td>185461.0</td>\n",
       "      <td>185262.0</td>\n",
       "      <td>184263.0</td>\n",
       "      <td>183359.0</td>\n",
       "      <td>182896.0</td>\n",
       "      <td>183302.0</td>\n",
       "      <td>183850.0</td>\n",
       "      <td>184323.0</td>\n",
       "      <td>184004.0</td>\n",
       "      <td>184393.0</td>\n",
       "      <td>184874.0</td>\n",
       "      <td>185468.0</td>\n",
       "      <td>185143.0</td>\n",
       "      <td>184016.0</td>\n",
       "      <td>183558.0</td>\n",
       "      <td>183651.0</td>\n",
       "      <td>183769.0</td>\n",
       "      <td>183295.0</td>\n",
       "      <td>183077.0</td>\n",
       "      <td>182343.0</td>\n",
       "      <td>181946.0</td>\n",
       "      <td>180672.0</td>\n",
       "      <td>179570.0</td>\n",
       "      <td>177714.0</td>\n",
       "      <td>176122.0</td>\n",
       "      <td>174785.0</td>\n",
       "      <td>174053.0</td>\n",
       "      <td>173051.0</td>\n",
       "      <td>171897.0</td>\n",
       "      <td>170067.0</td>\n",
       "      <td>168534.0</td>\n",
       "      <td>167708.0</td>\n",
       "      <td>167669.0</td>\n",
       "      <td>167622.0</td>\n",
       "      <td>167573.0</td>\n",
       "      <td>167346.0</td>\n",
       "      <td>167175.0</td>\n",
       "      <td>167000.0</td>\n",
       "      <td>166703.0</td>\n",
       "      <td>166631.0</td>\n",
       "      <td>166329.0</td>\n",
       "      <td>166607.0</td>\n",
       "      <td>167096.0</td>\n",
       "      <td>167747.0</td>\n",
       "      <td>168725.0</td>\n",
       "      <td>169508.0</td>\n",
       "      <td>170394.0</td>\n",
       "      <td>170337.0</td>\n",
       "      <td>169592.0</td>\n",
       "      <td>167491.0</td>\n",
       "      <td>165488.0</td>\n",
       "      <td>163970.0</td>\n",
       "      <td>162902.0</td>\n",
       "      <td>161966.0</td>\n",
       "      <td>161241.0</td>\n",
       "      <td>161488.0</td>\n",
       "      <td>161326.0</td>\n",
       "      <td>160817.0</td>\n",
       "      <td>159370.0</td>\n",
       "      <td>158064.0</td>\n",
       "      <td>157130.0</td>\n",
       "      <td>156624.0</td>\n",
       "      <td>156405.0</td>\n",
       "      <td>155915.0</td>\n",
       "      <td>155689.0</td>\n",
       "      <td>155512.0</td>\n",
       "      <td>155498.0</td>\n",
       "      <td>155935.0</td>\n",
       "      <td>156558.0</td>\n",
       "      <td>156797.0</td>\n",
       "      <td>156427.0</td>\n",
       "      <td>155345.0</td>\n",
       "      <td>154473.0</td>\n",
       "      <td>154002.0</td>\n",
       "      <td>153784.0</td>\n",
       "      <td>153578.0</td>\n",
       "      <td>152985.0</td>\n",
       "      <td>152508.0</td>\n",
       "      <td>151807.0</td>\n",
       "      <td>151572.0</td>\n",
       "      <td>152032.0</td>\n",
       "      <td>153197.0</td>\n",
       "      <td>154319.0</td>\n",
       "      <td>154912.0</td>\n",
       "      <td>154590.0</td>\n",
       "      <td>153730.0</td>\n",
       "      <td>153093.0</td>\n",
       "      <td>153027.0</td>\n",
       "      <td>153170.0</td>\n",
       "      <td>153407.0</td>\n",
       "      <td>153770.0</td>\n",
       "      <td>154052.0</td>\n",
       "      <td>152735.0</td>\n",
       "      <td>151932.0</td>\n",
       "      <td>151197.0</td>\n",
       "      <td>151529.0</td>\n",
       "      <td>151185.0</td>\n",
       "      <td>150982.0</td>\n",
       "      <td>151152.0</td>\n",
       "      <td>151573.0</td>\n",
       "      <td>152541.0</td>\n",
       "      <td>153163.0</td>\n",
       "      <td>153473.0</td>\n",
       "      <td>154117.0</td>\n",
       "      <td>155346.0</td>\n",
       "      <td>156034.0</td>\n",
       "      <td>155829.0</td>\n",
       "      <td>155165.0</td>\n",
       "      <td>155482.0</td>\n",
       "      <td>156306.0</td>\n",
       "      <td>156730.0</td>\n",
       "      <td>157046.0</td>\n",
       "      <td>156051.0</td>\n",
       "      <td>155744.0</td>\n",
       "      <td>155593.0</td>\n",
       "      <td>155782.0</td>\n",
       "      <td>156233.0</td>\n",
       "      <td>156414.0</td>\n",
       "      <td>157693.0</td>\n",
       "      <td>158424.0</td>\n",
       "      <td>159387.0</td>\n",
       "      <td>160022.0</td>\n",
       "      <td>161145.0</td>\n",
       "      <td>162362.0</td>\n",
       "      <td>164293.0</td>\n",
       "      <td>165834.0</td>\n",
       "      <td>166797.0</td>\n",
       "      <td>167640.0</td>\n",
       "      <td>168699.0</td>\n",
       "      <td>169919.0</td>\n",
       "      <td>170225.0</td>\n",
       "      <td>169976.0</td>\n",
       "      <td>170149.0</td>\n",
       "      <td>171026.0</td>\n",
       "      <td>173107.0</td>\n",
       "      <td>174567.0</td>\n",
       "      <td>176158.0</td>\n",
       "      <td>176461.0</td>\n",
       "      <td>177644.0</td>\n",
       "      <td>178936.0</td>\n",
       "      <td>180638.0</td>\n",
       "      <td>181664.0</td>\n",
       "      <td>182138.0</td>\n",
       "      <td>183150.0</td>\n",
       "      <td>184656.0</td>\n",
       "      <td>186732.0</td>\n",
       "      <td>188650.0</td>\n",
       "      <td>190755.0</td>\n",
       "      <td>192883.0</td>\n",
       "      <td>195542.0</td>\n",
       "      <td>197516.0</td>\n",
       "      <td>198968.0</td>\n",
       "      <td>199252.0</td>\n",
       "      <td>199840.0</td>\n",
       "      <td>199978.0</td>\n",
       "      <td>200839.0</td>\n",
       "      <td>201593.0</td>\n",
       "      <td>202632.0</td>\n",
       "      <td>203180.0</td>\n",
       "      <td>203531.0</td>\n",
       "      <td>204228.0</td>\n",
       "      <td>204670.0</td>\n",
       "      <td>205301.0</td>\n",
       "      <td>206452.0</td>\n",
       "      <td>208135.0</td>\n",
       "      <td>209251.0</td>\n",
       "      <td>210282.0</td>\n",
       "      <td>212368.0</td>\n",
       "      <td>216023.0</td>\n",
       "      <td>219027.0</td>\n",
       "      <td>222720.0</td>\n",
       "      <td>226121.0</td>\n",
       "      <td>229845.0</td>\n",
       "      <td>232754.0</td>\n",
       "      <td>235155.0</td>\n",
       "      <td>237287.0</td>\n",
       "      <td>239669.0</td>\n",
       "      <td>243054.0</td>\n",
       "      <td>246722.0</td>\n",
       "      <td>250520.0</td>\n",
       "      <td>253386.0</td>\n",
       "      <td>256032.0</td>\n",
       "      <td>257911.0</td>\n",
       "      <td>260104.0</td>\n",
       "      <td>262257.0</td>\n",
       "      <td>263715.0</td>\n",
       "      <td>264809.0</td>\n",
       "      <td>265684.0</td>\n",
       "      <td>267222.0</td>\n",
       "      <td>269460.0</td>\n",
       "      <td>272201.0</td>\n",
       "      <td>274446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>65791</td>\n",
       "      <td>340</td>\n",
       "      <td>19124</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>47155.0</td>\n",
       "      <td>47278.0</td>\n",
       "      <td>47408.0</td>\n",
       "      <td>47613.0</td>\n",
       "      <td>47956.0</td>\n",
       "      <td>48226.0</td>\n",
       "      <td>48586.0</td>\n",
       "      <td>48728.0</td>\n",
       "      <td>48955.0</td>\n",
       "      <td>49118.0</td>\n",
       "      <td>49275.0</td>\n",
       "      <td>49389.0</td>\n",
       "      <td>49523.0</td>\n",
       "      <td>49721.0</td>\n",
       "      <td>50009.0</td>\n",
       "      <td>50254.0</td>\n",
       "      <td>50481.0</td>\n",
       "      <td>50610.0</td>\n",
       "      <td>50741.0</td>\n",
       "      <td>50882.0</td>\n",
       "      <td>51036.0</td>\n",
       "      <td>51232.0</td>\n",
       "      <td>51416.0</td>\n",
       "      <td>51605.0</td>\n",
       "      <td>51744.0</td>\n",
       "      <td>51911.0</td>\n",
       "      <td>52107.0</td>\n",
       "      <td>52303.0</td>\n",
       "      <td>52536.0</td>\n",
       "      <td>52757.0</td>\n",
       "      <td>53075.0</td>\n",
       "      <td>53409.0</td>\n",
       "      <td>53737.0</td>\n",
       "      <td>53997.0</td>\n",
       "      <td>54232.0</td>\n",
       "      <td>54489.0</td>\n",
       "      <td>54885.0</td>\n",
       "      <td>55397.0</td>\n",
       "      <td>55953.0</td>\n",
       "      <td>56551.0</td>\n",
       "      <td>57038.0</td>\n",
       "      <td>57700.0</td>\n",
       "      <td>58370.0</td>\n",
       "      <td>59082.0</td>\n",
       "      <td>59803.0</td>\n",
       "      <td>60415.0</td>\n",
       "      <td>61013.0</td>\n",
       "      <td>61545.0</td>\n",
       "      <td>62079.0</td>\n",
       "      <td>62688.0</td>\n",
       "      <td>62923.0</td>\n",
       "      <td>63177.0</td>\n",
       "      <td>63518.0</td>\n",
       "      <td>64381.0</td>\n",
       "      <td>65520.0</td>\n",
       "      <td>66803.0</td>\n",
       "      <td>67730.0</td>\n",
       "      <td>68397.0</td>\n",
       "      <td>69036.0</td>\n",
       "      <td>70092.0</td>\n",
       "      <td>71384.0</td>\n",
       "      <td>72230.0</td>\n",
       "      <td>73336.0</td>\n",
       "      <td>74356.0</td>\n",
       "      <td>75894.0</td>\n",
       "      <td>77244.0</td>\n",
       "      <td>78885.0</td>\n",
       "      <td>80660.0</td>\n",
       "      <td>82392.0</td>\n",
       "      <td>83864.0</td>\n",
       "      <td>85158.0</td>\n",
       "      <td>86200.0</td>\n",
       "      <td>87088.0</td>\n",
       "      <td>87835.0</td>\n",
       "      <td>88441.0</td>\n",
       "      <td>89121.0</td>\n",
       "      <td>89893.0</td>\n",
       "      <td>91149.0</td>\n",
       "      <td>92529.0</td>\n",
       "      <td>93594.0</td>\n",
       "      <td>94165.0</td>\n",
       "      <td>94255.0</td>\n",
       "      <td>94451.0</td>\n",
       "      <td>94912.0</td>\n",
       "      <td>95741.0</td>\n",
       "      <td>96476.0</td>\n",
       "      <td>97222.0</td>\n",
       "      <td>97739.0</td>\n",
       "      <td>98489.0</td>\n",
       "      <td>99167.0</td>\n",
       "      <td>99733.0</td>\n",
       "      <td>99838.0</td>\n",
       "      <td>99550.0</td>\n",
       "      <td>99656.0</td>\n",
       "      <td>99854.0</td>\n",
       "      <td>100206.0</td>\n",
       "      <td>99986.0</td>\n",
       "      <td>100138.0</td>\n",
       "      <td>99941.0</td>\n",
       "      <td>99881.0</td>\n",
       "      <td>99048.0</td>\n",
       "      <td>98127.0</td>\n",
       "      <td>97134.0</td>\n",
       "      <td>96257.0</td>\n",
       "      <td>95602.0</td>\n",
       "      <td>95245.0</td>\n",
       "      <td>94981.0</td>\n",
       "      <td>94602.0</td>\n",
       "      <td>93616.0</td>\n",
       "      <td>92874.0</td>\n",
       "      <td>92481.0</td>\n",
       "      <td>92696.0</td>\n",
       "      <td>92952.0</td>\n",
       "      <td>92836.0</td>\n",
       "      <td>92506.0</td>\n",
       "      <td>92312.0</td>\n",
       "      <td>92424.0</td>\n",
       "      <td>92504.0</td>\n",
       "      <td>92351.0</td>\n",
       "      <td>92106.0</td>\n",
       "      <td>92212.0</td>\n",
       "      <td>92600.0</td>\n",
       "      <td>92870.0</td>\n",
       "      <td>93107.0</td>\n",
       "      <td>93176.0</td>\n",
       "      <td>93231.0</td>\n",
       "      <td>93047.0</td>\n",
       "      <td>92459.0</td>\n",
       "      <td>91309.0</td>\n",
       "      <td>90136.0</td>\n",
       "      <td>89183.0</td>\n",
       "      <td>88488.0</td>\n",
       "      <td>87790.0</td>\n",
       "      <td>87334.0</td>\n",
       "      <td>87670.0</td>\n",
       "      <td>87738.0</td>\n",
       "      <td>87671.0</td>\n",
       "      <td>86896.0</td>\n",
       "      <td>86154.0</td>\n",
       "      <td>85404.0</td>\n",
       "      <td>85260.0</td>\n",
       "      <td>85489.0</td>\n",
       "      <td>85828.0</td>\n",
       "      <td>85911.0</td>\n",
       "      <td>85622.0</td>\n",
       "      <td>85131.0</td>\n",
       "      <td>84994.0</td>\n",
       "      <td>84956.0</td>\n",
       "      <td>84468.0</td>\n",
       "      <td>83914.0</td>\n",
       "      <td>83458.0</td>\n",
       "      <td>83422.0</td>\n",
       "      <td>83138.0</td>\n",
       "      <td>82549.0</td>\n",
       "      <td>81900.0</td>\n",
       "      <td>81010.0</td>\n",
       "      <td>80409.0</td>\n",
       "      <td>79632.0</td>\n",
       "      <td>79181.0</td>\n",
       "      <td>78938.0</td>\n",
       "      <td>79119.0</td>\n",
       "      <td>78909.0</td>\n",
       "      <td>78519.0</td>\n",
       "      <td>78197.0</td>\n",
       "      <td>78087.0</td>\n",
       "      <td>78402.0</td>\n",
       "      <td>78807.0</td>\n",
       "      <td>79508.0</td>\n",
       "      <td>80187.0</td>\n",
       "      <td>80784.0</td>\n",
       "      <td>81285.0</td>\n",
       "      <td>81015.0</td>\n",
       "      <td>80428.0</td>\n",
       "      <td>79744.0</td>\n",
       "      <td>79366.0</td>\n",
       "      <td>79379.0</td>\n",
       "      <td>79472.0</td>\n",
       "      <td>79768.0</td>\n",
       "      <td>80261.0</td>\n",
       "      <td>80640.0</td>\n",
       "      <td>80999.0</td>\n",
       "      <td>81231.0</td>\n",
       "      <td>81906.0</td>\n",
       "      <td>82584.0</td>\n",
       "      <td>83036.0</td>\n",
       "      <td>83252.0</td>\n",
       "      <td>83482.0</td>\n",
       "      <td>83850.0</td>\n",
       "      <td>84256.0</td>\n",
       "      <td>84297.0</td>\n",
       "      <td>84424.0</td>\n",
       "      <td>84014.0</td>\n",
       "      <td>83963.0</td>\n",
       "      <td>83726.0</td>\n",
       "      <td>83392.0</td>\n",
       "      <td>83715.0</td>\n",
       "      <td>83905.0</td>\n",
       "      <td>85242.0</td>\n",
       "      <td>85904.0</td>\n",
       "      <td>86463.0</td>\n",
       "      <td>86536.0</td>\n",
       "      <td>86671.0</td>\n",
       "      <td>87371.0</td>\n",
       "      <td>88241.0</td>\n",
       "      <td>88805.0</td>\n",
       "      <td>89295.0</td>\n",
       "      <td>89547.0</td>\n",
       "      <td>90432.0</td>\n",
       "      <td>91290.0</td>\n",
       "      <td>92328.0</td>\n",
       "      <td>92990.0</td>\n",
       "      <td>93161.0</td>\n",
       "      <td>93299.0</td>\n",
       "      <td>93875.0</td>\n",
       "      <td>94738.0</td>\n",
       "      <td>95989.0</td>\n",
       "      <td>97270.0</td>\n",
       "      <td>98559.0</td>\n",
       "      <td>99638.0</td>\n",
       "      <td>100559.0</td>\n",
       "      <td>101698.0</td>\n",
       "      <td>102675.0</td>\n",
       "      <td>103840.0</td>\n",
       "      <td>104803.0</td>\n",
       "      <td>106231.0</td>\n",
       "      <td>107462.0</td>\n",
       "      <td>108663.0</td>\n",
       "      <td>109688.0</td>\n",
       "      <td>110644.0</td>\n",
       "      <td>111453.0</td>\n",
       "      <td>111999.0</td>\n",
       "      <td>112718.0</td>\n",
       "      <td>114024.0</td>\n",
       "      <td>115018.0</td>\n",
       "      <td>116064.0</td>\n",
       "      <td>117057.0</td>\n",
       "      <td>118288.0</td>\n",
       "      <td>119258.0</td>\n",
       "      <td>119999.0</td>\n",
       "      <td>121046.0</td>\n",
       "      <td>122377.0</td>\n",
       "      <td>123673.0</td>\n",
       "      <td>124884.0</td>\n",
       "      <td>125699.0</td>\n",
       "      <td>126276.0</td>\n",
       "      <td>126973.0</td>\n",
       "      <td>128643.0</td>\n",
       "      <td>130918.0</td>\n",
       "      <td>132866.0</td>\n",
       "      <td>134736.0</td>\n",
       "      <td>136673.0</td>\n",
       "      <td>138842.0</td>\n",
       "      <td>140682.0</td>\n",
       "      <td>142276.0</td>\n",
       "      <td>143882.0</td>\n",
       "      <td>145456.0</td>\n",
       "      <td>147599.0</td>\n",
       "      <td>149763.0</td>\n",
       "      <td>151782.0</td>\n",
       "      <td>153034.0</td>\n",
       "      <td>154373.0</td>\n",
       "      <td>156225.0</td>\n",
       "      <td>157780.0</td>\n",
       "      <td>159029.0</td>\n",
       "      <td>159274.0</td>\n",
       "      <td>159886.0</td>\n",
       "      <td>160780.0</td>\n",
       "      <td>161929.0</td>\n",
       "      <td>163625.0</td>\n",
       "      <td>165020.0</td>\n",
       "      <td>166009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>65787</td>\n",
       "      <td>426</td>\n",
       "      <td>19120</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>59285.0</td>\n",
       "      <td>59426.0</td>\n",
       "      <td>59583.0</td>\n",
       "      <td>59842.0</td>\n",
       "      <td>60283.0</td>\n",
       "      <td>60535.0</td>\n",
       "      <td>60926.0</td>\n",
       "      <td>61006.0</td>\n",
       "      <td>61287.0</td>\n",
       "      <td>61503.0</td>\n",
       "      <td>61796.0</td>\n",
       "      <td>62047.0</td>\n",
       "      <td>62201.0</td>\n",
       "      <td>62438.0</td>\n",
       "      <td>62602.0</td>\n",
       "      <td>62784.0</td>\n",
       "      <td>62835.0</td>\n",
       "      <td>62928.0</td>\n",
       "      <td>63095.0</td>\n",
       "      <td>63190.0</td>\n",
       "      <td>63341.0</td>\n",
       "      <td>63364.0</td>\n",
       "      <td>63532.0</td>\n",
       "      <td>63687.0</td>\n",
       "      <td>63928.0</td>\n",
       "      <td>64060.0</td>\n",
       "      <td>64241.0</td>\n",
       "      <td>64401.0</td>\n",
       "      <td>64644.0</td>\n",
       "      <td>64732.0</td>\n",
       "      <td>65012.0</td>\n",
       "      <td>65270.0</td>\n",
       "      <td>65741.0</td>\n",
       "      <td>65966.0</td>\n",
       "      <td>66236.0</td>\n",
       "      <td>66397.0</td>\n",
       "      <td>66714.0</td>\n",
       "      <td>67058.0</td>\n",
       "      <td>67336.0</td>\n",
       "      <td>67602.0</td>\n",
       "      <td>67994.0</td>\n",
       "      <td>68646.0</td>\n",
       "      <td>69335.0</td>\n",
       "      <td>69981.0</td>\n",
       "      <td>70569.0</td>\n",
       "      <td>71112.0</td>\n",
       "      <td>71704.0</td>\n",
       "      <td>72235.0</td>\n",
       "      <td>72709.0</td>\n",
       "      <td>73330.0</td>\n",
       "      <td>73746.0</td>\n",
       "      <td>74276.0</td>\n",
       "      <td>74643.0</td>\n",
       "      <td>75550.0</td>\n",
       "      <td>76924.0</td>\n",
       "      <td>78638.0</td>\n",
       "      <td>79962.0</td>\n",
       "      <td>80754.0</td>\n",
       "      <td>81255.0</td>\n",
       "      <td>82162.0</td>\n",
       "      <td>83373.0</td>\n",
       "      <td>84243.0</td>\n",
       "      <td>85085.0</td>\n",
       "      <td>85765.0</td>\n",
       "      <td>86841.0</td>\n",
       "      <td>87914.0</td>\n",
       "      <td>89420.0</td>\n",
       "      <td>91064.0</td>\n",
       "      <td>92680.0</td>\n",
       "      <td>94101.0</td>\n",
       "      <td>95481.0</td>\n",
       "      <td>96768.0</td>\n",
       "      <td>97527.0</td>\n",
       "      <td>98139.0</td>\n",
       "      <td>98600.0</td>\n",
       "      <td>99590.0</td>\n",
       "      <td>100777.0</td>\n",
       "      <td>102401.0</td>\n",
       "      <td>103944.0</td>\n",
       "      <td>105045.0</td>\n",
       "      <td>105856.0</td>\n",
       "      <td>106310.0</td>\n",
       "      <td>106816.0</td>\n",
       "      <td>107406.0</td>\n",
       "      <td>108362.0</td>\n",
       "      <td>109274.0</td>\n",
       "      <td>110067.0</td>\n",
       "      <td>110257.0</td>\n",
       "      <td>110736.0</td>\n",
       "      <td>111250.0</td>\n",
       "      <td>111937.0</td>\n",
       "      <td>112284.0</td>\n",
       "      <td>112116.0</td>\n",
       "      <td>112129.0</td>\n",
       "      <td>112210.0</td>\n",
       "      <td>112496.0</td>\n",
       "      <td>112280.0</td>\n",
       "      <td>112327.0</td>\n",
       "      <td>111987.0</td>\n",
       "      <td>111865.0</td>\n",
       "      <td>111468.0</td>\n",
       "      <td>110905.0</td>\n",
       "      <td>110061.0</td>\n",
       "      <td>109054.0</td>\n",
       "      <td>108236.0</td>\n",
       "      <td>107840.0</td>\n",
       "      <td>107229.0</td>\n",
       "      <td>106382.0</td>\n",
       "      <td>104857.0</td>\n",
       "      <td>103612.0</td>\n",
       "      <td>103169.0</td>\n",
       "      <td>103704.0</td>\n",
       "      <td>104468.0</td>\n",
       "      <td>104871.0</td>\n",
       "      <td>105113.0</td>\n",
       "      <td>105194.0</td>\n",
       "      <td>105273.0</td>\n",
       "      <td>104802.0</td>\n",
       "      <td>104411.0</td>\n",
       "      <td>103908.0</td>\n",
       "      <td>104409.0</td>\n",
       "      <td>105053.0</td>\n",
       "      <td>105918.0</td>\n",
       "      <td>106350.0</td>\n",
       "      <td>106543.0</td>\n",
       "      <td>106397.0</td>\n",
       "      <td>106030.0</td>\n",
       "      <td>105366.0</td>\n",
       "      <td>104268.0</td>\n",
       "      <td>103136.0</td>\n",
       "      <td>102228.0</td>\n",
       "      <td>101770.0</td>\n",
       "      <td>101097.0</td>\n",
       "      <td>100523.0</td>\n",
       "      <td>100815.0</td>\n",
       "      <td>100731.0</td>\n",
       "      <td>100628.0</td>\n",
       "      <td>99873.0</td>\n",
       "      <td>99574.0</td>\n",
       "      <td>99235.0</td>\n",
       "      <td>98796.0</td>\n",
       "      <td>98669.0</td>\n",
       "      <td>98506.0</td>\n",
       "      <td>98038.0</td>\n",
       "      <td>97484.0</td>\n",
       "      <td>96878.0</td>\n",
       "      <td>96600.0</td>\n",
       "      <td>96064.0</td>\n",
       "      <td>94953.0</td>\n",
       "      <td>94058.0</td>\n",
       "      <td>93175.0</td>\n",
       "      <td>92544.0</td>\n",
       "      <td>91654.0</td>\n",
       "      <td>90901.0</td>\n",
       "      <td>90383.0</td>\n",
       "      <td>90158.0</td>\n",
       "      <td>89810.0</td>\n",
       "      <td>89292.0</td>\n",
       "      <td>88503.0</td>\n",
       "      <td>87532.0</td>\n",
       "      <td>87245.0</td>\n",
       "      <td>86750.0</td>\n",
       "      <td>86414.0</td>\n",
       "      <td>85895.0</td>\n",
       "      <td>85664.0</td>\n",
       "      <td>85692.0</td>\n",
       "      <td>85681.0</td>\n",
       "      <td>85867.0</td>\n",
       "      <td>86401.0</td>\n",
       "      <td>87189.0</td>\n",
       "      <td>87978.0</td>\n",
       "      <td>88035.0</td>\n",
       "      <td>87756.0</td>\n",
       "      <td>87538.0</td>\n",
       "      <td>87516.0</td>\n",
       "      <td>87500.0</td>\n",
       "      <td>87455.0</td>\n",
       "      <td>87641.0</td>\n",
       "      <td>87890.0</td>\n",
       "      <td>88135.0</td>\n",
       "      <td>88066.0</td>\n",
       "      <td>88067.0</td>\n",
       "      <td>88359.0</td>\n",
       "      <td>89407.0</td>\n",
       "      <td>89879.0</td>\n",
       "      <td>90069.0</td>\n",
       "      <td>89567.0</td>\n",
       "      <td>89794.0</td>\n",
       "      <td>90353.0</td>\n",
       "      <td>90729.0</td>\n",
       "      <td>91108.0</td>\n",
       "      <td>90510.0</td>\n",
       "      <td>90018.0</td>\n",
       "      <td>89344.0</td>\n",
       "      <td>88990.0</td>\n",
       "      <td>89233.0</td>\n",
       "      <td>89568.0</td>\n",
       "      <td>90437.0</td>\n",
       "      <td>90991.0</td>\n",
       "      <td>91618.0</td>\n",
       "      <td>92120.0</td>\n",
       "      <td>92874.0</td>\n",
       "      <td>93934.0</td>\n",
       "      <td>95471.0</td>\n",
       "      <td>96238.0</td>\n",
       "      <td>96578.0</td>\n",
       "      <td>96384.0</td>\n",
       "      <td>96794.0</td>\n",
       "      <td>97384.0</td>\n",
       "      <td>98047.0</td>\n",
       "      <td>98752.0</td>\n",
       "      <td>99308.0</td>\n",
       "      <td>99863.0</td>\n",
       "      <td>100587.0</td>\n",
       "      <td>101182.0</td>\n",
       "      <td>101968.0</td>\n",
       "      <td>103354.0</td>\n",
       "      <td>104922.0</td>\n",
       "      <td>106401.0</td>\n",
       "      <td>107162.0</td>\n",
       "      <td>107883.0</td>\n",
       "      <td>108468.0</td>\n",
       "      <td>109210.0</td>\n",
       "      <td>109566.0</td>\n",
       "      <td>110661.0</td>\n",
       "      <td>111527.0</td>\n",
       "      <td>112971.0</td>\n",
       "      <td>113904.0</td>\n",
       "      <td>114742.0</td>\n",
       "      <td>115180.0</td>\n",
       "      <td>115528.0</td>\n",
       "      <td>116602.0</td>\n",
       "      <td>118029.0</td>\n",
       "      <td>119561.0</td>\n",
       "      <td>120776.0</td>\n",
       "      <td>121921.0</td>\n",
       "      <td>123224.0</td>\n",
       "      <td>124505.0</td>\n",
       "      <td>125578.0</td>\n",
       "      <td>126652.0</td>\n",
       "      <td>127560.0</td>\n",
       "      <td>128391.0</td>\n",
       "      <td>129214.0</td>\n",
       "      <td>129958.0</td>\n",
       "      <td>130488.0</td>\n",
       "      <td>130697.0</td>\n",
       "      <td>131571.0</td>\n",
       "      <td>133615.0</td>\n",
       "      <td>135661.0</td>\n",
       "      <td>138211.0</td>\n",
       "      <td>140279.0</td>\n",
       "      <td>142567.0</td>\n",
       "      <td>144675.0</td>\n",
       "      <td>146819.0</td>\n",
       "      <td>148779.0</td>\n",
       "      <td>150310.0</td>\n",
       "      <td>152475.0</td>\n",
       "      <td>154823.0</td>\n",
       "      <td>157243.0</td>\n",
       "      <td>158883.0</td>\n",
       "      <td>160251.0</td>\n",
       "      <td>161167.0</td>\n",
       "      <td>161807.0</td>\n",
       "      <td>162634.0</td>\n",
       "      <td>162972.0</td>\n",
       "      <td>163597.0</td>\n",
       "      <td>164008.0</td>\n",
       "      <td>164887.0</td>\n",
       "      <td>165860.0</td>\n",
       "      <td>167321.0</td>\n",
       "      <td>168524.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>65772</td>\n",
       "      <td>512</td>\n",
       "      <td>19104</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>74255.0</td>\n",
       "      <td>74311.0</td>\n",
       "      <td>73420.0</td>\n",
       "      <td>74074.0</td>\n",
       "      <td>75734.0</td>\n",
       "      <td>76763.0</td>\n",
       "      <td>75432.0</td>\n",
       "      <td>72962.0</td>\n",
       "      <td>72727.0</td>\n",
       "      <td>73290.0</td>\n",
       "      <td>74886.0</td>\n",
       "      <td>75703.0</td>\n",
       "      <td>77136.0</td>\n",
       "      <td>76687.0</td>\n",
       "      <td>75961.0</td>\n",
       "      <td>75429.0</td>\n",
       "      <td>74684.0</td>\n",
       "      <td>73577.0</td>\n",
       "      <td>70427.0</td>\n",
       "      <td>66836.0</td>\n",
       "      <td>62842.0</td>\n",
       "      <td>59484.0</td>\n",
       "      <td>57420.0</td>\n",
       "      <td>56159.0</td>\n",
       "      <td>54460.0</td>\n",
       "      <td>53705.0</td>\n",
       "      <td>53354.0</td>\n",
       "      <td>54161.0</td>\n",
       "      <td>55132.0</td>\n",
       "      <td>56825.0</td>\n",
       "      <td>59489.0</td>\n",
       "      <td>61141.0</td>\n",
       "      <td>62801.0</td>\n",
       "      <td>63571.0</td>\n",
       "      <td>64771.0</td>\n",
       "      <td>66118.0</td>\n",
       "      <td>69458.0</td>\n",
       "      <td>71424.0</td>\n",
       "      <td>71054.0</td>\n",
       "      <td>69524.0</td>\n",
       "      <td>68923.0</td>\n",
       "      <td>70123.0</td>\n",
       "      <td>68924.0</td>\n",
       "      <td>68029.0</td>\n",
       "      <td>67594.0</td>\n",
       "      <td>69885.0</td>\n",
       "      <td>72393.0</td>\n",
       "      <td>75732.0</td>\n",
       "      <td>78462.0</td>\n",
       "      <td>80597.0</td>\n",
       "      <td>81625.0</td>\n",
       "      <td>82957.0</td>\n",
       "      <td>86090.0</td>\n",
       "      <td>90569.0</td>\n",
       "      <td>94776.0</td>\n",
       "      <td>98868.0</td>\n",
       "      <td>100244.0</td>\n",
       "      <td>101244.0</td>\n",
       "      <td>101187.0</td>\n",
       "      <td>103106.0</td>\n",
       "      <td>104176.0</td>\n",
       "      <td>105997.0</td>\n",
       "      <td>106612.0</td>\n",
       "      <td>108608.0</td>\n",
       "      <td>108415.0</td>\n",
       "      <td>111372.0</td>\n",
       "      <td>115279.0</td>\n",
       "      <td>120360.0</td>\n",
       "      <td>124832.0</td>\n",
       "      <td>124231.0</td>\n",
       "      <td>123508.0</td>\n",
       "      <td>120037.0</td>\n",
       "      <td>119055.0</td>\n",
       "      <td>117620.0</td>\n",
       "      <td>116581.0</td>\n",
       "      <td>114039.0</td>\n",
       "      <td>111694.0</td>\n",
       "      <td>111634.0</td>\n",
       "      <td>112812.0</td>\n",
       "      <td>117029.0</td>\n",
       "      <td>119892.0</td>\n",
       "      <td>125002.0</td>\n",
       "      <td>125837.0</td>\n",
       "      <td>125404.0</td>\n",
       "      <td>125191.0</td>\n",
       "      <td>129275.0</td>\n",
       "      <td>133100.0</td>\n",
       "      <td>135720.0</td>\n",
       "      <td>133557.0</td>\n",
       "      <td>129974.0</td>\n",
       "      <td>128664.0</td>\n",
       "      <td>129022.0</td>\n",
       "      <td>131953.0</td>\n",
       "      <td>132874.0</td>\n",
       "      <td>131801.0</td>\n",
       "      <td>130653.0</td>\n",
       "      <td>129469.0</td>\n",
       "      <td>132144.0</td>\n",
       "      <td>133678.0</td>\n",
       "      <td>133951.0</td>\n",
       "      <td>133645.0</td>\n",
       "      <td>134489.0</td>\n",
       "      <td>136118.0</td>\n",
       "      <td>138275.0</td>\n",
       "      <td>138108.0</td>\n",
       "      <td>138675.0</td>\n",
       "      <td>138582.0</td>\n",
       "      <td>138958.0</td>\n",
       "      <td>136666.0</td>\n",
       "      <td>135071.0</td>\n",
       "      <td>135059.0</td>\n",
       "      <td>138225.0</td>\n",
       "      <td>139657.0</td>\n",
       "      <td>140254.0</td>\n",
       "      <td>140471.0</td>\n",
       "      <td>139708.0</td>\n",
       "      <td>139952.0</td>\n",
       "      <td>140422.0</td>\n",
       "      <td>142933.0</td>\n",
       "      <td>142296.0</td>\n",
       "      <td>142667.0</td>\n",
       "      <td>143506.0</td>\n",
       "      <td>146404.0</td>\n",
       "      <td>147608.0</td>\n",
       "      <td>147007.0</td>\n",
       "      <td>146597.0</td>\n",
       "      <td>144884.0</td>\n",
       "      <td>143841.0</td>\n",
       "      <td>139290.0</td>\n",
       "      <td>135684.0</td>\n",
       "      <td>130595.0</td>\n",
       "      <td>128748.0</td>\n",
       "      <td>126026.0</td>\n",
       "      <td>124332.0</td>\n",
       "      <td>125017.0</td>\n",
       "      <td>124836.0</td>\n",
       "      <td>125404.0</td>\n",
       "      <td>121829.0</td>\n",
       "      <td>120414.0</td>\n",
       "      <td>117467.0</td>\n",
       "      <td>116295.0</td>\n",
       "      <td>114123.0</td>\n",
       "      <td>113479.0</td>\n",
       "      <td>111740.0</td>\n",
       "      <td>107668.0</td>\n",
       "      <td>101551.0</td>\n",
       "      <td>97021.0</td>\n",
       "      <td>94019.0</td>\n",
       "      <td>92692.0</td>\n",
       "      <td>90642.0</td>\n",
       "      <td>89632.0</td>\n",
       "      <td>89190.0</td>\n",
       "      <td>89646.0</td>\n",
       "      <td>90767.0</td>\n",
       "      <td>92057.0</td>\n",
       "      <td>93573.0</td>\n",
       "      <td>95532.0</td>\n",
       "      <td>96827.0</td>\n",
       "      <td>98324.0</td>\n",
       "      <td>100714.0</td>\n",
       "      <td>103895.0</td>\n",
       "      <td>106680.0</td>\n",
       "      <td>108595.0</td>\n",
       "      <td>109549.0</td>\n",
       "      <td>111161.0</td>\n",
       "      <td>112782.0</td>\n",
       "      <td>113774.0</td>\n",
       "      <td>114854.0</td>\n",
       "      <td>112360.0</td>\n",
       "      <td>109890.0</td>\n",
       "      <td>106934.0</td>\n",
       "      <td>106550.0</td>\n",
       "      <td>106389.0</td>\n",
       "      <td>106369.0</td>\n",
       "      <td>107120.0</td>\n",
       "      <td>108935.0</td>\n",
       "      <td>109732.0</td>\n",
       "      <td>110166.0</td>\n",
       "      <td>110185.0</td>\n",
       "      <td>110746.0</td>\n",
       "      <td>114743.0</td>\n",
       "      <td>118835.0</td>\n",
       "      <td>123911.0</td>\n",
       "      <td>126603.0</td>\n",
       "      <td>128858.0</td>\n",
       "      <td>130365.0</td>\n",
       "      <td>131140.0</td>\n",
       "      <td>132559.0</td>\n",
       "      <td>133206.0</td>\n",
       "      <td>132999.0</td>\n",
       "      <td>132629.0</td>\n",
       "      <td>132356.0</td>\n",
       "      <td>132139.0</td>\n",
       "      <td>131735.0</td>\n",
       "      <td>130882.0</td>\n",
       "      <td>131097.0</td>\n",
       "      <td>130815.0</td>\n",
       "      <td>130642.0</td>\n",
       "      <td>129131.0</td>\n",
       "      <td>128057.0</td>\n",
       "      <td>128020.0</td>\n",
       "      <td>128184.0</td>\n",
       "      <td>128624.0</td>\n",
       "      <td>129960.0</td>\n",
       "      <td>131882.0</td>\n",
       "      <td>132739.0</td>\n",
       "      <td>133118.0</td>\n",
       "      <td>134098.0</td>\n",
       "      <td>136969.0</td>\n",
       "      <td>138824.0</td>\n",
       "      <td>141021.0</td>\n",
       "      <td>141497.0</td>\n",
       "      <td>142323.0</td>\n",
       "      <td>143175.0</td>\n",
       "      <td>145523.0</td>\n",
       "      <td>147700.0</td>\n",
       "      <td>149613.0</td>\n",
       "      <td>153357.0</td>\n",
       "      <td>158330.0</td>\n",
       "      <td>162564.0</td>\n",
       "      <td>165140.0</td>\n",
       "      <td>167502.0</td>\n",
       "      <td>169288.0</td>\n",
       "      <td>169718.0</td>\n",
       "      <td>169225.0</td>\n",
       "      <td>169003.0</td>\n",
       "      <td>170001.0</td>\n",
       "      <td>172179.0</td>\n",
       "      <td>174795.0</td>\n",
       "      <td>176565.0</td>\n",
       "      <td>176904.0</td>\n",
       "      <td>176388.0</td>\n",
       "      <td>176153.0</td>\n",
       "      <td>176110.0</td>\n",
       "      <td>176849.0</td>\n",
       "      <td>178179.0</td>\n",
       "      <td>179932.0</td>\n",
       "      <td>181731.0</td>\n",
       "      <td>181145.0</td>\n",
       "      <td>181805.0</td>\n",
       "      <td>182999.0</td>\n",
       "      <td>187313.0</td>\n",
       "      <td>188971.0</td>\n",
       "      <td>189183.0</td>\n",
       "      <td>187374.0</td>\n",
       "      <td>187973.0</td>\n",
       "      <td>190296.0</td>\n",
       "      <td>194804.0</td>\n",
       "      <td>199969.0</td>\n",
       "      <td>205333.0</td>\n",
       "      <td>210444.0</td>\n",
       "      <td>213002.0</td>\n",
       "      <td>215598.0</td>\n",
       "      <td>216806.0</td>\n",
       "      <td>220053.0</td>\n",
       "      <td>222065.0</td>\n",
       "      <td>223889.0</td>\n",
       "      <td>224518.0</td>\n",
       "      <td>223083.0</td>\n",
       "      <td>221879.0</td>\n",
       "      <td>219949.0</td>\n",
       "      <td>220270.0</td>\n",
       "      <td>221454.0</td>\n",
       "      <td>222006.0</td>\n",
       "      <td>220760.0</td>\n",
       "      <td>217933.0</td>\n",
       "      <td>216447.0</td>\n",
       "      <td>216424.0</td>\n",
       "      <td>218663.0</td>\n",
       "      <td>220453.0</td>\n",
       "      <td>223443.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     RegionID  SizeRank  RegionName RegionType StateName State          City  \\\n",
       "125     65810       126       19143        Zip        PA    PA  Philadelphia   \n",
       "247     65779       249       19111        Zip        PA    PA  Philadelphia   \n",
       "338     65791       340       19124        Zip        PA    PA  Philadelphia   \n",
       "423     65787       426       19120        Zip        PA    PA  Philadelphia   \n",
       "509     65772       512       19104        Zip        PA    PA  Philadelphia   \n",
       "\n",
       "                              Metro           CountyName  2000-01-31  \\\n",
       "125  Philadelphia-Camden-Wilmington  Philadelphia County     60701.0   \n",
       "247  Philadelphia-Camden-Wilmington  Philadelphia County     85062.0   \n",
       "338  Philadelphia-Camden-Wilmington  Philadelphia County     47155.0   \n",
       "423  Philadelphia-Camden-Wilmington  Philadelphia County     59285.0   \n",
       "509  Philadelphia-Camden-Wilmington  Philadelphia County     74255.0   \n",
       "\n",
       "     2000-02-29  2000-03-31  2000-04-30  2000-05-31  2000-06-30  2000-07-31  \\\n",
       "125     61009.0     61132.0     61497.0     62089.0     62804.0     63377.0   \n",
       "247     85325.0     85511.0     85937.0     86553.0     87106.0     87591.0   \n",
       "338     47278.0     47408.0     47613.0     47956.0     48226.0     48586.0   \n",
       "423     59426.0     59583.0     59842.0     60283.0     60535.0     60926.0   \n",
       "509     74311.0     73420.0     74074.0     75734.0     76763.0     75432.0   \n",
       "\n",
       "     2000-08-31  2000-09-30  2000-10-31  2000-11-30  2000-12-31  2001-01-31  \\\n",
       "125     63925.0     64313.0     64609.0     64756.0     65187.0     65676.0   \n",
       "247     87826.0     88161.0     88561.0     88958.0     89438.0     89950.0   \n",
       "338     48728.0     48955.0     49118.0     49275.0     49389.0     49523.0   \n",
       "423     61006.0     61287.0     61503.0     61796.0     62047.0     62201.0   \n",
       "509     72962.0     72727.0     73290.0     74886.0     75703.0     77136.0   \n",
       "\n",
       "     2001-02-28  2001-03-31  2001-04-30  2001-05-31  2001-06-30  2001-07-31  \\\n",
       "125     65861.0     65823.0     66081.0     66273.0     66797.0     67099.0   \n",
       "247     90515.0     91088.0     91677.0     92113.0     92536.0     92992.0   \n",
       "338     49721.0     50009.0     50254.0     50481.0     50610.0     50741.0   \n",
       "423     62438.0     62602.0     62784.0     62835.0     62928.0     63095.0   \n",
       "509     76687.0     75961.0     75429.0     74684.0     73577.0     70427.0   \n",
       "\n",
       "     2001-08-31  2001-09-30  2001-10-31  2001-11-30  2001-12-31  2002-01-31  \\\n",
       "125     67641.0     67686.0     67959.0     68203.0     68695.0     68447.0   \n",
       "247     93485.0     94003.0     94417.0     95089.0     95722.0     96345.0   \n",
       "338     50882.0     51036.0     51232.0     51416.0     51605.0     51744.0   \n",
       "423     63190.0     63341.0     63364.0     63532.0     63687.0     63928.0   \n",
       "509     66836.0     62842.0     59484.0     57420.0     56159.0     54460.0   \n",
       "\n",
       "     2002-02-28  2002-03-31  2002-04-30  2002-05-31  2002-06-30  2002-07-31  \\\n",
       "125     68180.0     67802.0     67913.0     68143.0     68498.0     68586.0   \n",
       "247     96941.0     97579.0     98372.0     99076.0     99886.0    100862.0   \n",
       "338     51911.0     52107.0     52303.0     52536.0     52757.0     53075.0   \n",
       "423     64060.0     64241.0     64401.0     64644.0     64732.0     65012.0   \n",
       "509     53705.0     53354.0     54161.0     55132.0     56825.0     59489.0   \n",
       "\n",
       "     2002-08-31  2002-09-30  2002-10-31  2002-11-30  2002-12-31  2003-01-31  \\\n",
       "125     68493.0     68414.0     68426.0     68588.0     68758.0     69396.0   \n",
       "247    101890.0    102984.0    104071.0    105134.0    106151.0    107061.0   \n",
       "338     53409.0     53737.0     53997.0     54232.0     54489.0     54885.0   \n",
       "423     65270.0     65741.0     65966.0     66236.0     66397.0     66714.0   \n",
       "509     61141.0     62801.0     63571.0     64771.0     66118.0     69458.0   \n",
       "\n",
       "     2003-02-28  2003-03-31  2003-04-30  2003-05-31  2003-06-30  2003-07-31  \\\n",
       "125     70405.0     71333.0     72108.0     71545.0     70832.0     69969.0   \n",
       "247    108177.0    109247.0    110301.0    111277.0    112438.0    114178.0   \n",
       "338     55397.0     55953.0     56551.0     57038.0     57700.0     58370.0   \n",
       "423     67058.0     67336.0     67602.0     67994.0     68646.0     69335.0   \n",
       "509     71424.0     71054.0     69524.0     68923.0     70123.0     68924.0   \n",
       "\n",
       "     2003-08-31  2003-09-30  2003-10-31  2003-11-30  2003-12-31  2004-01-31  \\\n",
       "125     69189.0     68933.0     68952.0     69436.0     69497.0     69166.0   \n",
       "247    115959.0    117757.0    119093.0    120471.0    121920.0    123332.0   \n",
       "338     59082.0     59803.0     60415.0     61013.0     61545.0     62079.0   \n",
       "423     69981.0     70569.0     71112.0     71704.0     72235.0     72709.0   \n",
       "509     68029.0     67594.0     69885.0     72393.0     75732.0     78462.0   \n",
       "\n",
       "     2004-02-29  2004-03-31  2004-04-30  2004-05-31  2004-06-30  2004-07-31  \\\n",
       "125     68806.0     68433.0     67859.0     68503.0     69640.0     72312.0   \n",
       "247    124897.0    126029.0    127138.0    128446.0    130704.0    133641.0   \n",
       "338     62688.0     62923.0     63177.0     63518.0     64381.0     65520.0   \n",
       "423     73330.0     73746.0     74276.0     74643.0     75550.0     76924.0   \n",
       "509     80597.0     81625.0     82957.0     86090.0     90569.0     94776.0   \n",
       "\n",
       "     2004-08-31  2004-09-30  2004-10-31  2004-11-30  2004-12-31  2005-01-31  \\\n",
       "125     74534.0     76922.0     77949.0     78439.0     78686.0     79661.0   \n",
       "247    136937.0    139741.0    141849.0    143846.0    146431.0    149653.0   \n",
       "338     66803.0     67730.0     68397.0     69036.0     70092.0     71384.0   \n",
       "423     78638.0     79962.0     80754.0     81255.0     82162.0     83373.0   \n",
       "509     98868.0    100244.0    101244.0    101187.0    103106.0    104176.0   \n",
       "\n",
       "     2005-02-28  2005-03-31  2005-04-30  2005-05-31  2005-06-30  2005-07-31  \\\n",
       "125     81557.0     83961.0     86307.0     88384.0     90337.0     92085.0   \n",
       "247    151750.0    153916.0    155841.0    158797.0    161690.0    165007.0   \n",
       "338     72230.0     73336.0     74356.0     75894.0     77244.0     78885.0   \n",
       "423     84243.0     85085.0     85765.0     86841.0     87914.0     89420.0   \n",
       "509    105997.0    106612.0    108608.0    108415.0    111372.0    115279.0   \n",
       "\n",
       "     2005-08-31  2005-09-30  2005-10-31  2005-11-30  2005-12-31  2006-01-31  \\\n",
       "125     93843.0     95070.0     96082.0     96754.0     97226.0     97366.0   \n",
       "247    168454.0    171486.0    173952.0    176189.0    178068.0    179278.0   \n",
       "338     80660.0     82392.0     83864.0     85158.0     86200.0     87088.0   \n",
       "423     91064.0     92680.0     94101.0     95481.0     96768.0     97527.0   \n",
       "509    120360.0    124832.0    124231.0    123508.0    120037.0    119055.0   \n",
       "\n",
       "     2006-02-28  2006-03-31  2006-04-30  2006-05-31  2006-06-30  2006-07-31  \\\n",
       "125     97250.0     97431.0     98005.0     98827.0     99968.0    101264.0   \n",
       "247    180165.0    180564.0    181480.0    182426.0    183849.0    185148.0   \n",
       "338     87835.0     88441.0     89121.0     89893.0     91149.0     92529.0   \n",
       "423     98139.0     98600.0     99590.0    100777.0    102401.0    103944.0   \n",
       "509    117620.0    116581.0    114039.0    111694.0    111634.0    112812.0   \n",
       "\n",
       "     2006-08-31  2006-09-30  2006-10-31  2006-11-30  2006-12-31  2007-01-31  \\\n",
       "125    102415.0    102880.0    103031.0    103011.0    103479.0    104487.0   \n",
       "247    185461.0    185262.0    184263.0    183359.0    182896.0    183302.0   \n",
       "338     93594.0     94165.0     94255.0     94451.0     94912.0     95741.0   \n",
       "423    105045.0    105856.0    106310.0    106816.0    107406.0    108362.0   \n",
       "509    117029.0    119892.0    125002.0    125837.0    125404.0    125191.0   \n",
       "\n",
       "     2007-02-28  2007-03-31  2007-04-30  2007-05-31  2007-06-30  2007-07-31  \\\n",
       "125    105486.0    106479.0    107086.0    107907.0    108633.0    109295.0   \n",
       "247    183850.0    184323.0    184004.0    184393.0    184874.0    185468.0   \n",
       "338     96476.0     97222.0     97739.0     98489.0     99167.0     99733.0   \n",
       "423    109274.0    110067.0    110257.0    110736.0    111250.0    111937.0   \n",
       "509    129275.0    133100.0    135720.0    133557.0    129974.0    128664.0   \n",
       "\n",
       "     2007-08-31  2007-09-30  2007-10-31  2007-11-30  2007-12-31  2008-01-31  \\\n",
       "125    109709.0    109799.0    110175.0    110832.0    111722.0    111804.0   \n",
       "247    185143.0    184016.0    183558.0    183651.0    183769.0    183295.0   \n",
       "338     99838.0     99550.0     99656.0     99854.0    100206.0     99986.0   \n",
       "423    112284.0    112116.0    112129.0    112210.0    112496.0    112280.0   \n",
       "509    129022.0    131953.0    132874.0    131801.0    130653.0    129469.0   \n",
       "\n",
       "     2008-02-29  2008-03-31  2008-04-30  2008-05-31  2008-06-30  2008-07-31  \\\n",
       "125    112139.0    111773.0    111921.0    111393.0    111219.0    110379.0   \n",
       "247    183077.0    182343.0    181946.0    180672.0    179570.0    177714.0   \n",
       "338    100138.0     99941.0     99881.0     99048.0     98127.0     97134.0   \n",
       "423    112327.0    111987.0    111865.0    111468.0    110905.0    110061.0   \n",
       "509    132144.0    133678.0    133951.0    133645.0    134489.0    136118.0   \n",
       "\n",
       "     2008-08-31  2008-09-30  2008-10-31  2008-11-30  2008-12-31  2009-01-31  \\\n",
       "125    109495.0    108215.0    107367.0    106351.0    105522.0    104364.0   \n",
       "247    176122.0    174785.0    174053.0    173051.0    171897.0    170067.0   \n",
       "338     96257.0     95602.0     95245.0     94981.0     94602.0     93616.0   \n",
       "423    109054.0    108236.0    107840.0    107229.0    106382.0    104857.0   \n",
       "509    138275.0    138108.0    138675.0    138582.0    138958.0    136666.0   \n",
       "\n",
       "     2009-02-28  2009-03-31  2009-04-30  2009-05-31  2009-06-30  2009-07-31  \\\n",
       "125    103805.0    103242.0    103065.0    102323.0    101444.0    101301.0   \n",
       "247    168534.0    167708.0    167669.0    167622.0    167573.0    167346.0   \n",
       "338     92874.0     92481.0     92696.0     92952.0     92836.0     92506.0   \n",
       "423    103612.0    103169.0    103704.0    104468.0    104871.0    105113.0   \n",
       "509    135071.0    135059.0    138225.0    139657.0    140254.0    140471.0   \n",
       "\n",
       "     2009-08-31  2009-09-30  2009-10-31  2009-11-30  2009-12-31  2010-01-31  \\\n",
       "125    101305.0    101310.0    100671.0    101311.0    101380.0    101148.0   \n",
       "247    167175.0    167000.0    166703.0    166631.0    166329.0    166607.0   \n",
       "338     92312.0     92424.0     92504.0     92351.0     92106.0     92212.0   \n",
       "423    105194.0    105273.0    104802.0    104411.0    103908.0    104409.0   \n",
       "509    139708.0    139952.0    140422.0    142933.0    142296.0    142667.0   \n",
       "\n",
       "     2010-02-28  2010-03-31  2010-04-30  2010-05-31  2010-06-30  2010-07-31  \\\n",
       "125     99991.0     99764.0     99667.0    100342.0    101206.0    101751.0   \n",
       "247    167096.0    167747.0    168725.0    169508.0    170394.0    170337.0   \n",
       "338     92600.0     92870.0     93107.0     93176.0     93231.0     93047.0   \n",
       "423    105053.0    105918.0    106350.0    106543.0    106397.0    106030.0   \n",
       "509    143506.0    146404.0    147608.0    147007.0    146597.0    144884.0   \n",
       "\n",
       "     2010-08-31  2010-09-30  2010-10-31  2010-11-30  2010-12-31  2011-01-31  \\\n",
       "125    101410.0     99899.0     99054.0     98190.0     97760.0     97027.0   \n",
       "247    169592.0    167491.0    165488.0    163970.0    162902.0    161966.0   \n",
       "338     92459.0     91309.0     90136.0     89183.0     88488.0     87790.0   \n",
       "423    105366.0    104268.0    103136.0    102228.0    101770.0    101097.0   \n",
       "509    143841.0    139290.0    135684.0    130595.0    128748.0    126026.0   \n",
       "\n",
       "     2011-02-28  2011-03-31  2011-04-30  2011-05-31  2011-06-30  2011-07-31  \\\n",
       "125     95989.0     95607.0     94610.0     93853.0     92438.0     91449.0   \n",
       "247    161241.0    161488.0    161326.0    160817.0    159370.0    158064.0   \n",
       "338     87334.0     87670.0     87738.0     87671.0     86896.0     86154.0   \n",
       "423    100523.0    100815.0    100731.0    100628.0     99873.0     99574.0   \n",
       "509    124332.0    125017.0    124836.0    125404.0    121829.0    120414.0   \n",
       "\n",
       "     2011-08-31  2011-09-30  2011-10-31  2011-11-30  2011-12-31  2012-01-31  \\\n",
       "125     90053.0     89361.0     88056.0     86772.0     85735.0     84583.0   \n",
       "247    157130.0    156624.0    156405.0    155915.0    155689.0    155512.0   \n",
       "338     85404.0     85260.0     85489.0     85828.0     85911.0     85622.0   \n",
       "423     99235.0     98796.0     98669.0     98506.0     98038.0     97484.0   \n",
       "509    117467.0    116295.0    114123.0    113479.0    111740.0    107668.0   \n",
       "\n",
       "     2012-02-29  2012-03-31  2012-04-30  2012-05-31  2012-06-30  2012-07-31  \\\n",
       "125     84007.0     82600.0     81821.0     80929.0     80279.0     79375.0   \n",
       "247    155498.0    155935.0    156558.0    156797.0    156427.0    155345.0   \n",
       "338     85131.0     84994.0     84956.0     84468.0     83914.0     83458.0   \n",
       "423     96878.0     96600.0     96064.0     94953.0     94058.0     93175.0   \n",
       "509    101551.0     97021.0     94019.0     92692.0     90642.0     89632.0   \n",
       "\n",
       "     2012-08-31  2012-09-30  2012-10-31  2012-11-30  2012-12-31  2013-01-31  \\\n",
       "125     78214.0     76773.0     75875.0     75539.0     75270.0     75758.0   \n",
       "247    154473.0    154002.0    153784.0    153578.0    152985.0    152508.0   \n",
       "338     83422.0     83138.0     82549.0     81900.0     81010.0     80409.0   \n",
       "423     92544.0     91654.0     90901.0     90383.0     90158.0     89810.0   \n",
       "509     89190.0     89646.0     90767.0     92057.0     93573.0     95532.0   \n",
       "\n",
       "     2013-02-28  2013-03-31  2013-04-30  2013-05-31  2013-06-30  2013-07-31  \\\n",
       "125     75887.0     76684.0     77167.0     77425.0     77025.0     76461.0   \n",
       "247    151807.0    151572.0    152032.0    153197.0    154319.0    154912.0   \n",
       "338     79632.0     79181.0     78938.0     79119.0     78909.0     78519.0   \n",
       "423     89292.0     88503.0     87532.0     87245.0     86750.0     86414.0   \n",
       "509     96827.0     98324.0    100714.0    103895.0    106680.0    108595.0   \n",
       "\n",
       "     2013-08-31  2013-09-30  2013-10-31  2013-11-30  2013-12-31  2014-01-31  \\\n",
       "125     77015.0     78310.0     79731.0     80026.0     80722.0     80064.0   \n",
       "247    154590.0    153730.0    153093.0    153027.0    153170.0    153407.0   \n",
       "338     78197.0     78087.0     78402.0     78807.0     79508.0     80187.0   \n",
       "423     85895.0     85664.0     85692.0     85681.0     85867.0     86401.0   \n",
       "509    109549.0    111161.0    112782.0    113774.0    114854.0    112360.0   \n",
       "\n",
       "     2014-02-28  2014-03-31  2014-04-30  2014-05-31  2014-06-30  2014-07-31  \\\n",
       "125     79452.0     78172.0     78217.0     78507.0     78747.0     78096.0   \n",
       "247    153770.0    154052.0    152735.0    151932.0    151197.0    151529.0   \n",
       "338     80784.0     81285.0     81015.0     80428.0     79744.0     79366.0   \n",
       "423     87189.0     87978.0     88035.0     87756.0     87538.0     87516.0   \n",
       "509    109890.0    106934.0    106550.0    106389.0    106369.0    107120.0   \n",
       "\n",
       "     2014-08-31  2014-09-30  2014-10-31  2014-11-30  2014-12-31  2015-01-31  \\\n",
       "125     76899.0     76226.0     76352.0     77600.0     78201.0     78684.0   \n",
       "247    151185.0    150982.0    151152.0    151573.0    152541.0    153163.0   \n",
       "338     79379.0     79472.0     79768.0     80261.0     80640.0     80999.0   \n",
       "423     87500.0     87455.0     87641.0     87890.0     88135.0     88066.0   \n",
       "509    108935.0    109732.0    110166.0    110185.0    110746.0    114743.0   \n",
       "\n",
       "     2015-02-28  2015-03-31  2015-04-30  2015-05-31  2015-06-30  2015-07-31  \\\n",
       "125     78404.0     78963.0     79121.0     78435.0     76924.0     76583.0   \n",
       "247    153473.0    154117.0    155346.0    156034.0    155829.0    155165.0   \n",
       "338     81231.0     81906.0     82584.0     83036.0     83252.0     83482.0   \n",
       "423     88067.0     88359.0     89407.0     89879.0     90069.0     89567.0   \n",
       "509    118835.0    123911.0    126603.0    128858.0    130365.0    131140.0   \n",
       "\n",
       "     2015-08-31  2015-09-30  2015-10-31  2015-11-30  2015-12-31  2016-01-31  \\\n",
       "125     77810.0     79529.0     80410.0     80371.0     79269.0     78491.0   \n",
       "247    155482.0    156306.0    156730.0    157046.0    156051.0    155744.0   \n",
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       "423     89794.0     90353.0     90729.0     91108.0     90510.0     90018.0   \n",
       "509    132559.0    133206.0    132999.0    132629.0    132356.0    132139.0   \n",
       "\n",
       "     2016-02-29  2016-03-31  2016-04-30  2016-05-31  2016-06-30  2016-07-31  \\\n",
       "125     78287.0     78132.0     78159.0     77924.0     78513.0     78295.0   \n",
       "247    155593.0    155782.0    156233.0    156414.0    157693.0    158424.0   \n",
       "338     83726.0     83392.0     83715.0     83905.0     85242.0     85904.0   \n",
       "423     89344.0     88990.0     89233.0     89568.0     90437.0     90991.0   \n",
       "509    131735.0    130882.0    131097.0    130815.0    130642.0    129131.0   \n",
       "\n",
       "     2016-08-31  2016-09-30  2016-10-31  2016-11-30  2016-12-31  2017-01-31  \\\n",
       "125     78311.0     78364.0     78514.0     79223.0     80707.0     81456.0   \n",
       "247    159387.0    160022.0    161145.0    162362.0    164293.0    165834.0   \n",
       "338     86463.0     86536.0     86671.0     87371.0     88241.0     88805.0   \n",
       "423     91618.0     92120.0     92874.0     93934.0     95471.0     96238.0   \n",
       "509    128057.0    128020.0    128184.0    128624.0    129960.0    131882.0   \n",
       "\n",
       "     2017-02-28  2017-03-31  2017-04-30  2017-05-31  2017-06-30  2017-07-31  \\\n",
       "125     80844.0     79670.0     79979.0     81003.0     81645.0     81964.0   \n",
       "247    166797.0    167640.0    168699.0    169919.0    170225.0    169976.0   \n",
       "338     89295.0     89547.0     90432.0     91290.0     92328.0     92990.0   \n",
       "423     96578.0     96384.0     96794.0     97384.0     98047.0     98752.0   \n",
       "509    132739.0    133118.0    134098.0    136969.0    138824.0    141021.0   \n",
       "\n",
       "     2017-08-31  2017-09-30  2017-10-31  2017-11-30  2017-12-31  2018-01-31  \\\n",
       "125     82782.0     84137.0     86169.0     87425.0     88374.0     90395.0   \n",
       "247    170149.0    171026.0    173107.0    174567.0    176158.0    176461.0   \n",
       "338     93161.0     93299.0     93875.0     94738.0     95989.0     97270.0   \n",
       "423     99308.0     99863.0    100587.0    101182.0    101968.0    103354.0   \n",
       "509    141497.0    142323.0    143175.0    145523.0    147700.0    149613.0   \n",
       "\n",
       "     2018-02-28  2018-03-31  2018-04-30  2018-05-31  2018-06-30  2018-07-31  \\\n",
       "125     93178.0     97105.0     99056.0    101865.0    104178.0    107011.0   \n",
       "247    177644.0    178936.0    180638.0    181664.0    182138.0    183150.0   \n",
       "338     98559.0     99638.0    100559.0    101698.0    102675.0    103840.0   \n",
       "423    104922.0    106401.0    107162.0    107883.0    108468.0    109210.0   \n",
       "509    153357.0    158330.0    162564.0    165140.0    167502.0    169288.0   \n",
       "\n",
       "     2018-08-31  2018-09-30  2018-10-31  2018-11-30  2018-12-31  2019-01-31  \\\n",
       "125    108961.0    110034.0    110958.0    112422.0    114015.0    115970.0   \n",
       "247    184656.0    186732.0    188650.0    190755.0    192883.0    195542.0   \n",
       "338    104803.0    106231.0    107462.0    108663.0    109688.0    110644.0   \n",
       "423    109566.0    110661.0    111527.0    112971.0    113904.0    114742.0   \n",
       "509    169718.0    169225.0    169003.0    170001.0    172179.0    174795.0   \n",
       "\n",
       "     2019-02-28  2019-03-31  2019-04-30  2019-05-31  2019-06-30  2019-07-31  \\\n",
       "125    117291.0    118019.0    118858.0    120055.0    122956.0    124885.0   \n",
       "247    197516.0    198968.0    199252.0    199840.0    199978.0    200839.0   \n",
       "338    111453.0    111999.0    112718.0    114024.0    115018.0    116064.0   \n",
       "423    115180.0    115528.0    116602.0    118029.0    119561.0    120776.0   \n",
       "509    176565.0    176904.0    176388.0    176153.0    176110.0    176849.0   \n",
       "\n",
       "     2019-08-31  2019-09-30  2019-10-31  2019-11-30  2019-12-31  2020-01-31  \\\n",
       "125    126050.0    126668.0    128030.0    129620.0    131469.0    134062.0   \n",
       "247    201593.0    202632.0    203180.0    203531.0    204228.0    204670.0   \n",
       "338    117057.0    118288.0    119258.0    119999.0    121046.0    122377.0   \n",
       "423    121921.0    123224.0    124505.0    125578.0    126652.0    127560.0   \n",
       "509    178179.0    179932.0    181731.0    181145.0    181805.0    182999.0   \n",
       "\n",
       "     2020-02-29  2020-03-31  2020-04-30  2020-05-31  2020-06-30  2020-07-31  \\\n",
       "125    136766.0    138666.0    140376.0    141110.0    142564.0    144531.0   \n",
       "247    205301.0    206452.0    208135.0    209251.0    210282.0    212368.0   \n",
       "338    123673.0    124884.0    125699.0    126276.0    126973.0    128643.0   \n",
       "423    128391.0    129214.0    129958.0    130488.0    130697.0    131571.0   \n",
       "509    187313.0    188971.0    189183.0    187374.0    187973.0    190296.0   \n",
       "\n",
       "     2020-08-31  2020-09-30  2020-10-31  2020-11-30  2020-12-31  2021-01-31  \\\n",
       "125    149060.0    152057.0    154865.0    157516.0    161147.0    164398.0   \n",
       "247    216023.0    219027.0    222720.0    226121.0    229845.0    232754.0   \n",
       "338    130918.0    132866.0    134736.0    136673.0    138842.0    140682.0   \n",
       "423    133615.0    135661.0    138211.0    140279.0    142567.0    144675.0   \n",
       "509    194804.0    199969.0    205333.0    210444.0    213002.0    215598.0   \n",
       "\n",
       "     2021-02-28  2021-03-31  2021-04-30  2021-05-31  2021-06-30  2021-07-31  \\\n",
       "125    167188.0    169351.0    171125.0    172719.0    174880.0    176204.0   \n",
       "247    235155.0    237287.0    239669.0    243054.0    246722.0    250520.0   \n",
       "338    142276.0    143882.0    145456.0    147599.0    149763.0    151782.0   \n",
       "423    146819.0    148779.0    150310.0    152475.0    154823.0    157243.0   \n",
       "509    216806.0    220053.0    222065.0    223889.0    224518.0    223083.0   \n",
       "\n",
       "     2021-08-31  2021-09-30  2021-10-31  2021-11-30  2021-12-31  2022-01-31  \\\n",
       "125    176015.0    174596.0    173114.0    172087.0    171445.0    171542.0   \n",
       "247    253386.0    256032.0    257911.0    260104.0    262257.0    263715.0   \n",
       "338    153034.0    154373.0    156225.0    157780.0    159029.0    159274.0   \n",
       "423    158883.0    160251.0    161167.0    161807.0    162634.0    162972.0   \n",
       "509    221879.0    219949.0    220270.0    221454.0    222006.0    220760.0   \n",
       "\n",
       "     2022-02-28  2022-03-31  2022-04-30  2022-05-31  2022-06-30  2022-07-31  \n",
       "125    171680.0    171878.0    171607.0    171333.0    171771.0    172611.0  \n",
       "247    264809.0    265684.0    267222.0    269460.0    272201.0    274446.0  \n",
       "338    159886.0    160780.0    161929.0    163625.0    165020.0    166009.0  \n",
       "423    163597.0    164008.0    164887.0    165860.0    167321.0    168524.0  \n",
       "509    217933.0    216447.0    216424.0    218663.0    220453.0    223443.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove data not in Philadelphia, Pennsylvania\n",
    "zillow = zillow[(zillow['City'] == 'Philadelphia') & (zillow['State'] == 'PA')]\n",
    "zillow.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeeba31c",
   "metadata": {},
   "source": [
    "To account for any errors in data entry for the city and state, we'll also check the zipcodes column.  We'll remove any rows with zipcodes not within the list of valid Philadelphia, PA zipcodes.  A list of zipcodes within Philadelphia, PA was obtained from https://www.zillow.com/browse/homes/pa/philadelphia-county/ and is replicated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0246836b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of zipcodes from: https://www.zillow.com/browse/homes/pa/philadelphia-county/\n",
    "list_of_zipcodes = [\n",
    "    '19120','19124','19111','19143','19134','19149','19148','19104','19140','19145',\n",
    "    '19139','19131','19144','19146','19147','19128','19135','19115','19141','19136',\n",
    "    '19154','19152','19151','19116','19121','19132','19114','19138','19142','19119',\n",
    "    '19125','19133','19130','19150','19103','19122','19126','19123','19107','19153',\n",
    "    '19106','19129','19118','19137','19102','19127','19019','19093','19092','19101',\n",
    "    '19099','19105','19109','19108','19110','19112','19160','19155','19162','19161',\n",
    "    '19171','19170','19173','19172','19176','19175','19178','19177','19181','19179',\n",
    "    '19183','19182','19185','19184','19188','19187','19192','19191','19194','19193',\n",
    "    '19196','19195','19244','19197','19255','19190']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c91ae89",
   "metadata": {},
   "source": [
    "Only keep rows from data with a zipcode within the list above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f82f3b46",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>2013-06-30</th>\n",
       "      <th>2013-07-31</th>\n",
       "      <th>2013-08-31</th>\n",
       "      <th>2013-09-30</th>\n",
       "      <th>2013-10-31</th>\n",
       "      <th>2013-11-30</th>\n",
       "      <th>2013-12-31</th>\n",
       "      <th>2014-01-31</th>\n",
       "      <th>2014-02-28</th>\n",
       "      <th>2014-03-31</th>\n",
       "      <th>2014-04-30</th>\n",
       "      <th>2014-05-31</th>\n",
       "      <th>2014-06-30</th>\n",
       "      <th>2014-07-31</th>\n",
       "      <th>2014-08-31</th>\n",
       "      <th>2014-09-30</th>\n",
       "      <th>2014-10-31</th>\n",
       "      <th>2014-11-30</th>\n",
       "      <th>2014-12-31</th>\n",
       "      <th>2015-01-31</th>\n",
       "      <th>2015-02-28</th>\n",
       "      <th>2015-03-31</th>\n",
       "      <th>2015-04-30</th>\n",
       "      <th>2015-05-31</th>\n",
       "      <th>2015-06-30</th>\n",
       "      <th>2015-07-31</th>\n",
       "      <th>2015-08-31</th>\n",
       "      <th>2015-09-30</th>\n",
       "      <th>2015-10-31</th>\n",
       "      <th>2015-11-30</th>\n",
       "      <th>2015-12-31</th>\n",
       "      <th>2016-01-31</th>\n",
       "      <th>2016-02-29</th>\n",
       "      <th>2016-03-31</th>\n",
       "      <th>2016-04-30</th>\n",
       "      <th>2016-05-31</th>\n",
       "      <th>2016-06-30</th>\n",
       "      <th>2016-07-31</th>\n",
       "      <th>2016-08-31</th>\n",
       "      <th>2016-09-30</th>\n",
       "      <th>2016-10-31</th>\n",
       "      <th>2016-11-30</th>\n",
       "      <th>2016-12-31</th>\n",
       "      <th>2017-01-31</th>\n",
       "      <th>2017-02-28</th>\n",
       "      <th>2017-03-31</th>\n",
       "      <th>2017-04-30</th>\n",
       "      <th>2017-05-31</th>\n",
       "      <th>2017-06-30</th>\n",
       "      <th>2017-07-31</th>\n",
       "      <th>2017-08-31</th>\n",
       "      <th>2017-09-30</th>\n",
       "      <th>2017-10-31</th>\n",
       "      <th>2017-11-30</th>\n",
       "      <th>2017-12-31</th>\n",
       "      <th>2018-01-31</th>\n",
       "      <th>2018-02-28</th>\n",
       "      <th>2018-03-31</th>\n",
       "      <th>2018-04-30</th>\n",
       "      <th>2018-05-31</th>\n",
       "      <th>2018-06-30</th>\n",
       "      <th>2018-07-31</th>\n",
       "      <th>2018-08-31</th>\n",
       "      <th>2018-09-30</th>\n",
       "      <th>2018-10-31</th>\n",
       "      <th>2018-11-30</th>\n",
       "      <th>2018-12-31</th>\n",
       "      <th>2019-01-31</th>\n",
       "      <th>2019-02-28</th>\n",
       "      <th>2019-03-31</th>\n",
       "      <th>2019-04-30</th>\n",
       "      <th>2019-05-31</th>\n",
       "      <th>2019-06-30</th>\n",
       "      <th>2019-07-31</th>\n",
       "      <th>2019-08-31</th>\n",
       "      <th>2019-09-30</th>\n",
       "      <th>2019-10-31</th>\n",
       "      <th>2019-11-30</th>\n",
       "      <th>2019-12-31</th>\n",
       "      <th>2020-01-31</th>\n",
       "      <th>2020-02-29</th>\n",
       "      <th>2020-03-31</th>\n",
       "      <th>2020-04-30</th>\n",
       "      <th>2020-05-31</th>\n",
       "      <th>2020-06-30</th>\n",
       "      <th>2020-07-31</th>\n",
       "      <th>2020-08-31</th>\n",
       "      <th>2020-09-30</th>\n",
       "      <th>2020-10-31</th>\n",
       "      <th>2020-11-30</th>\n",
       "      <th>2020-12-31</th>\n",
       "      <th>2021-01-31</th>\n",
       "      <th>2021-02-28</th>\n",
       "      <th>2021-03-31</th>\n",
       "      <th>2021-04-30</th>\n",
       "      <th>2021-05-31</th>\n",
       "      <th>2021-06-30</th>\n",
       "      <th>2021-07-31</th>\n",
       "      <th>2021-08-31</th>\n",
       "      <th>2021-09-30</th>\n",
       "      <th>2021-10-31</th>\n",
       "      <th>2021-11-30</th>\n",
       "      <th>2021-12-31</th>\n",
       "      <th>2022-01-31</th>\n",
       "      <th>2022-02-28</th>\n",
       "      <th>2022-03-31</th>\n",
       "      <th>2022-04-30</th>\n",
       "      <th>2022-05-31</th>\n",
       "      <th>2022-06-30</th>\n",
       "      <th>2022-07-31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>65810</td>\n",
       "      <td>126</td>\n",
       "      <td>19143</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>60701.0</td>\n",
       "      <td>61009.0</td>\n",
       "      <td>61132.0</td>\n",
       "      <td>61497.0</td>\n",
       "      <td>62089.0</td>\n",
       "      <td>62804.0</td>\n",
       "      <td>63377.0</td>\n",
       "      <td>63925.0</td>\n",
       "      <td>64313.0</td>\n",
       "      <td>64609.0</td>\n",
       "      <td>64756.0</td>\n",
       "      <td>65187.0</td>\n",
       "      <td>65676.0</td>\n",
       "      <td>65861.0</td>\n",
       "      <td>65823.0</td>\n",
       "      <td>66081.0</td>\n",
       "      <td>66273.0</td>\n",
       "      <td>66797.0</td>\n",
       "      <td>67099.0</td>\n",
       "      <td>67641.0</td>\n",
       "      <td>67686.0</td>\n",
       "      <td>67959.0</td>\n",
       "      <td>68203.0</td>\n",
       "      <td>68695.0</td>\n",
       "      <td>68447.0</td>\n",
       "      <td>68180.0</td>\n",
       "      <td>67802.0</td>\n",
       "      <td>67913.0</td>\n",
       "      <td>68143.0</td>\n",
       "      <td>68498.0</td>\n",
       "      <td>68586.0</td>\n",
       "      <td>68493.0</td>\n",
       "      <td>68414.0</td>\n",
       "      <td>68426.0</td>\n",
       "      <td>68588.0</td>\n",
       "      <td>68758.0</td>\n",
       "      <td>69396.0</td>\n",
       "      <td>70405.0</td>\n",
       "      <td>71333.0</td>\n",
       "      <td>72108.0</td>\n",
       "      <td>71545.0</td>\n",
       "      <td>70832.0</td>\n",
       "      <td>69969.0</td>\n",
       "      <td>69189.0</td>\n",
       "      <td>68933.0</td>\n",
       "      <td>68952.0</td>\n",
       "      <td>69436.0</td>\n",
       "      <td>69497.0</td>\n",
       "      <td>69166.0</td>\n",
       "      <td>68806.0</td>\n",
       "      <td>68433.0</td>\n",
       "      <td>67859.0</td>\n",
       "      <td>68503.0</td>\n",
       "      <td>69640.0</td>\n",
       "      <td>72312.0</td>\n",
       "      <td>74534.0</td>\n",
       "      <td>76922.0</td>\n",
