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jessking1019

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Feb 19th, 2020
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  1. #Categorizing income level and dafaulting on loans
  2. def income_level_debt(row):
  3. income = row['total_income']
  4. debt = row['debt']
  5. if income <= 30000:
  6. if debt == 1:
  7. return 'Defaulting Lower Class'
  8. return 'Lower Class'
  9. if income <= 80000:
  10. if debt == 1:
  11. return 'Defaulting Middle Class'
  12. return 'Middle Class'
  13. if income >= 80001:
  14. if debt == 1:
  15. return 'Defaulting Upper Class'
  16. return 'Upper Class'
  17.  
  18. #Value totals
  19. print(clean_data['income_level_debt'].value_counts())
  20. print()
  21. #income_debt_level total values
  22. print('Total values:', clean_data['income_level_debt'].value_counts().sum())
  23. #length of income_level_debt column
  24. print('Length of income_level_column:', len(clean_data['income_level_debt']))
  25. # Display necessary columns for this function
  26. tot_inc_debt = clean_data.loc[:,['debt', 'total_income', 'income_level_debt']]
  27. # Show the rows with 'None' under income_level_debt column
  28. print(tot_inc_debt[tot_inc_debt['income_level_debt'].isnull()])
  29.  
  30. # To check code my code using same values from the 'None' rows
  31. row_values = [80579, 0]
  32. row_columns = ['total_income', 'debt']
  33. row = pd.Series(data=row_values, index=row_columns)
  34. income_level_debt(row)
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