REF: remove NDFrame._convert

pandas/core/generic.py

@@ -6315,37 +6315,6 @@ def __deepcopy__(self: NDFrameT, memo=None) -> NDFrameT:
         """
         return self.copy(deep=True)
 
-    @final
-    def _convert(
-        self: NDFrameT,
-        *,
-        datetime: bool_t = False,
-        timedelta: bool_t = False,
-    ) -> NDFrameT:
-        """
-        Attempt to infer better dtype for object columns.
-
-        Parameters
-        ----------
-        datetime : bool, default False
-            If True, convert to date where possible.
-        timedelta : bool, default False
-            If True, convert to timedelta where possible.
-
-        Returns
-        -------
-        converted : same as input object
-        """
-        validate_bool_kwarg(datetime, "datetime")
-        validate_bool_kwarg(timedelta, "timedelta")
-        return self._constructor(
-            self._mgr.convert(
-                datetime=datetime,
-                timedelta=timedelta,
-                copy=True,
-            )
-        ).__finalize__(self)
-
     @final
     def infer_objects(self: NDFrameT) -> NDFrameT:
         """

pandas/core/groupby/generic.py

@@ -1681,9 +1681,8 @@ def _wrap_agged_manager(self, mgr: Manager2D) -> DataFrame:
         if self.axis == 1:
             result = result.T
 
-        # Note: we only need to pass datetime=True in order to get numeric
-        #  values converted
-        return self._reindex_output(result)._convert(datetime=True)
+        # Note: we really only care about inferring numeric dtypes here
+        return self._reindex_output(result).infer_objects()
 
     def _iterate_column_groupbys(self, obj: DataFrame | Series):
         for i, colname in enumerate(obj.columns):

pandas/plotting/_matplotlib/core.py

@@ -600,9 +600,9 @@ def _compute_plot_data(self):
             self.subplots = True
             data = reconstruct_data_with_by(self.data, by=self.by, cols=self.columns)
 
-        # GH16953, _convert is needed as fallback, for ``Series``
+        # GH16953, infer_objects is needed as fallback, for ``Series``
         # with ``dtype == object``
-        data = data._convert(datetime=True, timedelta=True)
+        data = data.infer_objects()
         include_type = [np.number, "datetime", "datetimetz", "timedelta"]
 
         # GH23719, allow plotting boolean

pandas/plotting/_matplotlib/hist.py

@@ -78,7 +78,7 @@ def _args_adjust(self) -> None:
 
     def _calculate_bins(self, data: DataFrame) -> np.ndarray:
         """Calculate bins given data"""
-        nd_values = data._convert(datetime=True)._get_numeric_data()
+        nd_values = data.infer_objects()._get_numeric_data()
         values = np.ravel(nd_values)
         values = values[~isna(values)]
 

pandas/tests/apply/test_frame_apply.py

@@ -461,7 +461,7 @@ def test_apply_convert_objects():
         }
     )
 
-    result = expected.apply(lambda x: x, axis=1)._convert(datetime=True)
+    result = expected.apply(lambda x: x, axis=1)
     tm.assert_frame_equal(result, expected)
 
 

pandas/tests/io/pytables/test_append.py

@@ -592,7 +592,6 @@ def check_col(key, name, size):
         df_dc.loc[df_dc.index[7:9], "string"] = "bar"
         df_dc["string2"] = "cool"
         df_dc["datetime"] = Timestamp("20010102")
-        df_dc = df_dc._convert(datetime=True)
         df_dc.loc[df_dc.index[3:5], ["A", "B", "datetime"]] = np.nan
 
         _maybe_remove(store, "df_dc")

pandas/tests/io/pytables/test_errors.py

@@ -75,7 +75,7 @@ def test_unimplemented_dtypes_table_columns(setup_path):
     df["obj1"] = "foo"
     df["obj2"] = "bar"
     df["datetime1"] = datetime.date(2001, 1, 2)
-    df = df._consolidate()._convert(datetime=True)
+    df = df._consolidate()
 
     with ensure_clean_store(setup_path) as store:
         # this fails because we have a date in the object block......

pandas/tests/io/pytables/test_put.py

@@ -197,7 +197,7 @@ def test_put_mixed_type(setup_path):
     df["datetime1"] = datetime.datetime(2001, 1, 2, 0, 0)
     df["datetime2"] = datetime.datetime(2001, 1, 3, 0, 0)
     df.loc[df.index[3:6], ["obj1"]] = np.nan
-    df = df._consolidate()._convert(datetime=True)
+    df = df._consolidate()
 
     with ensure_clean_store(setup_path) as store:
         _maybe_remove(store, "df")

pandas/tests/io/pytables/test_store.py

@@ -129,7 +129,7 @@ def test_repr(setup_path):
         df["datetime1"] = datetime.datetime(2001, 1, 2, 0, 0)
         df["datetime2"] = datetime.datetime(2001, 1, 3, 0, 0)
         df.loc[df.index[3:6], ["obj1"]] = np.nan
-        df = df._consolidate()._convert(datetime=True)
+        df = df._consolidate()
 
         with catch_warnings(record=True):
             simplefilter("ignore", pd.errors.PerformanceWarning)
@@ -444,7 +444,7 @@ def test_table_mixed_dtypes(setup_path):
     df["datetime1"] = datetime.datetime(2001, 1, 2, 0, 0)
     df["datetime2"] = datetime.datetime(2001, 1, 3, 0, 0)
     df.loc[df.index[3:6], ["obj1"]] = np.nan
-    df = df._consolidate()._convert(datetime=True)
+    df = df._consolidate()
 
     with ensure_clean_store(setup_path) as store:
         store.append("df1_mixed", df)

pandas/tests/io/test_html.py

@@ -627,7 +627,7 @@ def try_remove_ws(x):
         ]
         dfnew = df.applymap(try_remove_ws).replace(old, new)
         gtnew = ground_truth.applymap(try_remove_ws)
-        converted = dfnew._convert(datetime=True)
+        converted = dfnew
         date_cols = ["Closing Date", "Updated Date"]
         converted[date_cols] = converted[date_cols].apply(to_datetime)
         tm.assert_frame_equal(converted, gtnew)