notebooks/Querying Available Data Sets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c1995553-4d20-4983-b6fc-96f2823a82bd",
   "metadata": {},
   "source": [
    "# Querying Available Data Sets\n",
    "\n",
    "The US Census publishes dozens of data sets per year. They have published over\n",
    "1,300 data sets in all via their API. Some, like \n",
    "the *American Community Survey: 5-Year Estimates: Data Tables*\n",
    "are widely known and used, but there are plenty of other interesting and useful\n",
    "data sets you might want to load.\n",
    "\n",
    "`censusdis` supports querying avaialble data sets for a given year or for all time.\n",
    "\n",
    "We have not loaded all variables for all data sets for all years, but \n",
    "`censusdis` should be able to.\n",
    "If you encounter one it cannot load, please file and issue at \n",
    "https://github.com/vengroff/censusdis/issues and we will look into it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d2fb54-16df-4784-a4ed-d84d9731a313",
   "metadata": {},
   "source": [
    "## Imports and configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f0005ef-d7c2-42c7-a8f5-344260a4ec46",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import censusdis.data as ced\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db022f4-cdc5-477e-9656-e7e1af262601",
   "metadata": {},
   "source": [
    "## Data sets available for a given year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb62e82c-3118-4b1c-b930-e006ebed2f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "YEAR = 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c805533-4900-4eb7-bec0-3683102b9c6e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>DATASET</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>API BASE URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>abscb</td>\n",
       "      <td>Economic Surveys: Annual Business Survey: Char...</td>\n",
       "      <td>The Annual Business Survey (ABS) provides info...</td>\n",
       "      <td>http://api.census.gov/data/2020/abscb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>abscbo</td>\n",
       "      <td>Economic Surveys: Annual Business Survey: Char...</td>\n",
       "      <td>The Annual Business Survey (ABS) provides info...</td>\n",
       "      <td>http://api.census.gov/data/2020/abscbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>abscs</td>\n",
       "      <td>Economic Surveys: Annual Business Survey: Comp...</td>\n",
       "      <td>The Annual Business Survey (ABS) provides info...</td>\n",
       "      <td>http://api.census.gov/data/2020/abscs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>absmcb</td>\n",
       "      <td>Economic Surveys: Annual Business Survey: Modu...</td>\n",
       "      <td>The Annual Business Survey (ABS) provides info...</td>\n",
       "      <td>http://api.census.gov/data/2020/absmcb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>absnesd</td>\n",
       "      <td>Economic Surveys: Nonemployer Statistics by De...</td>\n",
       "      <td>The Nonemployer Statistics by Demographics (NE...</td>\n",
       "      <td>http://api.census.gov/data/2020/absnesd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>absnesdo</td>\n",
       "      <td>Economic Surveys: Nonemployer Statistics by De...</td>\n",
       "      <td>The Nonemployer Statistics by Demographics (NE...</td>\n",
       "      <td>http://api.census.gov/data/2020/absnesdo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>American Community Survey: 5-Year Estimates: D...</td>\n",
       "      <td>The American Community Survey (ACS) is an ongo...</td>\n",
       "      <td>http://api.census.gov/data/2020/acs/acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5/cprofile</td>\n",
       "      <td>American Community Survey: 5-Year Estimates: C...</td>\n",
       "      <td>The American Community Survey (ACS) is an ongo...</td>\n",
       "      <td>http://api.census.gov/data/2020/acs/acs5/cprofile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5/profile</td>\n",
       "      <td>American Community Survey: 5-Year Estimates: D...</td>\n",
       "      <td>The American Community Survey (ACS) is an ongo...</td>\n",
       "      <td>http://api.census.gov/data/2020/acs/acs5/profile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5/pums</td>\n",
       "      <td>2020 American Community Survey: 5-Year Estimat...</td>\n",
       "      <td>The American Community Survey (ACS) Public Use...</td>\n",
       "      <td>http://api.census.gov/data/2020/acs/acs5/pums</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5/pumspr</td>\n",
       "      <td>2020 American Community Survey: 5-Year Estimat...</td>\n",
       "      <td>The Public Use Microdata Sample (PUMS) for Pue...</td>\n",
       "      <td>http://api.census.gov/data/2020/acs/acs5/pumspr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5/subject</td>\n",
       "      <td>American Community Survey: 5-Year Estimates: S...</td>\n",
       "      <td>The American Community Survey (ACS) is an ongo...</td>\n",
       "      <td>http://api.census.gov/data/2020/acs/acs5/subject</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/flows</td>\n",
       "      <td>2016-2020 American Community Survey: Migration...</td>\n",
       "      <td>Migration flows are derived from the relations...</td>\n",
       "      <td>http://api.census.gov/data/2020/acs/flows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020</td>\n",
       "      <td>cbp</td>\n",
       "      <td>Economic Surveys: Business Patterns: County Bu...</td>\n",
       "      <td>County Business Patterns (CBP) is an annual se...</td>\n",
       "      <td>http://api.census.gov/data/2020/cbp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/arts/feb</td>\n",
       "      <td>Current Population Survey: Arts Benchmarking S...</td>\n",
       "      <td>ABS collects information about individual atte...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/arts/feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/asec/mar</td>\n",
       "      <td>Current Population Survey Annual Social and Ec...</td>\n",
       "      <td>The Annual Social and Economic Supplement or M...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/asec/mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/apr</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/aug</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/dec</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/feb</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/jan</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/jul</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/jul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/jun</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/jun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/mar</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/may</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/may</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/nov</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/oct</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/basic/sep</td>\n",
       "      <td>Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/basic/sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/dwjt/jan</td>\n",
       "      <td>Current Population Survey: Displaced Worker, E...</td>\n",
       "      <td>Displaced Workers: Provides data on workers wh...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/dwjt/jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/fertility/jun</td>\n",
       "      <td>Current Population Survey: Fertility Supplement</td>\n",
       "      <td>Provides data on the number of children that w...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/fertility/jun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/foodsec/dec</td>\n",
       "      <td>Current Population Survey: Food Security Suppl...</td>\n",
       "      <td>Provides data that will measure hunger and foo...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/foodsec/dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/school/oct</td>\n",
       "      <td>Current Population Survey: School Enrollment S...</td>\n",
       "      <td>Provides information on population 3 years old...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/school/oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/vets/aug</td>\n",
       "      <td>Current Population Survey: Veterans Supplement</td>\n",
       "      <td>Provides data for veterans of the United State...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/vets/aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020</td>\n",
       "      <td>cps/voting/nov</td>\n",
       "      <td>Current Population Survey: Voting Supplement</td>\n",
