Skip to content

Instantly share code, notes, and snippets.

@nealmcb

nealmcb/USAFacts_API_explore.ipynb

Last active May 2, 2017 05:03
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save nealmcb/7f00caacde5afbf069109b0cb542659d to your computer and use it in GitHub Desktop.
Save nealmcb/7f00caacde5afbf069109b0cb542659d to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
USAFacts_API_explore.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore the REST API of USAFacts.org\n",
"The [USAFacts](https://usafacts.org/) web site, launched in 2017-04, helps users explore a wide variety of US state and federal government data sources. See [USAFacts.org - Wikipedia](https://en.wikipedia.org/wiki/USAFacts.org) for more background.\n",
"\n",
"Geekwire writes [Former Microsoft CEO Steve Ballmer launches USAFacts, using business principles for unprecedented government analysis](https://www.geekwire.com/2017/steve-ballmer-launches-usafacts-using-business-principles-for-unprecedented-government-analysis/)\n",
"> the project is built on Microsoft Azure, using technologies including SQL Server. They use a REST API built in .NET for the backend. The front end is built in React and Victory, with Lunr JavaScript search technology. They hope to add PowerBI later to let people mix and match their own data sets and visualizations.\n",
"\n",
"The currently documented options for those of us that just like to play with the data are pretty limited though. The pages have a download icon that just says \"Download coming soon\". Their backend is undocumented as far as I can see.\n",
"\n",
"But the raw data seems pretty easy to access. Here are my first experiments in that vein, with Python 3.\n",
"\n",
"Thanks to Rashid Khan for inspiration and insights at [rashidkpc/timelion-usafacts: A timelion plugin for the usafacts.org API. Highly experimental.](https://github.com/rashidkpc/timelion-usafacts)\n",
"\n",
"### Compare and Contrast with Data USA\n",
"* [Data USA](https://datausa.io/) ([Wikipedia](https://en.wikipedia.org/wiki/Data_USA)) is a similar effort launched in 2016 by MIT, Deloitte, Macro Connections Group, and Datawheel. It is open source and has a powerful, documented API.\n",
"* **Update**: see a tiny example of API via Python at the bottom of this notebook\n",
"* Seems focused on education and jobs by locality, with data from 2014 to 2016\n",
"* Javascript examples on github: [DataUSA/datausa-tutorials: Holds tutorials for how to build things with the Data USA API and embedded visualizations.](https://github.com/DataUSA/datausa-tutorials)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## First set things up and define a function to pull a given set of tables by id"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import json\n",
"import requests\n",
"import tempfile\n",
"import subprocess"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def usafacts_data(ids):\n",
" \"Look up the given comma-separated ids from USAFacts backend, return as dictionary\"\n",
"\n",
" # Tip from timelion plugin:\n",
" # curl 'http://staging-usafacts-apiv2.azurewebsites.net/api/v2/metrics?ids=12818' -H 'Authorization: Basic d9448c61-936d-4717-8aa8-cba9a4903d57'\n",
"\n",
" token = 'd9448c61-936d-4717-8aa8-cba9a4903d57' # just seems to work for now....\n",
" metrics_url = 'http://staging-usafacts-apiv2.azurewebsites.net/api/v2/metrics'\n",
" headers = dict(Authorization='Basic %s' % token)\n",
"\n",
" if ids:\n",
" params = {'ids': ids}\n",
" else:\n",
" params = None\n",
" r = requests.get(metrics_url, params=params, headers=headers)\n",
" return r.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pull down US population data, by age group 5-14 years old, the metrics with \"id\" 12372"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"metricslist = usafacts_data('12372')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We got a list of all the metrics requested, even though there is just one in this case, so pull it out, and put it in a Pandas dataframe. Look at the first few rows."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"metrics = metricslist[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame(metrics['data'], dtype=np.float64)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1980.0</td>\n",
" <td>34942085.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1981.0</td>\n",
" <td>34360571.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1982.0</td>\n",
" <td>34102586.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1983.0</td>\n",
" <td>33921858.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1984.0</td>\n",
" <td>33788303.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x y\n",
"0 1980.0 34942085.0\n",
"1 1981.0 34360571.0\n",
"2 1982.0 34102586.0\n",
"3 1983.0 33921858.0\n",
"4 1984.0 33788303.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define a quick function to plot the data, labeling it according to metadata from the table"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def usafacts_plot(df, metrics):\n",
" \"Plot the data in the DataFrame, using title and axis labels from metrics\"\n",
" fig, ax = plt.subplots()\n",
" df.plot(kind='scatter', y='y', x='x', ax=ax)\n",
" ax.set(title=metrics['name'], xlabel=metrics['x_type'], ylabel=metrics['y_type'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAGFCAYAAAAmbFOlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns0xxcmJmZmaZ4uTEzMzMMuX/B/3igCkX74ClAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd326cada58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"usafacts_plot(df, metrics)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interesting variation there, for that age group!\n",
"\n",
"Show all the metadata, excluding the data, but including things like related tables (ancestors and children), etc."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"slide_title\": 0,\n",
" \"starts_at_zero\": false,\n",
" \"name\": \"5 to 14 years\",\n",
" \"sig_figs\": 0,\n",
" \"can_toggle_stacked_area\": false,\n",
" \"application_type\": 4,\n",
" \"rounding_unit\": 1,\n",
" \"id\": 12372,\n",
" \"ancestry\": {\n",
" \"ancestor_metrics\": [\n",
" 12818,\n",
" 12298\n",
" ],\n",
" \"mission\": 0,\n",
" \"topic\": 11519,\n",
" \"reporting_unit\": 11519\n",
" },\n",
" \"x_type\": \"Years\",\n",
" \"default_stacked_area\": false,\n",
" \"lexicon_name\": \"5 to 14 years\",\n",
" \"slide_id\": 0,\n",
" \"y_type\": \"People\",\n",
" \"type\": \"Other\",\n",
" \"chartable_description\": \"By Age Group\",\n",
" \"meta\": []\n",
"}\n"
]
}
],
"source": [
"metricsmeta = dict(metrics)\n",
"del metricsmeta['data']\n",
"print(json.dumps(metricsmeta, indent=2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# List of some or all tables of data by id\n",
"There's lots more info. Here's a peek"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"metricsall = usafacts_data('')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1377"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(metricsall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This seems to provide information on 1377 tables of data. Show details for the first 3."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'ancestry': {'ancestor_metrics': [],\n",
" 'mission': 0,\n",
" 'reporting_unit': 11519,\n",
" 'topic': 11519},\n",
" 'children': [11625],\n",
" 'id': 11662,\n",
" 'lexicon_name': 'Net divorce rate (Currently divorced as % of ever married)',\n",
" 'meta': [],\n",
" 'metrics': [],\n",
" 'name': 'Net divorce rate (Currently divorced as % of ever married)',\n",
" 'rounding_unit': 1,\n",
" 'sig_figs': 1,\n",
" 'slide_id': 0,\n",
" 'slide_title': '0',\n",
" 'starts_at_zero': False,\n",
" 'type': 'Other',\n",
" 'x_type': 'Years',\n",
" 'y_type': 'Percent'},\n",
" {'ancestry': {'ancestor_metrics': [11662],\n",
" 'mission': 0,\n",
" 'reporting_unit': 11519,\n",
" 'topic': 11519},\n",
" 'can_toggle_stacked_area': False,\n",
" 'chartable_children_description': 'By Gender',\n",
" 'children': [],\n",
" 'default_stacked_area': False,\n",
" 'id': 11625,\n",
" 'lexicon_name': 'By Gender',\n",
" 'meta': [],\n",
" 'metrics': [],\n",
" 'name': 'By Gender',\n",
" 'rounding_unit': 1,\n",
" 'sig_figs': 1,\n",
" 'slide_id': 0,\n",
" 'slide_title': '0',\n",
" 'starts_at_zero': False,\n",
" 'type': 'Other',\n",
" 'x_type': 'Years',\n",
" 'y_type': 'Percent'},\n",
" {'ancestry': {'ancestor_metrics': [],\n",
" 'mission': 0,\n",
" 'reporting_unit': 11519,\n",
" 'topic': 11519},\n",
" 'children': [11738],\n",
" 'id': 11775,\n",
" 'lexicon_name': 'Households',\n",
" 'meta': [],\n",
" 'metrics': [],\n",
" 'name': 'Households',\n",
" 'rounding_unit': 1000,\n",
" 'sig_figs': 0,\n",
" 'slide_id': 0,\n",
" 'slide_title': '0',\n",
