eland.groupby.DataFrameGroupBy.std#
- DataFrameGroupBy.std(numeric_only: bool = True) pd.DataFrame #
Compute the standard deviation 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
standard deviation value for each numeric column of each group
See Also#
Examples#
>>> df = ed.DataFrame( ... "http://localhost:9200", "flights", ... columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "DestCountry"] ... ) >>> df.groupby("DestCountry").std() AvgTicketPrice Cancelled dayOfWeek DestCountry AE 279.875500 0.367171 2.020634 AR 244.903626 0.355811 1.949901 AT 256.883342 0.381035 2.026411 AU 255.585377 0.336902 1.961486 CA 261.263054 0.341587 1.921980 ... ... ... ... RU 259.696213 0.338140 1.964815 SE 232.504297 0.357510 1.991340 TR 267.827572 0.333333 2.191454 US 272.774819 0.331242 1.939469 ZA 251.505568 0.356766 1.948258 [32 rows x 3 columns]