eland.groupby.DataFrameGroupBy.median#

DataFrameGroupBy.median(numeric_only: bool = True) → pd.DataFrame#

Compute the median 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: median absolute deviation value for each numeric column of each group

See Also#

pandas.core.groupby.GroupBy.median

Examples#

>>> df = ed.DataFrame(
...   "http://localhost:9200", "flights",
...   columns=["AvgTicketPrice", "Cancelled", "dayOfWeek", "timestamp", "DestCountry"]
... )
>>> df.groupby("DestCountry").median(numeric_only=False) 
             AvgTicketPrice  Cancelled  dayOfWeek               timestamp
DestCountry
AE               585.720490      False          2 2018-01-19 23:56:44.000
AR               678.447433      False          3 2018-01-22 10:18:50.000
AT               659.715592      False          3 2018-01-20 20:40:10.000
AU               689.241348      False          3 2018-01-22 18:46:11.000
CA               663.516057      False          3 2018-01-22 21:35:09.500
...                     ...        ...        ...                     ...
RU               670.714956      False          3 2018-01-20 16:48:16.000
SE               680.111084      False          3 2018-01-22 20:53:44.000
TR               441.681122      False          1 2018-01-13 23:17:27.000
US               600.591525      False          3 2018-01-22 04:09:50.000
ZA               633.935425      False          3 2018-01-23 17:42:57.000

[32 rows x 4 columns]