" 'starts_at_zero': False,\n",
" 'type': 'Other',\n",
" 'x_type': 'Years',\n",
" 'y_type': 'Items'}]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metricsall[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now try to list them all by name. The display may be incomplete, since the notebook may truncate the output to 1000 lines."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[(11662, 'Net divorce rate (Currently divorced as % of ever married)'),\n",
" (11625, 'By Gender'),\n",
" (11775, 'Households'),\n",
" (11738, 'By Household Type'),\n",
" (12818, 'Population'),\n",
" (12298, 'By Age Group'),\n",
" (12372, '5 to 14 years'),\n",
" (12557, '55 to 64 years'),\n",
" (12335, 'Under 5 years of age'),\n",
" (12520, '45 to 54 years'),\n",
" (12483, '35 to 44 years'),\n",
" (12594, '65+ years'),\n",
" (12409, '15 to 24 years'),\n",
" (12446, '25 to 34 years'),\n",
" (12595, 'Race / Ethnicity'),\n",
" (12780, 'Other / Mixed Race'),\n",
" (12669, 'Black or African American'),\n",
" (12632, 'White'),\n",
" (12706, 'Asian'),\n",
" (12743, 'American Indian and Alaska Native'),\n",
" (12781, 'Regional'),\n",
" (12223, 'Gender'),\n",
" (12297, 'Female'),\n",
" (12260, 'Male'),\n",
" (12185, 'Currently married'),\n",
" (11776, 'By gender'),\n",
" (11999, 'All women'),\n",
" (12000, 'By race / ethnicity'),\n",
" (12111, 'Asian'),\n",
" (12074, 'Black'),\n",
" (12148, 'Hispanic'),\n",
" (12037, 'White'),\n",
" (11813, 'All men'),\n",
" (11814, 'By race / ethnicity'),\n",
" (11888, 'Black'),\n",
" (11851, 'White'),\n",
" (11925, 'Asian'),\n",
" (11962, 'Hispanic'),\n",
" (11737, 'Family Households'),\n",
" (11700, 'By Family Type'),\n",
" (12855, 'Rate of Young Adults Living at Home'),\n",
" (11624, 'Average Household Size'),\n",
" (12892, 'Young Adults Living at Home'),\n",
" (11587, 'Average Family Size'),\n",
" (11699, 'Educational Attainment'),\n",
" (12222, 'Median Age'),\n",
" (13131, 'Population Change'),\n",
" (12929, 'Births'),\n",
" (13021, 'Individuals Granted Asylum'),\n",
" (12966, 'Deaths'),\n",
" (13094, 'Refugees'),\n",
" (12995, 'Green Cards'),\n",
" (13058, 'Naturalizations'),\n",
" (14783, 'Arrests'),\n",
" (13132, 'By Race'),\n",
" (13237, 'American Indian and Alaska Native'),\n",
" (13202, 'Black or African American'),\n",
" (13272, 'Asian or Pacific Islander'),\n",
" (13167, 'White'),\n",
" (13415, 'By Offense'),\n",
" (14290, 'Gambling'),\n",
" (14115, 'Vagrancy'),\n",
" (13695, 'Burglary'),\n",
" (14010, 'Sex Offense (except forcible rape and prostitution)'),\n",
" (13590, 'Disorderly Conduct'),\n",
" (14255, 'Murder and Non-Negligent Manslaughter'),\n",
" (13450, 'Drug Abuse Violations -Total'),\n",
" (14045, 'Forgery and Counterfeiting'),\n",
" (13870, 'Robbery'),\n",
" (13800, 'Weapons; Carrying, Possessing, etc.'),\n",
" (13905, 'Stolen Property; Buying, Receiving, Possessing'),\n",
" (13765, 'Fraud'),\n",
" (14150, 'Forcible Rape'),\n",
" (14325, 'Suspicion'),\n",
" (13940, 'Curfew and Loitering Law Violations'),\n",
" (13555, 'Assault'),\n",
" (13625, 'Drunkenness'),\n",
" (14395, 'All Other Offenses (except traffic)'),\n",
" (13660, 'Liquor Laws'),\n",
" (14220, 'Arson'),\n",
" (14185, 'Embezzlement'),\n",
" (14360, 'Runaways'),\n",
" (13975, 'Motor Vehicle Theft'),\n",
" (13485, 'Driving Under the Influence'),\n",
" (13520, 'Larceny-Theft'),\n",
" (14080, 'Prostitution and Commercialized Vice'),\n",
" (13835, 'Offenses Against the Family and Children'),\n",
" (13730, 'Vandalism'),\n",
" (13344, 'By Assault Type'),\n",
" (13379, 'Aggravated Assault'),\n",
" (13414, 'Other Assaults'),\n",
" (13273, 'By Drug Abuse Violation'),\n",
" (13308, 'Sale-Manufacturing-Total'),\n",
" (13343, 'Possession-SubTotal'),\n",
" (14678, 'By Age'),\n",
" (14748, '18 or over'),\n",
" (14713, 'Under 18'),\n",
" (14396, 'Under 18 By Race'),\n",
" (14501, 'American Indian and Alaska Native'),\n",
" (14536, 'Asian or Pacific Islander'),\n",
" (14466, 'Black or African American'),\n",
" (14431, 'White'),\n",
" (14537, 'Over 18 By Race'),\n",
" (14607, 'Black or African American'),\n",
" (14572, 'White'),\n",
" (14677, 'Asian or Pacific Islander'),\n",
" (14642, 'American Indian and Alaska Native'),\n",
" (15118, 'Violent crime Rate (per 100,000 persons)'),\n",
" (14784, 'By region'),\n",
" (14932, 'West'),\n",
" (14895, 'South'),\n",
" (14858, 'Midwest'),\n",
" (14821, 'Northeast'),\n",
" (14933, 'By offense'),\n",
" (15007, 'Rape'),\n",
" (14970, 'Murder and nonnegligent manslaughter'),\n",
" (15081, 'Aggravated assault'),\n",
" (15044, 'Robbery'),\n",
" (15415, 'Property crime rate (per 100,000 persons)'),\n",
" (15119, 'By region'),\n",
" (15267, 'West'),\n",
" (15156, 'Northeast'),\n",
" (15193, 'Midwest'),\n",
" (15230, 'South'),\n",
" (15268, 'By Offense'),\n",
" (15342, 'Burglary'),\n",
" (15379, 'Motor vehicle theft'),\n",
" (15305, 'Larceny-theft'),\n",
" (15602, 'Public safety officers'),\n",
" (15416, 'By Jurisdiction'),\n",
" (15453, 'State and Local Officers'),\n",
" (15454, 'By Power of Arrest'),\n",
" (15528, 'Other Employees'),\n",
" (15491, 'Persons with Power of Arrest'),\n",
" (15565, 'Federal Officers'),\n",
" (16382, 'Persons under sentence of death'),\n",
" (16417, 'Time on death row'),\n",
" (16521, 'Prisoners'),\n",
" (16854, 'Mean max prison sentence length (months)'),\n",
" (16722, 'By offense'),\n",
" (16810, 'Public-order offenses'),\n",
" (16744, 'Violent offenses'),\n",
" (16788, 'Drug offenses'),\n",
" (16832, 'Other offenses'),\n",
" (16766, 'Property offenses'),\n",
" (16987, 'Mean time served in prison (months)'),\n",
" (16855, 'By offense'),\n",
" (16965, 'Other offenses'),\n",
" (16899, 'Property offenses'),\n",
" (16921, 'Drug offenses'),\n",
" (16943, 'Public-order offenses'),\n",
" (16877, 'Violent offenses'),\n",
" (17120, 'Percent of mean maximum prison sentence served'),\n",
" (16988, 'By offense'),\n",
" (17076, 'Public-order offenses'),\n",
" (17054, 'Drug offenses'),\n",
" (17098, 'Other offenses'),\n",
" (17010, 'Violent offenses'),\n",
" (17032, 'Property offenses'),\n",
" (17474, 'Prison admissions'),\n",
" (17121, 'By type'),\n",
" (17199, 'Parole violations'),\n",
" (17160, 'New court commitments'),\n",
" (17200, 'By jurisdiction'),\n",
" (17356, 'State'),\n",
" (17357, 'State prison admissions by type'),\n",
" (17396, 'New Court Commitments'),\n",
" (17435, 'Parole violations'),\n",
" (17239, 'Federal'),\n",
" (17240, 'Federal prison admissions by type'),\n",
" (17318, 'Parole violations'),\n",
" (17279, 'New Court Commitments'),\n",
" (17495, 'Jail inmates'),\n",
" (17476, 'By ethnicity'),\n",
" (17478, 'By court outcome'),\n",
" (17475, 'By gender'),\n",
" (17477, 'By age group'),\n",
" (17527, 'Jail: Inmates held for ice'),\n",
" (17540, 'Persons under jail supervision outside of a jail facility'),\n",
" (17528, 'By supervision'),\n",
" (17552, 'Jail capacity occupied'),\n",
" (17880, 'Correctional population'),\n",
" (17553, 'By incarceration'),\n",
" (17771, 'Total Community Supervision Population'),\n",
" (17772, 'By supervision type'),\n",
" (17808, 'Probation'),\n",
" (17844, 'Parole'),\n",
" (17589, 'Incarcerated Population'),\n",
" (17590, 'By incarceration type'),\n",
" (17626, 'Local jail'),\n",
" (17662, 'Total Prisoners'),\n",
" (17663, 'By incarceration jurisdiction'),\n",
" (17699, 'State'),\n",
" (17735, 'Federal'),\n",
" (17968, 'Appeals Court Cases Filed'),\n",
" (17978, 'Appeals Court Cases Reversed'),\n",
" (18009, 'District Court Cases Filed'),\n",
" (17979, 'Cases Filed by Type'),\n",
" (17999, 'Criminal Cases'),\n",
" (17989, 'Civil Cases'),\n",
" (18040, 'District Court Cases Pending'),\n",
" (18010, 'Cases Pending by Type'),\n",
" (18030, 'Criminal Cases'),\n",
" (18020, 'Civil Cases'),\n",
" (18144, 'District Court Cases Terminated'),\n",
" (18041, 'Cases Terminated by Type'),\n",
" (18051, 'Civil Cases'),\n",
" (18052, 'Civil Cases by Outcome'),\n",
" (18072, 'Court Action'),\n",
" (18073, 'Civil Outcome by Timing of Resolution'),\n",
" (18083, 'Before Pretrial'),\n",
" (18093, 'During or After Pretrial'),\n",
" (18103, 'During or After Trial'),\n",
" (18104, 'Civil Outcome by Timing of Resolution by Trial Type'),\n",
" (18114, 'Nonjury'),\n",
" (18124, 'Jury'),\n",
" (18062, 'No Court Action'),\n",
" (18134, 'Criminal Cases'),\n",
" (18154, 'District Courts Criminal Defendants'),\n",
" (18164, 'Defendants per Criminal Case'),\n",
" (15980, 'Firearms licenses'),\n",
" (15953, 'By firearm license'),\n",
" (16007, 'Firearm background checks'),\n",
" (16023, 'Firearm deaths'),\n",
" (16079, 'Firearm inspections'),\n",
" (16106, 'Firearm inspections'),\n",
" (16024, 'Firearms inspections'),\n",
" (16078, 'Percent Total Business Entities Inspected'),\n",
" (16051, 'Percent Total Licensees Inspected'),\n",
