DEPR: Deprecate tshift and integrate it to shift

doc/source/user_guide/timeseries.rst

@@ -516,7 +516,7 @@ The ``DatetimeIndex`` class contains many time series related optimizations:
 * A large range of dates for various offsets are pre-computed and cached
   under the hood in order to make generating subsequent date ranges very fast
   (just have to grab a slice).
-* Fast shifting using the ``shift`` and ``tshift`` method on pandas objects.
+* Fast shifting using the ``shift`` method on pandas objects.
 * Unioning of overlapping ``DatetimeIndex`` objects with the same frequency is
   very fast (important for fast data alignment).
 * Quick access to date fields via properties such as ``year``, ``month``, etc.
@@ -1462,23 +1462,19 @@ the pandas objects.
 
 The ``shift`` method accepts an ``freq`` argument which can accept a
 ``DateOffset`` class or other ``timedelta``-like object or also an
-:ref:`offset alias <timeseries.offset_aliases>`:
+:ref:`offset alias <timeseries.offset_aliases>`.
+
+When ``freq`` is specified, ``shift`` method changes all the dates in the index
+rather than changing the alignment of the data and the index:
 
 .. ipython:: python
 
+   ts.shift(5, freq='D')
    ts.shift(5, freq=pd.offsets.BDay())
    ts.shift(5, freq='BM')
 
-Rather than changing the alignment of the data and the index, ``DataFrame`` and
-``Series`` objects also have a :meth:`~Series.tshift` convenience method that
-changes all the dates in the index by a specified number of offsets:
-
-.. ipython:: python
-
-   ts.tshift(5, freq='D')
-
-Note that with ``tshift``, the leading entry is no longer NaN because the data
-is not being realigned.
+Note that with when ``freq`` is specified, the leading entry is no longer NaN
+because the data is not being realigned.
 
