austin_housing_trends.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc95fac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries and dependencies\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "157c93a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the raw data from resources\n",
    "saleprice_df = pd.read_csv(\"resources/median_sale_price_all.csv\")\n",
    "rates_df = pd.read_csv(\"resources/rates.csv\")\n",
    "\n",
    "# source https://www.irs.gov/statistics/soi-tax-stats-adjusted-gross-income-agi-percentile-data-by-state\n",
    "df_13 = pd.read_csv(\"resources/13instateshares.csv\")\n",
    "df_14 = pd.read_csv(\"resources/14instateshares.csv\")\n",
    "df_15 = pd.read_csv(\"resources/15instateshares.csv\")\n",
    "df_16 = pd.read_csv(\"resources/16instateshares.csv\")\n",
    "df_17 = pd.read_csv(\"resources/17instateshares.csv\")\n",
    "df_18 = pd.read_csv(\"resources/18instateshares.csv\")\n",
    "df_19 = pd.read_csv(\"resources/19instateshares.csv\")\n",
    "df_20 = pd.read_csv(\"resources/20instateshares.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19ddcba4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2013.rename(columns = {'statefips': 'year'}, inplace=True)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2013['year'] = df_2013['year'].replace(48, 2013)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2014.rename(columns = {'statefips': 'year'}, inplace=True)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2014['year'] = df_2014['year'].replace(48, 2014)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2015.rename(columns = {'statefips': 'year'}, inplace=True)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2015['year'] = df_2015['year'].replace(48, 2015)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2016.rename(columns = {'statefips': 'year'}, inplace=True)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2016['year'] = df_2016['year'].replace(48, 2016)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2017.rename(columns = {'statefips': 'year'}, inplace=True)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2017['year'] = df_2017['year'].replace(48, 2017)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:32: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2018.rename(columns = {'statefips': 'year'}, inplace=True)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2018['year'] = df_2018['year'].replace(48, 2018)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:37: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2019.rename(columns = {'statefips': 'year'}, inplace=True)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:38: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2019['year'] = df_2019['year'].replace(48, 2019)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2020.rename(columns = {'statefips': 'year'}, inplace=True)\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\2862780272.py:43: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2020['year'] = df_2020['year'].replace(48, 2020)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>agi_01</th>\n",
       "      <th>agi_05</th>\n",
       "      <th>agi_10</th>\n",
       "      <th>agi_25</th>\n",
       "      <th>agi_50</th>\n",
       "      <th>agi_75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>453430.00</td>\n",
       "      <td>184606.0</td>\n",
       "      <td>128782.0</td>\n",
       "      <td>72895.0</td>\n",
       "      <td>35617.0</td>\n",
       "      <td>17444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>497715.00</td>\n",
       "      <td>195185.0</td>\n",
       "      <td>135237.0</td>\n",
       "      <td>76173.0</td>\n",
       "      <td>37424.0</td>\n",
       "      <td>18299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>496385.04</td>\n",
       "      <td>199076.0</td>\n",
       "      <td>138372.0</td>\n",
       "      <td>77638.0</td>\n",
       "      <td>38208.0</td>\n",
       "      <td>18802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>463308.00</td>\n",
       "      <td>195144.0</td>\n",
       "      <td>136695.0</td>\n",
       "      <td>76956.0</td>\n",
       "      <td>38194.0</td>\n",
       "      <td>18959.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>528049.00</td>\n",
       "      <td>209115.0</td>\n",
       "      <td>144514.0</td>\n",
       "      <td>80809.0</td>\n",
       "      <td>40022.0</td>\n",
       "      <td>19645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>542780.00</td>\n",
       "      <td>216799.0</td>\n",
       "      <td>149881.0</td>\n",
       "      <td>83900.0</td>\n",
       "      <td>41726.0</td>\n",
       "      <td>20613.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>547661.90</td>\n",
       "      <td>220593.0</td>\n",
       "      <td>152085.0</td>\n",
       "      <td>84587.0</td>\n",
       "      <td>42099.0</td>\n",
       "      <td>20729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>543478.70</td>\n",
       "      <td>219156.0</td>\n",
       "      <td>150387.0</td>\n",
       "      <td>82532.0</td>\n",
       "      <td>40878.0</td>\n",
       "      <td>20115.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     agi_01    agi_05    agi_10   agi_25   agi_50   agi_75\n",
       "0  2013  453430.00  184606.0  128782.0  72895.0  35617.0  17444.0\n",
       "1  2014  497715.00  195185.0  135237.0  76173.0  37424.0  18299.0\n",
       "2  2015  496385.04  199076.0  138372.0  77638.0  38208.0  18802.0\n",
       "3  2016  463308.00  195144.0  136695.0  76956.0  38194.0  18959.0\n",
       "4  2017  528049.00  209115.0  144514.0  80809.0  40022.0  19645.0\n",
       "5  2018  542780.00  216799.0  149881.0  83900.0  41726.0  20613.0\n",
       "6  2019  547661.90  220593.0  152085.0  84587.0  42099.0  20729.0\n",
       "7  2020  543478.70  219156.0  150387.0  82532.0  40878.0  20115.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create combined AGI DataFrame\n",
    "columns = ['statefips', 'state', 'agi_01', 'agi_05', 'agi_10', 'agi_25', 'agi_50', 'agi_75']\n",
    "x = ['2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']\n",
    "\n",
    "df_13_tx = df_13.loc[df_13['state'] == 'TX']\n",
    "df_2013 = df_13_tx[columns]\n",
    "df_2013.rename(columns = {'statefips': 'year'}, inplace=True)\n",
