def getListOfMissingValues():
    """
    desc: List of common words used to represent null that are often found in files as text
    """
    lst = ['NaN', 'NAN', 'nan', 'null', 'NULL', 'nul', 'NUL', 'none', 'NONE', '', ' ', '	']
    return lst
	
def advanceMissingValues(df):
    """
    desc: Count nulls and hardcoded text that represents nulls
    param p1: DataFrame name
    return: DataFrame of field names and count values
    """
    lstMissingVals = getListOfMissingValues()
    col_list = getListOfFieldNames(df)
    output = pd.DataFrame(col_list)
    output.rename(columns = {0:'FieldName'}, inplace = True)
    output['Count'] = ''
    
    #For each field name count nulls and other null type values
    for col in col_list:
        nullCnt = df[col].isnull().sum(axis=0)
        #For each missing value perform count on column
        missValCnt = 0
        for missVal in lstMissingVals:
            missValCnt = missValCnt + len(df[(df[col]==missVal)])
 
        cntTotal = nullCnt + missValCnt
        output.loc[output['FieldName'] == col, 'Count'] = cntTotal

    return output

#Test Setup
lst = ['NaN', 'NAN', 'nan', 'null', 'NULL', 'nul', 'NUL', 'none', 'NONE', '', ' ', '	' ,None]
mdf = pd.DataFrame(lst)
mdf.rename(columns = {0:'NullTypes'}, inplace = True)
print(mdf)

#Run Test
chk = advanceMissingValues(mdf)
chk