API: Remove IntervalArray from top-level

doc/source/reference/arrays.rst

@@ -288,12 +288,12 @@ Properties
    Interval.overlaps
    Interval.right
 
-A collection of intervals may be stored in an :class:`IntervalArray`.
+A collection of intervals may be stored in an :class:`arrays.IntervalArray`.
 
 .. autosummary::
    :toctree: api/
 
-   IntervalArray
+   arrays.IntervalArray
    IntervalDtype
 
 .. _api.arrays.integer_na:

doc/source/whatsnew/v0.24.0.rst

@@ -225,7 +225,7 @@ from the ``Series``:
    ser.array
    pser.array
 
-These return an instance of :class:`IntervalArray` or :class:`arrays.PeriodArray`,
+These return an instance of :class:`arrays.IntervalArray` or :class:`arrays.PeriodArray`,
 the new extension arrays that back interval and period data.
 
 .. warning::
@@ -411,7 +411,7 @@ Other Enhancements
 - :meth:`Categorical.from_codes` now can take a ``dtype`` parameter as an alternative to passing ``categories`` and ``ordered`` (:issue:`24398`).
 - New attribute ``__git_version__`` will return git commit sha of current build (:issue:`21295`).
 - Compatibility with Matplotlib 3.0 (:issue:`22790`).
-- Added :meth:`Interval.overlaps`, :meth:`IntervalArray.overlaps`, and :meth:`IntervalIndex.overlaps` for determining overlaps between interval-like objects (:issue:`21998`)
+- Added :meth:`Interval.overlaps`, :meth:`arrays.IntervalArray.overlaps`, and :meth:`IntervalIndex.overlaps` for determining overlaps between interval-like objects (:issue:`21998`)
 - :func:`read_fwf` now accepts keyword ``infer_nrows`` (:issue:`15138`).
 - :func:`~DataFrame.to_parquet` now supports writing a ``DataFrame`` as a directory of parquet files partitioned by a subset of the columns when ``engine = 'pyarrow'`` (:issue:`23283`)
 - :meth:`Timestamp.tz_localize`, :meth:`DatetimeIndex.tz_localize`, and :meth:`Series.tz_localize` have gained the ``nonexistent`` argument for alternative handling of nonexistent times. See :ref:`timeseries.timezone_nonexistent` (:issue:`8917`, :issue:`24466`)

pandas/core/api.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 
-from pandas.core.arrays import IntervalArray
 from pandas.core.arrays.integer import (
     Int8Dtype,
     Int16Dtype,

pandas/core/arrays/array_.py

@@ -50,7 +50,7 @@ def array(data,         # type: Sequence[object]
         ============================== =====================================
         Scalar Type                    Array Type
         ============================== =====================================
-        :class:`pandas.Interval`       :class:`pandas.IntervalArray`
+        :class:`pandas.Interval`       :class:`pandas.arrays.IntervalArray`
         :class:`pandas.Period`         :class:`pandas.arrays.PeriodArray`
         :class:`datetime.datetime`     :class:`pandas.arrays.DatetimeArray`
         :class:`datetime.timedelta`    :class:`pandas.arrays.TimedeltaArray`

pandas/core/arrays/interval.py

@@ -32,6 +32,7 @@
 
 _shared_docs_kwargs = dict(
     klass='IntervalArray',
+    qualname='arrays.IntervalArray',
     name=''
 )
 
@@ -115,7 +116,7 @@
     A new ``IntervalArray`` can be constructed directly from an array-like of
     ``Interval`` objects:
 
-    >>> pd.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
+    >>> pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
     IntervalArray([(0, 1], (1, 5]],
                   closed='right',
                   dtype='interval[int64]')
@@ -248,8 +249,8 @@ def _from_factorized(cls, values, original):
 
