group['1.96*std'] = 1.96*group['Revenue'].std() group['Outlier'] = abs(group['Revenue'] - group['Revenue'].mean()) > 1.96*group['Revenue'].std() return group Newdf = StateMonth.apply(s) Newdf # Convert data types df.Date = df.Date.astype('datetime64') df.StandardDate = df.StandardDate.astype('datetime64') df.DateSK = df.DateSK.astype('int') df.Day = df.Day.astype('int') df.DOWInMonth = df.DOWInMonth.astype('int') df.DayOfYear = df.DayOfYear.astype('int') df.WeekOfYear = df.WeekOfYear.astype('int') df.WeekOfMonth = df.WeekOfMonth.astype('int') df.Month = df.Month.astype('int') df.Quarter = df.Quarter.astype('int') df.Year = df.Year.astype('int') print 'Data Types' print df.dtypes #From Excel to DataFrame from pandas import DataFrame, ExcelFile import pandas as pd import json