data_analyze.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import censusdata\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.precision',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "censusdata.search('acs5',2019,'concept','housing costs as a percentage of household income');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable     | Table                          | Label                                                    | Type \n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "B25106_001E  | TENURE BY HOUSING COSTS AS A P | !! Estimate Total:                                       | int  \n",
      "B25106_002E  | TENURE BY HOUSING COSTS AS A P | !! !! Estimate Total: Owner-occupied housing units:      | int  \n",
      "B25106_003E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Owner-occupied housing units: L | int  \n",
      "B25106_004E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_005E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_006E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_007E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Owner-occupied housing units: $ | int  \n",
      "B25106_008E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_009E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_010E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_011E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Owner-occupied housing units: $ | int  \n",
      "B25106_012E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_013E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_014E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_015E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Owner-occupied housing units: $ | int  \n",
      "B25106_016E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_017E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_018E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_019E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Owner-occupied housing units: $ | int  \n",
      "B25106_020E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_021E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_022E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Owner-occupied housing units | int  \n",
      "B25106_023E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Owner-occupied housing units: Z | int  \n",
      "B25106_024E  | TENURE BY HOUSING COSTS AS A P | !! !! Estimate Total: Renter-occupied housing units:     | int  \n",
      "B25106_025E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Renter-occupied housing units:  | int  \n",
      "B25106_026E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_027E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_028E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_029E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Renter-occupied housing units:  | int  \n",
      "B25106_030E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_031E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_032E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_033E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Renter-occupied housing units:  | int  \n",
      "B25106_034E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_035E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_036E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_037E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Renter-occupied housing units:  | int  \n",
      "B25106_038E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_039E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_040E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_041E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Renter-occupied housing units:  | int  \n",
      "B25106_042E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_043E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_044E  | TENURE BY HOUSING COSTS AS A P | !! !! !! !! Estimate Total: Renter-occupied housing unit | int  \n",
      "B25106_045E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Renter-occupied housing units:  | int  \n",
      "B25106_046E  | TENURE BY HOUSING COSTS AS A P | !! !! !! Estimate Total: Renter-occupied housing units:  | int  \n",
      "-------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "censusdata.printtable(censusdata.censustable('acs5',2019,'B25106'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = censusdata.download(\n",
    "    'acs5',2019,\n",
    "    censusdata.censusgeo([('state','53'),('tract','*')]),\n",
    "    [\n",
    "        'B25106_001E',                   #total\n",
    "        'B25106_002E',                   #owner occupied\n",
    "        'B25106_003E','B25106_006E',     #income <$20k\n",
    "        'B25106_007E','B25106_010E',     #income $20kto35k\n",
    "        'B25106_011E','B25106_014E',     #income $35kto50k\n",
    "        'B25106_015E','B25106_018E',     #income $50kto75k\n",
    "        'B25106_019E','B25106_022E',     #income >$75k\n",
    "        'B25106_023E',                   #income negative\n",
    "        'B25106_024E',                   #renter\n",
    "        'B25106_025E','B25106_028E',     #income <$20k\n",
    "        'B25106_029E','B25106_032E',     #income $20kto35k\n",
    "        'B25106_033E','B25106_036E',     #income $35kto50k\n",
    "        'B25106_037E','B25106_040E',     #income $50kto75k\n",
    "        'B25106_041E','B25106_044E',     #income >$75k\n",
    "        'B25106_045E'                    #income negative\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = dt.rename(\n",
    "    columns={\n",
    "        'B25106_001E':'total',\n",
    "        'B25106_002E':'owner',\n",
    "        'B25106_024E':'renter'\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt['owner_burden']=dt.B25106_006E+dt.B25106_010E+dt.B25106_014E+dt.B25106_018E+dt.B25106_022E+dt.B25106_023E\n",
    "dt['renter_burden']=dt.B25106_028E+dt.B25106_032E+dt.B25106_036E+dt.B25106_040E+dt.B25106_044E+dt.B25106_045E\n",
    "dt['total_burden']=dt.owner_burden+dt.renter_burden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=dt.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt['county']=dt['index'].astype('str').str.split(',',expand=True)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt['tract']=dt['index'].astype('str').str.split(',',expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>county</th>\n",
       "      <th>total</th>\n",
       "      <th>total_burden</th>\n",
       "      <th>owner_burden</th>\n",
       "      <th>renter_burden</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams County</td>\n",
       "      <td>5973</td>\n",
       "      <td>1682</td>\n",
