def main():
# Create a list of dcids for California, Kentucky, and Maryland
ca, ky, md = 'geoId/06', 'geoId/21', 'geoId/24'
dcids = [ca, ky, md]
# Get the population of all employed individuals in the above states.
print('Get Populations for All Employed Individuals')
employed = dc.get_populations(dcids, 'Person', constraining_properties={
'employment': 'BLS_Employed'})
print(json.dumps(employed, indent=2))
# Get the count for all male / females for the above states in 2016
print('Get Population Counts for Employed Individuals in Maryland')
pop_dcids = [employed[md]]
obs = dc.get_observations(pop_dcids,
'count',
'measuredValue',
'2018-12',
observation_period='P1M',
measurement_method='BLSSeasonallyAdjusted')
print(json.dumps(obs, indent=2))
# Get all population and observation data of Mountain View.
print('Get Mountain View population and observation')
popobs = dc.get_pop_obs("geoId/0649670")
pprint.pprint(popobs)
def test_valid_dcid(self, urlopen):
""" Calling get_pop_obs with valid dcid returns valid results. """
# Set the API key
dc.set_api_key('TEST-API-KEY')
# Call get_pop_obs
pop_obs = dc.get_pop_obs('geoId/06085')
self.assertDictEqual(
pop_obs, {
'name': 'Mountain View',
'placeType': 'City',
'populations': {
'dc/p/013ldrstf6lnf': {
'numConstraints':
6,
'observations': [{
'marginOfError': 119,
'measuredProp': 'count',
'measuredValue': 225,
'measurementMethod': 'CensusACS5yrSurvey',
'observationDate': '2014'
}, {
'marginOfError': 108,
'measuredProp': 'count',
'measuredValue': 180,
'measurementMethod': 'CensusACS5yrSurvey',
'observationDate': '2012'
}],
'popType':
'Person',
'propertyValues': {
'age': 'Years16Onwards',
'gender': 'Male',
'income': 'USDollar30000To34999',
'incomeStatus': 'WithIncome',
'race': 'USC_HispanicOrLatinoRace',
'workExperience': 'USC_NotWorkedFullTime'
}
}
}
})
def main():
# Create a list of dcids for California, Kentucky, and Maryland
ca, ky, md = 'geoId/06', 'geoId/21', 'geoId/24'
dcids = [ca, ky, md]
# Get the population of all employed individuals in the above states.
utils._print_header('Get Populations for All Employed Individuals')
employed = dc.get_populations(
dcids,
'Person',
constraining_properties={'employment': 'BLS_Employed'})
print('> Printing all populations of employed individuals\n')
print(json.dumps(employed, indent=2))
# Get the count for all male / females for the above states in 2016
utils._print_header(
'Get Population Counts for Employed Individuals in Maryland')
pop_dcids = [employed[md]]
print('> Requesting observations for {} in December 2018\n'.format(
pop_dcids))
obs = dc.get_observations(pop_dcids,
'count',
'measuredValue',
'2018-12',
observation_period='P1M',
measurement_method='BLSSeasonallyAdjusted')
print(json.dumps(obs, indent=2))
# We perform the same workflow using a Pandas DataFrame. First, initialize a
# DataFrame with Santa Clara and Montgomery County.
utils._print_header('Initialize the DataFrame')
pd_frame = pd.DataFrame({'state': ['geoId/06', 'geoId/21', 'geoId/24']})
pd_frame['state_name'] = dc.get_property_values(pd_frame['state'], 'name')
pd_frame = dc.flatten_frame(pd_frame)
print(pd_frame)
# Get populations for employed individuals
utils._print_header('Add Population and Observation to DataFrame')
pd_frame['employed_pop'] = dc.get_populations(
pd_frame['state'],
'Person',
constraining_properties={'employment': 'BLS_Employed'})
# Add the observation for employed individuals
pd_frame['employed_count'] = dc.get_observations(
pd_frame['employed_pop'],
'count',
'measuredValue',
'2018-12',
observation_period='P1M',
measurement_method='BLSSeasonallyAdjusted')
print(pd_frame)
# Final dataframe. Use the convenience function "clean_frame" to convert
# columns to numerical types.
utils._print_header('Final Data Frame')
pd_frame = dc.clean_frame(pd_frame)
print(pd_frame)
# Get all population and observation data of Mountain View.
utils._print_header('Get Mountain View population and observation')
popobs = dc.get_pop_obs("geoId/0649670")
pprint.pprint(popobs)