main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\n",
    "import locale\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "# %matplotlib inline\n",
    "\n",
    "from modules.files import *\n",
    "from modules.data import *\n",
    "from modules.enums import *\n",
    "from modules.graphs import *\n",
    "\n",
    "pd.set_option('display.max_columns', None)  # or 1000\n",
    "pd.set_option('display.max_rows', None)  # or 1000\n",
    "pd.set_option('display.max_colwidth', None)  # or 199\n",
    "\n",
    "locale.setlocale( locale.LC_ALL, '')\n",
    "\n",
    "\n",
    "#import_csv(\"Neighborhood_zhvi_uc_condo_tier_0.33_0.67_sm_sa_mon.csv\")\n",
    "\n",
    "config = {\n",
    "            env_database_host: \"127.0.0.1\",\n",
    "            env_database_username: \"capstone_user\",\n",
    "            env_database_password: \"VGVyBFFGVrdky4rRFeWp!\"\n",
    "        }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#condo_data = import_csv(\"ingested_condo.csv\")\n",
    "#sfr_data = import_csv(\"ingested_singlefamilyresidence.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#df2 = df1.pivot(index=\"Date\", columns=[\"RegionName\", \"Housing Type\"], values=\"% Change\")\n",
    "#df2 = df2.resample(\"Y\").mean()\n",
    "#df2.head()\n",
    "#df2.plot(legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Housing Type</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZHVI Start</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>ZHVI End</th>\n",
       "      <th>End Date</th>\n",
       "      <th>ZHVI Min</th>\n",
       "      <th>ZHVI Max</th>\n",
       "      <th>Years Diff</th>\n",
       "      <th>% Per Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RegionName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90046</th>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>172655.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>2008581.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>172655.0</td>\n",
       "      <td>2008581.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>10.270304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11201</th>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>467848.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>2966753.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>467848.0</td>\n",
       "      <td>3049533.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>7.636416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11206</th>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>180911.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>1129253.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>180694.0</td>\n",
       "      <td>1133223.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>7.568796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20002</th>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "      <td>146125.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>802620.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>135970.0</td>\n",
       "      <td>802620.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>7.022114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90046</th>\n",
       "      <td>Condo</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>146200.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>786405.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>137765.0</td>\n",
       "      <td>786405.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>6.932941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11201</th>\n",
       "      <td>Condo</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>168301.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>841613.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>164529.0</td>\n",
       "      <td>910432.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>6.622689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11226</th>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>215405.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>1075986.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>211407.0</td>\n",
       "      <td>1075986.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>6.618044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10011</th>\n",
       "      <td>Condo</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>170150.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>846310.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>170150.0</td>\n",
       "      <td>1057748.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>6.599919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20009</th>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "      <td>185511.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>917701.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>179545.0</td>\n",
       "      <td>917701.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>6.576784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10025</th>\n",
       "      <td>Condo</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>223756.0</td>\n",
       "      <td>1996-01-31</td>\n",
       "      <td>1090032.0</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>222370.0</td>\n",
       "      <td>1338832.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>6.511621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Housing Type         City State  ZHVI Start Start Date  \\\n",
       "RegionName                                                                    \n",
       "90046       SingleFamilyResidence  Los Angeles    CA    172655.0 1996-01-31   \n",
       "11201       SingleFamilyResidence     New York    NY    467848.0 1996-01-31   \n",
       "11206       SingleFamilyResidence     New York    NY    180911.0 1996-01-31   \n",
       "20002       SingleFamilyResidence   Washington    DC    146125.0 1996-01-31   \n",
       "90046                       Condo  Los Angeles    CA    146200.0 1996-01-31   \n",
       "11201                       Condo     New York    NY    168301.0 1996-01-31   \n",
       "11226       SingleFamilyResidence     New York    NY    215405.0 1996-01-31   \n",
       "10011                       Condo     New York    NY    170150.0 1996-01-31   \n",
       "20009       SingleFamilyResidence   Washington    DC    185511.0 1996-01-31   \n",
       "10025                       Condo     New York    NY    223756.0 1996-01-31   \n",
       "\n",
       "             ZHVI End   End Date  ZHVI Min   ZHVI Max  Years Diff  % Per Year  \n",
       "RegionName                                                                     \n",
       "90046       2008581.0 2021-02-28  172655.0  2008581.0        25.1   10.270304  \n",
       "11201       2966753.0 2021-02-28  467848.0  3049533.0        25.1    7.636416  \n",
       "11206       1129253.0 2021-02-28  180694.0  1133223.0        25.1    7.568796  \n",
       "20002        802620.0 2021-02-28  135970.0   802620.0        25.1    7.022114  \n",
       "90046        786405.0 2021-02-28  137765.0   786405.0        25.1    6.932941  \n",
       "11201        841613.0 2021-02-28  164529.0   910432.0        25.1    6.622689  \n",
       "11226       1075986.0 2021-02-28  211407.0  1075986.0        25.1    6.618044  \n",
       "10011        846310.0 2021-02-28  170150.0  1057748.0        25.1    6.599919  \n",
       "20009        917701.0 2021-02-28  179545.0   917701.0        25.1    6.576784  \n",
       "10025       1090032.0 2021-02-28  222370.0  1338832.0        25.1    6.511621  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = get_baseline_data(False, True)\n",
    "\n",
    "df1 = data.copy(deep=True)\n",
    "\n",
    "data = df1.reset_index(\"Housing Type\") # remove multiindex to filter on housing type\n",
    "\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>% Per Year Min</th>\n",
       "      <th>% Per Year Max</th>\n",
       "      <th>% Per Year Avg</th>\n",
       "      <th>Years Diff Min</th>\n",
       "      <th>Years Diff Max</th>\n",
       "      <th>Years Diff Avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Housing Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Condo</th>\n",
       "      <td>-0.283760</td>\n",
       "      <td>6.932941</td>\n",
       "      <td>3.912264</td>\n",
       "      <td>5.1</td>\n",
       "      <td>25.1</td>\n",
       "      <td>22.268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SingleFamilyResidence</th>\n",
       "      <td>-1.209351</td>\n",
       "      <td>10.270304</td>\n",
       "      <td>3.637877</td>\n",
       "      <td>11.7</td>\n",
       "      <td>25.1</td>\n",
       "      <td>22.898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       % Per Year Min  % Per Year Max  % Per Year Avg  \\\n",
       "Housing Type                                                            \n",
       "Condo                       -0.283760        6.932941        3.912264   \n",
       "SingleFamilyResidence       -1.209351       10.270304        3.637877   \n",
       "\n",
       "                       Years Diff Min  Years Diff Max  Years Diff Avg  \n",
       "Housing Type                                                           \n",
       "Condo                             5.1            25.1          22.268  \n",
       "SingleFamilyResidence            11.7            25.1          22.898  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary table\n",
