BUG: tzinfo lost when concatenating multiindex arrays

Author: sinhrks

doc/source/v0.14.1.txt

@@ -70,3 +70,4 @@ Bug Fixes
 - Bug in ``DataFrame`` and ``Series`` bar and barh plot raises ``TypeError`` when ``bottom``
   and ``left`` keyword is specified (:issue:`7226`)
 - BUG in ``DataFrame.hist`` raises ``TypeError`` when it contains non numeric column (:issue:`7277`)
+- Bug in ``MultiIndex.append``, ``concat`` and ``pivot_table`` don't preserve timezone (:issue:`6606`)

pandas/core/index.py

@@ -2948,6 +2948,14 @@ def append(self, other):
         if not isinstance(other, (list, tuple)):
             other = [other]
 
+        if all((isinstance(o, MultiIndex) and o.nlevels >= self.nlevels) for o in other):
+            arrays = []
+            for i in range(self.nlevels):
+                label = self.get_level_values(i)
+                appended = [o.get_level_values(i) for o in other]
+                arrays.append(label.append(appended))
+            return MultiIndex.from_arrays(arrays, names=self.names)
+
         to_concat = (self.values,) + tuple(k.values for k in other)
         new_tuples = np.concatenate(to_concat)
 

pandas/tests/test_multilevel.py

@@ -1,4 +1,5 @@
 # pylint: disable-msg=W0612,E1101,W0141
+import datetime
 import nose
 
 from numpy.random import randn
@@ -70,6 +71,46 @@ def test_append(self):
         result = a['A'].append(b['A'])
         tm.assert_series_equal(result, self.frame['A'])
 
+    def test_append_index(self):
+
+        idx1 = Index([1.1, 1.2, 1.3])
+        idx2 = pd.date_range('2011-01-01', freq='D', periods=3, tz='Asia/Tokyo')
+        idx3 = Index(['A', 'B', 'C'])
+
+        midx_lv2 = MultiIndex.from_arrays([idx1, idx2])
+        midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3])
+
+        result = idx1.append(midx_lv2)
+        expected = Index([1.1, 1.2, 1.3,
+                         (1.1, datetime.datetime(2010, 12, 31, 15, 0)),
+                         (1.2, datetime.datetime(2011, 1, 1, 15, 0)),
+                         (1.3, datetime.datetime(2011, 1, 2, 15, 0))])
+        self.assert_(result.equals(expected))
+
+        result = midx_lv2.append(idx1)
+        expected = Index([(1.1, datetime.datetime(2010, 12, 31, 15, 0)),
+                          (1.2, datetime.datetime(2011, 1, 1, 15, 0)),
+                          (1.3, datetime.datetime(2011, 1, 2, 15, 0)),
+                           1.1, 1.2, 1.3])
+        self.assert_(result.equals(expected))
+
+        result = midx_lv2.append(midx_lv2)
+        expected = MultiIndex.from_arrays([idx1.append(idx1), idx2.append(idx2)])
+        self.assert_(result.equals(expected))
+
+        result = midx_lv2.append(midx_lv3)
+        self.assert_(result.equals(expected))
+
+        result = midx_lv3.append(midx_lv2)
+        expected = Index._simple_new(
+            np.array([(1.1, datetime.datetime(2010, 12, 31, 15, 0), 'A'),
+                      (1.2, datetime.datetime(2011, 1, 1, 15, 0), 'B'),
+                      (1.3, datetime.datetime(2011, 1, 2, 15, 0), 'C'),
+                      (1.1, datetime.datetime(2010, 12, 31, 15, 0)),
+                      (1.2, datetime.datetime(2011, 1, 1, 15, 0)),
+                      (1.3, datetime.datetime(2011, 1, 2, 15, 0))]), None)
+        self.assert_(result.equals(expected))
+
     def test_dataframe_constructor(self):
         multi = DataFrame(np.random.randn(4, 4),
                           index=[np.array(['a', 'a', 'b', 'b']),

pandas/tools/tests/test_merge.py

@@ -8,6 +8,7 @@
 import numpy as np
 import random
 
+import pandas as pd
 from pandas.compat import range, lrange, lzip, zip, StringIO
 from pandas import compat, _np_version_under1p7
 from pandas.tseries.index import DatetimeIndex
@@ -1497,6 +1498,26 @@ def test_concat_multiindex_with_keys(self):
         tm.assert_frame_equal(result.ix[1], frame)
         self.assertEqual(result.index.nlevels, 3)
 