       "      <td>77949.0</td>\n",
       "      <td>78439.0</td>\n",
       "      <td>78686.0</td>\n",
       "      <td>79661.0</td>\n",
       "      <td>81557.0</td>\n",
       "      <td>83961.0</td>\n",
       "      <td>86307.0</td>\n",
       "      <td>88384.0</td>\n",
       "      <td>90337.0</td>\n",
       "      <td>92085.0</td>\n",
       "      <td>93843.0</td>\n",
       "      <td>95070.0</td>\n",
       "      <td>96082.0</td>\n",
       "      <td>96754.0</td>\n",
       "      <td>97226.0</td>\n",
       "      <td>97366.0</td>\n",
       "      <td>97250.0</td>\n",
       "      <td>97431.0</td>\n",
       "      <td>98005.0</td>\n",
       "      <td>98827.0</td>\n",
       "      <td>99968.0</td>\n",
       "      <td>101264.0</td>\n",
       "      <td>102415.0</td>\n",
       "      <td>102880.0</td>\n",
       "      <td>103031.0</td>\n",
       "      <td>103011.0</td>\n",
       "      <td>103479.0</td>\n",
       "      <td>104487.0</td>\n",
       "      <td>105486.0</td>\n",
       "      <td>106479.0</td>\n",
       "      <td>107086.0</td>\n",
       "      <td>107907.0</td>\n",
       "      <td>108633.0</td>\n",
       "      <td>109295.0</td>\n",
       "      <td>109709.0</td>\n",
       "      <td>109799.0</td>\n",
       "      <td>110175.0</td>\n",
       "      <td>110832.0</td>\n",
       "      <td>111722.0</td>\n",
       "      <td>111804.0</td>\n",
       "      <td>112139.0</td>\n",
       "      <td>111773.0</td>\n",
       "      <td>111921.0</td>\n",
       "      <td>111393.0</td>\n",
       "      <td>111219.0</td>\n",
       "      <td>110379.0</td>\n",
       "      <td>109495.0</td>\n",
       "      <td>108215.0</td>\n",
       "      <td>107367.0</td>\n",
       "      <td>106351.0</td>\n",
       "      <td>105522.0</td>\n",
       "      <td>104364.0</td>\n",
       "      <td>103805.0</td>\n",
       "      <td>103242.0</td>\n",
       "      <td>103065.0</td>\n",
       "      <td>102323.0</td>\n",
       "      <td>101444.0</td>\n",
       "      <td>101301.0</td>\n",
       "      <td>101305.0</td>\n",
       "      <td>101310.0</td>\n",
       "      <td>100671.0</td>\n",
       "      <td>101311.0</td>\n",
       "      <td>101380.0</td>\n",
       "      <td>101148.0</td>\n",
       "      <td>99991.0</td>\n",
       "      <td>99764.0</td>\n",
       "      <td>99667.0</td>\n",
       "      <td>100342.0</td>\n",
       "      <td>101206.0</td>\n",
       "      <td>101751.0</td>\n",
       "      <td>101410.0</td>\n",
       "      <td>99899.0</td>\n",
       "      <td>99054.0</td>\n",
       "      <td>98190.0</td>\n",
       "      <td>97760.0</td>\n",
       "      <td>97027.0</td>\n",
       "      <td>95989.0</td>\n",
       "      <td>95607.0</td>\n",
       "      <td>94610.0</td>\n",
       "      <td>93853.0</td>\n",
       "      <td>92438.0</td>\n",
       "      <td>91449.0</td>\n",
       "      <td>90053.0</td>\n",
       "      <td>89361.0</td>\n",
       "      <td>88056.0</td>\n",
       "      <td>86772.0</td>\n",
       "      <td>85735.0</td>\n",
       "      <td>84583.0</td>\n",
       "      <td>84007.0</td>\n",
       "      <td>82600.0</td>\n",
       "      <td>81821.0</td>\n",
       "      <td>80929.0</td>\n",
       "      <td>80279.0</td>\n",
       "      <td>79375.0</td>\n",
       "      <td>78214.0</td>\n",
       "      <td>76773.0</td>\n",
       "      <td>75875.0</td>\n",
       "      <td>75539.0</td>\n",
       "      <td>75270.0</td>\n",
       "      <td>75758.0</td>\n",
       "      <td>75887.0</td>\n",
       "      <td>76684.0</td>\n",
       "      <td>77167.0</td>\n",
       "      <td>77425.0</td>\n",
       "      <td>77025.0</td>\n",
       "      <td>76461.0</td>\n",
       "      <td>77015.0</td>\n",
       "      <td>78310.0</td>\n",
       "      <td>79731.0</td>\n",
       "      <td>80026.0</td>\n",
       "      <td>80722.0</td>\n",
       "      <td>80064.0</td>\n",
       "      <td>79452.0</td>\n",
       "      <td>78172.0</td>\n",
       "      <td>78217.0</td>\n",
       "      <td>78507.0</td>\n",
       "      <td>78747.0</td>\n",
       "      <td>78096.0</td>\n",
       "      <td>76899.0</td>\n",
       "      <td>76226.0</td>\n",
       "      <td>76352.0</td>\n",
       "      <td>77600.0</td>\n",
       "      <td>78201.0</td>\n",
       "      <td>78684.0</td>\n",
       "      <td>78404.0</td>\n",
       "      <td>78963.0</td>\n",
       "      <td>79121.0</td>\n",
       "      <td>78435.0</td>\n",
       "      <td>76924.0</td>\n",
       "      <td>76583.0</td>\n",
       "      <td>77810.0</td>\n",
       "      <td>79529.0</td>\n",
       "      <td>80410.0</td>\n",
       "      <td>80371.0</td>\n",
       "      <td>79269.0</td>\n",
       "      <td>78491.0</td>\n",
       "      <td>78287.0</td>\n",
       "      <td>78132.0</td>\n",
       "      <td>78159.0</td>\n",
       "      <td>77924.0</td>\n",
       "      <td>78513.0</td>\n",
       "      <td>78295.0</td>\n",
       "      <td>78311.0</td>\n",
       "      <td>78364.0</td>\n",
       "      <td>78514.0</td>\n",
       "      <td>79223.0</td>\n",
       "      <td>80707.0</td>\n",
       "      <td>81456.0</td>\n",
       "      <td>80844.0</td>\n",
       "      <td>79670.0</td>\n",
       "      <td>79979.0</td>\n",
       "      <td>81003.0</td>\n",
       "      <td>81645.0</td>\n",
       "      <td>81964.0</td>\n",
       "      <td>82782.0</td>\n",
       "      <td>84137.0</td>\n",
       "      <td>86169.0</td>\n",
       "      <td>87425.0</td>\n",
       "      <td>88374.0</td>\n",
       "      <td>90395.0</td>\n",
       "      <td>93178.0</td>\n",
       "      <td>97105.0</td>\n",
       "      <td>99056.0</td>\n",
       "      <td>101865.0</td>\n",
       "      <td>104178.0</td>\n",
       "      <td>107011.0</td>\n",
       "      <td>108961.0</td>\n",
       "      <td>110034.0</td>\n",
       "      <td>110958.0</td>\n",
       "      <td>112422.0</td>\n",
       "      <td>114015.0</td>\n",
       "      <td>115970.0</td>\n",
       "      <td>117291.0</td>\n",
       "      <td>118019.0</td>\n",
       "      <td>118858.0</td>\n",
       "      <td>120055.0</td>\n",
       "      <td>122956.0</td>\n",
       "      <td>124885.0</td>\n",
       "      <td>126050.0</td>\n",
       "      <td>126668.0</td>\n",
       "      <td>128030.0</td>\n",
       "      <td>129620.0</td>\n",
       "      <td>131469.0</td>\n",
       "      <td>134062.0</td>\n",
       "      <td>136766.0</td>\n",
       "      <td>138666.0</td>\n",
       "      <td>140376.0</td>\n",
       "      <td>141110.0</td>\n",
       "      <td>142564.0</td>\n",
       "      <td>144531.0</td>\n",
       "      <td>149060.0</td>\n",
       "      <td>152057.0</td>\n",
       "      <td>154865.0</td>\n",
       "      <td>157516.0</td>\n",
       "      <td>161147.0</td>\n",
       "      <td>164398.0</td>\n",
       "      <td>167188.0</td>\n",
       "      <td>169351.0</td>\n",
       "      <td>171125.0</td>\n",
       "      <td>172719.0</td>\n",
       "      <td>174880.0</td>\n",
       "      <td>176204.0</td>\n",
       "      <td>176015.0</td>\n",
       "      <td>174596.0</td>\n",
       "      <td>173114.0</td>\n",
       "      <td>172087.0</td>\n",
       "      <td>171445.0</td>\n",
       "      <td>171542.0</td>\n",
       "      <td>171680.0</td>\n",
       "      <td>171878.0</td>\n",
       "      <td>171607.0</td>\n",
       "      <td>171333.0</td>\n",
       "      <td>171771.0</td>\n",
       "      <td>172611.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>65779</td>\n",
       "      <td>249</td>\n",
       "      <td>19111</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>85062.0</td>\n",
       "      <td>85325.0</td>\n",
       "      <td>85511.0</td>\n",
       "      <td>85937.0</td>\n",
       "      <td>86553.0</td>\n",
       "      <td>87106.0</td>\n",
       "      <td>87591.0</td>\n",
       "      <td>87826.0</td>\n",
       "      <td>88161.0</td>\n",
       "      <td>88561.0</td>\n",
       "      <td>88958.0</td>\n",
       "      <td>89438.0</td>\n",
       "      <td>89950.0</td>\n",
       "      <td>90515.0</td>\n",
       "      <td>91088.0</td>\n",
       "      <td>91677.0</td>\n",
       "      <td>92113.0</td>\n",
       "      <td>92536.0</td>\n",
       "      <td>92992.0</td>\n",
       "      <td>93485.0</td>\n",
       "      <td>94003.0</td>\n",
       "      <td>94417.0</td>\n",
       "      <td>95089.0</td>\n",
       "      <td>95722.0</td>\n",
       "      <td>96345.0</td>\n",
       "      <td>96941.0</td>\n",
       "      <td>97579.0</td>\n",
       "      <td>98372.0</td>\n",
       "      <td>99076.0</td>\n",
       "      <td>99886.0</td>\n",
       "      <td>100862.0</td>\n",
       "      <td>101890.0</td>\n",
       "      <td>102984.0</td>\n",
       "      <td>104071.0</td>\n",
       "      <td>105134.0</td>\n",
       "      <td>106151.0</td>\n",
       "      <td>107061.0</td>\n",
       "      <td>108177.0</td>\n",
       "      <td>109247.0</td>\n",
       "      <td>110301.0</td>\n",
       "      <td>111277.0</td>\n",
       "      <td>112438.0</td>\n",
       "      <td>114178.0</td>\n",
       "      <td>115959.0</td>\n",
       "      <td>117757.0</td>\n",
       "      <td>119093.0</td>\n",
       "      <td>120471.0</td>\n",
       "      <td>121920.0</td>\n",
       "      <td>123332.0</td>\n",
       "      <td>124897.0</td>\n",
       "      <td>126029.0</td>\n",
       "      <td>127138.0</td>\n",
       "      <td>128446.0</td>\n",
       "      <td>130704.0</td>\n",
       "      <td>133641.0</td>\n",
       "      <td>136937.0</td>\n",
       "      <td>139741.0</td>\n",
       "      <td>141849.0</td>\n",
       "      <td>143846.0</td>\n",
       "      <td>146431.0</td>\n",
       "      <td>149653.0</td>\n",
       "      <td>151750.0</td>\n",
       "      <td>153916.0</td>\n",
       "      <td>155841.0</td>\n",
       "      <td>158797.0</td>\n",
       "      <td>161690.0</td>\n",
       "      <td>165007.0</td>\n",
       "      <td>168454.0</td>\n",
       "      <td>171486.0</td>\n",
       "      <td>173952.0</td>\n",
       "      <td>176189.0</td>\n",
       "      <td>178068.0</td>\n",
       "      <td>179278.0</td>\n",
       "      <td>180165.0</td>\n",
       "      <td>180564.0</td>\n",
       "      <td>181480.0</td>\n",
       "      <td>182426.0</td>\n",
       "      <td>183849.0</td>\n",
       "      <td>185148.0</td>\n",
       "      <td>185461.0</td>\n",
       "      <td>185262.0</td>\n",
       "      <td>184263.0</td>\n",
       "      <td>183359.0</td>\n",
       "      <td>182896.0</td>\n",
       "      <td>183302.0</td>\n",
       "      <td>183850.0</td>\n",
       "      <td>184323.0</td>\n",
       "      <td>184004.0</td>\n",
       "      <td>184393.0</td>\n",
       "      <td>184874.0</td>\n",
       "      <td>185468.0</td>\n",
       "      <td>185143.0</td>\n",
       "      <td>184016.0</td>\n",
       "      <td>183558.0</td>\n",
       "      <td>183651.0</td>\n",
       "      <td>183769.0</td>\n",
       "      <td>183295.0</td>\n",
       "      <td>183077.0</td>\n",
       "      <td>182343.0</td>\n",
       "      <td>181946.0</td>\n",
       "      <td>180672.0</td>\n",
       "      <td>179570.0</td>\n",
       "      <td>177714.0</td>\n",
       "      <td>176122.0</td>\n",
       "      <td>174785.0</td>\n",
       "      <td>174053.0</td>\n",
       "      <td>173051.0</td>\n",
       "      <td>171897.0</td>\n",
       "      <td>170067.0</td>\n",
       "      <td>168534.0</td>\n",
       "      <td>167708.0</td>\n",
       "      <td>167669.0</td>\n",
       "      <td>167622.0</td>\n",
       "      <td>167573.0</td>\n",
       "      <td>167346.0</td>\n",
       "      <td>167175.0</td>\n",
       "      <td>167000.0</td>\n",
       "      <td>166703.0</td>\n",
       "      <td>166631.0</td>\n",
       "      <td>166329.0</td>\n",
       "      <td>166607.0</td>\n",
       "      <td>167096.0</td>\n",
       "      <td>167747.0</td>\n",
       "      <td>168725.0</td>\n",
       "      <td>169508.0</td>\n",
       "      <td>170394.0</td>\n",
       "      <td>170337.0</td>\n",
       "      <td>169592.0</td>\n",
       "      <td>167491.0</td>\n",
       "      <td>165488.0</td>\n",
       "      <td>163970.0</td>\n",
       "      <td>162902.0</td>\n",
       "      <td>161966.0</td>\n",
       "      <td>161241.0</td>\n",
       "      <td>161488.0</td>\n",
       "      <td>161326.0</td>\n",
       "      <td>160817.0</td>\n",
       "      <td>159370.0</td>\n",
       "      <td>158064.0</td>\n",
       "      <td>157130.0</td>\n",
       "      <td>156624.0</td>\n",
       "      <td>156405.0</td>\n",
       "      <td>155915.0</td>\n",
       "      <td>155689.0</td>\n",
       "      <td>155512.0</td>\n",
       "      <td>155498.0</td>\n",
       "      <td>155935.0</td>\n",
       "      <td>156558.0</td>\n",
       "      <td>156797.0</td>\n",
       "      <td>156427.0</td>\n",
       "      <td>155345.0</td>\n",
       "      <td>154473.0</td>\n",
       "      <td>154002.0</td>\n",
       "      <td>153784.0</td>\n",
       "      <td>153578.0</td>\n",
       "      <td>152985.0</td>\n",
       "      <td>152508.0</td>\n",
       "      <td>151807.0</td>\n",
       "      <td>151572.0</td>\n",
       "      <td>152032.0</td>\n",
       "      <td>153197.0</td>\n",
       "      <td>154319.0</td>\n",
       "      <td>154912.0</td>\n",
       "      <td>154590.0</td>\n",
       "      <td>153730.0</td>\n",
       "      <td>153093.0</td>\n",
       "      <td>153027.0</td>\n",
       "      <td>153170.0</td>\n",
       "      <td>153407.0</td>\n",
       "      <td>153770.0</td>\n",
       "      <td>154052.0</td>\n",
       "      <td>152735.0</td>\n",
       "      <td>151932.0</td>\n",
       "      <td>151197.0</td>\n",
       "      <td>151529.0</td>\n",
       "      <td>151185.0</td>\n",
       "      <td>150982.0</td>\n",
       "      <td>151152.0</td>\n",
       "      <td>151573.0</td>\n",
       "      <td>152541.0</td>\n",
       "      <td>153163.0</td>\n",
       "      <td>153473.0</td>\n",
       "      <td>154117.0</td>\n",
       "      <td>155346.0</td>\n",
       "      <td>156034.0</td>\n",
       "      <td>155829.0</td>\n",
       "      <td>155165.0</td>\n",
       "      <td>155482.0</td>\n",
       "      <td>156306.0</td>\n",
       "      <td>156730.0</td>\n",
       "      <td>157046.0</td>\n",
       "      <td>156051.0</td>\n",
       "      <td>155744.0</td>\n",
       "      <td>155593.0</td>\n",
       "      <td>155782.0</td>\n",
       "      <td>156233.0</td>\n",
       "      <td>156414.0</td>\n",
       "      <td>157693.0</td>\n",
       "      <td>158424.0</td>\n",
       "      <td>159387.0</td>\n",
       "      <td>160022.0</td>\n",
       "      <td>161145.0</td>\n",
       "      <td>162362.0</td>\n",
       "      <td>164293.0</td>\n",
       "      <td>165834.0</td>\n",
       "      <td>166797.0</td>\n",
       "      <td>167640.0</td>\n",
       "      <td>168699.0</td>\n",
       "      <td>169919.0</td>\n",
       "      <td>170225.0</td>\n",
       "      <td>169976.0</td>\n",
       "      <td>170149.0</td>\n",
       "      <td>171026.0</td>\n",
       "      <td>173107.0</td>\n",
       "      <td>174567.0</td>\n",
       "      <td>176158.0</td>\n",
       "      <td>176461.0</td>\n",
       "      <td>177644.0</td>\n",
       "      <td>178936.0</td>\n",
       "      <td>180638.0</td>\n",
       "      <td>181664.0</td>\n",
       "      <td>182138.0</td>\n",
       "      <td>183150.0</td>\n",
       "      <td>184656.0</td>\n",
       "      <td>186732.0</td>\n",
       "      <td>188650.0</td>\n",
       "      <td>190755.0</td>\n",
       "      <td>192883.0</td>\n",
       "      <td>195542.0</td>\n",
       "      <td>197516.0</td>\n",
       "      <td>198968.0</td>\n",
       "      <td>199252.0</td>\n",
       "      <td>199840.0</td>\n",
       "      <td>199978.0</td>\n",
       "      <td>200839.0</td>\n",
       "      <td>201593.0</td>\n",
       "      <td>202632.0</td>\n",
       "      <td>203180.0</td>\n",
       "      <td>203531.0</td>\n",
       "      <td>204228.0</td>\n",
       "      <td>204670.0</td>\n",
       "      <td>205301.0</td>\n",
       "      <td>206452.0</td>\n",
       "      <td>208135.0</td>\n",
       "      <td>209251.0</td>\n",
       "      <td>210282.0</td>\n",
       "      <td>212368.0</td>\n",
       "      <td>216023.0</td>\n",
       "      <td>219027.0</td>\n",
       "      <td>222720.0</td>\n",
       "      <td>226121.0</td>\n",
       "      <td>229845.0</td>\n",
       "      <td>232754.0</td>\n",
       "      <td>235155.0</td>\n",
       "      <td>237287.0</td>\n",
       "      <td>239669.0</td>\n",
       "      <td>243054.0</td>\n",
       "      <td>246722.0</td>\n",
       "      <td>250520.0</td>\n",
       "      <td>253386.0</td>\n",
       "      <td>256032.0</td>\n",
       "      <td>257911.0</td>\n",
       "      <td>260104.0</td>\n",
       "      <td>262257.0</td>\n",
       "      <td>263715.0</td>\n",
       "      <td>264809.0</td>\n",
       "      <td>265684.0</td>\n",
       "      <td>267222.0</td>\n",
       "      <td>269460.0</td>\n",
       "      <td>272201.0</td>\n",
       "      <td>274446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>65791</td>\n",
       "      <td>340</td>\n",
       "      <td>19124</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>47155.0</td>\n",
       "      <td>47278.0</td>\n",
       "      <td>47408.0</td>\n",
       "      <td>47613.0</td>\n",
       "      <td>47956.0</td>\n",
       "      <td>48226.0</td>\n",
       "      <td>48586.0</td>\n",
       "      <td>48728.0</td>\n",
       "      <td>48955.0</td>\n",
       "      <td>49118.0</td>\n",
       "      <td>49275.0</td>\n",
       "      <td>49389.0</td>\n",
       "      <td>49523.0</td>\n",
       "      <td>49721.0</td>\n",
       "      <td>50009.0</td>\n",
       "      <td>50254.0</td>\n",
       "      <td>50481.0</td>\n",
       "      <td>50610.0</td>\n",
       "      <td>50741.0</td>\n",
       "      <td>50882.0</td>\n",
       "      <td>51036.0</td>\n",
       "      <td>51232.0</td>\n",
       "      <td>51416.0</td>\n",
       "      <td>51605.0</td>\n",
       "      <td>51744.0</td>\n",
       "      <td>51911.0</td>\n",
       "      <td>52107.0</td>\n",
       "      <td>52303.0</td>\n",
       "      <td>52536.0</td>\n",
       "      <td>52757.0</td>\n",
       "      <td>53075.0</td>\n",
       "      <td>53409.0</td>\n",
       "      <td>53737.0</td>\n",
       "      <td>53997.0</td>\n",
       "      <td>54232.0</td>\n",
       "      <td>54489.0</td>\n",
       "      <td>54885.0</td>\n",
       "      <td>55397.0</td>\n",
       "      <td>55953.0</td>\n",
       "      <td>56551.0</td>\n",
       "      <td>57038.0</td>\n",
       "      <td>57700.0</td>\n",
       "      <td>58370.0</td>\n",
       "      <td>59082.0</td>\n",
       "      <td>59803.0</td>\n",
       "      <td>60415.0</td>\n",
       "      <td>61013.0</td>\n",
       "      <td>61545.0</td>\n",
       "      <td>62079.0</td>\n",
       "      <td>62688.0</td>\n",
       "      <td>62923.0</td>\n",
       "      <td>63177.0</td>\n",
       "      <td>63518.0</td>\n",
       "      <td>64381.0</td>\n",
       "      <td>65520.0</td>\n",
       "      <td>66803.0</td>\n",
       "      <td>67730.0</td>\n",
       "      <td>68397.0</td>\n",
       "      <td>69036.0</td>\n",
       "      <td>70092.0</td>\n",
       "      <td>71384.0</td>\n",
       "      <td>72230.0</td>\n",
       "      <td>73336.0</td>\n",
       "      <td>74356.0</td>\n",
       "      <td>75894.0</td>\n",
       "      <td>77244.0</td>\n",
       "      <td>78885.0</td>\n",
       "      <td>80660.0</td>\n",
       "      <td>82392.0</td>\n",
       "      <td>83864.0</td>\n",
       "      <td>85158.0</td>\n",
       "      <td>86200.0</td>\n",
       "      <td>87088.0</td>\n",
       "      <td>87835.0</td>\n",
       "      <td>88441.0</td>\n",
       "      <td>89121.0</td>\n",
       "      <td>89893.0</td>\n",
       "      <td>91149.0</td>\n",
       "      <td>92529.0</td>\n",
       "      <td>93594.0</td>\n",
       "      <td>94165.0</td>\n",
       "      <td>94255.0</td>\n",
       "      <td>94451.0</td>\n",
       "      <td>94912.0</td>\n",
       "      <td>95741.0</td>\n",
       "      <td>96476.0</td>\n",
       "      <td>97222.0</td>\n",
       "      <td>97739.0</td>\n",
       "      <td>98489.0</td>\n",
       "      <td>99167.0</td>\n",
       "      <td>99733.0</td>\n",
       "      <td>99838.0</td>\n",
       "      <td>99550.0</td>\n",
       "      <td>99656.0</td>\n",
       "      <td>99854.0</td>\n",
       "      <td>100206.0</td>\n",
       "      <td>99986.0</td>\n",
       "      <td>100138.0</td>\n",
       "      <td>99941.0</td>\n",
       "      <td>99881.0</td>\n",
       "      <td>99048.0</td>\n",
       "      <td>98127.0</td>\n",
       "      <td>97134.0</td>\n",
       "      <td>96257.0</td>\n",
       "      <td>95602.0</td>\n",
       "      <td>95245.0</td>\n",
       "      <td>94981.0</td>\n",
       "      <td>94602.0</td>\n",
       "      <td>93616.0</td>\n",
       "      <td>92874.0</td>\n",
       "      <td>92481.0</td>\n",
       "      <td>92696.0</td>\n",
       "      <td>92952.0</td>\n",
       "      <td>92836.0</td>\n",
       "      <td>92506.0</td>\n",
       "      <td>92312.0</td>\n",
       "      <td>92424.0</td>\n",
       "      <td>92504.0</td>\n",
       "      <td>92351.0</td>\n",
       "      <td>92106.0</td>\n",
       "      <td>92212.0</td>\n",
       "      <td>92600.0</td>\n",
       "      <td>92870.0</td>\n",
       "      <td>93107.0</td>\n",
       "      <td>93176.0</td>\n",
       "      <td>93231.0</td>\n",
       "      <td>93047.0</td>\n",
       "      <td>92459.0</td>\n",
       "      <td>91309.0</td>\n",
       "      <td>90136.0</td>\n",
       "      <td>89183.0</td>\n",
       "      <td>88488.0</td>\n",
       "      <td>87790.0</td>\n",
       "      <td>87334.0</td>\n",
       "      <td>87670.0</td>\n",
       "      <td>87738.0</td>\n",
       "      <td>87671.0</td>\n",
       "      <td>86896.0</td>\n",
       "      <td>86154.0</td>\n",
       "      <td>85404.0</td>\n",
       "      <td>85260.0</td>\n",
       "      <td>85489.0</td>\n",
       "      <td>85828.0</td>\n",
       "      <td>85911.0</td>\n",
       "      <td>85622.0</td>\n",
       "      <td>85131.0</td>\n",
       "      <td>84994.0</td>\n",
       "      <td>84956.0</td>\n",
       "      <td>84468.0</td>\n",
       "      <td>83914.0</td>\n",
       "      <td>83458.0</td>\n",
       "      <td>83422.0</td>\n",
       "      <td>83138.0</td>\n",
       "      <td>82549.0</td>\n",
       "      <td>81900.0</td>\n",
       "      <td>81010.0</td>\n",
       "      <td>80409.0</td>\n",
       "      <td>79632.0</td>\n",
       "      <td>79181.0</td>\n",
       "      <td>78938.0</td>\n",
       "      <td>79119.0</td>\n",
       "      <td>78909.0</td>\n",
       "      <td>78519.0</td>\n",
       "      <td>78197.0</td>\n",
       "      <td>78087.0</td>\n",
       "      <td>78402.0</td>\n",
       "      <td>78807.0</td>\n",
       "      <td>79508.0</td>\n",
       "      <td>80187.0</td>\n",
       "      <td>80784.0</td>\n",
       "      <td>81285.0</td>\n",
       "      <td>81015.0</td>\n",
       "      <td>80428.0</td>\n",
       "      <td>79744.0</td>\n",
       "      <td>79366.0</td>\n",
       "      <td>79379.0</td>\n",
       "      <td>79472.0</td>\n",
       "      <td>79768.0</td>\n",
       "      <td>80261.0</td>\n",
       "      <td>80640.0</td>\n",
       "      <td>80999.0</td>\n",
       "      <td>81231.0</td>\n",
       "      <td>81906.0</td>\n",
       "      <td>82584.0</td>\n",
       "      <td>83036.0</td>\n",
       "      <td>83252.0</td>\n",
       "      <td>83482.0</td>\n",
       "      <td>83850.0</td>\n",
       "      <td>84256.0</td>\n",
       "      <td>84297.0</td>\n",
       "      <td>84424.0</td>\n",
       "      <td>84014.0</td>\n",
       "      <td>83963.0</td>\n",
       "      <td>83726.0</td>\n",
       "      <td>83392.0</td>\n",
       "      <td>83715.0</td>\n",
       "      <td>83905.0</td>\n",
       "      <td>85242.0</td>\n",
       "      <td>85904.0</td>\n",
       "      <td>86463.0</td>\n",
       "      <td>86536.0</td>\n",
       "      <td>86671.0</td>\n",
       "      <td>87371.0</td>\n",
       "      <td>88241.0</td>\n",
       "      <td>88805.0</td>\n",
       "      <td>89295.0</td>\n",
       "      <td>89547.0</td>\n",
       "      <td>90432.0</td>\n",
       "      <td>91290.0</td>\n",
       "      <td>92328.0</td>\n",
       "      <td>92990.0</td>\n",
       "      <td>93161.0</td>\n",
       "      <td>93299.0</td>\n",
       "      <td>93875.0</td>\n",
       "      <td>94738.0</td>\n",
       "      <td>95989.0</td>\n",
       "      <td>97270.0</td>\n",
       "      <td>98559.0</td>\n",
       "      <td>99638.0</td>\n",
       "      <td>100559.0</td>\n",
       "      <td>101698.0</td>\n",
       "      <td>102675.0</td>\n",
       "      <td>103840.0</td>\n",
       "      <td>104803.0</td>\n",
       "      <td>106231.0</td>\n",
       "      <td>107462.0</td>\n",
       "      <td>108663.0</td>\n",
       "      <td>109688.0</td>\n",
       "      <td>110644.0</td>\n",
       "      <td>111453.0</td>\n",
       "      <td>111999.0</td>\n",
       "      <td>112718.0</td>\n",
       "      <td>114024.0</td>\n",
       "      <td>115018.0</td>\n",
       "      <td>116064.0</td>\n",
       "      <td>117057.0</td>\n",
       "      <td>118288.0</td>\n",
       "      <td>119258.0</td>\n",
       "      <td>119999.0</td>\n",
       "      <td>121046.0</td>\n",
       "      <td>122377.0</td>\n",
       "      <td>123673.0</td>\n",
       "      <td>124884.0</td>\n",
       "      <td>125699.0</td>\n",
       "      <td>126276.0</td>\n",
       "      <td>126973.0</td>\n",
       "      <td>128643.0</td>\n",
       "      <td>130918.0</td>\n",
       "      <td>132866.0</td>\n",
       "      <td>134736.0</td>\n",
       "      <td>136673.0</td>\n",
       "      <td>138842.0</td>\n",
       "      <td>140682.0</td>\n",
       "      <td>142276.0</td>\n",
       "      <td>143882.0</td>\n",
       "      <td>145456.0</td>\n",
       "      <td>147599.0</td>\n",
       "      <td>149763.0</td>\n",
       "      <td>151782.0</td>\n",
       "      <td>153034.0</td>\n",
       "      <td>154373.0</td>\n",
       "      <td>156225.0</td>\n",
       "      <td>157780.0</td>\n",
       "      <td>159029.0</td>\n",
       "      <td>159274.0</td>\n",
       "      <td>159886.0</td>\n",
       "      <td>160780.0</td>\n",
       "      <td>161929.0</td>\n",
       "      <td>163625.0</td>\n",
       "      <td>165020.0</td>\n",
       "      <td>166009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>65787</td>\n",
       "      <td>426</td>\n",
       "      <td>19120</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>59285.0</td>\n",
       "      <td>59426.0</td>\n",
       "      <td>59583.0</td>\n",
       "      <td>59842.0</td>\n",
       "      <td>60283.0</td>\n",
       "      <td>60535.0</td>\n",
       "      <td>60926.0</td>\n",
       "      <td>61006.0</td>\n",
       "      <td>61287.0</td>\n",
       "      <td>61503.0</td>\n",
       "      <td>61796.0</td>\n",
       "      <td>62047.0</td>\n",
       "      <td>62201.0</td>\n",
       "      <td>62438.0</td>\n",
       "      <td>62602.0</td>\n",
       "      <td>62784.0</td>\n",
       "      <td>62835.0</td>\n",
       "      <td>62928.0</td>\n",
       "      <td>63095.0</td>\n",
       "      <td>63190.0</td>\n",
       "      <td>63341.0</td>\n",
       "      <td>63364.0</td>\n",
       "      <td>63532.0</td>\n",
       "      <td>63687.0</td>\n",
       "      <td>63928.0</td>\n",
       "      <td>64060.0</td>\n",
       "      <td>64241.0</td>\n",
       "      <td>64401.0</td>\n",
       "      <td>64644.0</td>\n",
       "      <td>64732.0</td>\n",
       "      <td>65012.0</td>\n",
       "      <td>65270.0</td>\n",
       "      <td>65741.0</td>\n",
       "      <td>65966.0</td>\n",
       "      <td>66236.0</td>\n",
       "      <td>66397.0</td>\n",
       "      <td>66714.0</td>\n",
       "      <td>67058.0</td>\n",
       "      <td>67336.0</td>\n",
       "      <td>67602.0</td>\n",
       "      <td>67994.0</td>\n",
       "      <td>68646.0</td>\n",
       "      <td>69335.0</td>\n",
       "      <td>69981.0</td>\n",
       "      <td>70569.0</td>\n",
       "      <td>71112.0</td>\n",
       "      <td>71704.0</td>\n",
       "      <td>72235.0</td>\n",
       "      <td>72709.0</td>\n",
       "      <td>73330.0</td>\n",
       "      <td>73746.0</td>\n",
       "      <td>74276.0</td>\n",
       "      <td>74643.0</td>\n",
       "      <td>75550.0</td>\n",
       "      <td>76924.0</td>\n",
       "      <td>78638.0</td>\n",
       "      <td>79962.0</td>\n",
       "      <td>80754.0</td>\n",
       "      <td>81255.0</td>\n",
       "      <td>82162.0</td>\n",
       "      <td>83373.0</td>\n",
       "      <td>84243.0</td>\n",
       "      <td>85085.0</td>\n",
       "      <td>85765.0</td>\n",
       "      <td>86841.0</td>\n",
       "      <td>87914.0</td>\n",
       "      <td>89420.0</td>\n",
       "      <td>91064.0</td>\n",
       "      <td>92680.0</td>\n",
       "      <td>94101.0</td>\n",
       "      <td>95481.0</td>\n",
       "      <td>96768.0</td>\n",
       "      <td>97527.0</td>\n",
       "      <td>98139.0</td>\n",
       "      <td>98600.0</td>\n",
       "      <td>99590.0</td>\n",
       "      <td>100777.0</td>\n",
       "      <td>102401.0</td>\n",
       "      <td>103944.0</td>\n",
       "      <td>105045.0</td>\n",
       "      <td>105856.0</td>\n",
       "      <td>106310.0</td>\n",
       "      <td>106816.0</td>\n",
       "      <td>107406.0</td>\n",
       "      <td>108362.0</td>\n",
       "      <td>109274.0</td>\n",
       "      <td>110067.0</td>\n",
       "      <td>110257.0</td>\n",
       "      <td>110736.0</td>\n",
       "      <td>111250.0</td>\n",
       "      <td>111937.0</td>\n",
       "      <td>112284.0</td>\n",
       "      <td>112116.0</td>\n",
       "      <td>112129.0</td>\n",
       "      <td>112210.0</td>\n",
       "      <td>112496.0</td>\n",
       "      <td>112280.0</td>\n",
       "      <td>112327.0</td>\n",
       "      <td>111987.0</td>\n",
       "      <td>111865.0</td>\n",
       "      <td>111468.0</td>\n",
       "      <td>110905.0</td>\n",
       "      <td>110061.0</td>\n",
       "      <td>109054.0</td>\n",
       "      <td>108236.0</td>\n",
       "      <td>107840.0</td>\n",
       "      <td>107229.0</td>\n",
       "      <td>106382.0</td>\n",
       "      <td>104857.0</td>\n",
       "      <td>103612.0</td>\n",
       "      <td>103169.0</td>\n",
       "      <td>103704.0</td>\n",
       "      <td>104468.0</td>\n",
       "      <td>104871.0</td>\n",
       "      <td>105113.0</td>\n",
       "      <td>105194.0</td>\n",
       "      <td>105273.0</td>\n",
       "      <td>104802.0</td>\n",
       "      <td>104411.0</td>\n",
       "      <td>103908.0</td>\n",
       "      <td>104409.0</td>\n",
       "      <td>105053.0</td>\n",
       "      <td>105918.0</td>\n",
       "      <td>106350.0</td>\n",
       "      <td>106543.0</td>\n",
       "      <td>106397.0</td>\n",
       "      <td>106030.0</td>\n",
       "      <td>105366.0</td>\n",
       "      <td>104268.0</td>\n",
       "      <td>103136.0</td>\n",
       "      <td>102228.0</td>\n",
       "      <td>101770.0</td>\n",
       "      <td>101097.0</td>\n",
       "      <td>100523.0</td>\n",
       "      <td>100815.0</td>\n",
       "      <td>100731.0</td>\n",
       "      <td>100628.0</td>\n",
       "      <td>99873.0</td>\n",
       "      <td>99574.0</td>\n",
       "      <td>99235.0</td>\n",
       "      <td>98796.0</td>\n",
       "      <td>98669.0</td>\n",
       "      <td>98506.0</td>\n",
       "      <td>98038.0</td>\n",
       "      <td>97484.0</td>\n",
       "      <td>96878.0</td>\n",
       "      <td>96600.0</td>\n",
       "      <td>96064.0</td>\n",
       "      <td>94953.0</td>\n",
       "      <td>94058.0</td>\n",
       "      <td>93175.0</td>\n",
       "      <td>92544.0</td>\n",
       "      <td>91654.0</td>\n",
       "      <td>90901.0</td>\n",
       "      <td>90383.0</td>\n",
       "      <td>90158.0</td>\n",
       "      <td>89810.0</td>\n",
       "      <td>89292.0</td>\n",
       "      <td>88503.0</td>\n",
       "      <td>87532.0</td>\n",
       "      <td>87245.0</td>\n",
       "      <td>86750.0</td>\n",
       "      <td>86414.0</td>\n",
       "      <td>85895.0</td>\n",
       "      <td>85664.0</td>\n",
       "      <td>85692.0</td>\n",
       "      <td>85681.0</td>\n",
       "      <td>85867.0</td>\n",
       "      <td>86401.0</td>\n",
       "      <td>87189.0</td>\n",
       "      <td>87978.0</td>\n",
       "      <td>88035.0</td>\n",
       "      <td>87756.0</td>\n",
       "      <td>87538.0</td>\n",
       "      <td>87516.0</td>\n",
       "      <td>87500.0</td>\n",
       "      <td>87455.0</td>\n",
       "      <td>87641.0</td>\n",
       "      <td>87890.0</td>\n",
       "      <td>88135.0</td>\n",
       "      <td>88066.0</td>\n",
       "      <td>88067.0</td>\n",
       "      <td>88359.0</td>\n",
       "      <td>89407.0</td>\n",
       "      <td>89879.0</td>\n",
       "      <td>90069.0</td>\n",
       "      <td>89567.0</td>\n",
       "      <td>89794.0</td>\n",
       "      <td>90353.0</td>\n",
       "      <td>90729.0</td>\n",
       "      <td>91108.0</td>\n",
       "      <td>90510.0</td>\n",
       "      <td>90018.0</td>\n",
       "      <td>89344.0</td>\n",
       "      <td>88990.0</td>\n",
       "      <td>89233.0</td>\n",
       "      <td>89568.0</td>\n",
       "      <td>90437.0</td>\n",
       "      <td>90991.0</td>\n",
       "      <td>91618.0</td>\n",
       "      <td>92120.0</td>\n",
       "      <td>92874.0</td>\n",
       "      <td>93934.0</td>\n",
       "      <td>95471.0</td>\n",
       "      <td>96238.0</td>\n",
       "      <td>96578.0</td>\n",
       "      <td>96384.0</td>\n",
       "      <td>96794.0</td>\n",
       "      <td>97384.0</td>\n",
       "      <td>98047.0</td>\n",
       "      <td>98752.0</td>\n",
       "      <td>99308.0</td>\n",
       "      <td>99863.0</td>\n",
       "      <td>100587.0</td>\n",
       "      <td>101182.0</td>\n",
       "      <td>101968.0</td>\n",
       "      <td>103354.0</td>\n",
       "      <td>104922.0</td>\n",
       "      <td>106401.0</td>\n",
       "      <td>107162.0</td>\n",
       "      <td>107883.0</td>\n",
       "      <td>108468.0</td>\n",
       "      <td>109210.0</td>\n",
       "      <td>109566.0</td>\n",
       "      <td>110661.0</td>\n",
       "      <td>111527.0</td>\n",
       "      <td>112971.0</td>\n",