    "\n",
    "default_groupby = [\"Housing Type\"]\n",
    "\n",
    "summary_dataframe = pd.DataFrame()\n",
    "summary_dataframe[\"% Per Year Min\"] = data.groupby(default_groupby)[\"% Per Year\"].min()\n",
    "summary_dataframe[\"% Per Year Max\"] = data.groupby(default_groupby)[\"% Per Year\"].max()\n",
    "summary_dataframe[\"% Per Year Avg\"] = data.groupby(default_groupby)[\"% Per Year\"].mean()\n",
    "summary_dataframe[\"Years Diff Min\"] = data.groupby(default_groupby)[\"Years Diff\"].min()\n",
    "summary_dataframe[\"Years Diff Max\"] = data.groupby(default_groupby)[\"Years Diff\"].max()\n",
    "summary_dataframe[\"Years Diff Avg\"] = data.groupby(default_groupby)[\"Years Diff\"].mean()\n",
    "\n",
    "summary_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ZHVI End', ylabel='% Per Year'>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xdt3tZwoAo0d3v9tXAJAiU0tAZCC2bYMhveTUFy+G6dPjKY9IH6klINJXt9/edbefLQC0tXXd7SsASAlTS0AkH70tmzhlCixbFk9ZRApILQGRTNav7z6aJ5Ply7vu9hUApEypJSDS6dRTYcmS3MfkuZi6SLlQS0CSy7373X6mAHD55d1H8ygASIVRS0CS5b//O5h7J5e//S1YlEUkAXIGATOrBi5292tiKo9I4eVz915m62qIFErOdJC77wD+Z0xlESmMTZt679S9+ebuaR6RhMonHbTMzK4H7gTe69zo7k9HViqRvpo9G37849zHbNsWrLYlIjvlEwQ6JzRPXyTegRPy+YIwpbQCWOfunzazBcDJQKO7nxsecw6wt7tfm3fJRXpL84weDX/9azxlESlTvQYBdz9+gN/xVeA5YE8z2wuY4u4TzWyRmR0OvATMAqYN8Huk0q1cCfW7LJHa3aOPwjHHxFMekQqQ1+ggMzsF+BBQ27nN3a/K/omdnxsFnAJ8F7gUSAGDzcyAoUAHcDlwnbt39Ln00ictre3lt4DInnsGyyfmopy+SL/1+pyAmd0EfAb4CmDAmcD78zz/D4ErCC7+uPsWoAFYBbwKbAaOcvfFfS249M3ixnVMXbCUz9+ynKkLlnJ/47piFymz7du7d+pmCgBnnaVOXZECyedhsSlh7n6ju38H+CgwurcPmdmngbfcfWX6dndf6O6T3f0yYD4wz8y+ZGZ3mdk3s5xrtpmtMLMVGzZsyKPIkq6ltZ25DU20daTY0r6dto4UVzQ00dLaXuyiBb73va6LfraO27fe6rro33lnvOUTqWD5pIO2hn/+3czeB7QA4/L43FRgupl9iiCNtKeZ/cLdPw9gZkeEx70AXOvux5nZHWY23t1fTD+Ru98M3AxQX19fcrd+pZ5mad64lZqqKtqCBhkANVVVNG/cWrzyauy+SEnIpyWwxMyGA/8GPA28BtzR24fc/evuPsrdxwIzgaWdASA0H5gH1ADV4bYUsFu+hS8F5ZBmGTViKB2pVLdtHakUo0YMja8Qr77a+9j9H/5QaR6RmOUzOmh++LLBzJYAte6+eSBfamYzgKfcfX34/gkzWwM0ufvqgZw7Tulpls677Csamph68Mh+32FH0aqoGzaEhadP5IqGJmqqquhIpVh4+sToWwF1dfDOO7mP2b4dqqtzHyMikek1CJjZbsBlwBh3/2czG2Nmx7p7L9MtdnH3h4GH097fB9yX9n4OMCfvUpeIQqdZFjeuY26PC/X0yQdmPLavwWL65AOZevDI6NNWSvOIlJV80kE/A9oJOoQBmoGrIytRGRlImqWltZ3Vr2/a2Tnbl87b/qag6oYNYdLo4YUNAA0Nvad5li1TmkekROXTMfwP7v4ZMzsbwN23huP8E6+/aZZMd/zvr9s9r1ZFFCmoPtPdvkjFyCcIbDOzoQRTRWBm/0DQMhD6nmbJdhFfctExebUqijLSZ8uW4KGtXD73OfjFL6L5/hJX6qPDRHLJGgTM7Gh3fxK4EngQGG1miwiGfs6KpXRlom7YkLz/82e7iL+3bUderYrYRvqceCIsXZr7mHfegREjCvu9ZaYv/TgipShXS+A/zOxPwFzgn4CjCZ4Y/qq7vx1H4SpRrov4pNHDe21V9CcFlfedqtI8fVISqTmRAcoVBI4ELgb+BMx395/HU6TK1ttFPJ9WRV9SUD3vVL91ymFMOHCv4HN/boSjj85d4GuugUsu6WMt8xNlGiWOFE1JPoQn0kdZg4C7p4AfmtlvgSfM7EaCfgELdnsvSWLJphDDNfMJFpnuVD/30bG9nzyGxdSjTKPElaIpiYfwRAaot+Ulvwj8C/AN4AZ35QIGKv0OddLo4ZF+V/PGrdSYsXbBp3s/OMZ/2ijTKHGmaIr2EJ5IAeXqGH6cYIqIY939b7GVqILF1ol41VXw7W8zCViT5ZBpX/gR60aP5xdf+kjkwainKNMocadoYnsITyQiuVoC33b338VWkgoX+R1qHumbsXO7P+RdW4DURX9y71GmUYqRounL6DCRUpP1iWEFgMLqvENN13mH2i8bNvT+pO4//RO407KljdV/3ch3T5tAbU0VewwZRG1N1YBTFwN5cnnh6RMLWpY4zi1SifJaWUwGriB3qKNHQ3Nz7mP+/ncY2v2cnXeqk0YPZ9qH9i9I6mKgLZso0yhK0Yjkr7eO4SrgDHe/K6byVKx+dyIWeOx+oVIXhci9R5lGUYpGJD85g4C7p8zsIkBBoADyukP99a/h072M5rnjDvjMZ6IpZJ40PFKkMuSTDvqdmc0B7gTe69zo7r1MFC+ZZLxDLcMndTU8UqQy5BMEzg//vDBtmwMHFb44CdHRAYMH5zxk66AhfPCyBmprqlg29wTqYipaXyj3LlL+8llZLJ/1hCWUdcjk9dfDV76S87PPPdbIWb/9G1vat+/cVurTEBQy967ZOEXil+/KYpcSrCw228zGA4f2ZWWxpOj5MNjaqz/V62datrTtvPDtC3Q8uL7b/qTk2TUbp0hx5JMO+hmwEpgSvm8G7gYUBNK0tLbz/Z8/ytofnJ37wJ/9DGbNAsIL34Kl3SZ3u/DjB3P9Qy8yuLo6MXl2zcYpUjyRrSxmZrXAI8CQ8Hvucfdvm9kC4GSg0d3PDY89B9jb3a/tb0WK5ppr4NJLqQOezHLIuCvuZ8jgQUFuP7yoZbrwfeO+Zxg2pBowZh93EJ/9yJhEXAQ1G6dI8US5slg7cIK7t5pZDfCYmS0Fprj7RDNbZGaHAy8RLFIzrV81KIZeYuDD445k1lnf6bat50Ut04UPoLV9BwA3PPwSn/3ImAIWunRpuKlI8eQTBL5NP1YWC2ccbQ3f1oQ/AIPDlsRQoAO4HLjO3Tv6VvQYvfYajOulf/yVV7h/82CuaGii2gy27ei2u+dFLdOFL12S7oQ13FSkePIJAquA04GP0MeVxcysmqA/4WCCqaj/aGYN4Tn/AGwGjnL3q3o5z2xgNsCYMTHdHd91V+4HssyCeffTTIedQyafWb+Z+UuezXpRS7/wVZvxXi9Bo9JpuKlIcVi2JQLM7FTgp8B2YAfwGXdf1q8vMRsO3At8xd2fSdt+C3ADwSpmJwFN7n51rnPV19f7ihUr+lOM3HbsgBNOgEceyX7M0qVw/PF5nzKfIY+dx2QKGhodIyKFYmYr3b2+5/ZcLYHvEqwlsNbMPgIsBD7Wny93901m9jBB3v+ZsEBHhLtfAK519+PM7A4zG+/uL/bne/pswwb48pfhnnsy7586FR5+GAb1b569fMbQRzG5m4hIvrJOJQ1sd/e1AO6+HNijLyc2s33CFgBhx/IngLVph8wH5hH0FVSH21LAbn35nj579FGYMCFI5+y7764BYNGiYIoGd3jssX4HgP7oDAYKACISl1xXuH3N7NJs7939B72c+wDgv8J+gSrgrs4HzMxsBvCUu68P3z9hZmsI0kGr+1GP3j32GBx77K7bv/51+Nd/hWHDIvlaEZFSlisI/Jjud/893+fk7k3AEVn23Qfcl/Z+DjAn33P3S2s4UOnAA+Gmm3qfqVNEJAGyBgF3/062feWo5Zjjaf7rRuXbRUTSJGJlMc1LIyKSWa6O4YqQPj3DlvbttHWkuKKhiZbWfB56FhGpbBUfBAq+wLuISAXJOwiY2dFmttTMloWje8qC5qUREckuaxAws/17bLqUYGaEaQRj/MtC5/QMtTVV7DFkELU1VZqXRkQklKtj+CYzWwn8m7u3AZuAzxI80PVuDGUrGM1LIyKSWdaWgLvPABqBJeF8/5fQ9UTvjOiLVlh6GldEZFc5+wTc/f8BnwSGA78Cnnf369x9QwxlExGRiOXqE5huZo8BSwkmfZsJnGZmt4cLy0gBtLS2s/r1TbENWY37+0SktOXqE7ga+CjB4i8PuPuHgUvDhea/SxAUZADifohND82JSE+50kGbCS70M4G3Oje6+4vurgAwQHE/xKaH5kQkk1xB4DSCTuDtBKOCpIDifohND82JSCa5JpB7G/hRjGVJlLgfYtNDcyKSScVPG1GqenuIrdAduHpoTkQyybrGcKmKbI3hIsm0DnGUHbj5rHssIpWnP2sMSwx6rkOc3oHbRpC+uaKhiakHjyzIRTufdY9FJDmUDiox6sAVkThFFgTMbLSZPWRmz5nZn83sq+H2BWbWZGa3pR17Tuf+pFMHrojEKcqWwHbgMnf/IHA0cKGZTQKmuPtEoNrMDjezocAs4MYIy1KSMnX+qgNXROIUWZ+Au78BvBG+3mJmzwFjgMFmZgRPIncAlwPXuXtHVGUpRbk6fzXrqYjEJZY+ATMbCxwB/BFoAFYBrxI8lXyUuy/u5fOzzWyFma3YsKH8567L5+ndUpv1VHMOiVSmyEcHmdkwggv/Je7+LrAw/MHMbgHmmdmXgJOAJne/uuc53P1m4GYIhohGXeaodXb+do7+ga7O31K56KfTnEMilSvSloCZ1RAEgEXu/qse+44IX74AnOvuZwETwgnqKlo5df5qziGRyhbl6CADfgI85+4/yHDIfGAeUANUh9s6F60pCekpkEKmQ8qp81dDVkUqW5TpoKnAOcAaM2sMt/2ruz8QLlT/lLuvBzCzJ8xsDUE6aHWEZcpbegpka8d2zIzaQdW9pkPyfSK3XDp/y6nVIiJ9p2kjMmhpbWfqgqW0daQy7q+tqWLZ3BN2uXBXau78/sZ1XFGB9RJJEk0b0QeZOm7TVZvt0okb9XQPxVQurRYR6TtNG5FBphRIuve27eCZ9Zu7bav03HmpDVkVkcJQEMigZ8dtte16zPwlz3brJFbuXETKkdJBWaSnQDZv3caXFz1Na/uOnft7juvvDBw9c+e6cxaRUqYgkEPntMstre1sT3XvQM90l6/cuYiUG6WD8tCXcf3KnYtIOVFLIE+6yxeRSqQg0AdalUtEKo3SQSIiCaYgICKSYAoCIiIJpiAgIpJgCgIiIgmmICAikmAKAiIiCaYgICKSYAoCIiIJpiAgIpJgCgIiIgkWWRAws5+a2Vtm9kzatgVm1mRmt6VtO8fMvhpVOUREJLsoWwK3AtM635jZXsAUd58IVJvZ4WY2FJgF3BhhOUREJIvIgoC7PwK8k7YpBQw2MwOGAh3A5cB17t4RVTlERCS72PoE3H0L0ACsAl4FNgNHufvi3j5rZrPNbIWZrdiwYUPEJRURSQ5z996P6u/JzcYCS9x9QoZ9twA3AEcCJwFN7n51b+esr6/3FStWFLqoIiIVzcxWunt9z+1FGR1kZkeEL18AznX3s4AJZja+GOUREUmqYq0sNh+YDdQA1eG2FLBbkcojIpJIUQ4RvR14AjjUzJrN7Ivh9hnAU+6+3t03AU+Y2RrA3X11VOUREZFdRdonEAX1CYiI9F1J9QmIiEhpUBAQEUkwBQERkQRTEBARSTAFARGRBFMQEBFJMAUBEZEEUxAQEUkwBQERkQRTEBARSTAFARGRBFMQEBFJMAUBEZEEUxAQEUkwBQERkQRTEBARSTAFARGRBFMQEBFJsEiDgJlNM7PnzewlM/uXcNsCM2sys9vSjjvHzL4aZVlERGRXUS40Xw3cAJwMHAacbWaTgCnuPhGoNrPDzWwoMAu4MaqyiIhIZlG2BD4MvOTur7j7NuAOYDow2MwMGAp0AJcD17l7R4RlEZEEaGltZ/Xrm2hpbS92UcrGoAjPfSDwetr7ZuAjQAOwCvgDsBk4yt2vynUiM5sNzAYYM2ZMJIUVkfK2uHEdcxuaqKmqoiOVYuHpE5k++cBiF6vkRdkSsAzb3N0Xuvtkd78MmA/MM7MvmdldZvbNTCdy95vdvd7d6/fZZ58Iiywi5ailtZ25DU20daTY0r6dto4UVzQ0qUWQhyiDQDMwOu39KGB95xszOyJ8+QJwrrufBUwws/ERlklEKlDzxq3UVHW/nNVUVdG8cWuRSlQ+ogwCTwHjzWycmQ0GZgL3p+2fD8wDaoDqcFsK2C3CMolIBRo1YigdqVS3bR2pFKNGDC1SicpHZEHA3bcDFwG/AZ4D7nL3PwOY2QzgKXdf7+6bgCfMbE3wMV8dVZlEpDLVDRvCwtMnUltTxR5DBlFbU8XC0ydSN2xIsYtW8szdi12GPqmvr/cVK1YUuxi9amltp3njVkaNGKpfRJGY6P9ddma20t3re26PcnRQYmmUgkhx1A0boot/H2naiALTKAURKScKAgWmUQoiUk4UBApMoxREpJwoCBSYRimISDlRx3AEpk8+kKkHj9QoBREpeQoCEdEoBREpB0oHiYgkmIKAiEiCKQiIiCSYgoCISIIpCIiIJFjZTSBnZhuAvxS7HAU2Eni72IWIkepb2VTf0vR+d99lVa6yCwKVyMxWZJrdr1KpvpVN9S0vSgeJiCSYgoCISIIpCJSGm4tdgJipvpVN9S0j6hMQEUkwtQRERBJMQUBEJMEUBArMzMaa2ay095ea2bNm1mRmfzCz96ftO8/MXgx/zkvbPs7Mlofb7zSzweH2WWZ2ffi6ysz+y8x+amYWYxW76VnftO1nmJmbWX3atoqsr5mdFf4b/9nMfpm2vazrm+F3eYyZPWRmq8Lf50+l7SvruuaS4e/hODN72sy2m9kZPY5zM5uftm2kmXV01rUkubt+CvQDXAA8D7wOPAzsDxwP7Ja2/87w9d7AK+GfI8LXI8J9dwEzw9c3AReEr2cB1wNG0Bn1S6CqlOobbt8DeAR4Eqiv5PoC44FVaXXZtxLqm6WuN6eV9TDgtUqoaz/+HsYCE4HbgDPSjh0LvAys6vH5RuD6Ytcl249aAgViZnsA3wHOBb5F8Ev+nrs/5O5/Dw97EhgVvv4k8Dt3f8fdNwK/A6aFd0InAPeEx/0XMKPH110L1AHnunuKIshW33D3fGAh0Jb2kUqt7z8DN4R1wt3fCj9StvXNUVcH9gwP2wtYH74u27rmkuP/9Gvu3gRkKu9W4Lm0FvBnCAJhydKiMoWTAgYT/idx99cyHPNF4L/D1wcS3F10ag631QGb3H17j+2dPgs8B3w87ZhiyFhfMzsCGO3uS8xsTtrxlVrfQ8I/lwHVwJXu/iDlXd9sdb0S+K2ZfQXYHfhEeHw51zWXfP5PZ3IHMNPM/gbsIAiW74uigIWgIFAg7v6emZ0LfA/Y38wmAPM6WwFm9nmgHvhY+JFMuU/Psb3T08AHgA8DywpU/D7LVl/gGoI7pp4qtb6DCFJCHydo5T0a7ivb+uao69nAre7+72b2UeDn5V7XXHr7P53DgwSt4TeBOyMu5oApHVRA7n4/cCZBKmQf4DIAM/sE8A1guru3h4c3A6PTPj6K4I7hbWC4mQ3qsb3TWuAs4E4z+1BEVclLhvpeDkwAHjaz14CjgfvDpnEl1vcygnotdvcOd3+VIH88njKvb5a6fpEwteHuTwC1BJOnlXVdc8n2f7qXz2wDVobHNkRawAJQECgQMxtmXSN/thA0c/cI0yP/SRAA3kr7yG+Ak8xshJmNAE4CfuNBb9JDQOeog/OAxenf5e6PA/8b+LWZjYmsUjlkqe/u7j7S3ce6+1iCPpDp7r6CyqzvHsB9BJ3/mNlI4BCCjtGyrW+Ouv4VODE85oMEQWADZVzXXHL8PeTj34G57t4SSeEKqdg905XyQzAq4kFgBfAq8EeC/OfvCZqFjeHP/WmfOR94Kfz5Qtr2g4A/hdvvBoaE22eRNsoA+ALwLFBXKvXtcczDhKODKrW+BCmPH4TlWkM4Eqac65ujrocRpG1Wh7/LJ5V7Xfv593AUQevnPaAF+HN4/FjgmQzn6VbXUvvRtBEFZmZjCTq6bi1yUWKh+lauJNU1l0r/e1A6qPA2EdwlJcUmVN9KtYnk1DWXTVTw34NaAiIiCaaWgIhIgikIiIgkmIKAiEiCKQhIopjZaWbW2OMnZWYnh7NAPtPj+CvNbE446+XtPfaNNLMNZjbEzB5Omy8m/ZiHzez5tO+6p+cxvZT31vSZKkUKTdNGSKK4+73AvZ3vzWw28DmCB55yPaz0K+D/mtlu3jVtwBkEz3209zID8uc8eGBOpOSoJSCJFU7+Ng84x3uZwdLd3yWYHvvUtM0zgdszf6LX777VzK4zs8fN7JXOu30LXG/B+gS/Bvbtz/lF8qUgIIlkZjUEc9jPcfe/pu36h/RUEcGUBp1uJ7jwY2bvI5gi4qE8vm5R2jn/LW37AcAxwKeB/xNuOw04FDicYJrqKX2unEgfKB0kSTWf4HH/O3psf9ndJ3e+CadP7rQEuNHM9iSY+Owed9+Rx3dlSwfdF7ZAnjWz/cJtxwG3h+ddb2ZL86uOSP8oCEjimNnHgdOBf+zL59x9q5k9SHC3PhP42gCL0p72Or1TQU9wSmyUDpJECWe5/BnBSlZb+nGK24FLgf0IZkkttEcIFiSpNrMDCGcoFYmKWgKSNP+boLP1P3qM6Pk+sDyPz/+WYJnEn3j+c64sMrOt4eu33f0TOY69l2BJxjXACwQzV4pERnMHiYgkmNJBIiIJpiAgIpJgCgIiIgmmICAikmAKAiIiCaYgICKSYAoCIiIJ9v8BHLnoReM26JcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# highest condos mapped\n",