" (16133, 'Firearms enforcement'),\n",
" (16185, 'Firearms manufactured'),\n",
" (16159, 'By type of firearm manufactured'),\n",
" (16239, 'Firearms processed under NFA'),\n",
" (16212, 'By processor'),\n",
" (16320, 'Business entities inspected'),\n",
" (16347, 'Enforcement records check'),\n",
" (15674, 'Firefighters'),\n",
" (15638, 'By type of firefighter'),\n",
" (15746, 'Fires'),\n",
" (15710, 'By type of fire'),\n",
" (15818, 'Civilian deaths from fires'),\n",
" (15782, 'By type of fire'),\n",
" (15854, 'Civilian injuries from fires'),\n",
" (15890, 'Firefighter injuries'),\n",
" (15926, 'Firefighter deaths'),\n",
" (17919, 'Disaster declarations'),\n",
" (17881, 'By selected states'),\n",
" (17882, 'By disaster'),\n",
" (17958, 'Disaster aid'),\n",
" (17921, 'By selected states'),\n",
" (17920, 'By expenditure'),\n",
" (18526, 'Consumer finances complaints'),\n",
" (18165, 'By type of complaint'),\n",
" (18273, 'Credit reporting'),\n",
" (18201, 'Bank account or service'),\n",
" (18345, 'Other financial service'),\n",
" (18363, 'Prepaid card'),\n",
" (18237, 'Debt collection'),\n",
" (18291, 'Student loan'),\n",
" (18255, 'Credit card'),\n",
" (18309, 'Money transfers'),\n",
" (18183, 'Consumer Loan'),\n",
" (18219, 'Mortgage'),\n",
" (18327, 'Payday loan'),\n",
" (18364, 'By complaint outcome'),\n",
" (18436, 'Closed with monetary relief'),\n",
" (18508, 'Closed with relief'),\n",
" (18472, 'In progress'),\n",
" (18400, 'Closed'),\n",
" (18454, 'Untimely response'),\n",
" (18490, 'Closed without relief'),\n",
" (18382, 'Closed with explanation'),\n",
" (18418, 'Closed with non-monetary relief'),\n",
" (18554, 'Do Not Call List Complaints'),\n",
" (18540, 'Active Registrations on Do Not Call List '),\n",
" (18570, 'Consumer Fraud Complaints'),\n",
" (18749, 'SEC enforcement actions'),\n",
" (18571, 'By type'),\n",
" (18639, 'Insider Trading'),\n",
" (18707, 'Securities Offering'),\n",
" (18656, 'Investment Adviser/Investment Companies'),\n",
" (18673, 'Issuer Reporting and Disclosure'),\n",
" (18605, 'Delinquent Filings'),\n",
" (18724, 'Other'),\n",
" (18588, 'Broker-Dealer'),\n",
" (18690, 'Market Manipulation'),\n",
" (18622, 'Foreign Corrupt Practices Act'),\n",
" (19000, 'Merger transactions reported'),\n",
" (18750, 'by Type'),\n",
" (18975, '% Transportation'),\n",
" (18775, '% Consumer Goods and Services'),\n",
" (18925, '% Health Services'),\n",
" (18900, '% Energy and Natural Resources'),\n",
" (18825, '% Banking and Insurance'),\n",
" (18800, '% Other'),\n",
" (18850, '% Manufacturing'),\n",
" (18875, '% Information Technology'),\n",
" (18950, '% Chemicals and Pharmaceuticals'),\n",
" (19076, 'Merger investgations with second requests issued'),\n",
" (19001, 'By agency'),\n",
" (19051, 'DOJ'),\n",
" (19026, 'FTC'),\n",
" (19152, 'Transactions involving a request for early termination'),\n",
" (19077, 'by Outcome'),\n",
" (19102, 'Granted'),\n",
" (19127, 'Not Granted'),\n",
" (19179, 'Voluntary recall orders'),\n",
" (19315, 'Letters of advice'),\n",
" (19180, 'By type'),\n",
" (19261, 'Correct Future Production'),\n",
" (19207, 'Consumer Level Recall'),\n",
" (19234, 'Distribution Level Recall'),\n",
" (19288, 'Stop Sale and Correct Future Production'),\n",
" (19342, 'Civil fines'),\n",
" (19369, 'Criminal fines'),\n",
" (19451, 'Determinations and judicial actions'),\n",
" (19370, 'By penalty'),\n",
" (19397, 'Total Civil Penalties'),\n",
" (19424, 'Total Criminal Penalties'),\n",
" (19843, 'National product injury estimate'),\n",
" (19452, 'By type of product'),\n",
" (19790, 'Home Structures & Const. Mat.'),\n",
" (19738, 'Heating, Cooling, Vent. Equip.'),\n",
" (19504, 'Toys'),\n",
" (19530, 'Sports & Recreational Equipment'),\n",
" (19634, 'Yard & Garden Equipment'),\n",
" (19478, 'Child Nursery Equipment'),\n",
" (19608, 'Household Containers'),\n",
" (19686, 'Home Maintenance'),\n",
" (19556, \"Home Comm'n & Entertainment\"),\n",
" (19582, 'Personal Use Items'),\n",
" (19816, 'Miscellaneous Products'),\n",
" (19712, 'General Household Appliances'),\n",
" (19660, 'Home Workshop Equipment'),\n",
" (19764, 'Home Furnishings & Fixtures'),\n",
" (19898, 'Occupational safety (OSHA) inspections'),\n",
" (19844, 'By type'),\n",
" (19880, 'Total Unprogrammed Inspections'),\n",
" (19862, 'Total Programmed Inspections'),\n",
" (19915, 'Time to wage complaint resolution (avg. days)'),\n",
" (19936, 'Wage complaints registered'),\n",
" (19957, 'Concluded wage cases'),\n",
" (19978, 'Time enforcing wage rules (hours)'),\n",
" (20016, 'Number of workers covered by workers comp'),\n",
" (20042, 'Fatal workplace injuries'),\n",
" (20051, 'Nonfatal occupational injuries and illnesses'),\n",
" (20070, 'Workplace safety violations'),\n",
" (20052, 'By type'),\n",
" (20300, 'Workers comp benefits paid'),\n",
" (20071, 'By payee'),\n",
" (20109, 'For Medical and Hospitalization'),\n",
" (20147, 'For Compensation Payments'),\n",
" (20148, 'By Type'),\n",
" (20224, 'From State and Federal Funds'),\n",
" (20262, \"From Employers' Self-Insurance\"),\n",
" (20186, 'From Private Carriers'),\n",
" (20345, 'Accidents'),\n",
" (20301, 'By mode of transportation'),\n",
" (20334, 'Other (Railroad, Transit, Water)'),\n",
" (20312, 'Highway'),\n",
" (20323, 'Air'),\n",
" (20423, 'Fatalities'),\n",
" (20346, 'By type'),\n",
" (20357, 'Highway'),\n",
" (20368, '% Alcohol Involved'),\n",
" (20379, 'Air'),\n",
" (20390, 'Railroad'),\n",
" (20412, 'Waterborne and Pipeline'),\n",
" (20401, 'Transit'),\n",
" (20490, 'Safety belt and helmet use'),\n",
" (20424, 'By usage'),\n",
" (20468, 'Pickup Trucks'),\n",
" (20479, 'Motorcycle Helmet Use'),\n",
" (20446, 'Light Trucks'),\n",
" (20457, 'Vans and Sport Utility Vehicles'),\n",
" (20435, 'Passenger Cars'),\n",
" (20556, 'Estimated lives saved by use of restraint'),\n",
" (20491, 'By restraint'),\n",
" (20513, 'Air bags'),\n",
" (20546, 'Child restraints'),\n",
" (20524, 'Motorcycle helmets'),\n",
" (20535, 'Age 21 minimum legal drinking age'),\n",
" (20502, 'Safety belts'),\n",
" (20608, 'Hazardous materials incident'),\n",
" (20557, 'By incident'),\n",
" (20567, 'Air incidents'),\n",
" (20597, 'Water and Other incidents'),\n",
" (20577, 'Highway incidents'),\n",
" (20587, 'Rail incidents'),\n",
" (20619, 'Firearms found at TSA checkpoints'),\n",
" (20664, 'Pilot reported near-midair collisions'),\n",
" (20620, 'By severity'),\n",
" (20653, 'Other'),\n",
" (20642, 'Potential'),\n",
" (20631, 'Critical'),\n",
" (20675, 'Total bridges'),\n",
" (20686, 'Functionally obsolete bridges'),\n",
" (20697, 'Structurally deficient bridges'),\n",
" (20708, 'Licensed drivers'),\n",
" (20776, 'Vehicle registrations'),\n",
" (20743, 'By ownership'),\n",
" (20765, 'Total Publicly Owned (non-Mortorcycle)'),\n",
" (20754, 'Total Private and Commercial (non-Motorcycle)'),\n",
" (20709, 'By vehicle'),\n",
" (20742, 'Total Trucks'),\n",
" (20720, 'Total Automobiles'),\n",
" (20731, 'Total Buses'),\n",
" (20810, 'Motorcycle registrations'),\n",
" (20777, 'By ownership'),\n",
" (20788, 'Total Private and Commercial Motorcycles'),\n",
" (20799, 'Total Public Motorcycles'),\n",
" (22233, 'Child population'),\n",
" (22243, 'Child social services investigators'),\n",
" (22252, 'Child maltreatment case referrals'),\n",
" (22408, 'Child maltreatment investigations'),\n",
" (22253, 'By Reporter'),\n",
" (22261, '% Reported by Professionals'),\n",
" (22262, 'By Type of Professional'),\n",
" (22286, '% Foster Care Providers'),\n",
" (22310, '% Mental Health Personnel'),\n",
" (22294, '% legal and law Enforcement Personnel'),\n",
" (22318, '% Social Services Personnel'),\n",
" (22270, '% Child Daycare Providers'),\n",
" (22302, '% Medical Personnel'),\n",
" (22278, '% Education Personnel'),\n",
" (22375, '% Unclassified'),\n",
" (22376, 'By other source'),\n",
" (22392, '% Other'),\n",
" (22400, '% Unknown'),\n",
" (22384, '% Anonymous Sources'),\n",
" (22326, '% Reported by Nonprofessionals'),\n",
" (22327, 'By type of person'),\n",
" (22335, '% Alleged Perpetrators'),\n",
" (22359, '% Other Relatives'),\n",
" (22343, '% Alleged Victims'),\n",
" (22367, '% Parents'),\n",
" (22351, '% Friends and Neighbors'),\n",
" (22418, 'Average response time (hours)'),\n",
" (22428, 'Child fatalities (estimate)'),\n",
" (22438, 'Child victims (estimate)'),\n",
" (22446, 'National Estimate of Reports with a Disposition'),\n",
" (22456, 'States reporting'),\n",
" (22466, 'Workers per state'),\n",
" (22090, 'Children in foster care'),\n",
" (21976, 'By age group'),\n",
" (22014, '1-4 years old'),\n",
" (21995, '< 1 year old'),\n",
" (22052, '10 - 14 years old'),\n",
" (22071, '15 - 20 years old'),\n",
" (22033, '5 - 9 years old'),\n",
" (21784, 'By placement'),\n",
