cass_chart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import pprint\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "plt.style.use('./blog.matplotlibrc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>housing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1983</td>\n",
       "      <td>5520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1985</td>\n",
       "      <td>5676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1986</td>\n",
       "      <td>6228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1987</td>\n",
       "      <td>6372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1988</td>\n",
       "      <td>6552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1989</td>\n",
       "      <td>6648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1990</td>\n",
       "      <td>6792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1991</td>\n",
       "      <td>6972.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1992</td>\n",
       "      <td>7212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1993</td>\n",
       "      <td>7404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1994</td>\n",
       "      <td>8736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1995</td>\n",
       "      <td>8508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1996</td>\n",
       "      <td>9636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1997</td>\n",
       "      <td>9924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1998</td>\n",
       "      <td>10188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1999</td>\n",
       "      <td>10392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2000</td>\n",
       "      <td>10452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2001</td>\n",
       "      <td>12156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2002</td>\n",
       "      <td>12504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2003</td>\n",
       "      <td>12828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2004</td>\n",
       "      <td>12876.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2005</td>\n",
       "      <td>11940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2006</td>\n",
       "      <td>12324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2007</td>\n",
       "      <td>12816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2008</td>\n",
       "      <td>12024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2009</td>\n",
       "      <td>11988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010</td>\n",
       "      <td>12912.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011</td>\n",
       "      <td>13080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012</td>\n",
       "      <td>12804.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>13644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>13296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>14268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>14736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>15528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>15924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>16752.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  housing\n",
       "35  1983   5520.0\n",
       "34  1985   5676.0\n",
       "33  1986   6228.0\n",
       "32  1987   6372.0\n",
       "31  1988   6552.0\n",
       "30  1989   6648.0\n",
       "29  1990   6792.0\n",
       "28  1991   6972.0\n",
       "27  1992   7212.0\n",
       "26  1993   7404.0\n",
       "25  1994   8736.0\n",
       "24  1995   8508.0\n",
       "23  1996   9636.0\n",
       "22  1997   9924.0\n",
       "21  1998  10188.0\n",
       "20  1999  10392.0\n",
       "19  2000  10452.0\n",
       "18  2001  12156.0\n",
       "17  2002  12504.0\n",
       "16  2003  12828.0\n",
       "15  2004  12876.0\n",
       "14  2005  11940.0\n",
       "13  2006  12324.0\n",
       "12  2007  12816.0\n",
       "11  2008  12024.0\n",
       "10  2009  11988.0\n",
       "9   2010  12912.0\n",
       "8   2011  13080.0\n",
       "7   2012  12804.0\n",
       "6   2013  13644.0\n",
       "5   2014  13296.0\n",
       "4   2015  14268.0\n",
       "3   2016  14736.0\n",
       "2   2017  15528.0\n",
       "1   2018  15924.0\n",
       "0   2019  16752.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = pd.read_csv('data/FMR_All_1983_2019_rev.csv', encoding='latin-1')\n",
    "housing = (\n",
    "    housing\n",
    "    .loc[lambda df: (\n",
    "        (df['areaname'] == 'Raleigh, NC MSA')\n",
    "        & (df['cntyname'] == 'Wake County')\n",
    "    )]\n",
    "    [['areaname'] + [i for i in housing if i.startswith('fmr') and i.endswith('_3')]]\n",
    "    .melt(\n",
    "        id_vars=['areaname'],\n",
    "        var_name='year',\n",
    "        value_name='housing'\n",
    "    )\n",
    "    .drop('areaname', axis='columns')\n",
    "    .assign(year=lambda df: (\n",
    "        df.year.str.extract(r'fmr(\\d{2})_3', expand=False)\n",
    "        .astype(int)\n",
    "        .map(lambda x: x + 2000 if x < 50 else x + 1900)\n",
    "    ))\n",
    "    .assign(housing = lambda df: (df['housing'] * 12))\n",
    "    .sort_values('year')\n",
    ")\n",
    "housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1985    0.458400\n",
       "1986    0.458400\n",
       "1987    0.458400\n",
       "1988    0.524423\n",
       "1989    0.607085\n",
       "1990    0.697262\n",
       "1991    0.758991\n",
       "1992    0.839506\n",
       "1993    0.887815\n",
       "1994    0.912507\n",
       "1995    0.950081\n",
       "Name: deflator, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BASE_YEAR = 1996\n",
    "insurance_deflator = (\n",
    "    pd.read_excel(\n",
    "        'data/Table 21 Expenditures, Enrollment and Per Enrollee Estimates of Health Insurance.xlsx',\n",
