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Remove unused argument from means_and_comparisons utility function #1044

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Nov 9, 2016
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Remove unused argument from means_and_comparisons utility function #1044

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2 changes: 1 addition & 1 deletion taxcalc/dropq/dropq_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,7 @@ def create_dropq_difference_table(df1, df2, groupby, res_col, diff_col,
# Negative values are the magnitude of the tax decrease
df2[res_col + suffix] = df2[diff_col + suffix] - df1[diff_col]

diffs = means_and_comparisons(df2, res_col + suffix,
diffs = means_and_comparisons(res_col + suffix,
df.groupby('bins', as_index=False),
wsum + EPSILON)

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10 changes: 4 additions & 6 deletions taxcalc/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -248,15 +248,13 @@ def add_income_bins(pdf, compare_with='soi', bins=None, right=True,
return pdf


def means_and_comparisons(pdf, col_name, gpdf, weighted_total):
def means_and_comparisons(col_name, gpdf, weighted_total):
"""
Return Pandas DataFrame based specified grouped values of col_name in
specified gpdf Pandas DataFrame.
pdf: Pandas DataFrame for full results of calculation (NEVER USED)
Return new Pandas DataFrame based on grouped values of specified
col_name in specified gpdf Pandas DataFrame.
col_name: the column name to calculate against
gpdf: grouped Pandas DataFrame
"""
# pylint: disable=unused-argument
# Who has a tax cut, and who has a tax increase
diffs = gpdf.apply(weighted_count_lt_zero, col_name)
diffs = pd.DataFrame(data=diffs, columns=['tax_cut'])
Expand Down Expand Up @@ -514,7 +512,7 @@ def create_difference_table(recs1, recs2, groupby,
# Positive values are the magnitude of the tax increase
# Negative values are the magnitude of the tax decrease
res2['tax_diff'] = res2[income_to_present] - res1[income_to_present]
diffs = means_and_comparisons(res2, 'tax_diff',
diffs = means_and_comparisons('tax_diff',
pdf.groupby('bins', as_index=False),
(res2['tax_diff'] * res2['s006']).sum())
sum_row = get_sums(diffs)[diffs.columns.values.tolist()]
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