Get a Collection of Statistical Data for Multiple Places

`datacommons.get_stat_all(places, stat_vars)`

Returns a nested dict of all time series for places and stat_vars.

Arguments

places (Iterable of str): The dcids of the Place to query for.
stats_vars (Iterable of str): The dcids of the StatisticalVariable.

Returns

A nested dict mapping Places to StatisticalVariables and all available time series for each Place and StatisticalVariable pair.

The top level dict key is the Place dcid and the second level dict key is the StatisticalVariable dcid, with the object being an array of time series object with the following fields

val: a dict from date to statistical value.
importName: the import name of the observations.
provenanceDomain: the Provenance domain of the observations.
measurementMethod: the measurementMethod of the observations, if it exists.
observationPeriod: the observationPeriod of the observations, if it exists.
unit: the unit of the observations, if it exists.
scalingFactor: the scalingFactor of the observations, if it exists.

Raises

ValueError - If no statistical value found for any Place and StatisticalVariable combinations.

Be sure to initialize the library. Check the Python library setup guide for more details.

You can find a list of StatisticalVariables with human-readable names here.

Examples

We would like to get the population and the male population in Arkansas and California.

>>> import datacommons as dc
>>> dc.get_stat_all(["geoId/05"], ["Count_Person", "Count_Person_Male"])
{
  'geoId/05': {
    'Count_Person_Female': {
      'sourceSeries': [
        {
          'val': {
            '2001': 1376360
            '2002': 1382090,
            ...
            '2017': 1521170,
            '2018': 1527580,
          },
            'measurementMethod': 'OECDRegionalStatistics',
            'observationPeriod': 'P1Y',
            'importName': 'OECDRegionalDemography',
            'provenanceDomain': 'oecd.org'
        },
        {
          'val': {
            '2011': 1474641,
            '2012': 1485120
            ...
            '2017': 1516293,
            '2018': 1522259,
          },
          'measurementMethod': 'CensusACS5yrSurvey',
          'importName': 'CensusACS5YearSurvey',
          'provenanceDomain': 'census.gov'
        }
      ]
    },
    'Count_Person_Male': {
      'sourceSeries': [
        {
          'val': {
            '2001': 1315210,
            '2002': 1323840,
            ...
            '2017': 1475420,
            '2018': 1480140,
          },
          'measurementMethod': 'OECDRegionalStatistics',
          'observationPeriod': 'P1Y',
          'importName': 'OECDRegionalDemography',
          'provenanceDomain': 'oecd.org'
        },
        {
          'val': {
            '2011': 1421287
            '2012': 1431252,
            ...
            '2017': 1461651,
            '2018': 1468412,
          },
          'measurementMethod': 'CensusACS5yrSurvey',
          'importName': 'CensusACS5YearSurvey',
          'provenanceDomain': 'census.gov'
        }
      ]
    }
  }
}

In the next example, there is no data found so the API throws ValueError:

>>> dc.get_stat_all(['badGeoId'], ['BadStaVar'])
>>> Traceback (most recent call last):
    ...
    raise ValueError('No data in response.')