ENH: NDFrame.axis_ops

doc/source/user_guide/10min.rst

@@ -627,14 +627,14 @@ financial applications. See the :ref:`Time Series section <timeseries>`.
    rng = pd.date_range("3/6/2012 00:00", periods=5, freq="D")
    ts = pd.Series(np.random.randn(len(rng)), rng)
    ts
-   ts_utc = ts.tz_localize("UTC")
+   ts_utc = ts.axis_ops.tz_localize("UTC")
    ts_utc
 
 :meth:`Series.tz_convert` converts a timezones aware time series to another time zone:
 
 .. ipython:: python
 
-   ts_utc.tz_convert("US/Eastern")
+   ts_utc.axis_ops.tz_convert("US/Eastern")
 
 Converting between time span representations:
 
@@ -643,9 +643,9 @@ Converting between time span representations:
    rng = pd.date_range("1/1/2012", periods=5, freq="M")
    ts = pd.Series(np.random.randn(len(rng)), index=rng)
    ts
-   ps = ts.to_period()
+   ps = ts.axis_ops.to_period()
    ps
-   ps.to_timestamp()
+   ps.axis_ops.to_timestamp()
 
 Converting between period and timestamp enables some convenient arithmetic
 functions to be used. In the following example, we convert a quarterly

doc/source/user_guide/advanced.rst

@@ -501,27 +501,27 @@ values across a level. For instance:
    df2_aligned
 
 
-Swapping levels with ``swaplevel``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Swapping levels with ``axis_ops.swap_level``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The :meth:`~MultiIndex.swaplevel` method can switch the order of two levels:
 
 .. ipython:: python
 
    df[:5]
-   df[:5].swaplevel(0, 1, axis=0)
+   df[:5].axis_ops.swap_level(0, 1, axis=0)
 
 .. _advanced.reorderlevels:
 
-Reordering levels with ``reorder_levels``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Reordering levels with ``axis_ops.reorder_levels``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The :meth:`~MultiIndex.reorder_levels` method generalizes the ``swaplevel``
 method, allowing you to permute the hierarchical index levels in one step:
 
 .. ipython:: python
 
-   df[:5].reorder_levels([1, 0], axis=0)
+   df[:5].axis_ops.reorder_levels([1, 0], axis=0)
 
 .. _advanced.index_names:
 

doc/source/user_guide/timeseries.rst

@@ -647,7 +647,7 @@ We are stopping on the included end-point as it is part of the index:
    dft2
    dft2.loc["2013-01-05"]
    idx = pd.IndexSlice
-   dft2 = dft2.swaplevel(0, 1).sort_index()
+   dft2 = dft2.axis_ops.swap_level(0, 1).sort_index()
    dft2.loc[idx[:, "2013-01-05"], :]
 
 Slicing with string indexing also honors UTC offset.
@@ -2218,8 +2218,8 @@ frequencies ``Q-JAN`` through ``Q-DEC``.
 Converting between representations
 ----------------------------------
 
-Timestamped data can be converted to PeriodIndex-ed data using ``to_period``
-and vice-versa using ``to_timestamp``:
+Timestamped data can be converted to PeriodIndex-ed data using ``axis_ops.to_period``
+and vice-versa using ``axis_ops.to_timestamp``:
 
 .. ipython:: python
 
@@ -2229,18 +2229,18 @@ and vice-versa using ``to_timestamp``:
 
    ts
 
-   ps = ts.to_period()
+   ps = ts.axis_ops.to_period()
 
    ps
 
-   ps.to_timestamp()
+   ps.axis_ops.to_timestamp()
 
 Remember that 's' and 'e' can be used to return the timestamps at the start or
 end of the period:
 
 .. ipython:: python
 
-   ps.to_timestamp("D", how="s")
+   ps.axis_ops.to_timestamp("D", how="s")
 
