sample code

import csv
import pandas as pd
import numpy as np
import re
import string


myDataFrame = pd.read_csv('Loan_payments_data_2020_unclean.csv')

#convert assign the columns to a dictionary
columnsDict = {"columnName": list(myDataFrame.columns)}
columnsDataFrame = pd.DataFrame(columnsDict)

#apply regex to capitalize first char and any other that has '_' prepended
replacedColumns = columnsDataFrame['columnName'].str.replace(r'(?<![^_]).', lambda x: x.group().upper())

#assign the formatted columns to the columns property of myDataDrame
myDataFrame.columns = list(replacedColumns)



# =============================================================================
# genderList = myDataFrame.loc[:,"Gender"]
# =============================================================================


#clean data
myDataFrame['Gender'] = myDataFrame['Gender'].replace('f', 'Female')
myDataFrame['Gender'] = myDataFrame['Gender'].replace('m', 'Male')
myDataFrame['Gender'] = myDataFrame['Gender'].replace('male', 'Male')
myDataFrame['Gender'] = myDataFrame['Gender'].replace('female', 'Female')
#myDataFrame['Gender'] = myDataFrame['Gender'].str.replace('', 'NaN')
myDataFrame['Gender'].fillna('NaN')

myDataFrame.to_csv('new_Paymets_Loan.csv')