       "      <td>944</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asotin County</td>\n",
       "      <td>9101</td>\n",
       "      <td>2253</td>\n",
       "      <td>1127</td>\n",
       "      <td>1126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Benton County</td>\n",
       "      <td>72121</td>\n",
       "      <td>18468</td>\n",
       "      <td>8731</td>\n",
       "      <td>9737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chelan County</td>\n",
       "      <td>28384</td>\n",
       "      <td>7888</td>\n",
       "      <td>4151</td>\n",
       "      <td>3737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clallam County</td>\n",
       "      <td>32958</td>\n",
       "      <td>10277</td>\n",
       "      <td>5690</td>\n",
       "      <td>4587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Clark County</td>\n",
       "      <td>174661</td>\n",
       "      <td>55124</td>\n",
       "      <td>27502</td>\n",
       "      <td>27622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Columbia County</td>\n",
       "      <td>1795</td>\n",
       "      <td>474</td>\n",
       "      <td>295</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cowlitz County</td>\n",
       "      <td>41952</td>\n",
       "      <td>13794</td>\n",
       "      <td>6708</td>\n",
       "      <td>7086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Douglas County</td>\n",
       "      <td>15263</td>\n",
       "      <td>3580</td>\n",
       "      <td>2271</td>\n",
       "      <td>1309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ferry County</td>\n",
       "      <td>3060</td>\n",
       "      <td>843</td>\n",
       "      <td>527</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Franklin County</td>\n",
       "      <td>26723</td>\n",
       "      <td>6784</td>\n",
       "      <td>3026</td>\n",
       "      <td>3758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Garfield County</td>\n",
       "      <td>984</td>\n",
       "      <td>206</td>\n",
       "      <td>88</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Grant County</td>\n",
       "      <td>30818</td>\n",
       "      <td>7437</td>\n",
       "      <td>3713</td>\n",
       "      <td>3724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Grays Harbor County</td>\n",
       "      <td>28722</td>\n",
       "      <td>8875</td>\n",
       "      <td>4674</td>\n",
       "      <td>4201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Island County</td>\n",
       "      <td>34768</td>\n",
       "      <td>11877</td>\n",
       "      <td>7115</td>\n",
       "      <td>4762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>14705</td>\n",
       "      <td>4707</td>\n",
       "      <td>2970</td>\n",
       "      <td>1737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>King County</td>\n",
       "      <td>882028</td>\n",
       "      <td>297642</td>\n",
       "      <td>127415</td>\n",
       "      <td>170227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kitsap County</td>\n",
       "      <td>103913</td>\n",
       "      <td>32913</td>\n",
       "      <td>17407</td>\n",
       "      <td>15506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kittitas County</td>\n",
       "      <td>18347</td>\n",
       "      <td>6880</td>\n",
       "      <td>2745</td>\n",
       "      <td>4135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Klickitat County</td>\n",
       "      <td>8877</td>\n",
       "      <td>2709</td>\n",
       "      <td>1398</td>\n",
       "      <td>1311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Lewis County</td>\n",
       "      <td>30618</td>\n",
       "      <td>9108</td>\n",
       "      <td>4898</td>\n",
       "      <td>4210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Lincoln County</td>\n",
       "      <td>4525</td>\n",
       "      <td>942</td>\n",
       "      <td>680</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Mason County</td>\n",
       "      <td>24278</td>\n",
       "      <td>7565</td>\n",
       "      <td>5091</td>\n",
       "      <td>2474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Okanogan County</td>\n",
       "      <td>17675</td>\n",
       "      <td>4687</td>\n",
       "      <td>2554</td>\n",
       "      <td>2133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pacific County</td>\n",
       "      <td>9279</td>\n",
       "      <td>2530</td>\n",
       "      <td>1695</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pend Oreille County</td>\n",
       "      <td>5727</td>\n",
       "      <td>1464</td>\n",
       "      <td>866</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pierce County</td>\n",
       "      <td>323296</td>\n",
       "      <td>113635</td>\n",
       "      <td>52959</td>\n",
       "      <td>60676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>San Juan County</td>\n",
       "      <td>8252</td>\n",
       "      <td>2864</td>\n",
       "      <td>2073</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Skagit County</td>\n",
       "      <td>48493</td>\n",
       "      <td>15644</td>\n",
       "      <td>8367</td>\n",
       "      <td>7277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Skamania County</td>\n",
       "      <td>4816</td>\n",
       "      <td>1347</td>\n",
       "      <td>825</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Snohomish County</td>\n",
       "      <td>293823</td>\n",
       "      <td>96218</td>\n",
       "      <td>49401</td>\n",
       "      <td>46817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Spokane County</td>\n",
       "      <td>202811</td>\n",
       "      <td>66346</td>\n",
       "      <td>28731</td>\n",
       "      <td>37615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Stevens County</td>\n",
       "      <td>17554</td>\n",
       "      <td>4531</td>\n",
       "      <td>3011</td>\n",
       "      <td>1520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Thurston County</td>\n",
       "      <td>109983</td>\n",
       "      <td>36888</td>\n",
       "      <td>17148</td>\n",
       "      <td>19740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Wahkiakum County</td>\n",
       "      <td>1897</td>\n",
       "      <td>557</td>\n",
       "      <td>425</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Walla Walla County</td>\n",
       "      <td>22646</td>\n",
       "      <td>6752</td>\n",
       "      <td>2577</td>\n",
       "      <td>4175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Whatcom County</td>\n",
       "      <td>86523</td>\n",
       "      <td>30934</td>\n",
       "      <td>13676</td>\n",