    "    )\n",
    "    .iloc[[0, 23], 1:]\n",
    "    .T\n",
    "    .rename(columns={0: 'year', 23: 'deflator'})\n",
    "    .assign(year=lambda df: df['year'].astype(int))\n",
    "    .set_index('year')\n",
    "    ['deflator']\n",
    "    .reindex(list(range(1985, BASE_YEAR + 1)))\n",
    "    .fillna(method='bfill')\n",
    ")\n",
    "insurance_deflator = (\n",
    "    insurance_deflator\n",
    "    .div(insurance_deflator[BASE_YEAR])\n",
    "    .loc[1985: BASE_YEAR - 1]\n",
    ")\n",
    "insurance_deflator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>insurance_total</th>\n",
       "      <th>insurance_employee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>2270.915727</td>\n",
       "      <td>584.460548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>2270.915727</td>\n",
       "      <td>584.460548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987</td>\n",
       "      <td>2270.915727</td>\n",
       "      <td>584.460548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1988</td>\n",
       "      <td>2597.991412</td>\n",
       "      <td>668.639291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1989</td>\n",
       "      <td>3007.500805</td>\n",
       "      <td>774.033816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1990</td>\n",
       "      <td>3454.238325</td>\n",
       "      <td>889.009662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1991</td>\n",
       "      <td>3760.040794</td>\n",
       "      <td>967.713366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1992</td>\n",
       "      <td>4158.913580</td>\n",
       "      <td>1070.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1993</td>\n",
       "      <td>4398.237252</td>\n",
       "      <td>1131.964573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1994</td>\n",
       "      <td>4520.558239</td>\n",
       "      <td>1163.446055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1995</td>\n",
       "      <td>4706.698873</td>\n",
       "      <td>1211.352657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1996</td>\n",
       "      <td>4954.000000</td>\n",
       "      <td>1275.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1997</td>\n",
       "      <td>5332.000000</td>\n",
       "      <td>1305.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1998</td>\n",
       "      <td>5590.000000</td>\n",
       "      <td>1382.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1999</td>\n",
       "      <td>6058.000000</td>\n",
       "      <td>1438.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2000</td>\n",
       "      <td>6772.000000</td>\n",
       "      <td>1614.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2001</td>\n",
       "      <td>7509.000000</td>\n",
       "      <td>1741.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2002</td>\n",
       "      <td>8469.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2003</td>\n",
       "      <td>9249.000000</td>\n",
       "      <td>2283.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2004</td>\n",
       "      <td>10006.000000</td>\n",
       "      <td>2438.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2005</td>\n",
       "      <td>10728.000000</td>\n",
       "      <td>2585.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2006</td>\n",
       "      <td>11381.000000</td>\n",
       "      <td>2890.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2007</td>\n",
       "      <td>11839.500000</td>\n",
       "      <td>3142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2008</td>\n",
       "      <td>12298.000000</td>\n",
       "      <td>3394.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2009</td>\n",
       "      <td>13027.000000</td>\n",
       "      <td>3474.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2010</td>\n",
       "      <td>13871.000000</td>\n",
       "      <td>3721.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2011</td>\n",
       "      <td>15022.000000</td>\n",
       "      <td>3962.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2012</td>\n",
       "      <td>15473.000000</td>\n",
       "      <td>4236.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>16029.000000</td>\n",
       "      <td>4421.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014</td>\n",
       "      <td>16655.000000</td>\n",
       "      <td>4518.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2015</td>\n",
       "      <td>17322.000000</td>\n",
       "      <td>4710.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2016</td>\n",
       "      <td>17710.000000</td>\n",
       "      <td>4956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2017</td>\n",
       "      <td>18687.000000</td>\n",
       "      <td>5218.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2018</td>\n",
       "      <td>19565.000000</td>\n",
       "      <td>5431.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  insurance_total  insurance_employee\n",
       "0   1985      2270.915727          584.460548\n",
       "1   1986      2270.915727          584.460548\n",
       "2   1987      2270.915727          584.460548\n",
       "3   1988      2597.991412          668.639291\n",
       "4   1989      3007.500805          774.033816\n",
       "5   1990      3454.238325          889.009662\n",
       "6   1991      3760.040794          967.713366\n",
       "7   1992      4158.913580         1070.370370\n",
       "8   1993      4398.237252         1131.964573\n",
       "9   1994      4520.558239         1163.446055\n",
       "10  1995      4706.698873         1211.352657\n",
       "11  1996      4954.000000         1275.000000\n",
       "12  1997      5332.000000         1305.000000\n",
       "13  1998      5590.000000         1382.000000\n",
       "14  1999      6058.000000         1438.000000\n",
       "15  2000      6772.000000         1614.000000\n",