       "      <td>Provides demographic information on persons wh...</td>\n",
       "      <td>http://api.census.gov/data/2020/cps/voting/nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/cd118</td>\n",
       "      <td>Decennial Census: 118th Congressional District...</td>\n",
       "      <td>The Congressional District Summary File (118th...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/cd118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/ddhca</td>\n",
       "      <td>Decennial Census: Detailed Demographic and Hou...</td>\n",
       "      <td>This product provides the population counts an...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/ddhca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dhc</td>\n",
       "      <td>Decennial Census: Demographic and Housing Char...</td>\n",
       "      <td>This product will include topics such as age, ...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dhc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dhcas</td>\n",
       "      <td>Decennial Census of Island Areas: American Sam...</td>\n",
       "      <td>This product will include some of the demograp...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dhcas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dhcgu</td>\n",
       "      <td>Decennial Census of Island Areas: Guam Demogra...</td>\n",
       "      <td>This product will include some of the demograp...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dhcgu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dhcmp</td>\n",
       "      <td>Decennial Census of Island Areas: Commonwealth...</td>\n",
       "      <td>This product will include some of the demograp...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dhcmp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dhcvi</td>\n",
       "      <td>Decennial Census of Island Areas: U.S. Virgin ...</td>\n",
       "      <td>This product will include some of the demograp...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dhcvi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dp</td>\n",
       "      <td>Decennial Census: Demographic Profile</td>\n",
       "      <td>This product will include topics such as age, ...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dpas</td>\n",
       "      <td>Decennial Census of Island Areas: American Sam...</td>\n",
       "      <td>The U.S. Census Bureau conducts the Island Are...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dpas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dpgu</td>\n",
       "      <td>Decennial Census of Island Areas: Guam Demogra...</td>\n",
       "      <td>The U.S. Census Bureau conducts the Island Are...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dpgu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dpmp</td>\n",
       "      <td>2020 Commonwealth of the Northern Mariana Isla...</td>\n",
       "      <td>The U.S. Census Bureau conducts the Island Are...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dpmp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/dpvi</td>\n",
       "      <td>Decennial Census of Island Areas: U.S. Virgin ...</td>\n",
       "      <td>The U.S. Census Bureau conducts the Island Are...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/dpvi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/pes</td>\n",
       "      <td>Decennial Census: Decennial Post-Enumeration S...</td>\n",
       "      <td>Post-Enumeration Survey estimates of net cover...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/pes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/pl</td>\n",
       "      <td>Decennial Census: Redistricting Data (PL 94-171)</td>\n",
       "      <td>Public Law 94-171, enacted in 1975, directs th...</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/pl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2020</td>\n",
       "      <td>dec/responserate</td>\n",
       "      <td>Decennial Census: Decennial Self-Response Rate</td>\n",
       "      <td>Daily Decennial Self-Response Rates</td>\n",
       "      <td>http://api.census.gov/data/2020/dec/responserate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2020</td>\n",
       "      <td>nonemp</td>\n",
       "      <td>Economic Surveys: Nonemployer Statistics</td>\n",
       "      <td>Nonemployer Statistics is an annual series tha...</td>\n",
       "      <td>http://api.census.gov/data/2020/nonemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2020</td>\n",
       "      <td>pdb/blockgroup</td>\n",
       "      <td>Planning Database Block Group Level</td>\n",
       "      <td>The PDB is a database of U.S. housing, demogra...</td>\n",
       "      <td>http://api.census.gov/data/2020/pdb/blockgroup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2020</td>\n",
       "      <td>pdb/statecounty</td>\n",
       "      <td>2020 Planning Database - State and County Level</td>\n",
       "      <td>This is a one-time release of the Census Burea...</td>\n",
       "      <td>http://api.census.gov/data/2020/pdb/statecounty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2020</td>\n",
       "      <td>pdb/tract</td>\n",
       "      <td>Planning Database: Tract Level</td>\n",
       "      <td>The PDB is a database of U.S. housing, demogra...</td>\n",
       "      <td>http://api.census.gov/data/2020/pdb/tract</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR            DATASET  \\\n",
       "0   2020              abscb   \n",
       "1   2020             abscbo   \n",
       "2   2020              abscs   \n",
       "3   2020             absmcb   \n",
       "4   2020            absnesd   \n",
       "5   2020           absnesdo   \n",
       "6   2020           acs/acs5   \n",
       "7   2020  acs/acs5/cprofile   \n",
       "8   2020   acs/acs5/profile   \n",
       "9   2020      acs/acs5/pums   \n",
       "10  2020    acs/acs5/pumspr   \n",
       "11  2020   acs/acs5/subject   \n",
       "12  2020          acs/flows   \n",
       "13  2020                cbp   \n",
       "14  2020       cps/arts/feb   \n",
       "15  2020       cps/asec/mar   \n",
       "16  2020      cps/basic/apr   \n",
       "17  2020      cps/basic/aug   \n",
       "18  2020      cps/basic/dec   \n",
       "19  2020      cps/basic/feb   \n",
       "20  2020      cps/basic/jan   \n",
       "21  2020      cps/basic/jul   \n",
       "22  2020      cps/basic/jun   \n",
       "23  2020      cps/basic/mar   \n",
       "24  2020      cps/basic/may   \n",
       "25  2020      cps/basic/nov   \n",
       "26  2020      cps/basic/oct   \n",
       "27  2020      cps/basic/sep   \n",
       "28  2020       cps/dwjt/jan   \n",
       "29  2020  cps/fertility/jun   \n",
       "30  2020    cps/foodsec/dec   \n",
       "31  2020     cps/school/oct   \n",
       "32  2020       cps/vets/aug   \n",
       "33  2020     cps/voting/nov   \n",
       "34  2020          dec/cd118   \n",
       "35  2020          dec/ddhca   \n",
       "36  2020            dec/dhc   \n",
       "37  2020          dec/dhcas   \n",
       "38  2020          dec/dhcgu   \n",
       "39  2020          dec/dhcmp   \n",
       "40  2020          dec/dhcvi   \n",
       "41  2020             dec/dp   \n",
       "42  2020           dec/dpas   \n",
       "43  2020           dec/dpgu   \n",
       "44  2020           dec/dpmp   \n",
       "45  2020           dec/dpvi   \n",
       "46  2020            dec/pes   \n",
       "47  2020             dec/pl   \n",
       "48  2020   dec/responserate   \n",
       "49  2020             nonemp   \n",
       "50  2020     pdb/blockgroup   \n",
       "51  2020    pdb/statecounty   \n",
       "52  2020          pdb/tract   \n",
       "\n",
       "                                                TITLE  \\\n",
       "0   Economic Surveys: Annual Business Survey: Char...   \n",
       "1   Economic Surveys: Annual Business Survey: Char...   \n",
       "2   Economic Surveys: Annual Business Survey: Comp...   \n",
       "3   Economic Surveys: Annual Business Survey: Modu...   \n",
       "4   Economic Surveys: Nonemployer Statistics by De...   \n",
       "5   Economic Surveys: Nonemployer Statistics by De...   \n",
       "6   American Community Survey: 5-Year Estimates: D...   \n",
       "7   American Community Survey: 5-Year Estimates: C...   \n",
       "8   American Community Survey: 5-Year Estimates: D...   \n",
       "9   2020 American Community Survey: 5-Year Estimat...   \n",
       "10  2020 American Community Survey: 5-Year Estimat...   \n",
       "11  American Community Survey: 5-Year Estimates: S...   \n",
       "12  2016-2020 American Community Survey: Migration...   \n",
       "13  Economic Surveys: Business Patterns: County Bu...   \n",
       "14  Current Population Survey: Arts Benchmarking S...   \n",