    "condodata = data[data[\"Housing Type\"]==\"Condo\"]\n",
    "\n",
    "plot = condodata.plot(kind='scatter', x = 'ZHVI End', y = '% Per Year')\n",
    "\n",
    "def format_func(value, tick_number):\n",
    "    currency = locale.currency(value, grouping=True)\n",
    "    currency_no_decimal = currency.split('.')[0]        \n",
    "    if currency_no_decimal.find(',') != -1:\n",
    "        currency_no_decimal = currency_no_decimal[:currency_no_decimal.index(',')]\n",
    "        if currency.count(',') == 1:\n",
    "            currency_no_decimal+=\"K\"\n",
    "        elif currency.count(',') == 2:\n",
    "            currency_no_decimal+=\"M\"\n",
    "    return currency_no_decimal\n",
    "\n",
    "def percent_formatter(value, tick_number):\n",
    "    return \"{}%\".format(int(value))\n",
    "\n",
    "plot.xaxis.set_major_formatter(plt.FuncFormatter(format_func))\n",
    "plot.yaxis.set_major_formatter(plt.FuncFormatter(percent_formatter))\n",
    "\n",
    "\n",
    "# https://towardsdatascience.com/regression-plots-with-pandas-and-numpy-faf2edbfad4f\n",
    "d = np.polyfit(condodata['ZHVI End'],condodata['% Per Year'],1)\n",
    "f = np.poly1d(d)\n",
    "\n",
    "condodata.insert(6,'Trend',f(condodata['ZHVI End']))\n",
    "\n",
    "condodata.plot(x='ZHVI End', y='Trend',color='Red',ax=plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ZHVI End', ylabel='% Per Year'>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# highest sfr summary table\n",
    "sfrdata = data[data[\"Housing Type\"]==\"SingleFamilyResidence\"]\n",
    "\n",
    "plot = sfrdata.plot(kind='scatter', x = 'ZHVI End', y = '% Per Year')\n",
    "\n",
    "plot.xaxis.set_major_formatter(plt.FuncFormatter(format_func))\n",
    "plot.yaxis.set_major_formatter(plt.FuncFormatter(percent_formatter))\n",
    "\n",
    "d = np.polyfit(condodata['ZHVI End'],condodata['% Per Year'],1)\n",
    "f = np.poly1d(d)\n",
    "\n",
    "sfrdata.insert(len(sfrdata.columns),'Trend',f(sfrdata['ZHVI End']))\n",
    "\n",
    "sfrdata.plot(x='ZHVI End', y='Trend',color='Red',ax=plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.8/site-packages/scipy/stats/stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>final_zhvi</td>    <th>  R-squared:         </th> <td>   0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.2709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 06 Apr 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.615</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:31:31</td>     <th>  Log-Likelihood:    </th> <td> -153.79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    11</td>      <th>  AIC:               </th> <td>   311.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   312.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>   2.3e+05</td> <td> 6.95e+05</td> <td>    0.331</td> <td> 0.748</td> <td>-1.34e+06</td> <td>  1.8e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>percent_per_year</th> <td> 9.532e+04</td> <td> 1.83e+05</td> <td>    0.520</td> <td> 0.615</td> <td>-3.19e+05</td> <td>  5.1e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.037</td> <th>  Durbin-Watson:     </th> <td>   2.444</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.030</td> <th>  Jarque-Bera (JB):  </th> <td>   3.227</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.277</td> <th>  Prob(JB):          </th> <td>   0.199</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.722</td> <th>  Cond. No.          </th> <td>    29.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             final_zhvi   R-squared:                       0.029\n",
       "Model:                            OLS   Adj. R-squared:                 -0.079\n",
       "Method:                 Least Squares   F-statistic:                    0.2709\n",
       "Date:                Tue, 06 Apr 2021   Prob (F-statistic):              0.615\n",
       "Time:                        07:31:31   Log-Likelihood:                -153.79\n",
       "No. Observations:                  11   AIC:                             311.6\n",
       "Df Residuals:                       9   BIC:                             312.4\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           2.3e+05   6.95e+05      0.331      0.748   -1.34e+06     1.8e+06\n",
       "percent_per_year  9.532e+04   1.83e+05      0.520      0.615   -3.19e+05     5.1e+05\n",
       "==============================================================================\n",
       "Omnibus:                        7.037   Durbin-Watson:                   2.444\n",
       "Prob(Omnibus):                  0.030   Jarque-Bera (JB):                3.227\n",
       "Skew:                           1.277   Prob(JB):                        0.199\n",
       "Kurtosis:                       3.722   Cond. No.                         29.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "\n",
    "is_raw_data = True\n",
    "is_only_amfam_data = True\n",
    "    \n",
    "default_groupby = [zillow_column_region_name, custom_column_housing_type]\n",
    "\n",
    "raw_condo_data = import_csv(\"ingested_condo.csv\")\n",
    "raw_sfr_data = import_csv(\"ingested_singlefamilyresidence.csv\")\n",
    "\n",
    "# convert data to dataframes and merge together\n",
    "conda_data = get_dataframe_from_list(raw_condo_data)\n",
    "sfr_data = get_dataframe_from_list(raw_sfr_data)\n",
    "base_dataframe = pd.concat([conda_data, sfr_data])\n",
    "\n",
    "# temp while reading from file\n",
    "base_dataframe[custom_column_zhvi] = base_dataframe[custom_column_zhvi].astype(float) \n",
    "\n",
    "# massage data in dataframes\n",
    "base_dataframe[custom_column_date] = pd.to_datetime(base_dataframe[custom_column_date])  # convert to pandas date\n",
    "base_dataframe[custom_column_zhvi] = base_dataframe[custom_column_zhvi].replace([0],\n",
    "                                                                                np.nan)  # replace zero ZHVI with NAN to exclude from calculations\n",
    "base_dataframe.sort_values(\n",
    "    by=[custom_column_date])  # sorting the data allows for percent change to be calculated correctly\n",
    "\n",
    "# calculate first valid zhvi value per row\n",
    "first_valid_dataframe = base_dataframe.pivot(index=default_groupby, columns=custom_column_date,\n",
    "                                             values=custom_column_zhvi)\n",
    "first_valid_zhvi = first_valid_dataframe.apply(pd.Series.first_valid_index, axis=1)\n",
    "\n",
    "# create summary dataframe\n",
    "summary_dataframe = pd.DataFrame()\n",
    "summary_dataframe[zillow_column_city] = base_dataframe.groupby(default_groupby)[zillow_column_city].first()\n",
    "summary_dataframe[zillow_column_state] = base_dataframe.groupby(default_groupby)[zillow_column_state].first()\n",
    "\n",
    "if is_only_amfam_data:\n",
    "    summary_dataframe = filter_dataframe_by_amfam_states(summary_dataframe)\n",
    "\n",
    "# columns\n",
    "zhvi_start = base_dataframe.groupby(default_groupby)[\n",
    "    custom_column_zhvi].first()\n",
    "zhvi_end = base_dataframe.groupby(default_groupby)[\n",
    "    custom_column_zhvi].last()\n",
    "zhvi_min = base_dataframe.groupby(default_groupby)[custom_column_zhvi].min()\n",
    "zhvi_max = base_dataframe.groupby(default_groupby)[custom_column_zhvi].max()\n",
    "\n",
    "# format currencies\n",
    "if not is_raw_data:\n",
    "    zhvi_start = zhvi_start.apply(currency_formatter)\n",
    "    zhvi_end = zhvi_end.apply(currency_formatter)\n",
    "    zhvi_min = zhvi_min.apply(currency_formatter)\n",
    "    zhvi_max = zhvi_max.apply(currency_formatter)\n",
    "\n",
    "summary_dataframe[custom_column_zhvi_start] = zhvi_start\n",
    "summary_dataframe[custom_column_start_date] = first_valid_zhvi\n",
    "summary_dataframe['final_zhvi'] = zhvi_end\n",
    "summary_dataframe[custom_column_end_date] = base_dataframe.groupby(default_groupby)[custom_column_date].last()\n",
    "summary_dataframe['ZHVI Min'] = zhvi_min\n",
    "summary_dataframe['ZHVI Max'] = zhvi_max\n",
    "summary_dataframe[custom_column_years_difference] = round(\n",
    "    (summary_dataframe[custom_column_end_date] - summary_dataframe[custom_column_start_date]).dt.days / 365,\n",
    "    1)  # estimate years from number of days between start and end\n",
    "summary_dataframe[\"percent_per_year\"] = summary_dataframe.apply(\n",
    "    lambda row: get_home_appreciation_percentage_per_year(row[custom_column_zhvi_start],\n",
    "                                                          row[\"final_zhvi\"],\n",
    "                                                          row[custom_column_years_difference],\n",
    "                                                          is_raw_data), axis=1)\n",
    "summary_dataframe = summary_dataframe.sort_values(\"percent_per_year\",\n",
    "                                                      ascending=False)  # sort table to find highest movers\n",
    "    \n",
    "#summary_dataframe.head(10)\n",
    "\n",
    "# https://www.learndatasci.com/tutorials/predicting-housing-prices-linear-regression-using-python-pandas-statsmodels/\n",
    "# https://stackoverflow.com/questions/19991445/run-an-ols-regression-with-pandas-data-frame\n",
    "\n",
