Rename and reorder difference table columns

RELEASES.md

@@ -22,6 +22,9 @@ Release 0.12.0 on 2017-??-??
 - Change Calculator class constructor so that it makes a deep copy of each specified object for internal use
   [[#1582](https://github.com/open-source-economics/Tax-Calculator/pull/1582)
   by Martin Holmer]
+- Rename and reorder difference table columns
+  [[#1584](https://github.com/open-source-economics/Tax-Calculator/pull/1584)
+  by Martin Holmer]
 
 **New Features**
 - Add Calculator.reform_documentation that generates plain text documentation of a reform

taxcalc/tbi/__init__.py

@@ -1,4 +1,3 @@
 from taxcalc.tbi.tbi import (run_nth_year_tax_calc_model,
                              run_nth_year_gdp_elast_model,
-                             create_dict_table,
                              reform_warnings_errors)

taxcalc/tbi/tbi.py

@@ -22,17 +22,13 @@
                                    calculate,
                                    random_seed,
                                    summary,
+                                   create_dict_table,
                                    AGGR_ROW_NAMES)
-from taxcalc import (results, DIST_TABLE_LABELS,
-                     proportional_change_in_gdp,
-                     Growdiff, Growfactors, Policy)
+from taxcalc import (results, DIST_TABLE_LABELS, DIFF_TABLE_LABELS,
+                     proportional_change_in_gdp, Growdiff, Growfactors, Policy)
 
 
 # specify constants
-DIST_COLUMN_TYPES = [float] * len(DIST_TABLE_LABELS)
-
-DIFF_COLUMN_TYPES = [int, int, int, float, float, str, str, str, str]
-
 DEC_ROW_NAMES = ['perc0-10', 'perc10-20', 'perc20-30', 'perc30-40',
                  'perc40-50', 'perc50-60', 'perc60-70', 'perc70-80',
                  'perc80-90', 'perc90-100', 'all']
@@ -128,7 +124,7 @@ def append_year(pdf):
         pdf.columns = [str(col) + '_{}'.format(year_n) for col in pdf.columns]
         return pdf
 
-    # optionally return non-JSON results
+    # optionally return non-JSON-like results
     if not return_dict:
         res = dict()
         for tbl in summ:
@@ -137,10 +133,13 @@ def append_year(pdf):
         print('elapsed time for this run: {:.1f}'.format(elapsed_time))
         return res
 
-    # optionally construct JSON results tables for year n
+    # optionally construct JSON-like results dictionaries for year n
     dec_row_names_n = [x + '_' + str(year_n) for x in DEC_ROW_NAMES]
     bin_row_names_n = [x + '_' + str(year_n) for x in BIN_ROW_NAMES]
     agg_row_names_n = [x + '_' + str(year_n) for x in AGG_ROW_NAMES]
+    dist_column_types = [float] * len(DIST_TABLE_LABELS)
+    diff_column_types = [int, int, str, int, str, float, float, str, str]
+    assert len(diff_column_types) == len(DIFF_TABLE_LABELS)
     info = dict()
     for tbl in summ:
         info[tbl] = {'row_names': [], 'col_types': []}
@@ -151,9 +150,9 @@ def append_year(pdf):
         else:
             info[tbl]['row_names'] = agg_row_names_n
         if 'dist' in tbl:
-            info[tbl]['col_types'] = DIST_COLUMN_TYPES
+            info[tbl]['col_types'] = dist_column_types
         elif 'diff' in tbl:
-            info[tbl]['col_types'] = DIFF_COLUMN_TYPES
+            info[tbl]['col_types'] = diff_column_types
     res = dict()
     for tbl in summ:
         if 'aggr' in tbl:
@@ -164,8 +163,10 @@ def append_year(pdf):
             res[tbl] = create_dict_table(summ[tbl],
                                          row_names=info[tbl]['row_names'],
                                          column_types=info[tbl]['col_types'])
+
     elapsed_time = time.time() - start_time
     print('elapsed time for this run: {:.1f}'.format(elapsed_time))
+
     return res
 