+    def test_concat_multiindex_with_tz(self):
+        # GH 6606
+        df = DataFrame({'dt': [datetime(2014, 1, 1),
+                               datetime(2014, 1, 2),
+                               datetime(2014, 1, 3)],
+                        'b': ['A', 'B', 'C'],
+                        'c': [1, 2, 3], 'd': [4, 5, 6]})
+        df['dt'] = df['dt'].apply(lambda d: pd.Timestamp(d, tz='US/Pacific'))
+        df = df.set_index(['dt', 'b'])
+
+        exp_idx1 = pd.DatetimeIndex(['2014-01-01', '2014-01-02', '2014-01-03'] * 2,
+                                    tz='US/Pacific', name='dt')
+        exp_idx2 = Index(['A', 'B', 'C'] * 2, name='b')
+        exp_idx = pd.MultiIndex.from_arrays([exp_idx1, exp_idx2])
+        expected = DataFrame({'c': [1, 2, 3] * 2, 'd': [4, 5, 6] * 2},
+                             index=exp_idx, columns=['c', 'd'])
+
+        result = concat([df, df])
+        tm.assert_frame_equal(result, expected)
+
     def test_concat_keys_and_levels(self):
         df = DataFrame(np.random.randn(1, 3))
         df2 = DataFrame(np.random.randn(1, 4))

pandas/tools/tests/test_pivot.py

@@ -490,6 +490,41 @@ def test_pivot_timegrouper(self):
                              values='Quantity', aggfunc=np.sum)
         tm.assert_frame_equal(result, expected.T)
 
+    def test_pivot_datetime_tz(self):
+        dates1 = ['2011-07-19 07:00:00', '2011-07-19 08:00:00', '2011-07-19 09:00:00',
+                  '2011-07-19 07:00:00', '2011-07-19 08:00:00', '2011-07-19 09:00:00']
+        dates2 = ['2013-01-01 15:00:00', '2013-01-01 15:00:00', '2013-01-01 15:00:00',
+                  '2013-02-01 15:00:00', '2013-02-01 15:00:00', '2013-02-01 15:00:00']
+        df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'],
+                        'dt1': dates1, 'dt2': dates2,
+                        'value1': range(6), 'value2': [1, 2] * 3})
+        df['dt1'] = df['dt1'].apply(lambda d: pd.Timestamp(d, tz='US/Pacific'))
+        df['dt2'] = df['dt2'].apply(lambda d: pd.Timestamp(d, tz='Asia/Tokyo'))
+
+        exp_idx = pd.DatetimeIndex(['2011-07-19 07:00:00', '2011-07-19 08:00:00',
+                                    '2011-07-19 09:00:00'], tz='US/Pacific', name='dt1')
+        exp_col1 = Index(['value1', 'value1'])
+        exp_col2 = Index(['a', 'b'], name='label')
+        exp_col = MultiIndex.from_arrays([exp_col1, exp_col2])
+        expected = DataFrame([[0, 3], [1, 4], [2, 5]],
+                             index=exp_idx, columns=exp_col)
+        result = pivot_table(df, index=['dt1'], columns=['label'], values=['value1'])
+        tm.assert_frame_equal(result, expected)
+
+
+        exp_col1 = Index(['sum', 'sum', 'sum', 'sum', 'mean', 'mean', 'mean', 'mean'])
+        exp_col2 = Index(['value1', 'value1', 'value2', 'value2'] * 2)
+        exp_col3 = pd.DatetimeIndex(['2013-01-01 15:00:00', '2013-02-01 15:00:00'] * 4,
+                                    tz='Asia/Tokyo', name='dt2')
+        exp_col = MultiIndex.from_arrays([exp_col1, exp_col2, exp_col3])
+        expected = DataFrame(np.array([[0, 3, 1, 2, 0, 3, 1, 2], [1, 4, 2, 1, 1, 4, 2, 1],
+                              [2, 5, 1, 2, 2, 5, 1, 2]]), index=exp_idx, columns=exp_col)
+
+        result = pivot_table(df, index=['dt1'], columns=['dt2'], values=['value1', 'value2'],
+                             aggfunc=[np.sum, np.mean])
+        tm.assert_frame_equal(result, expected)
+
+
 class TestCrosstab(tm.TestCase):
 
     def setUp(self):