    "result = sm.ols(formula=\"final_zhvi ~ percent_per_year\", data=summary_dataframe).fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>RegionName</th>\n",
       "      <th>Housing Type</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Metro</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>Date</th>\n",
       "      <th>ZHVI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-01-31</td>\n",
       "      <td>223756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-02-29</td>\n",
       "      <td>222370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-03-31</td>\n",
       "      <td>222477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-04-30</td>\n",
       "      <td>222536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-05-31</td>\n",
       "      <td>223119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-06-30</td>\n",
       "      <td>223713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-07-31</td>\n",
       "      <td>223503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-08-31</td>\n",
       "      <td>224192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-09-30</td>\n",
       "      <td>224469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10025</td>\n",
       "      <td>Condo</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>1996-10-31</td>\n",
       "      <td>225933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionName Housing Type State      City                        Metro  \\\n",
       "0       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "1       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "2       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "3       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "4       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "5       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "6       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "7       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "8       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "9       10025        Condo    NY  New York  New York-Newark-Jersey City   \n",
       "\n",
       "        CountyName       Date    ZHVI  \n",
       "0  New York County 1996-01-31  223756  \n",
       "1  New York County 1996-02-29  222370  \n",
       "2  New York County 1996-03-31  222477  \n",
       "3  New York County 1996-04-30  222536  \n",
       "4  New York County 1996-05-31  223119  \n",
       "5  New York County 1996-06-30  223713  \n",
       "6  New York County 1996-07-31  223503  \n",
       "7  New York County 1996-08-31  224192  \n",
       "8  New York County 1996-09-30  224469  \n",
       "9  New York County 1996-10-31  225933  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mysql_zillow.get_zillow_data({\n",
    "            env_database_host: \"127.0.0.1\",\n",
    "            env_database_username: \"capstone_user\",\n",
    "            env_database_password: \"VGVyBFFGVrdky4rRFeWp!\"\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Starting ZHVI': 145784.0,\n",
       " 'Start Date': Timestamp('2008-09-30 00:00:00'),\n",
       " 'Final ZHVI': 215719.0,\n",
       " 'End Date': Timestamp('2021-02-28 00:00:00'),\n",
       " 'ZHVI Min': 144939.0,\n",
       " 'ZHVI Max': 215719.0,\n",
       " 'Years Diff': 12.4,\n",
       " '% Per Year': 3.2105459900252376}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = calculate_metrics({\n",
    "            env_database_host: \"127.0.0.1\",\n",
    "            env_database_username: \"capstone_user\",\n",
    "            env_database_password: \"VGVyBFFGVrdky4rRFeWp!\"\n",
    "        })\n",
    "\n",
    "def get_home_appreciation_percentage_per_year_2(initial_value, final_value, years):\n",
    "    \"\"\"\n",
    "    currency_formatter formats currency string to float\n",
    "    :param initial_value: initial home value\n",
    "    :param final_value: final home value\n",
    "    :param years: number of years to calculate appreciation over\n",
    "    :return: float\n",
    "    \"\"\"\n",
    "    # https://goodcalculators.com/home-appreciation-calculator/\n",
    "    years = float(years)\n",
    "    home_appreciation_percentage = (((final_value / initial_value) ** (1 / years)) - 1) * 100\n",
    "    if math.isnan(home_appreciation_percentage):\n",
    "        home_appreciation_percentage = 0\n",
    "    return home_appreciation_percentage\n",
    "\n",
    "def calculate_baseline_data(data):\n",
    "    \n",
    "    headers = {column_date: custom_column_date, column_zhvi: custom_column_zhvi}\n",
    "\n",
    "    # create dataframe\n",
    "    base_dataframe = pd.DataFrame(data).rename(columns=headers)\n",
    "            \n",
    "    # sorting the data allows for percent change to be calculated correctly\n",
    "    base_dataframe = base_dataframe.sort_values(custom_column_date,ascending=True)\n",
    "    \n",
    "    # replace zero ZHVI with NAN to exclude from calculations\n",
    "    base_dataframe[custom_column_zhvi] = base_dataframe[custom_column_zhvi].replace([0], np.nan) \n",
    "    \n",
    "    # convert to pandas date\n",
    "    base_dataframe[custom_column_date] = pd.to_datetime(base_dataframe[custom_column_date])\n",
    "\n",
    "    # create summary dataframe\n",
    "    summary_dataframe = {\n",
    "        custom_column_zhvi_start: base_dataframe[custom_column_zhvi].iat[base_dataframe[custom_column_zhvi].first_valid_index()],\n",
    "        custom_column_start_date: base_dataframe[custom_column_date].iat[base_dataframe[custom_column_zhvi].first_valid_index()],\n",
    "        custom_column_zhvi_end: base_dataframe[custom_column_zhvi].iat[-1],\n",
    "        custom_column_end_date: base_dataframe[custom_column_date].max(),\n",
    "        custom_column_zhvi_min: base_dataframe[custom_column_zhvi].min(),\n",
    "        custom_column_zhvi_max: base_dataframe[custom_column_zhvi].max()\n",
    "    }\n",
    "    \n",
    "    summary_dataframe[custom_column_years_difference] = round(\n",
    "        (summary_dataframe[custom_column_end_date] - summary_dataframe[custom_column_start_date]).days / 365,\n",
    "        1)  # estimate years from number of days between start and end\n",
    "    summary_dataframe[custom_column_appreciation] =  get_home_appreciation_percentage_per_year_2(summary_dataframe[custom_column_zhvi_start],\n",
    "                                                              summary_dataframe[custom_column_zhvi_end],\n",
    "                                                              summary_dataframe[custom_column_years_difference])\n",
    "    return summary_dataframe\n",
    "\n",
    "df = calculate_baseline_data(data)\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>RegionName</th>\n",
       "      <th>Housing Type</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Starting ZHVI</th>\n",
       "      <th>Final ZHVI</th>\n",
       "      <th>ZHVI Min</th>\n",
       "      <th>ZHVI Max</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Years Available</th>\n",
       "      <th>% Per Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60153</td>\n",
       "      <td>Condo</td>\n",
       "      <td>IL</td>\n",
       "      <td>Maywood</td>\n",
       "      <td>$35,422</td>\n",
       "      <td>$91,365</td>\n",
       "      <td>$91,365</td>\n",
       "      <td>$35,422</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>5.8</td>\n",
       "      <td>17.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85714</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>$48,599</td>\n",
       "      <td>$113,364</td>\n",
       "      <td>$113,364</td>\n",
       "      <td>$48,599</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>5.2</td>\n",
       "      <td>17.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89434</td>\n",
       "      <td>Condo</td>\n",
       "      <td>NV</td>\n",
       "      <td>Sparks</td>\n",
       "      <td>$67,721</td>\n",
       "      <td>$225,625</td>\n",
       "      <td>$225,625</td>\n",
       "      <td>$67,721</td>\n",
       "      <td>2012-09-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.4</td>\n",
       "      <td>15.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85051</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$31,515</td>\n",
       "      <td>$128,911</td>\n",
       "      <td>$128,911</td>\n",
       "      <td>$30,374</td>\n",
       "      <td>2010-08-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>10.5</td>\n",
       "      <td>14.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85202</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>$59,305</td>\n",
       "      <td>$186,984</td>\n",
       "      <td>$186,984</td>\n",
       "      <td>$59,305</td>\n",
       "      <td>2012-07-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.6</td>\n",
       "      <td>14.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85017</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$37,305</td>\n",
       "      <td>$114,155</td>\n",
       "      <td>$114,155</td>\n",
       "      <td>$37,305</td>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.72%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>85009</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$32,643</td>\n",
       "      <td>$111,812</td>\n",
       "      <td>$111,812</td>\n",
       "      <td>$31,550</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>9.9</td>\n",
