BUG: tz info lost by set_index and reindex

doc/source/release.rst

@@ -495,6 +495,8 @@ Bug Fixes
 - Bug in ``boxplot`` and ``hist`` draws unnecessary axes (:issue:`6769`)
 - Regression in ``groupby.nth()`` for out-of-bounds indexers (:issue:`6621`)
 - Bug in ``quantile`` with datetime values (:issue:`6965`)
+- Bug in ``Dataframe.set_index``, ``reindex`` and ``pivot`` don't preserve ``DatetimeIndex`` and ``PeriodIndex`` attributes (:issue:`3950`, :issue:`5878`, :issue:`6631`)
+- Bug in ``MultiIndex.get_level_values`` doesn't preserve ``DatetimeIndex`` and ``PeriodIndex`` attributes (:issue:`7092`)
 
 pandas 0.13.1
 -------------

pandas/core/frame.py

@@ -2220,7 +2220,7 @@ def set_index(self, keys, drop=True, append=False, inplace=False,
                 for i in range(self.index.nlevels):
                     arrays.append(self.index.get_level_values(i))
             else:
-                arrays.append(np.asarray(self.index))
+                arrays.append(self.index)
 
         to_remove = []
         for col in keys:
@@ -2232,9 +2232,12 @@ def set_index(self, keys, drop=True, append=False, inplace=False,
 
                 level = col.get_level_values(col.nlevels - 1)
                 names.extend(col.names)
-            elif isinstance(col, (Series, Index)):
+            elif isinstance(col, Series):
                 level = col.values
                 names.append(col.name)
+            elif isinstance(col, Index):
+                level = col
+                names.append(col.name)                
             elif isinstance(col, (list, np.ndarray)):
                 level = col
                 names.append(None)

pandas/core/index.py

@@ -114,9 +114,7 @@ def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False,
 
         # no class inference!
         if fastpath:
-            subarr = data.view(cls)
-            subarr.name = name
-            return subarr
+            return cls._simple_new(data, name)
 
         from pandas.tseries.period import PeriodIndex
         if isinstance(data, (np.ndarray, ABCSeries)):
@@ -185,6 +183,12 @@ def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False,
         subarr._set_names([name])
         return subarr
 
+    @classmethod
+    def _simple_new(cls, values, name, **kwargs):
+        result = values.view(cls)
+        result.name = name
+        return result
+
     def is_(self, other):
         """
         More flexible, faster check like ``is`` but that works through views
@@ -2588,11 +2592,12 @@ def get_level_values(self, level):
         values : ndarray
         """
         num = self._get_level_number(level)
-        unique_vals = self.levels[num]  # .values
+        unique = self.levels[num]  # .values
         labels = self.labels[num]
-        values = Index(com.take_1d(unique_vals.values, labels,
-                                   fill_value=unique_vals._na_value))
-        values.name = self.names[num]
+        filled = com.take_1d(unique.values, labels, fill_value=unique._na_value)
+        values = unique._simple_new(filled, self.names[num], 
+                                    freq=getattr(unique, 'freq', None),
+                                    tz=getattr(unique, 'tz', None))
         return values
 
     def format(self, space=2, sparsify=None, adjoin=True, names=False,

pandas/core/reshape.py

@@ -82,11 +82,10 @@ def __init__(self, values, index, level=-1, value_columns=None):
         labels = index.labels
 
         def _make_index(lev, lab):
-            if isinstance(lev, PeriodIndex):
-                i = lev.copy()
-            else:
-                i = lev.__class__(_make_index_array_level(lev.values, lab))
-                i.name = lev.name
+            values = _make_index_array_level(lev.values, lab)
+            i = lev._simple_new(values, lev.name, 
+                                freq=getattr(lev, 'freq', None),
+                                tz=getattr(lev, 'tz', None))
             return i
 
         self.new_index_levels = [_make_index(lev, lab)

pandas/tests/test_index.py

@@ -180,6 +180,19 @@ def test_index_ctor_infer_periodindex(self):
         assert_array_equal(rs, xp)
         tm.assert_isinstance(rs, PeriodIndex)
 
