API: Remove IntervalArray from top-level (#24926)

Author: TomAugspurger

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')],