" (21917, 'Runaway'),\n",
" (21822, 'Placed in Foster Family Home (Relative)'),\n",
" (21841, 'Placed in Foster Family Home (Non-Relative)'),\n",
" (21860, 'Placed in Group Home'),\n",
" (21936, 'Placed in Trial Home Visit'),\n",
" (21879, 'Placed in Institution'),\n",
" (21803, 'Placed in Pre-Adoptive Home'),\n",
" (21898, 'Placed in Supervised Independent Living'),\n",
" (21937, 'By gender'),\n",
" (21956, 'Male'),\n",
" (21975, 'Female'),\n",
" (21516, 'By race / ethnicity'),\n",
" (21611, 'American Indian/Alaska Native/Pacific Islander'),\n",
" (21535, 'White'),\n",
" (21573, 'Hispanic'),\n",
" (21630, 'Two or more races'),\n",
" (21649, 'Unknown'),\n",
" (21592, 'Asian'),\n",
" (21554, 'Black'),\n",
" (21650, 'By plan'),\n",
" (21707, 'Planned for Adoption'),\n",
" (21688, 'Plan to Live with Other Relative(s)'),\n",
" (21669, 'Plan to Reunify with Parent(s) or Principal Caretaker(s)'),\n",
" (21764, 'Planned for Guardianship'),\n",
" (21745, 'Emancipation'),\n",
" (21783, 'Case Plan Goal Not Yet Established'),\n",
" (21726, 'Planned for Long Term Foster Care'),\n",
" (22109, 'Children entering foster care'),\n",
" (22128, 'Children exiting foster care'),\n",
" (22147, 'Median time in foster care (months)'),\n",
" (22166, 'Adopted with public child welfare agency'),\n",
" (22185, 'Median age'),\n",
" (22204, 'Waiting adoption'),\n",
" (22223,\n",
" 'Waiting adoption whose parental rights (for all living parents) were terminated during'),\n",
" (20822, 'Children Enrolled in Chip'),\n",
" (20828, 'Children enrolled in medicaid'),\n",
" (20866, 'School lunches served'),\n",
" (21019, 'Children receiving school lunch'),\n",
" (20867, 'By program'),\n",
" (20905, 'Free'),\n",
" (20981, 'Full Price'),\n",
" (20943, 'Reduced Price'),\n",
" (21286, 'Children under 18'),\n",
" (21020, 'By family structure'),\n",
" (21058, 'Two parents'),\n",
" (21096, 'One Parent, Total'),\n",
" (21134, 'One Parent, Mother Only'),\n",
" (21172, 'One Parent, Father Only'),\n",
" (21210, 'No Parent, Other relatives'),\n",
" (21248, 'No Parent, Non-relatives'),\n",
" (21324, 'Children under 18 in poverty'),\n",
" (21500, 'Homeless students enrolled in Local educational agencies'),\n",
" (21325, 'By shelter'),\n",
" (21465, 'Hotels/Motels'),\n",
" (21430, 'Unsheltered'),\n",
" (21360, 'Shelters, transitional housing, awaiting foster care'),\n",
" (21395, 'Doubled-up'),\n",
" (23084, 'Armed forces'),\n",
" (22513, 'By service type'),\n",
" (22855, 'Total Reserve'),\n",
" (22551, 'Total Active Duty (incl cadets and midshipmen)'),\n",
" (22667, 'By branch'),\n",
" (22819, 'Air Force'),\n",
" (22781, 'Marine Corps'),\n",
" (22743, 'Navy'),\n",
" (22705, 'Army'),\n",
" (22552, 'By enlistment'),\n",
" (22628, 'Total Enlisted'),\n",
" (22590, 'Total Officer'),\n",
" (22666, 'Cadets-Midshipmen'),\n",
" (22893, 'Total Civilian'),\n",
" (22894, 'By branch'),\n",
" (23008, 'Air Force'),\n",
" (22932, 'Army'),\n",
" (22970, 'Navy'),\n",
" (23046, 'Other DOD'),\n",
" (23427, 'Armed forces deaths'),\n",
" (23085, 'By cause'),\n",
" (23275, 'Pending'),\n",
" (23389, 'Undetermined'),\n",
" (23161, 'Hostile Action'),\n",
" (23123, 'Accidents'),\n",
" (23313, 'Self-Inflicted'),\n",
" (23199, 'Homicide'),\n",
" (23237, 'Illness'),\n",
" (23351, 'Terrorist Attack'),\n",
" (24870, 'Active duty military'),\n",
" (23428, 'By region'),\n",
" (24293, 'Africa, Near East & South Asia'),\n",
" (24294, 'By branch'),\n",
" (24417, 'Marine'),\n",
" (24458, 'Air Force'),\n",
" (24376, 'Navy'),\n",
" (24335, 'Army'),\n",
" (24087, 'Western Hemisphere'),\n",
" (24088, 'By branch'),\n",
" (24170, 'Navy'),\n",
" (24129, 'Army'),\n",
" (24211, 'Marine'),\n",
" (24252, 'Air Force'),\n",
" (23881, 'East Asia and Pacific'),\n",
" (23882, 'By branch'),\n",
" (23923, 'Army'),\n",
" (24046, 'Air Force'),\n",
" (23964, 'Navy'),\n",
" (24005, 'Marine'),\n",
" (24499, 'Undistributed'),\n",
" (24500, 'By branch'),\n",
" (24623, 'Marine'),\n",
" (24541, 'Army'),\n",
" (24664, 'Air Force'),\n",
" (24582, 'Navy'),\n",
" (23469, 'U.S. and Territories'),\n",
" (23470, 'By branch'),\n",
" (23634, 'Air Force'),\n",
" (23511, 'Army'),\n",
" (23552, 'Navy'),\n",
" (23593, 'Marine'),\n",
" (23675, 'Europe'),\n",
" (23676, 'By branch'),\n",
" (23758, 'Navy'),\n",
" (23717, 'Army'),\n",
" (23840, 'Air Force'),\n",
" (23799, 'Marine'),\n",
" (24665, 'By branch'),\n",
" (24788, 'Marine'),\n",
" (24706, 'Army'),\n",
" (24829, 'Air Force'),\n",
" (24747, 'Navy'),\n",
" (25519, 'National defense spending'),\n",
" (25383, 'Expenditures'),\n",
" (25428, 'Expenditures'),\n",
" (25429, 'Investments'),\n",
" (25474, 'Investment 4'),\n",
" (24955, 'VA expenditures'),\n",
" (24871, 'By expense'),\n",
" (39604, 'Compensation & Pension'),\n",
" (24943, 'Construction'),\n",
" (24919, 'Loan Guaranty'),\n",
" (24883, 'Medical Care'),\n",
" (24931, 'Insurance & Indemnities'),\n",
" (24895, 'Education & Vocational Rehabilitation/ Employment'),\n",
" (24907, 'General Operating Expenses'),\n",
" (24979, 'Median income'),\n",
" (24967, 'By gender'),\n",
" (25323, 'Veteran population'),\n",
" (25334, 'Veteran Patients'),\n",
" (25346, 'In poverty'),\n",
" (25358, 'Labor force participation'),\n",
" (25370, 'Unemployment'),\n",
" (25382, 'With any disability'),\n",
" (26407, 'Foreign aid obligations'),\n",
" (25907, 'By region'),\n",
" (25986, 'World'),\n",
" (25960, 'Sub-Saharan Africa'),\n",
" (25947, 'South and Central Asia'),\n",
" (25973, 'Western Hemisphere'),\n",
" (25999, 'Select countries'),\n",
" (26000, 'By geography'),\n",
" (26156, 'Egypt'),\n",
" (26026, 'Israel'),\n",
" (26065, 'Kenya'),\n",
" (26130, 'South Africa'),\n",
" (26013, 'Afghanistan'),\n",
" (26078, 'Ethiopia'),\n",
" (26143, 'Iraq'),\n",
" (26039, 'Jordan'),\n",
" (26104, 'Tanzania'),\n",
" (26117, 'Colombia'),\n",
" (26091, 'Nigeria'),\n",
" (26052, 'Pakistan'),\n",
" (25934, 'Europe and Eurasia'),\n",
" (25921, 'East Asia and Oceania'),\n",
" (26157, 'By department'),\n",
" (26183, 'U.S. Agency for International Development'),\n",
" (26209, 'Department of Defense'),\n",
" (26235, 'Department of Agriculture'),\n",
" (26222, 'Department of the Treasury'),\n",
" (26248, 'Others'),\n",
" (26170, 'Department of State'),\n",
" (26196, 'Department of the Army'),\n",
" (26249, 'By type of assistance'),\n",
" (26262, 'Economic Assistance'),\n",
" (26275, 'Military Assistance'),\n",
" (26276, 'By sector'),\n",
" (26328, 'Education'),\n",
" (26315, 'Commodity Assistance'),\n",
" (26354, 'Health and Population'),\n",
" (26367, 'Humanitarian'),\n",
" (26380, 'Infrastructure'),\n",
" (26393, 'Other'),\n",
" (26302, 'Economic Growth'),\n",
" (26341, 'Governance'),\n",
" (26289, 'Agriculture'),\n",
" (26493, 'Passports Issued'),\n",
" (26450, 'Passports Issued By Type'),\n",
" (26494, 'US embassies'),\n",
" (26537, 'US passport applications'),\n",
" (26580, 'Valid US passports in circulation'),\n",
" (25584, 'Border apprehensions'),\n",
" (25520, 'By location'),\n",
" (25536, 'Coastal Border'),\n",
" (25568, 'Southwest Border'),\n",
" (25552, 'Northern Border'),\n",
" (25600, 'Airport firearm discoveries'),\n",
" (25665, 'Border patrol agents'),\n",
" (25601, 'By location'),\n",
" (25617, 'Southwest Border'),\n",
" (25633, 'Northern Border'),\n",
" (25649, 'Coastal Border'),\n",
" (25681, 'Domestic drug arrests'),\n",
" (25761, 'Domestic drug seizures (non-hallucinogens)'),\n",
" (25682, 'By type'),\n",
" (25698, 'Cocaine (kgs)'),\n",
" (25714, 'Heroin (kgs)'),\n",
" (25730, 'Marijuana (kgs)'),\n",
" (25746, 'Methamphetamine (kgs)'),\n",
" (25777, 'DEA domestic drug seizures (hallucinogens)'),\n",
" (25792, 'Intellectual Property Seizures'),\n",
" (25807, 'MSRP of intellectual property seizures'),\n",
" (25822, 'Value of intellectual property seizures'),\n",
" (25868, 'Persons removed or returned'),\n",
" (25906, 'Visas Granted'),\n",
" (27210, 'US Federal Funds Rate'),\n",
" (27246, 'Average US Inflation Rate'),\n",
" (27138, 'Stock Index: S&P 500'),\n",
" (26726, 'Crude Oil Spot Price'),\n",
" (26943, '30-Year mortgage rate'),\n",
" (26690, 'Gold Price'),\n",
" (26618, '10-year treasury rate'),\n",
" (26654, 'CPI: food price index'),\n",
" (26762, 'US Bank Prime Loan Rate'),\n",
" (26798, 'USD per 1 GBP'),\n",
" (26799, 'USD per 1 Euro'),\n",
" (26835, 'Existing Home Sales'),\n",
" (26871, 'Median New Home Sales Price'),\n",
" (26907, 'Median Home Values'),\n",
" (26979, 'New Home Sales'),\n",
" (26994, 'Stock Index: DAX'),\n",
" (27030, 'Stock Index: Dow Jones Industrial Average'),\n",
" (27066, 'Stock Index: NASDAQ Composite'),\n",
" (27102, 'Stock Index: Nikkei'),\n",
" (27174, 'Stock Index: VIX'),\n",
" (29724, 'Gross domestic product'),\n",
" (29762, 'Assets of failed banks'),\n",
" (29800, 'Deposits of failed banks'),\n",
