BUG: Fixes issue pandas-dev#3334: brittle margin computation in pivot…

…_table

Adds support for margin computation when all columns are used in rows and cols.

doc/source/v0.13.0.txt

@@ -29,6 +29,9 @@ Bug Fixes
 
   - Fixed bug in ``PeriodIndex.map`` where using ``str`` would return the str
     representation of the index (:issue:`4136`)
+
+  - Fixed some edge cases in pivot_table where margins did not compute if values is the index.
+
 
 See the :ref:`full release notes
 <release>` or issue tracker

pandas/tools/pivot.py

@@ -2,7 +2,6 @@
 
 from pandas import Series, DataFrame
 from pandas.core.index import MultiIndex
-from pandas.core.reshape import _unstack_multiple
 from pandas.tools.merge import concat
 from pandas.tools.util import cartesian_product
 import pandas.core.common as com
@@ -147,17 +146,64 @@ def pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean',
 DataFrame.pivot_table = pivot_table
 
 
-def _add_margins(table, data, values, rows=None, cols=None, aggfunc=np.mean):
-    grand_margin = {}
-    for k, v in data[values].iteritems():
-        try:
-            if isinstance(aggfunc, basestring):
-                grand_margin[k] = getattr(v, aggfunc)()
-            else:
-                grand_margin[k] = aggfunc(v)
-        except TypeError:
-            pass
+def _add_margins(table, data, values, rows, cols, aggfunc):
+
+    grand_margin = _compute_grand_margin(data, values, aggfunc)
+
+    if not values and isinstance(table, Series):
+        # If there are no values and the table is a series, then there is only
+        # one column in the data. Compute grand margin and return it.
+        row_key = ('All',) + ('',) * (len(rows) - 1) if len(rows) > 1 else 'All'
+        return table.append(Series({row_key: grand_margin['All']}))
+
+    if values:
+        marginal_result_set = _generate_marginal_results(table, data, values, rows, cols, aggfunc, grand_margin)
+        if not isinstance(marginal_result_set, tuple):
+            return marginal_result_set
+        result, margin_keys, row_margin = marginal_result_set
+    else:
+        marginal_result_set = _generate_marginal_results_without_values(table, data, rows, cols, aggfunc)
+        if not isinstance(marginal_result_set, tuple):
+            return marginal_result_set
+        result, margin_keys, row_margin = marginal_result_set
+
+    key = ('All',) + ('',) * (len(rows) - 1) if len(rows) > 1 else 'All'
+
+    row_margin = row_margin.reindex(result.columns)
+    # populate grand margin
+    for k in margin_keys:
+        if isinstance(k, basestring):
+            row_margin[k] = grand_margin[k]
+        else:
+            row_margin[k] = grand_margin[k[0]]
 
+    margin_dummy = DataFrame(row_margin, columns=[key]).T
+
+    row_names = result.index.names
+    result = result.append(margin_dummy)
+    result.index.names = row_names
+
+    return result
+
+
+def _compute_grand_margin(data, values, aggfunc):
+
+    if values:
+        grand_margin = {}
+        for k, v in data[values].iteritems():
+            try:
+                if isinstance(aggfunc, basestring):
+                    grand_margin[k] = getattr(v, aggfunc)()
+                else:
+                    grand_margin[k] = aggfunc(v)
+            except TypeError:
+                pass
+        return grand_margin
+    else:
+        return {'All': aggfunc(data.index)}
+
+
+def _generate_marginal_results(table, data, values, rows, cols, aggfunc, grand_margin):
     if len(cols) > 0:
         # need to "interleave" the margins
         table_pieces = []
@@ -201,23 +247,43 @@ def _all_key(key):
     else:
         row_margin = Series(np.nan, index=result.columns)
 
-    key = ('All',) + ('',) * (len(rows) - 1) if len(rows) > 1 else 'All'
+    return result, margin_keys, row_margin
 
