eland.groupby.DataFrameGroupBy.var¶
-
DataFrameGroupBy.var(numeric_only: bool = True) → pd.DataFrame¶ Compute the variance 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
variance 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").var() # doctest: +NORMALIZE_WHITESPACE AvgTicketPrice Cancelled dayOfWeek DestCountry AE 75789.979090 0.130443 3.950549 AR 59683.055316 0.125979 3.783429 AT 65726.669676 0.144610 4.090013 AU 65088.483446 0.113094 3.833562 CA 68149.950516 0.116496 3.688139 ... ... ... ... RU 67305.277617 0.114107 3.852666 SE 53740.570338 0.127062 3.942132 TR 61245.521047 0.094868 4.100420 US 74349.939410 0.109638 3.758700 ZA 62920.072901 0.126608 3.775609 <BLANKLINE> [32 rows x 3 columns]