將多個滾動功能應用於熊貓groupby滾動對象的多個列？

Question

我正在尋找以下方面：

1. 分組數據框

2. 對於每個組，生成時間窗口（給定時間單位）

3. 在結果結構中，采用每一列並應用多個滾動摘要統計功能，以便結果具有針對每個組/時間窗口組合的摘要統計。

這是一個示例數據集：

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

以上數據的期望輸出，按名稱分組並以1秒為窗口：

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

我嘗試使用列表推導來生成一堆數據幀，但過程確實很慢，我必須為每一列調用它。

Answer 1

讓我們將groupby與pd.Grouper一起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()

輸出：

             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