DataFrame.mode

DataFrame.mode(numeric_only: bool = False, dropna: bool = True, os_size: int = 10) → DataFrame[source]

Calculate mode of a DataFrame

Parameters

numeric_only: {True, False} Default is False

Which datatype to be returned - True: Returns all numeric or timestamp columns - False: Returns all columns

dropna: {True, False} Default is True

  • True: Don’t consider counts of NaN/NaT.

  • False: Consider counts of NaN/NaT.

os_size: default 10

number of rows to be returned if mode has multiple values

See Also

:pandas_api_docs:`pandas.DataFrame.mode`

Examples

>>> from tests import OPENSEARCH_TEST_CLIENT

>>> oml_ecommerce = oml.DataFrame(OPENSEARCH_TEST_CLIENT, 'ecommerce')
>>> oml_df = oml_ecommerce.filter(["total_quantity", "geoip.city_name", "customer_birth_date", "day_of_week", "taxful_total_price"])
>>> oml_df.mode(numeric_only=False)
   total_quantity geoip.city_name customer_birth_date day_of_week  taxful_total_price
0               2        New York                 NaT    Thursday               53.98

>>> oml_df.mode(numeric_only=True)
   total_quantity  taxful_total_price
0               2               53.98

>>> oml_df = oml_ecommerce.filter(["products.tax_amount","order_date"])
>>> oml_df.mode()
   products.tax_amount          order_date
0                  0.0 2016-12-02 20:36:58
1                  NaN 2016-12-04 23:44:10
2                  NaN 2016-12-08 06:21:36
3                  NaN 2016-12-08 09:38:53
4                  NaN 2016-12-12 11:38:24
5                  NaN 2016-12-12 19:46:34
6                  NaN 2016-12-14 18:00:00
7                  NaN 2016-12-15 11:38:24
8                  NaN 2016-12-22 19:39:22
9                  NaN 2016-12-24 06:21:36

>>> oml_df.mode(os_size = 3)
   products.tax_amount          order_date
0                  0.0 2016-12-02 20:36:58
1                  NaN 2016-12-04 23:44:10
2                  NaN 2016-12-08 06:21:36