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bibscy/Python

Created May 31, 2020 13:51
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Python
myDataFrame = pd.read_csv('Loan_payments_data_2020_unclean.csv')
columnsDict = {"columnName": list(myDataFrame.columns)}
columnsDataFrame = pd.DataFrame(columnsDict)
replacedColumns = columnsDataFrame['columnName'].str.replace(r'(?<![^_]).', lambda x: x.group().upper())
myDataFrame.columns = list(replacedColumns)
myDataFrame['Gender'] = myDataFrame['Gender'].str.replace('^\s*$', 'NaN')
# =============================================================================
# genderList = myDataFrame.loc[:,"Gender"]
# =============================================================================
myDataFrame['Gender'] = myDataFrame['Gender'].replace('f', 'Female')
myDataFrame['Gender'] = myDataFrame['Gender'].replace('m', 'Male')
myDataFrame['Gender'] = myDataFrame['Gender'].replace('male', 'Male')
myDataFrame['Gender'] = myDataFrame['Gender'].replace('female', 'Female')
myDataFrame['Gender'] = myDataFrame['Gender'].str.replace('^\s*$', 'NaN')
myDataFrame.to_csv('new_Paymets_Loan.csv', na_rep='NaN')
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