 Frequency conversion
 ~~~~~~~~~~~~~~~~~~~~

doc/source/whatsnew/v1.1.0.rst

@@ -737,6 +737,7 @@ Deprecations
 - :meth:`DatetimeIndex.week` and `DatetimeIndex.weekofyear` are deprecated and will be removed in a future version, use :meth:`DatetimeIndex.isocalendar().week` instead (:issue:`33595`)
 - :meth:`DatetimeArray.week` and `DatetimeArray.weekofyear` are deprecated and will be removed in a future version, use :meth:`DatetimeArray.isocalendar().week` instead (:issue:`33595`)
 - :meth:`DateOffset.__call__` is deprecated and will be removed in a future version, use ``offset + other`` instead (:issue:`34171`)
+- :meth:`DataFrame.tshift` and :meth:`Series.tshift` are deprecated and will be removed in a future version, use :meth:`DataFrame.shift` and :meth:`Series.shift` instead (:issue:`11631`)
 - Indexing an :class:`Index` object with a float key is deprecated, and will
   raise an ``IndexError`` in the future. You can manually convert to an integer key
   instead (:issue:`34191`).

pandas/core/generic.py

@@ -188,7 +188,7 @@ class NDFrame(PandasObject, SelectionMixin, indexing.IndexingMixin):
     ]
     _internal_names_set: Set[str] = set(_internal_names)
     _accessors: Set[str] = set()
-    _deprecations: FrozenSet[str] = frozenset(["get_values"])
+    _deprecations: FrozenSet[str] = frozenset(["get_values", "tshift"])
     _metadata: List[str] = []
     _is_copy = None
     _mgr: BlockManager
@@ -9130,7 +9130,9 @@ def shift(
         When `freq` is not passed, shift the index without realigning the data.
         If `freq` is passed (in this case, the index must be date or datetime,
         or it will raise a `NotImplementedError`), the index will be
-        increased using the periods and the `freq`.
+        increased using the periods and the `freq`. `freq` can be inferred
+        when specified as "infer" as long as either freq or inferred_freq
+        attribute is set in the index.
 
         Parameters
         ----------
@@ -9141,6 +9143,9 @@ def shift(
             If `freq` is specified then the index values are shifted but the
             data is not realigned. That is, use `freq` if you would like to
             extend the index when shifting and preserve the original data.
+            If `freq` is specified as "infer" then it will be inferred from
+            the freq or inferred_freq attributes of the index. If neither of
+            those attributes exist, a ValueError is thrown
         axis : {{0 or 'index', 1 or 'columns', None}}, default None
             Shift direction.
         fill_value : object, optional
@@ -9150,7 +9155,7 @@ def shift(
             For datetime, timedelta, or period data, etc. :attr:`NaT` is used.
             For extension dtypes, ``self.dtype.na_value`` is used.
 
-            .. versionchanged:: 0.24.0
+            .. versionchanged:: 1.1.0
 
         Returns
         -------
@@ -9167,46 +9172,99 @@ def shift(
 
         Examples
         --------
-        >>> df = pd.DataFrame({{'Col1': [10, 20, 15, 30, 45],
-        ...                    'Col2': [13, 23, 18, 33, 48],
-        ...                    'Col3': [17, 27, 22, 37, 52]}})
+        >>> df = pd.DataFrame({{"Col1": [10, 20, 15, 30, 45],
+        ...                    "Col2": [13, 23, 18, 33, 48],
+        ...                    "Col3": [17, 27, 22, 37, 52]}},
+        ...                   index=pd.date_range("2020-01-01", "2020-01-05"))
+        >>> df
+                    Col1  Col2  Col3
+        2020-01-01    10    13    17
+        2020-01-02    20    23    27
+        2020-01-03    15    18    22
+        2020-01-04    30    33    37
+        2020-01-05    45    48    52
 
         >>> df.shift(periods=3)
-           Col1  Col2  Col3
-        0   NaN   NaN   NaN
-        1   NaN   NaN   NaN
-        2   NaN   NaN   NaN
-        3  10.0  13.0  17.0
-        4  20.0  23.0  27.0
-
-        >>> df.shift(periods=1, axis='columns')
-           Col1  Col2  Col3
-        0   NaN  10.0  13.0
-        1   NaN  20.0  23.0
-        2   NaN  15.0  18.0
-        3   NaN  30.0  33.0
-        4   NaN  45.0  48.0
+                    Col1  Col2  Col3
+        2020-01-01   NaN   NaN   NaN
+        2020-01-02   NaN   NaN   NaN
+        2020-01-03   NaN   NaN   NaN
+        2020-01-04  10.0  13.0  17.0
+        2020-01-05  20.0  23.0  27.0
+
+        >>> df.shift(periods=1, axis="columns")
+                    Col1  Col2  Col3
+        2020-01-01   NaN  10.0  13.0
+        2020-01-02   NaN  20.0  23.0
+        2020-01-03   NaN  15.0  18.0
+        2020-01-04   NaN  30.0  33.0
+        2020-01-05   NaN  45.0  48.0
 
         >>> df.shift(periods=3, fill_value=0)
-           Col1  Col2  Col3
-        0     0     0     0
-        1     0     0     0
-        2     0     0     0
-        3    10    13    17
-        4    20    23    27