    "df_2013['year'] = df_2013['year'].replace(48, 2013)\n",
    "\n",
    "df_14_tx = df_14.loc[df_14['state'] == 'TX']\n",
    "df_2014 = df_14_tx[columns]\n",
    "df_2014.rename(columns = {'statefips': 'year'}, inplace=True)\n",
    "df_2014['year'] = df_2014['year'].replace(48, 2014)\n",
    "\n",
    "df_15_tx = df_15.loc[df_15['state'] == 'TX']\n",
    "df_2015 = df_15_tx[columns]\n",
    "df_2015.rename(columns = {'statefips': 'year'}, inplace=True)\n",
    "df_2015['year'] = df_2015['year'].replace(48, 2015)\n",
    "\n",
    "df_16_tx = df_16.loc[df_16['state'] == 'TX']\n",
    "df_2016 = df_16_tx[columns]\n",
    "df_2016.rename(columns = {'statefips': 'year'}, inplace=True)\n",
    "df_2016['year'] = df_2016['year'].replace(48, 2016)\n",
    "\n",
    "df_17_tx = df_17.loc[df_17['state'] == 'TX']\n",
    "df_2017 = df_17_tx[columns]\n",
    "df_2017.rename(columns = {'statefips': 'year'}, inplace=True)\n",
    "df_2017['year'] = df_2017['year'].replace(48, 2017)\n",
    "\n",
    "df_18_tx = df_18.loc[df_18['state'] == 'TX']\n",
    "df_2018 = df_18_tx[columns]\n",
    "df_2018.rename(columns = {'statefips': 'year'}, inplace=True)\n",
    "df_2018['year'] = df_2018['year'].replace(48, 2018)\n",
    "\n",
    "df_19_tx = df_19.loc[df_19['state'] == 'TX']\n",
    "df_2019 = df_19_tx[columns]\n",
    "df_2019.rename(columns = {'statefips': 'year'}, inplace=True)\n",
    "df_2019['year'] = df_2019['year'].replace(48, 2019)\n",
    "\n",
    "df_20_tx = df_20.loc[df_20['state'] == 'TX']\n",
    "df_2020 = df_20_tx[columns]\n",
    "df_2020.rename(columns = {'statefips': 'year'}, inplace=True)\n",
    "df_2020['year'] = df_2020['year'].replace(48, 2020)\n",
    "\n",
    "# Combine the DataFrames for Income\n",
    "df_all = pd.concat([df_2013, df_2014, df_2015, df_2016, df_2017, df_2018, df_2019, df_2020]).reset_index()\n",
    "income_all = df_all[['year', 'agi_01', 'agi_05', 'agi_10', 'agi_25', 'agi_50', 'agi_75']]\n",
    "income_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9152ec8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013 AGI</th>\n",
       "      <th>2020 AGI</th>\n",
       "      <th>Wealth Change</th>\n",
       "      <th>Percent Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>agi_01</th>\n",
       "      <td>453430.0</td>\n",
       "      <td>543478.7</td>\n",
       "      <td>90048.7</td>\n",
       "      <td>19.859449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agi_05</th>\n",
       "      <td>184606.0</td>\n",
       "      <td>219156.0</td>\n",
       "      <td>34550.0</td>\n",
       "      <td>18.715535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agi_10</th>\n",
       "      <td>128782.0</td>\n",
       "      <td>150387.0</td>\n",
       "      <td>21605.0</td>\n",
       "      <td>16.776413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agi_25</th>\n",
       "      <td>72895.0</td>\n",
       "      <td>82532.0</td>\n",
       "      <td>9637.0</td>\n",
       "      <td>13.220385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agi_50</th>\n",
       "      <td>35617.0</td>\n",
       "      <td>40878.0</td>\n",
       "      <td>5261.0</td>\n",
       "      <td>14.771036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agi_75</th>\n",
       "      <td>17444.0</td>\n",
       "      <td>20115.0</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>15.311855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        2013 AGI  2020 AGI  Wealth Change  Percent Change\n",
       "agi_01  453430.0  543478.7        90048.7       19.859449\n",
       "agi_05  184606.0  219156.0        34550.0       18.715535\n",
       "agi_10  128782.0  150387.0        21605.0       16.776413\n",
       "agi_25   72895.0   82532.0         9637.0       13.220385\n",
       "agi_50   35617.0   40878.0         5261.0       14.771036\n",
       "agi_75   17444.0   20115.0         2671.0       15.311855"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a summary Table of Wealth Change\n",
    "results = {}\n",
    "columns = ['agi_01', 'agi_05', 'agi_10', 'agi_25', 'agi_50', 'agi_75']\n",
    "\n",
    "# Loop through each column and calculate the change\n",
    "for column in columns:\n",
    "    difference = income_all[column][7] - income_all[column][0]\n",
    "    percent_diff = 100 * (income_all[column][7] - income_all[column][0]) / income_all[column][0]\n",
    "    results[column] = {'2013 AGI': income_all[column][0],\n",
    "                       '2020 AGI': income_all[column][7],\n",
    "                       'Wealth Change': difference,\n",
    "                      'Percent Change': percent_diff\n",
    "                      }\n",
    "\n",
    "# Convert the results dictionary to a DataFrame\n",
    "wealth_change = pd.DataFrame.from_dict(results, orient='index')\n",
    "wealth_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ec44acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot summary as a bar chart\n",
    "df = wealth_change[['2013 AGI', '2020 AGI']]\n",
    "figsize = (11,8)\n",
    "new_labels = ['AGI 1%', 'AGI 5%','AGI 10%','AGI 25%','AGI 50%','AGI 75%']\n",
    "\n",
    "df_plot = df.plot(kind='bar', figsize=figsize, \n",
    "              title='Change in Wealth from 2013 to 2020 in Texas', \n",
    "              legend=True, ylabel=\"Adjusted Gross Income ($)\").set_xticklabels(new_labels, rotation=60)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "137be817",
   "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>Date</th>\n",
       "      <th>Dallas, TX</th>\n",
       "      <th>Houston, TX</th>\n",
       "      <th>San Antonio, TX</th>\n",
       "      <th>Austin, TX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-02-29</td>\n",
       "      <td>137000.0</td>\n",
       "      <td>143900.0</td>\n",
       "      <td>135825.0</td>\n",
       "      <td>178950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-03-31</td>\n",
       "      <td>146700.0</td>\n",
       "      <td>143000.0</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>188000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-04-30</td>\n",
       "      <td>144000.0</td>\n",
       "      <td>143825.0</td>\n",
       "      <td>144850.0</td>\n",
       "      <td>184995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-05-31</td>\n",
       "      <td>149900.0</td>\n",