     Examples
     --------
-    >>> pd.%(klass)s.from_breaks([0, 1, 2, 3])
-    %(klass)s([(0, 1], (1, 2], (2, 3]]
+    >>> pd.%(qualname)s.from_breaks([0, 1, 2, 3])
+    %(klass)s([(0, 1], (1, 2], (2, 3]],
                   closed='right',
                   dtype='interval[int64]')
     """
@@ -311,7 +312,7 @@ def from_breaks(cls, breaks, closed='right', copy=False, dtype=None):
         Examples
         --------
         >>> %(klass)s.from_arrays([0, 1, 2], [1, 2, 3])
-        %(klass)s([(0, 1], (1, 2], (2, 3]]
+        %(klass)s([(0, 1], (1, 2], (2, 3]],
                      closed='right',
                      dtype='interval[int64]')
         """
@@ -354,16 +355,16 @@ def from_arrays(cls, left, right, closed='right', copy=False, dtype=None):
 
     Examples
     --------
-    >>> pd.%(klass)s.from_intervals([pd.Interval(0, 1),
+    >>> pd.%(qualname)s.from_intervals([pd.Interval(0, 1),
     ...                                  pd.Interval(1, 2)])
-    %(klass)s([(0, 1], (1, 2]]
+    %(klass)s([(0, 1], (1, 2]],
                   closed='right', dtype='interval[int64]')
 
     The generic Index constructor work identically when it infers an array
     of all intervals:
 
     >>> pd.Index([pd.Interval(0, 1), pd.Interval(1, 2)])
-    %(klass)s([(0, 1], (1, 2]]
+    %(klass)s([(0, 1], (1, 2]],
                   closed='right', dtype='interval[int64]')
     """
 
@@ -394,7 +395,7 @@ def from_arrays(cls, left, right, closed='right', copy=False, dtype=None):
 
     Examples
     --------
-    >>>  pd.%(klass)s.from_tuples([(0, 1), (1, 2)])
+    >>> pd.%(qualname)s.from_tuples([(0, 1), (1, 2)])
     %(klass)s([(0, 1], (1, 2]],
                 closed='right', dtype='interval[int64]')
     """
@@ -891,13 +892,13 @@ def closed(self):
 
         Examples
         --------
-        >>>  index = pd.interval_range(0, 3)
-        >>>  index
-        %(klass)s([(0, 1], (1, 2], (2, 3]]
+        >>> index = pd.interval_range(0, 3)
+        >>> index
+        IntervalIndex([(0, 1], (1, 2], (2, 3]],
               closed='right',
               dtype='interval[int64]')
-        >>>  index.set_closed('both')
-        %(klass)s([[0, 1], [1, 2], [2, 3]]
+        >>> index.set_closed('both')
+        IntervalIndex([[0, 1], [1, 2], [2, 3]],
               closed='both',
               dtype='interval[int64]')
         """
@@ -1039,7 +1040,7 @@ def repeat(self, repeats, axis=None):
 
         Examples
         --------
-        >>> intervals = pd.%(klass)s.from_tuples([(0, 1), (1, 3), (2, 4)])
+        >>> intervals = pd.%(qualname)s.from_tuples([(0, 1), (1, 3), (2, 4)])
         >>> intervals
         %(klass)s([(0, 1], (1, 3], (2, 4]],
               closed='right',

pandas/core/indexes/interval.py

@@ -38,6 +38,7 @@
 
 _index_doc_kwargs.update(
     dict(klass='IntervalIndex',
+         qualname="IntervalIndex",
          target_klass='IntervalIndex or list of Intervals',
          name=textwrap.dedent("""\
          name : object, optional
@@ -282,10 +283,10 @@ def contains(self, key):
         examples="""
         Examples
         --------
-        >>>  idx = pd.IntervalIndex.from_arrays([0, np.nan, 2], [1, np.nan, 3])
-        >>>  idx.to_tuples()
+        >>> idx = pd.IntervalIndex.from_arrays([0, np.nan, 2], [1, np.nan, 3])
+        >>> idx.to_tuples()
         Index([(0.0, 1.0), (nan, nan), (2.0, 3.0)], dtype='object')
-        >>>  idx.to_tuples(na_tuple=False)
+        >>> idx.to_tuples(na_tuple=False)
         Index([(0.0, 1.0), nan, (2.0, 3.0)], dtype='object')""",
     ))
     def to_tuples(self, na_tuple=True):
@@ -1201,47 +1202,47 @@ def interval_range(start=None, end=None, periods=None, freq=None,
     Numeric ``start`` and  ``end`` is supported.
 
