eland.groupby.DataFrameGroupBy.min¶
-
DataFrameGroupBy.min(numeric_only: bool = True) → pd.DataFrame¶ Compute the min 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
min value for each numeric column of each group
See also
Examples
>>> df = ed.DataFrame( ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"] ... ) >>> df.groupby("DestCountry").min(numeric_only=False) # doctest: +NORMALIZE_WHITESPACE AvgTicketPrice Cancelled dayOfWeek timestamp DestCountry AE 110.799911 False 0 2018-01-01 19:31:30 AR 125.589394 False 0 2018-01-01 01:30:47 AT 100.020531 False 0 2018-01-01 05:24:19 AU 102.294312 False 0 2018-01-01 00:00:00 CA 100.557251 False 0 2018-01-01 00:44:08 ... ... ... ... ... RU 101.004005 False 0 2018-01-01 01:01:51 SE 102.877190 False 0 2018-01-01 04:09:38 TR 142.876465 False 0 2018-01-01 06:45:17 US 100.145966 False 0 2018-01-01 00:06:27 ZA 102.002663 False 0 2018-01-01 06:44:44 <BLANKLINE> [32 rows x 4 columns]