eland.groupby.DataFrameGroupBy.mean¶
-
DataFrameGroupBy.mean(numeric_only: bool = True) → pd.DataFrame¶ Compute the mean value for each group.
- Parameters
- numeric_only: {True, False, None} Default is True
Which datatype to be returned - True: Returns all values as float64, NaN/NaT values are removed - None: Returns all values as the same dtype where possible, NaN/NaT are removed - False: Returns all values as the same dtype where possible, NaN/NaT are preserved
- Returns
- pandas.DataFrame
mean value for each numeric column of each group
See also
Examples
>>> df = ed.DataFrame( ... "localhost", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"] ... ) >>> df.groupby("DestCountry").mean(numeric_only=False) # doctest: +SKIP AvgTicketPrice Cancelled dayOfWeek timestamp DestCountry AE 605.132970 0.152174 2.695652 2018-01-21 16:58:07.891304443 AR 674.827252 0.147541 2.744262 2018-01-21 22:18:06.593442627 AT 646.650530 0.175066 2.872679 2018-01-21 15:54:42.469496094 AU 669.558832 0.129808 2.843750 2018-01-22 02:28:39.199519287 CA 648.747109 0.134534 2.951271 2018-01-22 14:40:47.165254150 ... ... ... ... ... RU 662.994963 0.131258 2.832206 2018-01-21 07:11:16.534506104 SE 660.612988 0.149020 2.682353 2018-01-22 07:48:23.447058838 TR 485.253247 0.100000 1.900000 2018-01-16 16:02:33.000000000 US 595.774391 0.125315 2.753900 2018-01-21 16:55:04.456970215 ZA 643.053057 0.148410 2.766784 2018-01-22 15:17:56.141342773 <BLANKLINE> [32 rows x 4 columns]