       "16  2001      7509.000000         1741.000000\n",
       "17  2002      8469.000000         1987.000000\n",
       "18  2003      9249.000000         2283.000000\n",
       "19  2004     10006.000000         2438.000000\n",
       "20  2005     10728.000000         2585.000000\n",
       "21  2006     11381.000000         2890.000000\n",
       "22  2007     11839.500000         3142.000000\n",
       "23  2008     12298.000000         3394.000000\n",
       "24  2009     13027.000000         3474.000000\n",
       "25  2010     13871.000000         3721.000000\n",
       "26  2011     15022.000000         3962.000000\n",
       "27  2012     15473.000000         4236.000000\n",
       "28  2013     16029.000000         4421.000000\n",
       "29  2014     16655.000000         4518.000000\n",
       "30  2015     17322.000000         4710.000000\n",
       "31  2016     17710.000000         4956.000000\n",
       "32  2017     18687.000000         5218.000000\n",
       "33  2018     19565.000000         5431.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance = (\n",
    "    pd.read_csv('data/insurance.csv')\n",
    "    .set_index('year')\n",
    "    .rename(columns=lambda x: f'insurance_{x}')\n",
    "    .reindex(list(range(1985, 2019)))\n",
    ")\n",
    "for var in insurance.columns:\n",
    "    insurance.loc[1985:BASE_YEAR - 1, var] = insurance.loc[BASE_YEAR, var] * insurance_deflator\n",
    "\n",
    "insurance = (\n",
    "    insurance\n",
    "    .astype(float)\n",
    "    .apply(pd.Series.interpolate)\n",
    "    .reset_index()\n",
    ")\n",
    "insurance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>transportation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975</td>\n",
       "      <td>2154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980</td>\n",
       "      <td>3176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>3484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>4954.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1991</td>\n",
       "      <td>5601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1992</td>\n",
       "      <td>5824.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1993</td>\n",
       "      <td>5804.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1994</td>\n",
       "      <td>5916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1995</td>\n",
       "      <td>6185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1996</td>\n",
       "      <td>6389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1997</td>\n",
       "      <td>6723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1998</td>\n",
       "      <td>6908.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1999</td>\n",
       "      <td>7050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000</td>\n",
       "      <td>7363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2001</td>\n",
       "      <td>7654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2002</td>\n",
       "      <td>7534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2003</td>\n",
       "      <td>7754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2004</td>\n",
       "      <td>8431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2005</td>\n",
       "      <td>8410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2006</td>\n",
       "      <td>7834.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2007</td>\n",
       "      <td>7823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2008</td>\n",
       "      <td>8121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2009</td>\n",
       "      <td>8096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2010</td>\n",
       "      <td>8487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2011</td>\n",
       "      <td>8776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2012</td>\n",
       "      <td>8946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>9122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014</td>\n",
       "      <td>8876.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2015</td>\n",
       "      <td>8698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2016</td>\n",
       "      <td>8558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2017</td>\n",
       "      <td>8468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2018</td>\n",
       "      <td>8849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019</td>\n",
       "      <td>9282.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  transportation\n",
       "0   1975          2154.0\n",
       "1   1980          3176.0\n",
       "2   1985          3484.0\n",
       "3   1990          4954.0\n",
       "4   1991          5601.0\n",
       "5   1992          5824.0\n",
       "6   1993          5804.0\n",
       "7   1994          5916.0\n",
       "8   1995          6185.0\n",
       "9   1996          6389.0\n",
       "10  1997          6723.0\n",
       "11  1998          6908.0\n",
       "12  1999          7050.0\n",
       "13  2000          7363.0\n",
       "14  2001          7654.0\n",
       "15  2002          7534.0\n",
       "16  2003          7754.0\n",
       "17  2004          8431.0\n",
       "18  2005          8410.0\n",
       "19  2006          7834.0\n",
       "20  2007          7823.0\n",
       "21  2008          8121.0\n",
       "22  2009          8096.0\n",
       "23  2010          8487.0\n",
       "24  2011          8776.0\n",
       "25  2012          8946.0\n",
       "26  2013          9122.0\n",
       "27  2014          8876.0\n",
       "28  2015          8698.0\n",
       "29  2016          8558.0\n",
       "30  2017          8468.0\n",
       "31  2018          8849.0\n",
       "32  2019          9282.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transportation = (\n",