     >>> pd.interval_range(start=0, end=5)
-    IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]]
+    IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
                   closed='right', dtype='interval[int64]')
 
     Additionally, datetime-like input is also supported.
 
     >>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
-                          end=pd.Timestamp('2017-01-04'))
+    ...                   end=pd.Timestamp('2017-01-04'))
     IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03],
-                   (2017-01-03, 2017-01-04]]
+                   (2017-01-03, 2017-01-04]],
                   closed='right', dtype='interval[datetime64[ns]]')
 
     The ``freq`` parameter specifies the frequency between the left and right.
     endpoints of the individual intervals within the ``IntervalIndex``.  For
     numeric ``start`` and ``end``, the frequency must also be numeric.
 
     >>> pd.interval_range(start=0, periods=4, freq=1.5)
-    IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]]
+    IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
                   closed='right', dtype='interval[float64]')
 
     Similarly, for datetime-like ``start`` and ``end``, the frequency must be
     convertible to a DateOffset.
 
     >>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
-                          periods=3, freq='MS')
+    ...                   periods=3, freq='MS')
     IntervalIndex([(2017-01-01, 2017-02-01], (2017-02-01, 2017-03-01],
-                   (2017-03-01, 2017-04-01]]
+                   (2017-03-01, 2017-04-01]],
                   closed='right', dtype='interval[datetime64[ns]]')
 
     Specify ``start``, ``end``, and ``periods``; the frequency is generated
     automatically (linearly spaced).
 
     >>> pd.interval_range(start=0, end=6, periods=4)
-    IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]]
+    IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
               closed='right',
               dtype='interval[float64]')
 
     The ``closed`` parameter specifies which endpoints of the individual
     intervals within the ``IntervalIndex`` are closed.
 
     >>> pd.interval_range(end=5, periods=4, closed='both')
-    IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]]
+    IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]],
                   closed='both', dtype='interval[int64]')
     """
     start = com.maybe_box_datetimelike(start)

pandas/tests/api/test_api.py

@@ -46,7 +46,6 @@ class TestPDApi(Base):
                'Series', 'SparseArray', 'SparseDataFrame', 'SparseDtype',
                'SparseSeries', 'Timedelta',
                'TimedeltaIndex', 'Timestamp', 'Interval', 'IntervalIndex',
-               'IntervalArray',
                'CategoricalDtype', 'PeriodDtype', 'IntervalDtype',
                'DatetimeTZDtype',
                'Int8Dtype', 'Int16Dtype', 'Int32Dtype', 'Int64Dtype',

pandas/tests/arrays/test_array.py

@@ -74,7 +74,7 @@
 
     # Interval
     ([pd.Interval(1, 2), pd.Interval(3, 4)], 'interval',
-     pd.IntervalArray.from_tuples([(1, 2), (3, 4)])),
+     pd.arrays.IntervalArray.from_tuples([(1, 2), (3, 4)])),
 
     # Sparse
     ([0, 1], 'Sparse[int64]', pd.SparseArray([0, 1], dtype='int64')),
@@ -129,7 +129,7 @@ def test_array_copy():
 
     # interval
     ([pd.Interval(0, 1), pd.Interval(1, 2)],
-     pd.IntervalArray.from_breaks([0, 1, 2])),
+     pd.arrays.IntervalArray.from_breaks([0, 1, 2])),
 
     # datetime
     ([pd.Timestamp('2000',), pd.Timestamp('2001')],