BUG: Respect dtype when calling pivot_table with margins=True

closes pandas-dev#17013

This fix actually exposed an occurrence of pandas-dev#17035 in an existing test
(as well as in one I added).

Author: Pietro Battiston <me@pietrobattiston.it>

Closes pandas-dev#17062 from toobaz/pivot_margin_int and squashes the following commits:

2737600 [Pietro Battiston] Removed now obsolete workaround
956c4f9 [Pietro Battiston] BUG: respect dtype when calling pivot_table with margins=True

Author: toobaz

Diff for: doc/source/whatsnew/v0.21.0.txt

@@ -311,6 +311,7 @@ Reshaping
 - Bug in merging with categorical dtypes with datetimelikes incorrectly raised a ``TypeError`` (:issue:`16900`)
 - Bug when using :func:`isin` on a large object series and large comparison array (:issue:`16012`)
 - Fixes regression from 0.20, :func:`Series.aggregate` and :func:`DataFrame.aggregate` allow dictionaries as return values again (:issue:`16741`)
+- Fixes dtype of result with integer dtype input, from :func:`pivot_table` when called with ``margins=True`` (:issue:`17013`)
 
 Numeric
 ^^^^^^^

Diff for: pandas/core/reshape/pivot.py

@@ -178,7 +178,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
             data = data[data.notna().all(axis=1)]
         table = _add_margins(table, data, values, rows=index,
                              cols=columns, aggfunc=aggfunc,
-                             margins_name=margins_name)
+                             margins_name=margins_name, fill_value=fill_value)
 
     # discard the top level
     if values_passed and not values_multi and not table.empty and \
@@ -199,7 +199,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
 
 
 def _add_margins(table, data, values, rows, cols, aggfunc,
-                 margins_name='All'):
+                 margins_name='All', fill_value=None):
     if not isinstance(margins_name, compat.string_types):
         raise ValueError('margins_name argument must be a string')
 
@@ -240,8 +240,7 @@ def _add_margins(table, data, values, rows, cols, aggfunc,
         if not isinstance(marginal_result_set, tuple):
             return marginal_result_set
         result, margin_keys, row_margin = marginal_result_set
-
-    row_margin = row_margin.reindex(result.columns)
+    row_margin = row_margin.reindex(result.columns, fill_value=fill_value)
     # populate grand margin
     for k in margin_keys:
         if isinstance(k, compat.string_types):
@@ -253,6 +252,9 @@ def _add_margins(table, data, values, rows, cols, aggfunc,
 
     row_names = result.index.names
     try:
+        for dtype in set(result.dtypes):
+            cols = result.select_dtypes([dtype]).columns
+            margin_dummy[cols] = margin_dummy[cols].astype(dtype)
         result = result.append(margin_dummy)
     except TypeError:
 
@@ -524,10 +526,6 @@ def crosstab(index, columns, values=None, rownames=None, colnames=None,
                            margins=margins, margins_name=margins_name,
                            dropna=dropna, **kwargs)
 
-    # GH 17013:
-    if values is None and margins:
-        table = table.fillna(0).astype(np.int64)
-
     # Post-process
     if normalize is not False:
         table = _normalize(table, normalize=normalize, margins=margins,

Diff for: pandas/tests/reshape/test_pivot.py

@@ -459,6 +459,41 @@ def _check_output(result, values_col, index=['A', 'B'],
 
         tm.assert_frame_equal(result['SALARY'], expected['SALARY'])
 
+    def test_margins_dtype(self):
+        # GH 17013
+
+        df = self.data.copy()
+        df[['D', 'E', 'F']] = np.arange(len(df) * 3).reshape(len(df), 3)
+
+        mi_val = list(product(['bar', 'foo'], ['one', 'two'])) + [('All', '')]
+        mi = MultiIndex.from_tuples(mi_val, names=('A', 'B'))
+        expected = DataFrame({'dull': [12, 21, 3, 9, 45],
+                              'shiny': [33, 0, 36, 51, 120]},
+                             index=mi).rename_axis('C', axis=1)
+        expected['All'] = expected['dull'] + expected['shiny']
+
+        result = df.pivot_table(values='D', index=['A', 'B'],
+                                       columns='C', margins=True,
+                                       aggfunc=np.sum, fill_value=0)
+
+        tm.assert_frame_equal(expected, result)
+
+    @pytest.mark.xfail(reason='GH 17035 (len of floats is casted back to '
+                              'floats)')
+    def test_margins_dtype_len(self):
+        mi_val = list(product(['bar', 'foo'], ['one', 'two'])) + [('All', '')]
+        mi = MultiIndex.from_tuples(mi_val, names=('A', 'B'))
+        expected = DataFrame({'dull': [1, 1, 2, 1, 5],
+                              'shiny': [2, 0, 2, 2, 6]},
+                             index=mi).rename_axis('C', axis=1)
+        expected['All'] = expected['dull'] + expected['shiny']
+
+        result = self.data.pivot_table(values='D', index=['A', 'B'],
+                                       columns='C', margins=True,
+                                       aggfunc=len, fill_value=0)
+
+        tm.assert_frame_equal(expected, result)
+
     def test_pivot_integer_columns(self):
         # caused by upstream bug in unstack
 
@@ -894,6 +929,8 @@ def test_pivot_table_margins_name_with_aggfunc_list(self):
         expected = pd.DataFrame(table.values, index=ix, columns=cols)
         tm.assert_frame_equal(table, expected)
 
+    @pytest.mark.xfail(reason='GH 17035 (np.mean of ints is casted back to '
+                              'ints)')
     def test_categorical_margins(self):
         # GH 10989
         df = pd.DataFrame({'x': np.arange(8),
@@ -904,14 +941,23 @@ def test_categorical_margins(self):
         expected.index = Index([0, 1, 'All'], name='y')
         expected.columns = Index([0, 1, 'All'], name='z')
 
-        data = df.copy()
-        table = data.pivot_table('x', 'y', 'z', margins=True)
+        table = df.pivot_table('x', 'y', 'z', margins=True)
         tm.assert_frame_equal(table, expected)
 
-        data = df.copy()
-        data.y = data.y.astype('category')
-        data.z = data.z.astype('category')
-        table = data.pivot_table('x', 'y', 'z', margins=True)
+    @pytest.mark.xfail(reason='GH 17035 (np.mean of ints is casted back to '
+                              'ints)')
+    def test_categorical_margins_category(self):
+        df = pd.DataFrame({'x': np.arange(8),
+                           'y': np.arange(8) // 4,
+                           'z': np.arange(8) % 2})
+
+        expected = pd.DataFrame([[1.0, 2.0, 1.5], [5, 6, 5.5], [3, 4, 3.5]])
+        expected.index = Index([0, 1, 'All'], name='y')
+        expected.columns = Index([0, 1, 'All'], name='z')
+
+        df.y = df.y.astype('category')
+        df.z = df.z.astype('category')
+        table = df.pivot_table('x', 'y', 'z', margins=True)
         tm.assert_frame_equal(table, expected)
 
     def test_categorical_aggfunc(self):