
        
        
            
                
                
                    df = pd.DataFrame({"A":[pd.np.nan,pd.np.nan], "B":[3,4]})
df
                
            
               A    B
0  NaN  3
1  NaN  4

Column-wise

To count the number of non-missing values for each column:


        
        
            
                
                
                    df.count()   # axis=0
                
            
            A  0
B  2
dtype: int64

Here, we have 0 non-NaN values in column A, and 2 non-NaN values in B.

Row-wise

To count the number of non-missing values for each row, set axis=1:


        
        
            
                
                
                    df.count(axis=1)
                
            
            0    1
1    1
dtype: int64

Here, we have 1 non-missing value in both row 0 and row 1.

Numeric and boolean columns/rows only

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":["a","b"], "B":[3,4]})
df
                
            
               A  B
0  a  3
1  b  4

To count only numeric and boolean columns, set numeric_only=True:


        
        
            
                
                
                    df.count(numeric_only=True)
                
            
            B    2
dtype: int64

Notice how column A is ignored since it is a non-numeric type.

Published by Arthur Yanagisawa

Edited by 0 others

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