How to use CensusData - 10 common examples
To help you get started, we’ve selected a few CensusData examples, based on popular ways it is used in public projects.
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jtleider / censusdata / test / test_censusgeo.py View on Github 
def test_eq(self):
self.assertEqual(censusdata.censusgeo([('state', '17'), ('county', '*')]), censusdata.censusgeo([('state', '17'), ('county', '*')]))def test_download_acs5_2015(self):
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '06'), ('place', '53000')]), ['B01001_001E', 'B01002_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 408073, 'B01002_001E': 36.3, 'B19013_001E': 54618}, [censusdata.censusgeo([('state', '06'), ('place', '53000')], 'Oakland city, California')]))
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '15'), ('county', '*')]), ['B01001_001E', 'B01002_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': [191482, 984178, 85, 69691, 160863], 'B01002_001E': [41.1, 36.9, 51.9, 41.6, 40], 'B19013_001E': [52108, 74460, 66250, 65101, 66476]},
[censusdata.censusgeo([('state', '15'), ('county', '001')], 'Hawaii County, Hawaii'), censusdata.censusgeo([('state', '15'), ('county', '003')], 'Honolulu County, Hawaii'),
censusdata.censusgeo([('state', '15'), ('county', '005')], 'Kalawao County, Hawaii'),
censusdata.censusgeo([('state', '15'), ('county', '007')], 'Kauai County, Hawaii'), censusdata.censusgeo([('state', '15'), ('county', '009')], 'Maui County, Hawaii')]))
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '17'), ('county', '031'), ('tract', '350100'), ('block group', '2')]), ['B01001_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 1293, 'B19013_001E': 49375}, [censusdata.censusgeo([('state', '17'), ('county', '031'), ('tract', '350100'), ('block group', '2')], 'Block Group 2, Census Tract 3501, Cook County, Illinois')]))
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('metropolitan statistical area/micropolitan statistical area', '16980')]), ['B01001_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 9534008, 'B19013_001E': 61828}, [censusdata.censusgeo([('metropolitan statistical area/micropolitan statistical area', '16980')], 'Chicago-Naperville-Elgin, IL-IN-WI Metro Area')]))
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '06')]), ['DP03_0021PE'], tabletype='profile'),
pd.DataFrame({'DP03_0021PE': 5.2}, [censusdata.censusgeo([('state', '06')], 'California')]))def test_download_acs5_2016(self):
assert_frame_equal(censusdata.download('acs5', 2016, censusdata.censusgeo([('state', '06'), ('place', '53000')]), ['B01001_001E', 'B01002_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 412040, 'B01002_001E': 36.2, 'B19013_001E': 57778}, [censusdata.censusgeo([('state', '06'), ('place', '53000')], 'Oakland city, California')]))
assert_frame_equal(censusdata.download('acs5', 2016, censusdata.censusgeo([('state', '15'), ('county', '*')]), ['B01001_001E', 'B01002_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': [193680, 986999, 91, 70447, 162456], 'B01002_001E': [41.8, 37.4, 56.5, 42.0, 40.5], 'B19013_001E': [53936, 77161, 65625, 68224, 68777]},
[censusdata.censusgeo([('state', '15'), ('county', '001')], 'Hawaii County, Hawaii'), censusdata.censusgeo([('state', '15'), ('county', '003')], 'Honolulu County, Hawaii'),
censusdata.censusgeo([('state', '15'), ('county', '005')], 'Kalawao County, Hawaii'),
censusdata.censusgeo([('state', '15'), ('county', '007')], 'Kauai County, Hawaii'), censusdata.censusgeo([('state', '15'), ('county', '009')], 'Maui County, Hawaii')]))
assert_frame_equal(censusdata.download('acs5', 2016, censusdata.censusgeo([('state', '17'), ('county', '031'), ('tract', '350100'), ('block group', '2')]), ['B01001_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 1374, 'B19013_001E': 44044}, [censusdata.censusgeo([('state', '17'), ('county', '031'), ('tract', '350100'), ('block group', '2')], 'Block Group 2, Census Tract 3501, Cook County, Illinois')]))
assert_frame_equal(censusdata.download('acs5', 2016, censusdata.censusgeo([('metropolitan statistical area/micropolitan statistical area', '16980')]), ['B01001_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 9528396, 'B19013_001E': 63327}, [censusdata.censusgeo([('metropolitan statistical area/micropolitan statistical area', '16980')], 'Chicago-Naperville-Elgin, IL-IN-WI Metro Area')]))
assert_frame_equal(censusdata.download('acs5', 2016, censusdata.censusgeo([('state', '06')]), ['DP03_0021PE'], tabletype='profile'),
pd.DataFrame({'DP03_0021PE': 5.2}, [censusdata.censusgeo([('state', '06')], 'California')]))def test_download_acs5_2015(self):
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '06'), ('place', '53000')]), ['B01001_001E', 'B01002_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 408073, 'B01002_001E': 36.3, 'B19013_001E': 54618}, [censusdata.censusgeo([('state', '06'), ('place', '53000')], 'Oakland city, California')]))
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '15'), ('county', '*')]), ['B01001_001E', 'B01002_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': [191482, 984178, 85, 69691, 160863], 'B01002_001E': [41.1, 36.9, 51.9, 41.6, 40], 'B19013_001E': [52108, 74460, 66250, 65101, 66476]},