       "      <td>13.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>85015</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$35,556</td>\n",
       "      <td>$127,412</td>\n",
       "      <td>$127,412</td>\n",
       "      <td>$34,187</td>\n",
       "      <td>2010-09-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>10.4</td>\n",
       "      <td>13.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60624</td>\n",
       "      <td>Condo</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>$66,847</td>\n",
       "      <td>$162,667</td>\n",
       "      <td>$162,667</td>\n",
       "      <td>$66,847</td>\n",
       "      <td>2013-10-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.3</td>\n",
       "      <td>12.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>89431</td>\n",
       "      <td>Condo</td>\n",
       "      <td>NV</td>\n",
       "      <td>Sparks</td>\n",
       "      <td>$85,885</td>\n",
       "      <td>$221,196</td>\n",
       "      <td>$221,196</td>\n",
       "      <td>$85,885</td>\n",
       "      <td>2013-05-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.8</td>\n",
       "      <td>12.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>85033</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$37,634</td>\n",
       "      <td>$124,888</td>\n",
       "      <td>$124,888</td>\n",
       "      <td>$37,634</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>9.9</td>\n",
       "      <td>12.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>85363</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Youngtown</td>\n",
       "      <td>$54,291</td>\n",
       "      <td>$151,763</td>\n",
       "      <td>$151,763</td>\n",
       "      <td>$54,291</td>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.7</td>\n",
       "      <td>12.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>80011</td>\n",
       "      <td>Condo</td>\n",
       "      <td>CO</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>$79,254</td>\n",
       "      <td>$209,658</td>\n",
       "      <td>$209,658</td>\n",
       "      <td>$79,254</td>\n",
       "      <td>2012-08-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.5</td>\n",
       "      <td>12.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>83642</td>\n",
       "      <td>Condo</td>\n",
       "      <td>ID</td>\n",
       "      <td>Meridian</td>\n",
       "      <td>$102,092</td>\n",
       "      <td>$256,715</td>\n",
       "      <td>$256,715</td>\n",
       "      <td>$99,464</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.2</td>\n",
       "      <td>11.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>80019</td>\n",
       "      <td>Condo</td>\n",
       "      <td>CO</td>\n",
       "      <td>Aurora</td>\n",
       "      <td>$95,633</td>\n",
       "      <td>$258,111</td>\n",
       "      <td>$258,111</td>\n",
       "      <td>$95,633</td>\n",
       "      <td>2012-03-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>85007</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$108,542</td>\n",
       "      <td>$345,023</td>\n",
       "      <td>$345,023</td>\n",
       "      <td>$98,188</td>\n",
       "      <td>2010-08-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>10.5</td>\n",
       "      <td>11.64%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>85210</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>$76,801</td>\n",
       "      <td>$190,627</td>\n",
       "      <td>$190,627</td>\n",
       "      <td>$76,801</td>\n",
       "      <td>2012-11-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.3</td>\n",
       "      <td>11.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>85035</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$44,507</td>\n",
       "      <td>$118,722</td>\n",
       "      <td>$118,722</td>\n",
       "      <td>$44,407</td>\n",
       "      <td>2011-12-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>9.2</td>\n",
       "      <td>11.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>45426</td>\n",
       "      <td>Condo</td>\n",
       "      <td>OH</td>\n",
       "      <td>Trotwood</td>\n",
       "      <td>$25,317</td>\n",
       "      <td>$49,398</td>\n",
       "      <td>$49,398</td>\n",
       "      <td>$25,317</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>6.7</td>\n",
       "      <td>10.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>85383</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Peoria</td>\n",
       "      <td>$126,833</td>\n",
       "      <td>$216,921</td>\n",
       "      <td>$216,921</td>\n",
       "      <td>$126,833</td>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>5.4</td>\n",
       "      <td>10.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>85040</td>\n",
       "      <td>Condo</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$66,608</td>\n",
       "      <td>$179,128</td>\n",
       "      <td>$179,128</td>\n",
       "      <td>$64,407</td>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>10.1</td>\n",
       "      <td>10.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>45223</td>\n",
       "      <td>Condo</td>\n",
       "      <td>OH</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>$56,764</td>\n",
       "      <td>$111,807</td>\n",
       "      <td>$111,807</td>\n",
       "      <td>$48,609</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.1</td>\n",
       "      <td>10.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>54545</td>\n",
       "      <td>Condo</td>\n",
       "      <td>WI</td>\n",
       "      <td>Manitowish Waters</td>\n",
       "      <td>$139,854</td>\n",
       "      <td>$282,011</td>\n",
       "      <td>$282,011</td>\n",
       "      <td>$139,410</td>\n",
       "      <td>2013-09-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.4</td>\n",
       "      <td>9.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>83287</td>\n",
       "      <td>Condo</td>\n",
       "      <td>ID</td>\n",
       "      <td>Fish Haven</td>\n",
       "      <td>$235,512</td>\n",
       "      <td>$388,269</td>\n",
       "      <td>$388,269</td>\n",
       "      <td>$235,512</td>\n",
       "      <td>2015-10-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>97305</td>\n",
       "      <td>Condo</td>\n",
       "      <td>OR</td>\n",
       "      <td>Hayesville</td>\n",
       "      <td>$83,387</td>\n",
       "      <td>$178,057</td>\n",
       "      <td>$178,057</td>\n",
       "      <td>$83,387</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.82%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85009</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$54,438</td>\n",
       "      <td>$194,019</td>\n",
       "      <td>$194,019</td>\n",
       "      <td>$42,626</td>\n",
       "      <td>2009-07-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>11.6</td>\n",
       "      <td>11.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80720</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>CO</td>\n",
       "      <td>Akron</td>\n",
       "      <td>$68,909</td>\n",
       "      <td>$139,863</td>\n",
       "      <td>$140,309</td>\n",
       "      <td>$68,909</td>\n",
       "      <td>2014-07-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>6.6</td>\n",
       "      <td>11.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97641</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>OR</td>\n",
       "      <td>Christmas Valley</td>\n",
       "      <td>$46,015</td>\n",
       "      <td>$98,141</td>\n",
       "      <td>$98,141</td>\n",
       "      <td>$46,015</td>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.1</td>\n",
       "      <td>11.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89030</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>NV</td>\n",
       "      <td>North Las Vegas</td>\n",
       "      <td>$66,403</td>\n",
       "      <td>$217,027</td>\n",
       "      <td>$217,027</td>\n",
       "      <td>$53,485</td>\n",
       "      <td>2009-07-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45225</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>OH</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>$23,442</td>\n",
       "      <td>$67,023</td>\n",
       "      <td>$70,375</td>\n",
       "      <td>$22,608</td>\n",
       "      <td>2010-10-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>10.3</td>\n",
       "      <td>10.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>97442</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>OR</td>\n",
       "      <td>Glendale</td>\n",
       "      <td>$88,672</td>\n",
       "      <td>$182,127</td>\n",
       "      <td>$182,127</td>\n",
       "      <td>$88,672</td>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.2</td>\n",
       "      <td>10.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80106</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>CO</td>\n",
       "      <td>Elbert</td>\n",
       "      <td>$271,053</td>\n",
       "      <td>$589,968</td>\n",
       "      <td>$589,968</td>\n",
       "      <td>$271,053</td>\n",
       "      <td>2013-05-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68642</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>NE</td>\n",
