【发布时间】:2017-08-01 18:34:53
【问题描述】:
我希望执行以下操作:
对数据框进行分组
为每个组生成时间窗口(给定时间单位)
在生成的结构中,获取每一列并应用多个滚动汇总统计函数,以便结果具有每个组/时间窗口组合的汇总统计。
这是一个示例数据集:
gps_time,name,val_x,val_y
2017-07-04 11:20:23.423,bob,0.963,0.201
2017-07-04 11:20:24.492,bob,0.964,0.203
2017-07-04 11:20:24.499,bob,0.962,0.210
2017-07-04 11:20:25.627,sarah,0.893,0.010
2017-07-04 11:20:28.627,sarah,0.894,0.012
2017-07-04 11:20:29.613,sarah,0.895,0.014
2017-07-04 11:20:29.630,larry,-0.423,0.231
2017-07-04 11:20:30.423,larry,-0.431,0.22
2017-07-04 11:20:30.428,larry,-0.432,0.222
以及上述数据的所需输出,按名称分组,窗口为 1 秒:
name,gps_time,val_x_mean,val_x_med,val_y_mean,val_y_med
bob,2017-07-04 11:20:23.423,0.963,0.963,0.201,0.201
bob,2017-07-04 11:20:24.492,0.963,0.963,0.2065,0.2065
sarah,2017-07-04 11:20:25.627,0.893,0.89,0.010,0.010
sarah,2017-07-04 11:20:28.627,0.8945,0.8945,0.013,0.013
larry,2017-07-04 11:20:30.423,-0.4287,-0.431,0.336,0.222
我尝试使用列表推导来生成一堆数据帧,但这个过程真的很慢,我必须为每一列调用它。
【问题讨论】:
标签: python pandas pandas-groupby