BUG: Ensure 'coerce' actually coerces datatypes

Changes behavior of convert objects so that passing 'coerce' will
ensure that data of the correct type is returned, even if all
values are null-types (NaN or NaT).

closes #9589

Author: Kevin Sheppard

pandas/core/common.py

@@ -1894,54 +1894,47 @@ def _possibly_convert_objects(values, convert_dates=True,
     if not hasattr(values, 'dtype'):
         values = np.array([values], dtype=np.object_)
 
-    # convert dates
-    if convert_dates and values.dtype == np.object_:
-
-        # we take an aggressive stance and convert to datetime64[ns]
+    # If not object, do not attempt conversion
+    if not is_object_dtype(values.dtype):
+        return values
+
+    # If 1 flag is coerce, ensure 2 others are False
+    conversions = (convert_dates, convert_numeric, convert_timedeltas)
+    if 'coerce' in conversions:
+        coerce_count = sum([c == 'coerce' for c in conversions])
+        if coerce_count > 1:
+            raise ValueError("'coerce' can be used at most once.")
+
+        # Immediate return if coerce
         if convert_dates == 'coerce':
-            new_values = _possibly_cast_to_datetime(
-                values, 'M8[ns]', coerce=True)
-
-            # if we are all nans then leave me alone
-            if not isnull(new_values).all():
-                values = new_values
-
-        else:
-            values = lib.maybe_convert_objects(
-                values, convert_datetime=convert_dates)
-
-    # convert timedeltas
-    if convert_timedeltas and values.dtype == np.object_:
-
-        if convert_timedeltas == 'coerce':
-            from pandas.tseries.timedeltas import to_timedelta
-            values = to_timedelta(values, coerce=True)
-
-            # if we are all nans then leave me alone
-            if not isnull(new_values).all():
-                values = new_values
-
-        else:
-            values = lib.maybe_convert_objects(
-                values, convert_timedelta=convert_timedeltas)
-
+            return pd.to_datetime(values, coerce=True, box=False)
+        elif convert_timedeltas == 'coerce':
+            return pd.to_timedelta(values, coerce=True, box=False)
+        elif convert_numeric == 'coerce':
+            return lib.maybe_convert_numeric(values, set(), coerce_numeric=True)
+
+    # Soft conversions
+    if convert_dates:
+        values = lib.maybe_convert_objects(values,
+                                           convert_datetime=convert_dates)
+
+    if convert_timedeltas and is_object_dtype(values.dtype):
+        # Object check to ensure only run if previous did not completely
+        # convert
+        values = lib.maybe_convert_objects(values,
+                                           convert_timedelta=convert_timedeltas)
     # convert to numeric
-    if values.dtype == np.object_:
-        if convert_numeric:
-            try:
-                new_values = lib.maybe_convert_numeric(
-                    values, set(), coerce_numeric=True)
-
-                # if we are all nans then leave me alone
-                if not isnull(new_values).all():
-                    values = new_values
-
-            except:
-                pass
-        else:
+    if convert_numeric and is_object_dtype(values.dtype):
+        # Only if previous failed
+        try:
+            converted = lib.maybe_convert_numeric(values,
+                                                   set(),
+                                                   coerce_numeric=True)
+            # If all NaNs, then do not-alter
+            values = converted if not isnull(converted).all() else values
 
-            # soft-conversion
-            values = lib.maybe_convert_objects(values)
+        except:
+            pass
 
     return values
 

pandas/core/groupby.py

@@ -2939,12 +2939,17 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
 
                 # if we have date/time like in the original, then coerce dates
                 # as we are stacking can easily have object dtypes here
-                if (self._selected_obj.ndim == 2
-                       and self._selected_obj.dtypes.isin(_DATELIKE_DTYPES).any()):
-                    cd = 'coerce'
+                if (self._selected_obj.ndim == 2 and
+                        self._selected_obj.dtypes.isin(_DATELIKE_DTYPES).any()):
+                    result = result.convert_objects(convert_dates=False,
+                                                    convert_numeric=True)
+                    date_cols = self._selected_obj.select_dtypes(
+                        include=list(_DATELIKE_DTYPES)).columns
+                    result[date_cols] = result[date_cols].convert_objects(
+                        convert_dates='coerce')
                 else:
-                    cd = True
-                result = result.convert_objects(convert_dates=cd)
+                    result = result.convert_objects(convert_dates=True)
+
                 return self._reindex_output(result)
 