[censusdata.censusgeo([('state', '15'), ('county', '001')], 'Hawaii County, Hawaii'), censusdata.censusgeo([('state', '15'), ('county', '003')], 'Honolulu County, Hawaii'),
censusdata.censusgeo([('state', '15'), ('county', '005')], 'Kalawao County, Hawaii'),
censusdata.censusgeo([('state', '15'), ('county', '007')], 'Kauai County, Hawaii'), censusdata.censusgeo([('state', '15'), ('county', '009')], 'Maui County, Hawaii')]))
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '17'), ('county', '031'), ('tract', '350100'), ('block group', '2')]), ['B01001_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 1293, 'B19013_001E': 49375}, [censusdata.censusgeo([('state', '17'), ('county', '031'), ('tract', '350100'), ('block group', '2')], 'Block Group 2, Census Tract 3501, Cook County, Illinois')]))
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('metropolitan statistical area/micropolitan statistical area', '16980')]), ['B01001_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 9534008, 'B19013_001E': 61828}, [censusdata.censusgeo([('metropolitan statistical area/micropolitan statistical area', '16980')], 'Chicago-Naperville-Elgin, IL-IN-WI Metro Area')]))
assert_frame_equal(censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '06')]), ['DP03_0021PE'], tabletype='profile'),
pd.DataFrame({'DP03_0021PE': 5.2}, [censusdata.censusgeo([('state', '06')], 'California')]))def test_download_error_tabletype(self):
self.assertRaises(ValueError, censusdata.download, 'acs5', 2015, censusdata.censusgeo([('state', '06')]), ['B19013_001E'], tabletype='cdetail')def test_download_acs1_2017(self):
assert_frame_equal(censusdata.download('acs1', 2017, censusdata.censusgeo([('state', '17')]), ['B19013_001E']),
pd.DataFrame({'B19013_001E': 62992}, [censusdata.censusgeo([('state', '17')], 'Illinois')]))def test_export(self):
data = censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '*')]), ['B01001_001E'])
try:
csv = StringIO.StringIO()
except NameError:
csv = io.StringIO()
censusdata.exportcsv(csv, data)
self.maxDiff = None
self.assertEqual(csv.getvalue(), textwrap.dedent("""\
state,NAME,B01001_001E
01,Alabama,4830620
02,Alaska,733375
04,Arizona,6641928
05,Arkansas,2958208
06,California,38421464
08,Colorado,5278906
09,Connecticut,3593222
10,Delaware,926454def test_download_acs5_2018(self):
assert_frame_equal(censusdata.download('acs5', 2018, censusdata.censusgeo([('state', '06'), ('place', '53000')]), ['B01001_001E', 'B01002_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 421042, 'B01002_001E': 36.5, 'B19013_001E': 68442}, [censusdata.censusgeo([('state', '06'), ('place', '53000')], 'Oakland city, California')]))
assert_frame_equal(censusdata.download('acs5', 2018, censusdata.censusgeo([('state', '15'), ('county', '*')]), ['B01001_001E', 'B01002_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': [197658, 165281, 71377, 75, 987638], 'B01002_001E': [42.3, 41.1, 42.4, 57.1, 37.6], 'B19013_001E': [59297, 77117, 78482, 61875, 82906]},
[censusdata.censusgeo([('state', '15'), ('county', '001')], 'Hawaii County, Hawaii'), censusdata.censusgeo([('state', '15'), ('county', '009')], 'Maui County, Hawaii'),
censusdata.censusgeo([('state', '15'), ('county', '007')], 'Kauai County, Hawaii'), censusdata.censusgeo([('state', '15'), ('county', '005')], 'Kalawao County, Hawaii'),
censusdata.censusgeo([('state', '15'), ('county', '003')], 'Honolulu County, Hawaii'),]))
assert_frame_equal(censusdata.download('acs5', 2018, censusdata.censusgeo([('state', '17'), ('county', '031'), ('tract', '350100'), ('block group', '2')]), ['B01001_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 1433, 'B19013_001E': 33750}, [censusdata.censusgeo([('state', '17'), ('county', '031'), ('tract', '350100'), ('block group', '2')], 'Block Group 2, Census Tract 3501, Cook County, Illinois')]))
assert_frame_equal(censusdata.download('acs5', 2018, censusdata.censusgeo([('metropolitan statistical area/micropolitan statistical area', '16980')]), ['B01001_001E', 'B19013_001E']),
pd.DataFrame({'B01001_001E': 9536428, 'B19013_001E': 68715}, [censusdata.censusgeo([('metropolitan statistical area/micropolitan statistical area', '16980')], 'Chicago-Naperville-Elgin, IL-IN-WI Metro Area')]))
assert_frame_equal(censusdata.download('acs5', 2018, censusdata.censusgeo([('state', '06')]), ['DP03_0021PE'], tabletype='profile'),
pd.DataFrame({'DP03_0021PE': 5.1}, [censusdata.censusgeo([('state', '06')], 'California')]))def test_download_acs1_201214(self):
medhhinc = {2012: 55137, 2013: 56210, 2014: 57444}
for year in range(2012, 2014+1):
assert_frame_equal(censusdata.download('acs1', year, censusdata.censusgeo([('state', '17')]), ['B19013_001E']),
pd.DataFrame({'B19013_001E': medhhinc[year]}, [censusdata.censusgeo([('state', '17')], 'Illinois')]))def test_download_acs5_200914(self):
medhhinc = {2009: 55222, 2010: 55735, 2011: 56576, 2012: 56853, 2013: 56797, 2014: 57166}
for year in range(2009, 2014+1):
assert_frame_equal(censusdata.download('acs5', year, censusdata.censusgeo([('state', '17')]), ['B19013_001E']),
pd.DataFrame({'B19013_001E': medhhinc[year]}, [censusdata.censusgeo([('state', '17')], 'Illinois')]))