       "15  Current Population Survey Annual Social and Ec...   \n",
       "16           Current Population Survey: Basic Monthly   \n",
       "17           Current Population Survey: Basic Monthly   \n",
       "18           Current Population Survey: Basic Monthly   \n",
       "19           Current Population Survey: Basic Monthly   \n",
       "20           Current Population Survey: Basic Monthly   \n",
       "21           Current Population Survey: Basic Monthly   \n",
       "22           Current Population Survey: Basic Monthly   \n",
       "23           Current Population Survey: Basic Monthly   \n",
       "24           Current Population Survey: Basic Monthly   \n",
       "25           Current Population Survey: Basic Monthly   \n",
       "26           Current Population Survey: Basic Monthly   \n",
       "27           Current Population Survey: Basic Monthly   \n",
       "28  Current Population Survey: Displaced Worker, E...   \n",
       "29    Current Population Survey: Fertility Supplement   \n",
       "30  Current Population Survey: Food Security Suppl...   \n",
       "31  Current Population Survey: School Enrollment S...   \n",
       "32     Current Population Survey: Veterans Supplement   \n",
       "33       Current Population Survey: Voting Supplement   \n",
       "34  Decennial Census: 118th Congressional District...   \n",
       "35  Decennial Census: Detailed Demographic and Hou...   \n",
       "36  Decennial Census: Demographic and Housing Char...   \n",
       "37  Decennial Census of Island Areas: American Sam...   \n",
       "38  Decennial Census of Island Areas: Guam Demogra...   \n",
       "39  Decennial Census of Island Areas: Commonwealth...   \n",
       "40  Decennial Census of Island Areas: U.S. Virgin ...   \n",
       "41              Decennial Census: Demographic Profile   \n",
       "42  Decennial Census of Island Areas: American Sam...   \n",
       "43  Decennial Census of Island Areas: Guam Demogra...   \n",
       "44  2020 Commonwealth of the Northern Mariana Isla...   \n",
       "45  Decennial Census of Island Areas: U.S. Virgin ...   \n",
       "46  Decennial Census: Decennial Post-Enumeration S...   \n",
       "47   Decennial Census: Redistricting Data (PL 94-171)   \n",
       "48     Decennial Census: Decennial Self-Response Rate   \n",
       "49           Economic Surveys: Nonemployer Statistics   \n",
       "50                Planning Database Block Group Level   \n",
       "51    2020 Planning Database - State and County Level   \n",
       "52                     Planning Database: Tract Level   \n",
       "\n",
       "                                          DESCRIPTION  \\\n",
       "0   The Annual Business Survey (ABS) provides info...   \n",
       "1   The Annual Business Survey (ABS) provides info...   \n",
       "2   The Annual Business Survey (ABS) provides info...   \n",
       "3   The Annual Business Survey (ABS) provides info...   \n",
       "4   The Nonemployer Statistics by Demographics (NE...   \n",
       "5   The Nonemployer Statistics by Demographics (NE...   \n",
       "6   The American Community Survey (ACS) is an ongo...   \n",
       "7   The American Community Survey (ACS) is an ongo...   \n",
       "8   The American Community Survey (ACS) is an ongo...   \n",
       "9   The American Community Survey (ACS) Public Use...   \n",
       "10  The Public Use Microdata Sample (PUMS) for Pue...   \n",
       "11  The American Community Survey (ACS) is an ongo...   \n",
       "12  Migration flows are derived from the relations...   \n",
       "13  County Business Patterns (CBP) is an annual se...   \n",
       "14  ABS collects information about individual atte...   \n",
       "15  The Annual Social and Economic Supplement or M...   \n",
       "16  To provide estimates of employment, unemployme...   \n",
       "17  To provide estimates of employment, unemployme...   \n",
       "18  To provide estimates of employment, unemployme...   \n",
       "19  To provide estimates of employment, unemployme...   \n",
       "20  To provide estimates of employment, unemployme...   \n",
       "21  To provide estimates of employment, unemployme...   \n",
       "22  To provide estimates of employment, unemployme...   \n",
       "23  To provide estimates of employment, unemployme...   \n",
       "24  To provide estimates of employment, unemployme...   \n",
       "25  To provide estimates of employment, unemployme...   \n",
       "26  To provide estimates of employment, unemployme...   \n",
       "27  To provide estimates of employment, unemployme...   \n",
       "28  Displaced Workers: Provides data on workers wh...   \n",
       "29  Provides data on the number of children that w...   \n",
       "30  Provides data that will measure hunger and foo...   \n",
       "31  Provides information on population 3 years old...   \n",
       "32  Provides data for veterans of the United State...   \n",
       "33  Provides demographic information on persons wh...   \n",
       "34  The Congressional District Summary File (118th...   \n",
       "35  This product provides the population counts an...   \n",
       "36  This product will include topics such as age, ...   \n",
       "37  This product will include some of the demograp...   \n",
       "38  This product will include some of the demograp...   \n",
       "39  This product will include some of the demograp...   \n",
       "40  This product will include some of the demograp...   \n",
       "41  This product will include topics such as age, ...   \n",
       "42  The U.S. Census Bureau conducts the Island Are...   \n",
       "43  The U.S. Census Bureau conducts the Island Are...   \n",
       "44  The U.S. Census Bureau conducts the Island Are...   \n",
       "45  The U.S. Census Bureau conducts the Island Are...   \n",
       "46  Post-Enumeration Survey estimates of net cover...   \n",
       "47  Public Law 94-171, enacted in 1975, directs th...   \n",
       "48                Daily Decennial Self-Response Rates   \n",
       "49  Nonemployer Statistics is an annual series tha...   \n",
       "50  The PDB is a database of U.S. housing, demogra...   \n",
       "51  This is a one-time release of the Census Burea...   \n",
       "52  The PDB is a database of U.S. housing, demogra...   \n",
       "\n",
       "                                         API BASE URL  \n",
       "0               http://api.census.gov/data/2020/abscb  \n",
       "1              http://api.census.gov/data/2020/abscbo  \n",
       "2               http://api.census.gov/data/2020/abscs  \n",
       "3              http://api.census.gov/data/2020/absmcb  \n",
       "4             http://api.census.gov/data/2020/absnesd  \n",
       "5            http://api.census.gov/data/2020/absnesdo  \n",
       "6            http://api.census.gov/data/2020/acs/acs5  \n",
       "7   http://api.census.gov/data/2020/acs/acs5/cprofile  \n",
       "8    http://api.census.gov/data/2020/acs/acs5/profile  \n",
       "9       http://api.census.gov/data/2020/acs/acs5/pums  \n",
       "10    http://api.census.gov/data/2020/acs/acs5/pumspr  \n",
       "11   http://api.census.gov/data/2020/acs/acs5/subject  \n",
       "12          http://api.census.gov/data/2020/acs/flows  \n",
       "13                http://api.census.gov/data/2020/cbp  \n",
       "14       http://api.census.gov/data/2020/cps/arts/feb  \n",
       "15       http://api.census.gov/data/2020/cps/asec/mar  \n",
       "16      http://api.census.gov/data/2020/cps/basic/apr  \n",
       "17      http://api.census.gov/data/2020/cps/basic/aug  \n",
       "18      http://api.census.gov/data/2020/cps/basic/dec  \n",
       "19      http://api.census.gov/data/2020/cps/basic/feb  \n",
       "20      http://api.census.gov/data/2020/cps/basic/jan  \n",
       "21      http://api.census.gov/data/2020/cps/basic/jul  \n",
       "22      http://api.census.gov/data/2020/cps/basic/jun  \n",
       "23      http://api.census.gov/data/2020/cps/basic/mar  \n",
       "24      http://api.census.gov/data/2020/cps/basic/may  \n",
       "25      http://api.census.gov/data/2020/cps/basic/nov  \n",
       "26      http://api.census.gov/data/2020/cps/basic/oct  \n",
       "27      http://api.census.gov/data/2020/cps/basic/sep  \n",
       "28       http://api.census.gov/data/2020/cps/dwjt/jan  \n",
       "29  http://api.census.gov/data/2020/cps/fertility/jun  \n",
       "30    http://api.census.gov/data/2020/cps/foodsec/dec  \n",
       "31     http://api.census.gov/data/2020/cps/school/oct  \n",
       "32       http://api.census.gov/data/2020/cps/vets/aug  \n",
       "33     http://api.census.gov/data/2020/cps/voting/nov  \n",
       "34          http://api.census.gov/data/2020/dec/cd118  \n",
       "35          http://api.census.gov/data/2020/dec/ddhca  \n",
