基于组by的其他列的Farword填充

Question

我是python的新手，我有struct for forword fill。我有数据帧(df_input)我需要转发填充d1列到d2列组的值名称&类型列

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)

我已经尝试了以下

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

提前感谢!

Answer 1

使用说明:

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

您需要用Series.mask填充d2列的值来替换缺失值:

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