notebooks/Visualizations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations\n",
    "\n",
    "In this notebook, we'll create visualizations that will be used to best communicate the model process in the presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import models relevant visualization models\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionID</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Metro</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>1996-04</th>\n",
       "      <th>1996-05</th>\n",
       "      <th>1996-06</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-07</th>\n",
       "      <th>2017-08</th>\n",
       "      <th>2017-09</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>84654</td>\n",
       "      <td>60657</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Cook</td>\n",
       "      <td>1</td>\n",
       "      <td>334200.0</td>\n",
       "      <td>335400.0</td>\n",
       "      <td>336500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1005500</td>\n",
       "      <td>1007500</td>\n",
       "      <td>1007800</td>\n",
       "      <td>1009600</td>\n",
       "      <td>1013300</td>\n",
       "      <td>1018700</td>\n",
       "      <td>1024400</td>\n",
       "      <td>1030700</td>\n",
       "      <td>1033800</td>\n",
       "      <td>1030600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90668</td>\n",
       "      <td>75070</td>\n",
       "      <td>McKinney</td>\n",
       "      <td>TX</td>\n",
       "      <td>Dallas-Fort Worth</td>\n",
       "      <td>Collin</td>\n",
       "      <td>2</td>\n",
       "      <td>235700.0</td>\n",
       "      <td>236900.0</td>\n",
       "      <td>236700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>308000</td>\n",
       "      <td>310000</td>\n",
       "      <td>312500</td>\n",
       "      <td>314100</td>\n",
       "      <td>315000</td>\n",
       "      <td>316600</td>\n",
       "      <td>318100</td>\n",
       "      <td>319600</td>\n",
       "      <td>321100</td>\n",
       "      <td>321800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91982</td>\n",
       "      <td>77494</td>\n",
       "      <td>Katy</td>\n",
       "      <td>TX</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Harris</td>\n",
       "      <td>3</td>\n",
       "      <td>210400.0</td>\n",
       "      <td>212200.0</td>\n",
       "      <td>212200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>321000</td>\n",
       "      <td>320600</td>\n",
       "      <td>320200</td>\n",
       "      <td>320400</td>\n",
       "      <td>320800</td>\n",
       "      <td>321200</td>\n",
       "      <td>321200</td>\n",
       "      <td>323000</td>\n",
       "      <td>326900</td>\n",
       "      <td>329900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84616</td>\n",
       "      <td>60614</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Cook</td>\n",
       "      <td>4</td>\n",
       "      <td>498100.0</td>\n",
       "      <td>500900.0</td>\n",
       "      <td>503100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1289800</td>\n",
       "      <td>1287700</td>\n",
       "      <td>1287400</td>\n",
       "      <td>1291500</td>\n",
       "      <td>1296600</td>\n",
       "      <td>1299000</td>\n",
       "      <td>1302700</td>\n",
       "      <td>1306400</td>\n",
       "      <td>1308500</td>\n",
       "      <td>1307000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>93144</td>\n",
       "      <td>79936</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>5</td>\n",
       "      <td>77300.0</td>\n",
       "      <td>77300.0</td>\n",
       "      <td>77300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>119100</td>\n",
       "      <td>119400</td>\n",
       "      <td>120000</td>\n",
       "      <td>120300</td>\n",
       "      <td>120300</td>\n",
       "      <td>120300</td>\n",
       "      <td>120300</td>\n",
       "      <td>120500</td>\n",
       "      <td>121000</td>\n",
       "      <td>121500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionID  RegionName      City State              Metro CountyName  \\\n",
       "0     84654       60657   Chicago    IL            Chicago       Cook   \n",
       "1     90668       75070  McKinney    TX  Dallas-Fort Worth     Collin   \n",
       "2     91982       77494      Katy    TX            Houston     Harris   \n",
       "3     84616       60614   Chicago    IL            Chicago       Cook   \n",
       "4     93144       79936   El Paso    TX            El Paso    El Paso   \n",
       "\n",
       "   SizeRank   1996-04   1996-05   1996-06  ...  2017-07  2017-08  2017-09  \\\n",
       "0         1  334200.0  335400.0  336500.0  ...  1005500  1007500  1007800   \n",
       "1         2  235700.0  236900.0  236700.0  ...   308000   310000   312500   \n",
       "2         3  210400.0  212200.0  212200.0  ...   321000   320600   320200   \n",
       "3         4  498100.0  500900.0  503100.0  ...  1289800  1287700  1287400   \n",
       "4         5   77300.0   77300.0   77300.0  ...   119100   119400   120000   \n",
       "\n",
       "   2017-10  2017-11  2017-12  2018-01  2018-02  2018-03  2018-04  \n",
       "0  1009600  1013300  1018700  1024400  1030700  1033800  1030600  \n",
