MAINT: Drop convert_objects from NDFrame

Deprecated since 0.17.0

xref gh-11173

Author: gfyoung

doc/source/10min.rst

@@ -96,17 +96,17 @@ will be completed:
    df2.apply              df2.combineMult
    df2.applymap           df2.compound
    df2.as_blocks          df2.consolidate
-   df2.asfreq             df2.convert_objects
-   df2.as_matrix          df2.copy
-   df2.astype             df2.corr
-   df2.at                 df2.corrwith
-   df2.at_time            df2.count
-   df2.axes               df2.cov
-   df2.B                  df2.cummax
-   df2.between_time       df2.cummin
-   df2.bfill              df2.cumprod
-   df2.blocks             df2.cumsum
-   df2.bool               df2.D
+   df2.asfreq             df2.copy
+   df2.as_matrix          df2.corr
+   df2.astype             df2.corrwith
+   df2.at                 df2.count
+   df2.at_time            df2.cov
+   df2.axes               df2.cummax
+   df2.B                  df2.cummin
+   df2.between_time       df2.cumprod
+   df2.bfill              df2.cumsum
+   df2.blocks             df2.D
+   df2.bool
 
 As you can see, the columns ``A``, ``B``, ``C``, and ``D`` are automatically
 tab completed. ``E`` is there as well; the rest of the attributes have been

doc/source/api.rst

@@ -760,7 +760,6 @@ Conversion
    :toctree: generated/
 
    DataFrame.astype
-   DataFrame.convert_objects
    DataFrame.copy
    DataFrame.isnull
    DataFrame.notnull

doc/source/whatsnew/v0.20.0.txt

@@ -813,6 +813,7 @@ Removal of prior version deprecations/changes
 - The ``take_last`` parameter has been dropped from ``duplicated()``, ``drop_duplicates()``, ``nlargest()``, and ``nsmallest()`` methods (:issue:`10236`, :issue:`10792`, :issue:`10920`)
 - ``Series``, ``Index``, and ``DataFrame`` have dropped the ``sort`` and ``order`` methods (:issue:`10726`)
 - The ``LongPanel`` and ``WidePanel`` classes have been removed (:issue:`10892`)
+- ``Series``, ``Index``, and ``DataFrame`` have dropped the ``convert_objects`` method (:issue:`11173`)
 
 .. _whatsnew_0200.performance:
 

pandas/core/frame.py

@@ -3753,7 +3753,7 @@ def combine(self, other, func, fill_value=None, overwrite=True):
 
             result[col] = arr
 
-        # convert_objects just in case
+        # Convert objects just in case.
         return self._constructor(result, index=new_index,
                                  columns=new_columns)._convert(datetime=True,
                                                                copy=False)

pandas/core/generic.py

@@ -3280,52 +3280,6 @@ def _convert(self, datetime=False, numeric=False, timedelta=False,
                                timedelta=timedelta, coerce=coerce,
                                copy=copy)).__finalize__(self)
 
-    # TODO: Remove in 0.18 or 2017, which ever is sooner
-    def convert_objects(self, convert_dates=True, convert_numeric=False,
-                        convert_timedeltas=True, copy=True):
-        """
-        Deprecated.
-
-        Attempt to infer better dtype for object columns
-
-        Parameters
-        ----------
-        convert_dates : boolean, default True
-            If True, convert to date where possible. If 'coerce', force
-            conversion, with unconvertible values becoming NaT.
-        convert_numeric : boolean, default False
-            If True, attempt to coerce to numbers (including strings), with
-            unconvertible values becoming NaN.
-        convert_timedeltas : boolean, default True
-            If True, convert to timedelta where possible. If 'coerce', force
-            conversion, with unconvertible values becoming NaT.
-        copy : boolean, default True
-            If True, return a copy even if no copy is necessary (e.g. no
-            conversion was done). Note: This is meant for internal use, and
-            should not be confused with inplace.
-
-        See Also
-        --------
-        pandas.to_datetime : Convert argument to datetime.
-        pandas.to_timedelta : Convert argument to timedelta.
-        pandas.to_numeric : Return a fixed frequency timedelta index,
-            with day as the default.
-
-        Returns
-        -------
-        converted : same as input object
-        """
-        from warnings import warn
-        warn("convert_objects is deprecated.  Use the data-type specific "
-             "converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.",
-             FutureWarning, stacklevel=2)
-
-        return self._constructor(
-            self._data.convert(convert_dates=convert_dates,
-                               convert_numeric=convert_numeric,
-                               convert_timedeltas=convert_timedeltas,
-                               copy=copy)).__finalize__(self)
-
     # ----------------------------------------------------------------------
     # Filling NA's
 

