本文整理汇总了Python中taxcalc.Calculator.distribution_tables方法的典型用法代码示例。如果您正苦于以下问题：Python Calculator.distribution_tables方法的具体用法？Python Calculator.distribution_tables怎么用？Python Calculator.distribution_tables使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类taxcalc.Calculator的用法示例。
在下文中一共展示了Calculator.distribution_tables方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_distribution_tables
# 需要导入模块: from taxcalc import Calculator [as 别名]
# 或者: from taxcalc.Calculator import distribution_tables [as 别名]
def test_distribution_tables(cps_subsample):
"""
Test distribution_tables method.
"""
pol = Policy()
recs = Records.cps_constructor(data=cps_subsample)
calc1 = Calculator(policy=pol, records=recs)
assert calc1.current_year == 2014
calc1.calc_all()
dt1, dt2 = calc1.distribution_tables(None, 'weighted_deciles')
assert isinstance(dt1, pd.DataFrame)
assert dt2 is None
dt1, dt2 = calc1.distribution_tables(calc1, 'weighted_deciles')
assert isinstance(dt1, pd.DataFrame)
assert isinstance(dt2, pd.DataFrame)
reform = {2014: {'_UBI_u18': [1000],
'_UBI_1820': [1000],
'_UBI_21': [1000]}}
pol.implement_reform(reform)
assert not pol.parameter_errors
calc2 = Calculator(policy=pol, records=recs)
calc2.calc_all()
dt1, dt2 = calc1.distribution_tables(calc2, 'weighted_deciles')
assert isinstance(dt1, pd.DataFrame)
assert isinstance(dt2, pd.DataFrame)示例2: test_dec_graph_plots
# 需要导入模块: from taxcalc import Calculator [as 别名]
# 或者: from taxcalc.Calculator import distribution_tables [as 别名]
def test_dec_graph_plots(cps_subsample):
pol = Policy()
rec = Records.cps_constructor(data=cps_subsample)
calc1 = Calculator(policy=pol, records=rec)
year = 2020
calc1.advance_to_year(year)
reform = {
'SS_Earnings_c': {year: 9e99}, # OASDI FICA tax on all earnings
'FICA_ss_trt': {year: 0.107484} # lower rate to keep revenue unchanged
}
pol.implement_reform(reform)
calc2 = Calculator(policy=pol, records=rec)
calc2.advance_to_year(year)
assert calc1.current_year == calc2.current_year
calc1.calc_all()
calc2.calc_all()
fig = calc1.decile_graph(calc2)
assert fig
dt1, dt2 = calc1.distribution_tables(calc2, 'weighted_deciles')
dta = dec_graph_data(dt1, dt2, year,
include_zero_incomes=True,
include_negative_incomes=False)
assert isinstance(dta, dict)
dta = dec_graph_data(dt1, dt2, year,
include_zero_incomes=False,
include_negative_incomes=True)
assert isinstance(dta, dict)
dta = dec_graph_data(dt1, dt2, year,
include_zero_incomes=False,
include_negative_incomes=False)
assert isinstance(dta, dict)示例3: test_dist_table_sum_row
# 需要导入模块: from taxcalc import Calculator [as 别名]
# 或者: from taxcalc.Calculator import distribution_tables [as 别名]
def test_dist_table_sum_row(cps_subsample):
rec = Records.cps_constructor(data=cps_subsample)
calc = Calculator(policy=Policy(), records=rec)
calc.calc_all()
# create three distribution tables and compare the ALL row contents
tb1, _ = calc.distribution_tables(None, 'standard_income_bins')
tb2, _ = calc.distribution_tables(None, 'soi_agi_bins')
tb3, _ = calc.distribution_tables(None, 'weighted_deciles')
tb4, _ = calc.distribution_tables(None, 'weighted_deciles',
pop_quantiles=True)
assert np.allclose(tb1.loc['ALL'], tb2.loc['ALL'])
assert np.allclose(tb1.loc['ALL'], tb3.loc['ALL'])
# make sure population count is larger than filing-unit count
assert tb4.at['ALL', 'count'] > tb1.at['ALL', 'count']
# make sure population table has same ALL row values as filing-unit table
for col in ['count', 'count_StandardDed', 'count_ItemDed', 'count_AMT']:
tb4.at['ALL', col] = tb1.at['ALL', col]
assert np.allclose(tb1.loc['ALL'], tb4.loc['ALL'])
# make sure population table has same ALL tax liabilities as diagnostic tbl
dgt = calc.diagnostic_table(1)
assert np.allclose([tb4.at['ALL', 'iitax'],
tb4.at['ALL', 'payrolltax']],
[dgt.at['Ind Income Tax ($b)', calc.current_year],
dgt.at['Payroll Taxes ($b)', calc.current_year]])示例4: test_create_tables
# 需要导入模块: from taxcalc import Calculator [as 别名]
# 或者: from taxcalc.Calculator import distribution_tables [as 别名]
#.........这里部分代码省略.........
if not np.allclose(diff[tabcol].values, expected,
atol=0.1, rtol=0.0):
test_failure = True
print('diff xdec', tabcol)
for val in diff[tabcol].values:
print('{:.1f},'.format(val))
tabcol = 'pc_aftertaxinc'
expected = [np.nan,
np.nan,
-0.3,
-1.0,
-0.7,
-0.5,
-0.7,
-0.7,
-0.7,
-0.7,
-0.7,
-0.3,
-0.6,
-0.6,
-0.3,
-0.1]
if not np.allclose(diff[tabcol].values, expected,
atol=0.1, rtol=0.0, equal_nan=True):
test_failure = True
print('diff xdec', tabcol)
for val in diff[tabcol].values:
print('{:.1f},'.format(val))
# test creating various distribution tables
dist, _ = calc2.distribution_tables(None, 'weighted_deciles')
assert isinstance(dist, pd.DataFrame)
tabcol = 'iitax'
expected = [0.0,
0.0,
-1.8,
2.0,
5.0,
6.7,
18.0,
26.1,
35.2,
82.7,
148.8,
734.1,
1056.9,
152.4,
234.7,
347.1]
if not np.allclose(dist[tabcol].values, expected,
atol=0.1, rtol=0.0):
test_failure = True
print('dist xdec', tabcol)
for val in dist[tabcol].values:
print('{:.1f},'.format(val))
tabcol = 'count_ItemDed'
expected = [0.0,
0.0,
0.4,
1.5,
2.5,
2.6,