DOC: Add experimental note to DatetimeArray and TimedeltaArray (pandas-dev#24882)

* DOC: Add experimental note to DatetimeArray and TimedeltaArray

Author: TomAugspurger

doc/source/user_guide/integer_na.rst

@@ -10,6 +10,12 @@ Nullable Integer Data Type
 
 .. versionadded:: 0.24.0
 
+.. note::
+
+   IntegerArray is currently experimental. Its API or implementation may
+   change without warning.
+
+
 In :ref:`missing_data`, we saw that pandas primarily uses ``NaN`` to represent
 missing data. Because ``NaN`` is a float, this forces an array of integers with
 any missing values to become floating point. In some cases, this may not matter

doc/source/whatsnew/v0.24.0.rst

@@ -32,6 +32,11 @@ Optional Integer NA Support
 Pandas has gained the ability to hold integer dtypes with missing values. This long requested feature is enabled through the use of :ref:`extension types <extending.extension-types>`.
 Here is an example of the usage.
 
+.. note::
+
+   IntegerArray is currently experimental. Its API or implementation may
+   change without warning.
+
 We can construct a ``Series`` with the specified dtype. The dtype string ``Int64`` is a pandas ``ExtensionDtype``. Specifying a list or array using the traditional missing value
 marker of ``np.nan`` will infer to integer dtype. The display of the ``Series`` will also use the ``NaN`` to indicate missing values in string outputs. (:issue:`20700`, :issue:`20747`, :issue:`22441`, :issue:`21789`, :issue:`22346`)
 
@@ -213,6 +218,9 @@ from the ``Series``:
    ser.array
    pser.array
 
+These return an instance of :class:`IntervalArray` or :class:`arrays.PeriodArray`,
+the new extension arrays that back interval and period data.
+
 .. warning::
 
    For backwards compatibility, :attr:`Series.values` continues to return

pandas/core/arrays/datetimes.py

@@ -218,6 +218,13 @@ class DatetimeArray(dtl.DatetimeLikeArrayMixin,
 
     .. versionadded:: 0.24.0
 
+    .. warning::
+
+       DatetimeArray is currently experimental, and its API may change
+       without warning. In particular, :attr:`DatetimeArray.dtype` is
+       expected to change to always be an instance of an ``ExtensionDtype``
+       subclass.
+
     Parameters
     ----------
     values : Series, Index, DatetimeArray, ndarray
@@ -511,6 +518,12 @@ def dtype(self):
         """
         The dtype for the DatetimeArray.
 
+        .. warning::
+
+           A future version of pandas will change dtype to never be a
+           ``numpy.dtype``. Instead, :attr:`DatetimeArray.dtype` will
+           always be an instance of an ``ExtensionDtype`` subclass.
+
         Returns
         -------
         numpy.dtype or DatetimeTZDtype

pandas/core/arrays/integer.py

@@ -225,24 +225,57 @@ class IntegerArray(ExtensionArray, ExtensionOpsMixin):
     """
     Array of integer (optional missing) values.
 
+    .. versionadded:: 0.24.0
+
+    .. warning::
+
+       IntegerArray is currently experimental, and its API or internal
+       implementation may change without warning.
+
     We represent an IntegerArray with 2 numpy arrays:
 
     - data: contains a numpy integer array of the appropriate dtype
     - mask: a boolean array holding a mask on the data, True is missing
 
     To construct an IntegerArray from generic array-like input, use
-    ``integer_array`` function instead.
+    :func:`pandas.array` with one of the integer dtypes (see examples).
+
+    See :ref:`integer_na` for more.
 
     Parameters
     ----------
-    values : integer 1D numpy array
-    mask : boolean 1D numpy array
+    values : numpy.ndarray
+        A 1-d integer-dtype array.
+    mask : numpy.ndarray
+        A 1-d boolean-dtype array indicating missing values.
     copy : bool, default False
+        Whether to copy the `values` and `mask`.
 
     Returns
     -------
     IntegerArray
 
+    Examples
+    --------
+    Create an IntegerArray with :func:`pandas.array`.
+
+    >>> int_array = pd.array([1, None, 3], dtype=pd.Int32Dtype())
+    >>> int_array
+    <IntegerArray>
+    [1, NaN, 3]
+    Length: 3, dtype: Int32
+
+    String aliases for the dtypes are also available. They are capitalized.
+
+    >>> pd.array([1, None, 3], dtype='Int32')
+    <IntegerArray>
+    [1, NaN, 3]
+    Length: 3, dtype: Int32
+
+    >>> pd.array([1, None, 3], dtype='UInt16')
+    <IntegerArray>
+    [1, NaN, 3]
+    Length: 3, dtype: UInt16
     """
 
     @cache_readonly

pandas/core/arrays/timedeltas.py

@@ -107,6 +107,29 @@ def wrapper(self, other):
 
 
 class TimedeltaArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps):
+    """
+    Pandas ExtensionArray for timedelta data.
+
+    .. versionadded:: 0.24.0
+
+    .. warning::
+
+       TimedeltaArray is currently experimental, and its API may change
+       without warning. In particular, :attr:`TimedeltaArray.dtype` is
+       expected to change to be an instance of an ``ExtensionDtype``
+       subclass.
+
+    Parameters
+    ----------
+    values : array-like
+        The timedelta data.
+
+    dtype : numpy.dtype
+        Currently, only ``numpy.dtype("timedelta64[ns]")`` is accepted.
+    freq : Offset, optional
+    copy : bool, default False
+        Whether to copy the underlying array of data.
+    """
     _typ = "timedeltaarray"
     _scalar_type = Timedelta
     __array_priority__ = 1000
@@ -128,6 +151,19 @@ def _box_func(self):
 
     @property
     def dtype(self):
+        """
+        The dtype for the TimedeltaArray.
+
+        .. warning::
+
+           A future version of pandas will change dtype to be an instance
+           of a :class:`pandas.api.extensions.ExtensionDtype` subclass,
+           not a ``numpy.dtype``.
+
+        Returns
+        -------
+        numpy.dtype
+        """
         return _TD_DTYPE
 
     # ----------------------------------------------------------------