+                    Col1  Col2  Col3
+        2020-01-01     0     0     0
+        2020-01-02     0     0     0
+        2020-01-03     0     0     0
+        2020-01-04    10    13    17
+        2020-01-05    20    23    27
+
+        >>> df.shift(periods=3, freq="D")
+                    Col1  Col2  Col3
+        2020-01-04    10    13    17
+        2020-01-05    20    23    27
+        2020-01-06    15    18    22
+        2020-01-07    30    33    37
+        2020-01-08    45    48    52
+
+        >>> df.shift(periods=3, freq="infer")
+                    Col1  Col2  Col3
+        2020-01-04    10    13    17
+        2020-01-05    20    23    27
+        2020-01-06    15    18    22
+        2020-01-07    30    33    37
+        2020-01-08    45    48    52
         """
         if periods == 0:
             return self.copy()
 
-        block_axis = self._get_block_manager_axis(axis)
         if freq is None:
+            # when freq is None, data is shifted, index is not
+            block_axis = self._get_block_manager_axis(axis)
             new_data = self._mgr.shift(
                 periods=periods, axis=block_axis, fill_value=fill_value
             )
+            return self._constructor(new_data).__finalize__(self, method="shift")
+
+        # when freq is given, index is shifted, data is not
+        index = self._get_axis(axis)
+
+        if freq == "infer":
+            freq = getattr(index, "freq", None)
+
+            if freq is None:
+                freq = getattr(index, "inferred_freq", None)
+
+            if freq is None:
+                msg = "Freq was not set in the index hence cannot be inferred"
+                raise ValueError(msg)
+
+        elif isinstance(freq, str):
+            freq = to_offset(freq)
+
+        if isinstance(index, PeriodIndex):
+            orig_freq = to_offset(index.freq)
+            if freq != orig_freq:
+                assert orig_freq is not None  # for mypy
+                raise ValueError(
+                    f"Given freq {freq.rule_code} does not match "
+                    f"PeriodIndex freq {orig_freq.rule_code}"
+                )
+            new_ax = index.shift(periods)
         else:
-            return self.tshift(periods, freq)
+            new_ax = index.shift(periods, freq)
 
-        return self._constructor(new_data).__finalize__(self, method="shift")
+        result = self.set_axis(new_ax, axis)
+        return result.__finalize__(self, method="shift")
 
     def slice_shift(self: FrameOrSeries, periods: int = 1, axis=0) -> FrameOrSeries:
         """
@@ -9251,6 +9309,9 @@ def tshift(
         """
         Shift the time index, using the index's frequency if available.
 
+        .. deprecated:: 1.1.0
+            Use `shift` instead.
+
         Parameters
         ----------
         periods : int
@@ -9271,39 +9332,19 @@ def tshift(
         attributes of the index. If neither of those attributes exist, a
         ValueError is thrown
         """
-        index = self._get_axis(axis)
-        if freq is None:
-            freq = getattr(index, "freq", None)
-
-        if freq is None:
-            freq = getattr(index, "inferred_freq", None)
+        warnings.warn(
+            (
+                "tshift is deprecated and will be removed in a future version. "
+                "Please use shift instead."
+            ),
+            FutureWarning,
+            stacklevel=2,
+        )
 
         if freq is None:
-            msg = "Freq was not given and was not set in the index"
-            raise ValueError(msg)
-
-        if periods == 0:
-            return self
-
-        if isinstance(freq, str):
-            freq = to_offset(freq)
-
-        axis = self._get_axis_number(axis)
-        if isinstance(index, PeriodIndex):
-            orig_freq = to_offset(index.freq)
-            if freq != orig_freq:
-                assert orig_freq is not None  # for mypy
-                raise ValueError(
-                    f"Given freq {freq.rule_code} does not match "
-                    f"PeriodIndex freq {orig_freq.rule_code}"
-                )
-            new_ax = index.shift(periods)
-        else:
-            new_ax = index.shift(periods, freq)
+            freq = "infer"
 
-        result = self.copy()
-        result.set_axis(new_ax, axis, inplace=True)
-        return result.__finalize__(self, method="tshift")
+        return self.shift(periods, freq, axis)
 
     def truncate(
         self: FrameOrSeries, before=None, after=None, axis=None, copy: bool_t = True

pandas/tests/frame/methods/test_shift.py

@@ -145,7 +145,10 @@ def test_shift_duplicate_columns(self):
         tm.assert_frame_equal(shifted[0], shifted[1])
         tm.assert_frame_equal(shifted[0], shifted[2])
 
+    @pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
