BUG: DTI.value_counts doesnt preserve tz

Author: sinhrks

doc/source/v0.15.0.txt

@@ -290,6 +290,12 @@ Bug Fixes
 
 
 
+- Bug in ``DatetimeIndex.value_counts`` doesn't preserve tz  (:issue:`7735`)
+- Bug in ``PeriodIndex.value_counts`` results in ``Int64Index`` (:issue:`7735`)
+
+
+
+
 
 
 

pandas/core/algorithms.py

@@ -197,6 +197,7 @@ def value_counts(values, sort=True, ascending=False, normalize=False,
     from pandas.core.series import Series
     from pandas.tools.tile import cut
 
+    is_period = getattr(values, 'inferred_type', None) == 'period'
     values = Series(values).values
     is_category = com.is_categorical_dtype(values.dtype)
 
@@ -212,11 +213,8 @@ def value_counts(values, sort=True, ascending=False, normalize=False,
         values = cat.codes
 
     dtype = values.dtype
-    if com.is_integer_dtype(dtype):
-        values = com._ensure_int64(values)
-        keys, counts = htable.value_count_int64(values)
 
-    elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)):
+    if issubclass(values.dtype.type, (np.datetime64, np.timedelta64)) or is_period:
         values = values.view(np.int64)
         keys, counts = htable.value_count_int64(values)
 
@@ -227,6 +225,10 @@ def value_counts(values, sort=True, ascending=False, normalize=False,
         # convert the keys back to the dtype we came in
         keys = keys.astype(dtype)
 
+    elif com.is_integer_dtype(dtype):
+        values = com._ensure_int64(values)
+        keys, counts = htable.value_count_int64(values)
+
     else:
         values = com._ensure_object(values)
         mask = com.isnull(values)

pandas/core/base.py

@@ -275,8 +275,18 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
         counts : Series
         """
         from pandas.core.algorithms import value_counts
-        return value_counts(self.values, sort=sort, ascending=ascending,
-                            normalize=normalize, bins=bins, dropna=dropna)
+        from pandas.tseries.api import DatetimeIndex, PeriodIndex
+        result = value_counts(self, sort=sort, ascending=ascending,
+                              normalize=normalize, bins=bins, dropna=dropna)
+
+        if isinstance(self, PeriodIndex):
+            # preserve freq
+            result.index = self._simple_new(result.index.values, self.name,
+                                            freq=self.freq)
+        elif isinstance(self, DatetimeIndex):
+            result.index = self._simple_new(result.index.values, self.name,
+                                            tz=getattr(self, 'tz', None))
+        return result
 
     def unique(self):
         """
@@ -542,5 +552,3 @@ def __sub__(self, other):
 
     def _add_delta(self, other):
         return NotImplemented
-
-

pandas/tests/test_base.py

@@ -267,8 +267,9 @@ def test_value_counts_unique_nunique(self):
                     # skips int64 because it doesn't allow to include nan or None
                     continue
 
-                if o.values.dtype == 'datetime64[ns]' and _np_version_under1p7:
-                    # Unable to assign None
+                if ((isinstance(o, Int64Index) and not isinstance(o,
+                    (DatetimeIndex, PeriodIndex)))):
+                    # skips int64 because it doesn't allow to include nan or None
                     continue
 
                 # special assign to the numpy array
@@ -283,12 +284,8 @@ def test_value_counts_unique_nunique(self):
                 else:
                     o = klass(np.repeat(values, range(1, len(o) + 1)))
 
-                if isinstance(o, DatetimeIndex):
-                    expected_s_na = Series(list(range(10, 2, -1)) + [3], index=values[9:0:-1])
-                    expected_s = Series(list(range(10, 2, -1)), index=values[9:1:-1])
-                else:
-                    expected_s_na = Series(list(range(10, 2, -1)) +[3], index=values[9:0:-1], dtype='int64')
-                    expected_s = Series(list(range(10, 2, -1)), index=values[9:1:-1], dtype='int64')
+                expected_s_na = Series(list(range(10, 2, -1)) +[3], index=values[9:0:-1], dtype='int64')
+                expected_s = Series(list(range(10, 2, -1)), index=values[9:1:-1], dtype='int64')
 
                 tm.assert_series_equal(o.value_counts(dropna=False), expected_s_na)
                 tm.assert_series_equal(o.value_counts(), expected_s)
@@ -709,6 +706,28 @@ def test_sub_isub(self):
             rng -= 1
             tm.assert_index_equal(rng, expected)
 
+    def test_value_counts(self):
+        # GH 7735
+        for tz in [None, 'UTC', 'Asia/Tokyo', 'US/Eastern']:
+            idx = pd.date_range('2011-01-01 09:00', freq='H', periods=10)
+            # create repeated values, 'n'th element is repeated by n+1 times
+            idx = DatetimeIndex(np.repeat(idx.values, range(1, len(idx) + 1)), tz=tz)
+
+            exp_idx = pd.date_range('2011-01-01 18:00', freq='-1H', periods=10, tz=tz)
+            expected = Series(range(10, 0, -1), index=exp_idx, dtype='int64')
+            tm.assert_series_equal(idx.value_counts(), expected)
+
+            idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 09:00', '2013-01-01 09:00',
+                                 '2013-01-01 08:00', '2013-01-01 08:00', pd.NaT], tz=tz)
+
+            exp_idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 08:00'], tz=tz)
+            expected = Series([3, 2], index=exp_idx)
+            tm.assert_series_equal(idx.value_counts(), expected)
+
+            exp_idx = DatetimeIndex(['2013-01-01 09:00', '2013-01-01 08:00', pd.NaT], tz=tz)
+            expected = Series([3, 2, 1], index=exp_idx)
+            tm.assert_series_equal(idx.value_counts(dropna=False), expected)
+
 
 class TestPeriodIndexOps(Ops):
     _allowed = '_allow_period_index_ops'
@@ -968,6 +987,30 @@ def test_sub_isub(self):
         rng -= 1
         tm.assert_index_equal(rng, expected)
 
+    def test_value_counts(self):
+        # GH 7735
+        idx = pd.period_range('2011-01-01 09:00', freq='H', periods=10)
+        # create repeated values, 'n'th element is repeated by n+1 times
+        idx = PeriodIndex(np.repeat(idx.values, range(1, len(idx) + 1)), freq='H')
+
+        exp_idx = PeriodIndex(['2011-01-01 18:00', '2011-01-01 17:00', '2011-01-01 16:00',
+                               '2011-01-01 15:00', '2011-01-01 14:00', '2011-01-01 13:00',
+                               '2011-01-01 12:00', '2011-01-01 11:00', '2011-01-01 10:00',
+                               '2011-01-01 09:00'], freq='H')
+        expected = Series(range(10, 0, -1), index=exp_idx, dtype='int64')
+        tm.assert_series_equal(idx.value_counts(), expected)
+
+        idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 09:00', '2013-01-01 09:00',
+                           '2013-01-01 08:00', '2013-01-01 08:00', pd.NaT], freq='H')
+
+        exp_idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 08:00'], freq='H')
+        expected = Series([3, 2], index=exp_idx)
+        tm.assert_series_equal(idx.value_counts(), expected)
+
+        exp_idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 08:00', pd.NaT], freq='H')
+        expected = Series([3, 2, 1], index=exp_idx)
+        tm.assert_series_equal(idx.value_counts(dropna=False), expected)
+
 
 if __name__ == '__main__':
     import nose