 Converting between period and timestamp enables some convenient arithmetic
 functions to be used. In the following example, we convert a quarterly
@@ -2460,8 +2460,8 @@ Operations between :class:`Series` in different time zones will yield UTC
 .. ipython:: python
 
    ts_utc = pd.Series(range(3), pd.date_range("20130101", periods=3, tz="UTC"))
-   eastern = ts_utc.tz_convert("US/Eastern")
-   berlin = ts_utc.tz_convert("Europe/Berlin")
+   eastern = ts_utc.axis_ops.tz_convert("US/Eastern")
+   berlin = ts_utc.axis_ops.tz_convert("Europe/Berlin")
    result = eastern + berlin
    result
    result.index

doc/source/whatsnew/v0.18.1.rst

@@ -190,6 +190,7 @@ Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiI
 On other levels
 
 .. ipython:: python
+   :okwarning:
 
    idx = pd.IndexSlice
    dft2 = dft2.swaplevel(0, 1).sort_index()

doc/source/whatsnew/v2.1.0.rst

@@ -84,6 +84,7 @@ Other enhancements
 - :meth:`arrays.SparseArray.map` now supports ``na_action`` (:issue:`52096`).
 - Add :meth:`diff()` and :meth:`round()` for :class:`Index` (:issue:`19708`)
 - Add dtype of categories to ``repr`` information of :class:`CategoricalDtype` (:issue:`52179`)
+- Added :meth:`Series.axis_ops` and :meth:`DataFrame.axis_ops` accessor for methods that operate on the object axes but not the data (:issue:`52461`)
 - Added to the escape mode "latex-math" preserving without escaping all characters between "\(" and "\)" in formatter (:issue:`51903`)
 - Adding ``engine_kwargs`` parameter to :meth:`DataFrame.read_excel` (:issue:`52214`)
 - Classes that are useful for type-hinting have been added to the public API in the new submodule ``pandas.api.typing`` (:issue:`48577`)
@@ -264,6 +265,7 @@ Deprecations
 - Deprecated unused "closed" and "normalize" keywords in the :class:`DatetimeIndex` constructor (:issue:`52628`)
 - Deprecated unused "closed" keyword in the :class:`TimedeltaIndex` constructor (:issue:`52628`)
 - Deprecated logical operation between two non boolean :class:`Series` with different indexes always coercing the result to bool dtype. In a future version, this will maintain the return type of the inputs. (:issue:`52500`, :issue:`52538`)
+- Deprecated :meth:`Series.tz_localize`, :meth:`Series.tz_convert`, :meth:`Series.to_timestamp`, :meth:`Series.to_period`, :meth:`Series.swaplevel`, :meth:`Series.reorder_level` and their :class:`DataFrame` counterparts, use ``obj.axis_ops.to_period``, ``obj.axis_ops.swap_level``, etc instead (:issue:`52461`)
 - Deprecated allowing ``downcast`` keyword other than ``None``, ``False``, "infer", or a dict with these as values in :meth:`Series.fillna`, :meth:`DataFrame.fillna` (:issue:`40988`)
 - Deprecated allowing arbitrary ``fill_value`` in :class:`SparseDtype`, in a future version the ``fill_value`` will need to be compatible with the ``dtype.subtype``, either a scalar that can be held by that subtype or ``NaN`` for integer or bool subtypes (:issue:`23124`)
 - Deprecated behavior of :func:`assert_series_equal` and :func:`assert_frame_equal` considering NA-like values (e.g. ``NaN`` vs ``None`` as equivalent) (:issue:`52081`)

pandas/conftest.py

@@ -145,6 +145,11 @@ def pytest_collection_modifyitems(items, config) -> None:
             "(Series|DataFrame).bool is now deprecated and will be removed "
             "in future version of pandas",
         ),
+        ("DataFrame.to_timestamp", "DataFrame.to_timestamp is deprecated"),
+        ("Series.swaplevel", "Series.swaplevel is deprecated"),
+        ("Series.to_timestamp", "Series.to_timestamp is deprecated"),
+        ("Series.to_period", "Series.to_period is deprecated"),
+        ("Series.reorder_levels", "Series.reorder_levels is deprecated"),
     ]
 
     for item in items: