根据时间计算非零元素的熊猫数据框中列的分组平均值

Question

您好，我想计算每个名称组在每个时间的平均天数列。每个组的每个时间的平均值应仅基于大于 0 的 Days 行的元素计算。任何反馈将不胜感激..

Name       Time        Days    Average
 
John    2021-12-02      0        0
John    2021-12-03      2        0 
John    2021-12-05      9        2
John    2021-12-07      0       5.5
John    2021-12-10     10       5.5
Larry   2021-12-02     20        0
Jim     2021-12-09     20        0
Jim     2021-12-10     20       20
Jim     2021-12-12     40       20
Jim     2021-12-12      0       26.6
Juli    2021-11-09      0        0
Juli    2021-11-10      0        0
Juli    2021-11-12     40        0
Juli    2021-11-18      0       40
Juli    2021-11-12      0       40
Juli    2021-11-18      2       40
Juli    2021-11-19      0       21

Answer 1

首先将0 替换为缺失值，然后将GroupBy.transform 与Series.expanding 的lambda 函数与mean 和Series.shift 一起使用，最后将NaNs 替换为0 为Series.fillna：

df['Avg'] = (df.assign(Days = df['Days'].replace(0,np.nan))
               .groupby('Name')['Days']
               .transform(lambda x: x.expanding().mean().shift())
               .fillna(0))
print (df)
     Name        Time  Days  Average        Avg
0    John  2021-12-02     0      0.0   0.000000
1    John  2021-12-03     2      0.0   0.000000
2    John  2021-12-05     9      2.0   2.000000
3    John  2021-12-07     0      5.5   5.500000
4    John  2021-12-10    10      5.5   5.500000
5   Larry  2021-12-02    20      0.0   0.000000
6     Jim  2021-12-09    20      0.0   0.000000
7     Jim  2021-12-10    20     20.0  20.000000
8     Jim  2021-12-12    40     20.0  20.000000
9     Jim  2021-12-12     0     26.6  26.666667
10   Juli  2021-11-09     0      0.0   0.000000
11   Juli  2021-11-10     0      0.0   0.000000
12   Juli  2021-11-12    40      0.0   0.000000
13   Juli  2021-11-18     0     40.0  40.000000
14   Juli  2021-11-12     0     40.0  40.000000
15   Juli  2021-11-18     2     40.0  40.000000
16   Juli  2021-11-19     0     21.0  21.000000