     def test_tshift(self, datetime_frame):
+        # TODO: remove this test when tshift deprecation is enforced
+
         # PeriodIndex
         ps = tm.makePeriodFrame()
         shifted = ps.tshift(1)
@@ -186,10 +189,60 @@ def test_tshift(self, datetime_frame):
         tm.assert_frame_equal(unshifted, inferred_ts)
 
         no_freq = datetime_frame.iloc[[0, 5, 7], :]
-        msg = "Freq was not given and was not set in the index"
+        msg = "Freq was not set in the index hence cannot be inferred"
         with pytest.raises(ValueError, match=msg):
             no_freq.tshift()
 
+    def test_tshift_deprecated(self, datetime_frame):
+        # GH#11631
+        with tm.assert_produces_warning(FutureWarning):
+            datetime_frame.tshift()
+
+    def test_shift_with_freq(self, datetime_frame):
+        # PeriodIndex
+        ps = tm.makePeriodFrame()
+        shifted = ps.shift(1, freq="infer")
+        unshifted = shifted.shift(-1, freq="infer")
+
+        tm.assert_frame_equal(unshifted, ps)
+
+        shifted2 = ps.shift(freq="B")
+        tm.assert_frame_equal(shifted, shifted2)
+
+        shifted3 = ps.shift(freq=offsets.BDay())
+        tm.assert_frame_equal(shifted, shifted3)
+
+        with pytest.raises(ValueError, match="does not match"):
+            ps.shift(freq="M")
+
+        # DatetimeIndex
+        shifted = datetime_frame.shift(1, freq="infer")
+        unshifted = shifted.shift(-1, freq="infer")
+
+        tm.assert_frame_equal(datetime_frame, unshifted)
+
+        shifted2 = datetime_frame.shift(freq=datetime_frame.index.freq)
+        tm.assert_frame_equal(shifted, shifted2)
+
+        inferred_ts = DataFrame(
+            datetime_frame.values,
+            Index(np.asarray(datetime_frame.index)),
+            columns=datetime_frame.columns,
+        )
+        shifted = inferred_ts.shift(1, freq="infer")
+
+        expected = datetime_frame.shift(1, freq="infer")
+        expected.index = expected.index._with_freq(None)
+        tm.assert_frame_equal(shifted, expected)
+
+        unshifted = shifted.shift(-1, freq="infer")
+        tm.assert_frame_equal(unshifted, inferred_ts)
+
+        no_freq = datetime_frame.iloc[[0, 5, 7], :]
+        msg = "Freq was not set in the index hence cannot be inferred"
+        with pytest.raises(ValueError, match=msg):
+            no_freq.shift(freq="infer")
+
     def test_shift_dt64values_int_fill_deprecated(self):
         # GH#31971
         ser = pd.Series([pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")])

pandas/tests/generic/test_finalize.py

@@ -438,11 +438,21 @@
     (pd.DataFrame, frame_data, operator.methodcaller("mask", np.array([[True]]))),
     (pd.Series, ([1, 2],), operator.methodcaller("slice_shift")),
     (pd.DataFrame, frame_data, operator.methodcaller("slice_shift")),
-    (pd.Series, (1, pd.date_range("2000", periods=4)), operator.methodcaller("tshift")),
-    (
-        pd.DataFrame,
-        ({"A": [1, 1, 1, 1]}, pd.date_range("2000", periods=4)),
-        operator.methodcaller("tshift"),
+    pytest.param(
+        (
+            pd.Series,
+            (1, pd.date_range("2000", periods=4)),
+            operator.methodcaller("tshift"),
+        ),
+        marks=pytest.mark.filterwarnings("ignore::FutureWarning"),
+    ),
+    pytest.param(
+        (
+            pd.DataFrame,
+            ({"A": [1, 1, 1, 1]}, pd.date_range("2000", periods=4)),
+            operator.methodcaller("tshift"),
+        ),
+        marks=pytest.mark.filterwarnings("ignore::FutureWarning"),
     ),
     (pd.Series, ([1, 2],), operator.methodcaller("truncate", before=0)),
     (pd.DataFrame, frame_data, operator.methodcaller("truncate", before=0)),

pandas/tests/groupby/test_groupby.py

@@ -1979,6 +1979,7 @@ def test_bool_aggs_dup_column_labels(bool_agg_func):
 @pytest.mark.parametrize(
     "idx", [pd.Index(["a", "a"]), pd.MultiIndex.from_tuples((("a", "a"), ("a", "a")))]
 )
+@pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
 def test_dup_labels_output_shape(groupby_func, idx):
     if groupby_func in {"size", "ngroup", "cumcount"}:
         pytest.skip("Not applicable")

pandas/tests/groupby/test_groupby_subclass.py

@@ -14,6 +14,7 @@
         tm.SubclassedSeries(np.arange(0, 10), name="A"),
     ],
 )
+@pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
 def test_groupby_preserves_subclass(obj, groupby_func):
     # GH28330 -- preserve subclass through groupby operations
 

pandas/tests/groupby/test_whitelist.py

@@ -339,6 +339,7 @@ def test_groupby_function_rename(mframe):
         assert f.__name__ == name
 
 
+@pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
 def test_groupby_selection_with_methods(df):
     # some methods which require DatetimeIndex
     rng = date_range("2014", periods=len(df))