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bankcredits-Data cleaning.py
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#!/usr/bin/env python
# coding: utf-8
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import pandas as pd
import numpy as np
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data = pd.read_csv('2_Full_Join_File.csv')
data.head()
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data.columns
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#Remove the irrelvant columns
data1=data.loc[:,['customer_no','feature_1', 'feature_4','feature_5','feature_6', 'feature_7','feature_11','feature_19','feature_23','feature_25', 'feature_26','feature_27','feature_29', 'feature_30', 'feature_31',
'feature_32', 'feature_33', 'feature_34', 'feature_35','feature_36','feature_37' ,'feature_40', 'feature_41',
'feature_42','feature_44' ,'feature_46','feature_48','feature_55','feature_56', 'feature_58', 'feature_59',
'feature_60','feature_62', 'feature_64', 'feature_66', 'feature_67', 'feature_68', 'feature_69', 'feature_71',
'feature_72','feature_78','feature_78', 'feature_79', 'Bad_label','tot_high_credit_amt', 'tot_cur_balance_amt', 'total_amt_past_due',
'tot_creditlimit', 'tot_cashlimit', 'enq_amt', 'total_enq_count']]
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data1.head()
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data1.head()
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data1.replace(['?','*','$',' ',' ',''],np.nan,inplace=True)
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data1.isnull().sum()
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data1.loc[:,['feature_36','feature_27','feature_37','feature_46','feature_48']].info()
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data1.loc[:,['feature_36','feature_27','feature_37','feature_46','feature_48']]=data1.loc[:,['feature_36','feature_27','feature_37','feature_46','feature_48']].replace(np.nan,'others')
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data1.shape
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from collections import Counter
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from collections import Counter
Counter(data1.feature_36)
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data1.shape
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data1.drop_duplicates(inplace = True)
data1.shape
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data1.isnull().sum()
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data2=data1.loc[:,['customer_no','feature_1', 'feature_4','feature_5','feature_6', 'feature_7','feature_11','feature_19','feature_23','feature_25', 'feature_26','feature_27','feature_29', 'feature_30', 'feature_31',
'feature_32', 'feature_33', 'feature_34', 'feature_35','feature_36','feature_37' ,'feature_40', 'feature_41',
'feature_42','feature_44' ,'feature_46','feature_48','feature_55','feature_56', 'feature_58', 'feature_59',
'feature_60','feature_62', 'feature_64', 'feature_66', 'feature_67', 'feature_68', 'feature_69', 'feature_71',
'feature_72', 'feature_78','feature_79','tot_high_credit_amt', 'tot_cur_balance_amt', 'total_amt_past_due',
'tot_creditlimit', 'tot_cashlimit', 'enq_amt', 'total_enq_countt','Bad_label']]
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data2.head()
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data2.shape
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data2.isnull().sum()
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print(data2.shape)
data2.dropna(inplace = True)
print(data2.shape)
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data2.isnull().sum()
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data2.shape
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data2.to_csv('D:bank details/4_Full_Join_Cleaned.csv',index = False)
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