       "      <td>113904.0</td>\n",
       "      <td>114742.0</td>\n",
       "      <td>115180.0</td>\n",
       "      <td>115528.0</td>\n",
       "      <td>116602.0</td>\n",
       "      <td>118029.0</td>\n",
       "      <td>119561.0</td>\n",
       "      <td>120776.0</td>\n",
       "      <td>121921.0</td>\n",
       "      <td>123224.0</td>\n",
       "      <td>124505.0</td>\n",
       "      <td>125578.0</td>\n",
       "      <td>126652.0</td>\n",
       "      <td>127560.0</td>\n",
       "      <td>128391.0</td>\n",
       "      <td>129214.0</td>\n",
       "      <td>129958.0</td>\n",
       "      <td>130488.0</td>\n",
       "      <td>130697.0</td>\n",
       "      <td>131571.0</td>\n",
       "      <td>133615.0</td>\n",
       "      <td>135661.0</td>\n",
       "      <td>138211.0</td>\n",
       "      <td>140279.0</td>\n",
       "      <td>142567.0</td>\n",
       "      <td>144675.0</td>\n",
       "      <td>146819.0</td>\n",
       "      <td>148779.0</td>\n",
       "      <td>150310.0</td>\n",
       "      <td>152475.0</td>\n",
       "      <td>154823.0</td>\n",
       "      <td>157243.0</td>\n",
       "      <td>158883.0</td>\n",
       "      <td>160251.0</td>\n",
       "      <td>161167.0</td>\n",
       "      <td>161807.0</td>\n",
       "      <td>162634.0</td>\n",
       "      <td>162972.0</td>\n",
       "      <td>163597.0</td>\n",
       "      <td>164008.0</td>\n",
       "      <td>164887.0</td>\n",
       "      <td>165860.0</td>\n",
       "      <td>167321.0</td>\n",
       "      <td>168524.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>65772</td>\n",
       "      <td>512</td>\n",
       "      <td>19104</td>\n",
       "      <td>Zip</td>\n",
       "      <td>PA</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Philadelphia-Camden-Wilmington</td>\n",
       "      <td>Philadelphia County</td>\n",
       "      <td>74255.0</td>\n",
       "      <td>74311.0</td>\n",
       "      <td>73420.0</td>\n",
       "      <td>74074.0</td>\n",
       "      <td>75734.0</td>\n",
       "      <td>76763.0</td>\n",
       "      <td>75432.0</td>\n",
       "      <td>72962.0</td>\n",
       "      <td>72727.0</td>\n",
       "      <td>73290.0</td>\n",
       "      <td>74886.0</td>\n",
       "      <td>75703.0</td>\n",
       "      <td>77136.0</td>\n",
       "      <td>76687.0</td>\n",
       "      <td>75961.0</td>\n",
       "      <td>75429.0</td>\n",
       "      <td>74684.0</td>\n",
       "      <td>73577.0</td>\n",
       "      <td>70427.0</td>\n",
       "      <td>66836.0</td>\n",
       "      <td>62842.0</td>\n",
       "      <td>59484.0</td>\n",
       "      <td>57420.0</td>\n",
       "      <td>56159.0</td>\n",
       "      <td>54460.0</td>\n",
       "      <td>53705.0</td>\n",
       "      <td>53354.0</td>\n",
       "      <td>54161.0</td>\n",
       "      <td>55132.0</td>\n",
       "      <td>56825.0</td>\n",
       "      <td>59489.0</td>\n",
       "      <td>61141.0</td>\n",
       "      <td>62801.0</td>\n",
       "      <td>63571.0</td>\n",
       "      <td>64771.0</td>\n",
       "      <td>66118.0</td>\n",
       "      <td>69458.0</td>\n",
       "      <td>71424.0</td>\n",
       "      <td>71054.0</td>\n",
       "      <td>69524.0</td>\n",
       "      <td>68923.0</td>\n",
       "      <td>70123.0</td>\n",
       "      <td>68924.0</td>\n",
       "      <td>68029.0</td>\n",
       "      <td>67594.0</td>\n",
       "      <td>69885.0</td>\n",
       "      <td>72393.0</td>\n",
       "      <td>75732.0</td>\n",
       "      <td>78462.0</td>\n",
       "      <td>80597.0</td>\n",
       "      <td>81625.0</td>\n",
       "      <td>82957.0</td>\n",
       "      <td>86090.0</td>\n",
       "      <td>90569.0</td>\n",
       "      <td>94776.0</td>\n",
       "      <td>98868.0</td>\n",
       "      <td>100244.0</td>\n",
       "      <td>101244.0</td>\n",
       "      <td>101187.0</td>\n",
       "      <td>103106.0</td>\n",
       "      <td>104176.0</td>\n",
       "      <td>105997.0</td>\n",
       "      <td>106612.0</td>\n",
       "      <td>108608.0</td>\n",
       "      <td>108415.0</td>\n",
       "      <td>111372.0</td>\n",
       "      <td>115279.0</td>\n",
       "      <td>120360.0</td>\n",
       "      <td>124832.0</td>\n",
       "      <td>124231.0</td>\n",
       "      <td>123508.0</td>\n",
       "      <td>120037.0</td>\n",
       "      <td>119055.0</td>\n",
       "      <td>117620.0</td>\n",
       "      <td>116581.0</td>\n",
       "      <td>114039.0</td>\n",
       "      <td>111694.0</td>\n",
       "      <td>111634.0</td>\n",
       "      <td>112812.0</td>\n",
       "      <td>117029.0</td>\n",
       "      <td>119892.0</td>\n",
       "      <td>125002.0</td>\n",
       "      <td>125837.0</td>\n",
       "      <td>125404.0</td>\n",
       "      <td>125191.0</td>\n",
       "      <td>129275.0</td>\n",
       "      <td>133100.0</td>\n",
       "      <td>135720.0</td>\n",
       "      <td>133557.0</td>\n",
       "      <td>129974.0</td>\n",
       "      <td>128664.0</td>\n",
       "      <td>129022.0</td>\n",
       "      <td>131953.0</td>\n",
       "      <td>132874.0</td>\n",
       "      <td>131801.0</td>\n",
       "      <td>130653.0</td>\n",
       "      <td>129469.0</td>\n",
       "      <td>132144.0</td>\n",
       "      <td>133678.0</td>\n",
       "      <td>133951.0</td>\n",
       "      <td>133645.0</td>\n",
       "      <td>134489.0</td>\n",
       "      <td>136118.0</td>\n",
       "      <td>138275.0</td>\n",
       "      <td>138108.0</td>\n",
       "      <td>138675.0</td>\n",
       "      <td>138582.0</td>\n",
       "      <td>138958.0</td>\n",
       "      <td>136666.0</td>\n",
       "      <td>135071.0</td>\n",
       "      <td>135059.0</td>\n",
       "      <td>138225.0</td>\n",
       "      <td>139657.0</td>\n",
       "      <td>140254.0</td>\n",
       "      <td>140471.0</td>\n",
       "      <td>139708.0</td>\n",
       "      <td>139952.0</td>\n",
       "      <td>140422.0</td>\n",
       "      <td>142933.0</td>\n",
       "      <td>142296.0</td>\n",
       "      <td>142667.0</td>\n",
       "      <td>143506.0</td>\n",
       "      <td>146404.0</td>\n",
       "      <td>147608.0</td>\n",
       "      <td>147007.0</td>\n",
       "      <td>146597.0</td>\n",
       "      <td>144884.0</td>\n",
       "      <td>143841.0</td>\n",
       "      <td>139290.0</td>\n",
       "      <td>135684.0</td>\n",
       "      <td>130595.0</td>\n",
       "      <td>128748.0</td>\n",
       "      <td>126026.0</td>\n",
       "      <td>124332.0</td>\n",
       "      <td>125017.0</td>\n",
       "      <td>124836.0</td>\n",
       "      <td>125404.0</td>\n",
       "      <td>121829.0</td>\n",
       "      <td>120414.0</td>\n",
       "      <td>117467.0</td>\n",
       "      <td>116295.0</td>\n",
       "      <td>114123.0</td>\n",
       "      <td>113479.0</td>\n",
       "      <td>111740.0</td>\n",
       "      <td>107668.0</td>\n",
       "      <td>101551.0</td>\n",
       "      <td>97021.0</td>\n",
       "      <td>94019.0</td>\n",
       "      <td>92692.0</td>\n",
       "      <td>90642.0</td>\n",
       "      <td>89632.0</td>\n",
       "      <td>89190.0</td>\n",
       "      <td>89646.0</td>\n",
       "      <td>90767.0</td>\n",
       "      <td>92057.0</td>\n",
       "      <td>93573.0</td>\n",
       "      <td>95532.0</td>\n",
       "      <td>96827.0</td>\n",
       "      <td>98324.0</td>\n",
       "      <td>100714.0</td>\n",
       "      <td>103895.0</td>\n",
       "      <td>106680.0</td>\n",
       "      <td>108595.0</td>\n",
       "      <td>109549.0</td>\n",
       "      <td>111161.0</td>\n",
       "      <td>112782.0</td>\n",
       "      <td>113774.0</td>\n",
       "      <td>114854.0</td>\n",
       "      <td>112360.0</td>\n",
       "      <td>109890.0</td>\n",
       "      <td>106934.0</td>\n",
       "      <td>106550.0</td>\n",
       "      <td>106389.0</td>\n",
       "      <td>106369.0</td>\n",
       "      <td>107120.0</td>\n",
       "      <td>108935.0</td>\n",
       "      <td>109732.0</td>\n",
       "      <td>110166.0</td>\n",
       "      <td>110185.0</td>\n",
       "      <td>110746.0</td>\n",
       "      <td>114743.0</td>\n",
       "      <td>118835.0</td>\n",
       "      <td>123911.0</td>\n",
       "      <td>126603.0</td>\n",
       "      <td>128858.0</td>\n",
       "      <td>130365.0</td>\n",
       "      <td>131140.0</td>\n",
       "      <td>132559.0</td>\n",
       "      <td>133206.0</td>\n",
       "      <td>132999.0</td>\n",
       "      <td>132629.0</td>\n",
       "      <td>132356.0</td>\n",
       "      <td>132139.0</td>\n",
       "      <td>131735.0</td>\n",
       "      <td>130882.0</td>\n",
       "      <td>131097.0</td>\n",
       "      <td>130815.0</td>\n",
       "      <td>130642.0</td>\n",
       "      <td>129131.0</td>\n",
       "      <td>128057.0</td>\n",
       "      <td>128020.0</td>\n",
       "      <td>128184.0</td>\n",
       "      <td>128624.0</td>\n",
       "      <td>129960.0</td>\n",
       "      <td>131882.0</td>\n",
       "      <td>132739.0</td>\n",
       "      <td>133118.0</td>\n",
       "      <td>134098.0</td>\n",
       "      <td>136969.0</td>\n",
       "      <td>138824.0</td>\n",
       "      <td>141021.0</td>\n",
       "      <td>141497.0</td>\n",
       "      <td>142323.0</td>\n",
       "      <td>143175.0</td>\n",
       "      <td>145523.0</td>\n",
       "      <td>147700.0</td>\n",
       "      <td>149613.0</td>\n",
       "      <td>153357.0</td>\n",
       "      <td>158330.0</td>\n",
       "      <td>162564.0</td>\n",
       "      <td>165140.0</td>\n",
       "      <td>167502.0</td>\n",
       "      <td>169288.0</td>\n",
       "      <td>169718.0</td>\n",
       "      <td>169225.0</td>\n",
       "      <td>169003.0</td>\n",
       "      <td>170001.0</td>\n",
       "      <td>172179.0</td>\n",
       "      <td>174795.0</td>\n",
       "      <td>176565.0</td>\n",
       "      <td>176904.0</td>\n",
       "      <td>176388.0</td>\n",
       "      <td>176153.0</td>\n",
       "      <td>176110.0</td>\n",
       "      <td>176849.0</td>\n",
       "      <td>178179.0</td>\n",
       "      <td>179932.0</td>\n",
       "      <td>181731.0</td>\n",
       "      <td>181145.0</td>\n",
       "      <td>181805.0</td>\n",
       "      <td>182999.0</td>\n",
       "      <td>187313.0</td>\n",
       "      <td>188971.0</td>\n",
       "      <td>189183.0</td>\n",
       "      <td>187374.0</td>\n",
       "      <td>187973.0</td>\n",
       "      <td>190296.0</td>\n",
       "      <td>194804.0</td>\n",
       "      <td>199969.0</td>\n",
       "      <td>205333.0</td>\n",
       "      <td>210444.0</td>\n",
       "      <td>213002.0</td>\n",
       "      <td>215598.0</td>\n",
       "      <td>216806.0</td>\n",
       "      <td>220053.0</td>\n",
       "      <td>222065.0</td>\n",
       "      <td>223889.0</td>\n",
       "      <td>224518.0</td>\n",
       "      <td>223083.0</td>\n",
       "      <td>221879.0</td>\n",
       "      <td>219949.0</td>\n",
       "      <td>220270.0</td>\n",
       "      <td>221454.0</td>\n",
       "      <td>222006.0</td>\n",
       "      <td>220760.0</td>\n",
       "      <td>217933.0</td>\n",
       "      <td>216447.0</td>\n",
       "      <td>216424.0</td>\n",
       "      <td>218663.0</td>\n",
       "      <td>220453.0</td>\n",
       "      <td>223443.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     RegionID  SizeRank zipcode RegionType StateName State          City  \\\n",
       "125     65810       126   19143        Zip        PA    PA  Philadelphia   \n",
       "247     65779       249   19111        Zip        PA    PA  Philadelphia   \n",
       "338     65791       340   19124        Zip        PA    PA  Philadelphia   \n",
       "423     65787       426   19120        Zip        PA    PA  Philadelphia   \n",
       "509     65772       512   19104        Zip        PA    PA  Philadelphia   \n",
       "\n",
       "                              Metro           CountyName  2000-01-31  \\\n",
       "125  Philadelphia-Camden-Wilmington  Philadelphia County     60701.0   \n",
       "247  Philadelphia-Camden-Wilmington  Philadelphia County     85062.0   \n",
       "338  Philadelphia-Camden-Wilmington  Philadelphia County     47155.0   \n",
       "423  Philadelphia-Camden-Wilmington  Philadelphia County     59285.0   \n",
       "509  Philadelphia-Camden-Wilmington  Philadelphia County     74255.0   \n",
       "\n",
       "     2000-02-29  2000-03-31  2000-04-30  2000-05-31  2000-06-30  2000-07-31  \\\n",
       "125     61009.0     61132.0     61497.0     62089.0     62804.0     63377.0   \n",
       "247     85325.0     85511.0     85937.0     86553.0     87106.0     87591.0   \n",
       "338     47278.0     47408.0     47613.0     47956.0     48226.0     48586.0   \n",
       "423     59426.0     59583.0     59842.0     60283.0     60535.0     60926.0   \n",
       "509     74311.0     73420.0     74074.0     75734.0     76763.0     75432.0   \n",
       "\n",
       "     2000-08-31  2000-09-30  2000-10-31  2000-11-30  2000-12-31  2001-01-31  \\\n",
       "125     63925.0     64313.0     64609.0     64756.0     65187.0     65676.0   \n",
       "247     87826.0     88161.0     88561.0     88958.0     89438.0     89950.0   \n",
       "338     48728.0     48955.0     49118.0     49275.0     49389.0     49523.0   \n",
       "423     61006.0     61287.0     61503.0     61796.0     62047.0     62201.0   \n",
       "509     72962.0     72727.0     73290.0     74886.0     75703.0     77136.0   \n",
       "\n",
       "     2001-02-28  2001-03-31  2001-04-30  2001-05-31  2001-06-30  2001-07-31  \\\n",
       "125     65861.0     65823.0     66081.0     66273.0     66797.0     67099.0   \n",
       "247     90515.0     91088.0     91677.0     92113.0     92536.0     92992.0   \n",
       "338     49721.0     50009.0     50254.0     50481.0     50610.0     50741.0   \n",
       "423     62438.0     62602.0     62784.0     62835.0     62928.0     63095.0   \n",
       "509     76687.0     75961.0     75429.0     74684.0     73577.0     70427.0   \n",
       "\n",
       "     2001-08-31  2001-09-30  2001-10-31  2001-11-30  2001-12-31  2002-01-31  \\\n",
       "125     67641.0     67686.0     67959.0     68203.0     68695.0     68447.0   \n",
       "247     93485.0     94003.0     94417.0     95089.0     95722.0     96345.0   \n",
       "338     50882.0     51036.0     51232.0     51416.0     51605.0     51744.0   \n",
       "423     63190.0     63341.0     63364.0     63532.0     63687.0     63928.0   \n",
       "509     66836.0     62842.0     59484.0     57420.0     56159.0     54460.0   \n",
       "\n",
       "     2002-02-28  2002-03-31  2002-04-30  2002-05-31  2002-06-30  2002-07-31  \\\n",
       "125     68180.0     67802.0     67913.0     68143.0     68498.0     68586.0   \n",
       "247     96941.0     97579.0     98372.0     99076.0     99886.0    100862.0   \n",
       "338     51911.0     52107.0     52303.0     52536.0     52757.0     53075.0   \n",
       "423     64060.0     64241.0     64401.0     64644.0     64732.0     65012.0   \n",
       "509     53705.0     53354.0     54161.0     55132.0     56825.0     59489.0   \n",
       "\n",
       "     2002-08-31  2002-09-30  2002-10-31  2002-11-30  2002-12-31  2003-01-31  \\\n",
       "125     68493.0     68414.0     68426.0     68588.0     68758.0     69396.0   \n",
       "247    101890.0    102984.0    104071.0    105134.0    106151.0    107061.0   \n",
       "338     53409.0     53737.0     53997.0     54232.0     54489.0     54885.0   \n",
       "423     65270.0     65741.0     65966.0     66236.0     66397.0     66714.0   \n",
       "509     61141.0     62801.0     63571.0     64771.0     66118.0     69458.0   \n",
       "\n",
       "     2003-02-28  2003-03-31  2003-04-30  2003-05-31  2003-06-30  2003-07-31  \\\n",
       "125     70405.0     71333.0     72108.0     71545.0     70832.0     69969.0   \n",
       "247    108177.0    109247.0    110301.0    111277.0    112438.0    114178.0   \n",
       "338     55397.0     55953.0     56551.0     57038.0     57700.0     58370.0   \n",
       "423     67058.0     67336.0     67602.0     67994.0     68646.0     69335.0   \n",
       "509     71424.0     71054.0     69524.0     68923.0     70123.0     68924.0   \n",
       "\n",
       "     2003-08-31  2003-09-30  2003-10-31  2003-11-30  2003-12-31  2004-01-31  \\\n",
       "125     69189.0     68933.0     68952.0     69436.0     69497.0     69166.0   \n",
       "247    115959.0    117757.0    119093.0    120471.0    121920.0    123332.0   \n",
       "338     59082.0     59803.0     60415.0     61013.0     61545.0     62079.0   \n",
       "423     69981.0     70569.0     71112.0     71704.0     72235.0     72709.0   \n",
       "509     68029.0     67594.0     69885.0     72393.0     75732.0     78462.0   \n",
       "\n",
       "     2004-02-29  2004-03-31  2004-04-30  2004-05-31  2004-06-30  2004-07-31  \\\n",
       "125     68806.0     68433.0     67859.0     68503.0     69640.0     72312.0   \n",
       "247    124897.0    126029.0    127138.0    128446.0    130704.0    133641.0   \n",
       "338     62688.0     62923.0     63177.0     63518.0     64381.0     65520.0   \n",
       "423     73330.0     73746.0     74276.0     74643.0     75550.0     76924.0   \n",
       "509     80597.0     81625.0     82957.0     86090.0     90569.0     94776.0   \n",
       "\n",
       "     2004-08-31  2004-09-30  2004-10-31  2004-11-30  2004-12-31  2005-01-31  \\\n",
       "125     74534.0     76922.0     77949.0     78439.0     78686.0     79661.0   \n",
       "247    136937.0    139741.0    141849.0    143846.0    146431.0    149653.0   \n",
       "338     66803.0     67730.0     68397.0     69036.0     70092.0     71384.0   \n",
       "423     78638.0     79962.0     80754.0     81255.0     82162.0     83373.0   \n",
       "509     98868.0    100244.0    101244.0    101187.0    103106.0    104176.0   \n",
       "\n",
       "     2005-02-28  2005-03-31  2005-04-30  2005-05-31  2005-06-30  2005-07-31  \\\n",
       "125     81557.0     83961.0     86307.0     88384.0     90337.0     92085.0   \n",
       "247    151750.0    153916.0    155841.0    158797.0    161690.0    165007.0   \n",
       "338     72230.0     73336.0     74356.0     75894.0     77244.0     78885.0   \n",
       "423     84243.0     85085.0     85765.0     86841.0     87914.0     89420.0   \n",
       "509    105997.0    106612.0    108608.0    108415.0    111372.0    115279.0   \n",
       "\n",
       "     2005-08-31  2005-09-30  2005-10-31  2005-11-30  2005-12-31  2006-01-31  \\\n",
       "125     93843.0     95070.0     96082.0     96754.0     97226.0     97366.0   \n",
       "247    168454.0    171486.0    173952.0    176189.0    178068.0    179278.0   \n",
       "338     80660.0     82392.0     83864.0     85158.0     86200.0     87088.0   \n",
       "423     91064.0     92680.0     94101.0     95481.0     96768.0     97527.0   \n",
       "509    120360.0    124832.0    124231.0    123508.0    120037.0    119055.0   \n",
       "\n",
       "     2006-02-28  2006-03-31  2006-04-30  2006-05-31  2006-06-30  2006-07-31  \\\n",
       "125     97250.0     97431.0     98005.0     98827.0     99968.0    101264.0   \n",
       "247    180165.0    180564.0    181480.0    182426.0    183849.0    185148.0   \n",
       "338     87835.0     88441.0     89121.0     89893.0     91149.0     92529.0   \n",
       "423     98139.0     98600.0     99590.0    100777.0    102401.0    103944.0   \n",
       "509    117620.0    116581.0    114039.0    111694.0    111634.0    112812.0   \n",
       "\n",
       "     2006-08-31  2006-09-30  2006-10-31  2006-11-30  2006-12-31  2007-01-31  \\\n",
       "125    102415.0    102880.0    103031.0    103011.0    103479.0    104487.0   \n",
       "247    185461.0    185262.0    184263.0    183359.0    182896.0    183302.0   \n",
       "338     93594.0     94165.0     94255.0     94451.0     94912.0     95741.0   \n",
       "423    105045.0    105856.0    106310.0    106816.0    107406.0    108362.0   \n",
       "509    117029.0    119892.0    125002.0    125837.0    125404.0    125191.0   \n",
       "\n",
       "     2007-02-28  2007-03-31  2007-04-30  2007-05-31  2007-06-30  2007-07-31  \\\n",
       "125    105486.0    106479.0    107086.0    107907.0    108633.0    109295.0   \n",
       "247    183850.0    184323.0    184004.0    184393.0    184874.0    185468.0   \n",
       "338     96476.0     97222.0     97739.0     98489.0     99167.0     99733.0   \n",
       "423    109274.0    110067.0    110257.0    110736.0    111250.0    111937.0   \n",
       "509    129275.0    133100.0    135720.0    133557.0    129974.0    128664.0   \n",
       "\n",
       "     2007-08-31  2007-09-30  2007-10-31  2007-11-30  2007-12-31  2008-01-31  \\\n",
       "125    109709.0    109799.0    110175.0    110832.0    111722.0    111804.0   \n",
       "247    185143.0    184016.0    183558.0    183651.0    183769.0    183295.0   \n",
       "338     99838.0     99550.0     99656.0     99854.0    100206.0     99986.0   \n",
       "423    112284.0    112116.0    112129.0    112210.0    112496.0    112280.0   \n",
       "509    129022.0    131953.0    132874.0    131801.0    130653.0    129469.0   \n",
       "\n",
       "     2008-02-29  2008-03-31  2008-04-30  2008-05-31  2008-06-30  2008-07-31  \\\n",
       "125    112139.0    111773.0    111921.0    111393.0    111219.0    110379.0   \n",
       "247    183077.0    182343.0    181946.0    180672.0    179570.0    177714.0   \n",
       "338    100138.0     99941.0     99881.0     99048.0     98127.0     97134.0   \n",
       "423    112327.0    111987.0    111865.0    111468.0    110905.0    110061.0   \n",
       "509    132144.0    133678.0    133951.0    133645.0    134489.0    136118.0   \n",
       "\n",
       "     2008-08-31  2008-09-30  2008-10-31  2008-11-30  2008-12-31  2009-01-31  \\\n",
       "125    109495.0    108215.0    107367.0    106351.0    105522.0    104364.0   \n",
       "247    176122.0    174785.0    174053.0    173051.0    171897.0    170067.0   \n",
       "338     96257.0     95602.0     95245.0     94981.0     94602.0     93616.0   \n",
       "423    109054.0    108236.0    107840.0    107229.0    106382.0    104857.0   \n",
       "509    138275.0    138108.0    138675.0    138582.0    138958.0    136666.0   \n",
       "\n",
       "     2009-02-28  2009-03-31  2009-04-30  2009-05-31  2009-06-30  2009-07-31  \\\n",
       "125    103805.0    103242.0    103065.0    102323.0    101444.0    101301.0   \n",
       "247    168534.0    167708.0    167669.0    167622.0    167573.0    167346.0   \n",
       "338     92874.0     92481.0     92696.0     92952.0     92836.0     92506.0   \n",
       "423    103612.0    103169.0    103704.0    104468.0    104871.0    105113.0   \n",
       "509    135071.0    135059.0    138225.0    139657.0    140254.0    140471.0   \n",
       "\n",
       "     2009-08-31  2009-09-30  2009-10-31  2009-11-30  2009-12-31  2010-01-31  \\\n",
       "125    101305.0    101310.0    100671.0    101311.0    101380.0    101148.0   \n",
       "247    167175.0    167000.0    166703.0    166631.0    166329.0    166607.0   \n",
       "338     92312.0     92424.0     92504.0     92351.0     92106.0     92212.0   \n",
       "423    105194.0    105273.0    104802.0    104411.0    103908.0    104409.0   \n",
       "509    139708.0    139952.0    140422.0    142933.0    142296.0    142667.0   \n",
       "\n",
       "     2010-02-28  2010-03-31  2010-04-30  2010-05-31  2010-06-30  2010-07-31  \\\n",
       "125     99991.0     99764.0     99667.0    100342.0    101206.0    101751.0   \n",
       "247    167096.0    167747.0    168725.0    169508.0    170394.0    170337.0   \n",
       "338     92600.0     92870.0     93107.0     93176.0     93231.0     93047.0   \n",
       "423    105053.0    105918.0    106350.0    106543.0    106397.0    106030.0   \n",
       "509    143506.0    146404.0    147608.0    147007.0    146597.0    144884.0   \n",
       "\n",
       "     2010-08-31  2010-09-30  2010-10-31  2010-11-30  2010-12-31  2011-01-31  \\\n",
       "125    101410.0     99899.0     99054.0     98190.0     97760.0     97027.0   \n",
       "247    169592.0    167491.0    165488.0    163970.0    162902.0    161966.0   \n",
       "338     92459.0     91309.0     90136.0     89183.0     88488.0     87790.0   \n",
       "423    105366.0    104268.0    103136.0    102228.0    101770.0    101097.0   \n",
       "509    143841.0    139290.0    135684.0    130595.0    128748.0    126026.0   \n",
       "\n",
       "     2011-02-28  2011-03-31  2011-04-30  2011-05-31  2011-06-30  2011-07-31  \\\n",
       "125     95989.0     95607.0     94610.0     93853.0     92438.0     91449.0   \n",
       "247    161241.0    161488.0    161326.0    160817.0    159370.0    158064.0   \n",
       "338     87334.0     87670.0     87738.0     87671.0     86896.0     86154.0   \n",
       "423    100523.0    100815.0    100731.0    100628.0     99873.0     99574.0   \n",
       "509    124332.0    125017.0    124836.0    125404.0    121829.0    120414.0   \n",
       "\n",
       "     2011-08-31  2011-09-30  2011-10-31  2011-11-30  2011-12-31  2012-01-31  \\\n",
       "125     90053.0     89361.0     88056.0     86772.0     85735.0     84583.0   \n",
       "247    157130.0    156624.0    156405.0    155915.0    155689.0    155512.0   \n",
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       "423     99235.0     98796.0     98669.0     98506.0     98038.0     97484.0   \n",
       "509    117467.0    116295.0    114123.0    113479.0    111740.0    107668.0   \n",
       "\n",
       "     2012-02-29  2012-03-31  2012-04-30  2012-05-31  2012-06-30  2012-07-31  \\\n",
       "125     84007.0     82600.0     81821.0     80929.0     80279.0     79375.0   \n",
       "247    155498.0    155935.0    156558.0    156797.0    156427.0    155345.0   \n",
       "338     85131.0     84994.0     84956.0     84468.0     83914.0     83458.0   \n",
       "423     96878.0     96600.0     96064.0     94953.0     94058.0     93175.0   \n",
       "509    101551.0     97021.0     94019.0     92692.0     90642.0     89632.0   \n",
       "\n",
       "     2012-08-31  2012-09-30  2012-10-31  2012-11-30  2012-12-31  2013-01-31  \\\n",
       "125     78214.0     76773.0     75875.0     75539.0     75270.0     75758.0   \n",
       "247    154473.0    154002.0    153784.0    153578.0    152985.0    152508.0   \n",
       "338     83422.0     83138.0     82549.0     81900.0     81010.0     80409.0   \n",
       "423     92544.0     91654.0     90901.0     90383.0     90158.0     89810.0   \n",
       "509     89190.0     89646.0     90767.0     92057.0     93573.0     95532.0   \n",
       "\n",
       "     2013-02-28  2013-03-31  2013-04-30  2013-05-31  2013-06-30  2013-07-31  \\\n",
       "125     75887.0     76684.0     77167.0     77425.0     77025.0     76461.0   \n",
       "247    151807.0    151572.0    152032.0    153197.0    154319.0    154912.0   \n",
       "338     79632.0     79181.0     78938.0     79119.0     78909.0     78519.0   \n",
       "423     89292.0     88503.0     87532.0     87245.0     86750.0     86414.0   \n",
       "509     96827.0     98324.0    100714.0    103895.0    106680.0    108595.0   \n",
       "\n",
       "     2013-08-31  2013-09-30  2013-10-31  2013-11-30  2013-12-31  2014-01-31  \\\n",
       "125     77015.0     78310.0     79731.0     80026.0     80722.0     80064.0   \n",
       "247    154590.0    153730.0    153093.0    153027.0    153170.0    153407.0   \n",
       "338     78197.0     78087.0     78402.0     78807.0     79508.0     80187.0   \n",
       "423     85895.0     85664.0     85692.0     85681.0     85867.0     86401.0   \n",
       "509    109549.0    111161.0    112782.0    113774.0    114854.0    112360.0   \n",
       "\n",
       "     2014-02-28  2014-03-31  2014-04-30  2014-05-31  2014-06-30  2014-07-31  \\\n",
       "125     79452.0     78172.0     78217.0     78507.0     78747.0     78096.0   \n",
       "247    153770.0    154052.0    152735.0    151932.0    151197.0    151529.0   \n",
       "338     80784.0     81285.0     81015.0     80428.0     79744.0     79366.0   \n",
       "423     87189.0     87978.0     88035.0     87756.0     87538.0     87516.0   \n",
       "509    109890.0    106934.0    106550.0    106389.0    106369.0    107120.0   \n",
       "\n",
       "     2014-08-31  2014-09-30  2014-10-31  2014-11-30  2014-12-31  2015-01-31  \\\n",
       "125     76899.0     76226.0     76352.0     77600.0     78201.0     78684.0   \n",
       "247    151185.0    150982.0    151152.0    151573.0    152541.0    153163.0   \n",
       "338     79379.0     79472.0     79768.0     80261.0     80640.0     80999.0   \n",
       "423     87500.0     87455.0     87641.0     87890.0     88135.0     88066.0   \n",
       "509    108935.0    109732.0    110166.0    110185.0    110746.0    114743.0   \n",
       "\n",
       "     2015-02-28  2015-03-31  2015-04-30  2015-05-31  2015-06-30  2015-07-31  \\\n",
       "125     78404.0     78963.0     79121.0     78435.0     76924.0     76583.0   \n",
       "247    153473.0    154117.0    155346.0    156034.0    155829.0    155165.0   \n",
       "338     81231.0     81906.0     82584.0     83036.0     83252.0     83482.0   \n",
       "423     88067.0     88359.0     89407.0     89879.0     90069.0     89567.0   \n",
       "509    118835.0    123911.0    126603.0    128858.0    130365.0    131140.0   \n",
       "\n",
       "     2015-08-31  2015-09-30  2015-10-31  2015-11-30  2015-12-31  2016-01-31  \\\n",
       "125     77810.0     79529.0     80410.0     80371.0     79269.0     78491.0   \n",
       "247    155482.0    156306.0    156730.0    157046.0    156051.0    155744.0   \n",
       "338     83850.0     84256.0     84297.0     84424.0     84014.0     83963.0   \n",
       "423     89794.0     90353.0     90729.0     91108.0     90510.0     90018.0   \n",
       "509    132559.0    133206.0    132999.0    132629.0    132356.0    132139.0   \n",
       "\n",
       "     2016-02-29  2016-03-31  2016-04-30  2016-05-31  2016-06-30  2016-07-31  \\\n",
       "125     78287.0     78132.0     78159.0     77924.0     78513.0     78295.0   \n",
       "247    155593.0    155782.0    156233.0    156414.0    157693.0    158424.0   \n",
       "338     83726.0     83392.0     83715.0     83905.0     85242.0     85904.0   \n",
       "423     89344.0     88990.0     89233.0     89568.0     90437.0     90991.0   \n",
       "509    131735.0    130882.0    131097.0    130815.0    130642.0    129131.0   \n",
       "\n",
       "     2016-08-31  2016-09-30  2016-10-31  2016-11-30  2016-12-31  2017-01-31  \\\n",
       "125     78311.0     78364.0     78514.0     79223.0     80707.0     81456.0   \n",
       "247    159387.0    160022.0    161145.0    162362.0    164293.0    165834.0   \n",
       "338     86463.0     86536.0     86671.0     87371.0     88241.0     88805.0   \n",
       "423     91618.0     92120.0     92874.0     93934.0     95471.0     96238.0   \n",
       "509    128057.0    128020.0    128184.0    128624.0    129960.0    131882.0   \n",
       "\n",
       "     2017-02-28  2017-03-31  2017-04-30  2017-05-31  2017-06-30  2017-07-31  \\\n",
       "125     80844.0     79670.0     79979.0     81003.0     81645.0     81964.0   \n",
       "247    166797.0    167640.0    168699.0    169919.0    170225.0    169976.0   \n",
       "338     89295.0     89547.0     90432.0     91290.0     92328.0     92990.0   \n",
       "423     96578.0     96384.0     96794.0     97384.0     98047.0     98752.0   \n",
       "509    132739.0    133118.0    134098.0    136969.0    138824.0    141021.0   \n",
       "\n",
       "     2017-08-31  2017-09-30  2017-10-31  2017-11-30  2017-12-31  2018-01-31  \\\n",
       "125     82782.0     84137.0     86169.0     87425.0     88374.0     90395.0   \n",
       "247    170149.0    171026.0    173107.0    174567.0    176158.0    176461.0   \n",
       "338     93161.0     93299.0     93875.0     94738.0     95989.0     97270.0   \n",
       "423     99308.0     99863.0    100587.0    101182.0    101968.0    103354.0   \n",
       "509    141497.0    142323.0    143175.0    145523.0    147700.0    149613.0   \n",
       "\n",
       "     2018-02-28  2018-03-31  2018-04-30  2018-05-31  2018-06-30  2018-07-31  \\\n",
       "125     93178.0     97105.0     99056.0    101865.0    104178.0    107011.0   \n",
       "247    177644.0    178936.0    180638.0    181664.0    182138.0    183150.0   \n",
       "338     98559.0     99638.0    100559.0    101698.0    102675.0    103840.0   \n",
       "423    104922.0    106401.0    107162.0    107883.0    108468.0    109210.0   \n",
       "509    153357.0    158330.0    162564.0    165140.0    167502.0    169288.0   \n",
       "\n",
       "     2018-08-31  2018-09-30  2018-10-31  2018-11-30  2018-12-31  2019-01-31  \\\n",
       "125    108961.0    110034.0    110958.0    112422.0    114015.0    115970.0   \n",
       "247    184656.0    186732.0    188650.0    190755.0    192883.0    195542.0   \n",
       "338    104803.0    106231.0    107462.0    108663.0    109688.0    110644.0   \n",
       "423    109566.0    110661.0    111527.0    112971.0    113904.0    114742.0   \n",
       "509    169718.0    169225.0    169003.0    170001.0    172179.0    174795.0   \n",
       "\n",
       "     2019-02-28  2019-03-31  2019-04-30  2019-05-31  2019-06-30  2019-07-31  \\\n",
       "125    117291.0    118019.0    118858.0    120055.0    122956.0    124885.0   \n",
       "247    197516.0    198968.0    199252.0    199840.0    199978.0    200839.0   \n",
       "338    111453.0    111999.0    112718.0    114024.0    115018.0    116064.0   \n",
       "423    115180.0    115528.0    116602.0    118029.0    119561.0    120776.0   \n",
       "509    176565.0    176904.0    176388.0    176153.0    176110.0    176849.0   \n",
       "\n",
       "     2019-08-31  2019-09-30  2019-10-31  2019-11-30  2019-12-31  2020-01-31  \\\n",
       "125    126050.0    126668.0    128030.0    129620.0    131469.0    134062.0   \n",
       "247    201593.0    202632.0    203180.0    203531.0    204228.0    204670.0   \n",
       "338    117057.0    118288.0    119258.0    119999.0    121046.0    122377.0   \n",
       "423    121921.0    123224.0    124505.0    125578.0    126652.0    127560.0   \n",
       "509    178179.0    179932.0    181731.0    181145.0    181805.0    182999.0   \n",
       "\n",
       "     2020-02-29  2020-03-31  2020-04-30  2020-05-31  2020-06-30  2020-07-31  \\\n",
       "125    136766.0    138666.0    140376.0    141110.0    142564.0    144531.0   \n",
       "247    205301.0    206452.0    208135.0    209251.0    210282.0    212368.0   \n",
