
        
        
            
                
                
                    df = pd.DataFrame({"A":[np.nan,4,5],"B":[6,7,np.nan],"C":[np.nan,8,9]}, index=["a","b","c"])
df
                
            
               A    B    C
a  NaN  6.0  NaN
b  4.0  7.0  8.0
c  5.0  NaN  9.0

To remove rows that contain at least one NaN:


        
        
            
                
                
                    df.dropna()
                
            
               A    B    C
b  4.0  7.0  8.0

Note that dropna() creates and return a new DataFrame - the original df is kept intact. This can be changed by setting inplace=True.

Rows with missing values for certain columns

Consider the same DataFrame as above:


        
        
            
                
                
                    df
                
            
               A    B    C
a  NaN  6.0  NaN
b  4.0  7.0  8.0
c  5.0  NaN  9.0

To remove rows where the value for column C is NaN:


        
        
            
                
                
                    df.dropna(subset=["C"])
                
            
               A    B    C
b  4.0  7.0  8.0
c  5.0  NaN  9.0

Rows with n missing values

Consider the same DataFrame as above:


        
        
            
                
                
                    df
                
            
               A    B    C
a  NaN  6.0  NaN
b  4.0  7.0  8.0
c  5.0  NaN  9.0

To remove rows that contain at least two NaN:


        
        
            
                
                
                    df.dropna(thresh=2)
                
            
               A    B    C
b  4.0  7.0  8.0
c  5.0  NaN  9.0

Rows with all missing values

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[np.nan,np.nan],"B":[6,np.nan]}, index=["a","b"])
                
            
               A    B
a  NaN  6.0
b  NaN  NaN

To remove rows where all entries are missing:


        
        
            
                
                
                    df.dropna(how="all")
                
            
               A    B
a  NaN  6.0