-
Notifications
You must be signed in to change notification settings - Fork 4
/
app_dist_tables_avg_total2.py
179 lines (164 loc) · 7.83 KB
/
app_dist_tables_avg_total2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
"""
app_dist_Tables00.py illustrates use of pitaxcalc-demo release 2.0.0
(India version).
USAGE: python app_dist_Tables00.py
"""
import locale
import pandas as pd
from taxcalc import *
import numpy as np
from babel.numbers import format_currency
def remove_decimal(S):
S = str(S)
S = S[:-3]
return S
def ind_currency(curr):
curr_str = format_currency(curr, 'INR', locale='en_IN').replace(u'\xa0', u' ')
return(remove_decimal(curr_str))
def convert_df(df, cols):
# breakup the dataframe into cols and others
df1 = df[cols].copy(deep=True)
cols_other = df.columns.difference(cols)
df2 = df[cols_other].copy(deep=True)
# strip the first row and make it into a list
for i in range(len(df)):
#print('i '+ str(i))
row = df1.loc[i].values.tolist()
#print(row)
# take the list and build a new list element by element
row1=[]
for j in range(len(row)):
#row1.append(format_it(str(row[i])))
#row1.append(format_it(row[i]))
#value_str = format_currency(row[j], 'INR', locale='en_IN').replace(u'\xa0', u' ')
value_str = ind_currency(row[j])
row1.append(value_str)
# replace the row with the changed list
df1.loc[i] = row1
# reassemble the dataframe
df = pd.concat([df2, df1], axis=1)
return(df)
# create Records object containing pit.csv and pit_weights.csv input data
recs = Records(data='pit.csv', weights='pit_weights.csv')
grecs = GSTRecords()
crecs = CorpRecords()
# create Policy object containing current-law policy
pol = Policy()
# specify Calculator object for current-law policy
calc1 = Calculator(policy=pol, records=recs, gstrecords=grecs, corprecords=crecs, verbose=False)
# specify Calculator object for reform in JSON file
reform = Calculator.read_json_param_objects('Budget2019_reform.json', None)
#print(reform['policy'])
pol.implement_reform(reform['policy'])
calc2 = Calculator(policy=pol, records=recs, gstrecords=grecs, corprecords=crecs, verbose=False)
# loop through years 2017, 2018, 2019, and 2020 and print out pitax
START_YEAR = 2017
END_YEAR = 2023
BASE_YEAR = 2019
wtd_tax_clp={}
wtd_tax_ref={}
wtd_tot={}
for year in range(START_YEAR, END_YEAR+1):
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()
wtd_tax_clp[year] = weighted_tax1
wtd_tax_ref[year] = weighted_tax2
wtd_tot[year] = total_weights
if (year>=BASE_YEAR):
print(f'**************** Total Tax Collection for {year}', end=' ')
print('****************')
print('\n')
print(f'Current Law: Tax Collection in Rs. Cr. for {year}:', end=' ')
print(f'{weighted_tax1 * 1e-7:,.2f}')
print(f'Reform : Tax Collection in Rs. Cr. for {year}:', end=' ')
print(f'{weighted_tax2 * 1e-7:,.2f}')
print(' Difference in Tax Collection:', end=' ')
print(f'{(weighted_tax2-weighted_tax1) * 1e-7:,.2f} Cr.')