       "36            http://api.census.gov/data/2020/dec/dhc  \n",
       "37          http://api.census.gov/data/2020/dec/dhcas  \n",
       "38          http://api.census.gov/data/2020/dec/dhcgu  \n",
       "39          http://api.census.gov/data/2020/dec/dhcmp  \n",
       "40          http://api.census.gov/data/2020/dec/dhcvi  \n",
       "41             http://api.census.gov/data/2020/dec/dp  \n",
       "42           http://api.census.gov/data/2020/dec/dpas  \n",
       "43           http://api.census.gov/data/2020/dec/dpgu  \n",
       "44           http://api.census.gov/data/2020/dec/dpmp  \n",
       "45           http://api.census.gov/data/2020/dec/dpvi  \n",
       "46            http://api.census.gov/data/2020/dec/pes  \n",
       "47             http://api.census.gov/data/2020/dec/pl  \n",
       "48   http://api.census.gov/data/2020/dec/responserate  \n",
       "49             http://api.census.gov/data/2020/nonemp  \n",
       "50     http://api.census.gov/data/2020/pdb/blockgroup  \n",
       "51    http://api.census.gov/data/2020/pdb/statecounty  \n",
       "52          http://api.census.gov/data/2020/pdb/tract  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_datasets_for_year = ced.variables.all_data_sets(year=YEAR)\n",
    "\n",
    "df_datasets_for_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1520d44-ef92-40a9-99b9-7e7d393bc72a",
   "metadata": {},
   "source": [
    "## Data sets available for all years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca560f11-9eea-49f8-ae47-595473b29878",
   "metadata": {
    "tags": []
   },
   "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>DATASET</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>API BASE URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>cbp</td>\n",
       "      <td>1986 County Business Patterns: Business Patterns</td>\n",
       "      <td>County Business Patterns (CBP) is an annual se...</td>\n",
       "      <td>http://api.census.gov/data/1986/cbp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1987</td>\n",
       "      <td>cbp</td>\n",
       "      <td>1987 County Business Patterns: Business Patterns</td>\n",
       "      <td>County Business Patterns (CBP) is an annual se...</td>\n",
       "      <td>http://api.census.gov/data/1987/cbp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1988</td>\n",
       "      <td>cbp</td>\n",
       "      <td>1988 County Business Patterns: Business Patterns</td>\n",
       "      <td>County Business Patterns (CBP) is an annual se...</td>\n",
       "      <td>http://api.census.gov/data/1988/cbp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1989</td>\n",
       "      <td>cbp</td>\n",
       "      <td>1989 County Business Patterns: Business Patterns</td>\n",
       "      <td>County Business Patterns (CBP) is an annual se...</td>\n",
       "      <td>http://api.census.gov/data/1989/cbp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1989</td>\n",
       "      <td>cps/basic/apr</td>\n",
       "      <td>Apr 1989 Current Population Survey: Basic Monthly</td>\n",
       "      <td>To provide estimates of employment, unemployme...</td>\n",
       "      <td>http://api.census.gov/data/1989/cps/basic/apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>timeseries</td>\n",
       "      <td>timeseries/pseo/earnings</td>\n",
       "      <td>Post-Secondary Employment Outcomes (PSEO): Pos...</td>\n",
       "      <td>Post-Secondary Employment Outcomes (PSEO) are ...</td>\n",
       "      <td>http://api.census.gov/data/timeseries/pseo/ear...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>timeseries</td>\n",
       "      <td>timeseries/pseo/flows</td>\n",
       "      <td>Post-Secondary Employment Outcomes (PSEO): Pos...</td>\n",
       "      <td>Post-Secondary Employment Outcomes (PSEO) are ...</td>\n",
       "      <td>http://api.census.gov/data/timeseries/pseo/flows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>timeseries</td>\n",
       "      <td>timeseries/qwi/rh</td>\n",
       "      <td>Time Series Longitudinal Employer-Household Dy...</td>\n",
       "      <td>The Quarterly Workforce Indicators (QWI) are a...</td>\n",
       "      <td>http://api.census.gov/data/timeseries/qwi/rh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>timeseries</td>\n",
       "      <td>timeseries/qwi/sa</td>\n",
       "      <td>Time Series Longitudinal Employer-Household Dy...</td>\n",
       "      <td>The Quarterly Workforce Indicators (QWI) are a...</td>\n",
       "      <td>http://api.census.gov/data/timeseries/qwi/sa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445</th>\n",
       "      <td>timeseries</td>\n",
       "      <td>timeseries/qwi/se</td>\n",
       "      <td>Time Series Longitudinal Employer-Household Dy...</td>\n",
       "      <td>The Quarterly Workforce Indicators (QWI) are a...</td>\n",
       "      <td>http://api.census.gov/data/timeseries/qwi/se</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1446 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            YEAR                   DATASET  \\\n",
       "0           1986                       cbp   \n",
       "1           1987                       cbp   \n",
       "2           1988                       cbp   \n",
       "3           1989                       cbp   \n",
       "4           1989             cps/basic/apr   \n",
       "...          ...                       ...   \n",
       "1441  timeseries  timeseries/pseo/earnings   \n",
       "1442  timeseries     timeseries/pseo/flows   \n",
       "1443  timeseries         timeseries/qwi/rh   \n",
       "1444  timeseries         timeseries/qwi/sa   \n",
       "1445  timeseries         timeseries/qwi/se   \n",
       "\n",
       "                                                  TITLE  \\\n",
       "0      1986 County Business Patterns: Business Patterns   \n",
       "1      1987 County Business Patterns: Business Patterns   \n",
       "2      1988 County Business Patterns: Business Patterns   \n",
       "3      1989 County Business Patterns: Business Patterns   \n",
       "4     Apr 1989 Current Population Survey: Basic Monthly   \n",
       "...                                                 ...   \n",
       "1441  Post-Secondary Employment Outcomes (PSEO): Pos...   \n",
       "1442  Post-Secondary Employment Outcomes (PSEO): Pos...   \n",
       "1443  Time Series Longitudinal Employer-Household Dy...   \n",
       "1444  Time Series Longitudinal Employer-Household Dy...   \n",
       "1445  Time Series Longitudinal Employer-Household Dy...   \n",
       "\n",
       "                                            DESCRIPTION  \\\n",
       "0     County Business Patterns (CBP) is an annual se...   \n",
       "1     County Business Patterns (CBP) is an annual se...   \n",
       "2     County Business Patterns (CBP) is an annual se...   \n",
       "3     County Business Patterns (CBP) is an annual se...   \n",
       "4     To provide estimates of employment, unemployme...   \n",
       "...                                                 ...   \n",
       "1441  Post-Secondary Employment Outcomes (PSEO) are ...   \n",
       "1442  Post-Secondary Employment Outcomes (PSEO) are ...   \n",
       "1443  The Quarterly Workforce Indicators (QWI) are a...   \n",
       "1444  The Quarterly Workforce Indicators (QWI) are a...   \n",
       "1445  The Quarterly Workforce Indicators (QWI) are a...   \n",
       "\n",
       "                                           API BASE URL  \n",
       "0                   http://api.census.gov/data/1986/cbp  \n",
       "1                   http://api.census.gov/data/1987/cbp  \n",
       "2                   http://api.census.gov/data/1988/cbp  \n",
       "3                   http://api.census.gov/data/1989/cbp  \n",
       "4         http://api.census.gov/data/1989/cps/basic/apr  \n",
       "...                                                 ...  \n",
       "1441  http://api.census.gov/data/timeseries/pseo/ear...  \n",
       "1442   http://api.census.gov/data/timeseries/pseo/flows  \n",
       "1443       http://api.census.gov/data/timeseries/qwi/rh  \n",
       "1444       http://api.census.gov/data/timeseries/qwi/sa  \n",
       "1445       http://api.census.gov/data/timeseries/qwi/se  \n",
       "\n",
       "[1446 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_datasets = ced.variables.all_data_sets()\n",
    "\n",
    "df_all_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0734fa25-dd6f-4005-bc69-0e0fb821b8f0",
   "metadata": {},
   "source": [
    "## How many datasets by year.\n",
    "\n",
    "Some older years do not have very many vintage data sets. Note also that we have some `'timeseries'` datasets that are multi-year.\n",
    "\n",