    "    pd.read_excel('data/table_03_17_100219.xlsx', skiprows=1)\n",
    "    .iloc[0, 1:]    \n",
    "    .astype(float)\n",
    "    .mul(15_000)\n",
    "    .div(100)\n",
    "    .reset_index()\n",
    "    .rename(columns={'index': 'year', 0: 'transportation'})\n",
    "    .assign(year=lambda df: df['year'].astype(int))\n",
    ")\n",
    "transportation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964</td>\n",
       "      <td>464.305425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1967</td>\n",
       "      <td>524.461151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1968</td>\n",
       "      <td>544.294238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
       "      <td>571.463599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1970</td>\n",
       "      <td>618.955297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1971</td>\n",
       "      <td>662.956828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1972</td>\n",
       "      <td>702.398245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1973</td>\n",
       "      <td>776.732200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1974</td>\n",
       "      <td>798.021915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1975</td>\n",
       "      <td>823.308878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1976</td>\n",
       "      <td>889.769427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1977</td>\n",
       "      <td>967.529403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1978</td>\n",
       "      <td>1018.887375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1979</td>\n",
       "      <td>1072.387392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1980</td>\n",
       "      <td>1163.691499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1981</td>\n",
       "      <td>1275.170833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1982</td>\n",
       "      <td>1435.423149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1983</td>\n",
       "      <td>1598.048220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1984</td>\n",
       "      <td>1716.478098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1985</td>\n",
       "      <td>1840.761775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1986</td>\n",
       "      <td>1929.253048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1987</td>\n",
       "      <td>2068.837776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1988</td>\n",
       "      <td>2201.552152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1989</td>\n",
       "      <td>2339.116259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1990</td>\n",
       "      <td>2487.642424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1991</td>\n",
       "      <td>2621.292203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1992</td>\n",
       "      <td>2846.641506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1993</td>\n",
       "      <td>3009.943668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1994</td>\n",
       "      <td>3182.568672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1995</td>\n",
       "      <td>3335.067361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1996</td>\n",
       "      <td>3506.923193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1997</td>\n",
       "      <td>3667.170890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1998</td>\n",
       "      <td>3836.690294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1999</td>\n",
       "      <td>4013.465740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2020</td>\n",
       "      <td>4137.042522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2001</td>\n",
       "      <td>4326.705797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2002</td>\n",
       "      <td>4598.094912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2003</td>\n",
       "      <td>4893.693545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2004</td>\n",
       "      <td>5337.132849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2005</td>\n",
       "      <td>5712.838668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2006</td>\n",
       "      <td>6054.179705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2007</td>\n",
       "      <td>6399.627783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2008</td>\n",
       "      <td>6714.489999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2009</td>\n",
       "      <td>7106.056328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2010</td>\n",
       "      <td>7517.979270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2011</td>\n",
       "      <td>7959.675548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2012</td>\n",
       "      <td>8393.261605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2013</td>\n",
       "      <td>8737.262409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2014</td>\n",
       "      <td>9049.785035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2015</td>\n",
       "      <td>9315.867495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2016</td>\n",
       "      <td>9602.166281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2017</td>\n",
       "      <td>9744.197742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2018</td>\n",
       "      <td>10024.922407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year       college\n",
       "0   1964    464.305425\n",
       "1   1967    524.461151\n",
       "2   1968    544.294238\n",
       "3   1969    571.463599\n",
       "5   1970    618.955297\n",
       "6   1971    662.956828\n",
       "7   1972    702.398245\n",
       "8   1973    776.732200\n",
       "9   1974    798.021915\n",
       "11  1975    823.308878\n",
       "12  1976    889.769427\n",
       "13  1977    967.529403\n",
       "14  1978   1018.887375\n",
       "15  1979   1072.387392\n",
