CensusAgg_part2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import os.path\n",
    "\n",
    "import geopandas as gpd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/projects/mpi/shared/Data'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set working directory\n",
    "\n",
    "os.chdir('/projects/mpi/shared/Data')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_cts.cpg\t\t    la_qtid_ct.csv\r\n",
      "all_cts.dbf\t\t    la_tag.csv\r\n",
      "all_cts.prj\t\t    __MACOSX\r\n",
      "all_cts.shp\t\t    NewYork.geojson\r\n",
      "all_cts.shx\t\t    nyc_all.csv\r\n",
      "Borough Boundaries\t    ny_combine.csv\r\n",
      "Boundaries - City.geojson   ny_ct_agg.csv\r\n",
      "cellphone data 2\t    ny_ct.csv\r\n",
      "cellphone data 2.zip\t    ny_qtid_ct.csv\r\n",
      "CENSUS_TRACTS_2010.cpg\t    SanFrancisco.geojson\r\n",
      "CENSUS_TRACTS_2010.dbf\t    sf_all.csv\r\n",
      "CENSUS_TRACTS_2010.prj\t    sf_combine.csv\r\n",
      "CENSUS_TRACTS_2010.sbn\t    sf_ct_agg.csv\r\n",
      "CENSUS_TRACTS_2010.sbx\t    sf_ct.csv\r\n",
      "CENSUS_TRACTS_2010.shp\t    sf_qtid_ct.csv\r\n",
      "CENSUS_TRACTS_2010.shp.xml  sf_tag.csv\r\n",
      "CENSUS_TRACTS_2010.shx\t    shapefiles\r\n",
      "chicago_all.csv\t\t    tl_2015_06037_addrfeat.cpg\r\n",
      "Chicago.geojson\t\t    tl_2015_06037_addrfeat.dbf\r\n",
      "chic_combine.csv\t    tl_2015_06037_addrfeat.prj\r\n",
      "chic_ct_agg.csv\t\t    tl_2015_06037_addrfeat.shp\r\n",
      "chic_ct.csv\t\t    tl_2015_06037_addrfeat.shp.ea.iso.xml\r\n",
      "chic_qtid_ct.csv\t    tl_2015_06037_addrfeat.shp.iso.xml\r\n",
      "la_all.csv\t\t    tl_2015_06037_addrfeat.shp.xml\r\n",
      "la_combine.csv\t\t    tl_2015_06037_addrfeat.shx\r\n",
      "la_ct_agg.csv\t\t    tl_2015_06037_addrfeat.zip\r\n",
      "la_ct.csv\r\n"
     ]
    }
   ],
   "source": [
    "# grab sptialjoined files\n",
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cts = gpd.read_file('all_cts.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e94f0a588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find out which STATEFP is\n",
    "fig= plt.figure(figsize = (15,20))\n",
    "for i in range(len(cts.STATEFP.unique())):\n",
    "    ax = fig.add_subplot(3,4,i+1)\n",
    "    cts[cts['STATEFP']==cts.STATEFP.unique()[i]].plot(ax = ax)\n",
    "    ax.set_title(\"the number of STATEFP is {0}\".format(cts.STATEFP.unique()[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>TRACTCE</th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>400600</td>\n",
       "      <td>1400000US06001400600</td>\n",
       "      <td>06001400600</td>\n",
       "      <td>4006</td>\n",
       "      <td>CT</td>\n",
       "      <td>297856</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.26807 37.844136, -122.26514 37....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>400900</td>\n",
       "      <td>1400000US06001400900</td>\n",
       "      <td>06001400900</td>\n",
       "      <td>4009</td>\n",
       "      <td>CT</td>\n",
       "      <td>420877</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.285576 37.839778, -122.283186 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>401400</td>\n",
       "      <td>1400000US06001401400</td>\n",
       "      <td>06001401400</td>\n",
       "      <td>4014</td>\n",
       "      <td>CT</td>\n",
       "      <td>758204</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.278611 37.826878, -122.268563 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>403000</td>\n",
       "      <td>1400000US06001403000</td>\n",
       "      <td>06001403000</td>\n",
       "      <td>4030</td>\n",
       "      <td>CT</td>\n",
       "      <td>352394</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.274757 37.79883299999999, -122....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>405902</td>\n",
       "      <td>1400000US06001405902</td>\n",
       "      <td>06001405902</td>\n",
       "      <td>4059.02</td>\n",
       "      <td>CT</td>\n",
       "      <td>487280</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.247175 37.789913, -122.243512 3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP TRACTCE              AFFGEOID        GEOID     NAME LSAD  \\\n",
       "0      06      001  400600  1400000US06001400600  06001400600     4006   CT   \n",
       "1      06      001  400900  1400000US06001400900  06001400900     4009   CT   \n",
       "2      06      001  401400  1400000US06001401400  06001401400     4014   CT   \n",
       "3      06      001  403000  1400000US06001403000  06001403000     4030   CT   \n",
       "4      06      001  405902  1400000US06001405902  06001405902  4059.02   CT   \n",
       "\n",
       "    ALAND  AWATER                                           geometry  \n",
       "0  297856       0  POLYGON ((-122.26807 37.844136, -122.26514 37....  \n",
       "1  420877       0  POLYGON ((-122.285576 37.839778, -122.283186 3...  \n",
       "2  758204       0  POLYGON ((-122.278611 37.826878, -122.268563 3...  \n",
       "3  352394       0  POLYGON ((-122.274757 37.79883299999999, -122....  \n",
       "4  487280       0  POLYGON ((-122.247175 37.789913, -122.243512 3...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['06', '09', '17', '34', '36', '18', '55', '26', '10', '25', '42',\n",
       "       '44'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 06:'CA',09:'CT',17:'IL',34:'NJ',36:'NY',18:'IN',55:'WI',26:'MI',10:'DE',25:'MA',42:'PA',44:'RI'\n",
    "\n",
    "# guessing\n",
    "# la&sf -> dest 06:'CA' -- from ---- 06:'CA'\n",
    "# ny -> dest 36:'NY' -- from ---- 09:'CT',34:'NJ',36:'NY',10:'DE',25:'MA',42:'PA',44:'RI'\n",
    "# chic -> dest 17:'IL' -- from ---- 17:'IL',18:'IN',55:'WI',26:'MI'\n",
    "cts.STATEFP.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_cts.cpg\t      chicago_all.csv\tla_ct.csv\tsf_all.csv\r\n",
      "all_cts.dbf\t      chic_combine.csv\tla_qtid_ct.csv\tsf_combine.csv\r\n",
      "all_cts.prj\t      chic_ct_agg.csv\t__MACOSX\tsf_ct_agg.csv\r\n",
      "all_cts.shp\t      chic_ct.csv\tnyc_all.csv\tsf_ct.csv\r\n",
      "all_cts.shx\t      chic_qtid_ct.csv\tny_combine.csv\tsf_qtid_ct.csv\r\n",
      "Borough Boundaries    la_all.csv\tny_ct_agg.csv\tshapefiles\r\n",
      "cellphone data 2      la_combine.csv\tny_ct.csv\r\n",
      "cellphone data 2.zip  la_ct_agg.csv\tny_qtid_ct.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "chic = pd.read_csv('chic_ct.csv')\n",
    "ny = pd.read_csv('ny_ct.csv')\n",
    "la = pd.read_csv('la_ct.csv')\n",
    "sf = pd.read_csv('sf_ct.csv')\n",
    "\n",
    "\n",
    "chic.sort_values(by='dest_ct',inplace = True)\n",
    "ny.sort_values(by='dest_ct',inplace = True)\n",
    "la.sort_values(by='dest_ct',inplace = True)\n",
    "sf.sort_values(by='dest_ct',inplace = True)\n",
    "\n",
    "ny['origin_ct'] = ny['origin_ct'].astype(str)\n",
    "ny['dest_ct'] = ny['dest_ct'].astype(str)\n",
    "chic['origin_ct'] = chic['origin_ct'].astype(str)\n",
    "chic['dest_ct'] = chic['dest_ct'].astype(str)\n",
    "la['origin_ct'] = la['origin_ct'].astype(str)\n",
    "la['dest_ct'] = la['dest_ct'].astype(str)\n",
    "sf['origin_ct'] = sf['origin_ct'].astype(str)\n",
    "sf['dest_ct'] = sf['dest_ct'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6766506 entries, 45681 to 1425261\n",
      "Data columns (total 7 columns):\n",
      "origin_ct      object\n",
      "dest_ct        object\n",
      "time_period    object\n",
      "origin_st      int64\n",
      "dest_st        int64\n",
      "od_counts      int64\n",
      "num_dates      int64\n",
      "dtypes: int64(4), object(3)\n",
      "memory usage: 413.0+ MB\n"
     ]
    }
   ],
   "source": [
    "ny.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[34, 36, 9, 10, 42, 44, 25]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "list(set([math.floor(i) for i in  ny['dest_ct'].astype(float)/1e9]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-705e3787e5f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'State Abbreviation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mq\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m  \u001b[0mny\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dest_ct'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m1e9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-36-705e3787e5f5>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'State Abbreviation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mq\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m  \u001b[0mny\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dest_ct'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m1e9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "list(set([math.floor(i) for i in  ny['dest_ct'].astype(float)/1e9]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download census data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/projects/mpi/user/jl9200')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from census import Census\n",
    "from us import states\n",
    "import json\n",
    "import urllib\n",
    "from Census_API import api_key\n",
    "import lxml\n",
    "import html5lib\n",
    "import requests\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Source: html version: https://api.census.gov/data/2016/acs/acs5/variables.html**\n",
    "\n",
    "We will trying to get census data from the giving state [STATEFP] and all the county by the giving key.\n",
    "\n",
    "Population key: B00002_001E [UNWEIGHTED SAMPLE HOUSING UNITS]\n",
    "\n",
    "Median Household income: B19013_001E []\n",
    "\n",
    "ALLOCATION OF PRIVATE VEHICLE OCCUPANCY : B99082_001E\n",
    "\n",
    "Estimate!!Total!!Public transportation (excluding taxicab) : B08006_008E\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "step1. lookup the keys data and the STATEFE code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "statecode = pd.read_html('https://www.mcc.co.mercer.pa.us/dps/state_fips_code_listing.htm',skiprows=0,header = 0)[0]\n",
    "df1 = statecode.iloc[:,:3]\n",
    "df2 = statecode.iloc[:,3:]\n",
    "df2.columns = df1.columns\n",
    "q = pd.concat([df1,df2],axis=0,ignore_index=True,sort=True).dropna()\n",
    "q.sort_values(by='FIPS Code',inplace = True)\n",
    "q['FIPS Code'] = q['FIPS Code'].astype(int)\n",
    "q.set_index('FIPS Code',inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['for',\n",
       " 'in',\n",
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       " 'B18101I_001E',\n",
       " 'B25127_072E',\n",
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       " 'B20005E_025E',\n",
       " 'B02019_004E',\n",
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       " 'B18101_009E',\n",
       " 'B19001D_016E',\n",
       " 'B22001_004E',\n",
       " 'B15011_026E',\n",
       " 'B19037D_023E',\n",
       " 'B25035_001E',\n",
       " 'B05008_062E',\n",
       " 'C24010G_002E',\n",
       " 'B19001D_015E',\n",
       " 'B22001_003E',\n",
       " 'B19037D_020E',\n",
       " 'B05008_061E',\n",
       " 'B15011_025E',\n",
       " 'C24010G_003E',\n",
       " 'B18101_007E',\n",
       " 'B22001_002E',\n",
       " 'B19001D_014E',\n",
       " 'B05008_060E',\n",
       " 'B19037D_021E',\n",
       " 'B15011_024E',\n",
       " 'C24010G_004E',\n",
       " 'B18101_008E',\n",
       " 'B19001D_013E',\n",
       " 'B22001_001E',\n",
       " 'B04006_058E',\n",
       " 'B02014_039E',\n",
       " 'B25127_039E',\n",
       " 'B02014_038E',\n",
       " 'B04006_059E',\n",
       " 'B25127_037E',\n",
       " 'B25127_036E',\n",
       " 'B15011_029E',\n",
       " 'B25127_038E',\n",
       " 'B15011_028E',\n",
       " 'C24010G_009E',\n",
       " 'B05004_010E',\n",
       " 'B02014_034E',\n",
       " 'B25127_034E',\n",
       " 'B05004_011E',\n",
       " 'B02014_035E',\n",
       " 'B25127_035E',\n",
       " 'B02014_036E',\n",
       " 'B25127_032E',\n",
       " 'B02014_037E',\n",
       " 'B25127_033E',\n",
       " 'B04006_057E',\n",
       " 'B25127_006E',\n",
       " 'B24121_212E',\n",
       " 'C27005_020E',\n",
       " 'B04006_056E',\n",
       " 'B25127_005E',\n",
       " 'B24121_213E',\n",
       " 'B04006_055E',\n",
       " 'B25127_008E',\n",
       " 'B16005E_011E',\n",
       " 'B24121_214E',\n",
       " 'B05007_040E',\n",
       " 'B04006_054E',\n",
       " 'B24121_215E',\n",
       " 'B25127_007E',\n",
       " 'C27005_021E',\n",
       " 'B06004A_002E',\n",
       " 'B04006_053E',\n",
       " 'B18101_020E',\n",
       " 'C27006_006E',\n",
       " 'C27006_007E',\n",
       " 'B19037D_009E',\n",
       " 'B06004A_001E',\n",
       " 'B25127_009E',\n",
       " 'B04006_052E',\n",
       " 'C27006_008E',\n",
       " 'B19037D_008E',\n",
       " 'B04006_051E',\n",