    "We will plot which of the most popular datasets are available in which years, and how many other datasets there are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47ed96ac-99e3-417d-b261-e19a7dc1e845",
   "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>0</th>\n",
       "      <th>ACS 1 Year</th>\n",
       "      <th>ACS 3 Year</th>\n",
       "      <th>ACS 5 Year</th>\n",
       "      <th>Population Estimates</th>\n",
       "      <th>Decennial Redistricting</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeseries</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0  ACS 1 Year  ACS 3 Year  ACS 5 Year  Population Estimates  \\\n",
       "0           1986         NaN         NaN         NaN                   NaN   \n",
       "1           1987         NaN         NaN         NaN                   NaN   \n",
       "2           1988         NaN         NaN         NaN                   NaN   \n",
       "3           1989         NaN         NaN         NaN                   NaN   \n",
       "4           1990         NaN         NaN         NaN                   NaN   \n",
       "...          ...         ...         ...         ...                   ...   \n",
       "2021         NaN         1.0         NaN         1.0                   1.0   \n",
       "2022         NaN         1.0         NaN         1.0                   NaN   \n",
       "2023         NaN         NaN         NaN         NaN                   NaN   \n",
       "2024         NaN         NaN         NaN         NaN                   NaN   \n",
       "timeseries   NaN         NaN         NaN         NaN                   NaN   \n",
       "\n",
       "            Decennial Redistricting  Other  \n",
       "0                               NaN    NaN  \n",
       "1                               NaN    NaN  \n",
       "2                               NaN    NaN  \n",
       "3                               NaN    NaN  \n",
       "4                               NaN    NaN  \n",
       "...                             ...    ...  \n",
       "2021                            NaN   55.0  \n",
       "2022                            NaN   38.0  \n",
       "2023                            NaN   13.0  \n",
       "2024                            NaN    1.0  \n",
       "timeseries                      NaN   61.0  \n",
       "\n",
       "[76 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_to_count = {\n",
    "    \"ACS 1 Year\": \"acs/acs1\",\n",
    "    \"ACS 3 Year\": \"acs/acs3\",\n",
    "    \"ACS 5 Year\": \"acs/acs5\",\n",
    "    \"Population Estimates\": \"pep/population\",\n",
    "    \"Decennial Redistricting\": \"dec/pl\",\n",
    "}\n",
    "\n",
    "s_counts_by_dataset = {\n",
    "    name: df_all_datasets[df_all_datasets.DATASET == dataset]\n",
    "    .groupby(\"YEAR\")[\"DATASET\"]\n",
    "    .count()\n",
    "    for name, dataset in datasets_to_count.items()\n",
    "}\n",
    "\n",
    "\n",
    "s_counts_other = {\n",
    "    \"Other\": df_all_datasets[~df_all_datasets.DATASET.isin(datasets_to_count)]\n",
    "    .groupby(\"YEAR\")[\"DATASET\"]\n",
    "    .count()\n",
    "}\n",
    "\n",
    "# Make all the indices strings so when pandas sorts them it does not\n",
    "# have a problem comparing ints and strings.\n",
    "s_counts_other[\"Other\"].index = s_counts_other[\"Other\"].index.map(str)\n",
    "for s_counts in s_counts_by_dataset.values():\n",
    "    s_counts.index = s_counts.index.map(str)\n",
    "\n",
    "\n",
    "df_years = pd.DataFrame([str(year) for year in range(1986, 2022)])\n",
    "\n",
    "df_dataset_counts = df_years.join(\n",
    "    pd.DataFrame(dict(s_counts_by_dataset, **s_counts_other)), how=\"outer\"\n",
    ")\n",
    "\n",
    "df_dataset_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d8da1a54-8020-47eb-9d98-0b6e61b2a0c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994',\n",
       "       '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003',\n",
       "       '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012',\n",
       "       '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021',\n",
       "       '2022', '2023', '2024', 'timeseries'],\n",
       "      dtype='object', name='YEAR')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_counts_other[\"Other\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1f59db7-8b64-4633-ad3c-f5fa1d09487a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
       "       '2014', '2015', '2016', '2017', '2018', '2019', '2021', '2022'],\n",
       "      dtype='object', name='YEAR')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_counts_by_dataset[\"ACS 1 Year\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39f9d182-eb70-40e9-9d04-c66ed710cb22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bdd81d9-b3fb-4b67-a072-9e83d4108e09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "740ed9c3-9b75-433c-b43d-43f021668515",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29c92955-1847-4f30-b23b-fb4af75b6696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_dataset_counts[datasets_to_count.keys()].plot.bar(stacked=True, figsize=(12, 8))\n",
    "\n",
    "ax.set_title(\"Selected Popular Datasets by Vintage\")\n",
    "\n",
    "ax.set_yticks(range(5))\n",
    "\n",
    "ax.grid(axis=\"y\")\n",
    "\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f89ec23e-db41-4927-9272-376875a571c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_dataset_counts.plot.bar(stacked=True, figsize=(12, 8))\n",
    "\n",
    "ax.set_title(\"All Available Datasets by Vintage\")\n",
    "\n",
    "ax.grid(axis=\"y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b12ed8f9-1863-440a-9ceb-a61abc5c0043",
   "metadata": {},
   "source": [
    "## List the groups in a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83a8f2d1-32dc-4d66-9c12-e582ed26855d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'acs/acs5'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = df_datasets_for_year.iloc[6][\"DATASET\"]\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d8c1536d-5195-497d-920a-8d8d51225749",
   "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>DATASET</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>GROUP</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>B01001</td>\n",
       "      <td>SEX BY AGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>B01001A</td>\n",
       "      <td>SEX BY AGE (WHITE ALONE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>B01001B</td>\n",
       "      <td>SEX BY AGE (BLACK OR AFRICAN AMERICAN ALONE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>B01001C</td>\n",
       "      <td>SEX BY AGE (AMERICAN INDIAN AND ALASKA NATIVE ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>B01001D</td>\n",
       "      <td>SEX BY AGE (ASIAN ALONE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>C27014</td>\n",
       "      <td>PUBLIC HEALTH INSURANCE BY WORK EXPERIENCE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>C27016</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY RATIO OF I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>C27017</td>\n",
       "      <td>PRIVATE HEALTH INSURANCE BY RATIO OF INCOME TO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>C27018</td>\n",
       "      <td>PUBLIC HEALTH INSURANCE BY RATIO OF INCOME TO ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>2020</td>\n",
       "      <td>C27021</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS  BY LIVING AR...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1140 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DATASET  YEAR    GROUP  \\\n",
       "0     acs/acs5  2020   B01001   \n",
       "1     acs/acs5  2020  B01001A   \n",
       "2     acs/acs5  2020  B01001B   \n",
       "3     acs/acs5  2020  B01001C   \n",
       "4     acs/acs5  2020  B01001D   \n",
       "...        ...   ...      ...   \n",
       "1135  acs/acs5  2020   C27014   \n",
       "1136  acs/acs5  2020   C27016   \n",
       "1137  acs/acs5  2020   C27017   \n",
       "1138  acs/acs5  2020   C27018   \n",
       "1139  acs/acs5  2020   C27021   \n",
       "\n",
       "                                            DESCRIPTION  \n",
       "0                                            SEX BY AGE  \n",
       "1                              SEX BY AGE (WHITE ALONE)  \n",
       "2          SEX BY AGE (BLACK OR AFRICAN AMERICAN ALONE)  \n",
       "3     SEX BY AGE (AMERICAN INDIAN AND ALASKA NATIVE ...  \n",
       "4                              SEX BY AGE (ASIAN ALONE)  \n",
       "...                                                 ...  \n",
       "1135         PUBLIC HEALTH INSURANCE BY WORK EXPERIENCE  \n",
       "1136  HEALTH INSURANCE COVERAGE STATUS BY RATIO OF I...  \n",
       "1137  PRIVATE HEALTH INSURANCE BY RATIO OF INCOME TO...  \n",
