feature-engineering-and-data-transformations/using-toy-exmaples/feature_cleaning/missing_data.py
def check_missing(data,output_path=None):
    """
    check the total number & percentage of missing values
    per variable of a pandas Dataframe
    """
    
    result = pd.concat([data.isnull().sum(),data.isnull().mean()],axis=1)
    result = result.rename(index=str,columns={0:'total missing',1:'proportion'})
    if output_path is not None:
        result.to_csv(output_path+'missing.csv')
        print('result saved at', output_path, 'missing.csv')
    return result

    check the total number & percentage of missing values
    per variable of a pandas Dataframe
    
check the total number & percentage of missing values
per variable of a pandas Dataframe
check the total number & percentage of missing values per variable of a pandas Dataframe
def check_missing(data,output_path=None):
    result = pd.concat([data.isnull().sum(),data.isnull().mean()],axis=1)
    result = result.rename(index=str,columns={0:'total missing',1:'proportion'})
    if output_path is not None:
        result.to_csv(output_path+'missing.csv')
        print('result saved at', output_path, 'missing.csv')
    return result
def check_missing ( data , output_path = None ) : result = pd . concat ( [ data . isnull ( ) . sum ( ) , data . isnull ( ) . mean ( ) ] , axis = 1 ) result = result . rename ( index = str , columns = { 0 : 'total missing' , 1 : 'proportion' } ) if output_path is not None : result . to_csv ( output_path + 'missing.csv' ) print ( 'result saved at' , output_path , 'missing.csv' ) return result
check the total number & percentage of missing values
per variable of a pandas Dataframe
check the total number & percentage of missing values per variable of a pandas Dataframe
"""
    check the total number & percentage of missing values
    per variable of a pandas Dataframe
    """
data
output_path