       " 'B24121_210E',\n",
       " 'B18101_022E',\n",
       " 'C27006_009E',\n",
       " 'B24121_211E',\n",
       " 'B04006_050E',\n",
       " 'B18101_021E',\n",
       " 'B24126_410E',\n",
       " 'B06004EPR_002E',\n",
       " 'B19037D_005E',\n",
       " 'B05008_044E',\n",
       " 'B05007_046E',\n",
       " 'C27006_002E',\n",
       " 'B18101_024E',\n",
       " 'B24126_411E',\n",
       " 'B05007_045E',\n",
       " 'B19037D_004E',\n",
       " 'B05008_045E',\n",
       " 'B06004EPR_001E',\n",
       " 'B18101_023E',\n",
       " 'C27006_003E',\n",
       " 'B05008_046E',\n",
       " ...]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "# look into the ACS 5yr data\n",
    "# download 5 year census data \n",
    "url = \"https://api.census.gov/data/2016/acs/acs5/variables.json\"\n",
    "resp = requests.request('GET',url)\n",
    "aff5 = json.loads(resp.text)\n",
    "aff5keys = list(aff5['variables'].keys())\n",
    "aff5keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22856"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censusMega = pd.read_html('https://api.census.gov/data/2016/acs/acs5/variables.html',skiprows=0)[0]\n",
    "len(censusMega)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "812"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censusMega2 = censusMega[censusMega['Label']=='Estimate!!Total']\n",
    "len(censusMega2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "491"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censusMega3 = censusMega2[~censusMega2['Group'].str[-1].str.isalpha()]\n",
    "len(censusMega3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "EMPLOYMENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNWEIGHTED SAMPLE COUNT OF THE POPULATION\n",
      "TOTAL POPULATION\n",
      "PLACE OF BIRTH FOR THE FOREIGN-BORN POPULATION IN THE UNITED STATES\n",
      "PLACE OF BIRTH BY YEAR OF ENTRY BY CITIZENSHIP STATUS FOR THE FOREIGN-BORN POPULATION\n",
      "SEX BY PLACE OF BIRTH BY YEAR OF ENTRY FOR THE FOREIGN-BORN POPULATION\n",
      "SEX BY AGE FOR THE FOREIGN-BORN POPULATION\n",
      "SEX BY AGE FOR THE FOREIGN-BORN POPULATION\n",
      "WORKER POPULATION FOR WORKPLACE GEOGRAPHY\n",
      "POPULATION UNDER 18 YEARS BY AGE\n",
      "RELATIONSHIP BY HOUSEHOLD TYPE (INCLUDING LIVING ALONE) FOR THE POPULATION 65 YEARS AND OVER\n",
      "GRANDPARENTS LIVING WITH OWN GRANDCHILDREN UNDER 18 YEARS BY RESPONSIBILITY FOR OWN GRANDCHILDREN BY LENGTH OF TIME RESPONSIBLE FOR OWN GRANDCHILDREN FOR THE POPULATION 30 YEARS AND OVER\n",
      "HOUSEHOLD TYPE BY RELATIVES AND NONRELATIVES FOR POPULATION IN HOUSEHOLDS\n",
      "POPULATION IN SUBFAMILIES BY SUBFAMILY TYPE BY RELATIONSHIP\n",
      "SEX BY MARITAL STATUS FOR THE POPULATION 15 YEARS AND OVER\n",
      "SEX BY MARITAL STATUS BY AGE FOR THE POPULATION 15 YEARS AND OVER\n",
      "MARRIAGES IN THE LAST YEAR BY SEX BY MARITAL STATUS FOR THE POPULATION 15 YEARS AND OVER\n",
      "MARRIAGES ENDING IN WIDOWHOOD IN THE LAST YEAR BY SEX BY MARITAL STATUS FOR THE POPULATION 15 YEARS AND OVER\n",
      "DIVORCES IN THE LAST YEAR BY SEX BY MARITAL STATUS FOR THE POPULATION 15 YEARS AND OVER\n",
      "MEDIAN DURATION OF CURRENT MARRIAGE IN YEARS BY SEX BY MARITAL STATUS FOR THE MARRIED POPULATION 15 YEARS AND OVER\n",
      "NUMBER OF TIMES MARRIED BY SEX BY MARITAL STATUS FOR THE POPULATION 15 YEARS AND OVER\n",
      "SCHOOL ENROLLMENT BY LEVEL OF SCHOOL FOR THE POPULATION 3 YEARS AND OVER\n",
      "SEX BY SCHOOL ENROLLMENT BY LEVEL OF SCHOOL BY TYPE OF SCHOOL FOR THE POPULATION 3 YEARS AND OVER\n",
      "SEX BY SCHOOL ENROLLMENT BY TYPE OF SCHOOL BY AGE FOR THE POPULATION 3 YEARS AND OVER\n",
      "SEX BY COLLEGE OR GRADUATE SCHOOL ENROLLMENT BY TYPE OF SCHOOL BY AGE FOR THE POPULATION 15 YEARS AND OVER\n",
      "SEX BY SCHOOL ENROLLMENT BY EDUCATIONAL ATTAINMENT BY EMPLOYMENT STATUS FOR THE POPULATION 16 TO 19 YEARS\n",
      "POVERTY STATUS IN THE PAST 12 MONTHS BY SCHOOL ENROLLMENT BY LEVEL OF SCHOOL FOR THE POPULATION 3 YEARS AND OVER\n",
      "SCHOOL ENROLLMENT BY DETAILED LEVEL OF SCHOOL FOR THE POPULATION 3 YEARS AND OVER\n",
      "SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER\n",
      "SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER\n",
      "EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER\n",
      "SEX BY AGE BY FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR FOR THE POPULATION 25 YEARS AND OVER\n",
      "LANGUAGE SPOKEN AT HOME BY ABILITY TO SPEAK ENGLISH FOR THE POPULATION 5 YEARS AND OVER\n",
      "AGE BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 5 YEARS AND OVER IN LIMITED ENGLISH SPEAKING HOUSEHOLDS\n",
      "AGE BY LANGUAGE SPOKEN AT HOME BY ABILITY TO SPEAK ENGLISH FOR THE POPULATION 5 YEARS AND OVER\n",
      "NATIVITY BY LANGUAGE SPOKEN AT HOME BY ABILITY TO SPEAK ENGLISH FOR THE POPULATION 5 YEARS AND OVER\n",
      "LANGUAGE SPOKEN AT HOME BY ABILITY TO SPEAK ENGLISH FOR THE POPULATION 5 YEARS AND OVER (HISPANIC OR LATINO)\n",
      "AGE BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 5 YEARS AND OVER\n",
      "CITIZENSHIP STATUS BY AGE BY LANGUAGE SPOKEN AT HOME AND ABILITY TO SPEAK ENGLISH FOR THE POPULATION 5 YEARS AND OVER\n",
      "POVERTY STATUS IN THE PAST 12 MONTHS BY AGE BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 5 YEARS AND OVER\n",
      "EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER\n",
      "MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) BY DISABILITY STATUS BY SEX FOR THE CIVILIAN NONINSTITUTIONALIZED POPULATION 16 YEARS AND OVER WITH EARNINGS\n",
      "SEX BY WORK EXPERIENCE IN THE PAST 12 MONTHS BY INCOME IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE POPULATION 15 YEARS AND OVER\n",
      "SEX BY EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE POPULATION 16 YEARS AND OVER WITH EARNINGS IN THE PAST 12 MONTHS\n",
      "MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) BY SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER\n",
      "SEX BY WORK EXPERIENCE IN THE PAST 12 MONTHS BY EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE POPULATION 16 YEARS AND OVER\n",
      "SEX BY AGE BY VETERAN STATUS FOR THE CIVILIAN POPULATION 18 YEARS AND OVER\n",
      "VETERAN STATUS BY EDUCATIONAL ATTAINMENT FOR THE CIVILIAN POPULATION 25 YEARS AND OVER\n",
      "MEDIAN INCOME IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) BY VETERAN STATUS BY SEX FOR THE CIVILIAN POPULATION 18 YEARS AND OVER WITH INCOME\n",
      "AGE BY VETERAN STATUS BY EMPLOYMENT STATUS FOR THE CIVILIAN POPULATION 18 TO 64 YEARS\n",
      "SEX BY AGE BY EMPLOYMENT STATUS FOR THE POPULATION 16 YEARS AND OVER\n",
      "EDUCATIONAL ATTAINMENT BY EMPLOYMENT STATUS FOR THE POPULATION 25 TO 64 YEARS\n",
      "SEX BY WORK STATUS IN THE PAST 12 MONTHS BY USUAL HOURS WORKED PER WEEK IN THE PAST 12 MONTHS BY WEEKS WORKED IN THE PAST 12 MONTHS FOR THE POPULATION 16 TO 64 YEARS\n",
      "POVERTY STATUS IN THE PAST 12 MONTHS BY DISABILITY STATUS BY EMPLOYMENT STATUS FOR THE POPULATION 20 TO 64 YEARS\n",
      "EMPLOYMENT STATUS FOR THE POPULATION 16 YEARS AND OVER\n",
      "SEX BY WORK STATUS IN THE PAST 12 MONTHS BY USUAL HOURS WORKED PER WEEK IN THE PAST 12 MONTHS BY WEEKS WORKED IN THE PAST 12 MONTHS FOR THE POPULATION 65 YEARS AND OVER\n",
      "FULL-TIME, YEAR-ROUND WORK STATUS IN THE PAST 12 MONTHS BY AGE FOR THE POPULATION 16 YEARS AND OVER\n",
      "OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "INDUSTRY BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "INDUSTRY BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "SEX BY CLASS OF WORKER FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "CLASS OF WORKER BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "SEX BY CLASS OF WORKER FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "CLASS OF WORKER BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER\n",
      "DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2016 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER\n",
      "DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER\n",
      "DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER\n",
      "TOTAL POPULATION IN OCCUPIED HOUSING UNITS BY TENURE\n",
      "TOTAL POPULATION IN OCCUPIED HOUSING UNITS BY TENURE BY UNITS IN STRUCTURE\n",
      "GROUP QUARTERS POPULATION\n",
      "TOTAL POPULATION COVERAGE RATE BY SEX\n",
      "TOTAL POPULATION COVERAGE RATE BY WEIGHTING RACE AND HISPANIC OR LATINO GROUPS\n",
      "GROUP QUARTERS POPULATION COVERAGE RATE\n",
      "ALLOCATION OF MOBILITY STATUS FOR THE POPULATION 1 YEAR AND OVER\n",
      "ALLOCATION OF RESIDENCE 1 YEAR AGO FOR THE POPULATION 1 YEAR AND OVER\n",
      "ALLOCATION OF LENGTH OF TIME GRANDPARENT RESPONSIBLE FOR OWN GRANDCHILDREN UNDER 18 YEARS FOR THE POPULATION 30 YEARS AND OVER\n",
      "ALLOCATION OF MARITAL STATUS FOR THE POPULATION 15 YEARS AND OVER\n",
      "ALLOCATION OF DIVORCED IN THE PAST 12 MONTHS FOR THE POPULATION 15 YEARS AND OVER\n",
      "ALLOCATION OF MARRIED IN THE PAST 12 MONTHS FOR THE POPULATION 15 YEARS AND OVER\n",
      "ALLOCATION OF WIDOWED IN THE PAST 12 MONTHS FOR THE POPULATION 15 YEARS AND OVER\n",
      "ALLOCATION OF TIMES MARRIED FOR THE POPULATION 15 YEARS AND OVER\n",
      "ALLOCATION OF YEAR LAST MARRIED FOR THE POPULATION 15 YEARS AND OVER\n",
      "ALLOCATION OF SCHOOL ENROLLMENT FOR THE POPULATION 3 YEARS AND OVER\n",
      "ALLOCATION OF GRADE ENROLLED FOR THE POPULATION 3 YEARS AND OVER ENROLLED IN SCHOOL\n",
      "ALLOCATION OF EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER\n",
      "ALLOCATION OF FIELD OF DEGREE FOR POPULATION 25 YEARS AND OVER\n",
      "ALLOCATION OF LANGUAGE STATUS FOR THE POPULATION 5 YEARS AND OVER\n",
      "ALLOCATION OF LANGUAGE SPOKEN AT HOME FOR THE POPULATION 5 YEARS AND OVER\n",
      "ALLOCATION OF ABILITY TO SPEAK ENGLISH FOR THE POPULATION 5 YEARS AND OVER\n",
      "ALLOCATION OF DISABILITY ITEMS FOR THE CIVILIAN NONINSTITUTIONALIZED POPULATION\n",
      "ALLOCATION OF HEARING DIFFICULTY FOR THE CIVILIAN NONINSTITUTIONALIZED POPULATION\n",
      "ALLOCATION OF VISION DIFFICULTY FOR THE CIVILIAN NONINSTITUTIONALIZED POPULATION\n",
      "ALLOCATION OF COGNITIVE DIFFICULTY FOR THE CIVILIAN NONINSTITUTIONALIZED POPULATION 5 YEARS AND OVER\n",
      "ALLOCATION OF AMBULATORY DIFFICULTY FOR THE CIVILIAN NONINSTITUTIONALIZED POPULATION 5 YEARS AND OVER\n",
      "ALLOCATION OF SELF-CARE DIFFICULTY FOR THE CIVILIAN NONINSTITUTIONALIZED POPULATION 5 YEARS AND OVER\n",
      "AGE BY ALLOCATION OF INDEPENDENT LIVING DIFFICULTY FOR THE CIVILIAN NONINSTITUTIONALIZED POPULATION 15 YEARS AND OVER\n",
      "ALLOCATION OF INDIVIDUALS' INCOME IN THE PAST 12 MONTHS FOR THE POPULATION 15 YEARS AND OVER - PERCENT OF INCOME ALLOCATED\n",
      "ALLOCATION OF EARNINGS IN THE PAST 12 MONTHS FOR THE POPULATION 16 YEARS AND OVER - PERCENT OF EARNINGS ALLOCATED\n",
      "ALLOCATION OF VETERAN STATUS FOR THE POPULATION 18 YEARS AND OVER\n",
      "ALLOCATION OF EMPLOYMENT STATUS FOR THE POPULATION 16 YEARS AND OVER\n",
      "ALLOCATION OF WORK STATUS IN THE PAST 12 MONTHS FOR THE POPULATION 16 YEARS AND OVER\n",
      "ALLOCATION OF USUAL HOURS WORKED PER WEEK IN THE PAST 12 MONTHS FOR THE POPULATION 16 YEARS AND OVER\n",
      "ALLOCATION OF WEEKS WORKED IN THE PAST 12 MONTHS FOR THE POPULATION 16 YEARS AND OVER\n",
      "ALLOCATION OF INDUSTRY FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "ALLOCATION OF OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "ALLOCATION OF CLASS OF WORKER FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "ALLOCATION OF INDUSTRY FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "ALLOCATION OF OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "ALLOCATION OF CLASS OF WORKER FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR FOR THE POPULATION 25 YEARS AND OVER\n",
      "LANGUAGE SPOKEN AT HOME FOR THE POPULATION 5 YEARS AND OVER\n",
      "AGE BY VETERAN STATUS BY POVERTY STATUS IN THE PAST 12 MONTHS BY DISABILITY STATUS FOR THE CIVILIAN POPULATION 18 YEARS AND OVER\n",
      "SEX BY OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "SEX BY OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "SEX BY INDUSTRY FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "SEX BY INDUSTRY FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "INDUSTRY BY OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "OCCUPATION BY CLASS OF WORKER FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n",
      "INDUSTRY BY CLASS OF WORKER FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER\n"
     ]
    }
   ],
   "source": [
    "for i in censusMega3[censusMega3['Concept'].str.contains('POPULATION')]['Concept']:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "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>Name</th>\n",