       "1138  PUBLIC HEALTH INSURANCE BY RATIO OF INCOME TO ...  \n",
       "1139  HEALTH INSURANCE COVERAGE STATUS  BY LIVING AR...  \n",
       "\n",
       "[1140 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_groups = ced.variables.all_groups(dataset, YEAR)\n",
    "\n",
    "df_groups"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b4a55f-86b1-4902-b319-f4660b144cd8",
   "metadata": {},
   "source": [
    "## List the variables in a group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c225c33c-35f3-4d70-a292-6a768e11439f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C27014'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = df_groups.iloc[-5][\"GROUP\"]\n",
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "807ae27d-9c04-4752-9f66-0976ca4a01a0",
   "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>DATASET</th>\n",
       "      <th>GROUP</th>\n",
       "      <th>VARIABLE</th>\n",
       "      <th>LABEL</th>\n",
       "      <th>SUGGESTED_WEIGHT</th>\n",
       "      <th>VALUES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_001E</td>\n",
       "      <td>Estimate!!Total:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_002E</td>\n",
       "      <td>Estimate!!Total:!!Worked full-time, year-round:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_003E</td>\n",
       "      <td>Estimate!!Total:!!Worked full-time, year-round...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_004E</td>\n",
       "      <td>Estimate!!Total:!!Worked full-time, year-round...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_005E</td>\n",
       "      <td>Estimate!!Total:!!Worked less than full-time, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_006E</td>\n",
       "      <td>Estimate!!Total:!!Worked less than full-time, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_007E</td>\n",
       "      <td>Estimate!!Total:!!Worked less than full-time, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_008E</td>\n",
       "      <td>Estimate!!Total:!!Did not work:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_009E</td>\n",
       "      <td>Estimate!!Total:!!Did not work:!!With public c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>acs/acs5</td>\n",
       "      <td>C27014</td>\n",
       "      <td>C27014_010E</td>\n",
       "      <td>Estimate!!Total:!!Did not work:!!No public cov...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR   DATASET   GROUP     VARIABLE  \\\n",
       "0  2020  acs/acs5  C27014  C27014_001E   \n",
       "1  2020  acs/acs5  C27014  C27014_002E   \n",
       "2  2020  acs/acs5  C27014  C27014_003E   \n",
       "3  2020  acs/acs5  C27014  C27014_004E   \n",
       "4  2020  acs/acs5  C27014  C27014_005E   \n",
       "5  2020  acs/acs5  C27014  C27014_006E   \n",
       "6  2020  acs/acs5  C27014  C27014_007E   \n",
       "7  2020  acs/acs5  C27014  C27014_008E   \n",
       "8  2020  acs/acs5  C27014  C27014_009E   \n",
       "9  2020  acs/acs5  C27014  C27014_010E   \n",
       "\n",
       "                                               LABEL  SUGGESTED_WEIGHT VALUES  \n",
       "0                                   Estimate!!Total:               NaN   None  \n",
       "1    Estimate!!Total:!!Worked full-time, year-round:               NaN   None  \n",
       "2  Estimate!!Total:!!Worked full-time, year-round...               NaN   None  \n",
       "3  Estimate!!Total:!!Worked full-time, year-round...               NaN   None  \n",
       "4  Estimate!!Total:!!Worked less than full-time, ...               NaN   None  \n",
       "5  Estimate!!Total:!!Worked less than full-time, ...               NaN   None  \n",
       "6  Estimate!!Total:!!Worked less than full-time, ...               NaN   None  \n",
       "7                    Estimate!!Total:!!Did not work:               NaN   None  \n",
       "8  Estimate!!Total:!!Did not work:!!With public c...               NaN   None  \n",
       "9  Estimate!!Total:!!Did not work:!!No public cov...               NaN   None  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_variables = ced.variables.all_variables(dataset, YEAR, group)\n",
    "df_variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0383caf-a164-4bd2-a020-3f81ef8d5a36",
   "metadata": {},
   "source": [
    "## Show the variables in a tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "12e9b79a-9b88-4bf6-9aa2-087f69d4ecde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+ Estimate\n",
       "    + Total: (C27014_001E)\n",
       "        + Worked full-time, year-round: (C27014_002E)\n",
       "            + With public coverage (C27014_003E)\n",
       "            + No public coverage (C27014_004E)\n",
       "        + Worked less than full-time, year-round: (C27014_005E)\n",
       "            + With public coverage (C27014_006E)\n",
       "            + No public coverage (C27014_007E)\n",
       "        + Did not work: (C27014_008E)\n",
       "            + With public coverage (C27014_009E)\n",
       "            + No public coverage (C27014_010E)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ced.variables.group_tree(dataset, YEAR, group)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ecafa3-e5fd-4871-9e3c-27efd523e783",
   "metadata": {},
   "source": [
    "## List all the leaves of the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "678c6900-3186-4347-a69b-6f09059b9f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C27014_003E',\n",
       " 'C27014_004E',\n",
       " 'C27014_006E',\n",
       " 'C27014_007E',\n",
       " 'C27014_009E',\n",
       " 'C27014_010E']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaves = ced.variables.group_leaves(dataset, YEAR, group)\n",
    "leaves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9091a6-5d06-4607-be47-88c57fc0813b",
   "metadata": {},
   "source": [
    "## Query the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8bcfe19c-ce7d-468d-894f-ba3d78486652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that these two are equivalent. Using `leaves_of_group=group`\n",
    "# could have saved us needing to construct `leaves` with the\n",
    "# separate call above.\n",
    "\n",
    "df_download_variables = ced.download(dataset, YEAR, leaves, state=\"*\")\n",
    "df_download_leaves_of_group = ced.download(\n",
    "    dataset, YEAR, leaves_of_group=group, state=\"*\"\n",
    ")\n",
    "\n",
    "df_download_leaves_of_group.equals(df_download_variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5f7afa8f-9767-4da8-a39b-087479ca3335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE</th>\n",
       "      <th>C27014_003E</th>\n",
       "      <th>C27014_004E</th>\n",
       "      <th>C27014_006E</th>\n",
       "      <th>C27014_007E</th>\n",
       "      <th>C27014_009E</th>\n",
       "      <th>C27014_010E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>282813</td>\n",
       "      <td>3936257</td>\n",
       "      <td>428403</td>\n",
       "      <td>1492576</td>\n",
       "      <td>691948</td>\n",
       "      <td>683856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06</td>\n",
       "      <td>1374376</td>\n",
       "      <td>10959296</td>\n",
       "      <td>1773518</td>\n",
       "      <td>4694829</td>\n",
       "      <td>2306967</td>\n",
       "      <td>2715925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>54298</td>\n",
       "      <td>456747</td>\n",
       "      <td>74180</td>\n",
       "      <td>163977</td>\n",
       "      <td>177283</td>\n",
       "      <td>112574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49</td>\n",
       "      <td>32811</td>\n",
       "      <td>955292</td>\n",
       "      <td>48745</td>\n",
       "      <td>472159</td>\n",
       "      <td>75240</td>\n",
       "      <td>217961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>682381</td>\n",
       "      <td>5703442</td>\n",
       "      <td>901892</td>\n",
       "      <td>2135736</td>\n",
       "      <td>1276372</td>\n",
       "      <td>1139733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>25265</td>\n",
       "      <td>257881</td>\n",
       "      <td>30201</td>\n",
       "      <td>78979</td>\n",
       "      <td>48885</td>\n",
       "      <td>30775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>02</td>\n",
       "      <td>23966</td>\n",
       "      <td>190562</td>\n",
       "      <td>32449</td>\n",
       "      <td>106897</td>\n",
       "      <td>34460</td>\n",
       "      <td>44862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>401669</td>\n",
       "      <td>6212125</td>\n",
       "      <td>399329</td>\n",
       "      <td>2513817</td>\n",
       "      <td>961401</td>\n",
       "      <td>1674842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>107049</td>\n",
       "      <td>1493945</td>\n",
