API: Uses pd.NA in IntegerArray

doc/source/user_guide/integer_na.rst

@@ -15,6 +15,10 @@ Nullable integer data type
    IntegerArray is currently experimental. Its API or implementation may
    change without warning.
 
+.. versionchanged:: 1.0.0
+
+   Now uses :attr:`pandas.NA` as the missing value rather
+   than :attr:`numpy.nan`.
 
 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
@@ -23,6 +27,9 @@ much. But if your integer column is, say, an identifier, casting to float can
 be problematic. Some integers cannot even be represented as floating point
 numbers.
 
+Construction
+------------
+
 Pandas can represent integer data with possibly missing values using
 :class:`arrays.IntegerArray`. This is an :ref:`extension types <extending.extension-types>`
 implemented within pandas.
@@ -39,6 +46,12 @@ NumPy's ``'int64'`` dtype:
 
    pd.array([1, 2, np.nan], dtype="Int64")
 
+All NA-like values are replaced with :attr:`pandas.NA`.
+
+.. ipython:: python
+
+   pd.array([1, 2, np.nan, None, pd.NA], dtype="Int64")
+
 This array can be stored in a :class:`DataFrame` or :class:`Series` like any
 NumPy array.
 
@@ -78,6 +91,9 @@ with the dtype.
    In the future, we may provide an option for :class:`Series` to infer a
    nullable-integer dtype.
 
+Operations
+----------
+
 Operations involving an integer array will behave similar to NumPy arrays.
 Missing values will be propagated, and the data will be coerced to another
 dtype if needed.
@@ -123,3 +139,15 @@ Reduction and groupby operations such as 'sum' work as well.
 
    df.sum()
    df.groupby('B').A.sum()
+
+Scalar NA Value
+---------------
+
+:class:`arrays.IntegerArray` uses :attr:`pandas.NA` as its scalar
+missing value. Slicing a single element that's missing will return
+:attr:`pandas.NA`
+
+.. ipython:: python
+
+   a = pd.array([1, None], dtype="Int64")
+   a[1]

doc/source/whatsnew/v1.0.0.rst

@@ -365,6 +365,64 @@ The following methods now also correctly output values for unobserved categories
 
 As a reminder, you can specify the ``dtype`` to disable all inference.
 
+:class:`arrays.IntegerArray` now uses :attr:`pandas.NA`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:class:`arrays.IntegerArray` now uses :attr:`pandas.NA` rather than
+:attr:`numpy.nan` as its missing value marker (:issue:`29964`).
+
+*pandas 0.25.x*
+
+.. code-block:: python
+
+   >>> a = pd.array([1, 2, None], dtype="Int64")
+   >>> a
+   <IntegerArray>
+   [1, 2, NaN]
+   Length: 3, dtype: Int64
+
+   >>> a[2]
+   nan
+
+*pandas 1.0.0*
+
+.. ipython:: python
+
+   a = pd.array([1, 2, None], dtype="Int64")
+   a[2]
+
+See :ref:`missing_data.NA` for more on the differences between :attr:`pandas.NA`
+and :attr:`numpy.nan`.
+
+:class:`arrays.IntegerArray` comparisons return :class:`arrays.BooleanArray`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Comparison operations on a :class:`arrays.IntegerArray` now returns a
+:class:`arrays.BooleanArray` rather than a NumPy array (:issue:`29964`).
+
+*pandas 0.25.x*
+
+.. code-block:: python
+
+   >>> a = pd.array([1, 2, None], dtype="Int64")
+   >>> a
+   <IntegerArray>
+   [1, 2, NaN]
+   Length: 3, dtype: Int64
+
+   >>> a > 1
+   array([False,  True, False])
+
+*pandas 1.0.0*
+
+.. ipython:: python
+
+   a = pd.array([1, 2, None], dtype="Int64")
+   a > 1
+
+Note that missing values now propagate, rather than always comparing unequal
+like :attr:`numpy.nan`. See :ref:`missing_data.NA` for more.
+
 By default :meth:`Categorical.min` now returns the minimum instead of np.nan
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

pandas/core/arrays/boolean.py

@@ -730,7 +730,6 @@ def all(self, skipna: bool = True, **kwargs):
     @classmethod
     def _create_logical_method(cls, op):
         def logical_method(self, other):
-
             if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
                 # Rely on pandas to unbox and dispatch to us.
                 return NotImplemented
@@ -777,8 +776,11 @@ def logical_method(self, other):
     @classmethod
     def _create_comparison_method(cls, op):
         def cmp_method(self, other):
+            from pandas.arrays import IntegerArray
 
-            if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
+            if isinstance(
+                other, (ABCDataFrame, ABCSeries, ABCIndexClass, IntegerArray)
+            ):
                 # Rely on pandas to unbox and dispatch to us.
                 return NotImplemented