       "      <th>Label</th>\n",
       "      <th>Concept</th>\n",
       "      <th>Required</th>\n",
       "      <th>Attributes</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Predicate Type</th>\n",
       "      <th>Group</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B00001_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>UNWEIGHTED SAMPLE COUNT OF THE POPULATION</td>\n",
       "      <td>not required</td>\n",
       "      <td>B00001_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B00001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>B01003_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>TOTAL POPULATION</td>\n",
       "      <td>not required</td>\n",
       "      <td>B01003_001M,  B01003_001MA,  B01003_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B01003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>B05006_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>PLACE OF BIRTH FOR THE FOREIGN-BORN POPULATION...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B05006_001M,  B05006_001MA,  B05006_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B05006</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>B05007_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>PLACE OF BIRTH BY YEAR OF ENTRY BY CITIZENSHIP...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B05007_001M,  B05007_001MA,  B05007_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B05007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>B05008_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY PLACE OF BIRTH BY YEAR OF ENTRY FOR THE...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B05008_001M,  B05008_001MA,  B05008_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B05008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>B05013_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY AGE FOR THE FOREIGN-BORN POPULATION</td>\n",
       "      <td>not required</td>\n",
       "      <td>B05013_001M,  B05013_001MA,  B05013_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B05013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1776</th>\n",
       "      <td>B05014_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY AGE FOR THE FOREIGN-BORN POPULATION</td>\n",
       "      <td>not required</td>\n",
       "      <td>B05014_001M,  B05014_001MA,  B05014_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B05014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6237</th>\n",
       "      <td>B08604_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>WORKER POPULATION FOR WORKPLACE GEOGRAPHY</td>\n",
       "      <td>not required</td>\n",
       "      <td>B08604_001M,  B08604_001MA,  B08604_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B08604</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6238</th>\n",
       "      <td>B09001_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>POPULATION UNDER 18 YEARS BY AGE</td>\n",
       "      <td>not required</td>\n",
       "      <td>B09001_001M,  B09001_001MA,  B09001_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B09001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6345</th>\n",
       "      <td>B09020_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>RELATIONSHIP BY HOUSEHOLD TYPE (INCLUDING LIVI...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B09020_001M,  B09020_001MA,  B09020_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B09020</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6406</th>\n",
       "      <td>B10050_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>GRANDPARENTS LIVING WITH OWN GRANDCHILDREN UND...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B10050_001M,  B10050_001MA,  B10050_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B10050</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6670</th>\n",
       "      <td>B11002_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>HOUSEHOLD TYPE BY RELATIVES AND NONRELATIVES F...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B11002_001M,  B11002_001MA,  B11002_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B11002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6922</th>\n",
       "      <td>B11014_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>POPULATION IN SUBFAMILIES BY SUBFAMILY TYPE BY...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B11014_001M,  B11014_001MA,  B11014_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B11014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6955</th>\n",
       "      <td>B12001_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY MARITAL STATUS FOR THE POPULATION 15 YE...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B12001_001M,  B12001_001MA,  B12001_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B12001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>B12002_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY MARITAL STATUS BY AGE FOR THE POPULATIO...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B12002_001M,  B12002_001MA,  B12002_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B12002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7354</th>\n",
       "      <td>B12501_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MARRIAGES IN THE LAST YEAR BY SEX BY MARITAL S...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B12501_001M,  B12501_001MA,  B12501_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B12501</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7365</th>\n",
       "      <td>B12502_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MARRIAGES ENDING IN WIDOWHOOD IN THE LAST YEAR...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B12502_001M,  B12502_001MA,  B12502_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B12502</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7376</th>\n",
       "      <td>B12503_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>DIVORCES IN THE LAST YEAR BY SEX BY MARITAL ST...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B12503_001M,  B12503_001MA,  B12503_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B12503</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7387</th>\n",
       "      <td>B12504_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MEDIAN DURATION OF CURRENT MARRIAGE IN YEARS B...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B12504_001M,  B12504_001MA,  B12504_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B12504</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7396</th>\n",
       "      <td>B12505_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>NUMBER OF TIMES MARRIED BY SEX BY MARITAL STAT...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B12505_001M,  B12505_001MA,  B12505_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B12505</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>B14001_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SCHOOL ENROLLMENT BY LEVEL OF SCHOOL FOR THE P...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B14001_001M,  B14001_001MA,  B14001_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B14001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7620</th>\n",
       "      <td>B14002_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY SCHOOL ENROLLMENT BY LEVEL OF SCHOOL BY...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B14002_001M,  B14002_001MA,  B14002_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B14002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7669</th>\n",
       "      <td>B14003_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY SCHOOL ENROLLMENT BY TYPE OF SCHOOL BY ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B14003_001M,  B14003_001MA,  B14003_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B14003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7726</th>\n",
       "      <td>B14004_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY COLLEGE OR GRADUATE SCHOOL ENROLLMENT B...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B14004_001M,  B14004_001MA,  B14004_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B14004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7759</th>\n",
       "      <td>B14005_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY SCHOOL ENROLLMENT BY EDUCATIONAL ATTAIN...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B14005_001M,  B14005_001MA,  B14005_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B14005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7788</th>\n",
       "      <td>B14006_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS BY SCHOOL...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B14006_001M,  B14006_001MA,  B14006_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B14006</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7809</th>\n",
       "      <td>B14007_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SCHOOL ENROLLMENT BY DETAILED LEVEL OF SCHOOL ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B14007_001M,  B14007_001MA,  B14007_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B14007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7999</th>\n",
       "      <td>B15001_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE P...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B15001_001M,  B15001_001MA,  B15001_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B15001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8082</th>\n",
       "      <td>B15002_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATI...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B15002_001M,  B15002_001MA,  B15002_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B15002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8117</th>\n",
       "      <td>B15003_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 Y...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B15003_001M,  B15003_001MA,  B15003_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B15003</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20892</th>\n",
       "      <td>B99181_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF DISABILITY ITEMS FOR THE CIVILIA...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99181_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99181</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20895</th>\n",
       "      <td>B99182_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF HEARING DIFFICULTY FOR THE CIVIL...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99182_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99182</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20898</th>\n",
       "      <td>B99183_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF VISION DIFFICULTY FOR THE CIVILI...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99183_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99183</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20901</th>\n",
       "      <td>B99184_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF COGNITIVE DIFFICULTY FOR THE CIV...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99184_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99184</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20904</th>\n",
       "      <td>B99185_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF AMBULATORY DIFFICULTY FOR THE CI...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99185_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99185</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20907</th>\n",
       "      <td>B99186_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF SELF-CARE DIFFICULTY FOR THE CIV...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99186_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99186</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20910</th>\n",
       "      <td>B99187_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>AGE BY ALLOCATION OF INDEPENDENT LIVING DIFFIC...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99187_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20917</th>\n",
       "      <td>B99191_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF INDIVIDUALS' INCOME IN THE PAST ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99191_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99191</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20949</th>\n",
       "      <td>B99201_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF EARNINGS IN THE PAST 12 MONTHS F...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99201_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99201</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20957</th>\n",