       "338    123673.0    124884.0    125699.0    126276.0    126973.0    128643.0   \n",
       "423    128391.0    129214.0    129958.0    130488.0    130697.0    131571.0   \n",
       "509    187313.0    188971.0    189183.0    187374.0    187973.0    190296.0   \n",
       "\n",
       "     2020-08-31  2020-09-30  2020-10-31  2020-11-30  2020-12-31  2021-01-31  \\\n",
       "125    149060.0    152057.0    154865.0    157516.0    161147.0    164398.0   \n",
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       "509    194804.0    199969.0    205333.0    210444.0    213002.0    215598.0   \n",
       "\n",
       "     2021-02-28  2021-03-31  2021-04-30  2021-05-31  2021-06-30  2021-07-31  \\\n",
       "125    167188.0    169351.0    171125.0    172719.0    174880.0    176204.0   \n",
       "247    235155.0    237287.0    239669.0    243054.0    246722.0    250520.0   \n",
       "338    142276.0    143882.0    145456.0    147599.0    149763.0    151782.0   \n",
       "423    146819.0    148779.0    150310.0    152475.0    154823.0    157243.0   \n",
       "509    216806.0    220053.0    222065.0    223889.0    224518.0    223083.0   \n",
       "\n",
       "     2021-08-31  2021-09-30  2021-10-31  2021-11-30  2021-12-31  2022-01-31  \\\n",
       "125    176015.0    174596.0    173114.0    172087.0    171445.0    171542.0   \n",
       "247    253386.0    256032.0    257911.0    260104.0    262257.0    263715.0   \n",
       "338    153034.0    154373.0    156225.0    157780.0    159029.0    159274.0   \n",
       "423    158883.0    160251.0    161167.0    161807.0    162634.0    162972.0   \n",
       "509    221879.0    219949.0    220270.0    221454.0    222006.0    220760.0   \n",
       "\n",
       "     2022-02-28  2022-03-31  2022-04-30  2022-05-31  2022-06-30  2022-07-31  \n",
       "125    171680.0    171878.0    171607.0    171333.0    171771.0    172611.0  \n",
       "247    264809.0    265684.0    267222.0    269460.0    272201.0    274446.0  \n",
       "338    159886.0    160780.0    161929.0    163625.0    165020.0    166009.0  \n",
       "423    163597.0    164008.0    164887.0    165860.0    167321.0    168524.0  \n",
       "509    217933.0    216447.0    216424.0    218663.0    220453.0    223443.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename RegionName columns and cast as strings\n",
    "zillow.rename(columns={'RegionName': 'zipcode'}, inplace=True)\n",
    "zillow['zipcode'] = zillow['zipcode'].astype('str')\n",
    "zillow = zillow[zillow['zipcode'].isin(list_of_zipcodes)]\n",
    "zillow.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c45432",
   "metadata": {},
   "source": [
    "We're only going to look at house values year over year rather than each month in order to smooth out fluctuations like seasonal changes, etc. that affect number of permits (due to weather constraints) and housing values.  We're interested in year-over-year changes within this analysis, not seasonal ones, so we'll need to simplify the columns in the data by taking the mean of the 12 months of the year.  We're also dropping the year 2022 from the data since the year is ongoing and thus it would be partial data.\n",
    "\n",
    "We'll keep those yearly housing value columns along with the 'zipcode' column, and drop all others as they're not relevant to this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5fe51768",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
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       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>zipcode</th>\n",
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       "      <th>247</th>\n",
       "      <td>87169.083333</td>\n",
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       "      <td>100774.250000</td>\n",
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       "      <td>247904.250000</td>\n",
       "      <td>19111.0</td>\n",
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       "      <th>338</th>\n",
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       "      <td>53024.750000</td>\n",
       "      <td>58146.000000</td>\n",
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       "      <td>78466.916667</td>\n",
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       "      <td>19124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>60626.583333</td>\n",
       "      <td>62999.750000</td>\n",
       "      <td>65052.333333</td>\n",
       "      <td>69190.500000</td>\n",
       "      <td>76995.750000</td>\n",
       "      <td>89394.583333</td>\n",
       "      <td>102700.916667</td>\n",
       "      <td>111093.166667</td>\n",
       "      <td>109969.500000</td>\n",
       "      <td>104448.500000</td>\n",
       "      <td>104789.000000</td>\n",
       "      <td>99707.083333</td>\n",
       "      <td>93737.666667</td>\n",
       "      <td>87028.750000</td>\n",
       "      <td>87586.166667</td>\n",
       "      <td>89659.000000</td>\n",
       "      <td>91216.500000</td>\n",
       "      <td>98590.416667</td>\n",
       "      <td>108835.750000</td>\n",
       "      <td>120191.500000</td>\n",
       "      <td>133184.333333</td>\n",
       "      <td>154988.833333</td>\n",
       "      <td>19120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>74463.083333</td>\n",
       "      <td>68886.833333</td>\n",
       "      <td>58794.000000</td>\n",
       "      <td>70255.250000</td>\n",
       "      <td>91643.750000</td>\n",
       "      <td>114452.250000</td>\n",
       "      <td>118049.916667</td>\n",
       "      <td>130982.000000</td>\n",
       "      <td>135507.666667</td>\n",
       "      <td>139226.166667</td>\n",
       "      <td>141402.583333</td>\n",
       "      <td>120080.166667</td>\n",
       "      <td>94038.166667</td>\n",
       "      <td>106057.250000</td>\n",
       "      <td>108781.333333</td>\n",
       "      <td>128183.666667</td>\n",
       "      <td>129940.500000</td>\n",
       "      <td>139072.416667</td>\n",
       "      <td>164660.000000</td>\n",
       "      <td>178046.333333</td>\n",
       "      <td>194805.083333</td>\n",
       "      <td>220964.166667</td>\n",
       "      <td>19104.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             2000          2001           2002           2003           2004  \\\n",
       "125  62949.916667  66982.833333   68354.000000   70132.916667   72604.083333   \n",
       "247  87169.083333  92798.916667  100774.250000  113989.916667  133582.583333   \n",
       "338  48307.250000  50625.833333   53024.750000   58146.000000   65528.666667   \n",
       "423  60626.583333  62999.750000   65052.333333   69190.500000   76995.750000   \n",
       "509  74463.083333  68886.833333   58794.000000   70255.250000   91643.750000   \n",
       "\n",
       "              2005           2006           2007           2008  \\\n",
       "125   90105.583333  100410.583333  108467.500000  109798.166667   \n",
       "247  163733.583333  182845.916667  184195.916667  178210.416667   \n",
       "338   78466.916667   91452.750000   98639.250000   97578.500000   \n",
       "423   89394.583333  102700.916667  111093.166667  109969.500000   \n",
       "509  114452.250000  118049.916667  130982.000000  135507.666667   \n",
       "\n",
       "              2009           2010           2011           2012  \\\n",
       "125  102126.750000  100015.166667   91745.833333   79605.416667   \n",
       "247  167529.750000  167488.083333  158836.250000  155074.500000   \n",
       "338   92638.166667   91818.166667   86595.416667   83713.500000   \n",
       "423  104448.500000  104789.000000   99707.083333   93737.666667   \n",
       "509  139226.166667  141402.583333  120080.166667   94038.166667   \n",
       "\n",
       "              2013           2014           2015           2016  \\\n",
       "125   77684.250000   78044.416667   78708.583333   78576.666667   \n",
       "247  153163.083333  152171.250000  155395.166667  158591.000000   \n",
       "338   78975.666667   80194.083333   83110.916667   85427.416667   \n",
       "423   87028.750000   87586.166667   89659.000000   91216.500000   \n",
       "509  106057.250000  108781.333333  128183.666667  129940.500000   \n",
       "\n",
       "              2017           2018           2019           2020  \\\n",
       "125   82954.000000  104098.166667  123322.583333  146060.000000   \n",
       "247  170341.416667  183692.250000  200591.583333  214182.916667   \n",
       "338   92145.750000  103423.833333  115630.666667  129380.000000   \n",
       "423   98590.416667  108835.750000  120191.500000  133184.333333   \n",
       "509  139072.416667  164660.000000  178046.333333  194805.083333   \n",
       "\n",
       "              2021  zipcode  \n",
       "125  171926.833333  19143.0  \n",
       "247  247904.250000  19111.0  \n",
       "338  150156.750000  19124.0  \n",
       "423  154988.833333  19120.0  \n",
       "509  220964.166667  19104.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_dict = {}\n",
    "year_list = [str(x) for x in range(2000,2022)]\n",
    "for col in zillow.columns:\n",
    "    for year in year_list:\n",
    "        if year in col:\n",
    "            groupby_dict[col] = year\n",
    "groupby_dict['zipcode'] = 'zipcode'\n",
    "\n",
    "zillow = zillow.groupby(groupby_dict, axis = 1).mean()\n",
    "zillow.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6e0138",
   "metadata": {},
   "source": [
    "We're going to alter the DataFrame so that the only columns are 'zipcode', 'year', and 'hv (housing value).  This will allow us to compute percent changes for previous years and will help us to merge this data with the permit data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "683a4d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>year</th>\n",
       "      <th>hv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19143</td>\n",
       "      <td>2000</td>\n",
       "      <td>62949.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19111</td>\n",
       "      <td>2000</td>\n",
       "      <td>87169.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19124</td>\n",
       "      <td>2000</td>\n",
       "      <td>48307.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19120</td>\n",
       "      <td>2000</td>\n",
       "      <td>60626.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19104</td>\n",
       "      <td>2000</td>\n",
       "      <td>74463.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zipcode  year            hv\n",
       "0   19143  2000  62949.916667\n",
       "1   19111  2000  87169.083333\n",
       "2   19124  2000  48307.250000\n",
       "3   19120  2000  60626.583333\n",
       "4   19104  2000  74463.083333"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow = zillow.melt(id_vars=[\"zipcode\"], var_name=\"year\", value_name=\"hv\")\n",
    "zillow['zipcode'] = zillow['zipcode'].astype('int').astype('str')\n",
    "zillow['year'] = zillow['year'].astype(int)\n",
    "zillow.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42393e52",
   "metadata": {},
   "source": [
    "Now we'll create columns showing house values (`hv_*yb`) from previous years (1, 3, and 5 years ago) along with columns showing the percent change (`pc_*yb`) from previous years (1, 3, and 5 years ago) along each zipcode in a given year.  We'll also create columns that show the house value one year in the future (`hv_1yf`) and percent change one year in the future(`pc_1yf`), utilizing the data within the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1640a5b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## First define a groupby function:\n",
    "def prev_prices(data):\n",
    "    data = data.sort_values(\"year\", ascending=True)\n",
    "    \n",
    "    # housing values 1 year, 3 years, and 5 years prior\n",
    "    data[\"hv_1yb\"] = data[\"hv\"].shift(1)\n",
    "    data[\"hv_3yb\"] = data[\"hv\"].shift(3)\n",
    "    data[\"hv_5yb\"] = data[\"hv\"].shift(5)\n",
    "    \n",
    "    # percent change since 1 year, 3 years, and 5 years prior\n",
    "    data[\"pc_1yb\"] = (data[\"hv\"] - data[\"hv_1yb\"]) / data[\"hv_1yb\"] * 100\n",
    "    data[\"pc_3yb\"] = (data[\"hv\"] - data[\"hv_3yb\"]) / data[\"hv_3yb\"] * 100\n",
    "    data[\"pc_5yb\"] = (data[\"hv\"] - data[\"hv_5yb\"]) / data[\"hv_5yb\"] * 100\n",
    "    \n",
    "    # housing values 1 year in the future\n",
    "    data[\"hv_1yf\"] = data[\"hv\"].shift(-1)\n",
    "    \n",
    "    # percent change 1 year in the future\n",
    "    data[\"pc_1yf\"] = (data[\"hv\"] - data[\"hv_1yf\"]) / data[\"hv_1yf\"] * 100\n",
    "\n",
    "    return data\n",
    "\n",
    "## Groupby and apply\n",
    "zillow = zillow.groupby([\"zipcode\"]).apply(prev_prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3d8dd16",
   "metadata": {},
   "source": [
    "Let's examine the data for a given zipcode to ensure the above code resulted in the expected output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "94feaa65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>year</th>\n",
       "      <th>hv</th>\n",
       "      <th>hv_1yb</th>\n",
       "      <th>hv_3yb</th>\n",
       "      <th>hv_5yb</th>\n",
       "      <th>pc_1yb</th>\n",
       "      <th>pc_3yb</th>\n",
       "      <th>pc_5yb</th>\n",
       "      <th>hv_1yf</th>\n",
       "      <th>pc_1yf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>19125</td>\n",
       "      <td>2000</td>\n",
       "      <td>59695.500000</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>61592.833333</td>\n",
       "      <td>-3.080445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>19125</td>\n",
       "      <td>2001</td>\n",
       "      <td>61592.833333</td>\n",
       "      <td>59695.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.178352</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66578.083333</td>\n",
       "      <td>-7.487824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>19125</td>\n",
       "      <td>2002</td>\n",
       "      <td>66578.083333</td>\n",
       "      <td>61592.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.093880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80227.833333</td>\n",
       "      <td>-17.013734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>19125</td>\n",
       "      <td>2003</td>\n",
       "      <td>80227.833333</td>\n",
       "      <td>66578.083333</td>\n",
       "      <td>59695.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.501867</td>\n",
       "      <td>34.395111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100972.250000</td>\n",
       "      <td>-20.544671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>19125</td>\n",
       "      <td>2004</td>\n",
       "      <td>100972.250000</td>\n",
       "      <td>80227.833333</td>\n",
       "      <td>61592.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.856883</td>\n",
       "      <td>63.935063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141262.500000</td>\n",
       "      <td>-28.521547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>19125</td>\n",
       "      <td>2005</td>\n",
       "      <td>141262.500000</td>\n",
       "      <td>100972.250000</td>\n",
       "      <td>66578.083333</td>\n",
       "      <td>59695.500000</td>\n",
       "      <td>39.902300</td>\n",
       "      <td>112.175678</td>\n",
       "      <td>136.638440</td>\n",
       "      <td>189545.500000</td>\n",
       "      <td>-25.473039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>19125</td>\n",
       "      <td>2006</td>\n",
       "      <td>189545.500000</td>\n",
       "      <td>141262.500000</td>\n",
       "      <td>80227.833333</td>\n",
       "      <td>61592.833333</td>\n",
       "      <td>34.179630</td>\n",
       "      <td>136.259029</td>\n",
       "      <td>207.739537</td>\n",
       "      <td>204270.416667</td>\n",
       "      <td>-7.208541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>19125</td>\n",
       "      <td>2007</td>\n",
       "      <td>204270.416667</td>\n",
       "      <td>189545.500000</td>\n",
       "      <td>100972.250000</td>\n",
       "      <td>66578.083333</td>\n",
       "      <td>7.768539</td>\n",
       "      <td>102.303521</td>\n",
       "      <td>206.813303</td>\n",
       "      <td>208613.000000</td>\n",
       "      <td>-2.081646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>19125</td>\n",
       "      <td>2008</td>\n",
       "      <td>208613.000000</td>\n",
       "      <td>204270.416667</td>\n",
       "      <td>141262.500000</td>\n",
       "      <td>80227.833333</td>\n",
       "      <td>2.125899</td>\n",
       "      <td>47.677551</td>\n",
       "      <td>160.025718</td>\n",
       "      <td>204203.833333</td>\n",
       "      <td>2.159199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>19125</td>\n",
       "      <td>2009</td>\n",
       "      <td>204203.833333</td>\n",
       "      <td>208613.000000</td>\n",
       "      <td>189545.500000</td>\n",
       "      <td>100972.250000</td>\n",
       "      <td>-2.113563</td>\n",
       "      <td>7.733411</td>\n",
       "      <td>102.237578</td>\n",
       "      <td>206431.500000</td>\n",
       "      <td>-1.079131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>19125</td>\n",
       "      <td>2010</td>\n",
       "      <td>206431.500000</td>\n",
       "      <td>204203.833333</td>\n",
       "      <td>204270.416667</td>\n",
       "      <td>141262.500000</td>\n",
       "      <td>1.090903</td>\n",
       "      <td>1.057952</td>\n",
       "      <td>46.133263</td>\n",
       "      <td>198072.750000</td>\n",
       "      <td>4.220040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>19125</td>\n",
       "      <td>2011</td>\n",
       "      <td>198072.750000</td>\n",
       "      <td>206431.500000</td>\n",
       "      <td>208613.000000</td>\n",
       "      <td>189545.500000</td>\n",
       "      <td>-4.049164</td>\n",
       "      <td>-5.052537</td>\n",
       "      <td>4.498788</td>\n",
       "      <td>190012.250000</td>\n",
       "      <td>4.242095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>19125</td>\n",
       "      <td>2012</td>\n",
       "      <td>190012.250000</td>\n",
       "      <td>198072.750000</td>\n",
       "      <td>204203.833333</td>\n",
       "      <td>204270.416667</td>\n",
       "      <td>-4.069464</td>\n",
       "      <td>-6.949714</td>\n",
       "      <td>-6.980045</td>\n",
       "      <td>207292.916667</td>\n",
       "      <td>-8.336352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>19125</td>\n",
       "      <td>2013</td>\n",
       "      <td>207292.916667</td>\n",
       "      <td>190012.250000</td>\n",
       "      <td>206431.500000</td>\n",
       "      <td>208613.000000</td>\n",
       "      <td>9.094501</td>\n",
       "      <td>0.417289</td>\n",
       "      <td>-0.632791</td>\n",
       "      <td>208096.583333</td>\n",
       "      <td>-0.386199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>19125</td>\n",
       "      <td>2014</td>\n",
       "      <td>208096.583333</td>\n",
       "      <td>207292.916667</td>\n",
       "      <td>198072.750000</td>\n",
       "      <td>204203.833333</td>\n",
       "      <td>0.387696</td>\n",
       "      <td>5.060683</td>\n",
       "      <td>1.906306</td>\n",
       "      <td>231614.500000</td>\n",
       "      <td>-10.153905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>19125</td>\n",
       "      <td>2015</td>\n",
       "      <td>231614.500000</td>\n",
       "      <td>208096.583333</td>\n",
       "      <td>190012.250000</td>\n",
       "      <td>206431.500000</td>\n",
       "      <td>11.301443</td>\n",
       "      <td>21.894509</td>\n",
       "      <td>12.199204</td>\n",
       "      <td>248214.583333</td>\n",
       "      <td>-6.687795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>19125</td>\n",
       "      <td>2016</td>\n",
       "      <td>248214.583333</td>\n",
       "      <td>231614.500000</td>\n",
       "      <td>207292.916667</td>\n",
       "      <td>198072.750000</td>\n",
       "      <td>7.167117</td>\n",
       "      <td>19.740986</td>\n",
       "      <td>25.314857</td>\n",
       "      <td>259411.000000</td>\n",
       "      <td>-4.316092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>19125</td>\n",
       "      <td>2017</td>\n",
       "      <td>259411.000000</td>\n",
       "      <td>248214.583333</td>\n",
       "      <td>208096.583333</td>\n",
       "      <td>190012.250000</td>\n",
       "      <td>4.510781</td>\n",
       "      <td>24.658942</td>\n",
       "      <td>36.523303</td>\n",
       "      <td>289231.750000</td>\n",
       "      <td>-10.310331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>19125</td>\n",
       "      <td>2018</td>\n",
       "      <td>289231.750000</td>\n",
       "      <td>259411.000000</td>\n",
       "      <td>231614.500000</td>\n",
       "      <td>207292.916667</td>\n",
       "      <td>11.495561</td>\n",
       "      <td>24.876357</td>\n",
       "      <td>39.528043</td>\n",
       "      <td>297306.250000</td>\n",
       "      <td>-2.715886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>19125</td>\n",
       "      <td>2019</td>\n",
       "      <td>297306.250000</td>\n",
       "      <td>289231.750000</td>\n",
       "      <td>248214.583333</td>\n",
       "      <td>208096.583333</td>\n",
       "      <td>2.791706</td>\n",
       "      <td>19.777914</td>\n",
       "      <td>42.869357</td>\n",
       "      <td>310467.083333</td>\n",
       "      <td>-4.239043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>19125</td>\n",
       "      <td>2020</td>\n",
       "      <td>310467.083333</td>\n",
       "      <td>297306.250000</td>\n",
       "      <td>259411.000000</td>\n",
       "      <td>231614.500000</td>\n",
       "      <td>4.426692</td>\n",
       "      <td>19.681541</td>\n",
       "      <td>34.044753</td>\n",
       "      <td>334596.750000</td>\n",
       "      <td>-7.211566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>19125</td>\n",
       "      <td>2021</td>\n",
       "      <td>334596.750000</td>\n",
       "      <td>310467.083333</td>\n",
       "      <td>289231.750000</td>\n",
       "      <td>248214.583333</td>\n",
       "      <td>7.772053</td>\n",
       "      <td>15.684654</td>\n",
       "      <td>34.801407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    zipcode  year             hv         hv_1yb         hv_3yb         hv_5yb  \\\n",
       "31    19125  2000   59695.500000            NaN            NaN            NaN   \n",
       "77    19125  2001   61592.833333   59695.500000            NaN            NaN   \n",
       "123   19125  2002   66578.083333   61592.833333            NaN            NaN   \n",
       "169   19125  2003   80227.833333   66578.083333   59695.500000            NaN   \n",
       "215   19125  2004  100972.250000   80227.833333   61592.833333            NaN   \n",
       "261   19125  2005  141262.500000  100972.250000   66578.083333   59695.500000   \n",
       "307   19125  2006  189545.500000  141262.500000   80227.833333   61592.833333   \n",
       "353   19125  2007  204270.416667  189545.500000  100972.250000   66578.083333   \n",
       "399   19125  2008  208613.000000  204270.416667  141262.500000   80227.833333   \n",
       "445   19125  2009  204203.833333  208613.000000  189545.500000  100972.250000   \n",
       "491   19125  2010  206431.500000  204203.833333  204270.416667  141262.500000   \n",
       "537   19125  2011  198072.750000  206431.500000  208613.000000  189545.500000   \n",
       "583   19125  2012  190012.250000  198072.750000  204203.833333  204270.416667   \n",
       "629   19125  2013  207292.916667  190012.250000  206431.500000  208613.000000   \n",
       "675   19125  2014  208096.583333  207292.916667  198072.750000  204203.833333   \n",
       "721   19125  2015  231614.500000  208096.583333  190012.250000  206431.500000   \n",
       "767   19125  2016  248214.583333  231614.500000  207292.916667  198072.750000   \n",
       "813   19125  2017  259411.000000  248214.583333  208096.583333  190012.250000   \n",
       "859   19125  2018  289231.750000  259411.000000  231614.500000  207292.916667   \n",
       "905   19125  2019  297306.250000  289231.750000  248214.583333  208096.583333   \n",
       "951   19125  2020  310467.083333  297306.250000  259411.000000  231614.500000   \n",
       "997   19125  2021  334596.750000  310467.083333  289231.750000  248214.583333   \n",
       "\n",
       "        pc_1yb      pc_3yb      pc_5yb         hv_1yf     pc_1yf  \n",
       "31         NaN         NaN         NaN   61592.833333  -3.080445  \n",
       "77    3.178352         NaN         NaN   66578.083333  -7.487824  \n",
       "123   8.093880         NaN         NaN   80227.833333 -17.013734  \n",
       "169  20.501867   34.395111         NaN  100972.250000 -20.544671  \n",
       "215  25.856883   63.935063         NaN  141262.500000 -28.521547  \n",
       "261  39.902300  112.175678  136.638440  189545.500000 -25.473039  \n",
       "307  34.179630  136.259029  207.739537  204270.416667  -7.208541  \n",
       "353   7.768539  102.303521  206.813303  208613.000000  -2.081646  \n",
       "399   2.125899   47.677551  160.025718  204203.833333   2.159199  \n",
       "445  -2.113563    7.733411  102.237578  206431.500000  -1.079131  \n",
       "491   1.090903    1.057952   46.133263  198072.750000   4.220040  \n",
       "537  -4.049164   -5.052537    4.498788  190012.250000   4.242095  \n",
       "583  -4.069464   -6.949714   -6.980045  207292.916667  -8.336352  \n",
       "629   9.094501    0.417289   -0.632791  208096.583333  -0.386199  \n",
       "675   0.387696    5.060683    1.906306  231614.500000 -10.153905  \n",
       "721  11.301443   21.894509   12.199204  248214.583333  -6.687795  \n",
       "767   7.167117   19.740986   25.314857  259411.000000  -4.316092  \n",
       "813   4.510781   24.658942   36.523303  289231.750000 -10.310331  \n",
       "859  11.495561   24.876357   39.528043  297306.250000  -2.715886  \n",
       "905   2.791706   19.777914   42.869357  310467.083333  -4.239043  \n",
       "951   4.426692   19.681541   34.044753  334596.750000  -7.211566  \n",
       "997   7.772053   15.684654   34.801407            NaN        NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check table on a given zipcode\n",
    "zillow[zillow['zipcode'] == '19125']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4d8df5",
   "metadata": {},
   "source": [
    "### Permit Data\n",
    "\n",
    "Now we'll load in the permit data, starting with `permits_2007-2015.csv` which has permit data from 2007-2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c568d1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objectid</th>\n",
       "      <th>permitnumber</th>\n",
       "      <th>addressobjectid</th>\n",
       "      <th>parcel_id_num</th>\n",
       "      <th>permittype</th>\n",
       "      <th>permitdescription</th>\n",
       "      <th>commercialorresidential</th>\n",
       "      <th>typeofwork</th>\n",
       "      <th>approvedscopeofwork</th>\n",
       "      <th>permitissuedate</th>\n",
       "      <th>status</th>\n",
       "      <th>applicanttype</th>\n",
       "      <th>contractorname</th>\n",
       "      <th>contractoraddress1</th>\n",
       "      <th>contractoraddress2</th>\n",
       "      <th>contractorcity</th>\n",
       "      <th>contractorstate</th>\n",
       "      <th>contractorzip</th>\n",
       "      <th>mostrecentinsp</th>\n",
       "      <th>opa_account_num</th>\n",
       "      <th>address</th>\n",
       "      <th>unit_type</th>\n",
       "      <th>unit_num</th>\n",
       "      <th>zip</th>\n",
       "      <th>censustract</th>\n",
       "      <th>council_district</th>\n",
       "      <th>opa_owner</th>\n",
       "      <th>systemofrecord</th>\n",
       "      <th>geocode_x</th>\n",
       "      <th>geocode_y</th>\n",
       "      <th>posse_jobid</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>100754</td>\n",
       "      <td>8402</td>\n",
       "      <td>336710</td>\n",
       "      <td>L_FFORM</td>\n",
       "      <td>FAST FORM BUILDING PERMIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-09-04 16:41:02</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>APPL</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>2008-01-29 15:20:00</td>\n",
       "      <td>632172000.0</td>\n",
       "      <td>126 ALEXIS LN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19115-3402</td>\n",
       "      <td>344.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>CHEN ZHI QIANG           ZHENG FANG</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2.720980e+06</td>\n",
       "      <td>290557.097420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.099475</td>\n",
       "      <td>-75.059753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>100029</td>\n",
       "      <td>740935</td>\n",
       "      <td>534979</td>\n",
       "      <td>BP_ADMINST</td>\n",
       "      <td>ADMINISTRATIVE PERMIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CO</td>\n",
       "      <td>CERTIFICATE OF OCCUPANCY</td>\n",
       "      <td>2007-08-30 16:37:33</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>OWNER</td>\n",
       "      <td>J J DELUCA CO INC</td>\n",
       "      <td>760 W SPROUL ROAD, STE 300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SPRINGFIELD</td>\n",
       "      <td>PA</td>\n",
       "      <td>19064-4001</td>\n",
       "      <td>2007-09-28 18:20:00</td>\n",
       "      <td>888112914.0</td>\n",
       "      <td>815-37 ARCH ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>621</td>\n",
       "      <td>19107-2403</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CHIANG MAGGIE</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2.696151e+06</td>\n",
       "      <td>236716.444730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.953782</td>\n",
       "      <td>-75.154127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1017</td>\n",
       "      <td>240571</td>\n",
       "      <td>360472</td>\n",
       "      <td>132411</td>\n",
       "      <td>ZP_USE</td>\n",
       "      <td>USE PERMIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SFRED</td>\n",
       "      <td>CREATE MOTHER IN LAW SUITE IN BASEMENT OF SFD.</td>\n",
       "      <td>2009-10-02 14:42:21</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>OWNER</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>652264500.0</td>\n",
       "      <td>8759 JACKSON ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19136-2141</td>\n",
       "      <td>349.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>KAMDAR PRAPTI M          KAMDAR MEHUL PANKAJ</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2.735362e+06</td>\n",
       "      <td>271794.924559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.046782</td>\n",
       "      <td>-75.010448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>100212</td>\n",
       "      <td>17535</td>\n",
       "      <td>111870</td>\n",
       "      <td>PP_PLUMBNG</td>\n",
       "      <td>PLUMBING PERMIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EZPLUM</td>\n",
       "      <td>AREA DRAIN, BASEMENT: LT, WC, LAV, HWH, 1ST FL...</td>\n",
       "      <td>2007-08-31 13:56:52</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>CONTRACTOR</td>\n",
       "      <td>ANTHONY S. RACHUBA JR</td>\n",
       "      <td>1660 FOULKROD STREET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PHILADELPHIA</td>\n",
       "      <td>PA</td>\n",
       "      <td>19124-</td>\n",
       "      <td>2008-05-20 13:32:00</td>\n",
       "      <td>361021600.0</td>\n",
       "      <td>1909 ANNIN ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19146-2907</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>GORDON JESSICA L</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2.690311e+06</td>\n",
       "      <td>230662.085428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.937639</td>\n",
       "      <td>-75.175576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>100984</td>\n",
       "      <td>419501</td>\n",
       "      <td>188921</td>\n",
       "      <td>L_FFORM</td>\n",
       "      <td>FAST FORM BUILDING PERMIT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-09-05 16:24:13</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>APPL</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>2015-10-17 15:45:00</td>\n",
       "      <td>291108000.0</td>\n",
       "      <td>2632 MASTER ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19121-0000</td>\n",
       "      <td>138.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>MOSHER JENNIFER LYNN     LANGHAM MICHAEL CHARLES</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2.688834e+06</td>\n",
       "      <td>244955.524768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.976977</td>\n",
       "      <td>-75.179360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   objectid permitnumber  addressobjectid parcel_id_num  permittype  \\\n",
       "0        32       100754             8402        336710     L_FFORM   \n",
       "1         2       100029           740935        534979  BP_ADMINST   \n",
       "2      1017       240571           360472        132411      ZP_USE   \n",
       "3        13       100212            17535        111870  PP_PLUMBNG   \n",
       "4        43       100984           419501        188921     L_FFORM   \n",
       "\n",
       "           permitdescription commercialorresidential typeofwork  \\\n",
       "0  FAST FORM BUILDING PERMIT                     NaN        NaN   \n",
       "1      ADMINISTRATIVE PERMIT                     NaN         CO   \n",
       "2                 USE PERMIT                     NaN      SFRED   \n",
       "3            PLUMBING PERMIT                     NaN     EZPLUM   \n",
       "4  FAST FORM BUILDING PERMIT                     NaN        NaN   \n",
       "\n",
       "                                 approvedscopeofwork      permitissuedate  \\\n",
       "0                                                NaN  2007-09-04 16:41:02   \n",
       "1                           CERTIFICATE OF OCCUPANCY  2007-08-30 16:37:33   \n",
       "2     CREATE MOTHER IN LAW SUITE IN BASEMENT OF SFD.  2009-10-02 14:42:21   \n",
       "3  AREA DRAIN, BASEMENT: LT, WC, LAV, HWH, 1ST FL...  2007-08-31 13:56:52   \n",
       "4                                                NaN  2007-09-05 16:24:13   \n",
       "\n",
       "      status applicanttype         contractorname          contractoraddress1  \\\n",
       "0  COMPLETED          APPL                    NaN                         NaN   \n",
       "1  COMPLETED         OWNER      J J DELUCA CO INC  760 W SPROUL ROAD, STE 300   \n",
       "2  COMPLETED         OWNER                    NaN                         NaN   \n",
       "3  COMPLETED    CONTRACTOR  ANTHONY S. RACHUBA JR        1660 FOULKROD STREET   \n",
       "4  COMPLETED          APPL                    NaN                         NaN   \n",
       "\n",
       "  contractoraddress2 contractorcity contractorstate contractorzip  \\\n",
       "0                NaN            NaN             NaN           NaN   \n",
       "1                NaN    SPRINGFIELD              PA    19064-4001   \n",
       "2                NaN            NaN             NaN           NaN   \n",
       "3                NaN   PHILADELPHIA              PA    19124-       \n",
       "4                NaN            NaN             NaN           NaN   \n",
       "\n",
       "        mostrecentinsp  opa_account_num          address unit_type unit_num  \\\n",
       "0  2008-01-29 15:20:00      632172000.0    126 ALEXIS LN       NaN      NaN   \n",
       "1  2007-09-28 18:20:00      888112914.0   815-37 ARCH ST       NaN      621   \n",
       "2                  NaN      652264500.0  8759 JACKSON ST       NaN      NaN   \n",
       "3  2008-05-20 13:32:00      361021600.0    1909 ANNIN ST       NaN      NaN   \n",
       "4  2015-10-17 15:45:00      291108000.0   2632 MASTER ST       NaN      NaN   \n",
       "\n",
       "          zip  censustract  council_district  \\\n",
       "0  19115-3402        344.0              10.0   \n",
       "1  19107-2403          2.0               1.0   \n",
       "2  19136-2141        349.0               6.0   \n",
       "3  19146-2907         21.0               2.0   \n",
       "4  19121-0000        138.0               5.0   \n",
       "\n",
       "                                          opa_owner systemofrecord  \\\n",
       "0               CHEN ZHI QIANG           ZHENG FANG         HANSEN   \n",
       "1                                     CHIANG MAGGIE         HANSEN   \n",
       "2      KAMDAR PRAPTI M          KAMDAR MEHUL PANKAJ         HANSEN   \n",
       "3                                  GORDON JESSICA L         HANSEN   \n",
       "4  MOSHER JENNIFER LYNN     LANGHAM MICHAEL CHARLES         HANSEN   \n",
       "\n",
       "      geocode_x      geocode_y  posse_jobid        lat        lng  \n",
       "0  2.720980e+06  290557.097420          NaN  40.099475 -75.059753  \n",
       "1  2.696151e+06  236716.444730          NaN  39.953782 -75.154127  \n",
       "2  2.735362e+06  271794.924559          NaN  40.046782 -75.010448  \n",
       "3  2.690311e+06  230662.085428          NaN  39.937639 -75.175576  \n",
       "4  2.688834e+06  244955.524768          NaN  39.976977 -75.179360  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: dtypes specified due to errors\n",
    "permits1 = pd.read_csv('data_files/permits_2007-2015.csv', dtype={'permitnumber':str, 'parcel_id_num':str})\n",
    "permits1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e57dc0",
   "metadata": {},
   "source": [
    "The relevant columnns in the data for this analysis would be the following:\n",
    "- Zipcode of Proposed Work ('zip' column)\n",
    "- Permit Description ('permitdescription' column)\n",
    "- Permit Issue Date ('permitissuedate' column)\n",
    "\n",
    "Let's isolate those columns, load in the other csv file (`permits_2016-2022.csv`), and combine the two DataFrames into one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba1c9cb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip</th>\n",
       "      <th>permitdescription</th>\n",
       "      <th>permitissuedate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19115-3402</td>\n",
       "      <td>FAST FORM BUILDING PERMIT</td>\n",
       "      <td>2007-09-04 16:41:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19107-2403</td>\n",
       "      <td>ADMINISTRATIVE PERMIT</td>\n",
       "      <td>2007-08-30 16:37:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19136-2141</td>\n",
       "      <td>USE PERMIT</td>\n",
       "      <td>2009-10-02 14:42:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19146-2907</td>\n",
       "      <td>PLUMBING PERMIT</td>\n",
       "      <td>2007-08-31 13:56:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19121-0000</td>\n",
       "      <td>FAST FORM BUILDING PERMIT</td>\n",
       "      <td>2007-09-05 16:24:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          zip          permitdescription      permitissuedate\n",
       "0  19115-3402  FAST FORM BUILDING PERMIT  2007-09-04 16:41:02\n",
       "1  19107-2403      ADMINISTRATIVE PERMIT  2007-08-30 16:37:33\n",
       "2  19136-2141                 USE PERMIT  2009-10-02 14:42:21\n",
       "3  19146-2907            PLUMBING PERMIT  2007-08-31 13:56:52\n",
       "4  19121-0000  FAST FORM BUILDING PERMIT  2007-09-05 16:24:13"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the other csv file\n",
    "col_list = ['zip', 'permitdescription', 'permitissuedate']\n",
    "permits1 = permits1[col_list]\n",
    "permits2 = pd.read_csv('data_files/permits_2016-2022.csv', usecols=col_list)\n",
    "\n",
    "# Combine\n",
    "permits_combined = permits = pd.concat([permits1, permits2], ignore_index = True)\n",
    "\n",
    "permits_combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43227e58",
   "metadata": {},
   "source": [
    "To match the Zillow data, The 'zip' column should be renamed to 'zipcode' and we should extract the year from the 'permitissuedate' columnn.  We'll also check zipcodes and remove any rows with zipcodes not in the list that was included above and used to process the Zillow data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6fd830e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>year</th>\n",
       "      <th>permitdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19115</td>\n",
       "      <td>2007</td>\n",
       "      <td>FAST FORM BUILDING PERMIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19107</td>\n",
       "      <td>2007</td>\n",
       "      <td>ADMINISTRATIVE PERMIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19136</td>\n",
       "      <td>2009</td>\n",
       "      <td>USE PERMIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19146</td>\n",
       "      <td>2007</td>\n",
       "      <td>PLUMBING PERMIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19121</td>\n",
       "      <td>2007</td>\n",
       "      <td>FAST FORM BUILDING PERMIT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zipcode  year          permitdescription\n",
       "0   19115  2007  FAST FORM BUILDING PERMIT\n",
       "1   19107  2007      ADMINISTRATIVE PERMIT\n",
       "2   19136  2009                 USE PERMIT\n",
       "3   19146  2007            PLUMBING PERMIT\n",
       "4   19121  2007  FAST FORM BUILDING PERMIT"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove trailing digits of zipcode and remove rows with invalid zipcodes\n",
    "permits_combined['zipcode'] = permits['zip'].str.split('-', expand=True)[0]\n",
    "permits_combined = permits_combined[permits_combined['zipcode'].isin(list_of_zipcodes)]\n",
    "\n",
    "# Extract year from 'permitissuedate'\n",
    "permits_combined['permitissuedate'] = pd.to_datetime(permits_combined['permitissuedate'])\n",
    "permits_combined['year'] = pd.DatetimeIndex(permits_combined['permitissuedate']).year\n",
    "\n",
    "# Select our new columns along with the 'permitdescription' column\n",
    "permits_combined = permits_combined[['zipcode', 'year', 'permitdescription']]\n",
    "permits_combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6a1508",
   "metadata": {},
   "source": [
    "---\n",
    "## Data Analysis\n",
    "### Setup\n",
    "Let's look at our two dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b451eb8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>year</th>\n",
       "      <th>hv</th>\n",
       "      <th>hv_1yb</th>\n",
       "      <th>hv_3yb</th>\n",
       "      <th>hv_5yb</th>\n",
       "      <th>pc_1yb</th>\n",
       "      <th>pc_3yb</th>\n",
       "      <th>pc_5yb</th>\n",
       "      <th>hv_1yf</th>\n",
       "      <th>pc_1yf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19143</td>\n",
       "      <td>2000</td>\n",
       "      <td>62949.916667</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>66982.833333</td>\n",
       "      <td>-6.020821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19111</td>\n",
       "      <td>2000</td>\n",
       "      <td>87169.083333</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>92798.916667</td>\n",
       "      <td>-6.066702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19124</td>\n",
       "      <td>2000</td>\n",
       "      <td>48307.250000</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>50625.833333</td>\n",
       "      <td>-4.579842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19120</td>\n",
       "      <td>2000</td>\n",
       "      <td>60626.583333</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>62999.750000</td>\n",
       "      <td>-3.766946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19104</td>\n",
       "      <td>2000</td>\n",
       "      <td>74463.083333</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>68886.833333</td>\n",
       "      <td>8.094798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zipcode  year            hv  hv_1yb  hv_3yb  hv_5yb  pc_1yb  pc_3yb  pc_5yb  \\\n",
       "0   19143  2000  62949.916667     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1   19111  2000  87169.083333     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2   19124  2000  48307.250000     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3   19120  2000  60626.583333     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4   19104  2000  74463.083333     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "         hv_1yf    pc_1yf  \n",
       "0  66982.833333 -6.020821  \n",
       "1  92798.916667 -6.066702  \n",
       "2  50625.833333 -4.579842  \n",
       "3  62999.750000 -3.766946  \n",
       "4  68886.833333  8.094798  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fb477255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>year</th>\n",
       "      <th>permitdescription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19115</td>\n",
       "      <td>2007</td>\n",
       "      <td>FAST FORM BUILDING PERMIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19107</td>\n",
       "      <td>2007</td>\n",
       "      <td>ADMINISTRATIVE PERMIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19136</td>\n",
       "      <td>2009</td>\n",
       "      <td>USE PERMIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19146</td>\n",
       "      <td>2007</td>\n",
       "      <td>PLUMBING PERMIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19121</td>\n",
       "      <td>2007</td>\n",
       "      <td>FAST FORM BUILDING PERMIT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zipcode  year          permitdescription\n",
       "0   19115  2007  FAST FORM BUILDING PERMIT\n",
       "1   19107  2007      ADMINISTRATIVE PERMIT\n",
       "2   19136  2009                 USE PERMIT\n",
       "3   19146  2007            PLUMBING PERMIT\n",
       "4   19121  2007  FAST FORM BUILDING PERMIT"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permits_combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ffa51d",
   "metadata": {},
   "source": [
    "Before combining these dataframes, we'll be calculating the number of permits grouped by both zipcode and year.  However, which permit types to include is still a question.  Let's take a look at the `permitdescription` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "263df8aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PLUMBING PERMIT                 188644\n",
       "ELECTRICAL PERMIT               133307\n",
       "ALTERATION PERMIT               100285\n",
       "MECHANICAL PERMIT                46057\n",
       "ZONING PERMIT                    37286\n",
       "ZONING/USE PERMIT                34460\n",
       "USE PERMIT                       28678\n",
       "SUPPRESSION PERMIT               23926\n",
       "ADMINISTRATIVE PERMIT            22940\n",
       "RESIDENTIAL BUILDING PERMIT      18053\n",
       "DEMOLITION PERMIT                15726\n",
       "MECHANICAL / FUEL GAS PERMIT     14935\n",
       "NEW CONSTRUCTION PERMIT          14443\n",
       "GENERAL PERMIT MINOR             10883\n",
       "COMMERCIAL BUILDING PERMIT        9780\n",
       "FIRE SUPPRESSION PERMIT           8369\n",
       "OPERATIONS PERMIT                 7880\n",
       "ADDITION PERMIT                   6532\n",
       "SIGN PERMIT                       4441\n",
       "FAST FORM BUILDING PERMIT         4376\n",
       "ZONING ADMIN REVIEW               2185\n",
       "GENERAL PERMIT                    1792\n",
       "SITE / UTILITY PERMIT              480\n",
       "PRELIMINARY APPROVAL                38\n",
       "5-YEAR TANK TESTING                  1\n",
       "Name: permitdescription, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permits_combined['permitdescription'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a6b265ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permit_type_list = permits_combined['permitdescription'].unique()\n",
    "len(permit_type_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bcd03ec",
   "metadata": {},
   "source": [
    "There are 25 unique permit types within the 'permitdescription' column.  Some of the ones that stand out as far as relevance for affecting housing values would be the following:\n",
    "- NEW CONSTRUCTION PERMIT\n",
    "- ZONING PERMIT\n",
    "- ALTERATION PERMIT\n",
    "- RESIDENTIAL BUILDING PERMIT\n",
    "- COMMERCIAL BUILDING PERMIT\n",
    "- ADDITION PERMIT\n",
    "- DEMOLITION PERMIT\n",
    "\n",
    "However, rather that just selecting the ones that sound obvious, we're going to do the following:\n",
    "- Loop through each permit type\n",
    "- Compute the number of permits per zipcode and year which will include creating the following columns:\n",
    "   - `no_permits` - number of permits in the given zipcode and year\n",
    "   - `np_1yb` - number of permits in the prior year\n",
    "   - `np_3yb` - number of permits in the prior 3 years\n",
    "   - `np_5yb` - number of permits in the prior 5 years\n",
    "- Combine the permit data and the Zillow data\n",
    "- Analyze correlation between the permit columns (shown above) VS the housing value (`hv`) and percent change columns (`pc_*`)\n",
    "\n",
    "Let's start by creating functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "382088a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute rolling permit data for 1y, 3y, and 5y ago\n",
    "def prev_permits(data):\n",
    "    data = data.sort_values(\"year\", ascending=True)\n",
    "    data[\"np_1yb\"] = data[\"no_permits\"].shift(1)\n",
    "    data[\"np_3yb\"] = data.rolling(3, closed=\"left\", min_periods=3).sum()[\"no_permits\"]\n",
    "    data[\"np_5yb\"] = data.rolling(5, closed=\"left\", min_periods=5).sum()[\"no_permits\"]\n",
    "    return data\n",
    "\n",
    "# Compute correlation using permit_type as 'all' if not specified\n",
    "def created_combined_df(permit_data, permit_type='all'):\n",
    "    \n",
    "    # Isolate the data to the rows related to the entered permit_type \n",
    "    if permit_type != 'all':\n",
    "        permit_data = permit_data[permit_data['permitdescription'].isin(permit_type)]\n",
    "\n",
    "    # Count number of permits by zipcode and year\n",
    "    permit_data = permit_data.groupby(['zipcode','year'])['zipcode'].count().reset_index(name='no_permits')\n",
    "\n",
    "    # Apply groupby function to add rolling sums to table\n",
    "    permit_data = permit_data.groupby([\"zipcode\"]).apply(prev_permits)\n",
    "\n",
    "    # Combine permit and housing data on zipcode and year\n",
    "    combined_data = pd.merge(permit_data, zillow, on=['zipcode','year'])\n",
    "    \n",
    "    # Drop rows with null values\n",
    "    combined_data = combined_data.dropna()\n",
    "    \n",
    "    return combined_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b778de97",
   "metadata": {},
   "source": [
    "### All Permit Types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56238364",
   "metadata": {},
   "source": [
    "First, before looping, we'll show an example of the correlation of the data using 'all' permit types.  The final table after running the function has the following columns:\n",
    "- `zipcode`: zipcode\n",
    "- `year`: year\n",
    "- `no_permits`: number of permits issued within a given year and zipcode\n",
    "- `np_1yb`, `np_3yb`, `np_5yb`: number of permits in the past year, 3 years, and 5 years respectively (rolling sums).\n",
    "- `hv`: average home value within a given year and zipcode\n",
    "- `hv_1yb`, `hv_3yb`, `hv_5yb`: average home value within a zipcode 1, 3, and 5 years prior to the year within the respective row.\n",
    "- `pc_1yb`, `pc_3yb`, `pc_5yb`: percentage change in average home value in the past year, past 3 years, and past 5 years, respectively.\n",
    "- `hv_1yf`: average home value within a zipcode 1 year into the future of the year within the respective row.\n",
    "- `pc_1yf`: percentage change in average home value in the next year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "add5b634",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>year</th>\n",
       "      <th>no_permits</th>\n",
       "      <th>np_1yb</th>\n",
       "      <th>np_3yb</th>\n",
       "      <th>np_5yb</th>\n",
       "      <th>hv</th>\n",
       "      <th>hv_1yb</th>\n",
       "      <th>hv_3yb</th>\n",
       "      <th>hv_5yb</th>\n",
       "      <th>pc_1yb</th>\n",
       "      <th>pc_3yb</th>\n",
       "      <th>pc_5yb</th>\n",
       "      <th>hv_1yf</th>\n",
       "      <th>pc_1yf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19102</td>\n",
       "      <td>2012</td>\n",
       "      <td>354</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>2689.0</td>\n",
       "      <td>320223.500000</td>\n",
       "      <td>322669.166667</td>\n",
       "      <td>365844.250000</td>\n",
       "      <td>374516.250000</td>\n",
       "      <td>-0.757949</td>\n",
       "      <td>-12.469992</td>\n",
       "      <td>-14.496767</td>\n",
       "      <td>328017.833333</td>\n",
       "      <td>-2.376192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19102</td>\n",
       "      <td>2013</td>\n",
       "      <td>507</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>2486.0</td>\n",
       "      <td>328017.833333</td>\n",
       "      <td>320223.500000</td>\n",
       "      <td>350226.333333</td>\n",
       "      <td>376604.416667</td>\n",
       "      <td>2.434029</td>\n",
       "      <td>-6.341185</td>\n",
       "      <td>-12.901225</td>\n",
       "      <td>342118.583333</td>\n",
       "      <td>-4.121597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19102</td>\n",
       "      <td>2014</td>\n",
       "      <td>533</td>\n",
       "      <td>507.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>342118.583333</td>\n",
       "      <td>328017.833333</td>\n",
       "      <td>322669.166667</td>\n",
       "      <td>365844.250000</td>\n",
       "      <td>4.298775</td>\n",
       "      <td>6.027665</td>\n",
       "      <td>-6.485182</td>\n",
       "      <td>348017.000000</td>\n",
       "      <td>-1.694865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19102</td>\n",
       "      <td>2015</td>\n",
       "      <td>744</td>\n",
       "      <td>533.0</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>2281.0</td>\n",
       "      <td>348017.000000</td>\n",
       "      <td>342118.583333</td>\n",
       "      <td>320223.500000</td>\n",
       "      <td>350226.333333</td>\n",
       "      <td>1.724085</td>\n",
       "      <td>8.679407</td>\n",
       "      <td>-0.630830</td>\n",
       "      <td>350041.750000</td>\n",
       "      <td>-0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19102</td>\n",
       "      <td>2016</td>\n",
       "      <td>769</td>\n",
       "      <td>744.0</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>350041.750000</td>\n",
       "      <td>348017.000000</td>\n",
       "      <td>328017.833333</td>\n",
       "      <td>322669.166667</td>\n",
       "      <td>0.581796</td>\n",
       "      <td>6.714244</td>\n",
       "      <td>8.483173</td>\n",
       "      <td>362175.250000</td>\n",
       "      <td>-3.350174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19102</td>\n",
       "      <td>2017</td>\n",
       "      <td>567</td>\n",
       "      <td>769.0</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>362175.250000</td>\n",
       "      <td>350041.750000</td>\n",
       "      <td>342118.583333</td>\n",
       "      <td>320223.500000</td>\n",
       "      <td>3.466301</td>\n",
       "      <td>5.862490</td>\n",
       "      <td>13.100772</td>\n",
       "      <td>360640.500000</td>\n",
       "      <td>0.425562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19102</td>\n",
       "      <td>2018</td>\n",
       "      <td>589</td>\n",
       "      <td>567.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>3120.0</td>\n",
       "      <td>360640.500000</td>\n",
       "      <td>362175.250000</td>\n",
       "      <td>348017.000000</td>\n",
       "      <td>328017.833333</td>\n",
       "      <td>-0.423759</td>\n",
       "      <td>3.627265</td>\n",
       "      <td>9.945394</td>\n",
       "      <td>351851.166667</td>\n",
       "      <td>2.498026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19102</td>\n",
       "      <td>2019</td>\n",
       "      <td>545</td>\n",
       "      <td>589.0</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>3202.0</td>\n",
       "      <td>351851.166667</td>\n",
       "      <td>360640.500000</td>\n",
       "      <td>350041.750000</td>\n",
       "      <td>342118.583333</td>\n",
       "      <td>-2.437145</td>\n",
       "      <td>0.516915</td>\n",
       "      <td>2.844798</td>\n",
       "      <td>351634.333333</td>\n",
       "      <td>0.061664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19102</td>\n",
       "      <td>2020</td>\n",
       "      <td>280</td>\n",
       "      <td>545.0</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>3214.0</td>\n",
       "      <td>351634.333333</td>\n",
       "      <td>351851.166667</td>\n",
       "      <td>362175.250000</td>\n",
       "      <td>348017.000000</td>\n",
       "      <td>-0.061626</td>\n",
       "      <td>-2.910446</td>\n",
       "      <td>1.039413</td>\n",
       "      <td>351424.750000</td>\n",
       "      <td>0.059638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19103</td>\n",
       "      <td>2012</td>\n",
       "      <td>2105</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>4829.0</td>\n",
       "      <td>9571.0</td>\n",
       "      <td>393150.916667</td>\n",
       "      <td>395331.000000</td>\n",
       "      <td>455700.833333</td>\n",
       "      <td>478987.666667</td>\n",
       "      <td>-0.551458</td>\n",
       "      <td>-13.726092</td>\n",
       "      <td>-17.920451</td>\n",
       "      <td>402539.000000</td>\n",
       "      <td>-2.332217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19103</td>\n",
       "      <td>2013</td>\n",
       "      <td>2165</td>\n",
       "      <td>2105.0</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>9209.0</td>\n",
       "      <td>402539.000000</td>\n",
       "      <td>393150.916667</td>\n",
       "      <td>429674.333333</td>\n",
       "      <td>480465.750000</td>\n",
       "      <td>2.387908</td>\n",
       "      <td>-6.315326</td>\n",
       "      <td>-16.219002</td>\n",
       "      <td>422837.250000</td>\n",
       "      <td>-4.800488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19103</td>\n",
       "      <td>2014</td>\n",
       "      <td>2500</td>\n",
       "      <td>2165.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>9099.0</td>\n",
       "      <td>422837.250000</td>\n",
       "      <td>402539.000000</td>\n",
       "      <td>395331.000000</td>\n",
       "      <td>455700.833333</td>\n",
       "      <td>5.042555</td>\n",
       "      <td>6.957777</td>\n",
       "      <td>-7.211658</td>\n",
       "      <td>439717.083333</td>\n",
       "      <td>-3.838794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19103</td>\n",
       "      <td>2015</td>\n",
       "      <td>2576</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>6770.0</td>\n",
       "      <td>10265.0</td>\n",
       "      <td>439717.083333</td>\n",
       "      <td>422837.250000</td>\n",
       "      <td>393150.916667</td>\n",
       "      <td>429674.333333</td>\n",
       "      <td>3.992040</td>\n",
       "      <td>11.844349</td>\n",
       "      <td>2.337293</td>\n",
       "      <td>441381.666667</td>\n",
       "      <td>-0.377130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19103</td>\n",
       "      <td>2016</td>\n",
       "      <td>2565</td>\n",
       "      <td>2576.0</td>\n",
       "      <td>7241.0</td>\n",
       "      <td>11275.0</td>\n",
       "      <td>441381.666667</td>\n",
       "      <td>439717.083333</td>\n",
       "      <td>402539.000000</td>\n",
       "      <td>395331.000000</td>\n",
       "      <td>0.378558</td>\n",
       "      <td>9.649417</td>\n",
       "      <td>11.648635</td>\n",
       "      <td>461093.250000</td>\n",
       "      <td>-4.274967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19103</td>\n",
       "      <td>2017</td>\n",
       "      <td>2461</td>\n",
       "      <td>2565.0</td>\n",
       "      <td>7641.0</td>\n",
       "      <td>11911.0</td>\n",
       "      <td>461093.250000</td>\n",
       "      <td>441381.666667</td>\n",
       "      <td>422837.250000</td>\n",
       "      <td>393150.916667</td>\n",
       "      <td>4.465882</td>\n",
       "      <td>9.047453</td>\n",
       "      <td>17.281489</td>\n",
       "      <td>477615.166667</td>\n",
       "      <td>-3.459253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19103</td>\n",
       "      <td>2018</td>\n",
       "      <td>2543</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>7602.0</td>\n",
       "      <td>12267.0</td>\n",
       "      <td>477615.166667</td>\n",
       "      <td>461093.250000</td>\n",
       "      <td>439717.083333</td>\n",
       "      <td>402539.000000</td>\n",
       "      <td>3.583205</td>\n",
       "      <td>8.618743</td>\n",
       "      <td>18.650657</td>\n",
       "      <td>481006.583333</td>\n",
       "      <td>-0.705067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19103</td>\n",
       "      <td>2019</td>\n",
       "      <td>2463</td>\n",
       "      <td>2543.0</td>\n",
       "      <td>7569.0</td>\n",
       "      <td>12645.0</td>\n",
       "      <td>481006.583333</td>\n",
       "      <td>477615.166667</td>\n",
       "      <td>441381.666667</td>\n",
       "      <td>422837.250000</td>\n",
       "      <td>0.710073</td>\n",
       "      <td>8.977472</td>\n",
       "      <td>13.756908</td>\n",
       "      <td>483876.333333</td>\n",
       "      <td>-0.593075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19103</td>\n",
       "      <td>2020</td>\n",
       "      <td>1463</td>\n",
       "      <td>2463.0</td>\n",
       "      <td>7467.0</td>\n",
       "      <td>12608.0</td>\n",
       "      <td>483876.333333</td>\n",
       "      <td>481006.583333</td>\n",
       "      <td>461093.250000</td>\n",
       "      <td>439717.083333</td>\n",
       "      <td>0.596613</td>\n",
       "      <td>4.941101</td>\n",
       "      <td>10.042651</td>\n",
       "      <td>491594.666667</td>\n",
       "      <td>-1.570060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>19104</td>\n",
       "      <td>2012</td>\n",
       "      <td>2124</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>5505.0</td>\n",
       "      <td>9884.0</td>\n",
       "      <td>94038.166667</td>\n",
       "      <td>120080.166667</td>\n",
       "      <td>139226.166667</td>\n",
       "      <td>130982.000000</td>\n",
       "      <td>-21.687178</td>\n",
       "      <td>-32.456543</td>\n",
       "      <td>-28.205275</td>\n",
       "      <td>106057.250000</td>\n",
       "      <td>-11.332637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>19104</td>\n",
       "      <td>2013</td>\n",
       "      <td>2163</td>\n",
       "      <td>2124.0</td>\n",
       "      <td>5860.0</td>\n",
       "      <td>9765.0</td>\n",
       "      <td>106057.250000</td>\n",
       "      <td>94038.166667</td>\n",
       "      <td>141402.583333</td>\n",
       "      <td>135507.666667</td>\n",
       "      <td>12.781069</td>\n",
       "      <td>-24.996243</td>\n",
       "      <td>-21.733395</td>\n",
       "      <td>108781.333333</td>\n",
       "      <td>-2.504183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zipcode  year  no_permits  np_1yb  np_3yb   np_5yb             hv  \\\n",
       "5    19102  2012         354   463.0  1257.0   2689.0  320223.500000   \n",
       "6    19102  2013         507   354.0  1241.0   2486.0  328017.833333   \n",
       "7    19102  2014         533   507.0  1324.0   2118.0  342118.583333   \n",
       "8    19102  2015         744   533.0  1394.0   2281.0  348017.000000   \n",
       "9    19102  2016         769   744.0  1784.0   2601.0  350041.750000   \n",
       "10   19102  2017         567   769.0  2046.0   2907.0  362175.250000   \n",
       "11   19102  2018         589   567.0  2080.0   3120.0  360640.500000   \n",
       "12   19102  2019         545   589.0  1925.0   3202.0  351851.166667   \n",
       "13   19102  2020         280   545.0  1701.0   3214.0  351634.333333   \n",
       "20   19103  2012        2105  1929.0  4829.0   9571.0  393150.916667   \n",
       "21   19103  2013        2165  2105.0  5600.0   9209.0  402539.000000   \n",
       "22   19103  2014        2500  2165.0  6199.0   9099.0  422837.250000   \n",
       "23   19103  2015        2576  2500.0  6770.0  10265.0  439717.083333   \n",
       "24   19103  2016        2565  2576.0  7241.0  11275.0  441381.666667   \n",
       "25   19103  2017        2461  2565.0  7641.0  11911.0  461093.250000   \n",
       "26   19103  2018        2543  2461.0  7602.0  12267.0  477615.166667   \n",
       "27   19103  2019        2463  2543.0  7569.0  12645.0  481006.583333   \n",
       "28   19103  2020        1463  2463.0  7467.0  12608.0  483876.333333   \n",
       "35   19104  2012        2124  1965.0  5505.0   9884.0   94038.166667   \n",
       "36   19104  2013        2163  2124.0  5860.0   9765.0  106057.250000   \n",
       "\n",
       "           hv_1yb         hv_3yb         hv_5yb     pc_1yb     pc_3yb  \\\n",
       "5   322669.166667  365844.250000  374516.250000  -0.757949 -12.469992   \n",
       "6   320223.500000  350226.333333  376604.416667   2.434029  -6.341185   \n",
       "7   328017.833333  322669.166667  365844.250000   4.298775   6.027665   \n",
       "8   342118.583333  320223.500000  350226.333333   1.724085   8.679407   \n",
       "9   348017.000000  328017.833333  322669.166667   0.581796   6.714244   \n",
       "10  350041.750000  342118.583333  320223.500000   3.466301   5.862490   \n",
       "11  362175.250000  348017.000000  328017.833333  -0.423759   3.627265   \n",
       "12  360640.500000  350041.750000  342118.583333  -2.437145   0.516915   \n",
       "13  351851.166667  362175.250000  348017.000000  -0.061626  -2.910446   \n",
       "20  395331.000000  455700.833333  478987.666667  -0.551458 -13.726092   \n",
       "21  393150.916667  429674.333333  480465.750000   2.387908  -6.315326   \n",
       "22  402539.000000  395331.000000  455700.833333   5.042555   6.957777   \n",
       "23  422837.250000  393150.916667  429674.333333   3.992040  11.844349   \n",
       "24  439717.083333  402539.000000  395331.000000   0.378558   9.649417   \n",
       "25  441381.666667  422837.250000  393150.916667   4.465882   9.047453   \n",
       "26  461093.250000  439717.083333  402539.000000   3.583205   8.618743   \n",
       "27  477615.166667  441381.666667  422837.250000   0.710073   8.977472   \n",
       "28  481006.583333  461093.250000  439717.083333   0.596613   4.941101   \n",
       "35  120080.166667  139226.166667  130982.000000 -21.687178 -32.456543   \n",
       "36   94038.166667  141402.583333  135507.666667  12.781069 -24.996243   \n",
       "\n",
       "       pc_5yb         hv_1yf     pc_1yf  \n",
       "5  -14.496767  328017.833333  -2.376192  \n",
       "6  -12.901225  342118.583333  -4.121597  \n",
       "7   -6.485182  348017.000000  -1.694865  \n",
       "8   -0.630830  350041.750000  -0.578431  \n",
       "9    8.483173  362175.250000  -3.350174  \n",
       "10  13.100772  360640.500000   0.425562  \n",
       "11   9.945394  351851.166667   2.498026  \n",
       "12   2.844798  351634.333333   0.061664  \n",
       "13   1.039413  351424.750000   0.059638  \n",
       "20 -17.920451  402539.000000  -2.332217  \n",
       "21 -16.219002  422837.250000  -4.800488  \n",
       "22  -7.211658  439717.083333  -3.838794  \n",
       "23   2.337293  441381.666667  -0.377130  \n",
       "24  11.648635  461093.250000  -4.274967  \n",
       "25  17.281489  477615.166667  -3.459253  \n",
       "26  18.650657  481006.583333  -0.705067  \n",
       "27  13.756908  483876.333333  -0.593075  \n",
       "28  10.042651  491594.666667  -1.570060  \n",
       "35 -28.205275  106057.250000 -11.332637  \n",
       "36 -21.733395  108781.333333  -2.504183  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data = created_combined_df(permits_combined)\n",
    "housing_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a1099e3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(housing_data.corr().round(2), annot = True)\n",
    "plt.title(\"Correlation Matrix for Housing Data (All Permit Types)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a712801",
   "metadata": {},
   "source": [
    "This is the full correlation matrix, however our focus is utilizing historic columns to predict columns in the future.  We can alter the heatmap to show only those relationships, per below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4057f1c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(housing_data.corr().iloc[1:12, 12:], annot = True)\n",
    "plt.title(\"Correlation Matrix for Housing Data (All Permit Types)\")\n",
    "plt.xlabel(\"House Value Columns (Future)\")\n",
    "plt.ylabel(\"Permit Columns & House Value Columns (Past & Current)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a9eab8",
   "metadata": {},
   "source": [