             else:

pandas/core/internals.py

@@ -1484,8 +1484,10 @@ def convert(self, convert_dates=True, convert_numeric=True, convert_timedeltas=T
         else:
 
             values = com._possibly_convert_objects(
-                self.values.ravel(), convert_dates=convert_dates,
-                convert_numeric=convert_numeric
+                self.values.ravel(),
+                convert_dates=convert_dates,
+                convert_numeric=convert_numeric,
+                convert_timedeltas=convert_timedeltas
             ).reshape(self.values.shape)
             blocks.append(make_block(values,
                                      ndim=self.ndim, placement=self.mgr_locs))

pandas/io/tests/test_html.py

@@ -541,8 +541,9 @@ def try_remove_ws(x):
         dfnew = df.applymap(try_remove_ws).replace(old, new)
         gtnew = ground_truth.applymap(try_remove_ws)
         converted = dfnew.convert_objects(convert_numeric=True)
-        tm.assert_frame_equal(converted.convert_objects(convert_dates='coerce'),
-                              gtnew)
+        date_cols = ['Closing Date','Updated Date']
+        converted[date_cols] = converted[date_cols].convert_objects(convert_dates='coerce')
+        tm.assert_frame_equal(converted,gtnew)
 
     @slow
     def test_gold_canyon(self):

pandas/tests/test_groupby.py

@@ -599,7 +599,7 @@ def f(grp):
             return grp.iloc[0]
         result = df.groupby('A').apply(f)[['C']]
         e = df.groupby('A').first()[['C']]
-        e.loc['Pony'] = np.nan
+        e.loc['Pony'] = pd.NaT
         assert_frame_equal(result,e)
 
         # scalar outputs

pandas/tests/test_series.py

@@ -5830,6 +5830,72 @@ def test_apply_dont_convert_dtype(self):
         self.assertEqual(result.dtype, object)
 
     def test_convert_objects(self):
+        # Tests: All to nans, coerce, true
+        # Test coercion returns correct type
+        s = Series(['a', 'b', 'c'])
+        results = s.convert_objects('coerce', False, False)
+        expected = Series([lib.NaT] * 3)
+        assert_series_equal(results, expected)
+
+        results = s.convert_objects(False, 'coerce', False)
+        expected = Series([np.nan] * 3)
+        assert_series_equal(results, expected)
+
+        expected = Series([lib.NaT] * 3, dtype=np.dtype('m8[ns]'))
+        results = s.convert_objects(False, False, 'coerce')
+        assert_series_equal(results, expected)
+
+        dt = datetime(2001, 1, 1, 0, 0)
+        td = dt - datetime(2000, 1, 1, 0, 0)
+
+        # Test coercion with mixed types
+        s = Series(['a', '3.1415', dt, td])
+        results = s.convert_objects('coerce',False,False)
+        expected = Series([lib.NaT, lib.NaT, dt, lib.NaT])
+        assert_series_equal(results, expected)
+
+        results = s.convert_objects(False, 'coerce',False)
+        expected = Series([nan, 3.1415, nan, nan])
+        assert_series_equal(results, expected)
+
+        results = s.convert_objects(False, False, 'coerce')
+        expected = Series([lib.NaT, lib.NaT, lib.NaT, td],
+                          dtype=np.dtype('m8[ns]'))
+        assert_series_equal(results, expected)
+
+        # Test standard conversion returns original
+        results = s.convert_objects(True, False, False)
+        assert_series_equal(results, s)
+        results = s.convert_objects(False, True, False)
+        expected = Series([nan, 3.1415, nan, nan])
+        assert_series_equal(results, expected)
+        results = s.convert_objects(False, False, True)
+        assert_series_equal(results, s)
+
+        # test pass-through and non-conversion when other types selected
+        s = Series(['1.0','2.0','3.0'])
+        results = s.convert_objects(True,True,True)
+        expected = Series([1.0,2.0,3.0])
+        assert_series_equal(results, expected)
+        results = s.convert_objects(True,False,True)
+        assert_series_equal(results, s)
+
+        s = Series([datetime(2001, 1, 1, 0, 0),datetime(2001, 1, 1, 0, 0)],
+                   dtype='O')
+        results = s.convert_objects(True,True,True)
+        expected = Series([datetime(2001, 1, 1, 0, 0),datetime(2001, 1, 1, 0, 0)])
+        assert_series_equal(results, expected)
+        results = s.convert_objects(False,True,True)
+        assert_series_equal(results, s)
+
+        td = datetime(2001, 1, 1, 0, 0) - datetime(2000, 1, 1, 0, 0)
+        s = Series([td, td], dtype='O')
+        results = s.convert_objects(True,True,True)
+        expected = Series([td, td])
+        assert_series_equal(results, expected)
+        results = s.convert_objects(True,True,False)
+        assert_series_equal(results, s)
+
 