       "      <td>B99211_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF VETERAN STATUS FOR THE POPULATIO...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99211_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99211</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20966</th>\n",
       "      <td>B99231_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF EMPLOYMENT STATUS FOR THE POPULA...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99231_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99231</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20969</th>\n",
       "      <td>B99232_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF WORK STATUS IN THE PAST 12 MONTH...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99232_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99232</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20972</th>\n",
       "      <td>B99233_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF USUAL HOURS WORKED PER WEEK IN T...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99233_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99233</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20977</th>\n",
       "      <td>B99234_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF WEEKS WORKED IN THE PAST 12 MONT...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99234_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99234</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20982</th>\n",
       "      <td>B99241_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF INDUSTRY FOR THE CIVILIAN EMPLOY...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99241_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99241</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20985</th>\n",
       "      <td>B99242_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF OCCUPATION FOR THE CIVILIAN EMPL...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99242_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99242</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20988</th>\n",
       "      <td>B99243_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF CLASS OF WORKER FOR THE CIVILIAN...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99243_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99243</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20991</th>\n",
       "      <td>B99244_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF INDUSTRY FOR THE FULL-TIME, YEAR...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99244_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99244</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20994</th>\n",
       "      <td>B99245_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF OCCUPATION FOR THE FULL-TIME, YE...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99245_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99245</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20997</th>\n",
       "      <td>B99246_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF CLASS OF WORKER FOR THE FULL-TIM...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99246_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99246</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21213</th>\n",
       "      <td>C15010_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR FOR...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C15010_001M,  C15010_001MA,  C15010_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C15010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21273</th>\n",
       "      <td>C16001_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>LANGUAGE SPOKEN AT HOME FOR THE POPULATION 5 Y...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C16001_001M,  C16001_001MA,  C16001_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C16001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21540</th>\n",
       "      <td>C21007_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>AGE BY VETERAN STATUS BY POVERTY STATUS IN THE...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C21007_001M,  C21007_001MA,  C21007_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C21007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21814</th>\n",
       "      <td>C24010_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY OCCUPATION FOR THE CIVILIAN EMPLOYED PO...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C24010_001M,  C24010_001MA,  C24010_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C24010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22004</th>\n",
       "      <td>C24020_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY OCCUPATION FOR THE FULL-TIME, YEAR-ROUN...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C24020_001M,  C24020_001MA,  C24020_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C24020</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22077</th>\n",
       "      <td>C24030_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY INDUSTRY FOR THE CIVILIAN EMPLOYED POPU...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C24030_001M,  C24030_001MA,  C24030_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C24030</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22132</th>\n",
       "      <td>C24040_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY INDUSTRY FOR THE FULL-TIME, YEAR-ROUND ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C24040_001M,  C24040_001MA,  C24040_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C24040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22187</th>\n",
       "      <td>C24050_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>INDUSTRY BY OCCUPATION FOR THE CIVILIAN EMPLOY...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C24050_001M,  C24050_001MA,  C24050_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C24050</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22271</th>\n",
       "      <td>C24060_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>OCCUPATION BY CLASS OF WORKER FOR THE CIVILIAN...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C24060_001M,  C24060_001MA,  C24060_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C24060</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22307</th>\n",
       "      <td>C24070_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>INDUSTRY BY CLASS OF WORKER FOR THE CIVILIAN E...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C24070_001M,  C24070_001MA,  C24070_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C24070</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Label  \\\n",
       "4      B00001_001E  Estimate!!Total   \n",
       "364    B01003_001E  Estimate!!Total   \n",
       "1275   B05006_001E  Estimate!!Total   \n",
       "1483   B05007_001E  Estimate!!Total   \n",
       "1589   B05008_001E  Estimate!!Total   \n",
       "1737   B05013_001E  Estimate!!Total   \n",
       "1776   B05014_001E  Estimate!!Total   \n",
       "6237   B08604_001E  Estimate!!Total   \n",
       "6238   B09001_001E  Estimate!!Total   \n",
       "6345   B09020_001E  Estimate!!Total   \n",
       "6406   B10050_001E  Estimate!!Total   \n",
       "6670   B11002_001E  Estimate!!Total   \n",
       "6922   B11014_001E  Estimate!!Total   \n",
       "6955   B12001_001E  Estimate!!Total   \n",
       "6974   B12002_001E  Estimate!!Total   \n",
       "7354   B12501_001E  Estimate!!Total   \n",
       "7365   B12502_001E  Estimate!!Total   \n",
       "7376   B12503_001E  Estimate!!Total   \n",
       "7387   B12504_001E  Estimate!!Total   \n",
       "7396   B12505_001E  Estimate!!Total   \n",
       "7610   B14001_001E  Estimate!!Total   \n",
       "7620   B14002_001E  Estimate!!Total   \n",
       "7669   B14003_001E  Estimate!!Total   \n",
       "7726   B14004_001E  Estimate!!Total   \n",
       "7759   B14005_001E  Estimate!!Total   \n",
       "7788   B14006_001E  Estimate!!Total   \n",
       "7809   B14007_001E  Estimate!!Total   \n",
       "7999   B15001_001E  Estimate!!Total   \n",
       "8082   B15002_001E  Estimate!!Total   \n",
       "8117   B15003_001E  Estimate!!Total   \n",
       "...            ...              ...   \n",
       "20892  B99181_001E  Estimate!!Total   \n",
       "20895  B99182_001E  Estimate!!Total   \n",
       "20898  B99183_001E  Estimate!!Total   \n",
       "20901  B99184_001E  Estimate!!Total   \n",
       "20904  B99185_001E  Estimate!!Total   \n",
       "20907  B99186_001E  Estimate!!Total   \n",
       "20910  B99187_001E  Estimate!!Total   \n",
       "20917  B99191_001E  Estimate!!Total   \n",
       "20949  B99201_001E  Estimate!!Total   \n",
       "20957  B99211_001E  Estimate!!Total   \n",
       "20966  B99231_001E  Estimate!!Total   \n",
       "20969  B99232_001E  Estimate!!Total   \n",
       "20972  B99233_001E  Estimate!!Total   \n",
       "20977  B99234_001E  Estimate!!Total   \n",
       "20982  B99241_001E  Estimate!!Total   \n",
       "20985  B99242_001E  Estimate!!Total   \n",
       "20988  B99243_001E  Estimate!!Total   \n",
       "20991  B99244_001E  Estimate!!Total   \n",
       "20994  B99245_001E  Estimate!!Total   \n",
       "20997  B99246_001E  Estimate!!Total   \n",
       "21213  C15010_001E  Estimate!!Total   \n",
       "21273  C16001_001E  Estimate!!Total   \n",
       "21540  C21007_001E  Estimate!!Total   \n",
       "21814  C24010_001E  Estimate!!Total   \n",
       "22004  C24020_001E  Estimate!!Total   \n",
       "22077  C24030_001E  Estimate!!Total   \n",
       "22132  C24040_001E  Estimate!!Total   \n",
       "22187  C24050_001E  Estimate!!Total   \n",
       "22271  C24060_001E  Estimate!!Total   \n",
       "22307  C24070_001E  Estimate!!Total   \n",
       "\n",
       "                                                 Concept      Required  \\\n",
       "4              UNWEIGHTED SAMPLE COUNT OF THE POPULATION  not required   \n",
       "364                                     TOTAL POPULATION  not required   \n",
       "1275   PLACE OF BIRTH FOR THE FOREIGN-BORN POPULATION...  not required   \n",
       "1483   PLACE OF BIRTH BY YEAR OF ENTRY BY CITIZENSHIP...  not required   \n",
       "1589   SEX BY PLACE OF BIRTH BY YEAR OF ENTRY FOR THE...  not required   \n",
       "1737          SEX BY AGE FOR THE FOREIGN-BORN POPULATION  not required   \n",
       "1776          SEX BY AGE FOR THE FOREIGN-BORN POPULATION  not required   \n",
       "6237           WORKER POPULATION FOR WORKPLACE GEOGRAPHY  not required   \n",
       "6238                    POPULATION UNDER 18 YEARS BY AGE  not required   \n",
       "6345   RELATIONSHIP BY HOUSEHOLD TYPE (INCLUDING LIVI...  not required   \n",
       "6406   GRANDPARENTS LIVING WITH OWN GRANDCHILDREN UND...  not required   \n",
       "6670   HOUSEHOLD TYPE BY RELATIVES AND NONRELATIVES F...  not required   \n",
       "6922   POPULATION IN SUBFAMILIES BY SUBFAMILY TYPE BY...  not required   \n",
       "6955   SEX BY MARITAL STATUS FOR THE POPULATION 15 YE...  not required   \n",
       "6974   SEX BY MARITAL STATUS BY AGE FOR THE POPULATIO...  not required   \n",
       "7354   MARRIAGES IN THE LAST YEAR BY SEX BY MARITAL S...  not required   \n",
       "7365   MARRIAGES ENDING IN WIDOWHOOD IN THE LAST YEAR...  not required   \n",
       "7376   DIVORCES IN THE LAST YEAR BY SEX BY MARITAL ST...  not required   \n",
       "7387   MEDIAN DURATION OF CURRENT MARRIAGE IN YEARS B...  not required   \n",
       "7396   NUMBER OF TIMES MARRIED BY SEX BY MARITAL STAT...  not required   \n",
       "7610   SCHOOL ENROLLMENT BY LEVEL OF SCHOOL FOR THE P...  not required   \n",
       "7620   SEX BY SCHOOL ENROLLMENT BY LEVEL OF SCHOOL BY...  not required   \n",
       "7669   SEX BY SCHOOL ENROLLMENT BY TYPE OF SCHOOL BY ...  not required   \n",
       "7726   SEX BY COLLEGE OR GRADUATE SCHOOL ENROLLMENT B...  not required   \n",
       "7759   SEX BY SCHOOL ENROLLMENT BY EDUCATIONAL ATTAIN...  not required   \n",
       "7788   POVERTY STATUS IN THE PAST 12 MONTHS BY SCHOOL...  not required   \n",
       "7809   SCHOOL ENROLLMENT BY DETAILED LEVEL OF SCHOOL ...  not required   \n",
       "7999   SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE P...  not required   \n",
       "8082   SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATI...  not required   \n",
       "8117   EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 Y...  not required   \n",
       "...                                                  ...           ...   \n",
       "20892  ALLOCATION OF DISABILITY ITEMS FOR THE CIVILIA...  not required   \n",
       "20895  ALLOCATION OF HEARING DIFFICULTY FOR THE CIVIL...  not required   \n",
