Farword 填充基于其他列与分组依据

Question

我是 python 的新手，我在 forword 填充处有结构。我有数据框（df_input）我需要按名称和类型列

将 d1 列填充到 d2 列组中的值

import pandas as pd
data_input = {'Name':['Renault', 'Renault', 'Renault', 'Renault','Renault','Renault','Renault','Renault','Renault'],
        'type':['Duster', 'Duster', 'Duster', 'Duster','Duster','Triber','Triber','Triber','Triber'],
         'd1':['nan','10','nan','nan','nan','nan','20','nan','nan'],
         'd2':['nan','nan','nan','200','nan','nan','nan','nan','200']}  

df_input = pd.DataFrame(data_input)


data_out = {'Name':['Renault', 'Renault', 'Renault', 'Renault','Renault','Renault','Renault','Renault','Renault'],
        'type':['Duster', 'Duster', 'Duster', 'Duster','Duster','Triber','Triber','Triber','Triber'],
         'd1':['nan','10','nan','nan','nan','nan','20','nan','nan'],
         'd2':['nan','nan','nan','200','nan','nan','nan','nan','200'],
         'Out_col':['nan','10','10','10','nan','nan','20','20','20']} 

df_out = pd.DataFrame(data_out)

我试过以下方法

df_out['Out_col']  = df_out.groupby(["Name","type"])["d1"].ffill()

提前致谢！

Answer 1

使用：

#strings nans to NaNs missing values
df_input = df_input.replace('nan', np.nan)

您需要用 Series.mask:

回填列 d2 的值来替换缺失值

s = df_input.groupby(["Name","type"])["d2"].bfill()
df_input['Out_col']  = df_input.groupby(["Name","type"])["d1"].ffill().mask(s.isna())
print (df_input)
      Name    type   d1   d2 Out_col
0  Renault  Duster  NaN  NaN     NaN
1  Renault  Duster   10  NaN      10
2  Renault  Duster  NaN  NaN      10
3  Renault  Duster  NaN  200      10
4  Renault  Duster  NaN  NaN     NaN
5  Renault  Triber  NaN  NaN     NaN
6  Renault  Triber   20  NaN      20
7  Renault  Triber  NaN  NaN      20
8  Renault  Triber  NaN  200      20

Farword 填充基于其他列与分组依据

Farword fill based on other column with group by

numpy

pandas