       "17  1980   1163.691499\n",
       "18  1981   1275.170833\n",
       "19  1982   1435.423149\n",
       "20  1983   1598.048220\n",
       "21  1984   1716.478098\n",
       "23  1985   1840.761775\n",
       "24  1986   1929.253048\n",
       "25  1987   2068.837776\n",
       "26  1988   2201.552152\n",
       "27  1989   2339.116259\n",
       "29  1990   2487.642424\n",
       "30  1991   2621.292203\n",
       "31  1992   2846.641506\n",
       "32  1993   3009.943668\n",
       "33  1994   3182.568672\n",
       "35  1995   3335.067361\n",
       "36  1996   3506.923193\n",
       "37  1997   3667.170890\n",
       "38  1998   3836.690294\n",
       "39  1999   4013.465740\n",
       "41  2020   4137.042522\n",
       "42  2001   4326.705797\n",
       "43  2002   4598.094912\n",
       "44  2003   4893.693545\n",
       "45  2004   5337.132849\n",
       "47  2005   5712.838668\n",
       "48  2006   6054.179705\n",
       "49  2007   6399.627783\n",
       "50  2008   6714.489999\n",
       "51  2009   7106.056328\n",
       "53  2010   7517.979270\n",
       "54  2011   7959.675548\n",
       "55  2012   8393.261605\n",
       "56  2013   8737.262409\n",
       "57  2014   9049.785035\n",
       "59  2015   9315.867495\n",
       "60  2016   9602.166281\n",
       "61  2017   9744.197742\n",
       "62  2018  10024.922407"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education = (\n",
    "    pd.read_excel(\n",
    "        'data/tabn330.10.xls', \n",
    "        skiprows=70,\n",
    "        names=['year', 'college'],\n",
    "        usecols='A,O'\n",
    "    )\n",
    "    .iloc[:63]\n",
    "    .dropna()\n",
    "    .assign(year=lambda df: (\n",
    "        df['year']\n",
    "        .str.extract(r'.*-(\\d\\d).*', expand=False)\n",
    "        .astype(int)\n",
    "        .map(lambda x: x + 2000 if x < 50 else x + 1900)\n",
    "    ))\n",
    "    .assign(college=lambda df: (\n",
    "        df['college']\n",
    "        .div(2)\n",
    "    ))\n",
    "\n",
    ")\n",
    "education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "wages = (\n",
    "    pd.read_excel(\n",
    "        'data/SeriesReport-20200228095134_66f31b.xlsx',\n",
    "        skiprows=19,\n",
    "        usecols='A,F',\n",
    "        names=['year', 'wages']\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "coti = (\n",
    "    housing\n",
    "    .merge(insurance, how='outer')\n",
    "    .merge(transportation, how='outer')\n",
    "    .merge(education, how='outer')\n",
    "    .merge(wages, how='outer')\n",
    "    .set_index('year')\n",
    "    .sort_index()\n",
    "    .apply(pd.Series.interpolate)\n",
    "    .loc[1985:2018]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>housing</th>\n",
       "      <th>insurance_total</th>\n",
       "      <th>insurance_employee</th>\n",
       "      <th>transportation</th>\n",
       "      <th>college</th>\n",
       "      <th>wages</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>5676.0</td>\n",
       "      <td>2270.915727</td>\n",
       "      <td>584.460548</td>\n",
       "      <td>3484.0</td>\n",
       "      <td>1840.761775</td>\n",
       "      <td>443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>6228.0</td>\n",
       "      <td>2270.915727</td>\n",
       "      <td>584.460548</td>\n",
       "      <td>3778.0</td>\n",
       "      <td>1929.253048</td>\n",
       "      <td>463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>6372.0</td>\n",
       "      <td>2270.915727</td>\n",
       "      <td>584.460548</td>\n",
       "      <td>4072.0</td>\n",
       "      <td>2068.837776</td>\n",
       "      <td>477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>6552.0</td>\n",
       "      <td>2597.991412</td>\n",
       "      <td>668.639291</td>\n",
       "      <td>4366.0</td>\n",
       "      <td>2201.552152</td>\n",
       "      <td>487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>6648.0</td>\n",
       "      <td>3007.500805</td>\n",
       "      <td>774.033816</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>2339.116259</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>6792.0</td>\n",
       "      <td>3454.238325</td>\n",
       "      <td>889.009662</td>\n",
       "      <td>4954.0</td>\n",
       "      <td>2487.642424</td>\n",
       "      <td>512.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>6972.0</td>\n",
       "      <td>3760.040794</td>\n",
       "      <td>967.713366</td>\n",
       "      <td>5601.0</td>\n",
       "      <td>2621.292203</td>\n",
       "      <td>523.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>7212.0</td>\n",
       "      <td>4158.913580</td>\n",
       "      <td>1070.370370</td>\n",
       "      <td>5824.0</td>\n",
       "      <td>2846.641506</td>\n",
       "      <td>536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>7404.0</td>\n",
       "      <td>4398.237252</td>\n",
       "      <td>1131.964573</td>\n",
       "      <td>5804.0</td>\n",
       "      <td>3009.943668</td>\n",
       "      <td>555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>8736.0</td>\n",
       "      <td>4520.558239</td>\n",
       "      <td>1163.446055</td>\n",
       "      <td>5916.0</td>\n",
       "      <td>3182.568672</td>\n",
       "      <td>576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>8508.0</td>\n",
       "      <td>4706.698873</td>\n",
       "      <td>1211.352657</td>\n",
       "      <td>6185.0</td>\n",
       "      <td>3335.067361</td>\n",
       "      <td>588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>9636.0</td>\n",
       "      <td>4954.000000</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>6389.0</td>\n",
       "      <td>3506.923193</td>\n",
       "      <td>599.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>9924.0</td>\n",
       "      <td>5332.000000</td>\n",
       "      <td>1305.000000</td>\n",