       "20898  ALLOCATION OF VISION DIFFICULTY FOR THE CIVILI...  not required   \n",
       "20901  ALLOCATION OF COGNITIVE DIFFICULTY FOR THE CIV...  not required   \n",
       "20904  ALLOCATION OF AMBULATORY DIFFICULTY FOR THE CI...  not required   \n",
       "20907  ALLOCATION OF SELF-CARE DIFFICULTY FOR THE CIV...  not required   \n",
       "20910  AGE BY ALLOCATION OF INDEPENDENT LIVING DIFFIC...  not required   \n",
       "20917  ALLOCATION OF INDIVIDUALS' INCOME IN THE PAST ...  not required   \n",
       "20949  ALLOCATION OF EARNINGS IN THE PAST 12 MONTHS F...  not required   \n",
       "20957  ALLOCATION OF VETERAN STATUS FOR THE POPULATIO...  not required   \n",
       "20966  ALLOCATION OF EMPLOYMENT STATUS FOR THE POPULA...  not required   \n",
       "20969  ALLOCATION OF WORK STATUS IN THE PAST 12 MONTH...  not required   \n",
       "20972  ALLOCATION OF USUAL HOURS WORKED PER WEEK IN T...  not required   \n",
       "20977  ALLOCATION OF WEEKS WORKED IN THE PAST 12 MONT...  not required   \n",
       "20982  ALLOCATION OF INDUSTRY FOR THE CIVILIAN EMPLOY...  not required   \n",
       "20985  ALLOCATION OF OCCUPATION FOR THE CIVILIAN EMPL...  not required   \n",
       "20988  ALLOCATION OF CLASS OF WORKER FOR THE CIVILIAN...  not required   \n",
       "20991  ALLOCATION OF INDUSTRY FOR THE FULL-TIME, YEAR...  not required   \n",
       "20994  ALLOCATION OF OCCUPATION FOR THE FULL-TIME, YE...  not required   \n",
       "20997  ALLOCATION OF CLASS OF WORKER FOR THE FULL-TIM...  not required   \n",
       "21213  FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR FOR...  not required   \n",
       "21273  LANGUAGE SPOKEN AT HOME FOR THE POPULATION 5 Y...  not required   \n",
       "21540  AGE BY VETERAN STATUS BY POVERTY STATUS IN THE...  not required   \n",
       "21814  SEX BY OCCUPATION FOR THE CIVILIAN EMPLOYED PO...  not required   \n",
       "22004  SEX BY OCCUPATION FOR THE FULL-TIME, YEAR-ROUN...  not required   \n",
       "22077  SEX BY INDUSTRY FOR THE CIVILIAN EMPLOYED POPU...  not required   \n",
       "22132  SEX BY INDUSTRY FOR THE FULL-TIME, YEAR-ROUND ...  not required   \n",
       "22187  INDUSTRY BY OCCUPATION FOR THE CIVILIAN EMPLOY...  not required   \n",
       "22271  OCCUPATION BY CLASS OF WORKER FOR THE CIVILIAN...  not required   \n",
       "22307  INDUSTRY BY CLASS OF WORKER FOR THE CIVILIAN E...  not required   \n",
       "\n",
       "                                      Attributes Limit Predicate Type   Group  \\\n",
       "4                                   B00001_001EA     0            int  B00001   \n",
       "364    B01003_001M,  B01003_001MA,  B01003_001EA     0            int  B01003   \n",
       "1275   B05006_001M,  B05006_001MA,  B05006_001EA     0            int  B05006   \n",
       "1483   B05007_001M,  B05007_001MA,  B05007_001EA     0            int  B05007   \n",
       "1589   B05008_001M,  B05008_001MA,  B05008_001EA     0            int  B05008   \n",
       "1737   B05013_001M,  B05013_001MA,  B05013_001EA     0            int  B05013   \n",
       "1776   B05014_001M,  B05014_001MA,  B05014_001EA     0            int  B05014   \n",
       "6237   B08604_001M,  B08604_001MA,  B08604_001EA     0            int  B08604   \n",
       "6238   B09001_001M,  B09001_001MA,  B09001_001EA     0            int  B09001   \n",
       "6345   B09020_001M,  B09020_001MA,  B09020_001EA     0            int  B09020   \n",
       "6406   B10050_001M,  B10050_001MA,  B10050_001EA     0            int  B10050   \n",
       "6670   B11002_001M,  B11002_001MA,  B11002_001EA     0            int  B11002   \n",
       "6922   B11014_001M,  B11014_001MA,  B11014_001EA     0            int  B11014   \n",
       "6955   B12001_001M,  B12001_001MA,  B12001_001EA     0            int  B12001   \n",
       "6974   B12002_001M,  B12002_001MA,  B12002_001EA     0            int  B12002   \n",
       "7354   B12501_001M,  B12501_001MA,  B12501_001EA     0            int  B12501   \n",
       "7365   B12502_001M,  B12502_001MA,  B12502_001EA     0            int  B12502   \n",
       "7376   B12503_001M,  B12503_001MA,  B12503_001EA     0            int  B12503   \n",
       "7387   B12504_001M,  B12504_001MA,  B12504_001EA     0            int  B12504   \n",
       "7396   B12505_001M,  B12505_001MA,  B12505_001EA     0            int  B12505   \n",
       "7610   B14001_001M,  B14001_001MA,  B14001_001EA     0            int  B14001   \n",
       "7620   B14002_001M,  B14002_001MA,  B14002_001EA     0            int  B14002   \n",
       "7669   B14003_001M,  B14003_001MA,  B14003_001EA     0            int  B14003   \n",
       "7726   B14004_001M,  B14004_001MA,  B14004_001EA     0            int  B14004   \n",
       "7759   B14005_001M,  B14005_001MA,  B14005_001EA     0            int  B14005   \n",
       "7788   B14006_001M,  B14006_001MA,  B14006_001EA     0            int  B14006   \n",
       "7809   B14007_001M,  B14007_001MA,  B14007_001EA     0            int  B14007   \n",
       "7999   B15001_001M,  B15001_001MA,  B15001_001EA     0            int  B15001   \n",
       "8082   B15002_001M,  B15002_001MA,  B15002_001EA     0            int  B15002   \n",
       "8117   B15003_001M,  B15003_001MA,  B15003_001EA     0            int  B15003   \n",
       "...                                          ...   ...            ...     ...   \n",
       "20892                               B99181_001EA     0            int  B99181   \n",
       "20895                               B99182_001EA     0            int  B99182   \n",
       "20898                               B99183_001EA     0            int  B99183   \n",
       "20901                               B99184_001EA     0            int  B99184   \n",
       "20904                               B99185_001EA     0            int  B99185   \n",
       "20907                               B99186_001EA     0            int  B99186   \n",
       "20910                               B99187_001EA     0            int  B99187   \n",
       "20917                               B99191_001EA     0            int  B99191   \n",
       "20949                               B99201_001EA     0            int  B99201   \n",
       "20957                               B99211_001EA     0            int  B99211   \n",
       "20966                               B99231_001EA     0            int  B99231   \n",
       "20969                               B99232_001EA     0            int  B99232   \n",
       "20972                               B99233_001EA     0            int  B99233   \n",
       "20977                               B99234_001EA     0            int  B99234   \n",
       "20982                               B99241_001EA     0            int  B99241   \n",
       "20985                               B99242_001EA     0            int  B99242   \n",
       "20988                               B99243_001EA     0            int  B99243   \n",
       "20991                               B99244_001EA     0            int  B99244   \n",
       "20994                               B99245_001EA     0            int  B99245   \n",
       "20997                               B99246_001EA     0            int  B99246   \n",
       "21213  C15010_001M,  C15010_001MA,  C15010_001EA     0            int  C15010   \n",
       "21273  C16001_001M,  C16001_001MA,  C16001_001EA     0            int  C16001   \n",
       "21540  C21007_001M,  C21007_001MA,  C21007_001EA     0            int  C21007   \n",
       "21814  C24010_001M,  C24010_001MA,  C24010_001EA     0            int  C24010   \n",
       "22004  C24020_001M,  C24020_001MA,  C24020_001EA     0            int  C24020   \n",
       "22077  C24030_001M,  C24030_001MA,  C24030_001EA     0            int  C24030   \n",
       "22132  C24040_001M,  C24040_001MA,  C24040_001EA     0            int  C24040   \n",
       "22187  C24050_001M,  C24050_001MA,  C24050_001EA     0            int  C24050   \n",
       "22271  C24060_001M,  C24060_001MA,  C24060_001EA     0            int  C24060   \n",
       "22307  C24070_001M,  C24070_001MA,  C24070_001EA     0            int  C24070   \n",
       "\n",
       "      Values  \n",
       "4        NaN  \n",
       "364      NaN  \n",
       "1275     NaN  \n",
       "1483     NaN  \n",
       "1589     NaN  \n",
       "1737     NaN  \n",
       "1776     NaN  \n",
       "6237     NaN  \n",
       "6238     NaN  \n",
       "6345     NaN  \n",
       "6406     NaN  \n",
       "6670     NaN  \n",
       "6922     NaN  \n",
       "6955     NaN  \n",
       "6974     NaN  \n",
       "7354     NaN  \n",
       "7365     NaN  \n",
       "7376     NaN  \n",
       "7387     NaN  \n",
       "7396     NaN  \n",
       "7610     NaN  \n",
       "7620     NaN  \n",
       "7669     NaN  \n",
       "7726     NaN  \n",
       "7759     NaN  \n",
       "7788     NaN  \n",
       "7809     NaN  \n",
       "7999     NaN  \n",
       "8082     NaN  \n",
       "8117     NaN  \n",
       "...      ...  \n",
       "20892    NaN  \n",
       "20895    NaN  \n",
       "20898    NaN  \n",
       "20901    NaN  \n",
       "20904    NaN  \n",
       "20907    NaN  \n",
       "20910    NaN  \n",
       "20917    NaN  \n",
       "20949    NaN  \n",
       "20957    NaN  \n",
       "20966    NaN  \n",
       "20969    NaN  \n",
       "20972    NaN  \n",
       "20977    NaN  \n",
       "20982    NaN  \n",
       "20985    NaN  \n",
       "20988    NaN  \n",
       "20991    NaN  \n",
       "20994    NaN  \n",
       "20997    NaN  \n",
       "21213    NaN  \n",
       "21273    NaN  \n",
       "21540    NaN  \n",
       "21814    NaN  \n",
       "22004    NaN  \n",
       "22077    NaN  \n",
       "22132    NaN  \n",
       "22187    NaN  \n",
       "22271    NaN  \n",
       "22307    NaN  \n",
       "\n",
       "[124 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censusMega3[censusMega3['Concept'].str.contains('POPULATION')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Label</th>\n",
       "      <th>Concept</th>\n",
       "      <th>Required</th>\n",
       "      <th>Attributes</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Predicate Type</th>\n",
       "      <th>Group</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>B05010_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 1...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B05010_001M,  B05010_001MA,  B05010_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B05010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>B06010_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>PLACE OF BIRTH BY INDIVIDUAL INCOME IN THE PAS...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B06010_001M,  B06010_001MA,  B06010_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B06010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>B07010_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>GEOGRAPHICAL MOBILITY IN THE PAST YEAR BY INDI...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B07010_001M,  B07010_001MA,  B07010_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B07010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6286</th>\n",
       "      <td>B09010_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>RECEIPT OF SUPPLEMENTAL SECURITY INCOME (SSI),...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B09010_001M,  B09010_001MA,  B09010_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B09010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7578</th>\n",
       "      <td>B13015_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>WOMEN 15 TO 50 YEARS WHO HAD A BIRTH IN THE PA...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B13015_001M,  B13015_001MA,  B13015_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B13015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>B17015_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILI...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B17015_001M,  B17015_001MA,  B17015_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B17015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10395</th>\n",
       "      <td>B17022_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 1...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B17022_001M,  B17022_001MA,  B17022_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B17022</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10511</th>\n",
       "      <td>B17024_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>AGE BY RATIO OF INCOME TO POVERTY LEVEL IN THE...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B17024_001M,  B17024_001MA,  B17024_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B17024</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10657</th>\n",