" (29838, 'Estimated loss of failed banks'),\n",
" (29876, 'Bankruptcies filed'),\n",
" (29913, 'Bankruptcies terminated'),\n",
" (29951, 'Patent applications filed'),\n",
" (29989, 'Patent applications pending'),\n",
" (30027, 'Patents issued'),\n",
" (30065, 'Businesses gaining jobs'),\n",
" (30103, 'Businesses losing jobs'),\n",
" (30141, 'Businesses less than a year old'),\n",
" (30179, 'Jobs created by businesses less than a year old'),\n",
" (30294, 'Net change in businesses'),\n",
" (30180, 'By reason'),\n",
" (30218, 'Opening Businesses'),\n",
" (30256, 'Closing Businesses'),\n",
" (30563, 'Net change in jobs'),\n",
" (30295, 'By type'),\n",
" (30448, 'Gross Job Losses'),\n",
" (30449, 'By reason'),\n",
" (30525, 'From Opening Businesses'),\n",
" (30487, 'From Expanding Businesses'),\n",
" (30333, 'Gross Job Gains'),\n",
" (30334, 'By reason'),\n",
" (30372, 'From Expanding Businesses'),\n",
" (30410, 'From Opening Businesses'),\n",
" (30674, 'Value of approved SBA loans'),\n",
" (30564, 'By SBA proram'),\n",
" (30619, 'Dealer Floor Plan 5'),\n",
" (30608, 'Recovery Act Assistance (506)'),\n",
" (30652, 'Microloan\\xa08'),\n",
" (30597, 'Debentures 3'),\n",
" (30663, 'Disaster 9'),\n",
" (30641, 'Refinance Assistance (504)'),\n",
" (30630, 'Recovery Act Mortgage Assistance (504 First Lien) 6'),\n",
" (30575, 'Loan Guaranties (7a)'),\n",
" (30586, 'Loan Guaranties (504)'),\n",
" (30785, 'Approved SBA loans'),\n",
" (30675, 'By SBA program'),\n",
" (30752, 'Refinance Assistance (504)'),\n",
" (30763, 'Microloan'),\n",
" (30708, 'Debentures 3'),\n",
" (30730, 'Dealer Floor Plan 5'),\n",
" (30719, 'Recovery Act Assistance (506)'),\n",
" (30774, 'Disaster 9'),\n",
" (30741, 'Recovery Act Mortgage Assistance (504 First Lien) 6'),\n",
" (30686, 'Loan Guaranties (7a)'),\n",
" (30697, 'Loan Guaranties (504)'),\n",
" (30823, 'Nonresidential private fixed investment'),\n",
" (30861, 'Residential Private Fixed Investment'),\n",
" (30898, 'Transportation Capital Stock'),\n",
" (30912, 'Average delay per commuter (hours per year)'),\n",
" (30926, 'Travel Time Index'),\n",
" (30936, 'Bridges'),\n",
" (30946, 'Functionally Obsolete'),\n",
" (30947, 'Federal R&D Outlays by Agency'),\n",
" (30948, 'Federal R&D Outlays by Agency'),\n",
" (30959, 'All Agencies'),\n",
" (29631, 'Federal R&D Outlays'),\n",
" (29463, 'By Agency'),\n",
" (29547, 'Department of Commerce'),\n",
" (29499, 'NASA'),\n",
" (29571, 'Department of Transportation'),\n",
" (29487, 'Department of Health & Human Svcs'),\n",
" (29475, 'Department of Defense'),\n",
" (29583, 'Department of Veterans Affairs'),\n",
" (29559, 'Department of the Interior'),\n",
" (29535, 'Department of Agriculture'),\n",
" (29511, 'Department of Energy'),\n",
" (29595, 'Environmental Protection Agency'),\n",
" (29619, 'All Other'),\n",
" (29607, 'Department of Homeland Security'),\n",
" (29523, 'National Science Foundation'),\n",
" (29704, 'Higher Education R&D Expenditures'),\n",
" (29632, 'By Type'),\n",
" (29692, 'All Other'),\n",
" (29644, 'Federal Government'),\n",
" (29680, 'Business'),\n",
" (29656, 'State & Local Government'),\n",
" (29668, 'Institution Funds'),\n",
" (27499, 'Average annual wage (2015 dollars)'),\n",
" (27247, 'By Sector'),\n",
" (27283, 'Utilities'),\n",
" (27271, 'Professional Services'),\n",
" (27259, 'Holding Companies'),\n",
" (27475, 'Agriculture'),\n",
" (27415, 'Real Estate'),\n",
" (27343, 'Wholesale Trade'),\n",
" (27355, 'Education'),\n",
" (27367, 'Construction'),\n",
" (27403, 'Transportation'),\n",
" (27391, 'Manufacturing'),\n",
" (27487, 'Accommodation & Food Services'),\n",
" (27427, 'Other Services'),\n",
" (27379, 'Health Care'),\n",
" (27451, 'Arts, Entertainment, Recreation'),\n",
" (27439, 'Administrative Support'),\n",
" (27307, 'Finance & Insurance'),\n",
" (27331, 'Government'),\n",
" (27295, 'Information'),\n",
" (27319, 'Mining'),\n",
" (27463, 'Retail Trade'),\n",
" (27752, 'Employment'),\n",
" (27500, 'By Sector'),\n",
" (27572, 'Mining'),\n",
" (27584, 'Government'),\n",
" (27512, 'Holding Companies'),\n",
" (27716, 'Retail Trade'),\n",
" (27608, 'Education'),\n",
" (27704, 'Arts, Entertainment, Recreation'),\n",
" (27668, 'Real Estate'),\n",
" (27656, 'Transportation'),\n",
" (27680, 'Other Services'),\n",
" (27536, 'Utilities'),\n",
" (27644, 'Manufacturing'),\n",
" (27692, 'Administrative Support'),\n",
" (27620, 'Construction'),\n",
" (27560, 'Finance & Insurance'),\n",
" (27524, 'Professional Services'),\n",
" (27596, 'Wholesale Trade'),\n",
" (27548, 'Information'),\n",
" (27728, 'Agriculture'),\n",
" (27632, 'Health Care'),\n",
" (27740, 'Accommodation & Food Services'),\n",
" (28005, 'Median annual wage (2015 dollars)'),\n",
" (27753, 'By Sector'),\n",
" (27993, 'Accommodation & Food Services'),\n",
" (27825, 'Mining'),\n",
" (27909, 'Transportation'),\n",
" (27921, 'Real Estate'),\n",
" (27777, 'Professional Services'),\n",
" (27837, 'Government'),\n",
" (27969, 'Retail Trade'),\n",
" (27933, 'Other Services'),\n",
" (27885, 'Health Care'),\n",
" (27849, 'Wholesale Trade'),\n",
" (27801, 'Information'),\n",
" (27945, 'Administrative Support'),\n",
" (27981, 'Agriculture'),\n",
" (27897, 'Manufacturing'),\n",
" (27789, 'Utilities'),\n",
" (27765, 'Holding Companies'),\n",
" (27873, 'Construction'),\n",
" (27813, 'Finance & Insurance'),\n",
" (27861, 'Education'),\n",
" (27957, 'Arts, Entertainment, Recreation'),\n",
" (28258, 'Wage Distribution'),\n",
" (28006, 'By Sector'),\n",
" (28042, 'Utilities'),\n",
" (28186, 'Other Services'),\n",
" (28030, 'Professional Services'),\n",
" (28114, 'Education'),\n",
" (28150, 'Manufacturing'),\n",
" (28198, 'Administrative Support'),\n",
" (28054, 'Information'),\n",
" (28066, 'Finance & Insurance'),\n",
" (28162, 'Transportation'),\n",
" (28078, 'Mining'),\n",
" (28246, 'Accommodation & Food Services'),\n",
" (28222, 'Retail Trade'),\n",
" (28090, 'Government'),\n",
" (28102, 'Wholesale Trade'),\n",
" (28174, 'Real Estate'),\n",
" (28126, 'Construction'),\n",
" (28234, 'Agriculture'),\n",
" (28138, 'Health Care'),\n",
" (28210, 'Arts, Entertainment, Recreation'),\n",
" (28018, 'Holding Companies'),\n",
" (28559, 'Average annual wage (2015 dollars)'),\n",
" (28259, 'By Occupation'),\n",
" (28343, 'Life, Physical, and Social Science'),\n",
" (28475, 'Production'),\n",
" (28319, 'Healthcare Practitioners and Technical'),\n",
" (28439, 'Cashiers'),\n",
" (28499, 'Healthcare Support'),\n",
" (28379, 'Construction and Extraction'),\n",
" (28271, 'Management'),\n",
" (28535, 'Personal Care and Service'),\n",
" (28511, 'Building and Grounds Cleaning and Maintenance'),\n",
" (28463, 'Office and Administrative Support'),\n",
" (28451, 'Retail Salespersons'),\n",
" (28427, 'Sales and Related'),\n",
" (28331, 'Business and Financial Operations'),\n",
" (28295, 'Computer and Mathematical'),\n",
" (28415, 'Protective Service'),\n",
" (28307, 'Architecture and Engineering'),\n",
" (28547, 'Food Preparation and Serving Related'),\n",
" (28487, 'Transportation and Material Moving'),\n",
" (28283, 'Legal'),\n",
" (28367, 'Education, Training, and Library'),\n",
" (28403, 'Installation, Maintenance, and Repair'),\n",
" (28523, 'Farming, Fishing, and Forestry'),\n",
" (28355, 'Arts, Design, Entertainment, Sports, and Media'),\n",
" (28391, 'Community and Social Service'),\n",
" (28860, 'Employment'),\n",
" (28560, 'By Occupation'),\n",
" (28632, '% Business and Financial Operations'),\n",
" (28692, '% Community and Social Service'),\n",
" (28728, '% Sales and Related'),\n",
" (28716, '% Protective Service'),\n",
" (28680, '% Construction and Extraction'),\n",
" (28620, '% Healthcare Practitioners and Technical'),\n",
" (28656, '% Arts, Design, Entertainment, Sports, and Media'),\n",
" (28836, '% Personal Care and Service'),\n",
" (28776, '% Production'),\n",
" (28608, '% Architecture and Engineering'),\n",
" (28848, '% Food Preparation and Serving Related'),\n",
" (28824, '% Farming, Fishing, and Forestry'),\n",
" (28764, '% Office and Administrative Support'),\n",
" (28644, '% B4:B2Food Preparation and Serving Related'),\n",
" (28812, '% Building and Grounds Cleaning and Maintenance'),\n",
" (28704, '% Installation, Maintenance, and Repair'),\n",
" (28668, '% Education, Training, and Library'),\n",
" (28788, '% Transportation and Material Moving'),\n",
" (28596, '% Computer and Mathematical'),\n",
" (28800, '% Healthcare Support'),\n",
" (28572, '% Management'),\n",
" (28752, '% \\xa0Retail Salespersons'),\n",
" (28740, '% Cashiers'),\n",
" (28584, '% Legal'),\n",
" (29161, 'Median annual wage (2015 dollars)'),\n",
" (28861, 'By Sector'),\n",
" (29065, 'Office and Administrative Support'),\n",
" (29089, 'Transportation and Material Moving'),\n",