       "      <td>149000.0</td>\n",
       "      <td>144900.0</td>\n",
       "      <td>190000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>192500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>355000.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>460000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2023-02-28</td>\n",
       "      <td>372000.0</td>\n",
       "      <td>308000.0</td>\n",
       "      <td>307000.0</td>\n",
       "      <td>450000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>374500.0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>305000.0</td>\n",
       "      <td>462990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2023-04-30</td>\n",
       "      <td>385000.0</td>\n",
       "      <td>320000.0</td>\n",
       "      <td>310000.0</td>\n",
       "      <td>479495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2023-05-31</td>\n",
       "      <td>395000.0</td>\n",
       "      <td>330000.0</td>\n",
       "      <td>318000.0</td>\n",
       "      <td>480000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0         Date Dallas, TX Houston, TX San Antonio, TX Austin, TX\n",
       "1   2008-02-29   137000.0    143900.0        135825.0   178950.0\n",
       "2   2008-03-31   146700.0    143000.0        140000.0   188000.0\n",
       "3   2008-04-30   144000.0    143825.0        144850.0   184995.0\n",
       "4   2008-05-31   149900.0    149000.0        144900.0   190000.0\n",
       "5   2008-06-30   155000.0    155000.0        152500.0   192500.0\n",
       "..         ...        ...         ...             ...        ...\n",
       "180 2023-01-31   355000.0    299900.0        300000.0   460000.0\n",
       "181 2023-02-28   372000.0    308000.0        307000.0   450000.0\n",
       "182 2023-03-31   374500.0    315000.0        305000.0   462990.0\n",
       "183 2023-04-30   385000.0    320000.0        310000.0   479495.0\n",
       "184 2023-05-31   395000.0    330000.0        318000.0   480000.0\n",
       "\n",
       "[184 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out our Saleprice DataFrame to only display the four major Texas MSA\n",
    "saleprice_tx = saleprice_df.loc[(saleprice_df[\"SizeRank\"] < 30) &\n",
    "                               (saleprice_df[\"StateName\"] == \"TX\")]\n",
    "\n",
    "# Drop unnecessary values to make transpose easier\n",
    "saleprice_tx = saleprice_tx.drop(['RegionID', 'SizeRank', 'RegionType', 'StateName'], axis=1)\n",
    "\n",
    "# Transpose the dataset to make columns contain specific information to only the MSA\n",
    "saleprice_t = saleprice_tx.transpose().reset_index()\n",
    "\n",
    "# This will move the city name into the index and make the DataFrame more readable\n",
    "saleprice_t.columns = saleprice_t.iloc[0]\n",
    "saleprice_t = saleprice_t.iloc[1:]\n",
    "saleprice_t.rename(columns = {\"RegionName\":\"Date\"}, inplace=True)\n",
    "\n",
    "# Convert the date to datetime\n",
    "saleprice_t['Date'] = pd.to_datetime(saleprice_t['Date'])\n",
    "saleprice_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ecf78c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot median sale price\n",
    "plt.plot(saleprice_t['Date'], saleprice_t['Dallas, TX'], label='Dallas', linestyle='-')\n",
    "plt.plot(saleprice_t['Date'], saleprice_t['Houston, TX'], label='Houston', linestyle='-')\n",
    "plt.plot(saleprice_t['Date'], saleprice_t['San Antonio, TX'], label='San Antonio', linestyle='-')\n",
    "plt.plot(saleprice_t['Date'], saleprice_t['Austin, TX'], label='Austin', linestyle='-')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Median Sale Price for Cities in Texas')\n",
    "plt.ylabel('Median Sale Price ($)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6743dfbe",
   "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>2013 Sale Price</th>\n",
       "      <th>2020 Sale Price</th>\n",
       "      <th>Sale Price Change</th>\n",
       "      <th>Percent Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dallas, TX</th>\n",
       "      <td>146000.0</td>\n",
       "      <td>296000.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>102.739726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houston, TX</th>\n",
       "      <td>140000.0</td>\n",
       "      <td>264000.0</td>\n",
       "      <td>124000.0</td>\n",
       "      <td>88.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Antonio, TX</th>\n",
       "      <td>143900.0</td>\n",
       "      <td>252000.0</td>\n",
       "      <td>108100.0</td>\n",
       "      <td>75.121612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin, TX</th>\n",
       "      <td>194000.0</td>\n",
       "      <td>380009.0</td>\n",
       "      <td>186009.0</td>\n",
       "      <td>95.880928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 2013 Sale Price  2020 Sale Price  Sale Price Change  \\\n",
       "Dallas, TX              146000.0         296000.0           150000.0   \n",
       "Houston, TX             140000.0         264000.0           124000.0   \n",
       "San Antonio, TX         143900.0         252000.0           108100.0   \n",
       "Austin, TX              194000.0         380009.0           186009.0   \n",
       "\n",
       "                 Percent Change  \n",
       "Dallas, TX           102.739726  \n",
       "Houston, TX           88.571429  \n",
       "San Antonio, TX       75.121612  \n",
       "Austin, TX            95.880928  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a summary Table of Home price change 2013 t0 current\n",
    "results = {}\n",
    "columns = ['Dallas, TX', 'Houston, TX', 'San Antonio, TX', 'Austin, TX']\n",
    "\n",
    "# Loop through each column and calculate the change\n",
    "for column in columns:\n",
    "    difference = saleprice_t[column][155] - saleprice_t[column][60]\n",
    "    percent_diff = 100 * (saleprice_t[column][155] - saleprice_t[column][60]) / saleprice_t[column][60]\n",
    "    results[column] = {'2013 Sale Price': saleprice_t[column][60],\n",
    "                       '2020 Sale Price': saleprice_t[column][155],\n",
    "                       'Sale Price Change': difference,\n",
    "                      'Percent Change': percent_diff\n",
    "                      }\n",
    "\n",
    "# Convert the results dictionary to a DataFrame\n",
