Strato-Query

Installation and usage

Python:

$ pip install strato-query

R:

library(devtools)
devtools::install_github('StratoDem/strato-query')

Authentication

# Example passing a StratoDem Analytics API token to a Python file using the API
$ API_TOKEN=my-api-token-here python examples/examples.py

Median household income for 80+ households across the US, by year

Python:

from strato_query.base_API_query import *
from strato_query.standard_filters import *
# Finds median household income in the US for those 80+ from 2010 to 2013
df = BaseAPIQuery.query_api_df(
 query_params=APIMedianQueryParams(
 query_type='MEDIAN',
 table='incomeforecast_us_annual_income_group_age',
 data_fields=('year', {'median_value': 'median_income'}),
 median_variable_name='income_g',
 data_filters=(
 GtrThanOrEqFilter(var='age_g', val=17).to_dict(),
 BetweenFilter(var='year', val=[2010, 2013]).to_dict(),
 ),
 groupby=('year',),
 order=('year',),
 aggregations=(),
 )
)
print('Median US household income 80+:')
print(df.head())

R:

library(stRatoquery)
# Finds median household income in the US for those 80+ from 2010 to 2013
df = submit_api_query(
 query = median_query_params(
 table = 'incomeforecast_us_annual_income_group_age',
 data_fields = api_fields(fields_list = list('year', 'geoid2', list(median_value = 'median_hhi'))),
 data_filters = list(
 ge_filter(filter_variable = 'age_g', filter_value = 17),
 between_filter(filter_variable = 'year', filter_value = c(2010, 2013))
 ),
 groupby=c('year'),
 median_variable_name='income_g',
 aggregations=list()
 ),
 apiToken = 'my-api-token-here')
print('Median US household income 80+:')
print(head(df))

Median US household income 80+:
 MEDIAN_VALUE YEAR
0 27645 2010
1 29269 2011
2 30474 2012
3 30712 2013

Population density in the Boston MSA

Python:

from strato_query.base_API_query import *
from strato_query.standard_filters import *
df = BaseAPIQuery.query_api_df(
 query_params=APIQueryParams(
 query_type='COUNT',
 table='populationforecast_metro_annual_population',
 data_fields=('year', 'cbsa', {'population': 'population'}),
 data_filters=(
 LessThanFilter(var='year', val=2015).to_dict(),
 EqFilter(var='cbsa', val=14454).to_dict(),
 ),
 aggregations=(dict(aggregation_func='sum', variable_name='population'),),
 groupby=('cbsa', 'year'),
 order=('year',),
 join=APIQueryParams(
 query_type='AREA',
 table='geocookbook_metro_na_shapes_full',
 data_fields=('cbsa', 'area', 'name'),
 data_filters=(),
 groupby=('cbsa', 'name'),
 aggregations=(),
 on=dict(left=('cbsa',), right=('cbsa',)),
 )
 )
)
df['POP_PER_SQ_MI'] = df['POPULATION'].div(df['AREA'])
df_final = df[['YEAR', 'NAME', 'POP_PER_SQ_MI']]
print('Population density in the Boston MSA up to 2015:')
print(df_final.head())
print('Results truncated')

R:

library(stRatoquery)
df = submit_api_query(
 query = api_query_params(
 table = 'populationforecast_metro_annual_population',
 data_fields = api_fields(fields_list = list('year', 'cbsa', list(population = 'population'))),
 data_filters = list(
 lt_filter(filter_variable = 'year', filter_value = 2015),
 eq_filter(filter_variable = 'cbsa', filter_value = 14454)
 ),
 groupby=c('year'),
 aggregations = list(sum_aggregation(variable_name = 'population')),
 join = api_query_params(
 table = 'geocookbook_metro_na_shapes_full',
 query_type = 'AREA',
 data_fields = api_fields(fields_list = list('cbsa', 'area', 'name')),
 data_filters = list(),
 groupby = c('cbsa', 'name'),
 aggregations = list(),
 on = list(left = c('cbsa'), right = c('cbsa'))
 )
 ),
 apiToken = 'my-api-token-here')

Population density in the Boston MSA up to 2015:
 YEAR NAME POP_PER_SQ_MI
0 2000 Boston, MA 1139.046639
1 2001 Boston, MA 1149.129937
2 2002 Boston, MA 1153.094740
3 2003 Boston, MA 1152.352351
4 2004 Boston, MA 1149.932307
Results truncated

Example use of query base class with API call and example filter

from strato_query.base_API_query import *
from strato_query.standard_filters import *
class ExampleAPIQuery(BaseAPIQuery):
 @classmethod
 def get_df_from_API_call(cls, **kwargs):
 # This API call will return the population 65+ in 2018 within 5 miles of the lat/long pair
 age_filter = GtrThanOrEqFilter(
 var='age_g',
 val=14).to_dict()
 year_filter = EqFilter(
 var='year',
 val=2018).to_dict()
 mile_radius_filter = dict(
 filter_type='mile_radius',
 filter_value=dict(
 latitude=26.606484,
 longitude=-81.851531,
 miles=5),
 filter_variable='')
 df = cls.query_api_df(
 query_params=APIQueryParams(
 table='populationforecast_tract_annual_population_age',
 data_fields=('POPULATION',),
 data_filters=(age_filter, year_filter, mile_radius_filter),
 query_type='COUNT',
 aggregations=(),
 groupby=()
 )
 )
 return df