
        
        
            
                
                
                    df = pd.DataFrame({"A":[np.nan,3,np.nan],"B":[4,np.nan,5],"C":[np.nan,7,8]}, index=["a","b","c"])
df
                
            
               A    B    C
a  NaN  4.0  NaN
b  3.0  NaN  7.0
c  NaN  5.0  8.0

Solution

To fill the missing values with the mean of the column:


        
        
            
                
                
                    df.fillna(df.mean())
                
            
               A    B    C
a  3.0  4.0  7.5
b  3.0  4.5  7.0
c  3.0  5.0  8.0

Here, a new DataFrame is returned, and the original df is kept intact.

Explanation

Here, df.mean() returns a Series that holds the mean of each column:


        
        
            
                
                
                    df.mean()
                
            
            A    3.0
B    4.5
C    7.5
dtype: float64

Conveniently, this Series provides the mapping as to which value should be used as the filler for each column. We then directly use fillna(~) to perform the filling.

Performing the fill in-place

The fillna(~) method allows for the filling to be performed in-place. Note that in-place means that the original DataFrame is directly modified, and no new DataFrame is returned.

Set inplace=True like so:


        
        
            
                
                
                    df.fillna(df.mean(), inplace=True)
df
                
            
               A    B    C
a  3.0  4.0  7.5
b  3.0  4.5  7.0
c  3.0  5.0  8.0

Published by Isshin Inada

Edited by 0 others

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