Show missing values in a Pandas DataFrame

The function below show any null, NaN, or empty values in the dataframe. It takes a dataframe as a parameter and returns a dataframe with columns that shows the count and percentage of any null, NaNs, and empty values.

def missing_values_col(df):
    Write or use a previously written function to return the
    total missing values and the percent missing values by column.
    null_count = df.isnull().sum()
    null_percentage = (null_count / df.shape[0]) * 100
    empty_count = pd.Series(((df == ' ') | (df == '')).sum())
    empty_percentage = (empty_count / df.shape[0]) * 100
    nan_count = pd.Series(((df == 'nan') | (df == 'NaN')).sum())
    nan_percentage = (nan_count / df.shape[0]) * 100
    return pd.DataFrame({'num_missing': null_count, 'missing_percentage': null_percentage,
                         'num_empty': empty_count, 'empty_percentage': empty_percentage,
                         'nan_count': nan_count, 'nan_percentage': nan_percentage})

Published by

Ednalyn C. De Dios

I’ve always been enamored with code and I love data science because of its inherent power to solve real problems. Having grown up in the Philippines, served in the United States Navy, and worked in the nonprofit sector, I am driven to make the world a better place. I have started and participated in numerous campaigns that aim to reduce domestic violence and child abuse in the community.

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