 
@@ -212,48 +213,3 @@ def run_nth_year_gdp_elast_model(year_n, start_year,
         return gdp_elast_total
     else:
         return gdp_effect
-
-
-def create_dict_table(dframe, row_names=None, column_types=None,
-                      num_decimals=2):
-    """
-    Create and return dictionary with JSON-like content from specified dframe.
-    """
-    # embedded formatted_string function
-    def formatted_string(val, _type, num_decimals):
-        """
-        Return formatted conversion of number val into a string.
-        """
-        float_types = [float, np.dtype('f8')]
-        int_types = [int, np.dtype('i8')]
-        frmat_str = "0:.{num}f".format(num=num_decimals)
-        frmat_str = "{" + frmat_str + "}"
-        try:
-            if _type in float_types or _type is None:
-                return frmat_str.format(val)
-            elif _type in int_types:
-                return str(int(val))
-            elif _type == str:
-                return str(val)
-            else:
-                raise NotImplementedError()
-        except ValueError:
-            # try making it a string - good luck!
-            return str(val)
-    # high-level create_dict_table function logic
-    out = dict()
-    if row_names is None:
-        row_names = [str(x) for x in list(dframe.index)]
-    else:
-        assert len(row_names) == len(dframe.index)
-    if column_types is None:
-        column_types = [dframe[col].dtype for col in dframe.columns]
-    else:
-        assert len(column_types) == len(dframe.columns)
-    for idx, row_name in zip(dframe.index, row_names):
-        row_out = out.get(row_name, [])
-        for col, dtype in zip(dframe.columns, column_types):
-            row_out.append(formatted_string(dframe.loc[idx, col],
-                                            dtype, num_decimals))
-        out[row_name] = row_out
-    return out

taxcalc/tbi/tbi_utils.py

@@ -584,3 +584,48 @@ def summary(df1, df2, mask):
 
     # return dictionary of summary results
     return summ
+
+
+def create_dict_table(dframe, row_names=None, column_types=None,
+                      num_decimals=2):
+    """
+    Create and return dictionary with JSON-like content from specified dframe.
+    """
+    # embedded formatted_string function
+    def formatted_string(val, _type, num_decimals):
+        """
+        Return formatted conversion of number val into a string.
+        """
+        float_types = [float, np.dtype('f8')]
+        int_types = [int, np.dtype('i8')]
+        frmat_str = "0:.{num}f".format(num=num_decimals)
+        frmat_str = "{" + frmat_str + "}"
+        try:
+            if _type in float_types or _type is None:
+                return frmat_str.format(val)
+            elif _type in int_types:
+                return str(int(val))
+            elif _type == str:
+                return str(val)
+            else:
+                raise NotImplementedError()
+        except ValueError:
+            # try making it a string - good luck!
+            return str(val)
+    # high-level create_dict_table function logic
+    out = dict()
+    if row_names is None:
+        row_names = [str(x) for x in list(dframe.index)]
+    else:
+        assert len(row_names) == len(dframe.index)
+    if column_types is None:
+        column_types = [dframe[col].dtype for col in dframe.columns]
+    else:
+        assert len(column_types) == len(dframe.columns)
+    for idx, row_name in zip(dframe.index, row_names):
+        row_out = out.get(row_name, [])
+        for col, dtype in zip(dframe.columns, column_types):
+            row_out.append(formatted_string(dframe.loc[idx, col],
+                                            dtype, num_decimals))
+        out[row_name] = row_out
+    return out

taxcalc/tests/test_utils.py

@@ -17,6 +17,7 @@
 from taxcalc import Policy, Records, Behavior, Calculator
 from taxcalc.utils import (STATS_COLUMNS,
                            DIST_TABLE_COLUMNS, DIST_TABLE_LABELS,
+                           DIFF_TABLE_COLUMNS, DIFF_TABLE_LABELS,
                            create_distribution_table, create_difference_table,
                            weighted_count_lt_zero, weighted_count_gt_zero,
                            weighted_count, weighted_sum, weighted_mean,
@@ -846,3 +847,9 @@ def test_bootstrap_se_ci():
     assert abs(bsd['se'] / 23.02 - 1) < 0.02
     assert abs(bsd['cilo'] / 45.9 - 1) < 0.02
     assert abs(bsd['cihi'] / 135.4 - 1) < 0.03
+
+
+def test_table_columns_labels():
+    # check that length of two lists are the same
+    assert len(DIST_TABLE_COLUMNS) == len(DIST_TABLE_LABELS)
+    assert len(DIFF_TABLE_COLUMNS) == len(DIFF_TABLE_LABELS)

taxcalc/utils.py

@@ -33,9 +33,8 @@
                  'payrolltax', 'combined', 's006']
 
 # Items in the DIST_TABLE_COLUMNS list below correspond to the items in the
-# DIST_TABLE_LABELS list below; this correspondence allows us to use
-# DIST_TABLE_LABELS to map a label to the correct column in the distribution
-# tables.
+# DIST_TABLE_LABELS list below; this correspondence allows us to use this
+# labels list to map a label to the correct column in a distribution table.
 DIST_TABLE_COLUMNS = ['s006',
                       'c00100',
                       'num_returns_StandardDed',
@@ -76,18 +75,29 @@
                      'Payroll Tax Liablities',
                      'Combined Payroll and Individual Income Tax Liabilities']
 
-# Following list is used in our difference table to label its columns.
-DIFF_TABLE_LABELS = ['Tax Units with Tax Cut',
+# Items in the DIFF_TABLE_COLUMNS list below correspond to the items in the
+# DIFF_TABLE_LABELS list below; this correspondence allows us to use this
+# labels list to map a label to the correct column in a difference table.
+DIFF_TABLE_COLUMNS = ['count',
+                      'tax_cut',
+                      'perc_cut',
+                      'tax_inc',
+                      'perc_inc',
+                      'mean',
+                      'tot_change',
+                      'share_of_change',
+                      'perc_aftertax']
+
+DIFF_TABLE_LABELS = ['All Tax Units',
+                     'Tax Units with Tax Cut',
+                     'Percent with Tax Cut',
                      'Tax Units with Tax Increase',
-                     'Count',
+                     'Percent with Tax Increase',
                      'Average Tax Change',
                      'Total Tax Difference',
-                     'Percent with Tax Increase',
-                     'Percent with Tax Decrease',
                      'Share of Overall Change',
                      'Change as % of Aftertax Income']
 
-
 WEBAPP_INCOME_BINS = [-9e99, 0, 9999, 19999, 29999, 39999, 49999, 74999, 99999,
                       199999, 499999, 1000000, 9e99]
 
@@ -436,13 +446,13 @@ def weighted_share_of_total(gpdf, colname, total):
         # print gpdf.count()  # show unweighted number of filing units per bin
         # create difference table statistics from gpdf in a new DataFrame
         diffs = pd.DataFrame()
+        diffs['count'] = gpdf.apply(weighted_count)
         diffs['tax_cut'] = gpdf.apply(weighted_count_lt_zero, 'tax_diff')
+        diffs['perc_cut'] = gpdf.apply(weighted_perc_cut, 'tax_diff')
         diffs['tax_inc'] = gpdf.apply(weighted_count_gt_zero, 'tax_diff')
-        diffs['count'] = gpdf.apply(weighted_count)
+        diffs['perc_inc'] = gpdf.apply(weighted_perc_inc, 'tax_diff')
         diffs['mean'] = gpdf.apply(weighted_mean, 'tax_diff')
         diffs['tot_change'] = gpdf.apply(weighted_sum, 'tax_diff')
-        diffs['perc_inc'] = gpdf.apply(weighted_perc_inc, 'tax_diff')
-        diffs['perc_cut'] = gpdf.apply(weighted_perc_cut, 'tax_diff')
         wtotal = (res2['tax_diff'] * res2['s006']).sum()
         diffs['share_of_change'] = gpdf.apply(weighted_share_of_total,
                                               'tax_diff', wtotal)