Dropna argument is now respected when false in pivot_table (pandas-dev#25738)

Author: alexander-ponomaroff

doc/source/whatsnew/v0.25.0.rst

@@ -394,6 +394,7 @@ Reshaping
 - Bug in :func:`merge` when merging by index name would sometimes result in an incorrectly numbered index (:issue:`24212`)
 - :func:`to_records` now accepts dtypes to its `column_dtypes` parameter (:issue:`24895`)
 - Bug in :func:`concat` where order of ``OrderedDict`` (and ``dict`` in Python 3.6+) is not respected, when passed in as  ``objs`` argument (:issue:`21510`)
+- Bug in :func:`pivot_table` where columns with ``NaN`` values are dropped even if ``dropna`` argument is ``False``, when the ``aggfunc`` argument contains a ``list`` (:issue:`22159`)
 - Bug in :func:`concat` where the resulting ``freq`` of two :class:`DatetimeIndex` with the same ``freq`` would be dropped (:issue:`3232`).
 - Bug in :func:`merge` where merging with equivalent Categorical dtypes was raising an error (:issue:`22501`)
 - Bug in :class:`DataFrame` constructor when passing non-empty tuples would cause a segmentation fault (:issue:`25691`)

pandas/core/reshape/pivot.py

@@ -34,7 +34,8 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
             table = pivot_table(data, values=values, index=index,
                                 columns=columns,
                                 fill_value=fill_value, aggfunc=func,
-                                margins=margins, margins_name=margins_name)
+                                margins=margins, dropna=dropna,
+                                margins_name=margins_name)
             pieces.append(table)
             keys.append(getattr(func, '__name__', func))
 

pandas/tests/reshape/test_pivot.py

@@ -1288,6 +1288,55 @@ def test_pivot_number_of_levels_larger_than_int32(self):
             df.pivot_table(index='ind1', columns='ind2',
                            values='count', aggfunc='count')
 
+    def test_pivot_table_aggfunc_dropna(self, dropna):
+        # GH 22159
+        df = pd.DataFrame({'fruit': ['apple', 'peach', 'apple'],
+                           'size': [1, 1, 2],
+                           'taste': [7, 6, 6]})
+
+        def ret_one(x):
+            return 1
+
+        def ret_sum(x):
+            return sum(x)
+
+        def ret_none(x):
+            return np.nan
+
+        result = pd.pivot_table(df, columns='fruit',
+                                aggfunc=[ret_sum, ret_none, ret_one],
+                                dropna=dropna)
+
+        data = [[3, 1, np.nan, np.nan, 1, 1], [13, 6, np.nan, np.nan, 1, 1]]
+        col = pd.MultiIndex.from_product([['ret_sum', 'ret_none', 'ret_one'],
+                                         ['apple', 'peach']],
+                                         names=[None, 'fruit'])
+        expected = pd.DataFrame(data, index=['size', 'taste'], columns=col)
+
+        if dropna:
+            expected = expected.dropna(axis='columns')
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_aggfunc_scalar_dropna(self, dropna):
+        # GH 22159
+        df = pd.DataFrame({'A': ['one', 'two', 'one'],
+                           'x': [3, np.nan, 2],
+                           'y': [1, np.nan, np.nan]})
+
+        result = pd.pivot_table(df, columns='A',
+                                aggfunc=np.mean,
+                                dropna=dropna)
+
+        data = [[2.5, np.nan], [1, np.nan]]
+        col = pd.Index(['one', 'two'], name='A')
+        expected = pd.DataFrame(data, index=['x', 'y'], columns=col)
+
+        if dropna:
+            expected = expected.dropna(axis='columns')
+
+        tm.assert_frame_equal(result, expected)
+
 
 class TestCrosstab(object):