Merge pull request #6731 from jreback/resample

BUG: Bug in resample when how=None resample freq is the same as the axis freq (GH5955)

Author: jreback

doc/source/release.rst

@@ -291,6 +291,7 @@ Bug Fixes
 - Bug in ``DataFrame.to_stata`` which incorrectly handles nan values and ignores 'with_index' keyword argument (:issue:`6685`)
 - Bug in resample with extra bins when using an evenly divisible frequency (:issue:`4076`)
 - Bug in consistency of groupby aggregation when passing a custom function (:issue:`6715`)
+- Bug in resample when ``how=None`` resample freq is the same as the axis frequency (:issue:`5955`)
 
 pandas 0.13.1
 -------------

pandas/tseries/resample.py

@@ -246,7 +246,9 @@ def _resample_timestamps(self):
 
         # Determine if we're downsampling
         if axlabels.freq is not None or axlabels.inferred_freq is not None:
+
             if len(grouper.binlabels) < len(axlabels) or self.how is not None:
+                # downsample
                 grouped = obj.groupby(grouper, axis=self.axis)
                 result = grouped.aggregate(self._agg_method)
             else:
@@ -259,8 +261,15 @@ def _resample_timestamps(self):
                 else:
                     res_index = binner[:-1]
 
-                result = obj.reindex(res_index, method=self.fill_method,
-                                     limit=self.limit)
+                # if we have the same frequency as our axis, then we are equal sampling
+                # even if how is None
+                if self.fill_method is None and self.limit is None and to_offset(
+                    axlabels.inferred_freq) == self.freq:
+                    result = obj.copy()
+                    result.index = res_index
+                else:
+                    result = obj.reindex(res_index, method=self.fill_method,
+                                         limit=self.limit)
         else:
             # Irregular data, have to use groupby
             grouped = obj.groupby(grouper, axis=self.axis)

pandas/tseries/tests/test_resample.py

@@ -160,6 +160,32 @@ def test_resample_basic_from_daily(self):
         self.assertEquals(result.irow(5), s['1/9/2005'])
         self.assertEqual(result.index.name, 'index')
 
+    def test_resample_upsampling_picked_but_not_correct(self):
+
+        # Test for issue #3020
+        dates = date_range('01-Jan-2014','05-Jan-2014', freq='D')
+        series = Series(1, index=dates)
+
+        result = series.resample('D')
+        self.assertEquals(result.index[0], dates[0])
+
+        # GH 5955
+        # incorrect deciding to upsample when the axis frequency matches the resample frequency
+
+        import datetime
+        s = Series(np.arange(1.,6),index=[datetime.datetime(1975, 1, i, 12, 0) for i in range(1, 6)])
+        expected = Series(np.arange(1.,6),index=date_range('19750101',periods=5,freq='D'))
+
+        result = s.resample('D',how='count')
+        assert_series_equal(result,Series(1,index=expected.index))
+
+        result1 = s.resample('D',how='sum')
+        result2 = s.resample('D',how='mean')
+        result3 = s.resample('D')
+        assert_series_equal(result1,expected)
+        assert_series_equal(result2,expected)
+        assert_series_equal(result3,expected)
+
     def test_resample_frame_basic(self):
         df = tm.makeTimeDataFrame()
 
@@ -1078,15 +1104,6 @@ def test_all_values_single_bin(self):
         result = s.resample("A", how='mean')
         tm.assert_almost_equal(result[0], s.mean())
 
-    def test_resample_doesnt_truncate(self):
-        # Test for issue #3020
-        import pandas as pd
-        dates = pd.date_range('01-Jan-2014','05-Jan-2014', freq='D')
-        series = Series(1, index=dates)
-
-        result = series.resample('D')
-        self.assertEquals(result.index[0], dates[0])
-
     def test_evenly_divisible_with_no_extra_bins(self):
         # 4076
         # when the frequency is evenly divisible, sometimes extra bins