Merge remote-tracking branch 'upstream/master'

Author: Seth Padowitz

doc/source/v0.14.1.txt

@@ -242,15 +242,18 @@ Bug Fixes
 - Bug in ``DatetimeIndex.to_period``, ``PeriodIndex.asobject``, ``PeriodIndex.to_timestamp`` doesn't preserve ``name`` (:issue:`7485`)
 - Bug in ``DatetimeIndex.to_period`` and ``PeriodIndex.to_timestanp`` handle ``NaT`` incorrectly (:issue:`7228`)
 
+- BUG in ``offsets.apply``, ''rollforward`` and ``rollback`` may return normal ``datetime`` (:issue:`7502`)
 
 
 - BUG in ``resample`` raises ``ValueError`` when target contains ``NaT`` (:issue:`7227`)
 
 - Bug in ``Timestamp.tz_localize`` resets ``nanosecond`` info (:issue:`7534`)
 
+
+
 - Bug in ``Index.astype(float)`` where it would return an ``object`` dtype
   ``Index`` (:issue:`7464`).
-
+- Bug in ``DataFrame.reset_index`` loses ``tz`` (:issue:`3950`)
 
 
 - Bug in non-monotonic ``Index.union`` may preserve ``name`` incorrectly (:issue:`7458`)

pandas/core/frame.py

@@ -2326,19 +2326,24 @@ def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
         else:
             new_obj = self.copy()
 
-        def _maybe_cast(values, labels=None):
-
-            if values.dtype == np.object_:
-                values = lib.maybe_convert_objects(values)
-
-            # if we have the labels, extract the values with a mask
-            if labels is not None:
-                mask = labels == -1
-                values = values.take(labels)
-                if mask.any():
-                    values, changed = com._maybe_upcast_putmask(
-                        values, mask, np.nan)
-
+        def _maybe_casted_values(index, labels=None):
+            if isinstance(index, PeriodIndex):
+                values = index.asobject
+            elif (isinstance(index, DatetimeIndex) and
+                  index.tz is not None):
+                values = index.asobject
+            else:
+                values = index.values
+                if values.dtype == np.object_:
+                    values = lib.maybe_convert_objects(values)
+
+                # if we have the labels, extract the values with a mask
+                if labels is not None:
+                    mask = labels == -1
+                    values = values.take(labels)
+                    if mask.any():
+                        values, changed = com._maybe_upcast_putmask(values,
+                                                                    mask, np.nan)
             return values
 
         new_index = np.arange(len(new_obj))
@@ -2371,7 +2376,7 @@ def _maybe_cast(values, labels=None):
                             col_name = tuple(name_lst)
 
                     # to ndarray and maybe infer different dtype
-                    level_values = _maybe_cast(lev.values, lab)
+                    level_values = _maybe_casted_values(lev, lab)
                     if level is None or i in level:
                         new_obj.insert(0, col_name, level_values)
 
@@ -2387,13 +2392,7 @@ def _maybe_cast(values, labels=None):
                     lev_num = self.columns._get_level_number(col_level)
                     name_lst[lev_num] = name
                     name = tuple(name_lst)
-            if isinstance(self.index, PeriodIndex):
-                values = self.index.asobject
-            elif (isinstance(self.index, DatetimeIndex) and
-                  self.index.tz is not None):
-                values = self.index.asobject
-            else:
-                values = _maybe_cast(self.index.values)
+            values = _maybe_casted_values(self.index)
             new_obj.insert(0, name, values)
 
         new_obj.index = new_index

pandas/stats/tests/test_moments.py

@@ -17,11 +17,6 @@
 
 N, K = 100, 10
 
-def _skip_if_no_scipy():
-    try:
-        import scipy.stats
-    except ImportError:
-        raise nose.SkipTest("no scipy.stats")
 
 class TestMoments(tm.TestCase):
 
@@ -68,7 +63,7 @@ def test_rolling_mean(self):
         self._check_moment_func(mom.rolling_mean, np.mean)
 
     def test_cmov_mean(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_mean
         except ImportError:
@@ -86,7 +81,7 @@ def test_cmov_mean(self):
         assert_series_equal(xp, rs)
 
     def test_cmov_window(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -104,7 +99,7 @@ def test_cmov_window(self):
         assert_series_equal(xp, rs)
 
     def test_cmov_window_corner(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -128,7 +123,7 @@ def test_cmov_window_corner(self):
         self.assertEqual(len(rs), 5)
 
     def test_cmov_window_frame(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -141,7 +136,7 @@ def test_cmov_window_frame(self):
         assert_frame_equal(DataFrame(xp), rs)
 
     def test_cmov_window_na_min_periods(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -158,7 +153,7 @@ def test_cmov_window_na_min_periods(self):
         assert_series_equal(xp, rs)
 
     def test_cmov_window_regular(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:
@@ -174,7 +169,7 @@ def test_cmov_window_regular(self):
             assert_series_equal(Series(xp), rs)
 
     def test_cmov_window_special(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         try:
             from scikits.timeseries.lib import cmov_window
         except ImportError:

pandas/tests/test_format.py

@@ -86,18 +86,6 @@ def skip_if_np_version_under1p7():
 
         raise nose.SkipTest('numpy >= 1.7 required')
 
