Tabular Null Value Check display Function for Pandas Dataframe

null_value_display.py

# Not my original function - looking for citation
def missing_check(df):
    null_val_sum = df.isnull().sum()
    total = df.isnull().sum().sort_values(ascending=False) # total null values
    percent = (null_val_sum/df.isnull().count()).sort_values(ascending=False)
    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
    return missing_data
'''
Example:
missing_data(df_with_nulls)

Output:
---------------------------
                    Total   Percent
HDI for year        19590  0.704170
country               554  0.019914
sex                   495  0.017793
population            459  0.016499
year                  447  0.016068
gdp_per_capita ($)    440  0.015816
age                   395  0.014198
suicides_no           390  0.014019
country-year          381  0.013695
suicides/100k pop     371  0.013336
 gdp_for_year ($)     370  0.013300
generation            368  0.013228
'''