       "      <td>17258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Whitman County</td>\n",
       "      <td>17999</td>\n",
       "      <td>7442</td>\n",
       "      <td>1504</td>\n",
       "      <td>5938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Yakima County</td>\n",
       "      <td>83048</td>\n",
       "      <td>24884</td>\n",
       "      <td>11758</td>\n",
       "      <td>13126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  county   total  total_burden  owner_burden  renter_burden\n",
       "0           Adams County    5973          1682           944            738\n",
       "1          Asotin County    9101          2253          1127           1126\n",
       "2          Benton County   72121         18468          8731           9737\n",
       "3          Chelan County   28384          7888          4151           3737\n",
       "4         Clallam County   32958         10277          5690           4587\n",
       "5           Clark County  174661         55124         27502          27622\n",
       "6        Columbia County    1795           474           295            179\n",
       "7         Cowlitz County   41952         13794          6708           7086\n",
       "8         Douglas County   15263          3580          2271           1309\n",
       "9           Ferry County    3060           843           527            316\n",
       "10       Franklin County   26723          6784          3026           3758\n",
       "11       Garfield County     984           206            88            118\n",
       "12          Grant County   30818          7437          3713           3724\n",
       "13   Grays Harbor County   28722          8875          4674           4201\n",
       "14         Island County   34768         11877          7115           4762\n",
       "15      Jefferson County   14705          4707          2970           1737\n",
       "16           King County  882028        297642        127415         170227\n",
       "17         Kitsap County  103913         32913         17407          15506\n",
       "18       Kittitas County   18347          6880          2745           4135\n",
       "19      Klickitat County    8877          2709          1398           1311\n",
       "20          Lewis County   30618          9108          4898           4210\n",
       "21        Lincoln County    4525           942           680            262\n",
       "22          Mason County   24278          7565          5091           2474\n",
       "23       Okanogan County   17675          4687          2554           2133\n",
       "24        Pacific County    9279          2530          1695            835\n",
       "25   Pend Oreille County    5727          1464           866            598\n",
       "26         Pierce County  323296        113635         52959          60676\n",
       "27       San Juan County    8252          2864          2073            791\n",
       "28         Skagit County   48493         15644          8367           7277\n",
       "29       Skamania County    4816          1347           825            522\n",
       "30      Snohomish County  293823         96218         49401          46817\n",
       "31        Spokane County  202811         66346         28731          37615\n",
       "32        Stevens County   17554          4531          3011           1520\n",
       "33       Thurston County  109983         36888         17148          19740\n",
       "34      Wahkiakum County    1897           557           425            132\n",
       "35    Walla Walla County   22646          6752          2577           4175\n",
       "36        Whatcom County   86523         30934         13676          17258\n",
       "37        Whitman County   17999          7442          1504           5938\n",
       "38         Yakima County   83048         24884         11758          13126"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_county=dt.groupby(by='county')[['total','total_burden','owner_burden','renter_burden']].sum().reset_index()\n",
    "dt_county"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_state=pd.DataFrame(\n",
    "    {\n",
    "        \"total\":dt_county.total.sum(),\n",
    "        \"total_burden\":dt_county.total_burden.sum(),\n",
    "        \"owner_burden\":dt_county.owner_burden.sum(),\n",
    "        \"renter_burden\":dt_county.renter_burden.sum(),\n",
    "        \"in20bl\":dt.B25106_006E.sum()+dt.B25106_028E.sum(),\n",
    "        \"in20to35\":dt.B25106_010E.sum()+dt.B25106_032E.sum(),\n",
    "        \"in35to50\":dt.B25106_014E.sum()+dt.B25106_036E.sum(),\n",
    "        \"in50to75\":dt.B25106_018E.sum()+dt.B25106_040E.sum(),\n",
    "        \"in75ab\":dt.B25106_022E.sum()+dt.B25106_044E.sum()\n",
    "    },\n",
    "    index=[0]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>in20bl</td>\n",
       "      <td>239854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>in20to35</td>\n",
       "      <td>216619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>in35to50</td>\n",
       "      <td>166605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>in50to75</td>\n",
       "      <td>164061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>in75ab</td>\n",
       "      <td>115584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   variable   value\n",
       "0    in20bl  239854\n",
       "1  in20to35  216619\n",
       "2  in35to50  166605\n",
       "3  in50to75  164061\n",
       "4    in75ab  115584"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_state_melt=dt_state.melt(value_vars=['in20bl','in20to35','in35to50','in50to75','in75ab'])\n",
    "dt_state_melt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0ad5db390>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_county[['county','owner_burden','renter_burden']].plot(kind='bar',x='county',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0af2cc7d0>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_county.plot(kind='scatter',x='total',y='total_burden')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.25, 1.25, -1.25, 1.25)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_state_melt.plot(\n",
    "    kind='pie',y='value',\n",
    "    labels=[\n",
    "        'income below $20K',\n",
    "        'income between $20K to $35K',\n",
    "        'income between $35K to $50K',\n",
    "        'income between $50K to 75K',\n",
    "        'income above $75K'\n",
    "    ],\n",
    "    legend=False,\n",
    "    title = 'Households Experiencing Housing Cost Burden in Washington State'\n",
    ").axis('off')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}