
        
        
            
                
                
                    df = pd.DataFrame({"A":[3,None],"B":[4,5]})
df
                
            
               A    B
0  3.0  4
1  NaN  5

To find columns with at least one NaN:


        
        
            
                
                
                    df.isna().any()
                
            
            A    True
B   False
dtype: bool

Explanation

Here, isna() returns a DataFrame of booleans where True corresponds to an entry with NaN value:


        
        
            
                
                
                    df.isna()
                
            
               A      B
0  False  False
1  True   False

We then call any(), which checks each column to see if there exists a non-zero value. If so, then True is returned for that column:


        
        
            
                
                
                    df.isna().any()
                
            
            A     True
B    False
dtype: bool

Remember, booleans True and False are synonymous to integers 1 and 0 respectively.

Finding columns with only missing values

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[pd.np.nan,pd.np.nan],"B":[4,5]})
df
                
            
               A    B
0  NaN  4
1  NaN  5

To find columns that contain only NaN:


        
        
            
                
                
                    df.isna().all()
                
            
            A     True
B    False
dtype: bool

Explanation

Once again, we use isna() to fetch a DataFrame of booleans where True corresponds to entries with NaN:


        
        
            
                
                
                    df.isna().all()
                
            
               A     B
0  True  False
1  True  False

Now, instead of any(), we use all(), which checks each column and returns True for that column if all its values are non-zero:


        
        
            
                
                
                    df.isna().all()
                
            
            A     True
B    False
dtype: bool

Published by Isshin Inada

Edited by 0 others

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