       "      <td>Humphrey</td>\n",
       "      <td>$131,112</td>\n",
       "      <td>$242,625</td>\n",
       "      <td>$242,625</td>\n",
       "      <td>$131,112</td>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>6.5</td>\n",
       "      <td>9.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>63622</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>MO</td>\n",
       "      <td>Belgrade</td>\n",
       "      <td>$62,281</td>\n",
       "      <td>$112,868</td>\n",
       "      <td>$114,787</td>\n",
       "      <td>$62,281</td>\n",
       "      <td>2014-11-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>6.3</td>\n",
       "      <td>9.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>85324</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Black Canyon City</td>\n",
       "      <td>$109,498</td>\n",
       "      <td>$270,716</td>\n",
       "      <td>$270,716</td>\n",
       "      <td>$102,797</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>9.9</td>\n",
       "      <td>9.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>89101</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>NV</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>$78,969</td>\n",
       "      <td>$223,993</td>\n",
       "      <td>$223,993</td>\n",
       "      <td>$57,322</td>\n",
       "      <td>2009-07-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>11.6</td>\n",
       "      <td>9.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>97469</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>OR</td>\n",
       "      <td>Riddle</td>\n",
       "      <td>$114,239</td>\n",
       "      <td>$196,759</td>\n",
       "      <td>$196,759</td>\n",
       "      <td>$114,239</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>68933</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>NE</td>\n",
       "      <td>Clay Center</td>\n",
       "      <td>$56,113</td>\n",
       "      <td>$111,306</td>\n",
       "      <td>$111,306</td>\n",
       "      <td>$54,954</td>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>80743</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>CO</td>\n",
       "      <td>Otis</td>\n",
       "      <td>$62,836</td>\n",
       "      <td>$111,704</td>\n",
       "      <td>$111,704</td>\n",
       "      <td>$62,836</td>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>6.5</td>\n",
       "      <td>9.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>89106</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>NV</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>$84,393</td>\n",
       "      <td>$235,551</td>\n",
       "      <td>$235,551</td>\n",
       "      <td>$64,915</td>\n",
       "      <td>2009-07-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>11.6</td>\n",
       "      <td>9.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>85033</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$79,978</td>\n",
       "      <td>$228,201</td>\n",
       "      <td>$228,201</td>\n",
       "      <td>$57,603</td>\n",
       "      <td>2009-02-28</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>45204</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>OH</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>$49,420</td>\n",
       "      <td>$85,988</td>\n",
       "      <td>$85,988</td>\n",
       "      <td>$48,454</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>44702</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>OH</td>\n",
       "      <td>Canton</td>\n",
       "      <td>$17,764</td>\n",
       "      <td>$32,165</td>\n",
       "      <td>$32,165</td>\n",
       "      <td>$17,764</td>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>85019</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>$84,006</td>\n",
       "      <td>$228,476</td>\n",
       "      <td>$228,476</td>\n",
       "      <td>$57,024</td>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>12.2</td>\n",
       "      <td>8.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>89115</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>NV</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>$91,986</td>\n",
       "      <td>$237,832</td>\n",
       "      <td>$237,832</td>\n",
       "      <td>$71,466</td>\n",
       "      <td>2009-07-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>11.6</td>\n",
       "      <td>8.53%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>69347</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>NE</td>\n",
       "      <td>Hay Springs</td>\n",
       "      <td>$173,716</td>\n",
       "      <td>$286,073</td>\n",
       "      <td>$286,073</td>\n",
       "      <td>$173,716</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>83610</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>ID</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>$134,893</td>\n",
       "      <td>$216,543</td>\n",
       "      <td>$216,543</td>\n",
       "      <td>$133,965</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>81152</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>CO</td>\n",
       "      <td>San Luis</td>\n",
       "      <td>$56,978</td>\n",
       "      <td>$109,374</td>\n",
       "      <td>$109,381</td>\n",
       "      <td>$56,978</td>\n",
       "      <td>2013-02-28</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>53212</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>WI</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>$67,188</td>\n",
       "      <td>$125,794</td>\n",
       "      <td>$125,794</td>\n",
       "      <td>$67,188</td>\n",
       "      <td>2013-04-30</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>81233</td>\n",
       "      <td>SingleFamilyResidence</td>\n",
       "      <td>CO</td>\n",
       "      <td>Howard</td>\n",
       "      <td>$234,030</td>\n",
       "      <td>$380,587</td>\n",
       "      <td>$380,587</td>\n",
       "      <td>$234,030</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.30%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RegionName           Housing Type State               City Starting ZHVI  \\\n",
       "0        60153                  Condo    IL            Maywood       $35,422   \n",
       "1        85714                  Condo    AZ             Tucson       $48,599   \n",
       "2        89434                  Condo    NV             Sparks       $67,721   \n",
       "3        85051                  Condo    AZ            Phoenix       $31,515   \n",
       "4        85202                  Condo    AZ               Mesa       $59,305   \n",
       "5        85017                  Condo    AZ            Phoenix       $37,305   \n",
       "6        85009                  Condo    AZ            Phoenix       $32,643   \n",
       "7        85015                  Condo    AZ            Phoenix       $35,556   \n",
       "8        60624                  Condo    IL            Chicago       $66,847   \n",
       "9        89431                  Condo    NV             Sparks       $85,885   \n",
       "10       85033                  Condo    AZ            Phoenix       $37,634   \n",
       "11       85363                  Condo    AZ          Youngtown       $54,291   \n",
       "12       80011                  Condo    CO             Aurora       $79,254   \n",
       "13       83642                  Condo    ID           Meridian      $102,092   \n",
       "14       80019                  Condo    CO             Aurora       $95,633   \n",
       "15       85007                  Condo    AZ            Phoenix      $108,542   \n",
       "16       85210                  Condo    AZ               Mesa       $76,801   \n",
       "17       85035                  Condo    AZ            Phoenix       $44,507   \n",
       "18       45426                  Condo    OH           Trotwood       $25,317   \n",
       "19       85383                  Condo    AZ             Peoria      $126,833   \n",
       "20       85040                  Condo    AZ            Phoenix       $66,608   \n",
       "21       45223                  Condo    OH         Cincinnati       $56,764   \n",
       "22       54545                  Condo    WI  Manitowish Waters      $139,854   \n",
       "23       83287                  Condo    ID         Fish Haven      $235,512   \n",
       "24       97305                  Condo    OR         Hayesville       $83,387   \n",
       "0        85009  SingleFamilyResidence    AZ            Phoenix       $54,438   \n",
       "1        80720  SingleFamilyResidence    CO              Akron       $68,909   \n",
       "2        97641  SingleFamilyResidence    OR   Christmas Valley       $46,015   \n",
       "3        89030  SingleFamilyResidence    NV    North Las Vegas       $66,403   \n",
       "4        45225  SingleFamilyResidence    OH         Cincinnati       $23,442   \n",
       "5        97442  SingleFamilyResidence    OR           Glendale       $88,672   \n",
       "6        80106  SingleFamilyResidence    CO             Elbert      $271,053   \n",
       "7        68642  SingleFamilyResidence    NE           Humphrey      $131,112   \n",
       "8        63622  SingleFamilyResidence    MO           Belgrade       $62,281   \n",
       "9        85324  SingleFamilyResidence    AZ  Black Canyon City      $109,498   \n",
       "10       89101  SingleFamilyResidence    NV          Las Vegas       $78,969   \n",