    "Notable from this is the moderate to strong correlations between the `pc_1yf` column looking one year forward, and the three `pc_*yb` columns looking backwards in the bottom righthand corner.  The permit columns (`no_permits` and `np_*yb`) appear to have a weak negative correlation with the `pc_1yf` column, and a very weak correlation with the `hv_1yf` column. Historic home values (`hv_*yb`) also have a very strong correlation with future housing values `hv_1yf`, which makes sense. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ebbca73",
   "metadata": {},
   "source": [
    "We're going to now loop through each of the unique values in the `permitdescription` column and compute the correlation between the Permit Columns and the House Value Columns, as we did above.  Instead of producing a heatmap for each of the 25 values, we'll display the max correlation coefficient in order to select relevant permits to use in our model.\n",
    "\n",
    "We'll do two loops:\n",
    "1. The first will look at the correlation between the Permit Columns and the `hv_1yf` House Value column\n",
    "2. The second will look at the correlation between the Permit Columns and the `pc_1yf` Percent Change column\n",
    "\n",
    "The reason for the two loops is that the permit types that may have a higher correlation with the house value over the years may not have as high of a correlation with percent change, and vice versa.  Sussing those specific permit types out will be the goal of the below loops."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc4d885",
   "metadata": {},
   "source": [
    "## Model Building\n",
    "\n",
    "### Future Property Values - Selected Permits\n",
    "\n",
    "We'll start with the first loop and build a model that predicts future property values, aka the `hv_1yf` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "723b5ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>year</th>\n",
       "      <th>no_permits</th>\n",
       "      <th>np_1yb</th>\n",
       "      <th>np_3yb</th>\n",
       "      <th>np_5yb</th>\n",
       "      <th>hv</th>\n",
       "      <th>hv_1yb</th>\n",
       "      <th>hv_3yb</th>\n",
       "      <th>hv_5yb</th>\n",
       "      <th>pc_1yb</th>\n",
       "      <th>pc_3yb</th>\n",
       "      <th>pc_5yb</th>\n",
       "      <th>hv_1yf</th>\n",
       "      <th>pc_1yf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19102</td>\n",
       "      <td>2012</td>\n",
       "      <td>354</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>2689.0</td>\n",
       "      <td>320223.500000</td>\n",
       "      <td>322669.166667</td>\n",
       "      <td>365844.250000</td>\n",
       "      <td>374516.250000</td>\n",
       "      <td>-0.757949</td>\n",
       "      <td>-12.469992</td>\n",
       "      <td>-14.496767</td>\n",
       "      <td>328017.833333</td>\n",
       "      <td>-2.376192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19102</td>\n",
       "      <td>2013</td>\n",
       "      <td>507</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>2486.0</td>\n",
       "      <td>328017.833333</td>\n",
       "      <td>320223.500000</td>\n",
       "      <td>350226.333333</td>\n",
       "      <td>376604.416667</td>\n",
       "      <td>2.434029</td>\n",
       "      <td>-6.341185</td>\n",
       "      <td>-12.901225</td>\n",
       "      <td>342118.583333</td>\n",
       "      <td>-4.121597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19102</td>\n",
       "      <td>2014</td>\n",
       "      <td>533</td>\n",
       "      <td>507.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>342118.583333</td>\n",
       "      <td>328017.833333</td>\n",
       "      <td>322669.166667</td>\n",
       "      <td>365844.250000</td>\n",
       "      <td>4.298775</td>\n",
       "      <td>6.027665</td>\n",
       "      <td>-6.485182</td>\n",
       "      <td>348017.000000</td>\n",
       "      <td>-1.694865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19102</td>\n",
       "      <td>2015</td>\n",
       "      <td>744</td>\n",
       "      <td>533.0</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>2281.0</td>\n",
       "      <td>348017.000000</td>\n",
       "      <td>342118.583333</td>\n",
       "      <td>320223.500000</td>\n",
       "      <td>350226.333333</td>\n",
       "      <td>1.724085</td>\n",
       "      <td>8.679407</td>\n",
       "      <td>-0.630830</td>\n",
       "      <td>350041.750000</td>\n",
       "      <td>-0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19102</td>\n",
       "      <td>2016</td>\n",
       "      <td>769</td>\n",
       "      <td>744.0</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>350041.750000</td>\n",
       "      <td>348017.000000</td>\n",
       "      <td>328017.833333</td>\n",
       "      <td>322669.166667</td>\n",
       "      <td>0.581796</td>\n",
       "      <td>6.714244</td>\n",
       "      <td>8.483173</td>\n",
       "      <td>362175.250000</td>\n",
       "      <td>-3.350174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zipcode  year  no_permits  np_1yb  np_3yb  np_5yb             hv  \\\n",
       "5   19102  2012         354   463.0  1257.0  2689.0  320223.500000   \n",
       "6   19102  2013         507   354.0  1241.0  2486.0  328017.833333   \n",
       "7   19102  2014         533   507.0  1324.0  2118.0  342118.583333   \n",
       "8   19102  2015         744   533.0  1394.0  2281.0  348017.000000   \n",
       "9   19102  2016         769   744.0  1784.0  2601.0  350041.750000   \n",
       "\n",
       "          hv_1yb         hv_3yb         hv_5yb    pc_1yb     pc_3yb  \\\n",
       "5  322669.166667  365844.250000  374516.250000 -0.757949 -12.469992   \n",
       "6  320223.500000  350226.333333  376604.416667  2.434029  -6.341185   \n",
       "7  328017.833333  322669.166667  365844.250000  4.298775   6.027665   \n",
       "8  342118.583333  320223.500000  350226.333333  1.724085   8.679407   \n",
       "9  348017.000000  328017.833333  322669.166667  0.581796   6.714244   \n",
       "\n",
       "      pc_5yb         hv_1yf    pc_1yf  \n",
       "5 -14.496767  328017.833333 -2.376192  \n",
       "6 -12.901225  342118.583333 -4.121597  \n",
       "7  -6.485182  348017.000000 -1.694865  \n",
       "8  -0.630830  350041.750000 -0.578431  \n",
       "9   8.483173  362175.250000 -3.350174  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "16b74fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FAST FORM BUILDING PERMIT', 'ADMINISTRATIVE PERMIT', 'USE PERMIT',\n",
       "       'PLUMBING PERMIT', 'ELECTRICAL PERMIT', 'ALTERATION PERMIT',\n",
       "       'ZONING/USE PERMIT', 'ZONING PERMIT', 'OPERATIONS PERMIT',\n",
       "       'ADDITION PERMIT', 'NEW CONSTRUCTION PERMIT', 'DEMOLITION PERMIT',\n",
       "       'SUPPRESSION PERMIT', 'MECHANICAL PERMIT', 'SIGN PERMIT',\n",
       "       'ZONING ADMIN REVIEW', 'FIRE SUPPRESSION PERMIT',\n",
       "       'RESIDENTIAL BUILDING PERMIT', 'GENERAL PERMIT',\n",
       "       'GENERAL PERMIT MINOR', 'COMMERCIAL BUILDING PERMIT',\n",
       "       'MECHANICAL / FUEL GAS PERMIT', '5-YEAR TANK TESTING',\n",
       "       'SITE / UTILITY PERMIT', 'PRELIMINARY APPROVAL'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permit_type_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "682b94e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAST FORM BUILDING PERMIT\n",
      "nan\n",
      "-----\n",
      "ADMINISTRATIVE PERMIT\n",
      "0.49\n",
      "-----\n",
      "USE PERMIT\n",
      "-0.09\n",
      "-----\n",
      "PLUMBING PERMIT\n",
      "0.08\n",
      "-----\n",
      "ELECTRICAL PERMIT\n",
      "0.35\n",
      "-----\n",
      "ALTERATION PERMIT\n",
      "0.26\n",
      "-----\n",
      "ZONING/USE PERMIT\n",
      "0.31\n",
      "-----\n",
      "ZONING PERMIT\n",
      "0.36\n",
      "-----\n",
      "OPERATIONS PERMIT\n",
      "0.23\n",
      "-----\n",
      "ADDITION PERMIT\n",
      "0.4\n",
      "-----\n",
      "NEW CONSTRUCTION PERMIT\n",
      "0.33\n",
      "-----\n",
      "DEMOLITION PERMIT\n",
      "0.37\n",
      "-----\n",
      "SUPPRESSION PERMIT\n",
      "0.49\n",
      "-----\n",
      "MECHANICAL PERMIT\n",
      "0.41\n",
      "-----\n",
      "SIGN PERMIT\n",
      "0.3\n",
      "-----\n",
      "ZONING ADMIN REVIEW\n",
      "0.48\n",
      "-----\n",
      "FIRE SUPPRESSION PERMIT\n",
      "0.51\n",
      "-----\n",
      "RESIDENTIAL BUILDING PERMIT\n",
      "0.48\n",
      "-----\n",
      "GENERAL PERMIT\n",
      "nan\n",
      "-----\n",
      "GENERAL PERMIT MINOR\n",
      "0.31\n",
      "-----\n",
      "COMMERCIAL BUILDING PERMIT\n",
      "0.19\n",
      "-----\n",
      "MECHANICAL / FUEL GAS PERMIT\n",
      "0.82\n",
      "-----\n",
      "5-YEAR TANK TESTING\n",
      "nan\n",
      "-----\n",
      "SITE / UTILITY PERMIT\n",
      "nan\n",
      "-----\n",
      "PRELIMINARY APPROVAL\n",
      "nan\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "selected_permits = []\n",
    "\n",
    "for permit_type in permit_type_list:\n",
    "    permit_data = created_combined_df(permits_combined, [permit_type])\n",
    "    \n",
    "    # 12 is the hv_1yf column, 1:5 are the permit columns.\n",
    "    correlation_mat = permit_data.corr().iloc[12, 1:5].round(2)\n",
    "    sorted_pairs = correlation_mat.sort_values(kind=\"quicksort\", ascending = False)\n",
    "    # Select permit_type with a correlation >= 0.4\n",
    "    if sorted_pairs[:1][0] >= 0.4:\n",
    "        selected_permits.append(permit_type)\n",
    "    print(permit_type)\n",
    "    print(sorted_pairs[:1][0])\n",
    "    print('-----')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa53d4e6",
   "metadata": {},
   "source": [
    "The following permits had a correlation coefficient over 0.4 with the `hv_1yf` column indicating a moderate correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab79c578",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ADMINISTRATIVE PERMIT',\n",
       " 'ADDITION PERMIT',\n",
       " 'SUPPRESSION PERMIT',\n",
       " 'MECHANICAL PERMIT',\n",
       " 'ZONING ADMIN REVIEW',\n",
       " 'FIRE SUPPRESSION PERMIT',\n",
       " 'RESIDENTIAL BUILDING PERMIT',\n",
       " 'MECHANICAL / FUEL GAS PERMIT']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_permits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389db986",
   "metadata": {},
   "source": [
    "These selected permits will be used going forward in building the model that will predict house values a year into the future.  First let's look at the revised correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3b924461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5Obh9+zaePXtW4uelUaNGePPNN9GgQQO88cYbaNeuHXr06FHgQVtzc3NkZWVp7X7JMDEZJo2qXbs2pFIprly5gnfffbfE9vmT5vy/tAGgUqVKBc4zMjJS+QX74hd5VFQULC0ti73GC5mZmejYsSPatGmD1atXK3+x1K9fHzk5OSXGXtK9FLY//4MoEolE+ctRHYMGDUL//v1hb2+PkSNHFtpm7Nix2LFjB+bNmwdvb29UqlQJX331FR4/fqzWNQr7eBfmxfRNaWlpkMlksLe3V+8mSqDu10BhTExMCp1RQp1p8F6+7ovEJP/nJjc3V/neysoKZ86cwbFjxxAREYFly5Zh3Lhx2L9/P5o1a1bkdQp7GOnlRLSk/0iWlkwmg0wmQ61atQAAW7duxbBhwzBnzhwEBQXBxsYGW7duxTfffFNsP6/6/eLk5ITU1NRCj5mZmaF9+/Zo3749Jk6ciBkzZmDKlCkYO3as8mOydetW1KlTp8C5hX29vco5r6tly5ZYt24djI2N4ebmpvzP7ov/4CxcuLDQ/4i6u7sr3xf19f3yQ8Evvi4K26fOf2Rex+DBg9GlSxckJSVhxYoVaNGiRYkPQb/8vaPO58XIyAh79uzB6dOnERERgW3btmHChAnYunUr3nrrLWXb1NTUAv8JInpdTIZJo+zt7dG5c2eEh4djxIgRKiNcwPNkIicnB15eXjAzM8OhQ4dQv3595fHDhw+rbKvrRfIRExOj8oOzONeuXUNSUhJmzpwJHx8fAM+T6Zd/iL9IXPLy8ortq379+jh06JDKvkOHDkEikaBevXpq30dJOnXqBDMzM9y/fx8ff/xxoW0OHz6M3r17o1evXgCe/yK6efMmXFxclG1MTU1LvKfiXLlyBWPGjMHy5cuxZ88efPjhhzh58qTKqHdJXnyeDx8+jC5duij3HzlyBE2aNHnl2DRx3Re/bOPj45XHZTJZgen0jIyM0LZtW7Rt2xbTp09HvXr18OuvvxabDBenXr16WLlyJR4/fqz83rlx4wbS0tJeqT8AmDt3LkxMTJT/OT18+DCaNGmCMWPGKNvkn/O5sK8Pdb5fCtO0aVM8ffoUMTExqF69erFtX/SblJSE+vXrw9zcHHfu3FH5PBVHnXOaNm0KANi3bx/ef//9QtuU5vvDwsKi0P+Aubi4oFq1arhx4wYGDhyoVl+apu7Pr5K0b98e1atXx88//4z169djzpw5BdqcPn0aeXl5ytHh48ePw9TUFLVq1YIQQq3PpUQigZ+fH/z8/DBp0iR06tQJa9asUfmZfunSJQQEBLzW/RDlx3mGSeN++uknmJiYoFmzZvj1119x9epVREdHY8OGDWjevDlu3boFS0tLjBw5ElOmTMHWrVtx69YtzJo1Czt27MCkSZNKfU0vLy/069cPAwcOxPr16xEdHY0LFy5g9erVmDt3bqHneHh4wMzMDIsXL8bt27exf/9+jBo1SmVkztHREVZWVti3bx8ePnyIR48eFdrX2LFjce7cOYwZMwbXr1/H3r17MWLECPTu3bvEBKA0pFIpLl26hAcPHsDa2rrQNnXr1sWOHTtw6tQpXL16FYMGDVJJ6gCgRo0aOHv2LG7fvo3k5GSVEc+SZGdn48MPP0S3bt3Qv39/rFixAo8ePVL5U7A6atWqhQ8++ABDhw7FP//8g+vXr2PUqFG4fPkyxo4dW6q+NH1dCwsLtG7dGt9//z0uXLiAs2fP4tNPP1VJ9nfs2IEFCxbg7NmziImJwfbt2xEbG/ta//np3bs3rKys8Omnn+LixYs4efIk+vfvDwsLC7VGjJOSkvDw4UPcvXsX+/btw0cffYQff/wRCxYsQI0aNQA8//q4dOkSduzYgdu3b2PhwoX4448/VPqpUaMGHj58iOPHjyM5ORmZmZlqfb8UxtfXF25ubir/WUxJSUG7du2wbt06nD9/Hvfu3cNff/2FiRMnokaNGvD19YWVlRUmTZqESZMmITw8HDdu3MCVK1ewefNmjB8/vtBrqXOOl5cXevfujaFDh2LDhg24ffs2Tp8+jYULF6rc/8GDBxEfH4/k5OQSP+5FmTlzJhYtWoQZM2bg8uXLuHHjBrZv344vvvjilfssDXV/fpVEIpFg0KBB+Pbbb5GTk4OPPvqoQJuUlBQMGzYM165dw99//40pU6Zg4MCBqFSpklqfl6ioKHz33Xc4efIkYmJisH//fly8eFHl++nJkyc4e/Ysunbt+mofEKKilH2ZMhkCmUwmxowZI2rXri3MzMyEk5OTaNu2rVi/fr1yKh11p1Zbv369yr7CpjUTQgi5XC7mzp0r6tatK0xMTISDg4No27at+O2335Rt8j9At3XrVuHl5SXMzMyEr6+viIyMLPDwzLp164Snp6cwNjZWe2o1R0dHMXjw4EKnVntZ/odVClPY1FEvy/8AXUxMjOjYsaOwtLRUzhLQr18/5XRmQghx+/ZtERgYKCpVqlRgarX8D43l3z948GBRo0YNlYe8jhw5IoyNjcWOHTuKjLOw+3/8+LFyijNTU1O1pzhT5+Pwsvyf95KuK4QQN27cEG3bthWWlpbCy8tLbNu2TeUBukOHDong4GDl9GxeXl5i9uzZyqfui3qALv9Dbvm/3l5MrWZqaiq8vLzE1q1bhZOTk8qDU/m96PvFy9LSUtSuXVv07dtXZQo8IZ5/3w0aNEjY2dkJa2tr8dFHH4nFixerfB3m5OSIjz76SNjZ2alMrabO90thpk2bJt544w3ldnZ2tpg4caJo0aKFsLOzU07x9sUXX4iYmBiVc1euXKmcmqxy5crCz89P/PTTT8rjhX1/lHROTk6OmDx5svDw8BAmJiaiatWqYtSoUcrje/bsEd7e3sLU1LTUU6vl9+eff4pWrVoJCwsLYW1tLRo3biymT59ebPyFfd0X9jDciwcwXzzcVth5hf38Kk5RP5OSkpKEiYmJGDRoUIFjL6ZWezFDkJWVlfj888+Vs028UNzn5fLly6Jz587CxcVFmJqaiurVq4uvv/5aZaaS1atXi7p165Z4D0SlJRGiFAWLRERUpu7fvw9PT0/s3LkTb7/9tq7DeSVpaWmoU6cO9u7dqyxToIrl6tWrqF+/Ps6cOVOgDKhdu3bw8vLCypUrtXZ9hUKBxo0bY/LkycoSMCJNYc0wEVE5smHDBlStWhU1atTA/fv3MW7cOHh4eGhs9gNdqFy5MjZs2KCyqhlVDM+ePcODBw8wceJEBAUFvXI9/Ot68OAB+vbty0SYtILJMBFROZKSkoLQ0FA8ePAA9vb2aN26NbZu3VqqhxPLo4qczBuyTZs2oV+/fqhfvz5+//13ncVRrVo1fPXVVzq7Puk3lkkQERERkcHibBJEREREZLCYDBMRERGRwWLNsBYtd/9E1yEQERGRHvkiboOuQwAA5Cbf0XifJo41Nd6nOjgyTEREREQGiyPDRERERFQ6itdb5rs8YTJMRERERKUjFLqOQGNYJkFEREREBosjw0RERERUOgqODBMRERERVXgcGSYiIiKiUhGsGSYiIiIiqvg4MkxEREREpaNHNcNMhomIiIiodFgmQURERERU8XFkmIiIiIhKR49WoOPIMBEREREZLI4MExEREVHp6FHNMJPhfLZs2QIfHx80atQIf//9N0JCQmBmZqbrsIiogqrWrhECpveBxEiK65sicX7JLpXjHh2bosXY9yEUAkKeh6hpG/Dw9E0AQIP+b8Lno3aARILrvx7EpVX/6OAOiIgKwdkkKpa8vDwYGRmp1bZXr17K97t370ZgYCCTYSJ6JRKpBK1nfIa/P56DjIRU9Pj7W9zbdxZpt+KVbR4cvYL7+84BAOx9qiFk6Qj81m4c7Oq6w+ejdvjzrVDk5crRZcM43D9wHul3E3V1O0REeqlMkmGZTIbZs2ejbt26uHnzJuzt7TFu3DjEx8djxYoVePbsGVxcXDBkyBBYWVkV2se0adPg6emJ6OhoZGVlYciQIfDy8kJ2djZWr16N2NhY5OXl4YMPPkCLFi0QGRmJc+fOIScnB8+ePUNQUBBOnToFhUKB2NhYvP3225DL5Th8+DBMTEwwceJEWFlZYcmSJWjWrBlSU1ORmpqK6dOnw8bGBlOmTMHSpUtx584dAEBwcDDeeuutsvjwEVEF5exbC+n3EvEkJgkAEL3jBDw7NsP5l5JheeYz5XsTCzNACACAnVcVJP53G/LsHABAwonrqNGpOS4s/bsM74CIqHBcge4VJCQkoFOnTpg/fz4sLS1x4sQJhIeHo3fv3vjhhx9QvXp1/P7778X2kZ2djRkzZqB///5YunQpAOCPP/5AgwYNMHv2bISGhmLDhg3Izs4GANy8eRPDhw9HaGgoACA2NhajRo3C7NmzsWnTJpiamuL7779H7dq1cejQIZVrdenSBfb29ggNDUVoaCju3buH1NRUzJs3D/PmzUNwcLAWPkpEpE8s3ezwNCFVuZ3xMBWV3OwKtPPs1Bw9I79Hp1++xqGvVgAAUm/Ewa1lXZhVtoKxuSmqt28MqyoOZRY7EZGhKLMyCWdnZ3h6egIAatasicTERGRkZKBevXoAgKCgICxYsKDYPtq0aQMAqFevHjIzM5GRkYGLFy/i7Nmz2LXreR1eTk4OkpOTAQCNGjVSGWmuX78+LCwsYGFhAUtLSzRv3hwAUL16dcTExJQYv0wmw+rVq9G0aVM0atSo9B8EIjIoEkgK7hQFd93bewb39p6BW8u6aD72ffz90RykRcfj/E9/oeumCZBnZCPlagwUcv2ZyoiIKjjWDJeeiYmJ8r1UKkVGRsZr9ymRSCCEwFdffYUqVaqoHIuOji5Q65s/BmNjY+X7vLzif8lYWVkhLCwM58+fx969exEVFYWhQ4cWaBcREYGIiAgAQI1Xuisi0hcZCamwcrNXbldytUfGw0dFtk84eQM2Hs4wt7NC9qOnuLH5EG5sfv5XK7/xPVVGmYmIdIplEq/P0tISVlZWuHbtGgDg8OHD8PHxKfacqKgoAMD169dhaWkJS0tLNG7cGHv27IH4/zq7u3fvaixGc3NzZclFeno6FAoFWrVqhQ8//LDI64SEhGDOnDmYM2eOxuIgoopJduEObGu4wrqaE6QmRvB6pxXu/3tOpY2Np4vyvWMDTxiZGiP70VMAgLmDDQDAqooDPDs3R/SOqLILnojIQOh0Nolhw4YpH6BzdnYudKT1ZVZWVpg8ebLyAToAeP/997F27Vp8/fXXAAAnJydMmDBBI/GFhIRg1qxZsLOzw2effYalS5dC8f9/Fvj44481cg0i0l8iT4GjU9ahy8ZxkEiluLHlEB7dfACfT9oDAK5tOIAaXVqgznttoJDnIS87BxFDwpXnd/x5FMztrKCQy3Hsm3XIeZypq1shIlKlRyvQScSLIdVybtq0aejTpw9q1aql61DUttz9E12HQERERHrki7gNug4BAPDs+qGSG5WSmXeQxvtUh0HMM0xEREREGqRHNcPlLhleuXIlbty4obKvS5cumDZtmm4CIiIiIiJVnE1CewYMGKDrEIiIiIjIQJS7ZJiIiIiIyjk9KpPQ2dRqRERERES6xpFhIiIiIiodPaoZ5sgwERERERksjgwTERERUakIoT+LbjAZJiIiIqLS4QN0REREREQVH0eGiYiIiKh0+AAdEREREVHFx5FhIiIiIiodPaoZZjJMRERERKWj0J/ZJFgmQUREREQGiyPDRERERFQ6LJMgddgohK5DICIiIqJiMBkmIiIiotLRo6nVmAwTERERUenoUZkEH6AjIiIiIoPFkWEiIiIiKh09KpPgyDARERERGSyODBMRERFR6XBkmIiIiIio4uPIMBERERGVihD6sxwzk2EiIiIiKh2WSRARERERVXwcGSYiIiKi0uGiG0REREREFR9HhomIiIiodPSoZpjJMBERERGVDsskKrYHDx7gm2++wccff4ydO3eW2P7KlSuYM2dOGURGRPrGrV0jdD0ShreOzYPP8LeLbGffuCZ6xa5Hta5+AACpmQk6/v0tOv07C10OzkWDr98rq5CJiAyKQY4MW1lZ4fPPP8fp06d1HQoR6TGJVIJms/ri4IezkZWQio67v8ODf84h/daDAu18v/kQDyMvKvcpnuXiwAczIc98BomxEUK2T0XCgQtIORdd1rdBRFQQyyR0RyaTYfbs2ahbty5u3rwJe3t7jBs3DrNmzYKnpyeio6ORlZWFIUOGwMvLq9A+bG1tYWtri3Pnzqns37x5M2xsbNClSxcAwKZNm2BrawsPDw9kZWUhLCwM8fHx8PHxwYABAyCVGuTAOhGpyb5JLTy9l4iMmCQAQMyOE3B/sxmu5kuG6/R7E7G7T8O+cU2V/fLMZwAAqYkRpCZGgBBlEzgRkQGpkNlcQkICOnXqhPnz58PS0hInTpwAAGRnZ2PGjBno378/li5dWup+27dvj0OHDgEAFAoFoqKiEBgYCACIjo7Gp59+innz5iExMRGnTp3S3A0RkV6ydLVHZnyKcjszIRUWbnYqbSxc7eDeuTmif4kocL5EKkGnf2fh3YtL8fDwZaT8d1vrMRMRqUUoNP/SkQo3MgwAzs7O8PT0BADUrFkTSUnPR13atGkDAKhXrx4yMzORkZGBSpUqlapfKysr3L17F48fP4anpyesra0BAF5eXnBxcQEAtG7dGtevX0erVq0K9BEREYGIiOe/1Bq/8h0SkV6QFLIv3+hu0+l9cH7mZghFwVFfoRDY+8YkmNhYInDVaNjWdcfjG3FaCpaIqBRYJqFbJiYmyvdSqRQ5OTmFtpNICvtNVLwOHTogMjISaWlpCA4OLvX5ISEhCAkJAQBs+qV3qc8nIv2RmZAKyyoOym1LN3tkPUxTaWPfuAYClg4HAJjZW6NKh8ZQ5OXhwd6zyja56ZmQHb8Gt+BGTIaJiDSsQpZJFCUqKgoAcP36dVhaWsLS0rLUffj5+eH8+fO4ffs2fH19lfujo6Mhk8mgUChw/PhxeHt7aypsItJTqefvwLqGKypVc4LUxAjV32mFuH1nVdrsajUau1p+iV0tv0TsX6dwZuJaPNh7Fmb21jCxef4zzMjcBC6B9ZEenaCL2yAiKkih0PxLRyrkyHBRrKysMHnyZOUDdEVJS0vDhAkTkJWVBYlEgt27dyvrj42NjVG/fn1UqlRJ5QG5OnXqYOPGjYiJiYGPjw/8/PzK4paIqAITeQqc+WYt2v06HhIjKe5sPoT0mw/g1acDACB6/f4iz7VwqYxWCwdDIpUCUglidp1EfMR/ZRU6EZHBkAihH48nT5s2DX369EGtWrVeqx+FQoHx48djzJgxcHNze62+NlVhmQQRERFpzkfxG3UdAgAg66/5Gu/T4q0xGu9THXpVJvG64uLiMHLkSDRs2PC1E2EiIiIiKv/0pkxi2rRpBfYdPHgQu3fvVtlXt25dDBgwoNA+3N3dER4ero3wiIiIiPQHZ5OoGIKDg19pRggiIiIiKoYO5wXWNJZJEBEREZHB0uuRYSIiIiLSAj0qk+DIMBEREREZLI4MExEREVHp6FHNMJNhIiIiIiodlkkQEREREVV8HBkmIiIiotLhyDARERERUcXHkWEiIiIiKh0hdB2BxjAZJiIiIqLSYZkEEREREVHFV+LIsFwux61bt3D//n1kZGSgUqVK8PDwQO3atWFszIFlIiIiIoOjRyPDRWaz6enp2L59Ow4dOgQrKytUrVoV5ubmyM7Oxp49e/D06VMEBQWhe/fusLGxKcuYKwwLhf7U0xARERHpoyKT4dDQUAQHByMsLAz29vYFjqempuLo0aMIDQ3FggULtBokEREREZUjhrACXVhYWLFlEPb29ujWrRu6dOmilcCIiIiIiLStyAfoXk6EV69eXWibtWvXsm6YiIiIyNAoFJp/6Yhas0kcOnSo0P2HDx/WaDBEREREVAEIofmXjhQ7rHvgwAEAQF5envL9CzKZDNbW1tqLjIiIiIjoJefPn8eaNWugUCjQoUMHdO/eXeV4ZmYmFi1ahJSUFOTl5eHtt99GcHBwsX0WmwwfOXIEwPPp1V68f8HW1hbDhg17hdsgIiIiogpNB2UNCoUCq1atwuTJk+Hg4ICJEyeiefPmcHd3V7bZu3cv3N3dMWHCBKSnp2PUqFEIDAwstqy32GQ4NDQUALB582Z8+OGHGroVIiIiIqLSiY6OhqurK1xcXAAAAQEBOH36tEoyLJFIkJ2dDSEEsrOzYWVlBam0+KpgtZ5+69KlC7Kzs2Fubg6FQoFDhw5BKpUiMDCwxAsQERERkZ7RwchwamoqHBwclNsODg64deuWSptOnTrh+++/xxdffIGsrCyMHj1aM8nwnDlzMHDgQNSoUQO//vorzp07ByMjI9y9exd9+/Yt/d0QERERUcWlhXmGIyIiEBERodwOCQlBSEjI/y5ZyEN2EolEZfvChQvw8PDA1KlTkZiYiO+++w7e3t6wtLQs8rpqJcMJCQnw9PQEABw9ehQzZsyAubk5xowZw2SYiIiIiF5b/uQ3PwcHB6SkpCi3U1JSYGdnp9Lm4MGD6N69OyQSCVxdXeHs7Iz4+Hh4eXkV2a9aNQ5SqRRyuRwxMTGwtLSEo6MjLC0tkZ2drc7pRERERKRHhEJo/FWSWrVqISEhATKZDHK5HFFRUWjevLlKG0dHR1y6dAkAkJaWhvj4eDg7Oxfbr1ojw76+vliwYAGePHmCgIAAAEBcXFyhyzQTEREREWmakZER+vXrh5kzZ0KhUCA4OBjVqlXDvn37AAAdO3bEe++9h59++glfffUVAKB3796wsbEptl+JKKwAI5/c3FwcOnQIRkZGaNu2LYyMjHDlyhWkpaWhdevWGrg9/bTd9WNdh0BERER6pPvDX3UdAgAgc9kojfdpOXihxvtUh1ojwyYmJgVqOOrXr6+VgIiIiIionNPCA3S6olYyDABnzpzB1atXkZ6errJ/+PDhGg9K206fPo0tW7ZAIpHAyMgIffv2hbe3d5Htr1y5gl27dmHChAllGCUR6QPn4EZo+N2nkBhJcX/jQdwK31Vou8q+NRH097c4/cUixP91CgDQ8fRC5D7NAvIUUOQpcOjNyWUZOhGRQVArGd66dSv+/fdfBAQE4MSJEwgJCcGxY8fg7++v7fi0omHDhmjevDkkEgnu37+PBQsW4Mcff9R1WESkb6QSNJ79OY71nI2shBS02zsDD/edw5ObDwq0qz/5IyRGXizQxbH3ZiIn9UkZBUxEpCY1HnirKNRKhg8ePIjJkyejevXqiIyMRN++fdGmTRts27ZN2/EVIJPJMHv2bNStWxc3b96Evb09xo0bh1mzZsHT0xPR0dHIysrCkCFDipxGw9zcXPn+2bNnyjnqNm/eDBsbG3Tp0gUAsGnTJtja2sLDwwNZWVkICwtDfHw8fHx8MGDAAC44QkTFsmvihad3E5EZIwMAxG0/Dtc3mxVIhmv1fxPxf5+CnW9NXYRJRGTQ1MrmMjIyUL16dQCAsbEx5HI5vLy8cPXqVa0GV5SEhAR06tQJ8+fPh6WlJU6cOAEAyM7OxowZM9C/f38sXbq02D5OnTqFL7/8ErNnz8aQIUMAAO3bt8ehQ4cAPF//OioqCoGBgQCeLwH46aefYt68eUhMTMSpU6e0eIdEpA8s3OyQFf+/OTGzE1Jh4aY6C4+5qx3curTA3XUR+U+HEAIBmyeg3T8z4fFJe63HS0SkNoVC8y8dUWtk2NXVFbGxsahWrZpyCgsrKytYWVlpO75COTs7KxcBqVmzJpKSkgAAbdq0AQDUq1cPmZmZyMjIQKVKlQrtw8/PD35+frh69Sq2bNmCKVOmwNnZGVZWVrh79y4eP34MT09PWFtbAwC8vLyUa2G3bt0a169fR6tWrbR8p0RUoeVbGQkAkG8Cn4bffYor320q9E+OR96ehuzENJg62qD1lol4Gh2PlBPXtRUtEZFBUisZ7tWrF548eV6z1rt3byxcuBDZ2dkYMGCAVoMriomJifK9VCpFTk5Ooe3yL9FXmHr16mHJkiVIT0+HjY0NOnTogMjISKSlpSE4OLjUsb28lCBTZSLDlhWfCosqDsptczd7ZD18pNKmcuMaaLF8BADA1N4aLh18IeQKJOw9g+zENABATnI6EvacgV2TWkyGiah80OFIrqaVmAw/ePAAubm5cHNzA/B8hHTx4sVaD+xVREVFoUGDBrh+/TosLS2LXIf64cOHcHFxgUQiwZ07dyCXy5UjwH5+ftiyZQvy8vIwatT/5tCLjo6GTCaDo6Mjjh8/jg4dOhTa98tLCW5fy3mGiQxZ2vnbsKrpCsvqTshKSIV7d3+cGRqu0uZfvy+V75su/AIP//0PCXvPwMjSDBKJBPKMbBhZmsEpqCFuzP+jjO+AiKgIJS9TUWEUmwxHRkZi+fLlqFSpEjIzMzFixIhyPYOElZUVJk+erHyArignTpzA4cOHYWRkBFNTU4wePVo5imxsbIz69eujUqVKKg/I1alTBxs3bkRMTAx8fHzg5+en9fshoopN5ClwcdJaBGya8HxqtU2ReHLjATw/ff6f6Xu/7C/yXDNHW7RcMxoAIDE2QtwfxyA7WHC2CSIiej3FrkA3evRofPzxx2jRogVOnTqFbdu2Ye7cuWUZn9qmTZuGPn36oFatWq/Vj0KhwPjx4zFmzBjlaPir4gp0REREpEnlZgW6+QM13qflmBUa71Mdxc4mkZqaihYtWgAAWrRogeTk5DIJSlfi4uIwcuRINGzY8LUTYSIiIiIq/9RegU4ikUBRjoulp02bVmDfwYMHsXv3bpV9devWLfLBP3d3d4SHhxd6jIiIiIj+n6EsupGdna1Se5uZmVmgFrek+Xx1KTg4+JVmhCAiIiKiYojyO0BaWsUmw6GhoWUVBxERERFRmSs2Ga5Xr15ZxUFEREREFYUelUkU+QDd7t27kZubW+zJubm5BWpyiYiIiIgqiiJHhtPS0jBy5Eg0adIE9erVQ5UqVWBubo7s7GzEx8fj6tWr+O+//xAUFFSW8RIRERGRjolyPKlCaRWZDH/88cd46623EBkZiQMHDiAmJgYZGRmwsrJC9erV0aRJE3z00UfKlduIiIiIyEDoUZlEsTXDNjY26NatG7p161ZW8RARERERlRm15xkmIiIiIgKgV1OrFbsCHRERERGRPuPIMBERERGVjh7VDHNkmIiIiIgMllojw+np6TA1NYW5uTkUCgUOHToEqVSKwMBASKXMp4mIiIgMih5NraZWJjtnzhwkJCQAADZt2oRdu3bhr7/+wi+//KLV4IiIiIioHFIIzb90RK1kOCEhAZ6engCAI0eOYNKkSQgNDUVUVJQ2YyMiIiIi0iq1yiSkUinkcjkSEhJgaWkJR0dHKBQKZGdnazu+Cs1cj6YdISIiIlLSoxxHrWTY19cXCxYswJMnTxAQEAAAiIuLg729vVaDIyIiIiLSJrWS4cGDB+PQoUMwMjJC27ZtAQBPnjzBBx98oNXgiIiIiKgc0qOp1dRKhk1MTBASEqKyr379+loJiIiIiIjKN6FHs0molQw/ffoUO3fuxP379wvUCU+fPl0rgRERERERaZtayfDChQshl8vh7+8PU1NTbcdEREREROWZoZVJ3Lx5EytXroSJiYm24yEiIiIiKjNqzTNcvXp1pKSkaDsWIiIiIqoI9GjRDbVGhhs0aIBZs2ahXbt2qFy5ssqx9u3bayMuIiIiIiqvDG2e4evXr8PBwQGXLl0qcIzJMBERERFVVGolw6GhodqOg4iIiIgqCkN7gO6FzMzMAlOrcRU6IiIiIqqo1EqGL168iJ9//hlJSUkFjm3ZskXjQRERERFR+SUMbWR42bJleO+999C6dWvOM0xEREREekOtZDg3NxfBwcGQStWaiY2IiIiI9JkejQyrld127doVO3bsgBD6ceNXrlzBZ599hrFjx2Ls2LH4/fffS2w/Z86cMoqOiPSJY3BjBB6bj8ATP6LGiG5FtrPxrYk343+Fy1stlfuMbSzhu3I02hydhzZH5qFy89plETIRUckUCs2/dEStkeGWLVti5syZ2L59O6ytrVWOhYeHayUwbfPx8cGECRN0HQYR6TOpBPXm9MPpnjORHZ8C/39mQfbPWWTcfFCgXd0pHyP54AWV3T4zPkPywfM4P2ABJCZGMLIwK8PgiYgMg1rJ8Pz58+Ht7Q1/f3+d1wzLZDLMnj0bdevWxc2bN2Fvb49x48Zh1qxZ8PT0RHR0NLKysjBkyBB4eXmVqu/NmzfDxsYGXbp0AQBs2rQJtra28PDwQFZWFsLCwhAfHw8fHx8MGDCAZSNEVKzKTb2Qefchsu7LAAAPt0fBpVNz3MmXDHsM6ITEv07B1reWcp+RlQXs/H1waeRSAIDIzYM8N7PsgiciKo6hlUnIZDIMHToUzZo1Q8OGDVVeupCQkIBOnTph/vz5sLS0xIkTJwAA2dnZmDFjBvr374+lS5cW28fNmzcxduxYzJo1C7GxsQCeLyBy6NAhAIBCoUBUVBQCAwMBANHR0fj0008xb948JCYm4tSpU1q8QyLSB2au9siK/99S9tnxqTBztc/Xxg4unVsgZt2/KvstPZyRk5KOhguHICBiNurPHwQjS44MExFpmlrJcPPmzXH58mVtx6I2Z2dneHp6AgBq1qypnPKtTZs2AIB69eohMzMTGRkZhZ5fo0YN/PTTTwgLC0OnTp0QFham7NfKygp3797FxYsX4enpqSwL8fLygouLC6RSKVq3bo3r168X2ndERAQmTJjAEgwiAiSF7VQdTfH57jPcmPFrgVEWibERbBrWQMy6fxEVMhF5mc9QY8Q72ouViKg0FELzLx1RezaJ77//Hj4+PrC1tVU5Nnz4cK0EVhwTExPle6lUipycnELbSSSF/iaCpaWl8n3Tpk2xatUqpKenw8bGBh06dEBkZCTS0tIQHBxc6thCQkIQEhICANi75sNSn09E+uNZQiosqjgot82r2OPZw0cqbWx8a8J32SgAgImDNRxDfCHy8pB25haexafi8bloAEDirpPFPoBHRFSW9GVSBUDNZLhatWqoVq2atmN5bVFRUWjQoAGuX78OS0tLlaT3ZWlpabC1tYVEIkF0dDQUCoVyBNjPzw9btmxBXl4eRo0apTwnOjoaMpkMjo6OOH78ODp06FAm90REFdfj/27DsqYrLKo7ITshFa7dA3BxyGKVNodbjFS+b7hwCGT/noNszxkAQFZ8CirVckPG7QQ4BDYo+OAdERG9NrWS4Q8++EDbcWiElZUVJk+erHyArignTpzAvn37YGRkBFNTU3z55ZfKUWRjY2PUr18flSpVUnlArk6dOti4cSNiYmLg4+MDPz8/rd8PEVVsIk+BqxPXoPnmSZAYSRG36SCe3ohDtU+f//Uo9peIYs+/NmkNGv00HFJTY2Tel+HSqGVlETYRUcn06AE6iVBjnLu4euEGDRpoNKBXNW3aNPTp0we1atUquXExFAoFxo8fjzFjxsDNze21+trrwjIJIiIi0pxOiZt1HQIAIH1gR433abNin8b7VIdaI8P5Z2ZIT0+HXC6Hg4NDhZ1nuDBxcXGYM2cO/Pz8XjsRJiIiItJbhjYynJ9CocC2bdtgYWGBt956SxtxacTBgwexe/dulX1169bFgAEDyuT6HBkmIiIiTSovI8OPPw/ReJ+2a4ovHdMWtUaG85NKpejRowcGDx5crpPh4ODgV5oRgoiIiIgMwyslwwBw8eJFrsBGREREZIj0qExCrWQ4/8wMOTk5yMnJKbNyAyIiIiIibVArGR4xYoTKtpmZGdzc3Iqcx5eIiIiI9JhC1wFojlrJcL169bQdBxERERFRmSsyGV68eHGRyxm/TBfLMRMRERGR7ghDqBl2dXUtyziIiIiIqKIwhGS4oizBTERERET0qtSeWu3y5cs4fPgwHj16BDs7O7Rt27bcLMVMRERERGVIjx6gU2ui4P379+PHH39E5cqV4efnBzs7OyxcuBAREbpZKYSIiIiISBPUGhneuXMnJk+eDE9PT+W+gIAAzJs3DyEhml+Oj4iIiIjKL4N4gO5lT548gbu7u8q+KlWq4OnTp1oJioiIiIjKMUMrk/D29sYvv/yCZ8+eAQCys7Oxfv161KlTR6vBERERERFpk0QIUeI496NHj/Djjz/i5s2bsLKywtOnT1GnTh2MGjUK9vb2ZRFnhZSbfEfXIRAREZEeMXGsqesQAACp7wZpvE/7Pw9pvE91qFUmYWdnh+nTpyMlJUU5m4SDg4O2YyMiIiIi0qpiyySSk5Nx8OBB5baDgwO8vLzg4OCAyMhIpKSkaD1AIiIiIipnFFp46UixyfDvv/+O3NzcQo/l5ubi999/10pQRERERFR+CYXmX7pSbDJ8+fJlBAYGFnosMDAQFy9e1EpQRERERERlodia4fT0dJiZmRV6zNTUFE+ePNFKUERERERUjhnK1Gp2dna4d+9eocfu3buHypUrayEkIiIiIqKyUWwy3Lp1a/z8889ITU1V2Z+amoqVK1cWWUJBRERERPpLn2qGiy2T6NGjB+7evYtRo0bBy8sLlStXRlpaGqKjo9GwYUP06NGjrOIkIiIiovJCj8ok1Fp04+LFi7h8+TKePHkCa2trNGzYEA0bNiyL+Co0LrpBREREmlReFt1IflPzi244/lOOF91o1KgRGjVqpO1YiIiIiKgC0GVZg6YVWzNMRERERKTP1BoZJiIiIiJ6gSPDRERERER6oFQjw8nJyUhNTUWdOnW0FQ8RERERlXMGNzKcnJyMKVOmYPTo0fjuu+8AACdOnMCyZcu0GpwuyGQyfPXVV7oOg4gMwORZ89G264fo/slgXYdCRFQ6QqL5l46olQz//PPPaNKkCdatWwdj4+eDyY0aNcLFixe1GhwRkT7r3uUNLJs/Q9dhEBFVGOfPn8eoUaMwYsQIbN++vdA2V65cwdixYzFmzBiEhoaW2KdaZRLR0dGYMGECpNL/5c6WlpbIzMxUL/IKRqFQYNmyZbh58ybs7e3Rt29fLFmyBLNnzwbwfPT4+++/xw8//KDjSImoImvu2xAPEhJ1HQYRUanpokxCoVBg1apVmDx5MhwcHDBx4kQ0b94c7u7uyjYZGRlYuXIlvvnmGzg6OuLx48cl9qvWyLCtrS0ePnyosi8uLg6Ojo6lvI2KISEhAZ06dcL8+fNhaWmJO3fuQC6XIzHx+S+tqKgo+Pv76zhKIiIiIsMRHR0NV1dXuLi4wNjYGAEBATh9+rRKm6NHj6Jly5bKHNXW1rbEftVKht9++23MnTsXBw8ehEKhwNGjR7FgwQK88847r3Ar5Z+zszM8PT0BADVr1kRSUhL8/f1x/PhxAMDx48cREBCgwwiJiIiIdEcoJBp/lSQ1NRUODg7KbQcHB6Smpqq0SUhIwNOnTzFt2jSMHz8ehw6VvKqdWmUS7du3h5WVFfbv3w8HBwccOnQIvXr1gp+fnzqnVzgmJibK91KpFDk5OWjbti0WLFigvGc3N7dCz42IiEBERAQA4LuvB2k/WCIiIqIypo0yiZdzKAAICQlBSEjI/64pRIFzJBLVJDovLw93797FlClTkJOTg8mTJ6N27dqoUqVKkddVe2o1Pz8/vU1+1eHq6gqpVIpt27YVOyr88icuN/lOWYVHREREVKHlT37zc3BwQEpKinI7JSUFdnZ2BdpYW1vD3Nwc5ubm8PHxwf37918/GT5w4ECRx9q3b69OF3rB398fGzZsQHh4uK5DISI9MDZ0Dk7/dxFpaeno0P0TDO3fB++9/aauwyIiKpHQwVRotWrVQkJCAmQyGezt7REVFYWRI0eqtGnevDlWr16NvLw8yOVyREdHo2vXrsX2KxGFjTnnM336dJXttLQ0PHz4EN7e3mpNWWGoODJMREREmmTiWFPXIQAAHvhrfjC06vGiB19fOHfuHNatWweFQoHg4GD06NED+/btAwB07NgRALBz504cPHgQUqkU7du310wyXJgDBw7gwYMH6NOnz6ucbhCYDBMREZEmlZdkOK6l5pNh95MlJ8PaoNZsEoVp165dseUTRERERKSfdDGbhLaoVTOsUKg+MpiTk4PDhw+jUqVKWgmKiIiIiKgsqJUMf/TRRwX22dvb44svvtB4QERERERUvr1akW35pFYynH/2BDMzM9jY2GglICIiIiKisqJWMuzk5KTtOIiIiIiogtBlja+mFZkMT506tcCqHoXJP+0aEREREVFFUWQybEiLaRARERGR+gxiZLhdu3ZlGAYRERERVRQG9wAd8HzVuejoaDx58gQvr9PBEWQiIiIiqqjUSoZPnTqFxYsXw83NDbGxsahWrRpiY2Ph7e3NZJiIiIjIwBhEmcTLtmzZgqFDh8Lf3x+ff/45vv/+exw8eBCxsbHajo+IiIiISGvUWo45OTkZ/v7+KvuCgoJw+PBhrQRFREREROWXEBKNv3RFrZFhGxsbpKWloXLlynBycsLNmzdhbW1dYJlmIiIiItJ/Qo9SQLWS4Q4dOuD69eto1aoVunbtiunTp0MikeCtt97SdnxERERERFpTbDK8YcMGtGvXDt27d1fuCwoKQv369ZGdnQ13d3dtx0dERERE5YxCh2UNmlZsMpyQkIDx48ejWrVqCAoKQuvWrWFjYwNHR8eyiq9iU+TpOgIiIiIiKoZEiOKnTX769CmOHTuGI0eO4O7du2jcuDGCgoLQrFkzGBurPU2xQcqV3dJ1CERERKRHTJxr6zoEAMAN784a77Pu9T0a71MdJSbDL0tISMDhw4dx9OhRZGZmIiAgAP3799dmfBUak2EiIiLSpPKSDF+v00XjfXrf3K3xPtWh1tRqL7i5ueG9997DRx99BHNzc/z777/aiouIiIiISOvUrnO4ceMGDh06hOPHj8Pa2hrBwcFo27atNmMjIiIionJI/bqC8q/YZFgmk+Hw4cM4fPgwnjx5gpYtW2L8+PHw9vYuq/iIiIiIiLSm2GR41KhRaNiwIXr27Ak/Pz+YmpqWVVxEREREVE4JhYFMrbZkyRLY29uXVSxERERERGWq2GSYiTARERER5Wcwi24QEREREeUn9CgZLtXUakRERERE+qRUybBCocCjR4+0FQsRERERVQBCaP6lK2qVSWRkZGDlypU4ceIEjI2NsX79epw5cwbR0dH48MMPtR0jEREREZFWqDUyvGLFClhaWuKnn36CsfHz/LlOnTqIiorSanBEREREVP4ohETjL11Ra2T40qVLWL58uTIRBgAbGxs8fvxYa4ERERERUflkcA/QWVpa4smTJyr7kpOTYWdnp5WgSiKTyfDVV1+9Vh979+7FiBEj0LNnT6Snp5fYfsmSJThx4sRrXZOIDM/Rk2fx1sdfoPOHA7Fyw9YCxx8/eYqRk2bg3c+G48NBo3Hrzj3lsfVbd6D7p0PxTp+hWP/bjjKMmojIcKiVDHfo0AHz5s3D5cuXIYTAzZs3sWTJErzxxhvajk9r6tatiylTpsDJyUnXoRCRnsrLy8OM+Uux9Ifp2Ln+J+yOOITbd2NU2qz45Td4166JP9eFY9Y3YzBn4c8AgFt37mHbrn+w6ef52LZmMQ5FncL92Ae6uA0iogL06QE6tZLhd955B/7+/li1ahXy8vKwdOlSNG/eHF26dNF2fEVSKBRYtmwZxowZgxkzZiAuLg4TJ05UHpfJZPj666+LPL9GjRpwdnYu0OfIkSOVI8UKhQIjRoxQbl+8eBFTp07FqFGjcPbsWS3cFRHpk0vXbqJ6VTdUq+IKExMTdO7QFgeOqv6F6fa9GLRq1hgAUNOjGh48lCE59RHu3I9Do3resDA3h7GxEZr7NsD+w8d1cRtERHpNrZphiUSCrl27omvXrtqOR20JCQkYNWoUBg8ejPnz5+POnTuQy+VITEyEi4sLoqKi4O/vX6o+pVIpAgMDceTIEXTt2hWXLl2Ch4cHbGxsAABJSUmYNm0aEhMTMX36dDRs2BCmpqbauD0i0gOypBS4Ov/vr08uTo64dO2GSpu6XjUQcSgKTRvVx6WrN5CQKENiUgq8anhg0c+/IO1xOszMTHHkxBnUr1u7rG+BiKhQ+rQCnVojw5cvX4ZMJgMApKWlITw8HD/99BPS0tK0GVuxnJ2d4enpCQCoWbMmkpKS4O/vj+PHn4+cHD9+HAEBAaXuNzg4GIcPHwYAHDx4EMHBwcpj/v7+kEqlcHNzg4uLC+Lj4wucHxERgQkTJmDChAmvcFdEpE8K+6ufBKq/QAZ88gHSn2Tgvc9HYOO2v+BduxaMjKSo5VkN/Xq/j4Gjp2Dw16Go41UDRkZGZRM4EVEJhJBo/KUraiXDq1atglT6vOm6deuQl5cHiUSC5cuXazW44piYmCjfS6VS5OXlISAgAMePH1cmqW5ubqXu19HREba2trh8+TJu3bqFJk2aKI9JJCV/okJCQjBnzhzMmTOn1NcmIv3i4uSAh7Ik5XZiUjKcHO1V2lhVssSMSV9i25rFmD15DB6lPYa7mysA4L23OmLr6oVYFz4XttbW8KhWpUzjJyIyBGolw6mpqXB0dEReXh4uXLiAL774AgMHDsTNmze1HV+puLq6QiqVYtu2ba80KvxC+/btsXjxYuVI8AsnTpyAQqHAw4cPkZiYiCpV+IuJiIrWwLsOYuLiERf/ELm5udiz/zCC27RUaZP+5Clyc3MBANt2/YNmjevDqpIlACDlURoAICFRhv2Hj6NzSFCZxk9EVBSDm2fYwsICaWlpiI2Nhbu7O8zNzSGXyyGXy7UdX6n5+/tjw4YNCA8PL7bd7t27sXPnTqSlpWHs2LFo0qQJBg8eDABo3rw5li5dqlIiATwfaZ42bRoeP36MgQMHsl6YiIplbGyESaMH44uvpiJPocC7Xd+AVw0PbNm+GwDQq3sX3Lkfi0kz58NIaoSantXw7YRRyvNHT56FtMdPYGxshG9GD4attZWuboWISG9JhCh5Movt27fjn3/+gVwuR9++fdG6dWtcvnwZv/76K2bNmlUWcZap27dvY926dfj2229fq59c2S0NRUREREQEmDiXjwdpT1TpofE+W8X/ofE+1aHWyHD37t3h5+cHqVQKV9fntWz29vbKkVR9sn37duzbtw8jR47UdShEREREpGVqjQxXZGFhYcqZMF7o3bs3fH19tX5tjgwTERGRJpWXkeEot/c03mdAwjaN96kOtUaGhwwZUuSxpUuXaiwYbRg7dqyuQyAiIiLSK7qcCk3T1EqGR4wYobL96NEj7N69G61bt9ZKUEREREREZUGtZLhevXoF9tWvXx8zZ87U6ZLMRERERFT2FLoOQIPUmme4MMbGxgVqcYmIiIiIKhK1Roa3bNmisv3s2TP8999/KquzEREREZFhEDCwmuGUlBSVbTMzM7z11lto27atVoIiIiIiovJLoUdzkamVDA8dOlTbcRARERERlTm1kmEAuHz5Mg4fPoxHjx7Bzs4Obdu2RYMGDbQZGxERERGVQwo9KpNQ6wG6/fv348cff0TlypXh5+cHOzs7LFy4EBEREdqOj4iIiIhIa9QaGd65cycmT54MT09P5b6AgADMmzcPISEh2oqNiIiIiMohg3uA7smTJ3B3d1fZV6VKFTx9+lQrQRERERFR+WVw8wx7e3vjl19+wbNnzwAA2dnZWL9+PerUqaPV4IiIiIiItEmtkeGBAwfixx9/RN++fWFlZYWnT5+iTp06GDVqlLbjIyIiIqJyxuDKJOzs7DB9+nQkJycjLS0NdnZ2cHBw0HZsFZ54+kjXIRAREZE+cdZ1APpH7anVAMDR0RGOjo7aioWIiIiIKgB9qhkuNhkeMmRIiR0sXbpUY8EQEREREZWlYpPhESNGKN8LITB37lxMmDBB60ERERERUfllMCPD9erVU9k2MjIqsI+IiIiIDIs+PUCn1tRqRERERET6qFQP0BERERERKfRnYLj4ZPjAgQMq23K5vMC+9u3baz4qIiIiIqIyUGwyfOTIEZVtLy+vAvuYDBMREREZFoUe1QwXmwyHhoaWVRxEREREVEEIXQegQXyAjoiIiIgMFh+gIyIiIqJS0ad5hjkyTEREREQGiyPDRERERFQqCon+PEBXqpHhc+fOITMzU7mdf5o1IiIiItJ/QgsvXSnVyPCff/6Jhw8fIigoCFKpFAcPHuTUakRExTh65iLmLluPPIUCPTq1w4Ceb6scf/wkA1MXrEBsggxmpib4dvQA1PasBgBIf5qBaT+uwq37cZBIJPh29AD4+tTWxW0QEemtUiXD3333HbKysjBx4kQkJCRg7ty52oqrWDKZDHPnzsW8efNeuY+lS5fizp07EELAzc0Nw4YNg7m5eZHtlyxZgmbNmqFVq1avfE0iMix5eQrMXLIOP88aD1dHe3w4aiqCWzZFLY+qyjYrt+yEd63qWDj1S9yJjcesJeuwcs5EAMDcZRvQunkjzJ88Erm5cmQ9e6arWyEiUmEwD9DNnz8fly5dUtm3bt06WFhY4O23367QZRKfffYZwsLC8MMPP8DR0RF79+7VdUhEpGcu3byN6lVcUM3NGSYmxugc1AoHT5xVaXM75gFaNq4PAKhZrQoeJCYj+dFjPM3IwtnL19HjzSAAgImJMWysKpX5PRAR6btik+ErV67Ax8dHub1s2TIkJiYiNDQUPXr0wNmzZ4s5W7sUCgWWLVuGMWPGYMaMGYiLi8PEiROVx2UyGb7++usiz7e0tAQACCGQk5Oj7HPkyJFIT09Xbo8YMUK5ffHiRUydOhWjRo3S6b0TUcUgS34EVyd75baLoz0SUx6ptKlbszoios4AAC7duI0EWTISk1MR91AGO1sbTJ7/Mz4YNhmhP65EZnZ2mcZPRFQUhUTzL10p8QG6J0+eQKFQYOnSpXjy5AkmTZoEc3NzGBsbqzxMV9YSEhLQqVMnzJ8/H5aWlrhz5w7kcjkSExMBAFFRUfD39y+2j59++gmDBg1CfHw8OnfuDKlUisDAQOWS05cuXYKHhwdsbGwAAElJSZg2bRomTJiAFStWKJNoIqLCiEIeCZHkW8K0/wdvI/1pBt4f9g1+3fkvvGt5wNhIiry8PFyLvodeXTtg65IZsDA3w6rf/iqr0ImIDEaxyXBQUBDGjx+PkSNH4sSJE/D394eJiQkAYPv27fDy8iqTIAvj7OwMT09PAEDNmjWRlJQEf39/HD9+HABw/PhxBAQEFNvH0KFDsXz5clStWhVRUVEAgODgYBw+fBgAcPDgQQQHByvb+/v7QyqVws3NDS4uLoiPjy/QZ0REBCZMmIAJEyZo4jaJqAJzcbTHw6RU5XZiciqcHSqrtLGqZIEZYwbh9yUzMevrL/Do8RNUdXGGi6M9XBzt0cj7+c/ZN9r44Vr0vTKMnoioaApINP7SlWIfoPv000/RuHFjGBkZoWrVqpg5cyY2bdoEqVSK3NxcTJ48uaziLOBFUg4AUqkUOTk5aNu2LRYsWAA/Pz8AgJubW4n9SKVSBAQEYOfOnQgODoajoyNsbW1x+fJl3Lp1CyNHjlS2lagxp15ISAhCQkIAADl3TpX2tohIjzSoUxP34x8i7qEMLg722HPoBOaOH6rSJv1pBizMzGBiYoxteyPRrGFdWFWygFUlC7g62eNuXAJquLvh5PkrqFW9ahFXIiIqW7qcCk3TSpxNonHjxsr3c+fOxeXLl5GXlwdvb29l3W154erqCqlUim3bthU7KiyEQGJiIlxdXSGEwJkzZ1ClShXl8fbt22Px4sUIDAyEVPq/wfMTJ04gKCgIMpkMiYmJKucQEeVnbGSESUM+xeDJYcjLU+Ddjm3h5eGO3/7eDwDo2bUD7sTG45sflkMqlaJW9aqY/uUA5fkTh3yKCd8vRW6uHO5uTvhu9CBd3QoRkd4q1dRqRkZGKslxeeTv748NGzYgPDy8yDZCCCxZskRZ8+zh4YEBA/73C6h58+ZYunSpSokE8Hykedq0aXj8+DEGDhwIU1NT7dwEEemNtn6+aOvnq7KvZ9cOyve+PrXx96ofCj3Xu5YHtiz6VpvhERG9El0+8KZpEiGEPo10a8Tt27exbt06fPvt6/0SYpkEERERaZJpTT9dhwAA+KXqJxrv89MHGzTepzpKNTJsCLZv3459+/ap1AoTERER0f/o06Ibep8Mh4WFQSaTqezr3bs3fH19C23fvXt3dO/eXfuBEREREVVQuiorOH/+PNasWQOFQoEOHToUmbNFR0fjm2++wejRo0tcPfiVkuHLly9DKpWiXr16r3J6mRo7dqyuQyAiIiKi16RQKLBq1SpMnjwZDg4OmDhxIpo3bw53d/cC7TZu3FjkwGd+JS66AQChoaG4fv06gOdlBAsXLsTChQvxxx9/lO4uiIiIiKjC08UKdNHR0XB1dYWLiwuMjY0REBCA06dPF2i3Z88etGzZUrloWknUSoZjY2NRp04dAMD+/fsRGhqKmTNn4t9//1XrIkREREREryM1NRUODg7KbQcHB6SmphZoc+rUKXTs2FHtftUqk3gx4cTDhw8BQDkcnZGRofaFiIiIiEg/aOMBuoiICERERCi3X17IDPhfPvqy/AuirV27Fr1791ZZJ6IkaiXDdevWxerVq/Ho0SO0aNECwPPE2NraWu0LEREREZF+0EYynD/5zc/BwQEpKSnK7ZSUFNjZ2am0uX37NhYuXAgASE9Px3///QepVKpcnbgwaiXDw4YNw65du2BjY4Nu3boBAOLj49GlSxd1TiciIiIiei21atVCQkICZDIZ7O3tERUVVWAq3CVLlqi8b9asWbGJMKBmMmxtbY2PP/5YZV/Tpk3VjZ2IiIiI9IjQwQp0RkZG6NevH2bOnAmFQoHg4GBUq1YN+/btA4BS1Qm/TK0V6ORyOSIjI3Hv3j1kZ2erHBs+fPgrXdgQcAU6IiIi0qTysgLdsmqaX4FucGw5XoEuPDwc9+/fR7NmzWBra6vtmIiIiIioHDO4FeguXLiA8PBwVKpUSdvxEBERERGVGbWSYUdHR+Tm5mo7FiIiIiKqAAxuZLht27YICwtD586dUblyZZVjDRo00EZcRERERFROlfjAWQWiVjK8d+9eAMCmTZtU9kskEoSHh2s+KiIiIiKiMqBWMvzynG2kPsWTZF2HQERERKRxCh1MraYt6q9VR0RERESkZ9QaGb537x7WrVtX6DzD+UsniIiIiEi/GdwDdAsXLkTLli3x+eefw9TUVNsxEREREVE5ZnDJcFpaGnr16gWJRI8KRIiIiIjI4KlVMxwUFISjR49qOxYiIiIiqgCEFl66otbIcPfu3TF58mT8+eefBZZjDg0N1UpgRERERETaplYyPH/+fDg7O8PPz481w0REREQGTp+mVlN7NonVq1fD2Fit5kRERESkx/TpATq1aoZ9fHwQFxen7ViIiIiIiMqUWkO9Tk5OmDFjBvz8/ArUDPfq1UsrgRERERFR+aTLB940Ta1kOCcnB02bNoVcLkdKSoq2YyIiIiIiKhNqJcNDhw7VdhxEREREVEEo9GhsWK1kODExschjLi4uGguGiIiIiKgsqZUMjxw5sshjW7Zs0VgwRERERFT+6dNsEmolw/kT3rS0NGzduhU+Pj5aCaokMpkMc+fOxbx58165jyVLluDq1auwtLQEAAwbNgyenp7Ftm/WrBlatWr1ytckIsNz7Pw1zF3zJxQKgXc7tET/7iEqx9OfZmLq0s2IS0yGqYkJpg/5ELWru+FZTi4+Dw1HrlwOeV4e3mjVGEN7dtbRXRARqdKfIgk1k+H8KleujL59+2LUqFFo06aNpmMqM3369GFyS0Rak6dQYNaqbVg+eTBcHCrj44kL0K55A9Ryd1W2WflnBLw9q+DHsf1w90EiZq3ahhVTh8LUxBgrQ4fC0twMufI89J26CG18fdCojqfuboiISA+pNc9wYeLj4/Hs2TNNxlIqCoUCy5Ytw5gxYzBjxgzExcVh4sSJyuMymQxff/11qfscOXIk0tPTldsjRoxQbl+8eBFTp07FqFGjcPbsWc3dDBHppcvRMajm6gh3F0eYGBujU0ATRJ6+rNLmTtxD+DWsAwCoUdUF8UmpSEl7AolEAktzMwCAPC8P8rw8QKJHSz4RUYWm0MJLV9QaGZ46dSokL/0QfvbsGWJjY/H+++9rLbCSJCQkYNSoURg8eDDmz5+PO3fuQC6XIzExES4uLoiKioK/v3+xfWzatAm///47GjRogN69e8PExASBgYE4cuQIunbtikuXLsHDwwM2NjYAgKSkJEybNg2JiYmYPn06GjZsyOWpiahIstQ0uDpUVm47O9ji0q0YlTZ1PKpi/8mLaOpdE5ei7yMh6RESU9PgUNkaeQoFPho/DzEPk9HrzTZoVNujjO+AiEj/qZUMt2/fXmXb3NwcHh4ecHNz00pQ6nB2dlbW+NasWRNJSUnw9/fH8ePH0b17dxw/fhxffvllked//PHHqFy5MuRyOZYvX44dO3bg/fffR3BwMMLCwtC1a1ccPHgQwcHBynP8/f0hlUrh5uYGFxcXxMfHF1tnTESGTRRSVJd/cLdf9w6Yu/ZP9BwbBq/qbvCuURVG0ud/tDOSSvFb2FikZ2Rh9A+rcSsmAbWr6+7nLhHRCwo9+kOVWslwu3bttBxG6ZmYmCjfS6VS5OTkoG3btliwYAH8/PwAoNhk3c7OTtlPcHAwdu3aBQBwdHSEra0tLl++jFu3bqnMpCFR40+UERERiIiIAABM+6ht6W+MiPSGi0NlPExJU27LUh7D2U51FU8rS3N8N/QjAIAQAl2Gf4eqzg4qbWwqWaBFvVqIOn+dyTARlQsGMc+wulOmlaflmF1dXSGVSrFt2zYEBAQU2/bRo0ews7ODEAKnT59GtWrVlMfat2+PxYsXIzAwEFLp/8qqT5w4gaCgIMhkMiQmJqJKlSoF+g0JCUFIyPOnxbMv7NbQnRFRRVS/VjXEJCQhTpYCF3tb7I36D7NHfqLSJj0jCxZmJjAxNsYf+0+gqU8tWFmaIzX9KYyNjGBTyQLZOTk4cekmPn+ng47uhIhIfxWZDFfUZZf9/f2xYcMGhIeHF9tu0aJFygfjPDw8MGjQIOWx5s2bY+nSpSolEsDzkeZp06bh8ePHGDhwIOuFiahYxkZGmNjvPQyZuRwKhQLdg1vCq5obftt3DADQs2Nr3H2QiMnhGyGVSlHT3QXTB38IAEh+lI7JS36FQqGAQgh09PdFULP6urwdIiIl/RkXBiRCFFbVZthu376NdevW4dtvv32tfjgyTERERJpk3riLrkMAAHzj+bHG+5x571eN96kOtecZTkhIwLFjx5Camgp7e3u0bt1apw/Qacv27duxb9++YlfdIyIiIjJkBrcC3ZkzZ7B48WI0bdoUTk5OiI+Px4QJEzBixAg0b95c2zG+lrCwMMhkMpV9vXv3hq+vb6Htu3fvju7du2s/MCIiIqIKyiAeoHvZpk2bMHbsWDRo0EC578qVK1i9enW5T4bHjh2r6xCIiIiIqJxSKxlOTU2Fj4+Pyj5vb+8K+5AdEREREb06/RkXVnM5Zk9PT+U8vC/89ddfXHCCiIiIiCo0tUaGBwwYgLlz52LPnj1wcHBASkoKzMzMMG7cOG3HR0RERETljME9QFe1alUsWLAAt27dUs4m4eXlBWNjtSejICIiIiI9YTAP0D19+hTR0dHw9fWFkZERvL29lcfOnz8PLy8vWFlZaT1IIiIiIiJtKLZmeNu2bbhz506hx+7evYs//vhDK0ERERERUfkltPDSlWKT4XPnziEkJKTQYyEhIThz5oxWgiIiIiIiKgvFlkmkpaXBxsam0GNWVlZ4/PixVoIiIiIiovJLnx6gK3ZkuFKlSoiPjy/0WEJCAiwtLbUSFBERERFRWSg2Gfbz88OaNWuQk5Ojsj8nJwfr1q1Dq1attBocEREREZU/Qgv/dKXYMolevXrh22+/xfDhw+Hr64vKlSsjLS0NFy5cgIODA3r27FlWcRIRERFROaFPZRLFJsMWFhb47rvvcOjQIVy6dAl37tyBlZUVevXqhbZt23KeYSIiIiKq0CRCCP2ZNbmcMTatqusQiIiISI/Icx7oOgQAwFBPzVcH/HTvN433qY5ia4aJiIiIiPQZ6xyIiIiIqFT0qayAyTARERERlYpCj9Jhtcokdu7cWej+v/76S6PBEBERERGVJbWS4W3btpVqPxERERHpL4UWXrpSbJnE5cuXAQAKhUL5/oXExERYWFhoLzIiIiIiIi0rNhleunQpgOcrzr14DwASiQSVK1dGv379tBsdEREREZU7ulwxTtOKTYaXLFkCAAgPD8fw4cPLJCAiIiIiKt/0aQU6tWqGmQgTERERkT4qcmR49OjRWLBgAQBgyJAhRXbwcvkEEREREek/gyiT+OKLL5TvR4wYUSbBEBERERGVpSKTYW9vb+X7evXqlUkwRERERFT+6VPNsFor0OXl5eHYsWO4e/cusrOzVY69PIJMRERERFSRqJUML168GDExMfD19YWtra22YyIiIiKickwhDKBm+GXnz5/H0qVLucgGEZEa3uzYDvPnfwsjqRSr12zC92FLCrRZMP9bdO7UHplZWejffzT+O38Z7u5VsHb1Qri4OkGhUGDlyo1YHL5Kec6woZ9j6NDPIZfLsWfPfkyYOLMsb4uISEl/UmE1k2F3d3c8ffpUb5LhJ0+eYP78+YiOjka7du3Qv3//YtvLZDLMnTsX8+bNK6MIiaiikkqlWLRwJjp1+QhxcQk4cXw3dv21D9eu3VK26dypPWp71YB3vTZo6dcUS8JnI6DN25DL5Rg7bjr+O38ZVlaVcOrkXkTsP4xr126hXVAAur39Jpo0DUFOTg6cnBx0eJdERPpDrWR4xIgRWLZsGRo3blygTCIoKEgrgWmTiYkJevXqhZiYGMTGxuo6HCLSI34tmuD27Xu4ezcGAPDbbzvQ7e03VZLht99+E+s3/g4AOHnqHGwr28LV1RkPH8rw8KEMAPD0aQauX7+FqlVcce3aLXzxxaf4PmwJcnJyAABJSSllfGdERP+j0KOxYbWS4cjISFy/fh0ZGRkwNTVV7pdIJDpJhmUyGWbNmgUvLy/cu3cPbm5uGD58OGJjY7F27Vo8e/YMxsbGmDp1aqGj2ebm5vD29sbDhw9V9h84cAAxMTHo27cvACAiIgIPHjxA586dkZeXh/DwcJXrmZmZlcXtElEFUqWqK2Lj4pXbcQ8S4NeiiUqbqlVcERf7vzYP4hJQtYqrMhEGAA8Pd/g2boCTp/4DANSuXRNt2vjhu2/HITv7GcaN/w5nzl7Q8t0QEek/tZLh3bt3Y+7cuXB3d9d2PGqLj4/H4MGD4e3tjZ9++gl79+7Fv//+iy+//BJeXl7IzMxUSdzVERAQgD///BOffPIJjI2NERkZiUGDBhV6vX/++QfdunXTxq0RUQUmkUgK7BP5HjQpqU2lSpb4bcsKjPk6FE+ePAUAGBsboXJlWwS0eRstmvti06/LULuuv4ajJyJSjz4tuqHWcsyVK1eGo6OjtmMpFQcHB+VcyG3btsWFCxdgZ2cHLy8vAIClpSWMjIxK1ae5uTnq16+Pc+fO4cGDB8jLy0P16tULvd7169cL7SMiIgITJkzAhAkTXvXWiKgCexCXgGruVZTb7lXdkJCQqNIm7kEC3Kv9r01VdzfE/38bY2NjbN2yAps2/Ynt2/eo9Pti+/SZ81AoFHB0tNfmrRARFUmhhZeuqJUMd+3aFYsWLcLNmzeRmJio8tKV/CMrmnq4r0OHDoiMjMTBgwfRrl27Iq9X2MgOAISEhGDOnDmYM2eORuIhoorl9Jnz8PKqAU/PajAxMUHPnu9g11/7VNr89dc+9On9PgCgpV9TpD9OV5ZIrPh5Hq5dj8aPC39WOWfHzn8QHNwawPOSCVNTUyQnp5bBHRER6Te1yiRWrXo+tc/Zs2cLHNuyZYtmI1JTcnIybt68iTp16uDo0aOoXbs2IiIiEB0dDS8vL2RlZcHU1LTUo8O1a9dGSkoK7t69i7CwsCKv9/IKfUREL+Tl5WHUl5Ox++9fYSSVYu26Lbh69SYGDewDAPh5xXrs3rMfnTq1x41rx5CZlYUBA8YAAFoHtECfT97HxUtXceb08wR6ypQ52LP3ANas3YyVK+bh/H/7kZOTi379v9TVLRIR6dUDdBKRv5gtHyEEEhMT4eTkVOrEUltkMhlmz54NHx8f3Lx5E66urhgxYgRiY2OxZs0a5OTkwNTUFFOmTIG5uXmhfQwbNgyZmZmQy+WoVKkSJk+erKyJ3r59O+7du4cvv/yy2OuV9ACdsWlVjd43ERERGTZ5zgNdhwAA+MDjHY33ufX+Do33qY4SR4YlEgnGjh2LdevWlUU8apNKpcqH217w8vLCzJnqTUK/ZEnBSfBfuH79Orp27arcdnZ2xoIFC14tUCIiIiI9Y3AP0Hl6eiIhIUHbsehcRkYGRo0aBVNTUzRs2FDX4RARERGVS/r0AF2JZRIAsHnzZhw5cgRBQUEFZpVo37691oJ7XefPn8fGjRtV9jk7O2Ps2LFlcn2WSRAREZEmlZcyiR4emp9e9o/7OzXepzrUeoDuxo0bcHZ2xrVr1wocK8/JsK+vL3x9fXUdBhEREZFeUWMstcJQKxkODQ3VdhxERERERGVOrWQYAJ48eYL//vsPaWlp6NatG1JTUyGEgIODgzbjIyIiIqJyRp+mVlPrAbqrV6/iyy+/xJEjR/D7778DAB4+fIgVK1ZoNTgiIiIiIm1SKxleu3YtvvzyS3zzzTfKuYa9vLxw+/ZtrQZHREREROWPPs0moVaZRFJSUoGpxoyNjZGXl6eVoIiIiIio/DK4eYbd3d1x/vx5lX2XLl1C9erVtRETEREREVGZUGtkuE+fPpg7dy6aNGmCnJwc/Pzzzzh79myZzddLREREROWHPj1Ap9aiGwCQmpqKI0eOICkpCY6OjggMDORMEiXgohtERESkSeVl0Y0u1btovM/dMbs13qc6ih0ZfvbsGbZt24bY2FjUqFED7777LkxMTMoqNiIiIiIqhwxm0Y1Vq1bh9u3baNKkCU6ePImnT5+iX79+ZRUbEREREZVDupr94fz581izZg0UCgU6dOiA7t27qxw/cuQIduzYAQAwNzfHgAED4OnpWWyfxT5Ad/78eUyePBmffPIJJk6ciLNnz77WDRARERERvQqFQoFVq1Zh0qRJWLBgAY4dO4a4uDiVNs7Ozpg2bRp++OEHvPfee/j5559L7LfEMgk7OzsAgKOjIzIzM1/jFoiIiIhIH+hiarXo6Gi4urrCxcUFABAQEIDTp0/D3d1d2aZu3brK97Vr10ZKSkqJ/RabDOfl5eHy5cvKbYVCobINAA0aNFDvDgxQDVtXXYdAREREpBdSU1NVJm9wcHDArVu3imx/4MABNGnSpMR+i02GbW1tsXTpUuW2lZWVyrZEIkF4eHiJFyEiIiIi/aGNqdUiIiIQERGh3A4JCUFISIhyu7CH9iQSSaF9Xb58GQcPHsS3335b4nWLTYaXLFlSYgdEREREZFi0MZtE/uQ3PwcHB5Wyh5SUFGU578vu37+P5cuXY+LEibC2ti7xumqtQEdEREREpEu1atVCQkICZDIZ5HI5oqKi0Lx5c5U2ycnJ+OGHHzB8+HBUqVJFrX7VWoGOiIiIiOgFXaxAZ2RkhH79+mHmzJlQKBQIDg5GtWrVsG/fPgBAx44d8fvvv+Pp06dYuXKl8pw5c+YU26/aK9BR6dV2aqbrEIiIiEiP3EoqH9PcBru/ofE+D8b9q/E+1cGRYSIiIiIqFV1MraYtrBkmIiIiIoPFkWEiIiIiKhWFHlXZMhkmIiIiolLRn1SYZRJEREREZMA4MkxEREREpaKLqdW0hSPDRERERGSwODJMRERERKWiTyPDTIaJiIiIqFT0ac02gyyTiI6OxtixY5WvU6dOFdteJpPhq6++KqPoiEifBLb3xz/HtyHi1HYMGtm3wPGaXp74bfcaXIk7jv5D+yj3u1Zxwfo/l2Pvsd+x+8hv+GzQR2UYNRGR4TDIkeFq1aphzpw5MDIywqNHjzB27Fg0a9YMRkZGug6NiPSIVCrFtDkT0PeDoXgYn4ht+9bjwN5DiL55V9kmLe0xvpsUhpAu7VTOzcvLw+zQBbh68ToqVbLEn/s34FjkCZVziYh0hWUSOiaTyTBr1ix4eXnh3r17cHNzw/DhwxEbG4u1a9fi2bNnMDY2xtSpU2FhYVHgfDMzM+X73NxcSCQSAMCBAwcQExODvn37AgAiIiLw4MEDdO7cGXl5eQgPD1e53sv9EBHl16hpfdy/F4vY+w8AAH9v34cOndupJLSpyY+QmvwI7d5oo3JuUmIykhKTAQAZGZm4ffMuXNycmQwTEWlYhS2TiI+PR0hICH744QdYWFhg7969+PHHH9G3b1+EhYVhypQpMDU1LfL8W7duYcyYMfjqq68wcOBAGBkZISAgAGfPnoVcLgcAREZGIjg4uNDr/fPPP2Vyn0RUcbm6OSPhQaJy+2F8IlzcnErdT9VqbqjX0BsXzl7WZHhERK9MaOGfrlTYZNjBwQHe3t4AgLZt2+LChQuws7ODl5cXAMDS0rLYsofatWtj/vz5mD17Nv7880/k5OTA3Nwc9evXx7lz5/DgwQPk5eWhevXqhV7v+vXrWr5DIqrw/v+vTi8r7UMnlpUsEL4mDDMn/4CnTzM0FRkR0WsRQmj8pSsVskwCgLK04QULCwvk5uaWuh93d3eYm5sjNjYWtWrVQocOHfDnn3+iSpUqaNeuXZHXy7/9QkREBCIiIkodBxHpn4fxiXCr6qLcdq3iAtnDZLXPNzY2RviaMOz8fQ/2/X1QGyESERm8CjsynJycjJs3bwIAjh49itq1a+PRo0eIjo4GAGRlZSEvL6/Qc2UymfJYUlIS4uPj4eT0/E+XtWvXRkpKCo4dO4bWrVsXeb0Xo8T5hYSEYM6cOZgzZ45mbpSIKqxL/12FZ41qcK9eBSYmxujavSP27z2k9vmzfpyC2zfvYs2yjVqMkoio9BQQGn/pSoUdGa5atSoiIyPx888/w9XVFZ07d0aDBg2wZs0a5OTkwNTUFFOmTCm0VOL69evYvn07jIyMIJVK0b9/f9jY2CiP+/v74969e7Cysiryeh07diyT+ySiiisvLw/TJ36P1b+Fw0hqhN837UD0jTv46LP3AACb1m2Do7MD/vx3PaysK0GhEOj7xUfo3PoD1K1fG+/2egvXr9zCzoO/AgDmzVyCQxHHdHlLRER6RyIq4KzJMpkMc+fOxbx587TS/5w5c9C1a1c0bNjwtfqp7dRMQxERERERAbeSzuo6BABAE9fWJTcqpf8e6uY/+xW2TEIbMjIyMGrUKJiamr52IkxERERE5V+FHBlW1/nz57Fxo2qtnbOzM8aOHVsm1+fIMBEREWlSeRkZbuwaoPE+LzyM0nif6qiwNcPq8PX1ha+vr67DICIiItIrupwXWNNYJkFEREREBkuvR4aJiIiISPMUelRly5FhIiIiIjJYHBkmIiIiolLRp5phJsNEREREVCoskyAiIiIi0gMcGSYiIiKiUtGnMgmODBMRERGRweLIMBERERGVij7VDDMZJiIiIqJSYZkEEREREZEe4MgwEREREZUKyyRILTXNnXUdAhEREREVg8kwEREREZUKa4aJiIiIiPQAR4aJiIiIqFSEUOg6BI1hMkxEREREpaJgmQQRERERUcXHkWEiIiIiKhWhR1OrcWSYiIiIiAwWR4aJiIiIqFT0qWaYyTARERERlQrLJIiIiIiI9ABHhomIiIioVBQcGSYiIiIiqvg4MkxEREREpSL4AB0RERERGSp9eoCOyTARURlp3q4ZBk8bDCMjKfZs2ovfftqqcjy4ezB6Dv0AAJCdkYXFk8Jx59pdXYRKRGQwDDIZlslkGD16NKpUqQIAqF27NgYNGlRs+7lz52LevHllFSIR6RmpVIphM4Zh4seTkJyQjMV/LcSJf08i5laMsk1i7EOM/WAcnj5+iubtmmPU3JEY1W20DqMmIioc5xnWA66urggLC9N1GERkIOr61kH8vXg8jHkIAIjceQj+HVupJMNXz15Tvr/+33U4ujmWeZxERIamQibDMpkMs2bNgpeXF+7duwc3NzcMHz4csbGxWLt2LZ49ewZjY2NMnToVFhYWavd74MABxMTEoG/fvgCAiIgIPHjwAJ07d0ZeXh7Cw8NVrmdmZqalOyQifePg6oik+CTldnJCMryb1C2yfacP38Tpg2fKIjQiolLTp5rhCju1Wnx8PEJCQvDDDz/AwsICe/fuxY8//oi+ffsiLCwMU6ZMgampaZHny2QyjBs3DqGhobh27floTEBAAM6ePQu5XA4AiIyMRHBwcKHX++eff7R/k0SkNySSgvuK+l3S2L8R3uzVEatmrdZuUEREVDFHhgHAwcEB3t7eAIC2bdvijz/+gJ2dHby8vAAAlpaWRZ5rZ2eHn376CdbW1rhz5w7CwsIwb948WFpaon79+jh37hyqVq2KvLw8VK9eHTKZrMD1du/ejW7duhXoOyIiAhEREVq4YyKqyJITkuFUxUm57ejmiJTElALtanh74suwLzG5zxQ8SXtSliESEalNnxbdqLDJsCTfMIuFhQVyc3PVOtfExAQmJiYAgJo1a8LFxQUJCQmoVasWOnTogD///BNVqlRBu3btirxe/u0XQkJCEBISAgB4c2NndW+HiPTcjQs3UdWzClyquSDlYQradQvCnBFzVdo4VXHC1BVTEDYqDA/uPtBRpEREJWOZRDmQnJyMmzdvAgCOHj2K2rVr49GjR4iOjgYAZGVlIS8vr9Bz09PToVAoAACJiYlISEiAi4sLgOczS6SkpODYsWNo3bp1kdd7MUpMRKQORZ4CS6YsxawNM7Di4M84/NcR3L8Zg66fdEHXT7oAAHp/+TGsK1tj+Mxh+GlvOBb/vVDHURMR6b8KOzJctWpVREZG4ueff4arqys6d+6MBg0aYM2aNcjJyYGpqSmmTJkCIyOjAudevXoVv/32G4yMjCCVSjFw4EBYWVkpj/v7++PevXsq+/Jfr2PHjmVyn0SkP04fPI3TB0+r7Pt7w27l+x/HLcSP45gAE1H5x6nVygGpVFpgbmAvLy/MnDmzxHNbtWqFVq1aFXn8+vXr6Nq1q3Lb2dkZCxYsePVgiYiIiKhcqrBlEtqQkZGBUaNGwdTUFA0bNtR1OERERETlkhBC4y9dkQh9qoDO5/z589i4caPKPmdnZ4wdO7ZMrv9mNT5AR0RERJrzT+weXYcAALCyrKHxPp9m6mb5+QpbJqEOX19f+Pr66joMIiIiIiqn9DoZJiIiIiLNE3r0AB1rhomIiIjIYHFkmIiIiIhKhSvQEREREZHB0qf5F1gmQUREREQGiyPDRERERFQqfICOiIiIiEgPcGSYiIiIiEpFn2qGmQwTERERUanoUzLMMgkiIiIiMlgcGSYiIiKiUtGfcWGODBMRERGRAZMIfSr6ICKqgCIiIhASEqLrMIiIDBJHhomIdCwiIkLXIRARGSwmw0RERERksJgMExEREZHBYjJMRKRjrBcmItIdPkBHRERERAaLI8NEREREZLCYDBMRERGRwWIyTESkATKZDF999dVr9bF3716MGDECPXv2RHp6eont09PTMWnSJIwbNw7Xrl17rWsTERkqLsdMRFRO1K1bF02bNsX06dPVan/p0iVUqVIFw4cP13JkRET6iyPDREQaolAosGzZMowZMwYzZsxAXFwcJk6cqDwuk8nw9ddfF3l+jRo14OzsXKDPkSNHKkeKFQoFRowYgdu3b2PDhg3477//MHbsWOTk5GjnpoiI9ByTYSIiDUlISECnTp0wf/58WFpa4s6dO5DL5UhMTAQAREVFwd/fv1R9SqVSBAYG4siRIwCejwZ7eHigVq1a6NWrFwICAhAWFgZTU1ON3w8RkSFgMkxEpCHOzs7w9PQEANSsWRNJSUnw9/fH8ePHAQDHjx9HQEBAqfsNDg7G4cOHAQAHDx5EcHCwxmImIjJ0TIaJiDTExMRE+V4qlSIvLw8BAQE4fvw44uPjAQBubm6l7tfR0RG2tra4fPkybt26hSZNmmgsZiIiQ8dkmIhIi1xdXSGVSrFt27ZXGhV+oX379li8eDH8/f0hlfJHNxGRpvAnKhGRlvn7++PIkSMl1gvv3r0bgwcPRkpKCsaOHYtly5YpjzVv3hzZ2dkskSAi0jAux0xEVAHcvn0b69atw7fffqvrUIiI9ArnGSYiKue2b9+Offv2YeTIkboOhYhI73BkmIiojIWFhUEmk6ns6927N3x9fXUTEBGRAWMyTEREREQGiw/QEREREZHBYjJMRERERAaLyTARVXi//fYbFi1apOswDN6wYcNw8eJFXYdBRFQqTIaJDEBhSUpkZCSmTJmio4hU3bx5E3369EFWVlaBY+PGjcPevXt1EBUgl8sxb948DBs2DD179sSVK1eKbLtp06YC057Fx8fjs88+Q0xMjNZivHLlCgYPHqxWW5lMhp49eyIvL6/U1zly5Aj69OmDPn36oHfv3ujVq5dyu0+fPqXuryQ9e/ZU6b9v374lnlOajwUR0QucWo2IdK5OnTqwt7fHyZMn0a5dO+X+mJgYxMXFoXXr1jqLzdvbG127dsX8+fOLbff+++9j/PjxiIiIQEhICIQQWL58Obp27Yrq1atrJJa8vDwYGRlppK/SCgwMRGBgIIDnSefixYtVFgXRhrCwMLi6umr1Gi/T5ceXiHSHyTARAQDi4uKwcuVK3Lt3D/b29vj444/RvHlzAMC0adMQGBiIDh06AHg+qrx//3589913EEJg3bp1OHr0KHJzc+Hk5ISRI0eievXqyM3NxaZNm3D8+HHI5XK0aNECffv2hampaYHrBwUF4dChQyrJ8OHDh9G0aVNYW1tjzZo1OHXqFDIzM+Hq6oq+ffvCx8enQD+FJWrDhg3DF198gUaNGkGhUGDnzp3Yv38/MjIy0KBBAwwaNAhWVlYF+jI2NkbXrl0BoMQlkE1MTDBkyBDMnj0bTZs2xdmzZ5GRkYEePXrgwYMHWL16Ne7cuQMbGxv06tVLuTTzuXPnsHnzZiQmJsLS0hLBwcHo2bMngOcjucOHD8fgwYOxdetWODs7Y/r06cXGMW3aNHh7e+PKlSu4f/8+6tSpg5EjR8LGxgahoaEAoBxlnTJlCry8vPDnn39i//79yMnJga+vL/r16wdLS8tir1OUe/fu4ZdffkFSUhJ8fX0xbNgw5ef77Nmz2Lx5M5KSkuDu7o6BAwfCw8OjVP337NkTixYtUibJS5YsgYODA7p3745Zs2ZBLpcrR6oXLlyITZs2wcHBAR9++CGAgl8fw4YNwxtvvIGjR48iPj4e69evx+3bt/HLL78gLi4OTk5O6Nu3L+rXr/9KHw8iKv9YJkFEkMvlmDt3Lho1aoSVK1eiX79+WLRoEeLj40s898KFC7h27RoWLlyItWvX4ssvv4S1tTUAYOPGjUhISEBYWBgWLVqE1NRU/P7774X207ZtW1y/fh3JyckAAIVCgaNHj6Jt27YAgFq1auH777/H6tWr0aZNG8yfPx85OTmlvtc9e/bg9OnTmDZtGpYvXw4rKyusXLmy1P0Upnbt2mjXrh3Cw8OxefNmDBkyBHK5HDNmzECbNm2wcuVKjBo1CqtWrUJsbCwAwMzMDMOHD8eaNWswYcIE/Pvvvzh16pRKv1evXsWCBQvwzTffqBXHsWPHMGTIEKxcuRJyuRy7du0CAGUivXbtWqxfvx516tRBZGQkIiMjERoaivDwcGRnZ2PVqlWv/DE4fvw4Jk2ahCVLliAmJgaRkZEAgDt37mDp0qUYNGgQVq9ejZCQEHz//ffIzc195Wu9zNzcHJMmTYKdnR3Wr1+P9evXw97eXq1zjx07hgkTJmDt2rV4/Pgx5syZgx49emD16tXo06cP5s2bh/T0dI3ESUTlD5NhIgMRFhaGvn37Kl8vJ4C3bt1CdnY2unfvDmNjYzRo0ABNmzbF0aNHS+zX2NgY2dnZePDgAYQQcHd3h52dHYQQ2L9/Pz777DNYWVnBwsICPXr0wLFjxwrtx9HREfXq1cPhw4cBAJcvX0Zubi6aNm0K4HmybG1tDSMjI7z99tuQy+VqJev5RURE4MMPP4SDgwNMTEzwwQcf4OTJk69UR1uYDz/8EA8fPkRgYCBq1aqFc+fOwcnJCcHBwTAyMkLNmjXRsmVLnDhxAgBQv359VK9eHVKpFB4eHmjdujWuXr2q0ucHH3wAc3PzQkfUC9OuXTtUqVIFpqam8Pf3x71794pse/ToUbz11ltwcXGBubk5Pv74Y0RFRb3yx6Nz586wt7eHlZUVmjVrprz2/v37ERISgtq1a0MqlaJdu3YwNjbGrVu3iuxr/Pjxyq/X1atXv1I86sbs6OgIU1NTHD58GE2aNEHTpk0hlUrRqFEj5eeRiPQTyySIDMTYsWPRqFEj5faLUgcAePToERwdHVVKAZycnJCamlpivw0aNMCbb76JVatWITk5GX5+fujTpw9yc3Px7NkzTJgwQdlWCAGFQlFkX0FBQfjjjz/Qo0cPHD58GK1bt4ax8fMfU7t27cKBAweQmpoKiUSCrKwsPHnypNQfh6SkJPzwww+QSCTKfVKpFI8fP1Z7JLE4pqamcHZ2RrVq1ZTXu3XrlsoDYHl5ecoR71u3buHXX39FTEwM5HI55HI5WrVqpdKng4NDqWKoXLmy8r2ZmRmys7OLbPvo0SM4OTkptx0dHZGXl/fKH4+Xr21qaqr8GkpOTsahQ4dUHoaUy+XFfo3NnTu3TGqGHR0dle+Tk5Nx4sQJnD17VrkvLy+PZRJEeozJMBHBzs4OycnJUCgUyoQ4OTkZbm5uAJ4nVM+ePVO2T0tLUzm/S5cu6NKlCx4/fowFCxZg586d6NmzJ0xNTTF//ny1kyo/Pz+sXLkSly9fxsmTJzFt2jQAwLVr17Bjxw5MnToV7u7ukEql+Pzzz1HYApr5Y1UoFCp/4nZwcMCQIUPg7e2tVkyvy8HBAfXq1Sty5o5FixbhzTffxMSJE2Fqaoq1a9cW+JP8y4n76yisHzs7OyQlJSm3k5OTYWRkBFtbW41c8wUHBwf06NEDPXr0eK1+CvtafPGfhcLur6Sv3cLiDAwM5KwURAaEZRJEhNq1a8Pc3Bw7d+6EXC7HlStXcPbsWeUsDp6enjh16hSePXuGhw8f4sCBA8pzo6OjcevWLcjlcpiZmcHExARSqRRSqRQdOnRQ1mECQGpqKs6fP19kHObm5mjVqhWWLl0KJycn1KpVCwCQlZUFIyMj2NjYQKFQ4Pfff0dmZmahfVSpUgW5ubk4d+4c5HI5tm3bplKX+sYbbygf4gKA9PR0nD59usiYcnNzlbXJcrkcOTk5hSbhRWnWrBkSEhJw+PBh5chvdHQ04uLilPdmZWUFU1NTREdHq1Wa8qpsbGwgkUiQmJio3Ne6dWv8/fffkMlkyM7OxqZNm+Dv76/xWRU6dOiAf//9F7du3YIQAtnZ2Th37lyh0+kVx9PTE0ePHoVCocD58+dVSkpsbW3x5MkTla8NT09P/Pfff3j69CnS0tKwe/fuYvsPDAzE2bNncf78eSgUCuTk5ODKlStISUkp3Q0TUYXBkWEigrGxMcaNG4eVK1fizz//hL29PYYPH46qVasCALp27Yrbt28rn/5v06YNLl26BOB5Mrdu3TokJibC1NQUjRs3Rrdu3QAAvXv3xu+//45vvvkGT548gb29Pd544w34+voWGUtQUBAiIyPx8ccfK/f5+vrC19cXo0aNgpmZGbp27aryp+2XWVpaYsCAAVi2bBkUCgW6deumUmbQpUsXAMCMGTPw6NEj2Nrawt/fHy1atCi0vy+//FKZOM+cORMAEB4eDmdnZ3U+tLCwsMDkyZOxbt06rFu3DkIIeHh44LPPPgMADBgwAL/88gtWr16NevXqwd/fHxkZGWr1XVpmZmbo0aMHpkyZgry8PEyaNAnBwcF49OgRQkNDkZOTg8aNG6Nfv34av3atWrXwxRdfYPXq1UhISICpqSm8vb0LnRGkOH379sWSJUvwzz//oEWLFiqft6pVq6J169YYPnw4FAoF5s+fj7Zt2+LSpUsYNmwYnJyc0K5dO/z1119F9u/o6Ihx48Zhw4YNWLhwIaRSKby8vDBw4MBXvnciKt8kojRDHEREREREeoRlEkRERERksJgMExEREZHBYjJMRERERAaLyTARERERGSwmw0RERERksJgMExEREZHBYjJMRERERAaLyTARERERGSwmw0RERERksP4PWTRV0CHbYUoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hv_model_data = created_combined_df(permits_combined, selected_permits)\n",
    "sns.heatmap(hv_model_data.corr().iloc[1:12, 12:13], annot = True)\n",
    "plt.title(\"Correlation Matrix for Housing Data (Select Permit Types)\")\n",
    "plt.xlabel(\"House Value 1 Year Into The Future\")\n",
    "plt.ylabel(\"Permit Columns & House Value Columns (Past)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe10b65",
   "metadata": {},
   "source": [
    "The correlation in the upper section of the matrix showing the relationship between the permit columns and `hv_1yf` increased from a high of 0.16 when using all permits to 0.46.  We now have several columns with moderate to strong correlations that we can utilize to build our model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "586baada",
   "metadata": {},
   "source": [
    "Let's first build a model only using the house value columns, without any permit data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "44941590",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'hv_1yb',\n",
    "    'hv_3yb',\n",
    "    'hv_5yb'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "602b2975",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degrees:  1\n",
      "Test RMSE:  12206.135989171082\n",
      "Train RMSE:  12890.160873679679\n",
      "-----\n",
      "Degrees:  2\n",
      "Test RMSE:  12699.230806365456\n",
      "Train RMSE:  11211.184031967718\n",
      "-----\n",
      "Degrees:  3\n",
      "Test RMSE:  11070.416067066311\n",
      "Train RMSE:  9992.762331970742\n",
      "-----\n",
      "Degrees:  4\n",
      "Test RMSE:  21148.7165538445\n",
      "Train RMSE:  12024.519560003699\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "X = hv_model_data[columns]\n",
    "y = hv_model_data['hv_1yf']\n",
    "degree_list = [_ for _ in range(1,5)]\n",
    "\n",
    "for d in degree_list:\n",
    "    poly = PolynomialFeatures(degree=d, include_bias=False)\n",
    "    poly_features = poly.fit_transform(X)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(poly_features, y, test_size=0.3, random_state=1)\n",
    "    poly_reg_model = LinearRegression()\n",
    "    poly_reg_model.fit(X_train, y_train)\n",
    "    \n",
    "    # Predict values and compute RMSE\n",
    "    test_predictions = poly_reg_model.predict(X_test)\n",
    "    test_rmse = np.sqrt(mean_squared_error(y_test, test_predictions))\n",
    "\n",
    "    # Compute train RMSE to determine if model is overfitting the data at higher degrees\n",
    "    train_predictions = poly_reg_model.predict(X_train)\n",
    "    train_rmse = np.sqrt(mean_squared_error(y_train, train_predictions))\n",
    "\n",
    "    print(\"Degrees: \",d)\n",
    "    print(\"Test RMSE: \",test_rmse)\n",
    "    print(\"Train RMSE: \", train_rmse)\n",
    "    print(\"-----\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288ee2c3",
   "metadata": {},
   "source": [
    "The model with 3 degrees has a lower Test RMSE than the other models, however the difference between the Test RMSE and Train RMSE indicate that the model with 3 degrees is overfitting the data.  The model with 1 degree (linear regression) will be used going forward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "44e8e7c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12206.135989171085\n"
     ]
    }
   ],
   "source": [
    "X = hv_model_data[columns]\n",
    "y = hv_model_data['hv_1yf']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "lin_reg_model = LinearRegression()\n",
    "lin_reg_model.fit(X_train, y_train)\n",
    "predictions = lin_reg_model.predict(X_test)\n",
    "rmse1 = np.sqrt(mean_squared_error(y_test, predictions))\n",
    "print(rmse1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fed2e6",
   "metadata": {},
   "source": [
    "Now let's add the permit columns to the model to determine if the model is more or less accurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b5025be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'no_permits',\n",
    "    'np_1yb',\n",
    "    'np_3yb',\n",
    "    'np_5yb',\n",
    "    'hv_1yb',\n",
    "    'hv_3yb',\n",
    "    'hv_5yb'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "605cd0db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11519.564598770725\n"
     ]
    }
   ],
   "source": [
    "X = hv_model_data[columns]\n",
    "y = hv_model_data['hv_1yf']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "lin_reg_model = LinearRegression()\n",
    "lin_reg_model.fit(X_train, y_train)\n",
    "predictions = lin_reg_model.predict(X_test)\n",
    "rmse2 = np.sqrt(mean_squared_error(y_test, predictions))\n",
    "print(rmse2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "60651427",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.62 % reduced error\n"
     ]
    }
   ],
   "source": [
    "print(((rmse1-rmse2)/rmse1*100).round(2), \"% reduced error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "280012f9",
   "metadata": {},
   "source": [
    "Our model designed to predict future housing values with permit data had about a 5% smaller error than the same model without permit data, which indicates that including permit data in a machine learning model may be beneficial in predicting future property values more accurately"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65805572",
   "metadata": {},
   "source": [
    "### Future Percent Change - Selected Permits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cede6537",
   "metadata": {},
   "source": [
    "We'll continue with the second loop and build a model that predicts YOY percent change in property values one year into the future. The original correlation matrix (reproduced below) indicated a negative correlation between the permit columns and the `pc_1yf` column, as such we looked for permit types with correlation values of -0.3 or lower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2b272694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(housing_data.corr().iloc[1:12, 13:], annot = True)\n",
    "plt.title(\"Correlation Matrix for Housing Data (All Permit Types)\")\n",
    "plt.xlabel(\"Percent Change In Housing Value 1 Year Into The Future\")\n",
    "plt.ylabel(\"Permit Columns & House Value Columns (Past)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cfe40e02",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAST FORM BUILDING PERMIT\n",
      "nan\n",
      "-----\n",
      "ADMINISTRATIVE PERMIT\n",
      "-0.01\n",
      "-----\n",
      "USE PERMIT\n",
      "-0.16\n",
      "-----\n",
      "PLUMBING PERMIT\n",
      "-0.24\n",
      "-----\n",
      "ELECTRICAL PERMIT\n",
      "-0.15\n",
      "-----\n",
      "ALTERATION PERMIT\n",
      "-0.15\n",
      "-----\n",
      "ZONING/USE PERMIT\n",
      "-0.11\n",
      "-----\n",
      "ZONING PERMIT\n",
      "-0.16\n",
      "-----\n",
      "OPERATIONS PERMIT\n",
      "-0.03\n",
      "-----\n",
      "ADDITION PERMIT\n",
      "-0.02\n",
      "-----\n",
      "NEW CONSTRUCTION PERMIT\n",
      "-0.2\n",
      "-----\n",
      "DEMOLITION PERMIT\n",
      "-0.31\n",
      "-----\n",
      "SUPPRESSION PERMIT\n",
      "-0.09\n",
      "-----\n",
      "MECHANICAL PERMIT\n",
      "-0.14\n",
      "-----\n",
      "SIGN PERMIT\n",
      "0.07\n",
      "-----\n",
      "ZONING ADMIN REVIEW\n",
      "-0.06\n",
      "-----\n",
      "FIRE SUPPRESSION PERMIT\n",
      "-0.27\n",
      "-----\n",
      "RESIDENTIAL BUILDING PERMIT\n",
      "0.28\n",
      "-----\n",
      "GENERAL PERMIT\n",
      "nan\n",
      "-----\n",
      "GENERAL PERMIT MINOR\n",
      "0.12\n",
      "-----\n",
      "COMMERCIAL BUILDING PERMIT\n",
      "0.18\n",
      "-----\n",
      "MECHANICAL / FUEL GAS PERMIT\n",
      "0.28\n",
      "-----\n",
      "5-YEAR TANK TESTING\n",
      "nan\n",
      "-----\n",
      "SITE / UTILITY PERMIT\n",
      "nan\n",
      "-----\n",
      "PRELIMINARY APPROVAL\n",
      "nan\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "selected_permits = []\n",
    "\n",
    "for permit_type in permit_type_list:\n",
    "    permit_data = created_combined_df(permits_combined, [permit_type])\n",
    "    \n",
    "    # 13 is the pc_1yf column, 1:5 are the permit columns.\n",
    "    correlation_mat = permit_data.corr().iloc[13, 1:5].round(2)\n",
    "    sorted_pairs = correlation_mat.sort_values(kind=\"quicksort\", ascending = True)\n",
    "    # Select permit_type with a correlation < -0.3 (negative correlation)\n",
    "    if sorted_pairs[:1][0] <= -0.3:\n",
    "        selected_permits.append(permit_type)\n",
    "    print(permit_type)\n",
    "    print(sorted_pairs[:1][0])\n",
    "    print('-----')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dc19416a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DEMOLITION PERMIT']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_permits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba29aab9",
   "metadata": {},
   "source": [
    "The original correlation matrix indicated a negative correlation between the permit columns and the `pc_1yf` column, as such we looked for permit types with correlation values of -0.3 or lower, which only resulted in a single permit type: \"DEMOLITION PERMIT\".  The revised correlation matrix is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aa034ecd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc_model_data = created_combined_df(permits_combined, selected_permits)\n",
    "sns.heatmap(pc_model_data.corr().iloc[1:12, 13:], annot = True)\n",
    "plt.title(\"Correlation Matrix for Housing Data (Demolition Permits)\")\n",
    "plt.xlabel(\"Percent Change In Housing Value 1 Year Into The Future\")\n",
    "plt.ylabel(\"Permit Columns & House Value Columns (Past)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2c98fc",
   "metadata": {},
   "source": [
    "For comparison the `no_permits` is the only value that moved further from 0.  The other permit columns moved closer to 0 when excluding the other permits.  This intuitively makes sense.  From a practical standpoint, we would expect demolition to affect the short term YOY property value as it can be disruptive, however long-term we'd expect it to not have as much of an effect, which is shown in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02bb7620",
   "metadata": {},
   "source": [
    "As before, we'll first build the model without permit columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5f0e3f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'hv_3yb',\n",
    "    'hv_5yb',\n",
    "    'pc_1yb',\n",
    "    'pc_3yb',\n",
    "    'pc_5yb'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "871735fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degrees:  1\n",
      "Test RMSE:  4.494920867497876\n",
      "Train RMSE:  4.180607440071173\n",
      "-----\n",
      "Degrees:  2\n",
      "Test RMSE:  4.58459380668673\n",
      "Train RMSE:  3.7398467716352606\n",
      "-----\n",
      "Degrees:  3\n",
      "Test RMSE:  13.614869896787466\n",
      "Train RMSE:  6.792951827404082\n",
      "-----\n",
      "Degrees:  4\n",
      "Test RMSE:  21.082491892479087\n",
      "Train RMSE:  10.8530869666231\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "X = pc_model_data[columns]\n",
    "y = pc_model_data['pc_1yf']\n",
    "degree_list = [_ for _ in range(1,5)]\n",
    "\n",
    "for d in degree_list:\n",
    "    poly = PolynomialFeatures(degree=d, include_bias=False)\n",
    "    poly_features = poly.fit_transform(X)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(poly_features, y, test_size=0.3, random_state=1)\n",
    "    poly_reg_model = LinearRegression()\n",
    "    poly_reg_model.fit(X_train, y_train)\n",
    "    \n",
    "    # Predict values and compute RMSE\n",
    "    test_predictions = poly_reg_model.predict(X_test)\n",
    "    test_rmse = np.sqrt(mean_squared_error(y_test, test_predictions))\n",
    "\n",
    "    # Compute train RMSE to determine if model is overfitting the data at higher degrees\n",
    "    train_predictions = poly_reg_model.predict(X_train)\n",
    "    train_rmse = np.sqrt(mean_squared_error(y_train, train_predictions))\n",
    "\n",
    "    print(\"Degrees: \",d)\n",
    "    print(\"Test RMSE: \",test_rmse)\n",
    "    print(\"Train RMSE: \", train_rmse)\n",
    "    print(\"-----\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298d9ce2",
   "metadata": {},
   "source": [
    "Our goal is to minimize Test RMSE as well as the difference between Test and Train RMSE, so we'll again use Linear Regression as our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "83120a12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.494920867497876\n"
     ]
    }
   ],
   "source": [
    "X = pc_model_data[columns]\n",
    "y = pc_model_data['pc_1yf']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "lin_reg_model = LinearRegression()\n",
    "lin_reg_model.fit(X_train, y_train)\n",
    "predictions = lin_reg_model.predict(X_test)\n",
    "rmse1 = np.sqrt(mean_squared_error(y_test, predictions))\n",
    "print(rmse1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf40023",
   "metadata": {},
   "source": [
    "We'll again add permit data into the model to determine its effect, only using columns with >= 0.2 correlation coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bd61933f",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'no_permits',\n",
    "    'np_1yb',\n",
    "    'np_3yb',\n",
    "    'hv_3yb',\n",
    "    'hv_5yb',\n",
    "    'pc_1yb',\n",
    "    'pc_3yb',\n",
    "    'pc_5yb'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9a652a14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.29374266814168\n"
     ]
    }
   ],
   "source": [
    "X = pc_model_data[columns]\n",
    "y = pc_model_data['pc_1yf']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "lin_reg_model = LinearRegression()\n",
    "lin_reg_model.fit(X_train, y_train)\n",
    "predictions = lin_reg_model.predict(X_test)\n",
    "rmse2 = np.sqrt(mean_squared_error(y_test, predictions))\n",
    "print(rmse2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3615d73b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.48 % reduced error\n"
     ]
    }
   ],
   "source": [
    "print(((rmse1-rmse2)/rmse1*100).round(2), \"% reduced error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "851f152a",
   "metadata": {},
   "source": [
    "Our model designed to predict future YOY change in housing values with permit data had about a 4% smaller error than the same model without permit data, which indicates that including permit data in a machine learning model may be beneficial in predicting YOY changes in property values more accurately."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f56ca3b",
   "metadata": {},
   "source": [
    "### Future Percent Change - All Permits\n",
    "\n",
    "For comparison, due to the similar correlations with the permit columns and `pc_1yf` when utilizing all permits VS just utilizing 'DEMOLITION PERMIT', let's also build a model using all permits.  As before, we'll start by omitting the permit columns, then add them in, and compare the RMSE between the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f2321afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(housing_data.corr().iloc[1:12, 12:13], annot = True)\n",
    "plt.title(\"Correlation Matrix for Housing Data (All Permit Types)\")\n",
    "plt.xlabel(\"Percent Change In Housing Value 1 Year Into The Future\")\n",
    "plt.ylabel(\"Permit Columns & House Value Columns (Past)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ac603e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'hv_3yb',\n",
    "    'hv_5yb',\n",
    "    'pc_1yb',\n",
    "    'pc_3yb',\n",
    "    'pc_5yb'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3be0b011",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.165870697068428\n"
     ]
    }
   ],
   "source": [
    "X = housing_data[columns]\n",
    "y = housing_data['pc_1yf']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "lin_reg_model = LinearRegression()\n",
    "lin_reg_model.fit(X_train, y_train)\n",
    "predictions = lin_reg_model.predict(X_test)\n",
    "rmse1 = np.sqrt(mean_squared_error(y_test, predictions))\n",
    "print(rmse1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9a85dc0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'no_permits',\n",
    "    'np_1yb',\n",
    "    'np_3yb',\n",
    "    'np_5yb',\n",
    "    'hv_3yb',\n",
    "    'hv_5yb',\n",
    "    'pc_1yb',\n",
    "    'pc_3yb',\n",
    "    'pc_5yb'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4f372302",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.849653289449073\n"
     ]
    }
   ],
   "source": [
    "X = housing_data[columns]\n",
    "y = housing_data['pc_1yf']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "lin_reg_model = LinearRegression()\n",
    "lin_reg_model.fit(X_train, y_train)\n",
    "predictions = lin_reg_model.predict(X_test)\n",
    "rmse2 = np.sqrt(mean_squared_error(y_test, predictions))\n",
    "print(rmse2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fe77e8ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.59 % reduced error\n"
     ]
    }
   ],
   "source": [
    "print(((rmse1-rmse2)/rmse1*100).round(2), \"% reduced error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9516432",
   "metadata": {},
   "source": [
    "Our model designed to predict future YOY change in housing values with permit data had about a 7% smaller error than the same model without permit data, which indicates that including permit data in a machine learning model may be beneficial in predicting YOY changes in property values more accurately. In addition, utilizing all permit types worked better for our model than just using 'DEMOLITION PERMIT, which makes sense given the correlation table."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee61547",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "The goal of this analysis, per the Introduction, was to determine whether the number of permits issued in a given zip code in prior years would help in building a model to predict future property values and property value YOY changes. This analysis showed that the addition of permit data into models predicting the property value one year into the future as well as predicting the YOY change in property value reduced the error of the model by around 4-7%, compared to models without the permit data.  \n",
    "\n",
    "This analysis supported the general belief that the inclusion of permit information may help make models that predict housing values more accurate.\n",
    "\n",
    "Again, the limitations as listed in the introduction were the following:\n",
    "- The permit data available ranges back from 2007, which only gives 15 years of analysis.  \n",
    "- The housing market crash of 2008 occured during the short timeframe available within our data.  This crash would be assumed to have affected housing price data and may overshadow any correlations between housing prices and permit issuance. \n",
    "- Not all renovation work on a building requires a permit, which would limit the data available within our permit dataset.  In addition, some work is occasionally performed without proper permitting, which would again limit data available in our dataset.\n",
    "\n",
    "Our analysis dropped null values in order to properly build the models.  This meant that years 2007-2011 were dropped due to null values in the `np_5yb` column. This helped give us some separation between the analysis and the housing crash of 2008 however it limited our data to years from 2012-2021, only 10 years of data.  It's for this reason our forecast focused only on 1 year into the future. Finding correlation 3 or 5 years into the future would have produced more rows with null values and further limited our already limited date range.\n",
    "\n",
    "### Next Steps\n",
    "\n",
    "To take this analysis a step further it would be worth looking at the effect of housing prices and number of permits issued at zipcodes surrounding a given zipcode, rather than limiting to just within a zipcode. It would also be worth seeing if this correlation is similar in other cities that have permit data available. Lastly, going into depth as to what work the permits were specifically related to may be helpful in finding further correlations. Though there were 25 different permit types, they were large umbrellas, digging further into the data within the permit datasets may help shed light on other correlations. This wasn't done initially in order to keep this analysis more top-level given the limitations stated in the introduction, but may be worth doing in the future.\n",
    "\n",
    "Finally, it will be helpful to revisit this analysis once more years haver passed and more permit data is available to see if the relationships noted above still hold up or if they change. Having more years of data would allow for forecasting further into the future which would be more helpful.  Projections years into the future can be affected by short-term market-related issues, that can typically be smoothed out when the forecast is extended several additional years. This analysis should be revisited in several years for that reason.  That would also allow us to view if the types of permits correlated with property values and YOY changes (outlined above) remain the same or change. "
   ]
  }
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