BUG+DEPR: undeprecate item, fix dt64/td64 output type (pandas-dev#30175)

doc/source/whatsnew/v1.0.0.rst

@@ -486,6 +486,7 @@ Documentation Improvements
 Deprecations
 ~~~~~~~~~~~~
 
+- :meth:`Series.item` and :meth:`Index.item` have been _undeprecated_ (:issue:`29250`)
 - ``Index.set_value`` has been deprecated. For a given index ``idx``, array ``arr``,
   value in ``idx`` of ``idx_val`` and a new value of ``val``, ``idx.set_value(arr, idx_val, val)``
   is equivalent to ``arr[idx.get_loc(idx_val)] = val``, which should be used instead (:issue:`28621`).
@@ -703,6 +704,8 @@ Datetimelike
 - Bug in :attr:`Timestamp.resolution` being a property instead of a class attribute (:issue:`29910`)
 - Bug in :func:`pandas.to_datetime` when called with ``None`` raising ``TypeError`` instead of returning ``NaT`` (:issue:`30011`)
 - Bug in :func:`pandas.to_datetime` failing for `deques` when using ``cache=True`` (the default) (:issue:`29403`)
+- Bug in :meth:`Series.item` with ``datetime64`` or ``timedelta64`` dtype, :meth:`DatetimeIndex.item`, and :meth:`TimedeltaIndex.item` returning an integer instead of a :class:`Timestamp` or :class:`Timedelta` (:issue:`30175`)
+-
 
 Timedelta
 ^^^^^^^^^

pandas/core/base.py

@@ -5,7 +5,6 @@
 from collections import OrderedDict
 import textwrap
 from typing import Dict, FrozenSet, List, Optional
-import warnings
 
 import numpy as np
 
@@ -26,6 +25,7 @@
     is_object_dtype,
     is_scalar,
     is_timedelta64_ns_dtype,
+    needs_i8_conversion,
 )
 from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
 from pandas.core.dtypes.missing import isna
@@ -659,19 +659,27 @@ def item(self):
         """
         Return the first element of the underlying data as a python scalar.
 
-        .. deprecated:: 0.25.0
-
         Returns
         -------
         scalar
             The first element of %(klass)s.
+
+        Raises
+        ------
+        ValueError
+            If the data is not length-1.
         """
-        warnings.warn(
-            "`item` has been deprecated and will be removed in a future version",
-            FutureWarning,
-            stacklevel=2,
-        )
-        return self.values.item()
+        if not (
+            is_extension_array_dtype(self.dtype) or needs_i8_conversion(self.dtype)
+        ):
+            # numpy returns ints instead of datetime64/timedelta64 objects,
+            #  which we need to wrap in Timestamp/Timedelta/Period regardless.
+            return self.values.item()
+
+        if len(self) == 1:
+            return next(iter(self))
+        else:
+            raise ValueError("can only convert an array of size 1 to a Python scalar")
 
     @property
     def nbytes(self):

pandas/core/indexes/period.py

@@ -1,5 +1,4 @@
 from datetime import datetime, timedelta
-import warnings
 import weakref
 
 import numpy as np
@@ -862,27 +861,6 @@ def __setstate__(self, state):
 
     _unpickle_compat = __setstate__
 
-    def item(self):
-        """
-        Return the first element of the underlying data as a python
-        scalar
-
-        .. deprecated:: 0.25.0
-
-        """
-        warnings.warn(
-            "`item` has been deprecated and will be removed in a future version",
-            FutureWarning,
-            stacklevel=2,
-        )
-        # TODO(DatetimeArray): remove
-        if len(self) == 1:
-            return self[0]
-        else:
-            # TODO: is this still necessary?
-            # copy numpy's message here because Py26 raises an IndexError
-            raise ValueError("can only convert an array of size 1 to a Python scalar")
-
     def memory_usage(self, deep=False):
         result = super().memory_usage(deep=deep)
         if hasattr(self, "_cache") and "_int64index" in self._cache:

pandas/tests/base/test_ops.py

@@ -236,15 +236,13 @@ def test_ndarray_compat_properties(self):
                 assert not hasattr(o, p)
 
             with pytest.raises(ValueError):
-                with tm.assert_produces_warning(FutureWarning):
-                    o.item()  # len > 1
+                o.item()  # len > 1
 
             assert o.ndim == 1
             assert o.size == len(o)
 
-        with tm.assert_produces_warning(FutureWarning):
-            assert Index([1]).item() == 1
-            assert Series([1]).item() == 1
+        assert Index([1]).item() == 1
+        assert Series([1]).item() == 1
 
     def test_value_counts_unique_nunique(self):
         for orig in self.objs:

pandas/tests/series/test_api.py

@@ -12,13 +12,14 @@
     DatetimeIndex,
     Index,
     Series,
+    Timedelta,
     TimedeltaIndex,
+    Timestamp,
     date_range,
     period_range,
     timedelta_range,
 )
 from pandas.core.arrays import PeriodArray
-from pandas.core.indexes.datetimes import Timestamp
 import pandas.util.testing as tm
 
 import pandas.io.formats.printing as printing
@@ -398,6 +399,50 @@ def test_numpy_unique(self, datetime_series):
         # it works!
         np.unique(datetime_series)
 
+    def test_item(self):
+        s = Series([1])
+        result = s.item()
+        assert result == 1
+        assert result == s.iloc[0]
+        assert isinstance(result, int)  # i.e. not np.int64
+
+        ser = Series([0.5], index=[3])
+        result = ser.item()
+        assert isinstance(result, float)
+        assert result == 0.5
+
+        ser = Series([1, 2])
+        msg = "can only convert an array of size 1"
+        with pytest.raises(ValueError, match=msg):
+            ser.item()
+
+        dti = pd.date_range("2016-01-01", periods=2)
+        with pytest.raises(ValueError, match=msg):
+            dti.item()
+        with pytest.raises(ValueError, match=msg):
+            Series(dti).item()
+
+        val = dti[:1].item()
+        assert isinstance(val, Timestamp)
+        val = Series(dti)[:1].item()
+        assert isinstance(val, Timestamp)
+
+        tdi = dti - dti
+        with pytest.raises(ValueError, match=msg):
+            tdi.item()
+        with pytest.raises(ValueError, match=msg):
+            Series(tdi).item()
+
+        val = tdi[:1].item()
+        assert isinstance(val, Timedelta)
+        val = Series(tdi)[:1].item()
+        assert isinstance(val, Timedelta)
+
+        # Case where ser[0] would not work
+        ser = Series(dti, index=[5, 6])
+        val = ser[:1].item()
+        assert val == dti[0]
+
     def test_ndarray_compat(self):
 
         # test numpy compat with Series as sub-class of NDFrame
@@ -414,13 +459,6 @@ def f(x):
         expected = tsdf.max()
         tm.assert_series_equal(result, expected)
 
-        # .item()
-        with tm.assert_produces_warning(FutureWarning):
-            s = Series([1])
-            result = s.item()
-            assert result == 1
-            assert s.item() == s.iloc[0]
-
         # using an ndarray like function
         s = Series(np.random.randn(10))
         result = Series(np.ones_like(s))