       "11       97469  SingleFamilyResidence    OR             Riddle      $114,239   \n",
       "12       68933  SingleFamilyResidence    NE        Clay Center       $56,113   \n",
       "13       80743  SingleFamilyResidence    CO               Otis       $62,836   \n",
       "14       89106  SingleFamilyResidence    NV          Las Vegas       $84,393   \n",
       "15       85033  SingleFamilyResidence    AZ            Phoenix       $79,978   \n",
       "16       45204  SingleFamilyResidence    OH         Cincinnati       $49,420   \n",
       "17       44702  SingleFamilyResidence    OH             Canton       $17,764   \n",
       "18       85019  SingleFamilyResidence    AZ            Phoenix       $84,006   \n",
       "19       89115  SingleFamilyResidence    NV          Las Vegas       $91,986   \n",
       "20       69347  SingleFamilyResidence    NE        Hay Springs      $173,716   \n",
       "21       83610  SingleFamilyResidence    ID          Cambridge      $134,893   \n",
       "22       81152  SingleFamilyResidence    CO           San Luis       $56,978   \n",
       "23       53212  SingleFamilyResidence    WI          Milwaukee       $67,188   \n",
       "24       81233  SingleFamilyResidence    CO             Howard      $234,030   \n",
       "\n",
       "   Final ZHVI  ZHVI Min  ZHVI Max Start Date   End Date  Years Available  \\\n",
       "0     $91,365   $91,365   $35,422 2015-04-30 2021-02-28              5.8   \n",
       "1    $113,364  $113,364   $48,599 2015-12-31 2021-02-28              5.2   \n",
       "2    $225,625  $225,625   $67,721 2012-09-30 2021-02-28              8.4   \n",
       "3    $128,911  $128,911   $30,374 2010-08-31 2021-02-28             10.5   \n",
       "4    $186,984  $186,984   $59,305 2012-07-31 2021-02-28              8.6   \n",
       "5    $114,155  $114,155   $37,305 2012-06-30 2021-02-28              8.7   \n",
       "6    $111,812  $111,812   $31,550 2011-03-31 2021-02-28              9.9   \n",
       "7    $127,412  $127,412   $34,187 2010-09-30 2021-02-28             10.4   \n",
       "8    $162,667  $162,667   $66,847 2013-10-31 2021-02-28              7.3   \n",
       "9    $221,196  $221,196   $85,885 2013-05-31 2021-02-28              7.8   \n",
       "10   $124,888  $124,888   $37,634 2011-03-31 2021-02-28              9.9   \n",
       "11   $151,763  $151,763   $54,291 2012-06-30 2021-02-28              8.7   \n",
       "12   $209,658  $209,658   $79,254 2012-08-31 2021-02-28              8.5   \n",
       "13   $256,715  $256,715   $99,464 2012-12-31 2021-02-28              8.2   \n",
       "14   $258,111  $258,111   $95,633 2012-03-31 2021-02-28              8.9   \n",
       "15   $345,023  $345,023   $98,188 2010-08-31 2021-02-28             10.5   \n",
       "16   $190,627  $190,627   $76,801 2012-11-30 2021-02-28              8.3   \n",
       "17   $118,722  $118,722   $44,407 2011-12-31 2021-02-28              9.2   \n",
       "18    $49,398   $49,398   $25,317 2014-06-30 2021-02-28              6.7   \n",
       "19   $216,921  $216,921  $126,833 2015-09-30 2021-02-28              5.4   \n",
       "20   $179,128  $179,128   $64,407 2011-01-31 2021-02-28             10.1   \n",
       "21   $111,807  $111,807   $48,609 2014-01-31 2021-02-28              7.1   \n",
       "22   $282,011  $282,011  $139,410 2013-09-30 2021-02-28              7.4   \n",
       "23   $388,269  $388,269  $235,512 2015-10-31 2021-02-28              5.3   \n",
       "24   $178,057  $178,057   $83,387 2013-01-31 2021-02-28              8.1   \n",
       "0    $194,019  $194,019   $42,626 2009-07-31 2021-02-28             11.6   \n",
       "1    $139,863  $140,309   $68,909 2014-07-31 2021-02-28              6.6   \n",
       "2     $98,141   $98,141   $46,015 2014-01-31 2021-02-28              7.1   \n",
       "3    $217,027  $217,027   $53,485 2009-07-31 2021-02-28             11.6   \n",
       "4     $67,023   $70,375   $22,608 2010-10-31 2021-02-28             10.3   \n",
       "5    $182,127  $182,127   $88,672 2013-12-31 2021-02-28              7.2   \n",
       "6    $589,968  $589,968  $271,053 2013-05-31 2021-02-28              7.8   \n",
       "7    $242,625  $242,625  $131,112 2014-08-31 2021-02-28              6.5   \n",
       "8    $112,868  $114,787   $62,281 2014-11-30 2021-02-28              6.3   \n",
       "9    $270,716  $270,716  $102,797 2011-03-31 2021-02-28              9.9   \n",
       "10   $223,993  $223,993   $57,322 2009-07-31 2021-02-28             11.6   \n",
       "11   $196,759  $196,759  $114,239 2015-01-31 2021-02-28              6.1   \n",
       "12   $111,306  $111,306   $54,954 2013-06-30 2021-02-28              7.7   \n",
       "13   $111,704  $111,704   $62,836 2014-08-31 2021-02-28              6.5   \n",
       "14   $235,551  $235,551   $64,915 2009-07-31 2021-02-28             11.6   \n",
       "15   $228,201  $228,201   $57,603 2009-02-28 2021-02-28             12.0   \n",
       "16    $85,988   $85,988   $48,454 2014-06-30 2021-02-28              6.7   \n",
       "17    $32,165   $32,165   $17,764 2013-12-31 2021-02-28              7.2   \n",
       "18   $228,476  $228,476   $57,024 2008-12-31 2021-02-28             12.2   \n",
       "19   $237,832  $237,832   $71,466 2009-07-31 2021-02-28             11.6   \n",
       "20   $286,073  $286,073  $173,716 2015-01-31 2021-02-28              6.1   \n",
       "21   $216,543  $216,543  $133,965 2015-05-31 2021-02-28              5.8   \n",
       "22   $109,374  $109,381   $56,978 2013-02-28 2021-02-28              8.0   \n",
       "23   $125,794  $125,794   $67,188 2013-04-30 2021-02-28              7.8   \n",
       "24   $380,587  $380,587  $234,030 2015-01-31 2021-02-28              6.1   \n",
       "\n",
       "   % Per Year  \n",
       "0      17.75%  \n",
       "1      17.69%  \n",
       "2      15.40%  \n",
       "3      14.36%  \n",
       "4      14.29%  \n",
       "5      13.72%  \n",
       "6      13.24%  \n",
       "7      13.06%  \n",
       "8      12.96%  \n",
       "9      12.89%  \n",
       "10     12.88%  \n",
       "11     12.54%  \n",
       "12     12.13%  \n",
       "13     11.90%  \n",
       "14     11.80%  \n",
       "15     11.64%  \n",
       "16     11.58%  \n",
       "17     11.25%  \n",
       "18     10.49%  \n",
       "19     10.45%  \n",
       "20     10.29%  \n",
       "21     10.02%  \n",
       "22      9.94%  \n",
       "23      9.89%  \n",
       "24      9.82%  \n",
       "0      11.58%  \n",
       "1      11.32%  \n",
       "2      11.26%  \n",
       "3      10.75%  \n",
       "4      10.74%  \n",
       "5      10.51%  \n",
       "6      10.49%  \n",
       "7       9.93%  \n",
       "8       9.90%  \n",
       "9       9.57%  \n",
       "10      9.40%  \n",
       "11      9.32%  \n",
       "12      9.30%  \n",
       "13      9.25%  \n",
       "14      9.25%  \n",
       "15      9.13%  \n",
       "16      8.62%  \n",
       "17      8.60%  \n",
       "18      8.55%  \n",
       "19      8.53%  \n",
       "20      8.52%  \n",
       "21      8.50%  \n",
       "22      8.49%  \n",
       "23      8.37%  \n",
       "24      8.30%  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condo_data = get_baseline_data(True, 1, {\n",
    "            env_database_host: \"127.0.0.1\",\n",
    "            env_database_username: \"capstone_user\",\n",
    "            env_database_password: \"VGVyBFFGVrdky4rRFeWp!\"\n",
    "        })\n",
    "sfr_data = get_baseline_data(True, 2, {\n",
    "            env_database_host: \"127.0.0.1\",\n",
    "            env_database_username: \"capstone_user\",\n",
    "            env_database_password: \"VGVyBFFGVrdky4rRFeWp!\"\n",
    "        })\n",
    "header_mappings = {\n",
    "    column_region_name: zillow_column_region_name,\n",
    "    column_housing_type: custom_column_housing_type,\n",
    "    column_state: zillow_column_state,\n",
    "    column_city: zillow_column_city,\n",
    "    column_zhvi_start: custom_column_zhvi_start,\n",
    "    column_zhvi_end: custom_column_zhvi_end,\n",
    "    column_zhvi_min: custom_column_zhvi_min,\n",
    "    column_zhvi_max: custom_column_zhvi_max,\n",
    "    column_date_start: custom_column_start_date,\n",
    "    column_date_end: custom_column_end_date,\n",
    "    column_date_difference: \"Years Available\",\n",
    "    column_zhvi_percent_change: custom_column_appreciation\n",
    "}\n",
    "df1 = pd.DataFrame(condo_data)\n",
    "df2 = pd.DataFrame(sfr_data)\n",
    "\n",
    "df = pd.concat([df1, df2]).rename(columns=header_mappings)\n",
    "\n",
    "df[custom_column_appreciation] = df[custom_column_appreciation].apply(\n",
    "            percent_formatter)\n",
    "df[custom_column_zhvi_start] = df[custom_column_zhvi_start].apply(\n",
    "    currency_formatter)\n",
    "df[custom_column_zhvi_end] = df[custom_column_zhvi_end].apply(currency_formatter)\n",
    "df[custom_column_zhvi_min] = df[custom_column_zhvi_min].apply(currency_formatter)\n",
    "df[custom_column_zhvi_max] = df[custom_column_zhvi_max].apply(currency_formatter)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Housing Type</th>\n",
       "      <th>Year</th>\n",
       "      <th>ZHVI Avg</th>\n",
       "      <th>amfam_only</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Condo</td>\n",
