随着滚动，更改滚动窗口大小

Question

我有一个像这样的pandas数据框：

>df

leg    speed
  1       10
  1       11
  1       12
  1       13
  1       12
  1       15
  1       19
  1       12
  2       10
  2       10
  2       12
  2       15
  2       19
  2       11
  :        :

我希望创建一个新列roll_speed，它取最近5个位置的滚动平均速度。但我想在其中加入更详细的条件。

1. Groupby leg(it doesn't take into account the speed of the rows in different leg.

2. I want the rolling window to be changed from 1 to 5 maximum according to the available rows. For example in leg == 1, in the first row there is only one row to calculate, so the rolling speed should be 10/1 = 10. For the second row, there are only two rows available for calculation, the rolling speed should be (10+11)/2 = 10.5.

   leg    speed   roll_speed
     1       10           10    # 10/1
     1       11           10.5  # (10+11)/2
     1       12           11    # (10+11+12)/3
     1       13           11.5  # (10+11+12+13)/4
     1       12           11.6  # (10+11+12+13+12)/5
     1       15           12.6  # (11+12+13+12+15)/5
     1       19           14.2  # (12+13+12+15+19)/5
     1       12           14.2  # (13+12+15+19+12)/5
     2       10           10    # 10/1
     2       10           10    # (10+10)/2
     2       12           10.7  # (10+10+12)/3
     2       15           11.8  # (10+10+12+15)/4
     2       19           13.2  # (10+10+12+15+19)/5
     2       11           13.4  # (10+12+15+19+11)/5
     :        :           
   

我的尝试：

df['roll_speed'] = df.speed.rolling(5).mean()

但是当计算可用行数少于五行时，它只会返回NA。我该如何解决这个问题？感谢任何帮助！

- Makoto Miyazaki

Answer 1

将参数min_periods设置为1

df['roll_speed'] = df.groupby('leg').speed.rolling(5, min_periods = 1).mean()\
.round(1).reset_index(drop = True)

    leg speed   roll_speed
0   1   10  10.0
1   1   11  10.5
2   1   12  11.0
3   1   13  11.5
4   1   12  11.6
5   1   15  12.6
6   1   19  14.2
7   1   12  14.2
8   2   10  10.0
9   2   10  10.0
10  2   12  10.7
11  2   15  11.8
12  2   19  13.2
13  2   11  13.4

- Vaishali

Answer 2

使用rolling(5)将为您获取每个组的前4次出现以外的所有结果。我们可以使用扩展的mean填充剩余值：

(df.groupby('leg').speed.rolling(5)
    .mean().fillna(df.groupby('leg').speed.expanding().mean())
).reset_index(drop=True)

0     10.000000
1     10.500000
2     11.000000
3     11.500000
4     11.600000
5     12.600000
6     14.200000
7     14.200000
8     10.000000
9     10.000000
10    10.666667
11    11.750000
12    13.200000
13    13.400000
Name: speed, dtype: float64

- user3483203