DataFrame.count

DataFrame.count() → Series[source]

Count non-NA cells for each column.

Counts are based on exists queries against ES.

This is inefficient, as it creates N queries (N is number of fields). An alternative approach is to use value_count aggregations. However, they have issues in that:

They can only be used with aggregatable fields (e.g. keyword not text)
For list fields they return multiple counts. E.g. tags=[‘opensearch-project’, ‘ml’] returns value_count=2 for a single document.

TODO - add additional pandas.DataFrame.count features

Returns

pandas.Series:: Summary of column counts

See Also

:pandas_api_docs:`pandas.DataFrame.count`

Examples

>>> from tests import OPENSEARCH_TEST_CLIENT

>>> df = oml.DataFrame(OPENSEARCH_TEST_CLIENT, 'ecommerce', columns=['customer_first_name', 'geoip.city_name'])
>>> df.count()
customer_first_name    4675
geoip.city_name        4094
dtype: int64