+    def test_constructor_simple_new(self):
+        idx = Index([1, 2, 3, 4, 5], name='int')
+        result = idx._simple_new(idx, 'int')
+        self.assert_(result.equals(idx))
+
+        idx = Index([1.1, np.nan, 2.2, 3.0], name='float')
+        result = idx._simple_new(idx, 'float')
+        self.assert_(result.equals(idx))
+
+        idx = Index(['A', 'B', 'C', np.nan], name='obj')
+        result = idx._simple_new(idx, 'obj')
+        self.assert_(result.equals(idx))
+
     def test_copy(self):
         i = Index([], name='Foo')
         i_copy = i.copy()

pandas/tests/test_multilevel.py

@@ -1989,6 +1989,78 @@ def test_datetimeindex(self):
         self.assert_(idx.levels[0].equals(expected1))
         self.assert_(idx.levels[1].equals(idx2))
 
+    def test_set_index_datetime(self):
+        # GH 3950
+        df = pd.DataFrame({'label':['a', 'a', 'a', 'b', 'b', 'b'],
+                           'datetime':['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'],
+                           'value':range(6)})
+        df.index = pd.to_datetime(df.pop('datetime'), utc=True)
+        df.index = df.index.tz_localize('UTC').tz_convert('US/Pacific')
+
+        expected = pd.DatetimeIndex(['2011-07-19 07:00:00', '2011-07-19 08:00:00', '2011-07-19 09:00:00'])
+        expected = expected.tz_localize('UTC').tz_convert('US/Pacific')
+
+        df = df.set_index('label', append=True)
+        self.assert_(df.index.levels[0].equals(expected))
+        self.assert_(df.index.levels[1].equals(pd.Index(['a', 'b'])))
+
+        df = df.swaplevel(0, 1)
+        self.assert_(df.index.levels[0].equals(pd.Index(['a', 'b'])))
+        self.assert_(df.index.levels[1].equals(expected))
+
+
+        df = DataFrame(np.random.random(6))
+        idx1 = pd.DatetimeIndex(['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'], tz='US/Eastern')
+        idx2 = pd.DatetimeIndex(['2012-04-01 09:00', '2012-04-01 09:00', '2012-04-01 09:00',
+                                 '2012-04-02 09:00', '2012-04-02 09:00', '2012-04-02 09:00'],
+                                tz='US/Eastern')
+        idx3 = pd.date_range('2011-01-01 09:00', periods=6, tz='Asia/Tokyo')
+
+        df = df.set_index(idx1)
+        df = df.set_index(idx2, append=True)
+        df = df.set_index(idx3, append=True)
+
+        expected1 = pd.DatetimeIndex(['2011-07-19 07:00:00', '2011-07-19 08:00:00',
+                                     '2011-07-19 09:00:00'], tz='US/Eastern')
+        expected2 = pd.DatetimeIndex(['2012-04-01 09:00', '2012-04-02 09:00'], tz='US/Eastern')
+
+        self.assert_(df.index.levels[0].equals(expected1))
+        self.assert_(df.index.levels[1].equals(expected2))
+        self.assert_(df.index.levels[2].equals(idx3))
+
+        # GH 7092
+        self.assert_(df.index.get_level_values(0).equals(idx1))
+        self.assert_(df.index.get_level_values(1).equals(idx2))
+        self.assert_(df.index.get_level_values(2).equals(idx3))
+
+    def test_set_index_period(self):
+        # GH 6631
+        df = DataFrame(np.random.random(6))
+        idx1 = pd.period_range('2011-01-01', periods=3, freq='M')
+        idx1 = idx1.append(idx1)
+        idx2 = pd.period_range('2013-01-01 09:00', periods=2, freq='H')
+        idx2 = idx2.append(idx2).append(idx2)
+        idx3 = pd.period_range('2005', periods=6, freq='Y')
+
+        df = df.set_index(idx1)
+        df = df.set_index(idx2, append=True)
+        df = df.set_index(idx3, append=True)
+
+        expected1 = pd.period_range('2011-01-01', periods=3, freq='M')
+        expected2 = pd.period_range('2013-01-01 09:00', periods=2, freq='H')
+
+        self.assert_(df.index.levels[0].equals(expected1))
+        self.assert_(df.index.levels[1].equals(expected2))
+        self.assert_(df.index.levels[2].equals(idx3))
+
+        self.assert_(df.index.get_level_values(0).equals(idx1))
+        self.assert_(df.index.get_level_values(1).equals(idx2))
+        self.assert_(df.index.get_level_values(2).equals(idx3))
+
 