       "1   314100   315000   316600   318100   319600   321100   321800  \n",
       "2   320400   320800   321200   321200   323000   326900   329900  \n",
       "3  1291500  1296600  1299000  1302700  1306400  1308500  1307000  \n",
       "4   120300   120300   120300   120300   120500   121000   121500  \n",
       "\n",
       "[5 rows x 272 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Read in raw data\n",
    "zillow_data = pd.read_csv('../data/raw/zillow_data.csv')\n",
    "zillow_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>1996-04</th>\n",
       "      <th>1996-05</th>\n",
       "      <th>1996-06</th>\n",
       "      <th>1996-07</th>\n",
       "      <th>1996-08</th>\n",
       "      <th>1996-09</th>\n",
       "      <th>...</th>\n",
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       "      <th>2017-10</th>\n",
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       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60657</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Cook</td>\n",
       "      <td>334200.0</td>\n",
       "      <td>335400.0</td>\n",
       "      <td>336500.0</td>\n",
       "      <td>337600.0</td>\n",
       "      <td>338500.0</td>\n",
       "      <td>339500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1005500</td>\n",
       "      <td>1007500</td>\n",
       "      <td>1007800</td>\n",
       "      <td>1009600</td>\n",
       "      <td>1013300</td>\n",
       "      <td>1018700</td>\n",
       "      <td>1024400</td>\n",
       "      <td>1030700</td>\n",
       "      <td>1033800</td>\n",
       "      <td>1030600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75070</td>\n",
       "      <td>McKinney</td>\n",
       "      <td>TX</td>\n",
       "      <td>Collin</td>\n",
       "      <td>235700.0</td>\n",
       "      <td>236900.0</td>\n",
       "      <td>236700.0</td>\n",
       "      <td>235400.0</td>\n",
       "      <td>233300.0</td>\n",
       "      <td>230600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>308000</td>\n",
       "      <td>310000</td>\n",
       "      <td>312500</td>\n",
       "      <td>314100</td>\n",
       "      <td>315000</td>\n",
       "      <td>316600</td>\n",
       "      <td>318100</td>\n",
       "      <td>319600</td>\n",
       "      <td>321100</td>\n",
       "      <td>321800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77494</td>\n",
       "      <td>Katy</td>\n",
       "      <td>TX</td>\n",
       "      <td>Harris</td>\n",
       "      <td>210400.0</td>\n",
       "      <td>212200.0</td>\n",
       "      <td>212200.0</td>\n",
       "      <td>210700.0</td>\n",
       "      <td>208300.0</td>\n",
       "      <td>205500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>321000</td>\n",
       "      <td>320600</td>\n",
       "      <td>320200</td>\n",
       "      <td>320400</td>\n",
       "      <td>320800</td>\n",
       "      <td>321200</td>\n",
       "      <td>321200</td>\n",
       "      <td>323000</td>\n",
       "      <td>326900</td>\n",
       "      <td>329900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60614</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Cook</td>\n",
       "      <td>498100.0</td>\n",
       "      <td>500900.0</td>\n",
       "      <td>503100.0</td>\n",
       "      <td>504600.0</td>\n",
       "      <td>505500.0</td>\n",
       "      <td>505700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1289800</td>\n",
       "      <td>1287700</td>\n",
       "      <td>1287400</td>\n",
       "      <td>1291500</td>\n",
       "      <td>1296600</td>\n",
       "      <td>1299000</td>\n",
       "      <td>1302700</td>\n",
       "      <td>1306400</td>\n",
       "      <td>1308500</td>\n",
       "      <td>1307000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79936</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>77300.0</td>\n",
       "      <td>77300.0</td>\n",
       "      <td>77300.0</td>\n",
       "      <td>77300.0</td>\n",
       "      <td>77400.0</td>\n",
       "      <td>77500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>119100</td>\n",
       "      <td>119400</td>\n",
       "      <td>120000</td>\n",
       "      <td>120300</td>\n",
       "      <td>120300</td>\n",
       "      <td>120300</td>\n",
       "      <td>120300</td>\n",
       "      <td>120500</td>\n",
       "      <td>121000</td>\n",
       "      <td>121500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 269 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionName      City State CountyName   1996-04   1996-05   1996-06  \\\n",
       "0       60657   Chicago    IL       Cook  334200.0  335400.0  336500.0   \n",
       "1       75070  McKinney    TX     Collin  235700.0  236900.0  236700.0   \n",
       "2       77494      Katy    TX     Harris  210400.0  212200.0  212200.0   \n",
       "3       60614   Chicago    IL       Cook  498100.0  500900.0  503100.0   \n",
       "4       79936   El Paso    TX    El Paso   77300.0   77300.0   77300.0   \n",
       "\n",
       "    1996-07   1996-08   1996-09  ...  2017-07  2017-08  2017-09  2017-10  \\\n",
       "0  337600.0  338500.0  339500.0  ...  1005500  1007500  1007800  1009600   \n",
