REF: IntervalIndex[IntervalArray]

doc/source/basics.rst

@@ -1924,11 +1924,24 @@ untouched. If the data is modified, it is because you did so explicitly.
 dtypes
 ------
 
-The main types stored in pandas objects are ``float``, ``int``, ``bool``,
-``datetime64[ns]`` and ``datetime64[ns, tz]``, ``timedelta[ns]``,
-``category`` and ``object``. In addition these dtypes have item sizes, e.g.
-``int64`` and ``int32``. See :ref:`Series with TZ <timeseries.timezone_series>`
-for more detail on ``datetime64[ns, tz]`` dtypes.
+For the most part, pandas uses NumPy arrays and dtypes for Series or individual
+columns of a DataFrame. The main types allowed in pandas objects are ``float``,
+``int``, ``bool``, and ``datetime64[ns]`` (note that NumPy does not support
+timezone-aware datetimes).
+
+In addition to NumPy's types, pandas :ref:`extends <extending.extension-types>`
+NumPy's type-system for a few cases.
+
+* :ref:`Categorical <categorical>`
+* :ref:`Datetime with Timezone <timeseries.timezone_series>`
+* :ref:`Period <timeseries.periods>`
+* :ref:`Interval <advanced.indexing.intervallindex>`
+
+Pandas uses the ``object`` dtype for storing strings.
+
+Finally, arbitrary objects may be stored using the ``object`` dtype, but should
+be avoided to the extent possible (for performance and interoperability with
+other libraries and methods. See :ref:`basics.object_conversion`).
 
 A convenient :attr:`~DataFrame.dtypes` attribute for DataFrame returns a Series
 with the data type of each column.

doc/source/whatsnew/v0.24.0.txt

@@ -66,6 +66,36 @@ Current Behavior:
 
     result
 
+
+.. _whatsnew_0240.enhancements.interval:
+
+Storing Interval Data in Series and DataFrame
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Interval data may now be stored in a ``Series`` or ``DataFrame``, in addition to an
+:class:`IntervalIndex` like previously (:issue:`19453`).
+
+.. ipython:: python
+
+   ser = pd.Series(pd.interval_range(0, 5))
+   ser
+   ser.dtype
+
+Previously, these would be cast to a NumPy array of ``Interval`` objects. In general,
+this should result in better performance when storing an array of intervals in
+a :class:`Series`.
+
+Note that the ``.values`` of a ``Series`` containing intervals is no longer a NumPy
+array, but rather an ``ExtensionArray``:
+
+.. ipython:: python
+
+   ser.values
+
+This is the same behavior as ``Series.values`` for categorical data. See
+:ref:`whatsnew_0240.api_breaking.interval_values` for more.
+
+
 .. _whatsnew_0240.enhancements.other:
 
 Other Enhancements
@@ -90,6 +120,45 @@ Other Enhancements
 Backwards incompatible API changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+
+.. _whatsnew_0240.api_breaking.interval_values:
+
+``IntervalIndex.values`` is now an ``IntervalArray``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :attr:`~Interval.values` attribute of an :class:`IntervalIndex` now returns an
+``IntervalArray``, rather than a NumPy array of :class:`Interval` objects (:issue:`19453`).
+
+Previous Behavior:
+
+.. code-block:: ipython
+
+   In [1]: idx = pd.interval_range(0, 4)
+
+   In [2]: idx.values
+   Out[2]:
+   array([Interval(0, 1, closed='right'), Interval(1, 2, closed='right'),
+          Interval(2, 3, closed='right'), Interval(3, 4, closed='right')],
+         dtype=object)
+
+New Behavior:
+
+.. ipython:: python
+
+   idx = pd.interval_range(0, 4)
+   idx.values
+
+This mirrors ``CateogricalIndex.values``, which returns a ``Categorical``.
+
+For situations where you need an ``ndarray`` of ``Interval`` objects, use
+:meth:`numpy.asarray` or ``idx.astype(object)``.
+
+.. ipython:: python
+
+   np.asarray(idx)
+   idx.values.astype(object)
+
+
 .. _whatsnew_0240.api.datetimelike.normalize:
 
 Tick DateOffset Normalize Restrictions
@@ -345,6 +414,7 @@ Interval
 ^^^^^^^^
 
 - Bug in the :class:`IntervalIndex` constructor where the ``closed`` parameter did not always override the inferred ``closed`` (:issue:`19370`)
+- Bug in the ``IntervalIndex`` repr where a trailing comma was missing after the list of intervals (:issue:`20611`)
 -
 -
 

pandas/_libs/interval.pyx

@@ -98,6 +98,26 @@ cdef class IntervalMixin(object):
             msg = 'cannot compute length between {left!r} and {right!r}'
             raise TypeError(msg.format(left=self.left, right=self.right))
 
+    def _check_closed_matches(self, other, name='other'):
+        """Check if the closed attribute of `other` matches.
+
+        Note that 'left' and 'right' are considered different from 'both'.
+
+        Parameters
+        ----------
+        other : Interval, IntervalIndex, IntervalArray
+        name : str
+            Name to use for 'other' in the error message.
+
+        Raises
+        ------
+        ValueError
+            When `other` is not closed exactly the same as self.
+        """
+        if self.closed != other.closed:
+            msg = "'{}.closed' is '{}', expected '{}'."
+            raise ValueError(msg.format(name, other.closed, self.closed))
+
 
 cdef _interval_like(other):
     return (hasattr(other, 'left')

pandas/core/arrays/__init__.py

@@ -2,5 +2,6 @@
                    ExtensionScalarOpsMixin)
 from .categorical import Categorical  # noqa
 from .datetimes import DatetimeArrayMixin  # noqa
+from .interval import IntervalArray  # noqa
 from .period import PeriodArrayMixin  # noqa
 from .timedelta import TimedeltaArrayMixin  # noqa

pandas/core/arrays/categorical.py

@@ -20,6 +20,7 @@
     _ensure_int64,
     _ensure_object,
     _ensure_platform_int,
+    is_extension_array_dtype,
     is_dtype_equal,
     is_datetimelike,
     is_datetime64_dtype,
@@ -1243,6 +1244,11 @@ def __array__(self, dtype=None):
         ret = take_1d(self.categories.values, self._codes)
         if dtype and not is_dtype_equal(dtype, self.categories.dtype):
             return np.asarray(ret, dtype)
+        if is_extension_array_dtype(ret):
+            # When we're a Categorical[ExtensionArray], like Interval,
+            # we need to ensure __array__ get's all the way to an
+            # ndarray.
+            ret = np.asarray(ret)
         return ret
 
     def __setstate__(self, state):