    "saleprice_change = pd.DataFrame.from_dict(results, orient='index')\n",
    "saleprice_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "073e65ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\1480024975.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  saleprice_year = saleprice_t.groupby(saleprice_t['Date'].dt.year)['Dallas, TX', 'Houston, TX', 'San Antonio, TX', 'Austin, TX'].mean().reset_index()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>year</th>\n",
       "      <th>agi_01</th>\n",
       "      <th>agi_05</th>\n",
       "      <th>agi_10</th>\n",
       "      <th>agi_25</th>\n",
       "      <th>agi_50</th>\n",
       "      <th>agi_75</th>\n",
       "      <th>Dallas, TX</th>\n",
       "      <th>Houston, TX</th>\n",
       "      <th>San Antonio, TX</th>\n",
       "      <th>Austin, TX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>453430.00</td>\n",
       "      <td>184606.0</td>\n",
       "      <td>128782.0</td>\n",
       "      <td>72895.0</td>\n",
       "      <td>35617.0</td>\n",
       "      <td>17444.0</td>\n",
       "      <td>170224.000000</td>\n",
       "      <td>166929.166667</td>\n",
       "      <td>158919.000000</td>\n",
       "      <td>213804.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>497715.00</td>\n",
       "      <td>195185.0</td>\n",
       "      <td>135237.0</td>\n",
       "      <td>76173.0</td>\n",
       "      <td>37424.0</td>\n",
       "      <td>18299.0</td>\n",
       "      <td>181825.000000</td>\n",
       "      <td>180522.750000</td>\n",
       "      <td>167904.166667</td>\n",
       "      <td>231716.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>496385.04</td>\n",
       "      <td>199076.0</td>\n",
       "      <td>138372.0</td>\n",
       "      <td>77638.0</td>\n",
       "      <td>38208.0</td>\n",
       "      <td>18802.0</td>\n",
       "      <td>200466.666667</td>\n",
       "      <td>193308.333333</td>\n",
       "      <td>178885.416667</td>\n",
       "      <td>253366.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>463308.00</td>\n",
       "      <td>195144.0</td>\n",
       "      <td>136695.0</td>\n",
       "      <td>76956.0</td>\n",
       "      <td>38194.0</td>\n",
       "      <td>18959.0</td>\n",
       "      <td>221000.000000</td>\n",
       "      <td>202937.500000</td>\n",
       "      <td>189564.583333</td>\n",
       "      <td>273659.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>528049.00</td>\n",
       "      <td>209115.0</td>\n",
       "      <td>144514.0</td>\n",
       "      <td>80809.0</td>\n",
       "      <td>40022.0</td>\n",
       "      <td>19645.0</td>\n",
       "      <td>242223.333333</td>\n",
       "      <td>212820.833333</td>\n",
       "      <td>198775.000000</td>\n",
       "      <td>290958.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>542780.00</td>\n",
       "      <td>216799.0</td>\n",
       "      <td>149881.0</td>\n",
       "      <td>83900.0</td>\n",
       "      <td>41726.0</td>\n",
       "      <td>20613.0</td>\n",
       "      <td>255800.000000</td>\n",
       "      <td>221631.333333</td>\n",
       "      <td>211902.083333</td>\n",
       "      <td>309124.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>547661.90</td>\n",
       "      <td>220593.0</td>\n",
       "      <td>152085.0</td>\n",
       "      <td>84587.0</td>\n",
       "      <td>42099.0</td>\n",
       "      <td>20729.0</td>\n",
       "      <td>262761.250000</td>\n",
       "      <td>230256.250000</td>\n",
       "      <td>220537.416667</td>\n",
       "      <td>320563.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>543478.70</td>\n",
       "      <td>219156.0</td>\n",
       "      <td>150387.0</td>\n",
       "      <td>82532.0</td>\n",
       "      <td>40878.0</td>\n",
       "      <td>20115.0</td>\n",
       "      <td>284408.333333</td>\n",
       "      <td>247583.333333</td>\n",
       "      <td>241730.833333</td>\n",
       "      <td>351267.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     agi_01    agi_05    agi_10   agi_25   agi_50   agi_75  \\\n",
       "0  2013  453430.00  184606.0  128782.0  72895.0  35617.0  17444.0   \n",
       "1  2014  497715.00  195185.0  135237.0  76173.0  37424.0  18299.0   \n",
       "2  2015  496385.04  199076.0  138372.0  77638.0  38208.0  18802.0   \n",
       "3  2016  463308.00  195144.0  136695.0  76956.0  38194.0  18959.0   \n",
       "4  2017  528049.00  209115.0  144514.0  80809.0  40022.0  19645.0   \n",
       "5  2018  542780.00  216799.0  149881.0  83900.0  41726.0  20613.0   \n",
       "6  2019  547661.90  220593.0  152085.0  84587.0  42099.0  20729.0   \n",
       "7  2020  543478.70  219156.0  150387.0  82532.0  40878.0  20115.0   \n",
       "\n",
       "      Dallas, TX    Houston, TX  San Antonio, TX     Austin, TX  \n",
       "0  170224.000000  166929.166667    158919.000000  213804.166667  \n",
       "1  181825.000000  180522.750000    167904.166667  231716.666667  \n",
       "2  200466.666667  193308.333333    178885.416667  253366.666667  \n",
       "3  221000.000000  202937.500000    189564.583333  273659.666667  \n",
       "4  242223.333333  212820.833333    198775.000000  290958.333333  \n",
       "5  255800.000000  221631.333333    211902.083333  309124.333333  \n",
       "6  262761.250000  230256.250000    220537.416667  320563.750000  \n",
       "7  284408.333333  247583.333333    241730.833333  351267.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new DataFrame that Combines AGI and Saleprice info\n",
    "saleprice_year = saleprice_t.groupby(saleprice_t['Date'].dt.year)['Dallas, TX', 'Houston, TX', 'San Antonio, TX', 'Austin, TX'].mean().reset_index()\n",
    "saleprice_year.rename(columns = {\"Date\":\"year\"}, inplace=True)\n",
    "saleprice_agi_df = pd.merge(income_all,saleprice_year, on='year')\n",
    "\n",
    "saleprice_agi_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "06e129ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>3.975577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.168868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>3.850577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>3.654038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>3.989808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>4.544615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>3.935769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>3.111698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021</td>\n",