" (28969, 'Education, Training, and Library'),\n",
" (29101, 'Healthcare Support'),\n",
" (29041, 'Cashiers'),\n",
" (29005, 'Installation, Maintenance, and Repair'),\n",
" (29149, 'Food Preparation and Serving Related'),\n",
" (28945, 'Life, Physical, and Social Science'),\n",
" (28981, 'Construction and Extraction'),\n",
" (29053, 'Retail Salespersons'),\n",
" (28933, 'Business and Financial Operations'),\n",
" (28993, 'Community and Social Service'),\n",
" (28909, 'Architecture and Engineering'),\n",
" (28873, 'Management'),\n",
" (28957, 'Arts, Design, Entertainment, Sports, and Media'),\n",
" (28921, 'Healthcare Practitioners and Technical'),\n",
" (28885, 'Legal'),\n",
" (29137, 'Personal Care and Service'),\n",
" (29077, 'Production'),\n",
" (29017, 'Protective Service'),\n",
" (29125, 'Farming, Fishing, and Forestry'),\n",
" (28897, 'Computer and Mathematical'),\n",
" (29113, 'Building and Grounds Cleaning and Maintenance'),\n",
" (29029, 'Sales and Related'),\n",
" (29462, 'Wage Distribution'),\n",
" (29162, 'By Occupation'),\n",
" (29258, '% Arts, Design, Entertainment, Sports, and Media'),\n",
" (29438, '% Personal Care and Service'),\n",
" (29378, '% Production'),\n",
" (29342, '% Cashiers'),\n",
" (29354, '% \\xa0Retail Salespersons'),\n",
" (29270, '% Education, Training, and Library'),\n",
" (29330, '% Sales and Related'),\n",
" (29402, '% Healthcare Support'),\n",
" (29222, '% Healthcare Practitioners and Technical'),\n",
" (29246, '% Life, Physical, and Social Science'),\n",
" (29174, '% Management'),\n",
" (29306, '% Installation, Maintenance, and Repair'),\n",
" (29210, '% Architecture and Engineering'),\n",
" (29186, '% Legal'),\n",
" (29282, '% Construction and Extraction'),\n",
" (29318, '% Protective Service'),\n",
" (29390, '% Transportation and Material Moving'),\n",
" (29450, '% Food Preparation and Serving Related'),\n",
" (29294, '% Community and Social Service'),\n",
" (29366, '% Office and Administrative Support'),\n",
" (29198, '% Computer and Mathematical'),\n",
" (29414, '% Building and Grounds Cleaning and Maintenance'),\n",
" (29426, '% Farming, Fishing, and Forestry'),\n",
" (29234, '% Business and Financial Operations'),\n",
" (31733, 'Depression'),\n",
" (31742, 'Asthma'),\n",
" (31750, 'Binge Drinking'),\n",
" (31759, 'Diabetes'),\n",
" (31768, 'Exercise'),\n",
" (31777, 'Obesity'),\n",
" (31786, 'Overweight'),\n",
" (31795, 'Smoking'),\n",
" (31828, 'All Drug Use 12 and Over'),\n",
" (31866, 'Total Reported Abortions (42 states reporting)'),\n",
" (31901, 'Infant mortality'),\n",
" (31805, 'Life Expectancy'),\n",
" (31796, 'Life Expectancy By Gender'),\n",
" (31815, 'Deaths'),\n",
" (31926, 'New Drugs Approved'),\n",
" (31951, 'Food Safety Procedures Performed'),\n",
" (31976, 'Food Safety Recalls'),\n",
" (32011, 'Covered by Private or Government Insurance'),\n",
" (31994, 'By Race'),\n",
" (32494, 'Expenditures of Healthcare Goods and Services'),\n",
" (32012, 'By Expenditure'),\n",
" (32457,\n",
" 'Total Administration and Total Net Cost of Health Insurance Expenditures'),\n",
" (32049, 'Personal Health Care'),\n",
" (32050, 'By Personal Healthcare Type'),\n",
" (32087, 'Hospital Expenditures'),\n",
" (32124, 'Physician and Clinical Expenditures'),\n",
" (32272, 'Other Non-Durable Medical Products Expenditures'),\n",
" (32235, 'Home Health Care Expenditures'),\n",
" (32346, 'Durable Medical Equipment Expenditures'),\n",
" (32161, 'Dental Services Expenditures'),\n",
" (32198, 'Other Professional Services Expenditures'),\n",
" (32420, 'Other Health, Residential, and Personal Care Expenditures'),\n",
" (32383, 'Nursing Care Facilities and Continuing Care Retirement Communities'),\n",
" ...]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[(m['id'], m['name']) for m in metricsall]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data USA API\n",
"\n",
"Based on https://github.com/DataUSA/datausa-api/wiki/Getting-Started"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"url = \"http://api.datausa.io/api/?show=geo&sumlevel=state&required=avg_wage\"\n",
"jd = requests.get(url).json()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'data': [[2014, '04000US01', 41185.2],\n",
" [2014, '04000US02', 51959.1],\n",
" [2014, '04000US04', 43582.8],\n",
" [2014, '04000US05', 38570.2],\n",
" [2014, '04000US06', 51851.5],\n",
" [2014, '04000US08', 49679.3],\n",
" [2014, '04000US09', 60977.6],\n",
" [2014, '04000US10', 47620.6],\n",
" [2014, '04000US11', 71820.1],\n",
" [2014, '04000US12', 42217.0],\n",
" [2014, '04000US13', 44957.1],\n",
" [2014, '04000US15', 44833.1],\n",
" [2014, '04000US16', 38357.1],\n",
" [2014, '04000US17', 49691.5],\n",
" [2014, '04000US18', 41072.2],\n",
" [2014, '04000US19', 41975.1],\n",
" [2014, '04000US20', 43453.9],\n",
" [2014, '04000US21', 39573.7],\n",
" [2014, '04000US22', 43441.9],\n",
" [2014, '04000US23', 40992.2],\n",
" [2014, '04000US24', 57025.9],\n",
" [2014, '04000US25', 58299.3],\n",
" [2014, '04000US26', 43528.2],\n",
" [2014, '04000US27', 48758.6],\n",
" [2014, '04000US28', 37638.8],\n",
" [2014, '04000US29', 42215.5],\n",
" [2014, '04000US30', 37732.0],\n",
" [2014, '04000US31', 40511.7],\n",
" [2014, '04000US32', 41918.2],\n",
" [2014, '04000US33', 51126.1],\n",
" [2014, '04000US34', 59716.3],\n",
" [2014, '04000US35', 38680.4],\n",
" [2014, '04000US36', 55258.8],\n",
" [2014, '04000US37', 42602.3],\n",
" [2014, '04000US38', 44515.7],\n",
" [2014, '04000US39', 43077.9],\n",
" [2014, '04000US40', 41001.7],\n",
" [2014, '04000US41', 42933.3],\n",
" [2014, '04000US42', 46524.3],\n",
" [2014, '04000US44', 47592.5],\n",
" [2014, '04000US45', 39836.5],\n",
" [2014, '04000US46', 38121.8],\n",
" [2014, '04000US47', 41518.3],\n",
" [2014, '04000US48', 46974.9],\n",
" [2014, '04000US49', 42599.9],\n",
" [2014, '04000US50', 43251.9],\n",
" [2014, '04000US51', 53508.3],\n",
" [2014, '04000US53', 51234.5],\n",
" [2014, '04000US54', 38862.0],\n",
" [2014, '04000US55', 42336.1],\n",
" [2014, '04000US56', 44894.6],\n",
" [2014, '04000US72', 25227.0],\n",
" [2015, '04000US72', 25186.3],\n",
" [2015, '04000US01', 41802.6],\n",
" [2015, '04000US02', 49974.0],\n",
" [2015, '04000US04', 44213.7],\n",
" [2015, '04000US05', 39881.4],\n",
" [2015, '04000US06', 53100.3],\n",
" [2015, '04000US08', 50447.0],\n",
" [2015, '04000US09', 61895.0],\n",
" [2015, '04000US10', 47956.1],\n",
" [2015, '04000US11', 73861.3],\n",
" [2015, '04000US12', 43357.2],\n",
" [2015, '04000US13', 45830.9],\n",
" [2015, '04000US15', 46559.5],\n",
" [2015, '04000US16', 37455.8],\n",
" [2015, '04000US17', 51341.4],\n",
" [2015, '04000US18', 42578.4],\n",
" [2015, '04000US19', 42066.9],\n",
" [2015, '04000US20', 44129.3],\n",
" [2015, '04000US21', 41198.1],\n",
" [2015, '04000US22', 44237.5],\n",
" [2015, '04000US23', 41440.4],\n",
" [2015, '04000US24', 58706.4],\n",
" [2015, '04000US25', 58715.5],\n",
" [2015, '04000US26', 44373.2],\n",
" [2015, '04000US27', 49828.1],\n",
" [2015, '04000US28', 37775.2],\n",
" [2015, '04000US29', 42985.4],\n",
" [2015, '04000US30', 41211.1],\n",
" [2015, '04000US31', 42916.0],\n",
" [2015, '04000US32', 42714.9],\n",
" [2015, '04000US33', 51736.8],\n",
" [2015, '04000US34', 61091.7],\n",
" [2015, '04000US35', 40246.0],\n",
" [2015, '04000US36', 56318.5],\n",
" [2015, '04000US37', 43811.0],\n",
" [2015, '04000US38', 44562.4],\n",
" [2015, '04000US39', 44280.4],\n",
" [2015, '04000US40', 42373.2],\n",
" [2015, '04000US41', 44959.3],\n",
" [2015, '04000US42', 47983.0],\n",
" [2015, '04000US44', 48137.9],\n",
" [2015, '04000US45', 40588.0],\n",
" [2015, '04000US46', 38294.7],\n",
" [2015, '04000US47', 42136.7],\n",
" [2015, '04000US48', 48573.4],\n",
" [2015, '04000US49', 43474.8],\n",
" [2015, '04000US50', 44057.2],\n",
" [2015, '04000US51', 53988.6],\n",
" [2015, '04000US53', 53413.8],\n",
" [2015, '04000US54', 40505.0],\n",
" [2015, '04000US55', 43733.5],\n",
" [2015, '04000US56', 46689.8]],\n",
" 'headers': ['year', 'geo', 'avg_wage'],\n",
" 'logic': [{'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all']},\n",
" 'table': 'pums_1yr.yg'},\n",
" {'dataset': 'ACS PUMS 5-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'soc': ['0', '1', '2', '3', 'all']},\n",
" 'table': 'pums_5yr.ygo'},\n",
" {'dataset': 'ACS PUMS 5-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'naics': ['0', '1', '2', 'all']},\n",
" 'table': 'pums_5yr.ygi'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'degree': ['all'],\n",
" 'geo': ['nation', 'state', 'puma', 'all']},\n",
" 'table': 'pums_1yr.ygd'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'race': ['all']},\n",
" 'table': 'pums_1yr.ygr'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'naics': ['0', '1', '2', 'all']},\n",