         s = Series([1., 2, 3], index=['a', 'b', 'c'])
         result = s.convert_objects(convert_dates=False, convert_numeric=True)
@@ -5848,20 +5914,19 @@ def test_convert_objects(self):
 
         r = s.copy().astype('O')
         r['a'] = 'garbled'
-        expected = s.copy()
-        expected['a'] = np.nan
         result = r.convert_objects(convert_dates=False, convert_numeric=True)
+        expected = s.copy()
+        expected['a'] = nan
         assert_series_equal(result, expected)
 
         # GH 4119, not converting a mixed type (e.g.floats and object)
         s = Series([1, 'na', 3, 4])
         result = s.convert_objects(convert_numeric=True)
-        expected = Series([1, np.nan, 3, 4])
+        expected = Series([1, nan, 3, 4])
         assert_series_equal(result, expected)
 
         s = Series([1, '', 3, 4])
         result = s.convert_objects(convert_numeric=True)
-        expected = Series([1, np.nan, 3, 4])
         assert_series_equal(result, expected)
 
         # dates
@@ -5885,23 +5950,28 @@ def test_convert_objects(self):
             [Timestamp(
                 '20010101'), Timestamp('20010102'), Timestamp('20010103'),
                           lib.NaT, lib.NaT, lib.NaT, Timestamp('20010104'), Timestamp('20010105')], dtype='M8[ns]')
-        result = s2.convert_objects(
-            convert_dates='coerce', convert_numeric=False)
+        result = s2.convert_objects(convert_dates='coerce',
+                                    convert_numeric=False,
+                                    convert_timedeltas=False)
         assert_series_equal(result, expected)
-        result = s2.convert_objects(
-            convert_dates='coerce', convert_numeric=True)
+        result = s2.convert_objects(convert_dates='coerce',
+                                    convert_numeric=False,
+                                    convert_timedeltas=False)
         assert_series_equal(result, expected)
 
         # preserver all-nans (if convert_dates='coerce')
         s = Series(['foo', 'bar', 1, 1.0], dtype='O')
-        result = s.convert_objects(
-            convert_dates='coerce', convert_numeric=False)
-        assert_series_equal(result, s)
+        result = s.convert_objects(convert_dates='coerce',
+                                    convert_numeric=False,
+                                    convert_timedeltas=False)
+        expected = Series([lib.NaT]*4)
+        assert_series_equal(result, expected)
 
         # preserver if non-object
         s = Series([1], dtype='float32')
-        result = s.convert_objects(
-            convert_dates='coerce', convert_numeric=False)
+        result = s.convert_objects(convert_dates='coerce',
+                                   convert_numeric=False,
+                                   convert_timedeltas=False)
         assert_series_equal(result, s)
 
         #r = s.copy()
@@ -5910,13 +5980,14 @@ def test_convert_objects(self):
         #self.assertEqual(result.dtype, 'M8[ns]')
 
         # dateutil parses some single letters into today's value as a date
+        expected = Series([lib.NaT])
         for x in 'abcdefghijklmnopqrstuvwxyz':
             s = Series([x])
             result = s.convert_objects(convert_dates='coerce')
-            assert_series_equal(result, s)
+            assert_series_equal(result, expected)
             s = Series([x.upper()])
             result = s.convert_objects(convert_dates='coerce')
-            assert_series_equal(result, s)
+            assert_series_equal(result, expected)
 
     def test_convert_objects_preserve_bool(self):
         s = Series([1, True, 3, 5], dtype=object)