       "      <td>2.957692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022</td>\n",
       "      <td>5.344038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023</td>\n",
       "      <td>6.684390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Week      Rate\n",
       "0   2013  3.975577\n",
       "1   2014  4.168868\n",
       "2   2015  3.850577\n",
       "3   2016  3.654038\n",
       "4   2017  3.989808\n",
       "5   2018  4.544615\n",
       "6   2019  3.935769\n",
       "7   2020  3.111698\n",
       "8   2021  2.957692\n",
       "9   2022  5.344038\n",
       "10  2023  6.684390"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Rates DataFrame\n",
    "rates_df['Week'] = pd.to_datetime(rates_df['Week'])\n",
    "rates_summary = rates_df.groupby(rates_df['Week'].dt.year)['Rate'].mean().reset_index()\n",
    "\n",
    "rates_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "476e5728",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\1901203008.py:48: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['Monthly Mortgage'] = monthly_mortgage\n",
      "C:\\Users\\grant\\AppData\\Local\\Temp\\ipykernel_11540\\1901203008.py:49: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['Annual Mortgage'] = annual_mortgage\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>Austin, TX</th>\n",
       "      <th>20% Downpayment</th>\n",
       "      <th>agi_01</th>\n",
       "      <th>agi_05</th>\n",
       "      <th>agi_10</th>\n",
       "      <th>agi_25</th>\n",
       "      <th>agi_50</th>\n",
       "      <th>agi_75</th>\n",
       "      <th>Monthly Mortgage</th>\n",
       "      <th>Annual Mortgage</th>\n",
       "      <th>HOA, Insurance, &amp; Taxes</th>\n",
       "      <th>Total Annual Housing Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>213804.166667</td>\n",
       "      <td>42760.833333</td>\n",
       "      <td>453430.00</td>\n",
       "      <td>184606.0</td>\n",
       "      <td>128782.0</td>\n",
       "      <td>72895.0</td>\n",
       "      <td>35617.0</td>\n",
       "      <td>17444.0</td>\n",
       "      <td>814.180546</td>\n",
       "      <td>9770.166548</td>\n",
       "      <td>2120.126141</td>\n",
       "      <td>11890.292688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>231716.666667</td>\n",
       "      <td>46343.333333</td>\n",
       "      <td>497715.00</td>\n",
       "      <td>195185.0</td>\n",
       "      <td>135237.0</td>\n",
       "      <td>76173.0</td>\n",
       "      <td>37424.0</td>\n",
       "      <td>18299.0</td>\n",
       "      <td>903.142301</td>\n",
       "      <td>10837.707612</td>\n",
       "      <td>2351.782552</td>\n",
       "      <td>13189.490164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>253366.666667</td>\n",
       "      <td>50673.333333</td>\n",
       "      <td>496385.04</td>\n",
       "      <td>199076.0</td>\n",
       "      <td>138372.0</td>\n",
       "      <td>77638.0</td>\n",
       "      <td>38208.0</td>\n",
       "      <td>18802.0</td>\n",
       "      <td>950.309816</td>\n",
       "      <td>11403.717793</td>\n",
       "      <td>2474.606761</td>\n",
       "      <td>13878.324554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>273659.666667</td>\n",
       "      <td>54731.933333</td>\n",
       "      <td>463308.00</td>\n",
       "      <td>195144.0</td>\n",
       "      <td>136695.0</td>\n",
       "      <td>76956.0</td>\n",
       "      <td>38194.0</td>\n",
       "      <td>18959.0</td>\n",
       "      <td>1002.004355</td>\n",
       "      <td>12024.052261</td>\n",
       "      <td>2609.219341</td>\n",
       "      <td>14633.271601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>290958.333333</td>\n",
       "      <td>58191.666667</td>\n",
       "      <td>528049.00</td>\n",
       "      <td>209115.0</td>\n",
       "      <td>144514.0</td>\n",
       "      <td>80809.0</td>\n",
       "      <td>40022.0</td>\n",
       "      <td>19645.0</td>\n",
       "      <td>1109.896369</td>\n",
       "      <td>13318.756429</td>\n",
       "      <td>2890.170145</td>\n",
       "      <td>16208.926574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>309124.333333</td>\n",
       "      <td>61824.866667</td>\n",
       "      <td>542780.00</td>\n",
       "      <td>216799.0</td>\n",
       "      <td>149881.0</td>\n",
       "      <td>83900.0</td>\n",
       "      <td>41726.0</td>\n",
       "      <td>20613.0</td>\n",
       "      <td>1259.594364</td>\n",
       "      <td>15115.132366</td>\n",
       "      <td>3279.983723</td>\n",
       "      <td>18395.116090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>320563.750000</td>\n",
       "      <td>64112.750000</td>\n",
       "      <td>547661.90</td>\n",
       "      <td>220593.0</td>\n",
       "      <td>152085.0</td>\n",
       "      <td>84587.0</td>\n",
       "      <td>42099.0</td>\n",
       "      <td>20729.0</td>\n",
       "      <td>1214.859023</td>\n",
       "      <td>14578.308276</td>\n",
       "      <td>3163.492896</td>\n",
       "      <td>17741.801171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>351267.000000</td>\n",
       "      <td>70253.400000</td>\n",
       "      <td>543478.70</td>\n",
       "      <td>219156.0</td>\n",
       "      <td>150387.0</td>\n",
       "      <td>82532.0</td>\n",
       "      <td>40878.0</td>\n",
       "      <td>20115.0</td>\n",
       "      <td>1201.760384</td>\n",
       "      <td>14421.124611</td>\n",
       "      <td>3129.384041</td>\n",
       "      <td>17550.508652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     Austin, TX  20% Downpayment     agi_01    agi_05    agi_10  \\\n",
       "0  2013  213804.166667     42760.833333  453430.00  184606.0  128782.0   \n",
       "1  2014  231716.666667     46343.333333  497715.00  195185.0  135237.0   \n",
       "2  2015  253366.666667     50673.333333  496385.04  199076.0  138372.0   \n",
       "3  2016  273659.666667     54731.933333  463308.00  195144.0  136695.0   \n",
       "4  2017  290958.333333     58191.666667  528049.00  209115.0  144514.0   \n",
       "5  2018  309124.333333     61824.866667  542780.00  216799.0  149881.0   \n",
       "6  2019  320563.750000     64112.750000  547661.90  220593.0  152085.0   \n",
       "7  2020  351267.000000     70253.400000  543478.70  219156.0  150387.0   \n",
       "\n",
       "    agi_25   agi_50   agi_75  Monthly Mortgage  Annual Mortgage  \\\n",
       "0  72895.0  35617.0  17444.0        814.180546      9770.166548   \n",
       "1  76173.0  37424.0  18299.0        903.142301     10837.707612   \n",
       "2  77638.0  38208.0  18802.0        950.309816     11403.717793   \n",
       "3  76956.0  38194.0  18959.0       1002.004355     12024.052261   \n",
       "4  80809.0  40022.0  19645.0       1109.896369     13318.756429   \n",
       "5  83900.0  41726.0  20613.0       1259.594364     15115.132366   \n",
       "6  84587.0  42099.0  20729.0       1214.859023     14578.308276   \n",
       "7  82532.0  40878.0  20115.0       1201.760384     14421.124611   \n",
       "\n",
       "   HOA, Insurance, & Taxes  Total Annual Housing Cost  \n",
       "0              2120.126141               11890.292688  \n",
       "1              2351.782552               13189.490164  \n",
       "2              2474.606761               13878.324554  \n",
       "3              2609.219341               14633.271601  \n",
       "4              2890.170145               16208.926574  \n",
       "5              3279.983723               18395.116090  \n",
       "6              3163.492896               17741.801171  \n",
       "7              3129.384041               17550.508652  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deep dive into {city}\n",
    "city = 'Austin, TX'\n",
    "saleprice_agi_df['20% Downpayment'] = saleprice_agi_df[city] * 0.20\n",
    "\n",
    "df1 =saleprice_agi_df[['year', city, '20% Downpayment', 'agi_01', 'agi_05', \n",
    "                       'agi_10', 'agi_25', 'agi_50', 'agi_75']]\n",
    "\n",
    "# Create an empty list to store the monthly payment\n",
    "monthly_mortgage = []\n",
    "annual_mortgage = []\n",
    "hoa_tax_insurance_annual = []\n",
    "total_annual_payment = []\n",
    "\n",
    "house_prices = df1[city]\n",
    "rates = rates_summary['Rate'] / 100  # Convert rates to decimal form\n",
    "down_payment_percent = 20\n",
    "loan_term_years = 30\n",
    "hoa_factor = 0.022\n",
    "insurance_factor = 0.17\n",
    "tax_factor = 0.025\n",
    "\n",
    "# Calculate monthly payment for each house price and rate\n",
    "for house_price, rate in zip(house_prices, rates):\n",
    "    # Calculate down payment\n",
    "    down_payment = house_price * (down_payment_percent / 100)\n",
    "\n",
    "    # Calculate loan amount\n",
    "    loan_amount = house_price - down_payment\n",
    "\n",
    "    # Calculate monthly interest rate\n",
    "    monthly_interest_rate = rate / 12\n",
    "\n",
    "    # Calculate total number of payments (loan term in months)\n",
    "    total_payments = loan_term_years * 12\n",
    "\n",
    "    # Calculate monthly payment using the formula\n",
    "    monthly_payment = loan_amount * (monthly_interest_rate * (1 + monthly_interest_rate)**total_payments) / ((1 + monthly_interest_rate)**total_payments - 1)\n",
    "    yearly_payment = 12 * monthly_payment\n",
    "    hoa_annual = yearly_payment * (hoa_factor + insurance_factor + tax_factor)\n",
    "    total_annual = yearly_payment + hoa_annual\n",
    "    \n",
    "    monthly_mortgage.append(monthly_payment)\n",
    "    annual_mortgage.append(yearly_payment)\n",
    "    hoa_tax_insurance_annual.append(hoa_annual)\n",
    "    total_annual_payment.append(total_annual)    \n",
    "    \n",
    "# Add the new columns\n",
    "df1['Monthly Mortgage'] = monthly_mortgage\n",
    "df1['Annual Mortgage'] = annual_mortgage\n",
    "df1['HOA, Insurance, & Taxes'] = hoa_tax_insurance_annual\n",
    "df1['Total Annual Housing Cost'] = total_annual_payment\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "495fc1a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>year</th>\n",
       "      <th>Median Sale Price</th>\n",
       "      <th>20% Downpayment</th>\n",
       "      <th>Monthly Mortgage</th>\n",
       "      <th>Annual Mortgage</th>\n",
       "      <th>HOA, Insurance, &amp; Taxes</th>\n",
       "      <th>Total Annual Housing Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>213804.166667</td>\n",
       "      <td>42760.833333</td>\n",
       "      <td>814.180546</td>\n",
       "      <td>9770.166548</td>\n",
       "      <td>2120.126141</td>\n",
       "      <td>11890.292688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>231716.666667</td>\n",
       "      <td>46343.333333</td>\n",
       "      <td>903.142301</td>\n",
       "      <td>10837.707612</td>\n",
       "      <td>2351.782552</td>\n",
       "      <td>13189.490164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>253366.666667</td>\n",
       "      <td>50673.333333</td>\n",
       "      <td>950.309816</td>\n",
       "      <td>11403.717793</td>\n",
       "      <td>2474.606761</td>\n",
       "      <td>13878.324554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>273659.666667</td>\n",
       "      <td>54731.933333</td>\n",
       "      <td>1002.004355</td>\n",
       "      <td>12024.052261</td>\n",
       "      <td>2609.219341</td>\n",
       "      <td>14633.271601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>290958.333333</td>\n",
       "      <td>58191.666667</td>\n",
       "      <td>1109.896369</td>\n",
       "      <td>13318.756429</td>\n",
       "      <td>2890.170145</td>\n",
       "      <td>16208.926574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>309124.333333</td>\n",
       "      <td>61824.866667</td>\n",
       "      <td>1259.594364</td>\n",
       "      <td>15115.132366</td>\n",
       "      <td>3279.983723</td>\n",
       "      <td>18395.116090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>320563.750000</td>\n",
       "      <td>64112.750000</td>\n",
       "      <td>1214.859023</td>\n",
       "      <td>14578.308276</td>\n",
       "      <td>3163.492896</td>\n",
       "      <td>17741.801171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>351267.000000</td>\n",
       "      <td>70253.400000</td>\n",
       "      <td>1201.760384</td>\n",
       "      <td>14421.124611</td>\n",
       "      <td>3129.384041</td>\n",
