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app14.py

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+"""
+app1.py illustrates use of pitaxcalc-demo release 2.0.0 (India version).
+USAGE: python app2.py
+"""
+import locale
+from taxcalc import *
+
+
+def gti_format(gti):
+    str_gti = str(gti)
+    str_gti = str_gti.replace('(', '')
+    str_gti = str_gti.replace(']', '')
+    split_gti = str_gti.split(', ')
+    float1 = int(float(split_gti[0]))
+    float2 = int(float(split_gti[1]))
+    lc1 = locale.currency(float1, grouping=True)
+    lc1 = lc1.replace('? ', '')
+    lc2 = locale.currency(float2, grouping=True)
+    lc2 = lc2.replace('? ', '')
+    pt1 = f'{lc1}'
+    pt2 = f'{lc2}'
+    final_str = f'Rs. {pt1:16} - {pt2:15}'
+    return final_str
+
+
+locale.setlocale(locale.LC_ALL, '')
+
+# create Records object containing pit.csv and pit_weights.csv input data
+recs = Records(data='pitBigData.csv', weights='pit_weightsBD.csv')
+
+# create Policy object containing current-law policy
+pol = Policy()
+
+# specify Calculator object for current-law policy
+calc1 = Calculator(policy=pol, records=recs, verbose=False)
+
+# specify Calculator object for reform in JSON file
+reform = Calculator.read_json_param_objects('app14_reform.json', None)
+pol.implement_reform(reform['policy'])
+calc2 = Calculator(policy=pol, records=recs, verbose=False)
+# loop through years 2017, 2018, and 2019 and print out pitax
+for year in range(2017, 2020):
+    calc1.advance_to_year(year)
+    calc2.advance_to_year(year)
+    calc1.calc_all()
+    calc2.calc_all()
+    weighted_tax1 = calc1.weighted_total('pitax')
+    weighted_tax2 = calc2.weighted_total('pitax')
+    total_weights = calc1.total_weight()
+    print(f'Tax 1 for {year}: {weighted_tax1 * 1e-9:,.2f}')
+    print(f'Tax 2 for {year}: {weighted_tax2 * 1e-9:,.2f}')
+    print(f'Total weight for {year}: {total_weights * 1e-6:,.2f}')
+
+# dump out records for 2019
+dump_vars = ['FILING_SEQ_NO', 'AGEGRP', 'SALARIES', 'INCOME_HP',
+             'TOTAL_PROFTS_GAINS_BP', 'TOTAL_INCOME_OS', 'GTI', 'TTI']
+dumpdf = calc1.dataframe(dump_vars)
+dumpdf['pitax1'] = calc1.array('pitax')
+dumpdf['pitax2'] = calc2.array('pitax')
+dumpdf['pitax_diff'] = dumpdf['pitax2'] - dumpdf['pitax1']
+dumpdf['percent change in tax'] = np.where(dumpdf['pitax_diff'] == 0.0,
+                                           0.0, (dumpdf['pitax_diff'] /
+                                                 dumpdf['pitax1'] * 100))
+dumpdf['Zero change'] = np.where(dumpdf['percent change in tax'] == 0.0, 1, 0)
+column_order = dumpdf.columns
+
+assert len(dumpdf.index) == calc1.array_len
+
+dumpdf.to_csv('app14-dump.csv', columns=column_order,
+              index=False, float_format='%.0f')
+pd.options.display.float_format = 'Rs.{:,.0f}'.format
+# converting the result into deciles and getting mean of 3 variables each yr
+df1 = dumpdf.groupby(pd.qcut(dumpdf.GTI, 10))['pitax1', 'pitax2',
+                                              'pitax_diff'].mean()
+# making it more beautiful
+renames = {'pitax1': 'Base', 'pitax2': 'Reform', 'pitax_diff': 'Difference'}
+df1 = df1.reset_index()
+df1['GTI'] = df1['GTI'].apply(gti_format)
+df1.rename(renames, axis=1, inplace=True)
+df1.index = [i for i in range(1, 11)]
+print('\nIndividual Level - Average by Decile 2019')
+print(df1)
+df1.to_csv('Decilemeans.csv')  # conversion to csv files
+
+df2 = dumpdf.groupby(pd.qcut(dumpdf.GTI, 10))['pitax1', 'pitax2',
+                                              'pitax_diff'].sum()
+df2 = df2.reset_index()
+df2['GTI'] = df2['GTI'].apply(gti_format)
+df2.rename(renames, axis=1, inplace=True)
+df2.index = [i for i in range(1, 11)]
+print('\nAggregate Tax Liability by Decile 2019')
+print(df2)
+df2.to_csv('Decilesum.csv')

app14_reform.json

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+{
+  "policy": {
+	// increased cess rate from 3% to 4%
+  "_cess_rate": {"2019": [0.04]},
+  // reduce rate 2 from 5% to 0%
+  "_rate2":{"2019": [0.0]}
+  }
+}

app1_reform.json

@@ -9,4 +9,4 @@
     // reduce surcharge rate from 15% to 10%
 	"_surcharge_rate": {"2017": [0.10]}
     }
-}
+}