Pandas: If condition on multiple columns having null values and fillna with 0-CodePudding

I have a below dataframe, and my requirement is that, if both columns have np.nan then no change, if either of column has empty value then fill na with '0' value. I wrote this code but why its not working. Please suggest.

import pandas as pd
import numpy a np

data = {'Age': [np.nan, np.nan, 22, np.nan, 50,99],
        'Salary': [217, np.nan, 262, 352, 570, np.nan]}
df = pd.DataFrame(data)
print(df)

cond1 = (df['Age'].isnull()) & (df['Salary'].isnull())
if cond1 is False:
    df['Age'] = df['Age'].fillna(0)
    df['Salary'] = df['Salary'].fillna(0)

print(df)

CodePudding user response：

You can just assign it with update

c = ['Age','Salary']
df.update(df.loc[~df[c].isna().all(1),c].fillna(0))

df
Out[341]: 
    Age  Salary
0   0.0   217.0
1   NaN     NaN
2  22.0   262.0
3   0.0   352.0
4  50.0   570.0
5  99.0     0.0

CodePudding user response：

c1 = df['Age'].isna()
c2 = df['Salary'].isna()

df[np.c_[c1 & ~c2, ~c1 & c2]]=0   
df
    Age  Salary
0   0.0   217.0
1   NaN     NaN
2  22.0   262.0
3   0.0   352.0
4  50.0   570.0
5  99.0     0.0

CodePudding user response：

tmp=df.loc[(df['Age'].isna() & df['Salary'].isna())]
df.fillna(0,inplace=True)
df.loc[tmp.index]=np.nan

This might be a bit less sophisticated than the other answers but worked for me:

I first save the row(s) containing both Nan values at the same time
then fillna the original df as per normal
then set np.nan back to the location where we saved both rows containing Nan at the same time