Retrieve a collection of statistical data for multiple places

Returns a nested dict of all time series for places and stat_vars. Note that in Data Commons, a StatisticalVariable is any type of statistical metric that can be measured at a place and time. See the full list of StatisticalVariables.

General information about this method

Signature:

datacommons.get_stat_all(places, stat_vars)

Required arguments

places: The DCID IDs of the Place objects to query for. (Here DCID stands for Data Commons ID, the unique identifier assigned to all entities in Data Commons.)
stat_vars: The dcids of the StatisticalVariables.

Assembling the information you will need for a call to the get_stat_all method

Going into more detail on how to assemble the values for the required arguments:

place: For this parameter, you will need to specify the DCID (the unique ID assigned by Data Commons to each node in the graph) of the place you are interested in.
stat_var: The statistical variable whose value you are interested in.

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

What to expect in the function return

The method’s return value will always be an object in the following form:

{
    "<dcid>": {
      "stat_var": {
        "sourceSeries": [
          {
            "val": {
              <"time series">
            }
            "measurementMethod": "<String>",
            "observationPeriod": "<String>",
            "importName": "<String>",
            "provenanceDomain": "<String>"
          }
          ...
        ]
      }
      ...
    }
    ...
}

For more information on the key terms in this sample response, see the glossary.

Examples

Example 1: Retrieve the total population as well as the male population of Arkansas.

>>> 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'
        }
      ]
    }
  }
}

Example 2: Retrieve the populations of people with doctoral degrees in Minnesota and Wisconsin.

>>> datacommons.get_stat_all(["geoId/27","geoId/55"], ["Count_Person_EducationalAttainmentDoctorateDegree"])
{'geoId/27': {'Count_Person_EducationalAttainmentDoctorateDegree': {'sourceSeries': [{'val': {'2016': 50039, '2017': 52737, '2018': 54303, '2012': 40961, '2013': 42511, '2014': 44713, '2015': 47323}, 'measurementMethod': 'CensusACS5yrSurvey', 'importName': 'CensusACS5YearSurvey', 'provenanceDomain': 'census.gov', 'provenanceUrl': 'https://www.census.gov/'}]}}, 'geoId/55': {'Count_Person_EducationalAttainmentDoctorateDegree': {'sourceSeries': [{'val': {'2017': 43737, '2018': 46071, '2012': 38052, '2013': 38711, '2014': 40133, '2015': 41387, '2016': 42590}, 'measurementMethod': 'CensusACS5yrSurvey', 'importName': 'CensusACS5YearSurvey', 'provenanceDomain': 'census.gov', 'provenanceUrl': 'https://www.census.gov/'}]}}}

Error returns

When no data is found, the API returns a dictionary with no values:

>>> import datacommons as dc
>>> dc.get_stat_all(["bad value"],["another bad value"])
{'bad value': {'another bad value': {}}}

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