eland.groupby.DataFrameGroupBy.quantile#
- DataFrameGroupBy.quantile(q: Union[int, float, List[int], List[float]] = 0.5) pd.DataFrame#
用于对给定的 DataFrame 进行分组并计算分位数。
参数#
- q
浮点数或类似数组，默认值 0.5 0 <= q <= 1 之间的值，要计算的分位数。
返回值#
- pandas.DataFrame
每个分组列的分位数值
参见#
示例#
>>> 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