       "1  235400.0  233300.0  230600.0  ...   308000   310000   312500   314100   \n",
       "2  210700.0  208300.0  205500.0  ...   321000   320600   320200   320400   \n",
       "3  504600.0  505500.0  505700.0  ...  1289800  1287700  1287400  1291500   \n",
       "4   77300.0   77400.0   77500.0  ...   119100   119400   120000   120300   \n",
       "\n",
       "   2017-11  2017-12  2018-01  2018-02  2018-03  2018-04  \n",
       "0  1013300  1018700  1024400  1030700  1033800  1030600  \n",
       "1   315000   316600   318100   319600   321100   321800  \n",
       "2   320800   321200   321200   323000   326900   329900  \n",
       "3  1296600  1299000  1302700  1306400  1308500  1307000  \n",
       "4   120300   120300   120300   120500   121000   121500  \n",
       "\n",
       "[5 rows x 269 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Eliminate extraneous columns (RegionID (zillow's index), Size rank)\n",
    "new_zillow = zillow_data.drop(['RegionID', 'Metro', 'SizeRank'], axis=1)\n",
    "new_zillow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60657</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Cook</td>\n",
       "      <td>1996-04</td>\n",
       "      <td>334200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75070</td>\n",
       "      <td>McKinney</td>\n",
       "      <td>TX</td>\n",
       "      <td>Collin</td>\n",
       "      <td>1996-04</td>\n",
       "      <td>235700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77494</td>\n",
       "      <td>Katy</td>\n",
       "      <td>TX</td>\n",
       "      <td>Harris</td>\n",
       "      <td>1996-04</td>\n",
       "      <td>210400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60614</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Cook</td>\n",
       "      <td>1996-04</td>\n",
       "      <td>498100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79936</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>1996-04</td>\n",
       "      <td>77300.0</td>\n",
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      ],
      "text/plain": [
       "   RegionName      City State CountyName     time     value\n",
       "0       60657   Chicago    IL       Cook  1996-04  334200.0\n",
       "1       75070  McKinney    TX     Collin  1996-04  235700.0\n",
       "2       77494      Katy    TX     Harris  1996-04  210400.0\n",
       "3       60614   Chicago    IL       Cook  1996-04  498100.0\n",
       "4       79936   El Paso    TX    El Paso  1996-04   77300.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create pivot table in preparation for conversion to datetime objects\n",
    "zillow_melted = pd.melt(new_zillow,\n",
    "                        id_vars=['RegionName', 'City', 'State', 'CountyName'],\n",
    "                        var_name='time')\n",
    "zillow_melted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>City</th>\n",
       "      <th>State</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60657</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Cook</td>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>334200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75070</td>\n",
       "      <td>McKinney</td>\n",
       "      <td>TX</td>\n",
       "      <td>Collin</td>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>235700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77494</td>\n",
       "      <td>Katy</td>\n",
       "      <td>TX</td>\n",
       "      <td>Harris</td>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>210400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60614</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Cook</td>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>498100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79936</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>77300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionName      City State CountyName       time     value\n",
       "0       60657   Chicago    IL       Cook 1996-04-01  334200.0\n",
       "1       75070  McKinney    TX     Collin 1996-04-01  235700.0\n",
       "2       77494      Katy    TX     Harris 1996-04-01  210400.0\n",
       "3       60614   Chicago    IL       Cook 1996-04-01  498100.0\n",
       "4       79936   El Paso    TX    El Paso 1996-04-01   77300.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Convert time information to datetime objects\n",
    "## Set time as index for time-series analysis\n",
    "zillow_melted['time'] = pd.to_datetime(zillow_melted['time'], infer_datetime_format=True)\n",
    "zillow_melted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(zillow_melted['time'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "zillow_melted.set_index('time', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "zillow_tableau = zillow_melted.drop(['RegionName', 'City'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>State</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>value</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1996-04-01</th>\n",
       "      <td>IL</td>\n",
       "      <td>Cook</td>\n",
       "      <td>334200.0</td>\n",
       "      <td>334200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-04-01</th>\n",
       "      <td>TX</td>\n",
       "      <td>Collin</td>\n",