       "      <td>B17026_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>RATIO OF INCOME TO POVERTY LEVEL OF FAMILIES I...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B17026_001M,  B17026_001MA,  B17026_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B17026</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11044</th>\n",
       "      <td>B19001_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 201...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19001_001M,  B19001_001MA,  B19001_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11224</th>\n",
       "      <td>B19019_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19019_001M,  B19019_001MA,  B19019_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19019</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11242</th>\n",
       "      <td>B19037_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>AGE OF HOUSEHOLDER BY HOUSEHOLD INCOME IN THE ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19037_001M,  B19037_001MA,  B19037_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19037</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11945</th>\n",
       "      <td>B19052_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>WAGE OR SALARY INCOME IN THE PAST 12 MONTHS FO...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19052_001M,  B19052_001MA,  B19052_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19052</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11948</th>\n",
       "      <td>B19053_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SELF-EMPLOYMENT INCOME IN THE PAST 12 MONTHS F...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19053_001M,  B19053_001MA,  B19053_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19053</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11951</th>\n",
       "      <td>B19054_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>INTEREST, DIVIDENDS, OR NET RENTAL INCOME IN T...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19054_001M,  B19054_001MA,  B19054_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19054</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11954</th>\n",
       "      <td>B19055_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SOCIAL SECURITY INCOME IN THE PAST 12 MONTHS F...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19055_001M,  B19055_001MA,  B19055_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19055</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11957</th>\n",
       "      <td>B19056_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SUPPLEMENTAL SECURITY INCOME (SSI) IN THE PAST...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19056_001M,  B19056_001MA,  B19056_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19056</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11960</th>\n",
       "      <td>B19057_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>PUBLIC ASSISTANCE INCOME IN THE PAST 12 MONTHS...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19057_001M,  B19057_001MA,  B19057_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19057</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11963</th>\n",
       "      <td>B19058_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>PUBLIC ASSISTANCE INCOME OR FOOD STAMPS/SNAP I...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19058_001M,  B19058_001MA,  B19058_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19058</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11966</th>\n",
       "      <td>B19059_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>RETIREMENT INCOME IN THE PAST 12 MONTHS FOR HO...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19059_001M,  B19059_001MA,  B19059_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19059</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11969</th>\n",
       "      <td>B19060_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>OTHER TYPES OF INCOME IN THE PAST 12 MONTHS FO...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19060_001M,  B19060_001MA,  B19060_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19060</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>B19101_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>FAMILY INCOME IN THE PAST 12 MONTHS (IN 2016 I...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19101_001M,  B19101_001MA,  B19101_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19101</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12179</th>\n",
       "      <td>B19119_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MEDIAN FAMILY INCOME IN THE PAST 12 MONTHS (IN...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19119_001M,  B19119_001MA,  B19119_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19119</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12186</th>\n",
       "      <td>B19121_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MEDIAN FAMILY INCOME IN THE PAST 12 MONTHS (IN...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19121_001M,  B19121_001MA,  B19121_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19121</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12191</th>\n",
       "      <td>B19125_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MEDIAN FAMILY INCOME IN THE PAST 12 MONTHS (IN...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19125_001M,  B19125_001MA,  B19125_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19125</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12253</th>\n",
       "      <td>B19131_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>FAMILY TYPE BY PRESENCE OF OWN CHILDREN UNDER ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19131_001M,  B19131_001MA,  B19131_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19131</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12360</th>\n",
       "      <td>B19201_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>NONFAMILY HOUSEHOLD INCOME IN THE PAST 12 MONT...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19201_001M,  B19201_001MA,  B19201_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19201</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12438</th>\n",
       "      <td>B19325_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>SEX BY WORK EXPERIENCE IN THE PAST 12 MONTHS B...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B19325_001M,  B19325_001MA,  B19325_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B19325</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13697</th>\n",
       "      <td>B21004_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MEDIAN INCOME IN THE PAST 12 MONTHS (IN 2016 I...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B21004_001M,  B21004_001MA,  B21004_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B21004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13858</th>\n",
       "      <td>B22008_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B22008_001M,  B22008_001MA,  B22008_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B22008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18901</th>\n",
       "      <td>B25070_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25070_001M,  B25070_001MA,  B25070_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25070</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18913</th>\n",
       "      <td>B25072_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>AGE OF HOUSEHOLDER BY GROSS RENT AS A PERCENTA...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25072_001M,  B25072_001MA,  B25072_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25072</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18942</th>\n",
       "      <td>B25074_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>HOUSEHOLD INCOME BY GROSS RENT AS A PERCENTAGE...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25074_001M,  B25074_001MA,  B25074_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25074</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19137</th>\n",
       "      <td>B25091_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MORTGAGE STATUS BY SELECTED MONTHLY OWNER COST...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25091_001M,  B25091_001MA,  B25091_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25091</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19163</th>\n",
       "      <td>B25093_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>AGE OF HOUSEHOLDER BY SELECTED MONTHLY OWNER C...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25093_001M,  B25093_001MA,  B25093_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25093</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19210</th>\n",
       "      <td>B25095_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>HOUSEHOLD INCOME BY SELECTED MONTHLY OWNER COS...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25095_001M,  B25095_001MA,  B25095_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25095</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19307</th>\n",
       "      <td>B25098_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MORTGAGE STATUS BY HOUSEHOLD INCOME IN THE PAS...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25098_001M,  B25098_001MA,  B25098_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25098</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19329</th>\n",
       "      <td>B25100_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MORTGAGE STATUS BY RATIO OF VALUE TO HOUSEHOLD...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25100_001M,  B25100_001MA,  B25100_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19342</th>\n",
       "      <td>B25101_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>MORTGAGE STATUS BY MONTHLY HOUSING COSTS AS A ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25101_001M,  B25101_001MA,  B25101_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25101</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19423</th>\n",
       "      <td>B25106_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>TENURE BY HOUSING COSTS AS A PERCENTAGE OF HOU...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25106_001M,  B25106_001MA,  B25106_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25106</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19612</th>\n",
       "      <td>B25118_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>TENURE BY HOUSEHOLD INCOME IN THE PAST 12 MONT...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25118_001M,  B25118_001MA,  B25118_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25118</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19645</th>\n",
       "      <td>B25121_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 201...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25121_001M,  B25121_001MA,  B25121_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25121</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19751</th>\n",
       "      <td>B25122_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 201...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B25122_001M,  B25122_001MA,  B25122_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B25122</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20549</th>\n",
       "      <td>B27015_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS AND TYPE BY H...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B27015_001M,  B27015_001MA,  B27015_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B27015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20917</th>\n",
       "      <td>B99191_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF INDIVIDUALS' INCOME IN THE PAST ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99191_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99191</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20925</th>\n",
       "      <td>B99192_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF HOUSEHOLD INCOME IN THE PAST 12 ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99192_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99192</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20933</th>\n",
       "      <td>B99193_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF FAMILY INCOME IN THE PAST 12 MON...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99193_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99193</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20941</th>\n",
       "      <td>B99194_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>ALLOCATION OF NONFAMILY HOUSEHOLD INCOME IN TH...</td>\n",
       "      <td>not required</td>\n",