       "      <td>108716</td>\n",
       "      <td>580082</td>\n",
       "      <td>276030</td>\n",
       "      <td>370390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38</td>\n",
       "      <td>14537</td>\n",
       "      <td>256412</td>\n",
       "      <td>16776</td>\n",
       "      <td>104423</td>\n",
       "      <td>19607</td>\n",
       "      <td>32869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>23</td>\n",
       "      <td>25533</td>\n",
       "      <td>407962</td>\n",
       "      <td>41683</td>\n",
       "      <td>178827</td>\n",
       "      <td>77363</td>\n",
       "      <td>58090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>168501</td>\n",
       "      <td>3314750</td>\n",
       "      <td>166185</td>\n",
       "      <td>1264296</td>\n",
       "      <td>462749</td>\n",
       "      <td>857110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>01</td>\n",
       "      <td>75843</td>\n",
       "      <td>1423256</td>\n",
       "      <td>78067</td>\n",
       "      <td>540687</td>\n",
       "      <td>320987</td>\n",
       "      <td>406785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>33</td>\n",
       "      <td>23957</td>\n",
       "      <td>461188</td>\n",
       "      <td>38029</td>\n",
       "      <td>182053</td>\n",
       "      <td>53264</td>\n",
       "      <td>66680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>41</td>\n",
       "      <td>119163</td>\n",
       "      <td>1175171</td>\n",
       "      <td>198499</td>\n",
       "      <td>532910</td>\n",
       "      <td>234140</td>\n",
       "      <td>233120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>56</td>\n",
       "      <td>8863</td>\n",
       "      <td>183814</td>\n",
       "      <td>10046</td>\n",
       "      <td>79873</td>\n",
       "      <td>17303</td>\n",
       "      <td>36047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>04</td>\n",
       "      <td>209241</td>\n",
       "      <td>1980455</td>\n",
       "      <td>251539</td>\n",
       "      <td>758185</td>\n",
       "      <td>396259</td>\n",
       "      <td>479384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>22</td>\n",
       "      <td>148697</td>\n",
       "      <td>1249862</td>\n",
       "      <td>173016</td>\n",
       "      <td>453971</td>\n",
       "      <td>363999</td>\n",
       "      <td>314243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>131642</td>\n",
       "      <td>2055323</td>\n",
       "      <td>198159</td>\n",
       "      <td>809014</td>\n",
       "      <td>337807</td>\n",
       "      <td>386602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>28632</td>\n",
       "      <td>499391</td>\n",
       "      <td>37380</td>\n",
       "      <td>245311</td>\n",
       "      <td>64417</td>\n",
       "      <td>114182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>09</td>\n",
       "      <td>92423</td>\n",
       "      <td>1081766</td>\n",
       "      <td>154354</td>\n",
       "      <td>443137</td>\n",
       "      <td>168766</td>\n",
       "      <td>197937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15</td>\n",
       "      <td>33642</td>\n",
       "      <td>417542</td>\n",
       "      <td>43196</td>\n",
       "      <td>157676</td>\n",
       "      <td>58403</td>\n",
       "      <td>80596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17</td>\n",
       "      <td>288510</td>\n",
       "      <td>3980277</td>\n",
       "      <td>409366</td>\n",
       "      <td>1511031</td>\n",
       "      <td>581603</td>\n",
       "      <td>816258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>235053</td>\n",
       "      <td>2122254</td>\n",
       "      <td>326100</td>\n",
       "      <td>878593</td>\n",
       "      <td>357411</td>\n",
       "      <td>315603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>48</td>\n",
       "      <td>429774</td>\n",
       "      <td>9129322</td>\n",
       "      <td>392899</td>\n",
       "      <td>3507585</td>\n",
       "      <td>964519</td>\n",
       "      <td>2447273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>30</td>\n",
       "      <td>31841</td>\n",
       "      <td>295037</td>\n",
       "      <td>45156</td>\n",
       "      <td>140213</td>\n",
       "      <td>47166</td>\n",
       "      <td>53451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31</td>\n",
       "      <td>29308</td>\n",
       "      <td>656923</td>\n",
       "      <td>31713</td>\n",
       "      <td>244400</td>\n",
       "      <td>50326</td>\n",
       "      <td>90420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>39</td>\n",
       "      <td>303814</td>\n",
       "      <td>3472694</td>\n",
       "      <td>462996</td>\n",
       "      <td>1321470</td>\n",
       "      <td>661439</td>\n",
       "      <td>609880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>08</td>\n",
       "      <td>161908</td>\n",
       "      <td>1816067</td>\n",
       "      <td>214518</td>\n",
       "      <td>726399</td>\n",
       "      <td>228801</td>\n",
       "      <td>316622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>34</td>\n",
       "      <td>173745</td>\n",
       "      <td>2838870</td>\n",
       "      <td>243804</td>\n",
       "      <td>1099656</td>\n",
       "      <td>366326</td>\n",
       "      <td>593718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>24</td>\n",
       "      <td>166291</td>\n",
       "      <td>1986115</td>\n",
       "      <td>190913</td>\n",
       "      <td>673236</td>\n",
       "      <td>257918</td>\n",
       "      <td>338402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>51</td>\n",
       "      <td>163082</td>\n",
       "      <td>2750475</td>\n",
       "      <td>159265</td>\n",
       "      <td>1045318</td>\n",
       "      <td>313113</td>\n",
       "      <td>577403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>50</td>\n",
       "      <td>21272</td>\n",
       "      <td>185998</td>\n",
       "      <td>32007</td>\n",
       "      <td>79282</td>\n",
       "      <td>34732</td>\n",
       "      <td>21750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>37</td>\n",
       "      <td>179393</td>\n",
       "      <td>3137513</td>\n",
       "      <td>214270</td>\n",
       "      <td>1263978</td>\n",
       "      <td>517007</td>\n",
       "      <td>770802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>05</td>\n",
       "      <td>91675</td>\n",
       "      <td>838456</td>\n",
       "      <td>99700</td>\n",
       "      <td>275666</td>\n",
       "      <td>237071</td>\n",
       "      <td>178118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>53</td>\n",
       "      <td>187247</td>\n",
       "      <td>2253788</td>\n",
       "      <td>291772</td>\n",
       "      <td>960869</td>\n",
       "      <td>361407</td>\n",
       "      <td>456895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>20</td>\n",
       "      <td>43783</td>\n",
       "      <td>944358</td>\n",
       "      <td>45237</td>\n",
       "      <td>369139</td>\n",
       "      <td>93556</td>\n",
       "      <td>169884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>40</td>\n",
       "      <td>65805</td>\n",
       "      <td>1175836</td>\n",
       "      <td>64873</td>\n",
       "      <td>457040</td>\n",
       "      <td>179996</td>\n",
       "      <td>314931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>55</td>\n",
       "      <td>115916</td>\n",
       "      <td>1907149</td>\n",
       "      <td>177397</td>\n",
       "      <td>727003</td>\n",
       "      <td>236847</td>\n",
       "      <td>260607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>28</td>\n",
       "      <td>46154</td>\n",
       "      <td>832804</td>\n",
       "      <td>53174</td>\n",
       "      <td>319814</td>\n",
       "      <td>206865</td>\n",
       "      <td>243551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>29</td>\n",
       "      <td>87163</td>\n",
       "      <td>1940917</td>\n",
       "      <td>110536</td>\n",
       "      <td>751474</td>\n",
       "      <td>278097</td>\n",
       "      <td>392648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>26</td>\n",
       "      <td>251826</td>\n",
       "      <td>2796716</td>\n",
       "      <td>413718</td>\n",
       "      <td>1228631</td>\n",
       "      <td>644554</td>\n",
       "      <td>562436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>44</td>\n",
       "      <td>30234</td>\n",
       "      <td>320975</td>\n",
       "      <td>43515</td>\n",
       "      <td>134701</td>\n",
       "      <td>64969</td>\n",
       "      <td>49605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>27</td>\n",
