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})