       "      <td>6723.0</td>\n",
       "      <td>3667.170890</td>\n",
       "      <td>615.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>10188.0</td>\n",
       "      <td>5590.000000</td>\n",
       "      <td>1382.000000</td>\n",
       "      <td>6908.0</td>\n",
       "      <td>3836.690294</td>\n",
       "      <td>639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>10392.0</td>\n",
       "      <td>6058.000000</td>\n",
       "      <td>1438.000000</td>\n",
       "      <td>7050.0</td>\n",
       "      <td>4013.465740</td>\n",
       "      <td>668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>10452.0</td>\n",
       "      <td>6772.000000</td>\n",
       "      <td>1614.000000</td>\n",
       "      <td>7363.0</td>\n",
       "      <td>4170.085768</td>\n",
       "      <td>693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>12156.0</td>\n",
       "      <td>7509.000000</td>\n",
       "      <td>1741.000000</td>\n",
       "      <td>7654.0</td>\n",
       "      <td>4326.705797</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>12504.0</td>\n",
       "      <td>8469.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>7534.0</td>\n",
       "      <td>4598.094912</td>\n",
       "      <td>732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>12828.0</td>\n",
       "      <td>9249.000000</td>\n",
       "      <td>2283.000000</td>\n",
       "      <td>7754.0</td>\n",
       "      <td>4893.693545</td>\n",
       "      <td>744.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>12876.0</td>\n",
       "      <td>10006.000000</td>\n",
       "      <td>2438.000000</td>\n",
       "      <td>8431.0</td>\n",
       "      <td>5337.132849</td>\n",
       "      <td>762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>11940.0</td>\n",
       "      <td>10728.000000</td>\n",
       "      <td>2585.000000</td>\n",
       "      <td>8410.0</td>\n",
       "      <td>5712.838668</td>\n",
       "      <td>771.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>12324.0</td>\n",
       "      <td>11381.000000</td>\n",
       "      <td>2890.000000</td>\n",
       "      <td>7834.0</td>\n",
       "      <td>6054.179705</td>\n",
       "      <td>797.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>12816.0</td>\n",
       "      <td>11839.500000</td>\n",
       "      <td>3142.000000</td>\n",
       "      <td>7823.0</td>\n",
       "      <td>6399.627783</td>\n",
       "      <td>823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>12024.0</td>\n",
       "      <td>12298.000000</td>\n",
       "      <td>3394.000000</td>\n",
       "      <td>8121.0</td>\n",
       "      <td>6714.489999</td>\n",
       "      <td>857.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>11988.0</td>\n",
       "      <td>13027.000000</td>\n",
       "      <td>3474.000000</td>\n",
       "      <td>8096.0</td>\n",
       "      <td>7106.056328</td>\n",
       "      <td>873.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>12912.0</td>\n",
       "      <td>13871.000000</td>\n",
       "      <td>3721.000000</td>\n",
       "      <td>8487.0</td>\n",
       "      <td>7517.979270</td>\n",
       "      <td>874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>13080.0</td>\n",
       "      <td>15022.000000</td>\n",
       "      <td>3962.000000</td>\n",
       "      <td>8776.0</td>\n",
       "      <td>7959.675548</td>\n",
       "      <td>886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>12804.0</td>\n",
       "      <td>15473.000000</td>\n",
       "      <td>4236.000000</td>\n",
       "      <td>8946.0</td>\n",
       "      <td>8393.261605</td>\n",
       "      <td>910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>13644.0</td>\n",
       "      <td>16029.000000</td>\n",
       "      <td>4421.000000</td>\n",
       "      <td>9122.0</td>\n",
       "      <td>8737.262409</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>13296.0</td>\n",
       "      <td>16655.000000</td>\n",
       "      <td>4518.000000</td>\n",
       "      <td>8876.0</td>\n",
       "      <td>9049.785035</td>\n",
       "      <td>922.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>14268.0</td>\n",
       "      <td>17322.000000</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>8698.0</td>\n",
       "      <td>9315.867495</td>\n",
       "      <td>947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>14736.0</td>\n",
       "      <td>17710.000000</td>\n",
       "      <td>4956.000000</td>\n",
       "      <td>8558.0</td>\n",
       "      <td>9602.166281</td>\n",
       "      <td>969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>15528.0</td>\n",
       "      <td>18687.000000</td>\n",
       "      <td>5218.000000</td>\n",
       "      <td>8468.0</td>\n",
       "      <td>9744.197742</td>\n",
       "      <td>996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>19565.000000</td>\n",
       "      <td>5431.000000</td>\n",
       "      <td>8849.0</td>\n",
       "      <td>10024.922407</td>\n",
       "      <td>1026.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      housing  insurance_total  insurance_employee  transportation  \\\n",
       "year                                                                 \n",
       "1985   5676.0      2270.915727          584.460548          3484.0   \n",
       "1986   6228.0      2270.915727          584.460548          3778.0   \n",
       "1987   6372.0      2270.915727          584.460548          4072.0   \n",
       "1988   6552.0      2597.991412          668.639291          4366.0   \n",
       "1989   6648.0      3007.500805          774.033816          4660.0   \n",
       "1990   6792.0      3454.238325          889.009662          4954.0   \n",
       "1991   6972.0      3760.040794          967.713366          5601.0   \n",
       "1992   7212.0      4158.913580         1070.370370          5824.0   \n",