       "      <td>B99194_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>B99194</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21325</th>\n",
       "      <td>C17002_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 1...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C17002_001M,  C17002_001MA,  C17002_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C17002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21389</th>\n",
       "      <td>C18131_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 1...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C18131_001M,  C18131_001MA,  C18131_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C18131</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22697</th>\n",
       "      <td>C27016_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY RATIO OF I...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C27016_001M,  C27016_001MA,  C27016_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C27016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22738</th>\n",
       "      <td>C27017_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>PRIVATE HEALTH INSURANCE BY RATIO OF INCOME TO...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C27017_001M,  C27017_001MA,  C27017_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C27017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22779</th>\n",
       "      <td>C27018_001E</td>\n",
       "      <td>Estimate!!Total</td>\n",
       "      <td>PUBLIC HEALTH INSURANCE BY RATIO OF INCOME TO ...</td>\n",
       "      <td>not required</td>\n",
       "      <td>C27018_001M,  C27018_001MA,  C27018_001EA</td>\n",
       "      <td>0</td>\n",
       "      <td>int</td>\n",
       "      <td>C27018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Label  \\\n",
       "1695   B05010_001E  Estimate!!Total   \n",
       "2245   B06010_001E  Estimate!!Total   \n",
       "2957   B07010_001E  Estimate!!Total   \n",
       "6286   B09010_001E  Estimate!!Total   \n",
       "7578   B13015_001E  Estimate!!Total   \n",
       "9979   B17015_001E  Estimate!!Total   \n",
       "10395  B17022_001E  Estimate!!Total   \n",
       "10511  B17024_001E  Estimate!!Total   \n",
       "10657  B17026_001E  Estimate!!Total   \n",
       "11044  B19001_001E  Estimate!!Total   \n",
       "11224  B19019_001E  Estimate!!Total   \n",
       "11242  B19037_001E  Estimate!!Total   \n",
       "11945  B19052_001E  Estimate!!Total   \n",
       "11948  B19053_001E  Estimate!!Total   \n",
       "11951  B19054_001E  Estimate!!Total   \n",
       "11954  B19055_001E  Estimate!!Total   \n",
       "11957  B19056_001E  Estimate!!Total   \n",
       "11960  B19057_001E  Estimate!!Total   \n",
       "11963  B19058_001E  Estimate!!Total   \n",
       "11966  B19059_001E  Estimate!!Total   \n",
       "11969  B19060_001E  Estimate!!Total   \n",
       "11999  B19101_001E  Estimate!!Total   \n",
       "12179  B19119_001E  Estimate!!Total   \n",
       "12186  B19121_001E  Estimate!!Total   \n",
       "12191  B19125_001E  Estimate!!Total   \n",
       "12253  B19131_001E  Estimate!!Total   \n",
       "12360  B19201_001E  Estimate!!Total   \n",
       "12438  B19325_001E  Estimate!!Total   \n",
       "13697  B21004_001E  Estimate!!Total   \n",
       "13858  B22008_001E  Estimate!!Total   \n",
       "18901  B25070_001E  Estimate!!Total   \n",
       "18913  B25072_001E  Estimate!!Total   \n",
       "18942  B25074_001E  Estimate!!Total   \n",
       "19137  B25091_001E  Estimate!!Total   \n",
       "19163  B25093_001E  Estimate!!Total   \n",
       "19210  B25095_001E  Estimate!!Total   \n",
       "19307  B25098_001E  Estimate!!Total   \n",
       "19329  B25100_001E  Estimate!!Total   \n",
       "19342  B25101_001E  Estimate!!Total   \n",
       "19423  B25106_001E  Estimate!!Total   \n",
       "19612  B25118_001E  Estimate!!Total   \n",
       "19645  B25121_001E  Estimate!!Total   \n",
       "19751  B25122_001E  Estimate!!Total   \n",
       "20549  B27015_001E  Estimate!!Total   \n",
       "20917  B99191_001E  Estimate!!Total   \n",
       "20925  B99192_001E  Estimate!!Total   \n",
       "20933  B99193_001E  Estimate!!Total   \n",
       "20941  B99194_001E  Estimate!!Total   \n",
       "21325  C17002_001E  Estimate!!Total   \n",
       "21389  C18131_001E  Estimate!!Total   \n",
       "22697  C27016_001E  Estimate!!Total   \n",
       "22738  C27017_001E  Estimate!!Total   \n",
       "22779  C27018_001E  Estimate!!Total   \n",
       "\n",
       "                                                 Concept      Required  \\\n",
       "1695   RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 1...  not required   \n",
       "2245   PLACE OF BIRTH BY INDIVIDUAL INCOME IN THE PAS...  not required   \n",
       "2957   GEOGRAPHICAL MOBILITY IN THE PAST YEAR BY INDI...  not required   \n",
       "6286   RECEIPT OF SUPPLEMENTAL SECURITY INCOME (SSI),...  not required   \n",
       "7578   WOMEN 15 TO 50 YEARS WHO HAD A BIRTH IN THE PA...  not required   \n",
       "9979   POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILI...  not required   \n",
       "10395  RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 1...  not required   \n",
       "10511  AGE BY RATIO OF INCOME TO POVERTY LEVEL IN THE...  not required   \n",
       "10657  RATIO OF INCOME TO POVERTY LEVEL OF FAMILIES I...  not required   \n",
       "11044  HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 201...  not required   \n",
       "11224  MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS ...  not required   \n",
       "11242  AGE OF HOUSEHOLDER BY HOUSEHOLD INCOME IN THE ...  not required   \n",
       "11945  WAGE OR SALARY INCOME IN THE PAST 12 MONTHS FO...  not required   \n",
       "11948  SELF-EMPLOYMENT INCOME IN THE PAST 12 MONTHS F...  not required   \n",
       "11951  INTEREST, DIVIDENDS, OR NET RENTAL INCOME IN T...  not required   \n",
       "11954  SOCIAL SECURITY INCOME IN THE PAST 12 MONTHS F...  not required   \n",
       "11957  SUPPLEMENTAL SECURITY INCOME (SSI) IN THE PAST...  not required   \n",
       "11960  PUBLIC ASSISTANCE INCOME IN THE PAST 12 MONTHS...  not required   \n",
       "11963  PUBLIC ASSISTANCE INCOME OR FOOD STAMPS/SNAP I...  not required   \n",
       "11966  RETIREMENT INCOME IN THE PAST 12 MONTHS FOR HO...  not required   \n",
       "11969  OTHER TYPES OF INCOME IN THE PAST 12 MONTHS FO...  not required   \n",
       "11999  FAMILY INCOME IN THE PAST 12 MONTHS (IN 2016 I...  not required   \n",
       "12179  MEDIAN FAMILY INCOME IN THE PAST 12 MONTHS (IN...  not required   \n",
       "12186  MEDIAN FAMILY INCOME IN THE PAST 12 MONTHS (IN...  not required   \n",
       "12191  MEDIAN FAMILY INCOME IN THE PAST 12 MONTHS (IN...  not required   \n",
       "12253  FAMILY TYPE BY PRESENCE OF OWN CHILDREN UNDER ...  not required   \n",
       "12360  NONFAMILY HOUSEHOLD INCOME IN THE PAST 12 MONT...  not required   \n",
       "12438  SEX BY WORK EXPERIENCE IN THE PAST 12 MONTHS B...  not required   \n",
       "13697  MEDIAN INCOME IN THE PAST 12 MONTHS (IN 2016 I...  not required   \n",
       "13858  MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS ...  not required   \n",
       "18901  GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME...  not required   \n",
       "18913  AGE OF HOUSEHOLDER BY GROSS RENT AS A PERCENTA...  not required   \n",
       "18942  HOUSEHOLD INCOME BY GROSS RENT AS A PERCENTAGE...  not required   \n",
       "19137  MORTGAGE STATUS BY SELECTED MONTHLY OWNER COST...  not required   \n",
       "19163  AGE OF HOUSEHOLDER BY SELECTED MONTHLY OWNER C...  not required   \n",
       "19210  HOUSEHOLD INCOME BY SELECTED MONTHLY OWNER COS...  not required   \n",
       "19307  MORTGAGE STATUS BY HOUSEHOLD INCOME IN THE PAS...  not required   \n",
       "19329  MORTGAGE STATUS BY RATIO OF VALUE TO HOUSEHOLD...  not required   \n",
       "19342  MORTGAGE STATUS BY MONTHLY HOUSING COSTS AS A ...  not required   \n",
       "19423  TENURE BY HOUSING COSTS AS A PERCENTAGE OF HOU...  not required   \n",
       "19612  TENURE BY HOUSEHOLD INCOME IN THE PAST 12 MONT...  not required   \n",
       "19645  HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 201...  not required   \n",
       "19751  HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 201...  not required   \n",
       "20549  HEALTH INSURANCE COVERAGE STATUS AND TYPE BY H...  not required   \n",
       "20917  ALLOCATION OF INDIVIDUALS' INCOME IN THE PAST ...  not required   \n",
       "20925  ALLOCATION OF HOUSEHOLD INCOME IN THE PAST 12 ...  not required   \n",
       "20933  ALLOCATION OF FAMILY INCOME IN THE PAST 12 MON...  not required   \n",
       "20941  ALLOCATION OF NONFAMILY HOUSEHOLD INCOME IN TH...  not required   \n",
       "21325  RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 1...  not required   \n",
       "21389  RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 1...  not required   \n",
       "22697  HEALTH INSURANCE COVERAGE STATUS BY RATIO OF I...  not required   \n",
       "22738  PRIVATE HEALTH INSURANCE BY RATIO OF INCOME TO...  not required   \n",
       "22779  PUBLIC HEALTH INSURANCE BY RATIO OF INCOME TO ...  not required   \n",
       "\n",
       "                                      Attributes Limit Predicate Type   Group  \\\n",
       "1695   B05010_001M,  B05010_001MA,  B05010_001EA     0            int  B05010   \n",
       "2245   B06010_001M,  B06010_001MA,  B06010_001EA     0            int  B06010   \n",
       "2957   B07010_001M,  B07010_001MA,  B07010_001EA     0            int  B07010   \n",
       "6286   B09010_001M,  B09010_001MA,  B09010_001EA     0            int  B09010   \n",
       "7578   B13015_001M,  B13015_001MA,  B13015_001EA     0            int  B13015   \n",
       "9979   B17015_001M,  B17015_001MA,  B17015_001EA     0            int  B17015   \n",
       "10395  B17022_001M,  B17022_001MA,  B17022_001EA     0            int  B17022   \n",
       "10511  B17024_001M,  B17024_001MA,  B17024_001EA     0            int  B17024   \n",
       "10657  B17026_001M,  B17026_001MA,  B17026_001EA     0            int  B17026   \n",
       "11044  B19001_001M,  B19001_001MA,  B19001_001EA     0            int  B19001   \n",
       "11224  B19019_001M,  B19019_001MA,  B19019_001EA     0            int  B19019   \n",
       "11242  B19037_001M,  B19037_001MA,  B19037_001EA     0            int  B19037   \n",
       "11945  B19052_001M,  B19052_001MA,  B19052_001EA     0            int  B19052   \n",
       "11948  B19053_001M,  B19053_001MA,  B19053_001EA     0            int  B19053   \n",
       "11951  B19054_001M,  B19054_001MA,  B19054_001EA     0            int  B19054   \n",
       "11954  B19055_001M,  B19055_001MA,  B19055_001EA     0            int  B19055   \n",
       "11957  B19056_001M,  B19056_001MA,  B19056_001EA     0            int  B19056   \n",
       "11960  B19057_001M,  B19057_001MA,  B19057_001EA     0            int  B19057   \n",
       "11963  B19058_001M,  B19058_001MA,  B19058_001EA     0            int  B19058   \n",
       "11966  B19059_001M,  B19059_001MA,  B19059_001EA     0            int  B19059   \n",
       "11969  B19060_001M,  B19060_001MA,  B19060_001EA     0            int  B19060   \n",
       "11999  B19101_001M,  B19101_001MA,  B19101_001EA     0            int  B19101   \n",
       "12179  B19119_001M,  B19119_001MA,  B19119_001EA     0            int  B19119   \n",
       "12186  B19121_001M,  B19121_001MA,  B19121_001EA     0            int  B19121   \n",
       "12191  B19125_001M,  B19125_001MA,  B19125_001EA     0            int  B19125   \n",
       "12253  B19131_001M,  B19131_001MA,  B19131_001EA     0            int  B19131   \n",
       "12360  B19201_001M,  B19201_001MA,  B19201_001EA     0            int  B19201   \n",
       "12438  B19325_001M,  B19325_001MA,  B19325_001EA     0            int  B19325   \n",
       "13697  B21004_001M,  B21004_001MA,  B21004_001EA     0            int  B21004   \n",
       "13858  B22008_001M,  B22008_001MA,  B22008_001EA     0            int  B22008   \n",
       "18901  B25070_001M,  B25070_001MA,  B25070_001EA     0            int  B25070   \n",
       "18913  B25072_001M,  B25072_001MA,  B25072_001EA     0            int  B25072   \n",
       "18942  B25074_001M,  B25074_001MA,  B25074_001EA     0            int  B25074   \n",
       "19137  B25091_001M,  B25091_001MA,  B25091_001EA     0            int  B25091   \n",
       "19163  B25093_001M,  B25093_001MA,  B25093_001EA     0            int  B25093   \n",
       "19210  B25095_001M,  B25095_001MA,  B25095_001EA     0            int  B25095   \n",
       "19307  B25098_001M,  B25098_001MA,  B25098_001EA     0            int  B25098   \n",