" 'table': 'pums_1yr.ygi'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'soc': ['0', '1', '2', '3', 'all']},\n",
" 'table': 'pums_1yr.ygo'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'wage_bin': ['all']},\n",
" 'table': 'pums_1yr.ygw'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'sex': ['all']},\n",
" 'table': 'pums_1yr.ygs'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'cip': ['2', 'all'],\n",
" 'geo': ['nation', 'state', 'puma', 'all']},\n",
" 'table': 'pums_1yr.ygc'},\n",
" {'dataset': 'ACS PUMS 5-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'birthplace': ['all'],\n",
" 'geo': ['nation', 'state', 'puma', 'all']},\n",
" 'table': 'pums_5yr.ygb_v2'},\n",
" {'dataset': 'Growth',\n",
" 'link': 'http://bls.gov',\n",
" 'org': 'Bureau of Labor Statistics',\n",
" 'supported_levels': {'geo': ['all', 'nation', 'state', 'msa'],\n",
" 'soc': ['all', '0', '1', '2', '3']},\n",
" 'table': 'bls.oes_ygo'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'birthplace': ['all'],\n",
" 'geo': ['nation', 'state', 'puma', 'all']},\n",
" 'table': 'pums_1yr.ygb_v2'},\n",
" {'dataset': 'ACS PUMS 5-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'race': ['all'],\n",
" 'soc': ['0', '1', '2', '3', 'all']},\n",
" 'table': 'pums_5yr.ygor'},\n",
" {'dataset': 'ACS PUMS 5-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'sex': ['all'],\n",
" 'soc': ['0', '1', '2', '3', 'all']},\n",
" 'table': 'pums_5yr.ygos'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'race': ['all'],\n",
" 'soc': ['0', '1', '2', '3', 'all']},\n",
" 'table': 'pums_1yr.ygor'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'sex': ['all'],\n",
" 'soc': ['0', '1', '2', '3', 'all']},\n",
" 'table': 'pums_1yr.ygos'},\n",
" {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all'],\n",
" 'naics': ['0', '1', '2', 'all'],\n",
" 'soc': ['0', '1', '2', '3', 'all']},\n",
" 'table': 'pums_1yr.ygio'}],\n",
" 'source': {'dataset': 'ACS PUMS 1-year Estimate',\n",
" 'link': 'http://census.gov/programs-surveys/acs/technical-documentation/pums.html',\n",
" 'org': 'Census Bureau',\n",
" 'supported_levels': {'geo': ['nation', 'state', 'puma', 'all']},\n",
" 'table': 'pums_1yr.yg'},\n",
" 'subs': {}}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jd"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wages = pd.DataFrame(jd['data'], columns=jd['headers'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>geo</th>\n",
" <th>avg_wage</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014</td>\n",
" <td>04000US01</td>\n",
" <td>41185.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014</td>\n",
" <td>04000US02</td>\n",
" <td>51959.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>04000US04</td>\n",
" <td>43582.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>04000US05</td>\n",
" <td>38570.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014</td>\n",
" <td>04000US06</td>\n",
" <td>51851.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2014</td>\n",
" <td>04000US08</td>\n",
" <td>49679.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2014</td>\n",
" <td>04000US09</td>\n",
" <td>60977.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2014</td>\n",
" <td>04000US10</td>\n",
" <td>47620.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2014</td>\n",
" <td>04000US11</td>\n",
" <td>71820.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2014</td>\n",
" <td>04000US12</td>\n",
" <td>42217.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2014</td>\n",
" <td>04000US13</td>\n",
" <td>44957.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2014</td>\n",
" <td>04000US15</td>\n",
" <td>44833.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2014</td>\n",
" <td>04000US16</td>\n",
" <td>38357.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2014</td>\n",
" <td>04000US17</td>\n",
" <td>49691.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2014</td>\n",
" <td>04000US18</td>\n",
" <td>41072.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2014</td>\n",
" <td>04000US19</td>\n",
" <td>41975.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2014</td>\n",
" <td>04000US20</td>\n",
" <td>43453.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2014</td>\n",
" <td>04000US21</td>\n",
" <td>39573.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2014</td>\n",
" <td>04000US22</td>\n",
" <td>43441.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2014</td>\n",
" <td>04000US23</td>\n",
" <td>40992.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2014</td>\n",
" <td>04000US24</td>\n",
" <td>57025.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2014</td>\n",
" <td>04000US25</td>\n",
" <td>58299.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2014</td>\n",
" <td>04000US26</td>\n",
" <td>43528.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2014</td>\n",
" <td>04000US27</td>\n",
" <td>48758.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2014</td>\n",
" <td>04000US28</td>\n",
" <td>37638.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2014</td>\n",
" <td>04000US29</td>\n",
" <td>42215.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2014</td>\n",
" <td>04000US30</td>\n",
" <td>37732.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2014</td>\n",
" <td>04000US31</td>\n",
" <td>40511.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2014</td>\n",
" <td>04000US32</td>\n",
" <td>41918.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2014</td>\n",
" <td>04000US33</td>\n",
" <td>51126.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>2015</td>\n",
" <td>04000US25</td>\n",
" <td>58715.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>2015</td>\n",
" <td>04000US26</td>\n",
" <td>44373.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>2015</td>\n",
" <td>04000US27</td>\n",
" <td>49828.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>2015</td>\n",
" <td>04000US28</td>\n",
" <td>37775.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>2015</td>\n",
" <td>04000US29</td>\n",
" <td>42985.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>2015</td>\n",
" <td>04000US30</td>\n",
" <td>41211.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>2015</td>\n",
" <td>04000US31</td>\n",
" <td>42916.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>2015</td>\n",
" <td>04000US32</td>\n",
" <td>42714.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>2015</td>\n",
" <td>04000US33</td>\n",
" <td>51736.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>2015</td>\n",
" <td>04000US34</td>\n",
" <td>61091.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>2015</td>\n",
" <td>04000US35</td>\n",
" <td>40246.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>2015</td>\n",
" <td>04000US36</td>\n",
" <td>56318.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2015</td>\n",
" <td>04000US37</td>\n",
" <td>43811.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2015</td>\n",
" <td>04000US38</td>\n",
" <td>44562.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2015</td>\n",
" <td>04000US39</td>\n",
" <td>44280.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>2015</td>\n",
" <td>04000US40</td>\n",
" <td>42373.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>2015</td>\n",
" <td>04000US41</td>\n",
" <td>44959.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>2015</td>\n",
" <td>04000US42</td>\n",
" <td>47983.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>2015</td>\n",
" <td>04000US44</td>\n",
" <td>48137.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>2015</td>\n",
" <td>04000US45</td>\n",
" <td>40588.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>2015</td>\n",
" <td>04000US46</td>\n",
" <td>38294.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>2015</td>\n",
" <td>04000US47</td>\n",
" <td>42136.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>2015</td>\n",
" <td>04000US48</td>\n",
" <td>48573.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>2015</td>\n",
" <td>04000US49</td>\n",
" <td>43474.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>2015</td>\n",
" <td>04000US50</td>\n",
" <td>44057.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>2015</td>\n",
" <td>04000US51</td>\n",
" <td>53988.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>2015</td>\n",
" <td>04000US53</td>\n",
" <td>53413.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>2015</td>\n",
" <td>04000US54</td>\n",
" <td>40505.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>2015</td>\n",
" <td>04000US55</td>\n",
" <td>43733.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>2015</td>\n",
" <td>04000US56</td>\n",
" <td>46689.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>104 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" year geo avg_wage\n",
