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Time Series Table as pd.DataFrame
2
Pandas
API

Retrieve time series DataFrame

Returns a pandas.DataFrame with places as index and dates as columns, where each cell is the observed statistic for its place and date for the stat_var.

See the full list of StatisticalVariable classes.

General information about this method

Signature:

datacommons_pandas.build_time_series_dataframe(places, stat_var)

Required arguments:

NOTE: In Data Commons, dcid stands for Data Commons ID and indicates the unique identifier assigned to every node in the knowledge graph.

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

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

  • places: Data Commons uniquely identifies nodes by assigning them DCIDs, or Data Commons IDs. Your query will need to specify the DCIDs for the places of interest.

  • stat_var: This argument specifies the statistical variable whose measurement you seek.

For more information, check out the glossary.

Examples

Example 1: Retrieve the count of men in the state of California.

>>> datacommons_pandas.build_time_series_dataframe("geoId/05", "Count_Person_Male")
             2010     2011     2012  ...     2017     2018     2019
place                                ...                           
geoId/05  1430837  1447850  1449265  ...  1479682  1476680  1474705

[1 rows x 10 columns]

Example 2: Compare the historic populations of Sudan and South Sudan.

>>> datacommons_pandas.build_time_series_dataframe(["country/SSD","country/SDN"], "Count_Person")
                   2019     2019-06
place                              
country/SDN         NaN  41592539.0
country/SSD  12778250.0         NaN

Error Returns

If a nonexistent place is passed as an argument, it will not render in the dataframe, as follows:

>>> datacommons_pandas.build_time_series_dataframe(["geoId/123123123123123123","geoId/36"], "Count_Person")
              2001      2002      2003  ...      2017      2018      2019
place                                   ...                              
geoId/36  19082800  19137800  19175900  ...  19589600  19530400  19453600

[1 rows x 19 columns]

If you do not pass a required positional argument, a TypeError is returned:

>>> datacommons_pandas.build_time_series_dataframe(["geoId/123123123123123123","geoId/36"])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: build_time_series_dataframe() missing 1 required positional argument: 'stat_var'