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- #Categorizing income level and dafaulting on loans
- def income_level_debt(row):
- income = row['total_income']
- debt = row['debt']
- if income <= 30000:
- if debt == 1:
- return 'Defaulting Lower Class'
- return 'Lower Class'
- if income <= 80000:
- if debt == 1:
- return 'Defaulting Middle Class'
- return 'Middle Class'
- if income >= 80001:
- if debt == 1:
- return 'Defaulting Upper Class'
- return 'Upper Class'
- #Value totals
- print(clean_data['income_level_debt'].value_counts())
- print()
- #income_debt_level total values
- print('Total values:', clean_data['income_level_debt'].value_counts().sum())
- #length of income_level_debt column
- print('Length of income_level_column:', len(clean_data['income_level_debt']))
- # Display necessary columns for this function
- tot_inc_debt = clean_data.loc[:,['debt', 'total_income', 'income_level_debt']]
- # Show the rows with 'None' under income_level_debt column
- print(tot_inc_debt[tot_inc_debt['income_level_debt'].isnull()])
- # To check code my code using same values from the 'None' rows
- row_values = [80579, 0]
- row_columns = ['total_income', 'debt']
- row = pd.Series(data=row_values, index=row_columns)
- income_level_debt(row)
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