 if __name__ == '__main__':
 

pandas/tools/tests/test_pivot.py

@@ -3,7 +3,7 @@
 import numpy as np
 from numpy.testing import assert_equal
 
-import pandas
+import pandas as pd
 from pandas import DataFrame, Series, Index, MultiIndex, Grouper
 from pandas.tools.merge import concat
 from pandas.tools.pivot import pivot_table, crosstab
@@ -181,6 +181,42 @@ def test_pivot_index_with_nan(self):
                              columns = Index(['C1','C2','C3','C4'],name='b'))
         tm.assert_frame_equal(result, expected)
 
+    def test_pivot_with_tz(self):
+        # GH 5878
+        df = DataFrame({'dt1': [datetime.datetime(2013, 1, 1, 9, 0),
+                                   datetime.datetime(2013, 1, 2, 9, 0),
+                                   datetime.datetime(2013, 1, 1, 9, 0),
+                                   datetime.datetime(2013, 1, 2, 9, 0)],
+                           'dt2': [datetime.datetime(2014, 1, 1, 9, 0),
+                                   datetime.datetime(2014, 1, 1, 9, 0),
+                                   datetime.datetime(2014, 1, 2, 9, 0),
+                                   datetime.datetime(2014, 1, 2, 9, 0)],
+                           'data1': range(4), 'data2': range(4)})
+
+        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_col1 = Index(['data1', 'data1', 'data2', 'data2'])
+        exp_col2 = pd.DatetimeIndex(['2014/01/01 09:00', '2014/01/02 09:00'] * 2,
+                                    name='dt2', tz='Asia/Tokyo')
+        exp_col = pd.MultiIndex.from_arrays([exp_col1, exp_col2])
+        expected = DataFrame([[0, 2, 0, 2], [1, 3, 1, 3]],
+                             index=pd.DatetimeIndex(['2013/01/01 09:00', '2013/01/02 09:00'],
+                                                    name='dt1', tz='US/Pacific'),
+                             columns=exp_col)
+
+        pv =  df.pivot(index='dt1', columns='dt2')
+        tm.assert_frame_equal(pv, expected)
+
+        expected = DataFrame([[0, 2], [1, 3]],
+                     index=pd.DatetimeIndex(['2013/01/01 09:00', '2013/01/02 09:00'],
+                                            name='dt1', tz='US/Pacific'),
+                     columns=pd.DatetimeIndex(['2014/01/01 09:00', '2014/01/02 09:00'],
+                                            name='dt2', tz='Asia/Tokyo'))
+
+        pv =  df.pivot(index='dt1', columns='dt2', values='data1')
+        tm.assert_frame_equal(pv, expected)
+
     def test_margins(self):
         def _check_output(res, col, index=['A', 'B'], columns=['C']):
             cmarg = res['All'][:-1]
@@ -235,7 +271,7 @@ def test_pivot_integer_columns(self):
         d = datetime.date.min
         data = list(product(['foo', 'bar'], ['A', 'B', 'C'], ['x1', 'x2'],
                             [d + datetime.timedelta(i) for i in range(20)], [1.0]))
-        df = pandas.DataFrame(data)
+        df = DataFrame(data)
         table = df.pivot_table(values=4, index=[0, 1, 3], columns=[2])
 
