Question

首先，我想按name，group和place列进行分组。然后，我想获得相邻两个月的平均值y。最后，我想将平均值添加到原始数据框。

The origin dataframe:

import pandas as pd
df = pd.DataFrame({"name":["Amy", "Amy", "Amy", "Bob", "Bob", "Bob", "Bob", "Bob", "Bob"],
                  "group":[1, 1, 1, 1, 1, 1, 2, 2, 2],
                   "place":['a', 'a', "a", 'b', 'b', 'b', 'b', 'b', 'b' ],
                   "yearmonth": ["2019-01", "2019-02", "2019-03", "2019-01", "2019-02", "2019-03", "2019-01", "2019-02", "2019-03"],
                    "y":[1, 2, 3, 1, 2, 0, 2, 0, 0]
})

print(df)

Dataframe:

name  group place yearmonth  y
0  Amy      1     a   2019-01  1
1  Amy      1     a   2019-02  2
2  Amy      1     a   2019-03  3
3  Bob      1     b   2019-01  1
4  Bob      1     b   2019-02  2
5  Bob      1     b   2019-03  0
6  Bob      2     b   2019-01  2
7  Bob      2     b   2019-02  0
8  Bob      2     b   2019-03  0

Expected Result:

name  group place yearmonth  y   average_2months
0  Amy      1     a   2019-01  1  nan
1  Amy      1     a   2019-02  2  1.5
2  Amy      1     a   2019-03  3  2.5
3  Bob      1     b   2019-01  1  nan
4  Bob      1     b   2019-02  2  1.5
5  Bob      1     b   2019-03  0  1.0
6  Bob      2     b   2019-01  2  nan
7  Bob      2     b   2019-02  0  1.0
8  Bob      2     b   2019-03  0  0.0

What I tried:

现在，我现在如何获取相邻两个月的平均值。但是，我不知道如何将其添加到原始数据框。

tmp = df.groupby(['name', 'group', 'place'])['y'].rolling(2).mean()
print(tmp)

tmp:

name  group  place   
Amy   1      a      0    NaN
                    1    1.5
                    2    2.5
Bob   1      b      3    NaN
                    4    1.5
                    5    1.0
      2      b      6    NaN
                    7    1.0
                    8    0.0
Name: y, dtype: float64

Answer 1

索引的第4级是您的原始索引

df['new']=temp.reset_index(level=[0,1,2], drop=True)

按多列分组后如何获取移动窗口平均值

1 个答案: