
        
        
            
                
                
                    df = pd.DataFrame({"A":[np.nan,3,np.nan],"B":[4,5,6],"C":[np.nan,7,8]}, index=["a","b","c"])
df
                
            
               A    B  C
a  NaN  4  NaN
b  3.0  5  7.0
c  NaN  6  8.0

Solution

To count the number of rows that contain at least one missing value:


        
        
            
                
                
                    df.isna().any(axis=1).sum()
                
            
            2

Explanation

We first use isna() method to get a DataFrame of booleans where True indicates the presence of NaN:


        
        
            
                
                
                    df.isna()
                
            
               A      B      C
a  True   False  True
b  False  False  False
c  True   False  False

We then use any(axis=1), which returns a Series of booleans where True indicates a row with at least one True:


        
        
            
                
                
                    df.isna().any(axis=1)
                
            
            a     True
b    False
c     True
dtype: bool

In Pandas, True is internally represented as a 1, while False as a 0. Therefore, taking the sum of this Series will return the number of rows with at least one missing value:


        
        
            
                
                
                    df.isna().any(axis=1).sum()
                
            
            2

With all missing values

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[np.nan,np.nan],"B":[3,np.nan]}, index=["a","b"])
df
                
            
               A    B
a  NaN  3.0
b  NaN  NaN

Solution

To get the number of rows with all missing values:


        
        
            
                
                
                    df.isna().all(axis=1).sum()
                
            
            1

Explanation

Once again, we first use isna() to get a DataFrame of booleans where True indicates the presence of NaN:


        
        
            
                
                
                    df.isna()
                
            
               A     B
a  True  False
b  True  True

Next, we use all(axis=1) to get a Series of booleans where True indicates a row with all Trues:


        
        
            
                
                
                    df.isna().all(axis=1)
                
            
            a    False
b     True
dtype: bool

In Pandas, True is internally represented as a 1, while False as a 0, so taking the summation tells us the number of rows with all missing column values:


        
        
            
                
                
                    df.isna().all(axis=1).sum()
                
            
            1