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        "author": "Michael Clawar, Raaid Arshad, Eric Linden",
        "author_email": "tech@stratodem.com",
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        "description": "# Strato-Query\ntools to help create queries to StratoDem's API\n\n## Installation and usage\n#### Python:\n```\n$ pip install strato-query\n```\n\n#### R:\n```\nlibrary(devtools)\ndevtools::install_github('StratoDem/strato-query')\n```\n\n### Authentication\n`strato_query` looks for an `API_TOKEN` environment variable.\n```bash\n# Example passing a StratoDem Analytics API token to a Python file using the API\n$ API_TOKEN=my-api-token-here python examples/examples.py\n```\n\n### Median household income for 80+ households across the US, by year\n#### Python:\n```python\nfrom strato_query.base_API_query import *\nfrom strato_query.standard_filters import *\n\n\n# Finds median household income in the US for those 80+ from 2010 to 2013\ndf = BaseAPIQuery.query_api_df(\n    query_params=APIMedianQueryParams(\n        query_type='MEDIAN',\n        table='incomeforecast_us_annual_income_group_age',\n        data_fields=('year', {'median_value': 'median_income'}),\n        median_variable_name='income_g',\n        data_filters=(\n            GtrThanOrEqFilter(var='age_g', val=17).to_dict(),\n            BetweenFilter(var='year', val=[2010, 2013]).to_dict(),\n        ),\n        groupby=('year',),\n        order=('year',),\n        aggregations=(),\n    )\n)\n\nprint('Median US household income 80+:')\nprint(df.head())\n```\n#### R:\n```R\nlibrary(stRatoquery)\n\n\n# Finds median household income in the US for those 80+ from 2010 to 2013\ndf = submit_api_query(\n  query = median_query_params(\n    table = 'incomeforecast_us_annual_income_group_age',\n    data_fields = api_fields(fields_list = list('year', 'geoid2', list(median_value = 'median_hhi'))),\n    data_filters = list(\n        ge_filter(filter_variable = 'age_g', filter_value = 17),\n        between_filter(filter_variable = 'year', filter_value = c(2010, 2013))\n    ),\n    groupby=c('year'),\n    median_variable_name='income_g',\n    aggregations=list()\n  ),\n  apiToken = 'my-api-token-here')\n\nprint('Median US household income 80+:')\nprint(head(df))\n```\n\nOutput:\n```\nMedian US household income 80+:\n   MEDIAN_VALUE  YEAR\n0         27645  2010\n1         29269  2011\n2         30474  2012\n3         30712  2013\n```\n\n### Population density in the Boston MSA\n#### Python:\n```python\nfrom strato_query.base_API_query import *\nfrom strato_query.standard_filters import *\n\n\ndf = BaseAPIQuery.query_api_df(\n    query_params=APIQueryParams(\n        query_type='COUNT',\n        table='populationforecast_metro_annual_population',\n        data_fields=('year', 'cbsa', {'population': 'population'}),\n        data_filters=(\n            LessThanFilter(var='year', val=2015).to_dict(),\n            EqFilter(var='cbsa', val=14454).to_dict(),\n        ),\n        aggregations=(dict(aggregation_func='sum', variable_name='population'),),\n        groupby=('cbsa', 'year'),\n        order=('year',),\n        join=APIQueryParams(\n            query_type='AREA',\n            table='geocookbook_metro_na_shapes_full',\n            data_fields=('cbsa', 'area', 'name'),\n            data_filters=(),\n            groupby=('cbsa', 'name'),\n            aggregations=(),\n            on=dict(left=('cbsa',), right=('cbsa',)),\n        )\n    )\n)\n\ndf['POP_PER_SQ_MI'] = df['POPULATION'].div(df['AREA'])\ndf_final = df[['YEAR', 'NAME', 'POP_PER_SQ_MI']]\n\nprint('Population density in the Boston MSA up to 2015:')\nprint(df_final.head())\nprint('Results truncated')\n```\n\n#### R:\n```R\nlibrary(stRatoquery)\n\ndf = submit_api_query(\n  query = api_query_params(\n    table = 'populationforecast_metro_annual_population',\n    data_fields = api_fields(fields_list = list('year', 'cbsa', list(population = 'population'))),\n    data_filters = list(\n        lt_filter(filter_variable = 'year', filter_value = 2015),\n        eq_filter(filter_variable = 'cbsa', filter_value = 14454)\n    ),\n    groupby=c('year'),\n    aggregations = list(sum_aggregation(variable_name = 'population')),\n    join = api_query_params(\n        table = 'geocookbook_metro_na_shapes_full',\n        query_type = 'AREA',\n        data_fields = api_fields(fields_list = list('cbsa', 'area', 'name')),\n        data_filters = list(),\n        groupby = c('cbsa', 'name'),\n        aggregations = list(),\n        on = list(left = c('cbsa'), right = c('cbsa'))\n    )\n  ),\n  apiToken = 'my-api-token-here')\n```\n\nOutput:\n```\nPopulation density in the Boston MSA up to 2015:\n   YEAR        NAME  POP_PER_SQ_MI\n0  2000  Boston, MA    1139.046639\n1  2001  Boston, MA    1149.129937\n2  2002  Boston, MA    1153.094740\n3  2003  Boston, MA    1152.352351\n4  2004  Boston, MA    1149.932307\nResults truncated\n```\n\n### Example use of query base class with API call and example filter\n```python\nfrom strato_query.base_API_query import *\nfrom strato_query.standard_filters import *\n\n\nclass ExampleAPIQuery(BaseAPIQuery):\n    @classmethod\n    def get_df_from_API_call(cls, **kwargs):\n        # This API call will return the population 65+ in 2018 within 5 miles of the lat/long pair\n        age_filter = GtrThanOrEqFilter(\n            var='age_g',\n            val=14).to_dict()\n\n        year_filter = EqFilter(\n            var='year',\n            val=2018).to_dict()\n\n        mile_radius_filter = dict(\n            filter_type='mile_radius',\n            filter_value=dict(\n                latitude=26.606484,\n                longitude=-81.851531,\n                miles=5),\n            filter_variable='')\n\n        df = cls.query_api_df(\n            query_params=APIQueryParams(\n                table='populationforecast_tract_annual_population_age',\n                data_fields=('POPULATION',),\n                data_filters=(age_filter, year_filter, mile_radius_filter),\n                query_type='COUNT',\n                aggregations=(),\n                groupby=()\n            )\n        )\n\n        return 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