       "      <td>152178</td>\n",
       "      <td>1813873</td>\n",
       "      <td>222561</td>\n",
       "      <td>705573</td>\n",
       "      <td>196156</td>\n",
       "      <td>219438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>19</td>\n",
       "      <td>80034</td>\n",
       "      <td>1017463</td>\n",
       "      <td>108695</td>\n",
       "      <td>361520</td>\n",
       "      <td>125274</td>\n",
       "      <td>128039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>35</td>\n",
       "      <td>97998</td>\n",
       "      <td>497507</td>\n",
       "      <td>111490</td>\n",
       "      <td>181157</td>\n",
       "      <td>170661</td>\n",
       "      <td>131417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>32</td>\n",
       "      <td>77213</td>\n",
       "      <td>898084</td>\n",
       "      <td>94827</td>\n",
       "      <td>361572</td>\n",
       "      <td>153875</td>\n",
       "      <td>213596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>10</td>\n",
       "      <td>25494</td>\n",
       "      <td>285568</td>\n",
       "      <td>36096</td>\n",
       "      <td>102394</td>\n",
       "      <td>52158</td>\n",
       "      <td>54952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>72</td>\n",
       "      <td>121979</td>\n",
       "      <td>535502</td>\n",
       "      <td>208400</td>\n",
       "      <td>190812</td>\n",
       "      <td>609747</td>\n",
       "      <td>249120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>21</td>\n",
       "      <td>145764</td>\n",
       "      <td>1217060</td>\n",
       "      <td>191290</td>\n",
       "      <td>434073</td>\n",
       "      <td>387212</td>\n",
       "      <td>231214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>46</td>\n",
       "      <td>14846</td>\n",
       "      <td>287885</td>\n",
       "      <td>16505</td>\n",
       "      <td>109530</td>\n",
       "      <td>26141</td>\n",
       "      <td>40652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>47</td>\n",
       "      <td>160642</td>\n",
       "      <td>2031150</td>\n",
       "      <td>166729</td>\n",
       "      <td>760774</td>\n",
       "      <td>398301</td>\n",
       "      <td>475430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATE  C27014_003E  C27014_004E  C27014_006E  C27014_007E  C27014_009E  \\\n",
       "0     42       282813      3936257       428403      1492576       691948   \n",
       "1     06      1374376     10959296      1773518      4694829      2306967   \n",
       "2     54        54298       456747        74180       163977       177283   \n",
       "3     49        32811       955292        48745       472159        75240   \n",
       "4     36       682381      5703442       901892      2135736      1276372   \n",
       "5     11        25265       257881        30201        78979        48885   \n",
       "6     02        23966       190562        32449       106897        34460   \n",
       "7     12       401669      6212125       399329      2513817       961401   \n",
       "8     45       107049      1493945       108716       580082       276030   \n",
       "9     38        14537       256412        16776       104423        19607   \n",
       "10    23        25533       407962        41683       178827        77363   \n",
       "11    13       168501      3314750       166185      1264296       462749   \n",
       "12    01        75843      1423256        78067       540687       320987   \n",
       "13    33        23957       461188        38029       182053        53264   \n",
       "14    41       119163      1175171       198499       532910       234140   \n",
       "15    56         8863       183814        10046        79873        17303   \n",
       "16    04       209241      1980455       251539       758185       396259   \n",
       "17    22       148697      1249862       173016       453971       363999   \n",
       "18    18       131642      2055323       198159       809014       337807   \n",
       "19    16        28632       499391        37380       245311        64417   \n",
       "20    09        92423      1081766       154354       443137       168766   \n",
       "21    15        33642       417542        43196       157676        58403   \n",
       "22    17       288510      3980277       409366      1511031       581603   \n",
       "23    25       235053      2122254       326100       878593       357411   \n",
       "24    48       429774      9129322       392899      3507585       964519   \n",
       "25    30        31841       295037        45156       140213        47166   \n",
       "26    31        29308       656923        31713       244400        50326   \n",
       "27    39       303814      3472694       462996      1321470       661439   \n",
       "28    08       161908      1816067       214518       726399       228801   \n",
       "29    34       173745      2838870       243804      1099656       366326   \n",
       "30    24       166291      1986115       190913       673236       257918   \n",
       "31    51       163082      2750475       159265      1045318       313113   \n",
       "32    50        21272       185998        32007        79282        34732   \n",
       "33    37       179393      3137513       214270      1263978       517007   \n",
       "34    05        91675       838456        99700       275666       237071   \n",
       "35    53       187247      2253788       291772       960869       361407   \n",
       "36    20        43783       944358        45237       369139        93556   \n",
       "37    40        65805      1175836        64873       457040       179996   \n",
       "38    55       115916      1907149       177397       727003       236847   \n",
       "39    28        46154       832804        53174       319814       206865   \n",
       "40    29        87163      1940917       110536       751474       278097   \n",
       "41    26       251826      2796716       413718      1228631       644554   \n",
       "42    44        30234       320975        43515       134701        64969   \n",
       "43    27       152178      1813873       222561       705573       196156   \n",
       "44    19        80034      1017463       108695       361520       125274   \n",
       "45    35        97998       497507       111490       181157       170661   \n",
       "46    32        77213       898084        94827       361572       153875   \n",
       "47    10        25494       285568        36096       102394        52158   \n",
       "48    72       121979       535502       208400       190812       609747   \n",
       "49    21       145764      1217060       191290       434073       387212   \n",
       "50    46        14846       287885        16505       109530        26141   \n",
       "51    47       160642      2031150       166729       760774       398301   \n",
       "\n",
       "    C27014_010E  \n",
       "0        683856  \n",
       "1       2715925  \n",
       "2        112574  \n",
       "3        217961  \n",
       "4       1139733  \n",
       "5         30775  \n",
       "6         44862  \n",
       "7       1674842  \n",
       "8        370390  \n",
       "9         32869  \n",
       "10        58090  \n",
       "11       857110  \n",
       "12       406785  \n",
       "13        66680  \n",
       "14       233120  \n",
       "15        36047  \n",
       "16       479384  \n",
       "17       314243  \n",
       "18       386602  \n",
       "19       114182  \n",
       "20       197937  \n",
       "21        80596  \n",
       "22       816258  \n",
       "23       315603  \n",
       "24      2447273  \n",
       "25        53451  \n",
       "26        90420  \n",
       "27       609880  \n",
       "28       316622  \n",
       "29       593718  \n",
       "30       338402  \n",
       "31       577403  \n",
       "32        21750  \n",
       "33       770802  \n",
       "34       178118  \n",
       "35       456895  \n",
       "36       169884  \n",
       "37       314931  \n",
       "38       260607  \n",
       "39       243551  \n",
       "40       392648  \n",
       "41       562436  \n",
       "42        49605  \n",
       "43       219438  \n",
       "44       128039  \n",
       "45       131417  \n",
       "46       213596  \n",
       "47        54952  \n",
       "48       249120  \n",
       "49       231214  \n",
       "50        40652  \n",
       "51       475430  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_download_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd18e8fd-d452-4bdd-9604-3c36d9441fde",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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