Overview

The following data set is information obtained about counties in the United States from 2007 through 2014 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics.

http://www.census.gov/quickfacts/table/PST045215/00

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"White Alone, not Hispanic or Latino" float 75.6
"Two or More Races" float 1.8
"Asian Alone" float 1.1
"Native Hawaiian and Other Pacific Islander Alone" float 0.1
"White Alone" float 77.9
"Hispanic or Latino" float 2.7
"Black Alone" float 18.7
"American Indian and Alaska Native Alone" float 0.5
Key Type Example Value Comment
"Percent Under 18 Years" float 25.2
"Percent 65 and Older" float 13.8
"Percent Under 5 Years" float 6.0
Key Type Example Value Comment
"Retail Sales" int 5981 $100,000 of dollars
"Merchant Wholesaler Sales" int 0 $100,000 of dollars
"Accommodation and Food Services Sales" int 881 $100,000 of dollars
"Retail Sales per Capita" int 12003
Key Type Example Value Comment
"Ethnicities" dict { } percent, 2014
"Age" dict { } percent, 2014
"Miscellaneous" dict { }
"Sales" dict { } 2007
"County" str "Autauga County"
[Preview ]
"State" str "AL"
[Preview ]
"Income" dict { } 2009-2013
"Education" dict { } percent, 2009-2013
"Employment" dict { }
"Housing" dict { }
"Population" dict { }
Value Count
"Washington County" 30
"Jefferson County" 25
"Franklin County" 24
"Jackson County" 23
"Lincoln County" 23
"Madison County" 19
"Clay County" 18
"Montgomery County" 18
"Union County" 17
"Marion County" 17
"Monroe County" 17
"Wayne County" 16
"Warren County" 14
"Grant County" 14
"Greene County" 14
"Carroll County" 13
"Douglas County" 12
"Clark County" 12
"Adams County" 12
"Polk County" 12
"Marshall County" 12
"Johnson County" 12
"Lee County" 12
"Lake County" 12
"Crawford County" 11
"Scott County" 11
"Lawrence County" 11
"Morgan County" 11
"Fayette County" 11
"Calhoun County" 11
"Hamilton County" 10
"Pike County" 10
"Logan County" 10
"Henry County" 10
"Hancock County" 10
"Perry County" 10
"Putnam County" 9
"Shelby County" 9
"Knox County" 9
"Brown County" 9
"Cass County" 9
"Benton County" 9
"Clinton County" 9
"Orange County" 8
"Randolph County" 8
"Jasper County" 8
"Cherokee County" 8
"Harrison County" 8
"Columbia County" 8
"Butler County" 8
"Fulton County" 8
"Boone County" 8
"Cumberland County" 8
"Mercer County" 8
"Lewis County" 7
"Howard County" 7
"Webster County" 7
"Taylor County" 7
"Pulaski County" 7
"Newton County" 6
"Garfield County" 6
"Delaware County" 6
"Custer County" 6
"Hardin County" 6
"Richland County" 6
"Mason County" 6
"Martin County" 6
"Sullivan County" 6
"Floyd County" 6
"Jones County" 6
"DeKalb County" 6
"Macon County" 6
"Clarke County" 5
"Lafayette County" 5
"Anderson County" 5
"Saline County" 5
"Essex County" 5
"Kent County" 5
"Pierce County" 5
"Livingston County" 5
"Campbell County" 5
"Decatur County" 5
"Mitchell County" 5
"Carter County" 5
"Lyon County" 5
"Dallas County" 5
"Sheridan County" 5
"Houston County" 5
"York County" 5
"Henderson County" 5
"White County" 5
"Allen County" 4
"Middlesex County" 4
"Carbon County" 4
"Caldwell County" 4
"Dawson County" 4
"Iron County" 4
"Sherman County" 4
"Wheeler County" 4
"Camden County" 4
... ...
Value Count
"TX" 254
"GA" 159
"VA" 134
"KY" 120
"MO" 115
"KS" 105
"IL" 102
"NC" 100
"IA" 99
"TN" 95
"NE" 93
"IN" 92
"OH" 88
"MN" 87
"MI" 83
"MS" 82
"OK" 77
"AR" 75
"WI" 72
"AL" 67
"PA" 67
"FL" 67
"SD" 66
"CO" 64
"LA" 64
"NY" 62
"CA" 58
"MT" 56
"WV" 55
"ND" 53
"SC" 46
"ID" 44
"WA" 39
"OR" 36
"NM" 33
"UT" 29
"AK" 29
"MD" 24
"WY" 23
"NJ" 21
"NV" 17
"ME" 16
"AZ" 15
"MA" 14
"VT" 14
"NH" 10
"CT" 8
"RI" 5
"HI" 5
"DE" 3
"DC" 1
Key Type Example Value Comment
"Private Non-farm Establishments" int 817 2013
"Private Non-farm Employment Percent Change" float 2.1 2012-2013
"Firms" dict { } 2007
"Nonemployer Establishments" int 2947 2013
"Private Non-farm Employment" int 10120 2013
Key Type Example Value Comment
"Median Houseold Income" int 53682
"Per Capita Income" int 24571 2013 dollars
"Persons Below Poverty Level" float 12.1 percent
Key Type Example Value Comment
"Manufacturers Shipments" int 0 $100,000 of dollars, 2007
"Foreign Born" float 1.6 percent 2009-2013
"Percent Female" float 51.4 2014
"Language Other than English at Home" float 3.5 age 5+, percent, 2009-2013
"Living in Same House +1 Years" float 85.0 percent, 2009-2013
"Mean Travel Time to Work" float 26.2 minutes, workers aged 16+, 2009-2013
"Building Permits" int 131 2014
"Veterans" int 5922 2009-2013
"Land Area" float 594.44 Square Miles, 2010
Key Type Example Value Comment
"Total" int 4067
"Hispanic-Owned" float 0.7
"Native Hawaiian and Other Pacific Islander-Owned" float 0.0
"American Indian-Owned" float 0.0
"Black-Owned" float 15.2
"Asian-Owned" float 1.3
"Women-Owned" float 31.7
Key Type Example Value Comment
"Housing Units" int 22751 2014
"Units in Multi-Unit Structures" float 8.3 percent, 2009-2013
"Median Value of Owner-Occupied Units" int 136200 2009-2013
"Households" int 20071 2009-2013
"Persons per Household" float 2.71 2009-2013
"Homeownership Rate" float 76.8 2009-2013
Key Type Example Value Comment
"2014 Population" int 55395
"2010 Population" int 54571
"Population per Square Mile" float 91.8 2010
"Population Percent Change" float 1.5 April 1, 2010 to July 1, 2014
Key Type Example Value Comment
"Bachelor's Degree or Higher" float 20.9
"High School or Higher" float 85.6

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import county_demographics
list_of_report = county_demographics.get_all_counties()
Additionally, some of the functions can return a sample of the Big Data using an extra argument. If you use this sampled Big Data, it may be much faster. When you are sure your code is correct, you can remove the argument to use the full dataset.
import county_demographics
# These may be slow!
list_of_report = county_demographics.get_all_counties(test=True)

Documentation

 county_demographics.get_all_counties(test=False)

Returns the report for each county from the dataset.