       "1993   7404.0      4398.237252         1131.964573          5804.0   \n",
       "1994   8736.0      4520.558239         1163.446055          5916.0   \n",
       "1995   8508.0      4706.698873         1211.352657          6185.0   \n",
       "1996   9636.0      4954.000000         1275.000000          6389.0   \n",
       "1997   9924.0      5332.000000         1305.000000          6723.0   \n",
       "1998  10188.0      5590.000000         1382.000000          6908.0   \n",
       "1999  10392.0      6058.000000         1438.000000          7050.0   \n",
       "2000  10452.0      6772.000000         1614.000000          7363.0   \n",
       "2001  12156.0      7509.000000         1741.000000          7654.0   \n",
       "2002  12504.0      8469.000000         1987.000000          7534.0   \n",
       "2003  12828.0      9249.000000         2283.000000          7754.0   \n",
       "2004  12876.0     10006.000000         2438.000000          8431.0   \n",
       "2005  11940.0     10728.000000         2585.000000          8410.0   \n",
       "2006  12324.0     11381.000000         2890.000000          7834.0   \n",
       "2007  12816.0     11839.500000         3142.000000          7823.0   \n",
       "2008  12024.0     12298.000000         3394.000000          8121.0   \n",
       "2009  11988.0     13027.000000         3474.000000          8096.0   \n",
       "2010  12912.0     13871.000000         3721.000000          8487.0   \n",
       "2011  13080.0     15022.000000         3962.000000          8776.0   \n",
       "2012  12804.0     15473.000000         4236.000000          8946.0   \n",
       "2013  13644.0     16029.000000         4421.000000          9122.0   \n",
       "2014  13296.0     16655.000000         4518.000000          8876.0   \n",
       "2015  14268.0     17322.000000         4710.000000          8698.0   \n",
       "2016  14736.0     17710.000000         4956.000000          8558.0   \n",
       "2017  15528.0     18687.000000         5218.000000          8468.0   \n",
       "2018  15924.0     19565.000000         5431.000000          8849.0   \n",
       "\n",
       "           college   wages  \n",
       "year                        \n",
       "1985   1840.761775   443.0  \n",
       "1986   1929.253048   463.0  \n",
       "1987   2068.837776   477.0  \n",
       "1988   2201.552152   487.0  \n",
       "1989   2339.116259   500.0  \n",
       "1990   2487.642424   512.0  \n",
       "1991   2621.292203   523.0  \n",
       "1992   2846.641506   536.0  \n",
       "1993   3009.943668   555.0  \n",
       "1994   3182.568672   576.0  \n",
       "1995   3335.067361   588.0  \n",
       "1996   3506.923193   599.0  \n",
       "1997   3667.170890   615.0  \n",
       "1998   3836.690294   639.0  \n",
       "1999   4013.465740   668.0  \n",
       "2000   4170.085768   693.0  \n",
       "2001   4326.705797   720.0  \n",
       "2002   4598.094912   732.0  \n",
       "2003   4893.693545   744.0  \n",
       "2004   5337.132849   762.0  \n",
       "2005   5712.838668   771.0  \n",
       "2006   6054.179705   797.0  \n",
       "2007   6399.627783   823.0  \n",
       "2008   6714.489999   857.0  \n",
       "2009   7106.056328   873.0  \n",
       "2010   7517.979270   874.0  \n",
       "2011   7959.675548   886.0  \n",
       "2012   8393.261605   910.0  \n",
       "2013   8737.262409   912.0  \n",
       "2014   9049.785035   922.0  \n",
       "2015   9315.867495   947.0  \n",
       "2016   9602.166281   969.0  \n",
       "2017   9744.197742   996.0  \n",
       "2018  10024.922407  1026.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weeks_of_work_total = (\n",
    "    coti[['housing', 'insurance_total', 'transportation', 'college']]\n",
    "    .sum(axis='columns')\n",
    "    .div(coti['wages'])\n",
    ")\n",
    "weeks_of_work_employee = (\n",
    "    coti[['housing', 'insurance_employee', 'transportation', 'college']]\n",
    "    .sum(axis='columns')\n",
    "    .div(coti['wages'])\n",
    ")\n",
    "fig, ax = plt.subplots()\n",
    "ax.axhline(52, color='black', ls='--', lw=1, alpha=.5)\n",
    "ax.text(1986, 53, '52 weeks')\n",
    "\n",
    "ax.plot(weeks_of_work_total, lw=2)\n",
    "ax.text(2018.25, weeks_of_work_total[2018], 'With employer\\ncosts improperly\\nincluded', va='center')\n",
    "ax.plot(weeks_of_work_employee, lw=2)\n",
    "ax.text(2018.25, weeks_of_work_employee[2018], 'With only\\nemployee costs\\nincluded', va='center')\n",
    "\n",
    "ax.set(\n",
    "    ybound=(20, 60),\n",
    "    xbound=(1985, 2018),\n",
    "    title=\"Weeks of Income Needed to Cover\\nMajor Houshold Expenditures\",\n",
    "    xticks=list(range(1985, 2020, 5)),\n",
    "    yticks=(20, 40, 60),\n",
    ")\n",
    "pos = ax.get_position()\n",
    "ax.set_position([pos.x0, pos.y0, pos.width * .9, pos.height])\n",
    "fig.text(pos.x0 + pos.width * .9, 0, \"Sources: HUD, AHRQ, CMS, ED, BTS, BLS.\\nOliverSherouse.com\", size='small', ha='right')\n",
    "fig.savefig('weeks', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>employee</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>26.15</td>\n",
       "      <td>29.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>27.04</td>\n",
       "      <td>30.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>27.46</td>\n",
       "      <td>30.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>28.31</td>\n",
       "      <td>32.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>28.84</td>\n",
       "      <td>33.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>29.54</td>\n",
       "      <td>34.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>30.90</td>\n",
       "      <td>36.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>31.63</td>\n",
       "      <td>37.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>31.26</td>\n",
       "      <td>37.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>32.98</td>\n",
       "      <td>38.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>32.72</td>\n",
       "      <td>38.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>34.74</td>\n",
       "      <td>40.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>35.15</td>\n",
       "      <td>41.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>34.92</td>\n",
       "      <td>41.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>34.27</td>\n",
       "      <td>41.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>34.05</td>\n",
       "      <td>41.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>35.94</td>\n",
       "      <td>43.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>36.37</td>\n",
       "      <td>45.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>37.31</td>\n",
       "      <td>46.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>38.17</td>\n",
       "      <td>48.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>37.16</td>\n",
       "      <td>47.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>36.51</td>\n",
       "      <td>47.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>36.67</td>\n",
       "      <td>47.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>35.30</td>\n",
       "      <td>45.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>35.12</td>\n",
       "      <td>46.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>37.34</td>\n",
       "      <td>48.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>38.12</td>\n",
       "      <td>50.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>37.78</td>\n",
       "      <td>50.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>39.39</td>\n",
       "      <td>52.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>38.76</td>\n",
       "      <td>51.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>39.06</td>\n",
       "      <td>52.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>39.06</td>\n",
       "      <td>52.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>39.11</td>\n",
       "      <td>52.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>39.21</td>\n",
       "      <td>52.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      employee  total\n",
       "year                 \n",
       "1985     26.15  29.96\n",
       "1986     27.04  30.68\n",
       "1987     27.46  30.99\n",
       "1988     28.31  32.27\n",
       "1989     28.84  33.31\n",
       "1990     29.54  34.55\n",
       "1991     30.90  36.24\n",
       "1992     31.63  37.39\n",
       "1993     31.26  37.15\n",
       "1994     32.98  38.81\n",
       "1995     32.72  38.66\n",
       "1996     34.74  40.88\n",
       "1997     35.15  41.70\n",
       "1998     34.92  41.51\n",
       "1999     34.27  41.19\n",
       "2000     34.05  41.50\n",
       "2001     35.94  43.95\n",
       "2002     36.37  45.23\n",
       "2003     37.31  46.67\n",
       "2004     38.17  48.10\n",
       "2005     37.16  47.72\n",
       "2006     36.51  47.17\n",
       "2007     36.67  47.24\n",
       "2008     35.30  45.69\n",
       "2009     35.12  46.07\n",
       "2010     37.34  48.96\n",
       "2011     38.12  50.61\n",
       "2012     37.78  50.13\n",
       "2013     39.39  52.12\n",
       "2014     38.76  51.93\n",
       "2015     39.06  52.38\n",
       "2016     39.06  52.23\n",
       "2017     39.11  52.64\n",
       "2018     39.21  52.99"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    'employee': weeks_of_work_employee,\n",
    "    'total': weeks_of_work_total\n",
    "}).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_area_chart(coti, insurance, title=''):\n",
    "    insurance_var = f'insurance_{insurance}'\n",
    "    toplines = coti[['housing', insurance_var, 'transportation', 'college']].cumsum(axis='columns')\n",
    "    baselines = toplines.shift(axis='columns').fillna(0)\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    for var, topline in toplines.items():\n",
    "        baseline = baselines[var]\n",
    "        ax.fill_between(coti.index, baseline, topline)\n",
    "        ax.text(\n",
    "            2018.25,\n",
    "            baseline[2018] + (topline - baseline)[2018] * .5,\n",
    "            var.split('_')[0].replace('insurance', 'health insurance').title(),\n",
    "            va='center',\n",
    "        )\n",
    "\n",
    "    ax.plot(coti.index, coti['wages'] * 52, color='black', lw=3)\n",
    "    ax.text(2018.25, coti['wages'][2018] * 52, 'Median Male\\nIncome\\n(52 Weeks)', va='bottom')\n",
    "\n",
    "    ax.set_axisbelow(True)\n",
    "    ax.grid(axis='y', linestyle='--')\n",
    "\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "\n",
    "    ax.set(\n",
    "        xbound=(1985, 2018),\n",
    "        xticks=list(range(1985, 2020, 4)),\n",
    "        ybound=(0, 60000),\n",
    "        yticks=(0, 20000, 40000, 60000),\n",
    "        yticklabels=['', '20', '40', '60'],\n",
    "        ylabel='Current Dollars (thousands)',\n",
    "        title=title,\n",
    "    )\n",
    "    pos = ax.get_position()\n",
    "    ax.set_position([pos.x0, pos.y0, pos.width * .9, pos.height])\n",
    "    fig.text(pos.x0 + pos.width * .9, 0, \"Sources: HUD, AHRQ, CMS, ED, BTS, BLS.\\nOliverSherouse.com\", ha='right')\n",
    "    fig.savefig(f'expenses_{insurance}_insurance', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_area_chart(\n",
    "    coti,\n",
    "    'total',\n",
    "    'Real Income vs. Major Expenses\\nwith Employer-Paid\\nHealth Insurance Premiums Improperly Included'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_area_chart(\n",
    "    coti,\n",
    "    'employee',\n",
    "    'Real Income vs. Major Expenses\\nwith Only Employee-Paid\\nHealth Insurance Premiums Included'\n",
    ")"
   ]
  }
 ],
 "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}