print('\n')
print(f'Representing: {total_weights * 1e-5:,.2f} Lakh taxpayers')
print('\n')
for output_in_averages in [False, True]:
output_categories = 'standard_income_bins'
# pd.options.display.float_format = '{:,.3f}'.format
# dt1, dt2 = calc1.distribution_tables(calc2, 'weighted_deciles')
dt1, dt2 = calc1.distribution_tables(calc2, output_categories,
averages=output_in_averages,
scaling=True)
dt2['pitax_diff'] = dt2['pitax'] - dt1['pitax']
if (output_categories == 'standard_income_bins'):
dt1.rename_axis('Income_Bracket', inplace=True)
dt2.rename_axis('Income_Bracket', inplace=True)
else:
dt1.rename_axis('Decile', inplace=True)
dt2.rename_axis('Decile', inplace=True)
dt1 = dt1.reset_index().copy()
dt2 = dt2.reset_index().copy()
dt1 = dt1.fillna(0)
dt2 = dt2.fillna(0)
if output_in_averages:
print('*************************** Average Tax Burden ', end=' ')
print(f'(in Rs.) per Taxpayer for {year} ***************************')
pd.options.display.float_format = '{:,.0f}'.format
else:
print('***************** Distribution Tables ', end=' ')
print(f'for Total Tax Collection (in Rs. crores) for {year} *********')
pd.options.display.float_format = '{:,.3f}'.format
# list of columns for printing in rupees
col_list1 = list(dt1.columns)
col_list1.remove('Income_Bracket')
col_list1.remove('weight')
print('\n')
print(' *** CURRENT-LAW DISTRIBUTION TABLE ***')
#print('\n')
print(convert_df(dt1, col_list1))
print('\n')
print(' *** POLICY-REFORM DISTRIBUTION TABLE ***')
#print('\n')
col_list2 = col_list1
col_list2.append('pitax_diff')
print(convert_df(dt2, col_list2))
print('\n')
# print text version of each complete distribution table to a file
if output_in_averages:
with open('dist-table-all-clp-avg-'+str(year)+'.txt', 'w') as dfile:
dt1.to_string(dfile)
with open('dist-table-all-ref-avg-'+str(year)+'.txt', 'w') as dfile:
dt2.to_string(dfile)
# print text version of each partial distribution table to a file
to_include = ['weight', 'GTI', 'TTI', 'pitax']
with open('dist-table-part-clp-avg-'+str(year)+'.txt', 'w') as dfile:
dt1.to_string(dfile, columns=to_include)
with open('dist-table-part-ref-avg-'+str(year)+'.txt', 'w') as dfile:
dt2.to_string(dfile, columns=to_include)
else:
with open('dist-table-all-clp-total-'+str(year)+'.txt', 'w') as dfile:
dt1.to_string(dfile)
with open('dist-table-all-ref-total-'+str(year)+'.txt', 'w') as dfile:
dt2.to_string(dfile)
# print text version of each partial distribution table to a file
to_include = ['weight', 'GTI', 'TTI', 'pitax']
with open('dist-table-part-clp-total-'+str(year)+'.txt', 'w') as dfile:
dt1.to_string(dfile, columns=to_include)
with open('dist-table-part-ref-total-'+str(year)+'.txt', 'w') as dfile:
dt2.to_string(dfile, columns=to_include)
# Print the total taxes in the end
for year in range(BASE_YEAR, END_YEAR+1):
wtd_tax_clp_rs = ind_currency(wtd_tax_clp[year] * 1e-7)
wtd_tax_ref_rs = ind_currency(wtd_tax_ref[year] * 1e-7)
wtd_tax_diff_rs = ind_currency((wtd_tax_ref[year]-wtd_tax_clp[year]) * 1e-7)
print(f'**************** Total Tax Collection for {year}', end=' ')
print('****************')
#print('\n')
print(f'Current Law: Tax Collection in Rs. Cr. for {year}:', end=' ')
print(f'{ind_currency(wtd_tax_clp[year] * 1e-7)}')
print(f'Reform : Tax Collection in Rs. Cr. for {year}:', end=' ')
print(f'{ind_currency(wtd_tax_ref[year] * 1e-7)}')
print(' Difference in Tax Collection:', end=' ')
print(f'{ind_currency((wtd_tax_ref[year]-wtd_tax_clp[year]) * 1e-7)} Cr.')
print(f'Representing: {wtd_tot[year] * 1e-5:,.2f} Lakh taxpayers')
print('\n')