
        
        
            
                
                
                    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

Rows with at least one missing value

Solution

To get rows with missing values in df:


        
        
            
                
                
                    df[df.isna().any(axis=1)]
                
            
               A    B  C
a  NaN  4  NaN
c  NaN  6  8.0

Explanation

The isna() method returns a DataFrame of booleans where True indicates the presence of a missing value:


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

We then use any(axis=1) to obtain a Series where True represents the presence of at least one True in each row:


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

The parameter axis=1 is needed here since the default behaviour of any(~) is to scan through each column (as opposed to each row).

With this boolean mask, we can then extract rows that correspond to True using [] syntax:


        
        
            
                
                
                    df[df.isna().any(axis=1)]
                
            
               A    B  C
a  NaN  4  NaN
c  NaN  6  8.0

Rows with missing value for a certain column

We show the same df here for your reference:


        
        
            
                
                
                    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 get rows with missing value in column C:


        
        
            
                
                
                    df[df["C"].isna()]
                
            
               A    B  C
a  NaN  4  NaN

Explanation

We first begin by extracting column C as a Series:


        
        
            
                
                
                    df["C"]
                
            
            a    NaN
b    7.0
c    8.0
Name: C, dtype: float64

Next, we use the Series' isna() method to get a Series of booleans where True indicates the presence of NaN:


        
        
            
                
                
                    df["C"].isna()
                
            
            a     True
b    False
c    False
Name: C, dtype: bool

Finally, we pass in this boolean mask to extract the rows corresponding to True using [~] syntax:


        
        
            
                
                
                    df[df["C"].isna()]
                
            
               A    B  C
a  NaN  4  NaN

Rows with missing value for multiple columns

We show the same df here for your reference:


        
        
            
                
                
                    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 get rows with missing value in columns A and C:


        
        
            
                
                
                    df[df[["A","C"]].isna().all(axis=1)]
                
            
               A    B  C
a  NaN  4  NaN

Explanation

We first fetch columns A and C as a DataFrame using [~] syntax:


        
        
            
                
                
                    df[["A","C"]]
                
            
               A    C
a  NaN  NaN
b  3.0  7.0
c  NaN  8.0

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


        
        
            
                
                
                    df[["A","C"]].isna()
                
            
               A      C
a  True   True
b  False  False
c  True   False

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


        
        
            
                
                
                    df[["A","C"]].isna().all(axis=1)
                
            
            a     True
b    False
c    False
dtype: bool

Finally, we use the [] syntax to extract the rows corresponding to True:


        
        
            
                
                
                    df[df[["A","C"]].isna().all(axis=1)]
                
            
               A    B   C
a  NaN  4   NaN

Rows with missing values for all columns

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[np.nan,np.nan],"B":[np.nan,4]}, index=["a","b"])
df
                
            
               A    B
a  NaN  NaN
b  NaN  4.0

Solution

To get rows with missing values for all columns:


        
        
            
                
                
                    df[df.isna().all(axis=1)]
                
            
               A    B
a  NaN  NaN

Explanation

The logic is exactly the same as the case for getting rows with at least one missing value, except that we use all(~) instead of any(~). The difference is as follows:

all(~) returns a Series of booleans where True indicates a row with all missing column values (axis=1).
any(~) returns a Series of booleans where True indicates a row with at least one missing column value.