-def _skip_if_no_pytz():
-    try:
-        import pytz
-    except ImportError:
-        raise nose.SkipTest("pytz not installed")
-
-def _skip_if_no_dateutil():
-    try:
-        import dateutil
-    except ImportError:
-        raise nose.SkipTest("dateutil not installed")
-
 
 class TestDataFrameFormatting(tm.TestCase):
     _multiprocess_can_split_ = True
@@ -2930,7 +2918,7 @@ def test_no_tz(self):
         self.assertEqual(str(ts_nanos_micros), "1970-01-01 00:00:00.000001200")
 
     def test_tz_pytz(self):
-        _skip_if_no_pytz()
+        tm._skip_if_no_pytz()
 
         import pytz
 
@@ -2944,7 +2932,7 @@ def test_tz_pytz(self):
         self.assertEqual(str(dt_datetime_us), str(Timestamp(dt_datetime_us)))
 
     def test_tz_dateutil(self):
-        _skip_if_no_dateutil()
+        tm._skip_if_no_dateutil()
         import dateutil
         utc = dateutil.tz.tzutc()
 

pandas/tests/test_frame.py

@@ -52,12 +52,6 @@
 
 from numpy.testing.decorators import slow
 
-def _skip_if_no_scipy():
-    try:
-        import scipy.stats
-    except ImportError:
-        raise nose.SkipTest("no scipy.stats module")
-
 #---------------------------------------------------------------------
 # DataFrame test cases
 
@@ -2182,6 +2176,20 @@ def test_set_index_cast_datetimeindex(self):
         df.pop('ts')
         assert_frame_equal(df, expected)
 
+        # GH 3950
+        # reset_index with single level
+        for tz in ['UTC', 'Asia/Tokyo', 'US/Eastern']:
+            idx = pd.date_range('1/1/2011', periods=5, freq='D', tz=tz, name='idx')
+            df = pd.DataFrame({'a': range(5), 'b': ['A', 'B', 'C', 'D', 'E']}, index=idx) 
+
+            expected = pd.DataFrame({'idx': [datetime(2011, 1, 1), datetime(2011, 1, 2),
+                                             datetime(2011, 1, 3), datetime(2011, 1, 4),
+                                             datetime(2011, 1, 5)], 
+                                     'a': range(5), 'b': ['A', 'B', 'C', 'D', 'E']},
+                                     columns=['idx', 'a', 'b'])
+            expected['idx'] = expected['idx'].apply(lambda d: pd.Timestamp(d, tz=tz))
+            assert_frame_equal(df.reset_index(), expected)
+
     def test_set_index_multiindexcolumns(self):
         columns = MultiIndex.from_tuples([('foo', 1), ('foo', 2), ('bar', 1)])
         df = DataFrame(np.random.randn(3, 3), columns=columns)
@@ -6739,28 +6747,28 @@ def _check_method(self, method='pearson', check_minp=False):
             expected.ix['A', 'B'] = expected.ix['B', 'A'] = nan
 
     def test_corr_pearson(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
         self._check_method('pearson')
 
     def test_corr_kendall(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
         self._check_method('kendall')
 
     def test_corr_spearman(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
         self._check_method('spearman')
 
     def test_corr_non_numeric(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
@@ -6770,7 +6778,7 @@ def test_corr_non_numeric(self):
         assert_frame_equal(result, expected)
 
     def test_corr_nooverlap(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
 
         # nothing in common
         for meth in ['pearson', 'kendall', 'spearman']:
@@ -6783,7 +6791,7 @@ def test_corr_nooverlap(self):
             self.assertEqual(rs.ix['B', 'B'], 1)
 
     def test_corr_constant(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
 
         # constant --> all NA
 
@@ -10957,7 +10965,7 @@ def test_sem(self):
             nanops._USE_BOTTLENECK = True
 
     def test_skew(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         from scipy.stats import skew
 
         def alt(x):
@@ -10968,7 +10976,7 @@ def alt(x):
         self._check_stat_op('skew', alt)
 
     def test_kurt(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
 
         from scipy.stats import kurtosis
 
@@ -11320,7 +11328,7 @@ def test_cumprod(self):
         df.cumprod(1)
 
     def test_rank(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         from scipy.stats import rankdata
 
         self.frame['A'][::2] = np.nan
@@ -11412,7 +11420,7 @@ def test_rank2(self):
         assert_frame_equal(df.rank(), exp)
 
     def test_rank_na_option(self):
-        _skip_if_no_scipy()
+        tm._skip_if_no_scipy()
         from scipy.stats import rankdata
 
         self.frame['A'][::2] = np.nan