Pandas replace all values in a row with NaN if one value is NaN

Question

i have the following data frame

                             X                Y
   DateTime                
2019-07-22 00:00:00         0.5               0.6
2019-07-22 00:01:00         NaN               0.8
2019-07-22 00:02:00         0.7               NaN
2019-07-22 00:03:00         0.4               0.3

I want to replace all items in a row with NaN if one value in the row is NaN, maintaining the index as:

                             X                Y
   DateTime                
2019-07-22 00:00:00         0.5               0.6
2019-07-22 00:01:00         NaN               NaN
2019-07-22 00:02:00         NaN               NaN
2019-07-22 00:03:00         0.4               0.3

How do i do this?

Answer 1

Use DataFrame.mask with boolean mask:

df1 = df.mask(df.isna().any(axis=1))
print (df1)
                       X    Y
DateTime                     
2019-07-22 00:00:00  0.5  0.6
2019-07-22 00:01:00  NaN  NaN
2019-07-22 00:02:00  NaN  NaN
2019-07-22 00:03:00  0.4  0.3

Details :

Test missing values by DataFrame.isna :

print (df.isna())
                         X      Y
DateTime                         
2019-07-22 00:00:00  False  False
2019-07-22 00:01:00   True  False
2019-07-22 00:02:00  False   True
2019-07-22 00:03:00  False  False

And then test if at least one True per rows by DataFrame.any :

print (df.isna().any(axis=1))
DateTime
2019-07-22 00:00:00    False
2019-07-22 00:01:00     True
2019-07-22 00:02:00     True
2019-07-22 00:03:00    False
dtype: bool