pandas/core/internals.py

@@ -764,7 +764,7 @@ def _is_empty_indexer(indexer):
             values = self._try_coerce_and_cast_result(values, dtype)
             block = self.make_block(transf(values), fastpath=True)
 
-            # may have to soft convert_objects here
+            # May have to soft convert objects here
             if block.is_object and not self.is_object:
                 block = block.convert(numeric=False)
 
@@ -1846,12 +1846,13 @@ def is_bool(self):
         """
         return lib.is_bool_array(self.values.ravel())
 
-    # TODO: Refactor when convert_objects is removed since there will be 1 path
     def convert(self, *args, **kwargs):
-        """ attempt to coerce any object types to better types return a copy of
-        the block (if copy = True) by definition we ARE an ObjectBlock!!!!!
+        """
+        Attempt to coerce any object types to more specific data types.
+        If copy = True, return a copy of the block.
 
-        can return multiple blocks!
+        NOTE: This function can can return multiple blocks!
+        NOTE: By definition, we are an ObjectBlock!
         """
 
         if args:

pandas/tests/groupby/test_groupby.py

@@ -3876,8 +3876,8 @@ def test_groupby_blacklist(self):
 
         blacklist = [
             'eval', 'query', 'abs', 'where',
-            'mask', 'align', 'groupby', 'clip', 'astype',
-            'at', 'combine', 'consolidate', 'convert_objects',
+            'mask', 'align', 'groupby', 'clip',
+            'astype', 'at', 'combine', 'consolidate',
         ]
         to_methods = [method for method in dir(df) if method.startswith('to_')]
 

pandas/tests/series/test_internals.py

@@ -16,131 +16,6 @@
 
 class TestSeriesInternals(tm.TestCase):
 