       "      <td>17550.508652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  Median Sale Price  20% Downpayment  Monthly Mortgage  \\\n",
       "0  2013      213804.166667     42760.833333        814.180546   \n",
       "1  2014      231716.666667     46343.333333        903.142301   \n",
       "2  2015      253366.666667     50673.333333        950.309816   \n",
       "3  2016      273659.666667     54731.933333       1002.004355   \n",
       "4  2017      290958.333333     58191.666667       1109.896369   \n",
       "5  2018      309124.333333     61824.866667       1259.594364   \n",
       "6  2019      320563.750000     64112.750000       1214.859023   \n",
       "7  2020      351267.000000     70253.400000       1201.760384   \n",
       "\n",
       "   Annual Mortgage  HOA, Insurance, & Taxes  Total Annual Housing Cost  \n",
       "0      9770.166548              2120.126141               11890.292688  \n",
       "1     10837.707612              2351.782552               13189.490164  \n",
       "2     11403.717793              2474.606761               13878.324554  \n",
       "3     12024.052261              2609.219341               14633.271601  \n",
       "4     13318.756429              2890.170145               16208.926574  \n",
       "5     15115.132366              3279.983723               18395.116090  \n",
       "6     14578.308276              3163.492896               17741.801171  \n",
       "7     14421.124611              3129.384041               17550.508652  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the final Austin Summary Table\n",
    "austin_housing = df1.copy()\n",
    "austin_summary_table = austin_housing.drop(columns=['agi_01', 'agi_05', 'agi_10', \n",
    "                                                    'agi_25', 'agi_50', 'agi_75'])\n",
    "\n",
    "austin_summary_table.rename(columns={'Austin, TX': 'Median Sale Price'}, inplace=True)\n",
    "\n",
    "austin_summary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47aa605e",
   "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>year</th>\n",
       "      <th>agi_01</th>\n",
       "      <th>agi_05</th>\n",
       "      <th>agi_10</th>\n",
       "      <th>agi_25</th>\n",
       "      <th>agi_50</th>\n",
       "      <th>agi_75</th>\n",
       "      <th>Total Annual Housing Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>453430.00</td>\n",
       "      <td>184606.0</td>\n",
       "      <td>128782.0</td>\n",
       "      <td>72895.0</td>\n",
       "      <td>35617.0</td>\n",
       "      <td>17444.0</td>\n",
       "      <td>11890.292688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>497715.00</td>\n",
       "      <td>195185.0</td>\n",
       "      <td>135237.0</td>\n",
       "      <td>76173.0</td>\n",
       "      <td>37424.0</td>\n",
       "      <td>18299.0</td>\n",
       "      <td>13189.490164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>496385.04</td>\n",
       "      <td>199076.0</td>\n",
       "      <td>138372.0</td>\n",
       "      <td>77638.0</td>\n",
       "      <td>38208.0</td>\n",
       "      <td>18802.0</td>\n",
       "      <td>13878.324554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>463308.00</td>\n",
       "      <td>195144.0</td>\n",
       "      <td>136695.0</td>\n",
       "      <td>76956.0</td>\n",
       "      <td>38194.0</td>\n",
       "      <td>18959.0</td>\n",
       "      <td>14633.271601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>528049.00</td>\n",
       "      <td>209115.0</td>\n",
       "      <td>144514.0</td>\n",
       "      <td>80809.0</td>\n",
       "      <td>40022.0</td>\n",
       "      <td>19645.0</td>\n",
       "      <td>16208.926574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>542780.00</td>\n",
       "      <td>216799.0</td>\n",
       "      <td>149881.0</td>\n",
       "      <td>83900.0</td>\n",
       "      <td>41726.0</td>\n",
       "      <td>20613.0</td>\n",
       "      <td>18395.116090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>547661.90</td>\n",
       "      <td>220593.0</td>\n",
       "      <td>152085.0</td>\n",
       "      <td>84587.0</td>\n",
       "      <td>42099.0</td>\n",
       "      <td>20729.0</td>\n",
       "      <td>17741.801171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>543478.70</td>\n",
       "      <td>219156.0</td>\n",
       "      <td>150387.0</td>\n",
       "      <td>82532.0</td>\n",
       "      <td>40878.0</td>\n",
       "      <td>20115.0</td>\n",
       "      <td>17550.508652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     agi_01    agi_05    agi_10   agi_25   agi_50   agi_75  \\\n",
       "0  2013  453430.00  184606.0  128782.0  72895.0  35617.0  17444.0   \n",
       "1  2014  497715.00  195185.0  135237.0  76173.0  37424.0  18299.0   \n",
       "2  2015  496385.04  199076.0  138372.0  77638.0  38208.0  18802.0   \n",
       "3  2016  463308.00  195144.0  136695.0  76956.0  38194.0  18959.0   \n",
       "4  2017  528049.00  209115.0  144514.0  80809.0  40022.0  19645.0   \n",
       "5  2018  542780.00  216799.0  149881.0  83900.0  41726.0  20613.0   \n",
       "6  2019  547661.90  220593.0  152085.0  84587.0  42099.0  20729.0   \n",
       "7  2020  543478.70  219156.0  150387.0  82532.0  40878.0  20115.0   \n",
       "\n",
       "   Total Annual Housing Cost  \n",
       "0               11890.292688  \n",
       "1               13189.490164  \n",
       "2               13878.324554  \n",
       "3               14633.271601  \n",
       "4               16208.926574  \n",
       "5               18395.116090  \n",
       "6               17741.801171  \n",
       "7               17550.508652  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate which income brackets violate the 28% rule:\n",
    "# 28% Rule: Yourhousing expenses should not exceed 28% of your gross annual income. \n",
    "#This includes mortgage payments, interest, property taxes, homeowners insurance, and HOA fees.\n",
    "\n",
    "austin_affordability = df1.drop(columns= ['Austin, TX', '20% Downpayment', 'Monthly Mortgage', 'Annual Mortgage', 'HOA, Insurance, & Taxes'])\n",
    "\n",
    "austin_affordability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0aea1378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>year</th>\n",
       "      <th>Total Annual Housing Cost</th>\n",
       "      <th>agi_1 %</th>\n",
       "      <th>agi_5 %</th>\n",
       "      <th>agi_10 %</th>\n",
       "      <th>agi_25 %</th>\n",
       "      <th>agi_50 %</th>\n",
       "      <th>agi_75 %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>11890.292688</td>\n",
       "      <td>2.62%</td>\n",
       "      <td>6.44%</td>\n",
       "      <td>9.23%</td>\n",
       "      <td>16.31%</td>\n",
       "      <td>33.38%</td>\n",
       "      <td>68.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>13189.490164</td>\n",
       "      <td>2.65%</td>\n",
       "      <td>6.76%</td>\n",
       "      <td>9.75%</td>\n",
       "      <td>17.32%</td>\n",
       "      <td>35.24%</td>\n",
       "      <td>72.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>13878.324554</td>\n",
       "      <td>2.80%</td>\n",
       "      <td>6.97%</td>\n",
       "      <td>10.03%</td>\n",
       "      <td>17.88%</td>\n",
       "      <td>36.32%</td>\n",
       "      <td>73.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>14633.271601</td>\n",
       "      <td>3.16%</td>\n",
       "      <td>7.50%</td>\n",
       "      <td>10.71%</td>\n",
       "      <td>19.02%</td>\n",
       "      <td>38.31%</td>\n",
       "      <td>77.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>16208.926574</td>\n",
       "      <td>3.07%</td>\n",
       "      <td>7.75%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>20.06%</td>\n",
       "      <td>40.50%</td>\n",
       "      <td>82.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>18395.116090</td>\n",
       "      <td>3.39%</td>\n",
       "      <td>8.48%</td>\n",
       "      <td>12.27%</td>\n",
       "      <td>21.93%</td>\n",
       "      <td>44.09%</td>\n",
       "      <td>89.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>17741.801171</td>\n",
       "      <td>3.24%</td>\n",
       "      <td>8.04%</td>\n",
       "      <td>11.67%</td>\n",
       "      <td>20.97%</td>\n",
       "      <td>42.14%</td>\n",
       "      <td>85.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>17550.508652</td>\n",
       "      <td>3.23%</td>\n",
       "      <td>8.01%</td>\n",
       "      <td>11.67%</td>\n",
       "      <td>21.27%</td>\n",
       "      <td>42.93%</td>\n",
       "      <td>87.25%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  Total Annual Housing Cost agi_1 % agi_5 % agi_10 % agi_25 % agi_50 %  \\\n",
       "0  2013               11890.292688   2.62%   6.44%    9.23%   16.31%   33.38%   \n",
       "1  2014               13189.490164   2.65%   6.76%    9.75%   17.32%   35.24%   \n",
       "2  2015               13878.324554   2.80%   6.97%   10.03%   17.88%   36.32%   \n",
       "3  2016               14633.271601   3.16%   7.50%   10.71%   19.02%   38.31%   \n",
       "4  2017               16208.926574   3.07%   7.75%   11.22%   20.06%   40.50%   \n",
       "5  2018               18395.116090   3.39%   8.48%   12.27%   21.93%   44.09%   \n",
       "6  2019               17741.801171   3.24%   8.04%   11.67%   20.97%   42.14%   \n",
       "7  2020               17550.508652   3.23%   8.01%   11.67%   21.27%   42.93%   \n",
       "\n",
       "  agi_75 %  \n",
       "0   68.16%  \n",
       "1   72.08%  \n",
       "2   73.81%  \n",
       "3   77.18%  \n",
       "4   82.51%  \n",
       "5   89.24%  \n",
       "6   85.59%  \n",
       "7   87.25%  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the 28% rule and update the table\n",
    "austin_affordability['agi_1 %'] = (austin_affordability['Total Annual Housing Cost'] / austin_affordability['agi_01'] * 100).apply(lambda x: f'{x:.2f}%')\n",
    "austin_affordability['agi_5 %'] = (austin_affordability['Total Annual Housing Cost'] / austin_affordability['agi_05'] * 100).apply(lambda x: f'{x:.2f}%')\n",
    "austin_affordability['agi_10 %'] = (austin_affordability['Total Annual Housing Cost'] / austin_affordability['agi_10'] * 100).apply(lambda x: f'{x:.2f}%')\n",
    "austin_affordability['agi_25 %'] = (austin_affordability['Total Annual Housing Cost'] / austin_affordability['agi_25'] * 100).apply(lambda x: f'{x:.2f}%')\n",
    "austin_affordability['agi_50 %'] = (austin_affordability['Total Annual Housing Cost'] / austin_affordability['agi_50'] * 100).apply(lambda x: f'{x:.2f}%')\n",
    "austin_affordability['agi_75 %'] = (austin_affordability['Total Annual Housing Cost'] / austin_affordability['agi_75'] * 100).apply(lambda x: f'{x:.2f}%')\n",
    "austin_affordability.drop(columns=['agi_01', 'agi_05', 'agi_10', 'agi_25', 'agi_50', 'agi_75'], inplace=True)\n",
    "\n",
    "\n",
    "austin_affordability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce10d3d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>Total Annual Housing Cost</th>\n",
       "      <th>agi_1 %</th>\n",
       "      <th>agi_5 %</th>\n",
       "      <th>agi_10 %</th>\n",
       "      <th>agi_25 %</th>\n",
       "      <th>agi_50 %</th>\n",
       "      <th>agi_75 %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>11890.292688</td>\n",
       "      <td>2.62%</td>\n",
       "      <td>6.44%</td>\n",
       "      <td>9.23%</td>\n",
       "      <td>16.31%</td>\n",
       "      <td>33.38%</td>\n",
       "      <td>68.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>17550.508652</td>\n",
       "      <td>3.23%</td>\n",
       "      <td>8.01%</td>\n",
       "      <td>11.67%</td>\n",
       "      <td>21.27%</td>\n",
       "      <td>42.93%</td>\n",
       "      <td>87.25%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  Total Annual Housing Cost agi_1 % agi_5 % agi_10 % agi_25 % agi_50 %  \\\n",
       "0  2013               11890.292688   2.62%   6.44%    9.23%   16.31%   33.38%   \n",
       "7  2020               17550.508652   3.23%   8.01%   11.67%   21.27%   42.93%   \n",
       "\n",
       "  agi_75 %  \n",
       "0   68.16%  \n",
       "7   87.25%  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = austin_affordability.loc[(austin_affordability['year'] == 2013) |\n",
    "                             (austin_affordability['year'] == 2020)]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c33b4db2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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