ENH pandas-dev#15972 added margins_name parameter for crosstab

Author: Christoph Möhl

doc/source/whatsnew/v0.20.0.txt

@@ -514,7 +514,7 @@ Other Enhancements
 - ``parallel_coordinates()`` has gained a ``sort_labels`` keyword argument that sorts class labels and the colors assigned to them (:issue:`15908`)
 - Options added to allow one to turn on/off using ``bottleneck`` and ``numexpr``, see :ref:`here <basics.accelerate>` (:issue:`16157`)
 - ``DataFrame.style.bar()`` now accepts two more options to further customize the bar chart. Bar alignment is set with ``align='left'|'mid'|'zero'``, the default is "left", which is backward compatible; You can now pass a list of ``color=[color_negative, color_positive]``. (:issue:`14757`)
-
+- ``Crosstab`` has gained a ``margins_name`` parameter to define the name of the row / column that will contain the totals when margins is True(:issue:`15972`)
 
 .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations
 

pandas/core/reshape/pivot.py

@@ -388,7 +388,8 @@ def _convert_by(by):
 
 
 def crosstab(index, columns, values=None, rownames=None, colnames=None,
-             aggfunc=None, margins=False, dropna=True, normalize=False):
+             aggfunc=None, margins=False, margins_name='All', dropna=True,
+             normalize=False):
     """
     Compute a simple cross-tabulation of two (or more) factors. By default
     computes a frequency table of the factors unless an array of values and an
@@ -411,6 +412,12 @@ def crosstab(index, columns, values=None, rownames=None, colnames=None,
         If passed, must match number of column arrays passed
     margins : boolean, default False
         Add row/column margins (subtotals)
+    margins_name : string, default 'All'
+        Name of the row / column that will contain the totals
+        when margins is True.
+
+        .. versionadded:: 0.20.0
+
     dropna : boolean, default True
         Do not include columns whose entries are all NaN
     normalize : boolean, {'all', 'index', 'columns'}, or {0,1}, default False
@@ -490,23 +497,26 @@ def crosstab(index, columns, values=None, rownames=None, colnames=None,
         df = DataFrame(data)
         df['__dummy__'] = 0
         table = df.pivot_table('__dummy__', index=rownames, columns=colnames,
-                               aggfunc=len, margins=margins, dropna=dropna)
+                               aggfunc=len, margins=margins,
+                               margins_name=margins_name, dropna=dropna)
         table = table.fillna(0).astype(np.int64)
 
     else:
         data['__dummy__'] = values
         df = DataFrame(data)
         table = df.pivot_table('__dummy__', index=rownames, columns=colnames,
-                               aggfunc=aggfunc, margins=margins, dropna=dropna)
+                               aggfunc=aggfunc, margins=margins,
+                               margins_name=margins_name, dropna=dropna)
 
     # Post-process
     if normalize is not False:
-        table = _normalize(table, normalize=normalize, margins=margins)
+        table = _normalize(table, normalize=normalize, margins=margins,
+                           margins_name=margins_name)
 
     return table
 
 
-def _normalize(table, normalize, margins):
+def _normalize(table, normalize, margins, margins_name='All'):
 
     if not isinstance(normalize, bool) and not isinstance(normalize,
                                                           compat.string_types):
@@ -537,9 +547,9 @@ def _normalize(table, normalize, margins):
 
     elif margins is True:
 
-        column_margin = table.loc[:, 'All'].drop('All')
-        index_margin = table.loc['All', :].drop('All')
-        table = table.drop('All', axis=1).drop('All')
+        column_margin = table.loc[:, margins_name].drop(margins_name)
+        index_margin = table.loc[margins_name, :].drop(margins_name)
+        table = table.drop(margins_name, axis=1).drop(margins_name)
         # to keep index and columns names
         table_index_names = table.index.names
         table_columns_names = table.columns.names
@@ -561,8 +571,10 @@ def _normalize(table, normalize, margins):
         elif normalize == "all" or normalize is True:
             column_margin = column_margin / column_margin.sum()
             index_margin = index_margin / index_margin.sum()
-            index_margin.loc['All'] = 1
+            index_margin.loc[margins_name] = 1
             table = concat([table, column_margin], axis=1)
+            print('index margin')
+            print(index_margin)
             table = table.append(index_margin)
 
             table = table.fillna(0)

pandas/tests/reshape/test_pivot.py

@@ -1071,6 +1071,42 @@ def test_crosstab_margins(self):
         exp_rows = exp_rows.fillna(0).astype(np.int64)
         tm.assert_series_equal(all_rows, exp_rows)
 
+    def test_crosstab_margins_set_margin_name(self):
+        a = np.random.randint(0, 7, size=100)
+        b = np.random.randint(0, 3, size=100)
+        c = np.random.randint(0, 5, size=100)
+
+        df = DataFrame({'a': a, 'b': b, 'c': c})
+
+        result = crosstab(a, [b, c], rownames=['a'], colnames=('b', 'c'),
+                          margins=True, margins_name='TOTAL')
+
+        assert result.index.names == ('a',)
+        assert result.columns.names == ['b', 'c']
+
+        all_cols = result['TOTAL', '']
+        exp_cols = df.groupby(['a']).size().astype('i8')
+        # to keep index.name
+        exp_margin = Series([len(df)], index=Index(['TOTAL'], name='a'))
+        exp_cols = exp_cols.append(exp_margin)
+        exp_cols.name = ('TOTAL', '')
+
+        tm.assert_series_equal(all_cols, exp_cols)
+
+        all_rows = result.loc['TOTAL']
+        exp_rows = df.groupby(['b', 'c']).size().astype('i8')
+        exp_rows = exp_rows.append(Series([len(df)], index=[('TOTAL', '')]))
+        exp_rows.name = 'TOTAL'
+
+        exp_rows = exp_rows.reindex(all_rows.index)
+        exp_rows = exp_rows.fillna(0).astype(np.int64)
+        tm.assert_series_equal(all_rows, exp_rows)
+
+        for margins_name in [666, None, ['a', 'b']]:
+            with pytest.raises(ValueError):
+                crosstab(a, [b, c], rownames=['a'], colnames=('b', 'c'),
+                         margins=True, margins_name=margins_name)
+
     def test_crosstab_pass_values(self):
         a = np.random.randint(0, 7, size=100)
         b = np.random.randint(0, 3, size=100)