       "      <td>1996</td>\n",
       "      <td>56248</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Condo</td>\n",
       "      <td>1997</td>\n",
       "      <td>60296</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Condo</td>\n",
       "      <td>1998</td>\n",
       "      <td>66242</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Condo</td>\n",
       "      <td>1999</td>\n",
       "      <td>73342</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Condo</td>\n",
       "      <td>2000</td>\n",
       "      <td>82131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Housing Type  Year  ZHVI Avg  amfam_only\n",
       "0        Condo  1996     56248           0\n",
       "1        Condo  1997     60296           0\n",
       "2        Condo  1998     66242           0\n",
       "3        Condo  1999     73342           0\n",
       "4        Condo  2000     82131           0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_zhvi_trend_graph(config)\n",
    "graph_title_suffix = \"(All States)\"\n",
    "# build condo graph\n",
    "graph_title = \"Condos {}\".format(graph_title_suffix)\n",
    "# https://towardsdatascience.com/regression-plots-with-pandas-and-numpy-faf2edbfad4f\n",
    "# d = np.polyfit(df[custom_column_year],df[custom_column_zhvi_avg],1)\n",
    "# f = np.poly1d(d)\n",
    "\n",
    "# df.insert(2,'Trend',f(df[custom_column_zhvi_avg]))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condo_data = df[df[custom_column_housing_type] == zillow_data_type_condo]\n",
    "sfr_data = df[df[custom_column_housing_type] == zillow_data_type_sfr]\n",
    "condo_amfam_data = condo_data[condo_data[column_amfam_only] == 1]\n",
    "condo_all_data = condo_data[condo_data[column_amfam_only] == 0]\n",
    "sfr_amfam_data = sfr_data[sfr_data[column_amfam_only] == 1]\n",
    "sfr_all_data = sfr_data[sfr_data[column_amfam_only] == 0]\n",
    "\n",
    "\n",
    "def currency_formatter(value, tick_number):\n",
    "    if math.isnan(value):\n",
    "        value = 0\n",
    "    return \"${}\".format(locale.format_string('%d', value, True))\n",
    "\n",
    "fig1 = plt.figure() #Creating new figure\n",
    "ax1 = fig1.add_subplot() #Creating axis\n",
    "ax1.set_title(graph_title)\n",
    "ax1.plot(sfr_all_data[custom_column_year], sfr_all_data[custom_column_zhvi_avg],  linestyle=\"solid\", label=\"SFR (All States)\")\n",
    "ax1.plot(sfr_all_data[custom_column_year], condo_all_data[custom_column_zhvi_avg], linestyle=\"dotted\", label=\"Condo (All States)\")\n",
    "ax1.plot(sfr_all_data[custom_column_year], sfr_amfam_data[custom_column_zhvi_avg], linestyle=\"dashed\", label=\"SFR (AmFam States)\")\n",
    "ax1.plot(sfr_all_data[custom_column_year], condo_amfam_data[custom_column_zhvi_avg], linestyle=\"dashdot\", label=\"Condo (AmFam States)\")\n",
    "ax1.yaxis.set_major_formatter(plt.FuncFormatter(currency_formatter))\n",
    "ax1.legend()\n",
    "fig1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021\n"
     ]
    }
   ],
   "source": [
    "for a in range(2021, 2022):\n",
    "    print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import locale\n",
    "\n",
    "locale.setlocale(locale.LC_ALL, '')\n",
    "\n",
    "\n",
    "def formatter_currency(value, tick_number):\n",
    "    \"\"\"\n",
    "    formatter_currency translates raw tick label to formatted currency\n",
    "    :param value: plot value for an axis tick label\n",
    "    :param tick_number: the index of the axis tick\n",
    "    :return: axis tick label string\n",
    "    \"\"\"\n",
    "    currency = locale.currency(value, grouping=True)\n",
    "    currency_no_decimal = currency.split('.')[0]\n",
    "    if currency_no_decimal.find(',') != -1:\n",
    "        currency_no_decimal = currency_no_decimal[:currency_no_decimal.index(',')]\n",
    "        if currency.count(',') == 1:\n",
    "            currency_no_decimal += \"K\"\n",
    "        elif currency.count(',') == 2:\n",
    "            currency_no_decimal += \"M\"\n",
    "    return currency_no_decimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter imagemagick unavailable; using Pillow instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making animation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing mplot3d toolkits\n",
    "from mpl_toolkits import mplot3d\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.animation as animation\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "  \n",
    "# syntax for 3-D projection\n",
    "ax = plt.axes(projection ='3d')\n",
    "  \n",
    "# defining axes\n",
    "z = [\n",
    "194019,\n",
    "139863,\n",
    "98141,\n",
    "217027,\n",
    "67023,\n",
    "182127,\n",
    "589968,\n",
    "242625,\n",
    "112868,\n",
    "270716,\n",
    "223993,\n",
    "196759,\n",
    "111306,\n",
    "111704,\n",
    "235551,\n",
    "228201,\n",
    "85988,\n",
    "32165,\n",
    "228476,\n",
    "237832,\n",
    "286073,\n",
    "216543,\n",
    "109374,\n",
    "125794,\n",
    "380587\n",
    "] #final zhvi\n",
    "x = [47810,\n",
    "2880,\n",
    "1313,\n",
    "53928,\n",
    "9560,\n",
    "1903,\n",
    "5847,\n",
    "1721,\n",
    "1011,\n",
    "2892,\n",
    "46055,\n",
    "2596,\n",
    "1009,\n",
    "1137,\n",
    "26480,\n",
    "64832,\n",
    "6972,\n",
    "785,\n",
    "27813,\n",
    "58794,\n",
    "1249,\n",
    "942,\n",
    "1220,\n",
    "31408,\n",
    "1005] # population\n",
    "y = [\n",
    "    0.44,\n",
    "0,\n",
    "0,\n",
    "0.57,\n",
    "0.73,\n",
    "0,\n",
    "0,\n",
    "0.58,\n",
    "0.99,\n",
    "0,\n",
    "0.87,\n",
    "0,\n",
    "0,\n",
    "0,\n",
    "1.44,\n",
    "0.59,\n",
    "0.86,\n",
    "5.10,\n",
    "1.65,\n",
    "0.12,\n",
    "0,\n",
    "0,\n",
    "0,\n",
    "1.91,\n",
    "1.92\n",
    "]\n",
    "ax.scatter(x, y, z)\n",
    "\n",
    "# syntax for plotting\n",
    "ax.set_title('Highest Increasing Zip Codes With AmFam Agency Density (SFR)')\n",
    "ax.set_zlabel('Final ZHVI')\n",
    "ax.set_xlabel('Population')\n",
    "ax.set_ylabel('Agency Density')\n",
    "ax.zaxis.set_major_formatter(plt.FuncFormatter(formatter_currency))\n",
    "#plt.show()\n",
    "\n",
    "#https://stackoverflow.com/questions/51457738/animating-a-3d-scatterplot-with-matplotlib-to-gif-ends-up-empty\n",
    "def rotate(angle):\n",
    "    ax.view_init(azim=angle)\n",
    "\n",
    "print(\"Making animation\")\n",
    "rot_animation = animation.FuncAnimation(fig, rotate, frames=np.arange(0, 362, 2), interval=200)\n",
    "rot_animation.save('sfr_rotation.gif', dpi=80, writer='imagemagick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter imagemagick unavailable; using Pillow instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making animation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://www.geeksforgeeks.org/three-dimensional-plotting-in-python-using-matplotlib/ \n",
    "\n",
    "# importing mplot3d toolkits\n",
    "from mpl_toolkits import mplot3d\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.animation as animation\n",
    "  \n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "  \n",
    "# syntax for 3-D projection\n",
    "ax = plt.axes(projection ='3d')\n",
    "  \n",
    "# defining axes\n",
    "z = [\n",
    "91365,\n",
    "113364,\n",
    "225625,\n",
    "128911,\n",
    "186984,\n",
    "114155,\n",
    "111812,\n",
    "127412,\n",
    "162667,\n",
    "221196,\n",
    "124888,\n",
    "151763,\n",
    "209658,\n",
    "256715,\n",
    "258111,\n",
    "345023,\n",
    "190627,\n",
    "118722,\n",
    "49398,\n",
    "216921,\n",
    "179128,\n",
    "111807,\n",
    "282011,\n",
    "388269,\n",
    "178057\n",
    "] #final zhvi\n",
    "x = [\n",
    "24106,\n",
    "15009,\n",
    "26107,\n",
    "45313,\n",
    "40364,\n",
    "42656,\n",
    "47810,\n",
    "37644,\n",
    "34892,\n",
    "39042,\n",
    "64832,\n",
    "6625,\n",
    "52172,\n",
    "41893,\n",
    "2771,\n",
    "14428,\n",
    "36464,\n",
    "59660,\n",
    "15239,\n",
    "55327,\n",
    "29352,\n",
    "12360,\n",
    "889,\n",
    "213,\n",
    "40176\n",
    "] # population\n",
    "y = [\n",
    "2.49,\n",
    "1.80,\n",
    "0.23,\n",
    "1.32,\n",
    "1.34,\n",
    "1.17,\n",
    "0.44,\n",
    "1.17,\n",
    "1.72,\n",
    "0.38,\n",
    "0.59,\n",
    "7.70,\n",
    "0.52,\n",
    "0.48,\n",
    "4.69,\n",
    "2.36,\n",
    "1.51,\n",
    "0.49,\n",
    "0.33,\n",
    "0.43,\n",
    "0.95,\n",
    "0.57,\n",
    "0,\n",
    "0,\n",
    "0.10\n",
    "]\n",
    "ax.scatter(x, y, z)\n",
    "\n",
    "# syntax for plotting\n",
    "ax.set_title('Highest Increasing Zip Codes With AmFam Agency Density (Condo)')\n",
    "ax.set_zlabel('Final ZHVI')\n",
    "ax.set_xlabel('Population')\n",
    "ax.set_ylabel('Agency Density')\n",
    "ax.zaxis.set_major_formatter(plt.FuncFormatter(formatter_currency))\n",
    "#plt.show()\n",
    "\n",
    "def rotate(angle):\n",
    "    ax.view_init(azim=angle)\n",
    "\n",
    "#https://stackoverflow.com/questions/51457738/animating-a-3d-scatterplot-with-matplotlib-to-gif-ends-up-empty\n",
    "print(\"Making animation\")\n",
    "rot_animation = animation.FuncAnimation(fig, rotate, frames=np.arange(0, 362, 2), interval=200)\n",
    "rot_animation.save('condo_rotation.gif', dpi=80, writer='imagemagick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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