         df2 = df.rename(columns=str)
@@ -286,7 +322,7 @@ def test_pivot_columns_lexsorted(self):
         iproduct = np.random.randint(0, len(products), n)
         items['Index'] = products['Index'][iproduct]
         items['Symbol'] = products['Symbol'][iproduct]
-        dr = pandas.date_range(datetime.date(2000, 1, 1), datetime.date(2010, 12, 31))
+        dr = pd.date_range(datetime.date(2000, 1, 1), datetime.date(2010, 12, 31))
         dates = dr[np.random.randint(0, len(dr), n)]
         items['Year'] = dates.year
         items['Month'] = dates.month

pandas/tseries/period.py

@@ -669,6 +669,13 @@ def _from_arraylike(cls, data, freq, tz):
 
         return data, freq
 
+    @classmethod
+    def _simple_new(cls, values, name, freq=None, **kwargs):
+        result = values.view(cls)
+        result.name = name
+        result.freq = freq
+        return result
+
     def __contains__(self, key):
         if not isinstance(key, Period) or key.freq != self.freq:
             if isinstance(key, compat.string_types):

pandas/tseries/tests/test_period.py

@@ -1194,6 +1194,14 @@ def test_constructor_datetime64arr(self):
 
         self.assertRaises(ValueError, PeriodIndex, vals, freq='D')
 
+    def test_constructor_simple_new(self):
+        idx = period_range('2007-01', name='p', periods=20, freq='M')
+        result = idx._simple_new(idx, 'p', freq=idx.freq)
+        self.assert_(result.equals(idx))
+
+        result = idx._simple_new(idx.astype('i8'), 'p', freq=idx.freq)
+        self.assert_(result.equals(idx))
+
     def test_is_(self):
         create_index = lambda: PeriodIndex(freq='A', start='1/1/2001',
                                            end='12/1/2009')
@@ -1390,6 +1398,17 @@ def test_frame_setitem(self):
         tm.assert_isinstance(rs.index, PeriodIndex)
         self.assert_(rs.index.equals(rng))
 
+    def test_period_set_index_reindex(self):
+        # GH 6631
+        df = DataFrame(np.random.random(6))
+        idx1 = period_range('2011/01/01', periods=6, freq='M')
+        idx2 = period_range('2013', periods=6, freq='A')
+
+        df = df.set_index(idx1)
+        self.assert_(df.index.equals(idx1))
+        df = df.reindex(idx2)
+        self.assert_(df.index.equals(idx2))
+
     def test_nested_dict_frame_constructor(self):
         rng = period_range('1/1/2000', periods=5)
         df = DataFrame(randn(10, 5), columns=rng)

pandas/tseries/tests/test_timeseries.py

@@ -2510,6 +2510,17 @@ def test_dti_reset_index_round_trip(self):
         self.assertEquals(df.index[0], stamp)
         self.assertEquals(df.reset_index()['Date'][0], stamp)
 
+    def test_dti_set_index_reindex(self):
+        # GH 6631
+        df = DataFrame(np.random.random(6))
+        idx1 = date_range('2011/01/01', periods=6, freq='M', tz='US/Eastern')
+        idx2 = date_range('2013', periods=6, freq='A', tz='Asia/Tokyo')
+
+        df = df.set_index(idx1)
+        self.assert_(df.index.equals(idx1))
+        df = df.reindex(idx2)
+        self.assert_(df.index.equals(idx2))
+
     def test_datetimeindex_union_join_empty(self):
         dti = DatetimeIndex(start='1/1/2001', end='2/1/2001', freq='D')
         empty = Index([])