"0 2014 04000US01 41185.2\n",
"1 2014 04000US02 51959.1\n",
"2 2014 04000US04 43582.8\n",
"3 2014 04000US05 38570.2\n",
"4 2014 04000US06 51851.5\n",
"5 2014 04000US08 49679.3\n",
"6 2014 04000US09 60977.6\n",
"7 2014 04000US10 47620.6\n",
"8 2014 04000US11 71820.1\n",
"9 2014 04000US12 42217.0\n",
"10 2014 04000US13 44957.1\n",
"11 2014 04000US15 44833.1\n",
"12 2014 04000US16 38357.1\n",
"13 2014 04000US17 49691.5\n",
"14 2014 04000US18 41072.2\n",
"15 2014 04000US19 41975.1\n",
"16 2014 04000US20 43453.9\n",
"17 2014 04000US21 39573.7\n",
"18 2014 04000US22 43441.9\n",
"19 2014 04000US23 40992.2\n",
"20 2014 04000US24 57025.9\n",
"21 2014 04000US25 58299.3\n",
"22 2014 04000US26 43528.2\n",
"23 2014 04000US27 48758.6\n",
"24 2014 04000US28 37638.8\n",
"25 2014 04000US29 42215.5\n",
"26 2014 04000US30 37732.0\n",
"27 2014 04000US31 40511.7\n",
"28 2014 04000US32 41918.2\n",
"29 2014 04000US33 51126.1\n",
".. ... ... ...\n",
"74 2015 04000US25 58715.5\n",
"75 2015 04000US26 44373.2\n",
"76 2015 04000US27 49828.1\n",
"77 2015 04000US28 37775.2\n",
"78 2015 04000US29 42985.4\n",
"79 2015 04000US30 41211.1\n",
"80 2015 04000US31 42916.0\n",
"81 2015 04000US32 42714.9\n",
"82 2015 04000US33 51736.8\n",
"83 2015 04000US34 61091.7\n",
"84 2015 04000US35 40246.0\n",
"85 2015 04000US36 56318.5\n",
"86 2015 04000US37 43811.0\n",
"87 2015 04000US38 44562.4\n",
"88 2015 04000US39 44280.4\n",
"89 2015 04000US40 42373.2\n",
"90 2015 04000US41 44959.3\n",
"91 2015 04000US42 47983.0\n",
"92 2015 04000US44 48137.9\n",
"93 2015 04000US45 40588.0\n",
"94 2015 04000US46 38294.7\n",
"95 2015 04000US47 42136.7\n",
"96 2015 04000US48 48573.4\n",
"97 2015 04000US49 43474.8\n",
"98 2015 04000US50 44057.2\n",
"99 2015 04000US51 53988.6\n",
"100 2015 04000US53 53413.8\n",
"101 2015 04000US54 40505.0\n",
"102 2015 04000US55 43733.5\n",
"103 2015 04000US56 46689.8\n",
"\n",
"[104 rows x 3 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wages"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"logic = pd.DataFrame(jd['logic'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset</th>\n",
" <th>link</th>\n",
" <th>org</th>\n",
" <th>supported_levels</th>\n",
" <th>table</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'geo': ['nation', 'state', 'puma', 'all']}</td>\n",
" <td>pums_1yr.yg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ACS PUMS 5-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'geo': ['nation', 'state', 'puma', 'all'], 's...</td>\n",
" <td>pums_5yr.ygo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ACS PUMS 5-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'naics': ['0', '1', '2', 'all'], 'geo': ['nat...</td>\n",
" <td>pums_5yr.ygi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'degree': ['all'], 'geo': ['nation', 'state',...</td>\n",
" <td>pums_1yr.ygd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'race': ['all'], 'geo': ['nation', 'state', '...</td>\n",
" <td>pums_1yr.ygr</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'naics': ['0', '1', '2', 'all'], 'geo': ['nat...</td>\n",
" <td>pums_1yr.ygi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'geo': ['nation', 'state', 'puma', 'all'], 's...</td>\n",
" <td>pums_1yr.ygo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'geo': ['nation', 'state', 'puma', 'all'], 'w...</td>\n",
" <td>pums_1yr.ygw</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'geo': ['nation', 'state', 'puma', 'all'], 's...</td>\n",
" <td>pums_1yr.ygs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'geo': ['nation', 'state', 'puma', 'all'], 'c...</td>\n",
" <td>pums_1yr.ygc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>ACS PUMS 5-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'birthplace': ['all'], 'geo': ['nation', 'sta...</td>\n",
" <td>pums_5yr.ygb_v2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Growth</td>\n",
" <td>http://bls.gov</td>\n",
" <td>Bureau of Labor Statistics</td>\n",
" <td>{'geo': ['all', 'nation', 'state', 'msa'], 'so...</td>\n",
" <td>bls.oes_ygo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'birthplace': ['all'], 'geo': ['nation', 'sta...</td>\n",
" <td>pums_1yr.ygb_v2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>ACS PUMS 5-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'race': ['all'], 'soc': ['0', '1', '2', '3', ...</td>\n",
" <td>pums_5yr.ygor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>ACS PUMS 5-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'sex': ['all'], 'geo': ['nation', 'state', 'p...</td>\n",
" <td>pums_5yr.ygos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'race': ['all'], 'soc': ['0', '1', '2', '3', ...</td>\n",
" <td>pums_1yr.ygor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'sex': ['all'], 'geo': ['nation', 'state', 'p...</td>\n",
" <td>pums_1yr.ygos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>ACS PUMS 1-year Estimate</td>\n",
" <td>http://census.gov/programs-surveys/acs/technic...</td>\n",
" <td>Census Bureau</td>\n",
" <td>{'naics': ['0', '1', '2', 'all'], 'geo': ['nat...</td>\n",
" <td>pums_1yr.ygio</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset \\\n",
"0 ACS PUMS 1-year Estimate \n",
"1 ACS PUMS 5-year Estimate \n",
"2 ACS PUMS 5-year Estimate \n",
"3 ACS PUMS 1-year Estimate \n",
"4 ACS PUMS 1-year Estimate \n",
"5 ACS PUMS 1-year Estimate \n",
"6 ACS PUMS 1-year Estimate \n",
"7 ACS PUMS 1-year Estimate \n",
"8 ACS PUMS 1-year Estimate \n",
"9 ACS PUMS 1-year Estimate \n",
"10 ACS PUMS 5-year Estimate \n",
"11 Growth \n",
"12 ACS PUMS 1-year Estimate \n",
"13 ACS PUMS 5-year Estimate \n",
"14 ACS PUMS 5-year Estimate \n",
"15 ACS PUMS 1-year Estimate \n",
"16 ACS PUMS 1-year Estimate \n",
"17 ACS PUMS 1-year Estimate \n",
"\n",
" link \\\n",
"0 http://census.gov/programs-surveys/acs/technic... \n",
"1 http://census.gov/programs-surveys/acs/technic... \n",
"2 http://census.gov/programs-surveys/acs/technic... \n",
"3 http://census.gov/programs-surveys/acs/technic... \n",
"4 http://census.gov/programs-surveys/acs/technic... \n",
"5 http://census.gov/programs-surveys/acs/technic... \n",
"6 http://census.gov/programs-surveys/acs/technic... \n",
"7 http://census.gov/programs-surveys/acs/technic... \n",
"8 http://census.gov/programs-surveys/acs/technic... \n",
"9 http://census.gov/programs-surveys/acs/technic... \n",
"10 http://census.gov/programs-surveys/acs/technic... \n",
"11 http://bls.gov \n",
"12 http://census.gov/programs-surveys/acs/technic... \n",
"13 http://census.gov/programs-surveys/acs/technic... \n",
"14 http://census.gov/programs-surveys/acs/technic... \n",
"15 http://census.gov/programs-surveys/acs/technic... \n",
"16 http://census.gov/programs-surveys/acs/technic... \n",
"17 http://census.gov/programs-surveys/acs/technic... \n",
"\n",
" org \\\n",
"0 Census Bureau \n",
"1 Census Bureau \n",
"2 Census Bureau \n",
"3 Census Bureau \n",
"4 Census Bureau \n",
"5 Census Bureau \n",
"6 Census Bureau \n",
"7 Census Bureau \n",
"8 Census Bureau \n",
"9 Census Bureau \n",
"10 Census Bureau \n",
"11 Bureau of Labor Statistics \n",
"12 Census Bureau \n",
"13 Census Bureau \n",
"14 Census Bureau \n",
"15 Census Bureau \n",
"16 Census Bureau \n",
"17 Census Bureau \n",
"\n",
" supported_levels table \n",
"0 {'geo': ['nation', 'state', 'puma', 'all']} pums_1yr.yg \n",
"1 {'geo': ['nation', 'state', 'puma', 'all'], 's... pums_5yr.ygo \n",
"2 {'naics': ['0', '1', '2', 'all'], 'geo': ['nat... pums_5yr.ygi \n",
"3 {'degree': ['all'], 'geo': ['nation', 'state',... pums_1yr.ygd \n",
"4 {'race': ['all'], 'geo': ['nation', 'state', '... pums_1yr.ygr \n",
"5 {'naics': ['0', '1', '2', 'all'], 'geo': ['nat... pums_1yr.ygi \n",
"6 {'geo': ['nation', 'state', 'puma', 'all'], 's... pums_1yr.ygo \n",
"7 {'geo': ['nation', 'state', 'puma', 'all'], 'w... pums_1yr.ygw \n",
"8 {'geo': ['nation', 'state', 'puma', 'all'], 's... pums_1yr.ygs \n",
"9 {'geo': ['nation', 'state', 'puma', 'all'], 'c... pums_1yr.ygc \n",
"10 {'birthplace': ['all'], 'geo': ['nation', 'sta... pums_5yr.ygb_v2 \n",
"11 {'geo': ['all', 'nation', 'state', 'msa'], 'so... bls.oes_ygo \n",
"12 {'birthplace': ['all'], 'geo': ['nation', 'sta... pums_1yr.ygb_v2 \n",
"13 {'race': ['all'], 'soc': ['0', '1', '2', '3', ... pums_5yr.ygor \n",
"14 {'sex': ['all'], 'geo': ['nation', 'state', 'p... pums_5yr.ygos \n",
"15 {'race': ['all'], 'soc': ['0', '1', '2', '3', ... pums_1yr.ygor \n",
"16 {'sex': ['all'], 'geo': ['nation', 'state', 'p... pums_1yr.ygos \n",
"17 {'naics': ['0', '1', '2', 'all'], 'geo': ['nat... pums_1yr.ygio "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logic"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "81px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment