added test for crosstab norm with multiindex

Author: Christoph Möhl

pandas/core/reshape/pivot.py

@@ -564,8 +564,8 @@ def _normalize(table, normalize, margins, margins_name='All'):
         raise ValueError("Not a valid normalize argument")
 
     table = f(table)
-    table = table.fillna(0)       
-
+    table = table.fillna(0)
+    
     return table
 
 

pandas/tests/reshape/test_pivot.py

@@ -1325,6 +1325,73 @@ def test_crosstab_normalize(self):
                                 margins=True)
         tm.assert_frame_equal(test_case, norm_sum)
 
+    def test_crosstab_norm_margins_with_multiindex(self):
+        # GH 15150
+        a = np.array(['foo', 'bar', 'foo', 'bar','bar', 'foo'])
+        b = np.array(['one', 'one', 'two', 'one','two', 'two'])
+        c = np.array(['dull', 'shiny', 'dull', 'dull','dull', 'shiny'])
+        d = np.array(['a', 'a', 'b', 'a','b', 'b'])
+        expected_col_colnorm = MultiIndex(levels=[['All', 'dull', 'shiny'],
+                                                  ['', 'a', 'b']],
+                                          labels=[[1, 1, 2, 2, 0],
+                                                  [1, 2, 1, 2, 0]],
+                                          names=['col_0', 'col_1'])
+        expected_index_colnorm = MultiIndex(levels=[['All', 'bar', 'foo'],
+                                                    ['', 'one', 'two']],
+                                            labels=[[1, 1, 2, 2],
+                                                    [1, 2, 1, 2]],
+                                            names=['row_0', 'row_1'])
+        expected_data_colnorm = np.array([[.5, 0., 1., 0., .333333],
+                                          [0., .5, 0., 0., .166667],
+                                          [.5, 0., 0., 0., .166667],
+                                          [0., .5, 0., 1., .333333]])
+        expected_colnorm = pd.DataFrame(expected_data_colnorm,
+                                        index=expected_index_colnorm,
+                                        columns=expected_col_colnorm)
+        expected_col_indexnorm = MultiIndex(levels=[['All', 'dull', 'shiny'],
+                                                    ['', 'a', 'b']],
+                                            labels=[[1, 1, 2, 2],
+                                                    [1, 2, 1, 2]],
+                                            names=['col_0', 'col_1'])
+        expected_index_indexnorm = MultiIndex(levels=[['All', 'bar', 'foo'],
+                                                      ['', 'one', 'two']],
+                                              labels=[[1, 1, 2, 2, 0],
+                                                      [1, 2, 1, 2, 0]],
+                                              names=['row_0', 'row_1'])
+        expected_data_indexnorm = np.array([[.5, 0., .5, 0.],
+                                            [0., 1., 0., 0.],
+                                            [1., 0., 0., 0.],
+                                            [0., .5, 0., .5],
+                                            [.33333333, .33333333,
+                                             .16666667, .16666667]])
+        expected_indexnorm = pd.DataFrame(expected_data_indexnorm,
+                                 index=expected_index_indexnorm,
+                                 columns=expected_col_indexnorm)
+        expected_data_allnorm = np.array([[0.16666667, 0., .16666667,
+                                           0., .33333333],
+                                          [0. ,.16666667, 0.,
+                                           0., .16666667],
+                                          [.16666667, 0., 0.,
+                                           0., .16666667],
+                                          [0., .16666667, 0.,
+                                           .16666667, .33333333],
+                                          [0.33333333, .33333333, .16666667,
+                                           .16666667, 1.]])
+        expected_allnorm = pd.DataFrame(expected_data_allnorm,
+                                 index=expected_index_indexnorm,
+                                 columns=expected_col_colnorm)
+
+        result_colnorm = pd.crosstab([a, b], [c,d], normalize='columns',
+                                     margins=True)
+        result_indexnorm = pd.crosstab([a, b], [c,d], normalize='index',
+                                       margins=True)
+        result_allnorm = pd.crosstab([a, b], [c,d], normalize='all',
+                                       margins=True)
+
+        tm.assert_frame_equal(result_colnorm, expected_colnorm)
+        tm.assert_frame_equal(result_indexnorm, expected_indexnorm)
+        tm.assert_frame_equal(result_allnorm, expected_allnorm)
+
     def test_crosstab_with_empties(self):
         # Check handling of empties
         df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4],