       "19329  B25100_001M,  B25100_001MA,  B25100_001EA     0            int  B25100   \n",
       "19342  B25101_001M,  B25101_001MA,  B25101_001EA     0            int  B25101   \n",
       "19423  B25106_001M,  B25106_001MA,  B25106_001EA     0            int  B25106   \n",
       "19612  B25118_001M,  B25118_001MA,  B25118_001EA     0            int  B25118   \n",
       "19645  B25121_001M,  B25121_001MA,  B25121_001EA     0            int  B25121   \n",
       "19751  B25122_001M,  B25122_001MA,  B25122_001EA     0            int  B25122   \n",
       "20549  B27015_001M,  B27015_001MA,  B27015_001EA     0            int  B27015   \n",
       "20917                               B99191_001EA     0            int  B99191   \n",
       "20925                               B99192_001EA     0            int  B99192   \n",
       "20933                               B99193_001EA     0            int  B99193   \n",
       "20941                               B99194_001EA     0            int  B99194   \n",
       "21325  C17002_001M,  C17002_001MA,  C17002_001EA     0            int  C17002   \n",
       "21389  C18131_001M,  C18131_001MA,  C18131_001EA     0            int  C18131   \n",
       "22697  C27016_001M,  C27016_001MA,  C27016_001EA     0            int  C27016   \n",
       "22738  C27017_001M,  C27017_001MA,  C27017_001EA     0            int  C27017   \n",
       "22779  C27018_001M,  C27018_001MA,  C27018_001EA     0            int  C27018   \n",
       "\n",
       "      Values  \n",
       "1695     NaN  \n",
       "2245     NaN  \n",
       "2957     NaN  \n",
       "6286     NaN  \n",
       "7578     NaN  \n",
       "9979     NaN  \n",
       "10395    NaN  \n",
       "10511    NaN  \n",
       "10657    NaN  \n",
       "11044    NaN  \n",
       "11224    NaN  \n",
       "11242    NaN  \n",
       "11945    NaN  \n",
       "11948    NaN  \n",
       "11951    NaN  \n",
       "11954    NaN  \n",
       "11957    NaN  \n",
       "11960    NaN  \n",
       "11963    NaN  \n",
       "11966    NaN  \n",
       "11969    NaN  \n",
       "11999    NaN  \n",
       "12179    NaN  \n",
       "12186    NaN  \n",
       "12191    NaN  \n",
       "12253    NaN  \n",
       "12360    NaN  \n",
       "12438    NaN  \n",
       "13697    NaN  \n",
       "13858    NaN  \n",
       "18901    NaN  \n",
       "18913    NaN  \n",
       "18942    NaN  \n",
       "19137    NaN  \n",
       "19163    NaN  \n",
       "19210    NaN  \n",
       "19307    NaN  \n",
       "19329    NaN  \n",
       "19342    NaN  \n",
       "19423    NaN  \n",
       "19612    NaN  \n",
       "19645    NaN  \n",
       "19751    NaN  \n",
       "20549    NaN  \n",
       "20917    NaN  \n",
       "20925    NaN  \n",
       "20933    NaN  \n",
       "20941    NaN  \n",
       "21325    NaN  \n",
       "21389    NaN  \n",
       "22697    NaN  \n",
       "22738    NaN  \n",
       "22779    NaN  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censusMega3[censusMega3['Concept'].str.contains('INCOME')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "step2. get census data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ny': [34, 36, 9, 10, 42, 44, 25],\n",
       " 'chic': [17, 18, 26, 55],\n",
       " 'la': [6],\n",
       " 'sf': [6]}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "# list(set([math.floor(i) for i in  ny['dest_ct']/1e9]))\n",
    "\n",
    "statelist = {'ny':list(set([math.floor(i) for i in  ny['dest_ct'].astype(float)/1e9])),\n",
    "            'chic':list(set([math.floor(i) for i in  chic['dest_ct'].astype(float)/1e9])),\n",
    "            'la':list(set([math.floor(i) for i in  la['dest_ct'].astype(float)/1e9])),\n",
    "            'sf':list(set([math.floor(i) for i in  sf['dest_ct'].astype(float)/1e9]))}\n",
    "statelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'int' object has no attribute 'astype'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-1302f0133671>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m'%.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatelist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ny'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatelist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ny'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'astype'"
     ]
    }
   ],
   "source": [
    "'%.2f' % 0.2\n",
    "for i in range(len(statelist['ny'])):\n",
    "    print (str('%.2f' % float(statelist['ny'][i]/100)).split('.')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n",
      "36\n",
      "09\n",
      "10\n",
      "42\n",
      "44\n",
      "25\n",
      "17\n",
      "18\n",
      "26\n",
      "55\n",
      "06\n",
      "06\n"
     ]
    }
   ],
   "source": [
    "keylist = {'B00002_001E':'pop',\n",
    "           'B19013_001E':'medianincome',\n",
    "           'B99082_001E':'vehicle_occupancy',\n",
    "           'B08015_001E':'AggregateVehiclesInCommuting',\n",
    "           'B08006_008E':'publictransport',\n",
    "           'B06009_005E':'Bachelordegree',\n",
    "           'B10059_002E':'pastYearbelowPovertyLevel100',\n",
    "           'B07001_033E':'Movedwithinsamecounty',\n",
    "           'B07001_081E':'Movedfromabroad',\n",
    "           'B992703_001E':'AllocationPublicHealthInsurance'}\n",
    "\n",
    "#fip_list = ['005', '047', '061', '081', '085'] #BX, BK, MN, QN, SI\n",
    "cities = ['ny','chic','la','sf']\n",
    "def get_Census_Key(city,keylist):\n",
    "    c = Census(api_key)\n",
    "    df_city=[]\n",
    "\n",
    "    for i in range(len(statelist[city])):\n",
    "        print(str('%.2f' % float(statelist[city][i]/100)).split('.')[1])\n",
    "        dff = []\n",
    "        for j in keylist.keys():\n",
    "            df = c.acs.state_county_tract(('NAME',j),str('%.2f' % float(statelist[city][i]/100)).split('.')[1],'*',Census.ALL,year=2016)\n",
    "            df = pd.DataFrame(df)\n",
    "            #df.rename(index = str,columns={j:keylist[j]},inplace=True)\n",
    "            dff.append(df)\n",
    "        dffs = pd.concat(dff,axis=1)\n",
    "        df_city.append(dffs)\n",
    "    df_cities = pd.concat(df_city,axis=0)\n",
    "    dffall = df_cities.loc[:,~df_cities.columns.duplicated()]\n",
    "    dffall = dffall.reindex(sorted(dffall.columns),axis=1)\n",
    "    return dffall\n",
    "def makeDF(city,keylist):\n",
    "    df = get_Census_Key(city,keylist)\n",
    "    df.columns =[keylist[i] if i in keylist.keys() else i for i in df.columns]\n",
    "    df['GEOID'] = df['state']+df['county']+df['tract']\n",
    "    return df\n",
    "\n",
    "ny_census = makeDF('ny',keylist)\n",
    "chic_census = makeDF('chic',keylist)\n",
    "la_census = makeDF('la',keylist)\n",
    "sf_census = makeDF('sf',keylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "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>pop</th>\n",
       "      <th>Bachelordegree</th>\n",
       "      <th>Movedwithinsamecounty</th>\n",
       "      <th>Movedfromabroad</th>\n",
       "      <th>publictransport</th>\n",
       "      <th>AggregateVehiclesInCommuting</th>\n",
       "      <th>pastYearbelowPovertyLevel100</th>\n",
       "      <th>medianincome</th>\n",
       "      <th>vehicle_occupancy</th>\n",
       "      <th>AllocationPublicHealthInsurance</th>\n",
       "      <th>NAME</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>tract</th>\n",
       "      <th>GEOID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177417.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>Census Tract 4001, Alameda County, California</td>\n",
       "      <td>001</td>\n",
       "      <td>06</td>\n",
       "      <td>400100</td>\n",
       "      <td>06001400100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>144.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153125.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>Census Tract 4002, Alameda County, California</td>\n",
       "      <td>001</td>\n",
       "      <td>06</td>\n",
       "      <td>400200</td>\n",
       "      <td>06001400200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>144.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85313.0</td>\n",
       "      <td>3089.0</td>\n",
       "      <td>5236.0</td>\n",
       "      <td>Census Tract 4003, Alameda County, California</td>\n",
       "      <td>001</td>\n",
       "      <td>06</td>\n",
       "      <td>400300</td>\n",
       "      <td>06001400300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>172.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99539.0</td>\n",
       "      <td>2367.0</td>\n",
       "      <td>4165.0</td>\n",
       "      <td>Census Tract 4004, Alameda County, California</td>\n",
       "      <td>001</td>\n",
       "      <td>06</td>\n",
       "      <td>400400</td>\n",
       "      <td>06001400400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148.0</td>\n",
       "      <td>1053.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83650.0</td>\n",
       "      <td>2327.0</td>\n",
       "      <td>3748.0</td>\n",
       "      <td>Census Tract 4005, Alameda County, California</td>\n",
       "      <td>001</td>\n",
       "      <td>06</td>\n",
       "      <td>400500</td>\n",
       "      <td>06001400500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pop  Bachelordegree  Movedwithinsamecounty  Movedfromabroad  \\\n",
       "0  144.0           781.0                  267.0             20.0   \n",
       "1  144.0           557.0                   77.0             37.0   \n",
       "2  144.0          1332.0                  198.0             30.0   \n",
       "3  172.0          1123.0                  165.0              7.0   \n",
       "4  148.0          1053.0                  297.0             10.0   \n",
       "\n",
       "   publictransport  AggregateVehiclesInCommuting  \\\n",
       "0            182.0                         945.0   \n",
       "1            317.0                         605.0   \n",
       "2            892.0                        1455.0   \n",
       "3            660.0                         935.0   \n",
       "4            566.0                        1095.0   \n",
       "\n",
       "   pastYearbelowPovertyLevel100  medianincome  vehicle_occupancy  \\\n",
       "0                           0.0      177417.0             1550.0   \n",
       "1                           0.0      153125.0             1223.0   \n",
       "2                           0.0       85313.0             3089.0   \n",
       "3                           0.0       99539.0             2367.0   \n",
       "4                           0.0       83650.0             2327.0   \n",
       "\n",
       "   AllocationPublicHealthInsurance  \\\n",
       "0                           3018.0   \n",
       "1                           1960.0   \n",
       "2                           5236.0   \n",
       "3                           4165.0   \n",
       "4                           3748.0   \n",
       "\n",
       "                                            NAME county state   tract  \\\n",
       "0  Census Tract 4001, Alameda County, California    001    06  400100   \n",
       "1  Census Tract 4002, Alameda County, California    001    06  400200   \n",
       "2  Census Tract 4003, Alameda County, California    001    06  400300   \n",
       "3  Census Tract 4004, Alameda County, California    001    06  400400   \n",
       "4  Census Tract 4005, Alameda County, California    001    06  400500   \n",
       "\n",
       "         GEOID  \n",
       "0  06001400100  \n",
       "1  06001400200  \n",
       "2  06001400300  \n",
       "3  06001400400  \n",
       "4  06001400500  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cts' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-65ee834d0683>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'cts' is not defined"
     ]
    }
   ],
   "source": [
    "len(cts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "step3. aggreagate census and od-pair data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "ny_combine = ny.merge(ny_census,left_on='dest_ct',right_on='GEOID')\n",
    "la_combine = la.merge(la_census,left_on='dest_ct',right_on='GEOID')\n",
    "sf_combine = sf.merge(sf_census,left_on='dest_ct',right_on='GEOID')\n",
    "chic_combine = chic.merge(chic_census,left_on='dest_ct',right_on='GEOID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "ny_combine.to_csv('ny_combine.csv')\n",
    "chic_combine.to_csv('chic_combine.csv')\n",
    "la_combine.to_csv('la_combine.csv')\n",
    "sf_combine.to_csv('sf_combine.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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