       "      <td>235700.0</td>\n",
       "      <td>235700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-04-01</th>\n",
       "      <td>TX</td>\n",
       "      <td>Harris</td>\n",
       "      <td>210400.0</td>\n",
       "      <td>210400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-04-01</th>\n",
       "      <td>IL</td>\n",
       "      <td>Cook</td>\n",
       "      <td>498100.0</td>\n",
       "      <td>498100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-04-01</th>\n",
       "      <td>TX</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>77300.0</td>\n",
       "      <td>77300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           State CountyName     value     price\n",
       "time                                           \n",
       "1996-04-01    IL       Cook  334200.0  334200.0\n",
       "1996-04-01    TX     Collin  235700.0  235700.0\n",
       "1996-04-01    TX     Harris  210400.0  210400.0\n",
       "1996-04-01    IL       Cook  498100.0  498100.0\n",
       "1996-04-01    TX    El Paso   77300.0   77300.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow_tableau.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "zillow_tableau['price'] = zillow_tableau['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "zillow_tableau.drop('value', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_zillow = zillow_tableau.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_zillow = zillow_tableau.resample('M').max()\n",
    "min_zillow = zillow_tableau.resample('M').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1996-04-30</th>\n",
       "      <td>118299.123063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-05-31</th>\n",
       "      <td>118419.044139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-06-30</th>\n",
       "      <td>118537.423268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-07-31</th>\n",
       "      <td>118653.069278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-31</th>\n",
       "      <td>118780.254312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    price\n",
       "time                     \n",
       "1996-04-30  118299.123063\n",
       "1996-05-31  118419.044139\n",
       "1996-06-30  118537.423268\n",
       "1996-07-31  118653.069278\n",
       "1996-08-31  118780.254312"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_zillow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('dark')\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "sns.lineplot(data=mean_zillow, ax=ax\n",
    "            )\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Mean Price')\n",
    "ax.set_title('Nationwide House Value Change');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>State</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1996-04-30</th>\n",
       "      <td>WY</td>\n",
       "      <td>Yuma</td>\n",
       "      <td>3676700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-05-31</th>\n",
       "      <td>WY</td>\n",
       "      <td>Yuma</td>\n",
       "      <td>3704200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-06-30</th>\n",
       "      <td>WY</td>\n",
       "      <td>Yuma</td>\n",
       "      <td>3729600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-07-31</th>\n",
       "      <td>WY</td>\n",
       "      <td>Yuma</td>\n",
       "      <td>3754600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-31</th>\n",
       "      <td>WY</td>\n",
       "      <td>Yuma</td>\n",
       "      <td>3781800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           State CountyName      price\n",
       "time                                  \n",
       "1996-04-30    WY       Yuma  3676700.0\n",
       "1996-05-31    WY       Yuma  3704200.0\n",
       "1996-06-30    WY       Yuma  3729600.0\n",
       "1996-07-31    WY       Yuma  3754600.0\n",
       "1996-08-31    WY       Yuma  3781800.0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_zillow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>State</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1996-04-30</th>\n",
       "      <td>AK</td>\n",
       "      <td>Acadia</td>\n",
       "      <td>11300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-05-31</th>\n",
       "      <td>AK</td>\n",
       "      <td>Acadia</td>\n",
       "      <td>11500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-06-30</th>\n",
       "      <td>AK</td>\n",
       "      <td>Acadia</td>\n",
       "      <td>11600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-07-31</th>\n",
       "      <td>AK</td>\n",
       "      <td>Acadia</td>\n",
       "      <td>11800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-31</th>\n",
       "      <td>AK</td>\n",
       "      <td>Acadia</td>\n",
       "      <td>11800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           State CountyName    price\n",
       "time                                \n",
       "1996-04-30    AK     Acadia  11300.0\n",
       "1996-05-31    AK     Acadia  11500.0\n",
       "1996-06-30    AK     Acadia  11600.0\n",
       "1996-07-31    AK     Acadia  11800.0\n",
       "1996-08-31    AK     Acadia  11800.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_zillow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}