eland.groupby.DataFrameGroupBy.quantile#

DataFrameGroupBy.quantile(q: Union[int, float, List[int], List[float]] = 0.5) pd.DataFrame#

Used to groupby and calculate quantile for a given DataFrame.

Parameters#

q:

float or array like, default 0.5 Value between 0 <= q <= 1, the quantile(s) to compute.

Returns#

pandas.DataFrame

quantile value for each grouped column

See Also#

pandas.core.groupby.GroupBy.quantile

Examples#

>>> ed_df = ed.DataFrame('http://localhost:9200', 'flights')
>>> ed_flights = ed_df.filter(["AvgTicketPrice", "FlightDelayMin", "dayOfWeek", "timestamp"])
>>> ed_flights.groupby(["dayOfWeek", "Cancelled"]).quantile() 
                     AvgTicketPrice  FlightDelayMin
dayOfWeek Cancelled
0         False          572.290384             0.0
          True           578.140564             0.0
1         False          567.980560             0.0
          True           582.618713             0.0
2         False          590.170986             0.0
          True           579.811890             0.0
3         False          574.131340             0.0
          True           572.852264             0.0
4         False          591.533699             0.0
          True           582.877014             0.0
5         False          791.622625             0.0
          True           793.362946             0.0
6         False          817.378523             0.0
          True           766.855530             0.0
>>> ed_flights.groupby(["dayOfWeek", "Cancelled"]).quantile(q=[.2, .5]) 
                         AvgTicketPrice  FlightDelayMin
dayOfWeek Cancelled
0         False     0.2      319.925979             0.0
                    0.5      572.290384             0.0
          True      0.2      325.704562             0.0
                    0.5      578.140564             0.0
1         False     0.2      327.311007             0.0
                    0.5      567.980560             0.0
          True      0.2      336.839572             0.0
                    0.5      582.618713             0.0
2         False     0.2      332.323011             0.0
                    0.5      590.170986             0.0
          True      0.2      314.472537             0.0
                    0.5      579.811890             0.0
3         False     0.2      327.652659             0.0
                    0.5      574.131340             0.0
          True      0.2      298.483032             0.0
                    0.5      572.852264             0.0
4         False     0.2      314.290205             0.0
                    0.5      591.533699             0.0
          True      0.2      325.024850             0.0
                    0.5      582.877014             0.0
5         False     0.2      567.362137             0.0
                    0.5      791.622625             0.0
          True      0.2      568.323944             0.0
                    0.5      793.362946             0.0
6         False     0.2      568.489746             0.0
                    0.5      817.378523             0.0
          True      0.2      523.890680             0.0
                    0.5      766.855530             0.0