Using column B as the reference how can I replace NaN value

>>> a
A   B  
1   1     
Nan 3     
1   1    
Nan 1
Nan 2      
5   3       
1   1    
2   2 

I want result like this.

>> result 
A   B  
1   1     
5   3     
1   1    
1   1
2   2      
5   3       
1   1    
2   2   

I tried merging on the column b but couldn't figure out b=a.groupby('B').reset_index() dfM = pd.merge(a,b,on='B', how ='left')

CodePudding user response：

We need a map from values in column B to the values in A.

mapping = a.dropna().drop_duplicates().set_index("B")["A"]

It looks like this

B
1    1.0
3    5.0
2    2.0
Name: A, dtype: float64

Filling null values becomes irrelevant at this point. We can just map B to get column A

a["B"].map(mapping)

This gives you

0    1.0
1    5.0
2    1.0
3    1.0
4    2.0
5    5.0
6    1.0
7    2.0
Name: B, dtype: float64

Cast to int and use it to overwrite column A in your original dataframe if you need to.