-    row_margin = row_margin.reindex(result.columns)
-    # populate grand margin
-    for k in margin_keys:
-        if len(cols) > 0:
-            row_margin[k] = grand_margin[k[0]]
-        else:
-            row_margin[k] = grand_margin[k]
 
-    margin_dummy = DataFrame(row_margin, columns=[key]).T
+def _generate_marginal_results_without_values(table, data, rows, cols, aggfunc):
+    if len(cols) > 0:
+        # need to "interleave" the margins
+        margin_keys = []
 
-    row_names = result.index.names
-    result = result.append(margin_dummy)
-    result.index.names = row_names
+        def _all_key():
+            if len(cols) == 1:
+                return 'All'
+            return ('All', ) + ('', ) * (len(cols) - 1)
 
-    return result
+        if len(rows) > 0:
+            margin = data[rows].groupby(rows).apply(aggfunc)
+            all_key = _all_key()
+            table[all_key] = margin
+            result = table
+            margin_keys.append(all_key)
+
+        else:
+            margin = data.groupby(level=0, axis=0).apply(aggfunc)
+            all_key = _all_key()
+            table[all_key] = margin
+            result = table
+            margin_keys.append(all_key)
+            return result
+    else:
+        result = table
+        margin_keys = table.columns
+
+    if len(cols):
+        row_margin = data[cols].groupby(cols).apply(aggfunc)
+    else:
+        row_margin = Series(np.nan, index=result.columns)
+
+    return result, margin_keys, row_margin
 
 
 def _convert_by(by):

pandas/tools/tests/test_pivot.py

@@ -151,7 +151,7 @@ def test_pivot_index_with_nan(self):
         nan = np.nan
         df = DataFrame({"a":['R1', 'R2', nan, 'R4'], 'b':["C1", "C2", "C3" , "C4"], "c":[10, 15, nan , 20]})
         result = df.pivot('a','b','c')
-        expected = DataFrame([[nan,nan,nan,nan],[nan,10,nan,nan], 
+        expected = DataFrame([[nan,nan,nan,nan],[nan,10,nan,nan],
                               [nan,nan,nan,nan],[nan,nan,15,20]],
                              index = Index(['R1','R2',nan,'R4'],name='a'),
                              columns = Index(['C1','C2','C3','C4'],name='b'))
@@ -290,6 +290,28 @@ def test_pivot_complex_aggfunc(self):
 
         tm.assert_frame_equal(result, expected)
 
+    def test_margins_no_values_no_cols(self):
+        # Regression test on pivot table: no values or cols passed.
+        result = self.data[['A', 'B']].pivot_table(rows=['A', 'B'], aggfunc=len, margins=True)
+        result_list = result.tolist()
+        self.assertEqual(sum(result_list[:-1]), result_list[-1])
+
+    def test_margins_no_values_two_rows(self):
+        # Regression test on pivot table: no values passed but rows are a multi-index
+        result = self.data[['A', 'B', 'C']].pivot_table(rows=['A', 'B'], cols='C', aggfunc=len, margins=True)
+        self.assertEqual(result.All.tolist(), [3.0, 1.0, 4.0, 3.0, 11.0])
+
+    def test_margins_no_values_one_row_one_col(self):
+        # Regression test on pivot table: no values passed but row and col defined
+        result = self.data[['A', 'B']].pivot_table(rows='A', cols='B', aggfunc=len, margins=True)
+        self.assertEqual(result.All.tolist(), [4.0, 7.0, 11.0])
+
+    def test_margins_no_values_two_row_two_cols(self):
+        # Regression test on pivot table: no values passed but rows and cols are multi-indexed
+        self.data['D'] = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']
+        result = self.data[['A', 'B', 'C', 'D']].pivot_table(rows=['A', 'B'], cols=['C', 'D'], aggfunc=len, margins=True)
+        self.assertEqual(result.All.tolist(), [3.0, 1.0, 4.0, 3.0, 11.0])
+
 
 class TestCrosstab(unittest.TestCase):