我希望执行以下操作:
分组数据框
对于每个组,生成时间窗口(给定时间单位)
在结果结构中,采用每一列并应用多个滚动汇总统计功能,以便结果具有针对每个组/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,并具有1秒的窗口:
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,但是过程确实很慢,我必须为每一列调用它。
让我们用groupby
用pd.Grouper
:
df_out = df.groupby([pd.Grouper(freq='S', key='gps_time'),'name']).agg(['mean','median'])
df_out.columns = df_out.columns.map('_'.join)
df_out.reset_index()
输出:
gps_time name val_x_mean val_x_median val_y_mean \
0 2017-07-04 11:20:23 bob 0.9630 0.9630 0.2010
1 2017-07-04 11:20:24 bob 0.9630 0.9630 0.2065
2 2017-07-04 11:20:25 sarah 0.8930 0.8930 0.0100
3 2017-07-04 11:20:28 sarah 0.8940 0.8940 0.0120
4 2017-07-04 11:20:29 larry -0.4230 -0.4230 0.2310
5 2017-07-04 11:20:29 sarah 0.8950 0.8950 0.0140
6 2017-07-04 11:20:30 larry -0.4315 -0.4315 0.2210
val_y_median
0 0.2010
1 0.2065
2 0.0100
3 0.0120
4 0.2310
5 0.0140
6 0.2210
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我来说两句