def test_add_quantile_bins():
dfx = pd.DataFrame(data=DATA, columns=['expanded_income', 's006', 'label'])
dfb = add_quantile_bins(dfx, 'expanded_income', 100,
weight_by_income_measure=False)
bin_labels = dfb['bins'].unique()
default_labels = set(range(1, 101))
for lab in bin_labels:
assert lab in default_labels
dfb = add_quantile_bins(dfx, 'expanded_income', 100,
weight_by_income_measure=True)
assert 'bins' in dfb
def fuzz(df1, df2, bin_type, imeasure, suffix, cols_to_fuzz):
"""
Fuzz some df2 records in each bin defined by bin_type and imeasure.
The fuzzed records have their post-reform tax results (in df2)
set to their pre-reform tax results (in df1).
"""
# pylint: disable=too-many-arguments
assert bin_type == 'dec' or bin_type == 'bin' or bin_type == 'agg'
if bin_type == 'dec':
df2 = add_quantile_bins(df2, imeasure, 10)
elif bin_type == 'bin':
df2 = add_income_bins(df2, imeasure, bins=WEBAPP_INCOME_BINS)
else:
df2 = add_quantile_bins(df2, imeasure, 1)
gdf2 = df2.groupby('bins')
df2['nofuzz'] = gdf2['mask'].transform(chooser)
for col in cols_to_fuzz:
df2[col + suffix] = (df2[col] * df2['nofuzz'] -
df1[col] * df2['nofuzz'] + df1[col])
def test_add_quantile_bins():
dfx = pd.DataFrame(data=DATA, columns=['expanded_income', 's006', 'label'])
dfb = add_quantile_bins(dfx, 'expanded_income', 100,
weight_by_income_measure=False)
bin_labels = dfb['bins'].unique()
default_labels = set(range(1, 101))
for lab in bin_labels:
assert lab in default_labels
# custom labels
dfb = add_quantile_bins(dfx, 'expanded_income', 100,
weight_by_income_measure=True)
assert 'bins' in dfb
custom_labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
dfb = add_quantile_bins(dfx, 'expanded_income', 10,
labels=custom_labels)
assert 'bins' in dfb
bin_labels = dfb['bins'].unique()
for lab in bin_labels:
assert lab in custom_labels
def create(df1, df2, bin_type, imeasure, suffix, cols_to_fuzz, do_fuzzing):
"""
Create additional df2 columns. If do_fuzzing is True, also
fuzz some df2 records in each bin defined by bin_type and imeasure
with the fuzzed records having their post-reform tax results (in df2)
set to their pre-reform tax results (in df1).
"""
# pylint: disable=too-many-arguments
assert bin_type == 'dec' or bin_type == 'bin' or bin_type == 'agg'
if bin_type == 'dec':
df2 = add_quantile_bins(df2, imeasure, 10)
elif bin_type == 'bin':
df2 = add_income_bins(df2, imeasure, bins=STANDARD_INCOME_BINS)
else:
df2 = add_quantile_bins(df2, imeasure, 1)
gdf2 = df2.groupby('bins')
if do_fuzzing:
df2['nofuzz'] = gdf2['mask'].transform(chooser)
else: # never do any results fuzzing
df2['nofuzz'] = np.ones(df2.shape[0], dtype=np.int8)
for col in cols_to_fuzz:
df2[col + suffix] = (df2[col] * df2['nofuzz'] -
df1[col] * df2['nofuzz'] + df1[col])
def write_decile_table(dfx, tfile, tkind='Totals'):
"""
Write to tfile the tkind decile table using dfx DataFrame.
"""
dfx = add_quantile_bins(dfx, 'expanded_income', 10,
weight_by_income_measure=False)
gdfx = dfx.groupby('bins', as_index=False)
rtns_series = gdfx.apply(unweighted_sum, 's006')
xinc_series = gdfx.apply(weighted_sum, 'expanded_income')
itax_series = gdfx.apply(weighted_sum, 'iitax')
ptax_series = gdfx.apply(weighted_sum, 'payrolltax')
htax_series = gdfx.apply(weighted_sum, 'lumpsum_tax')
ctax_series = gdfx.apply(weighted_sum, 'combined')
# write decile table to text file
row = 'Weighted Tax {} by Baseline Expanded-Income Decile\n'
tfile.write(row.format(tkind))
rowfmt = '{}{}{}{}{}{}\n'
row = rowfmt.format(' Returns',
' ExpInc',
' IncTax',
' PayTax',
' LSTax',
' AllTax')
tfile.write(row)
row = rowfmt.format(' (#m)',
' ($b)',
' ($b)',
' ($b)',
' ($b)',
' ($b)')
tfile.write(row)
rowfmt = '{:9.2f}{:10.1f}{:10.1f}{:10.1f}{:10.1f}{:10.1f}\n'
for decile in range(0, 10):
row = '{:2d}'.format(decile)
row += rowfmt.format(rtns_series[decile] * 1e-6,
xinc_series[decile] * 1e-9,
itax_series[decile] * 1e-9,
ptax_series[decile] * 1e-9,
htax_series[decile] * 1e-9,
ctax_series[decile] * 1e-9)
tfile.write(row)
row = ' A'
row += rowfmt.format(rtns_series.sum() * 1e-6,
xinc_series.sum() * 1e-9,
itax_series.sum() * 1e-9,
ptax_series.sum() * 1e-9,
htax_series.sum() * 1e-9,
ctax_series.sum() * 1e-9)
tfile.write(row)