-    def test_convert_objects(self):
-
-        s = Series([1., 2, 3], index=['a', 'b', 'c'])
-        with tm.assert_produces_warning(FutureWarning):
-            result = s.convert_objects(convert_dates=False,
-                                       convert_numeric=True)
-        assert_series_equal(result, s)
-
-        # force numeric conversion
-        r = s.copy().astype('O')
-        r['a'] = '1'
-        with tm.assert_produces_warning(FutureWarning):
-            result = r.convert_objects(convert_dates=False,
-                                       convert_numeric=True)
-        assert_series_equal(result, s)
-
-        r = s.copy().astype('O')
-        r['a'] = '1.'
-        with tm.assert_produces_warning(FutureWarning):
-            result = r.convert_objects(convert_dates=False,
-                                       convert_numeric=True)
-        assert_series_equal(result, s)
-
-        r = s.copy().astype('O')
-        r['a'] = 'garbled'
-        expected = s.copy()
-        expected['a'] = np.nan
-        with tm.assert_produces_warning(FutureWarning):
-            result = r.convert_objects(convert_dates=False,
-                                       convert_numeric=True)
-        assert_series_equal(result, expected)
-
-        # GH 4119, not converting a mixed type (e.g.floats and object)
-        s = Series([1, 'na', 3, 4])
-        with tm.assert_produces_warning(FutureWarning):
-            result = s.convert_objects(convert_numeric=True)
-        expected = Series([1, np.nan, 3, 4])
-        assert_series_equal(result, expected)
-
-        s = Series([1, '', 3, 4])
-        with tm.assert_produces_warning(FutureWarning):
-            result = s.convert_objects(convert_numeric=True)
-        expected = Series([1, np.nan, 3, 4])
-        assert_series_equal(result, expected)
-
-        # dates
-        s = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 2, 0, 0),
-                    datetime(2001, 1, 3, 0, 0)])
-        s2 = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 2, 0, 0),
-                     datetime(2001, 1, 3, 0, 0), 'foo', 1.0, 1,
-                     Timestamp('20010104'), '20010105'],
-                    dtype='O')
-        with tm.assert_produces_warning(FutureWarning):
-            result = s.convert_objects(convert_dates=True,
-                                       convert_numeric=False)
-        expected = Series([Timestamp('20010101'), Timestamp('20010102'),
-                           Timestamp('20010103')], dtype='M8[ns]')
-        assert_series_equal(result, expected)
-
-        with tm.assert_produces_warning(FutureWarning):
-            result = s.convert_objects(convert_dates='coerce',
-                                       convert_numeric=False)
-        with tm.assert_produces_warning(FutureWarning):
-            result = s.convert_objects(convert_dates='coerce',
-                                       convert_numeric=True)
-        assert_series_equal(result, expected)
-
-        expected = Series([Timestamp('20010101'), Timestamp('20010102'),
-                           Timestamp('20010103'),
-                           lib.NaT, lib.NaT, lib.NaT, Timestamp('20010104'),
-                           Timestamp('20010105')], dtype='M8[ns]')
-        with tm.assert_produces_warning(FutureWarning):
-            result = s2.convert_objects(convert_dates='coerce',
-                                        convert_numeric=False)
-        assert_series_equal(result, expected)
-        with tm.assert_produces_warning(FutureWarning):
-            result = s2.convert_objects(convert_dates='coerce',
-                                        convert_numeric=True)
-        assert_series_equal(result, expected)
-
-        # preserver all-nans (if convert_dates='coerce')
-        s = Series(['foo', 'bar', 1, 1.0], dtype='O')
-        with tm.assert_produces_warning(FutureWarning):
-            result = s.convert_objects(convert_dates='coerce',
-                                       convert_numeric=False)
-        expected = Series([lib.NaT] * 2 + [Timestamp(1)] * 2)
-        assert_series_equal(result, expected)
-
-        # preserver if non-object
-        s = Series([1], dtype='float32')
-        with tm.assert_produces_warning(FutureWarning):
-            result = s.convert_objects(convert_dates='coerce',
-                                       convert_numeric=False)
-        assert_series_equal(result, s)
-
-        # r = s.copy()
-        # r[0] = np.nan
-        # result = r.convert_objects(convert_dates=True,convert_numeric=False)
-        # self.assertEqual(result.dtype, 'M8[ns]')
-
-        # dateutil parses some single letters into today's value as a date
-        for x in 'abcdefghijklmnopqrstuvwxyz':
-            s = Series([x])
-            with tm.assert_produces_warning(FutureWarning):
-                result = s.convert_objects(convert_dates='coerce')
-            assert_series_equal(result, s)
-            s = Series([x.upper()])
-            with tm.assert_produces_warning(FutureWarning):
-                result = s.convert_objects(convert_dates='coerce')
-            assert_series_equal(result, s)
-
-    def test_convert_objects_preserve_bool(self):
-        s = Series([1, True, 3, 5], dtype=object)
-        with tm.assert_produces_warning(FutureWarning):
-            r = s.convert_objects(convert_numeric=True)
-        e = Series([1, 1, 3, 5], dtype='i8')
-        tm.assert_series_equal(r, e)
-
-    def test_convert_objects_preserve_all_bool(self):
-        s = Series([False, True, False, False], dtype=object)
-        with tm.assert_produces_warning(FutureWarning):
-            r = s.convert_objects(convert_numeric=True)
-        e = Series([False, True, False, False], dtype=bool)
-        tm.assert_series_equal(r, e)
-
     # GH 10265
     def test_convert(self):
         # Tests: All to nans, coerce, true