What's new in 3.1.0 (Month XX, 2026)

.. _whatsnew_310.enhancements.enhancement1:

enhancement1
^^^^^^^^^^^^

.. _whatsnew_310.enhancements.enhancement2:

enhancement2
^^^^^^^^^^^^

.. _whatsnew_310.enhancements.other:

Other enhancements
^^^^^^^^^^^^^^^^^^
- :meth:`.DataFrameGroupBy.agg` now allows for the provided ``func`` to return a NumPy array (:issue:`63957`)
- Added :meth:`ExtensionArray.count` (:issue:`64450`)
- Display formatting for float sequences in DataFrame cells now respects the ``display.precision`` option (:issue:`60503`).
- Improved the precision of float parsing in :func:`read_csv` (:issue:`64395`)
- Improved the string ``repr`` of :class:`pd.core.arrays.SparseArray` (:issue:`64547`)

.. ---------------------------------------------------------------------------
.. _whatsnew_310.notable_bug_fixes:

Notable bug fixes

.. _whatsnew_310.api_breaking.deps:

Increased minimum versions for dependencies
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Some minimum supported versions of dependencies were updated.
If installed, we now require:

+-----------------+-----------------+----------+---------+
| Package         | Minimum Version | Required | Changed |
+=================+=================+==========+=========+
|                 |                 |    X     |    X    |
+-----------------+-----------------+----------+---------+

For `optional libraries <https://pandas.pydata.org/docs/getting_started/install.html>`_ the general recommendation is to use the latest version.
The following table lists the lowest version per library that is currently being tested throughout the development of pandas.
Optional libraries below the lowest tested version may still work, but are not considered supported.

+-----------------+-----------------+---------+
| Package         | Minimum Version | Changed |
+=================+=================+=========+
|                 |                 |    X    |
+-----------------+-----------------+---------+

See :ref:`install.dependencies` and :ref:`install.optional_dependencies` for more.

.. _whatsnew_310.api_breaking.other:

Other API changes
^^^^^^^^^^^^^^^^^
- APIs that accept an ``engine="numba"`` parameter with ``engine_kwargs`` will no longer pass through a ``nopython`` argument to ``numba.jit``. This argument has had no effect since numba 0.59.0 (:issue:`64483`).
-

.. ---------------------------------------------------------------------------
.. _whatsnew_310.deprecations:

Deprecations
~~~~~~~~~~~~
- Deprecated :meth:`.DataFrameGroupBy.agg` and :meth:`.Resampler.agg` unpacking a scalar when the provided ``func`` returns a Series or array of length 1; in the future this will result in the Series or array being in the result. Users should unpack the scalar in ``func`` itself (:issue:`64014`)
- Deprecated arithmetic operations between pandas objects (:class:`DataFrame`, :class:`Series`, :class:`Index`, and pandas-implemented :class:`ExtensionArray` subclasses) and list-likes other than ``list``, ``np.ndarray``, :class:`ExtensionArray`, :class:`Index`, :class:`Series`, :class:`DataFrame`. For e.g. ``tuple`` or ``range``, explicitly cast these to a supported object instead. In a future version, these will be treated as scalar-like for pointwise operation (:issue:`62423`)
- Deprecated automatic dtype promotion when reindexing with a ``fill_value`` that cannot be held by the original dtype. Explicitly cast to a common dtype instead (:issue:`53910`)
- Deprecated the ``.name`` property of offset objects (e.g., :class:`~pandas.tseries.offsets.Day`, :class:`~pandas.tseries.offsets.Hour`). Use ``.rule_code`` instead (:issue:`64207`)
-

.. ---------------------------------------------------------------------------
.. _whatsnew_310.performance:

Performance improvements
~~~~~~~~~~~~~~~~~~~~~~~~
- Performance improvement in :meth:`DataFrame.__getitem__` when selecting a
  single column by label on a :class:`DataFrame` with duplicate column names.
  (:issue:`64126`).
- Performance improvement in :func:`bdate_range` and :func:`date_range` with ``freq="B"`` or ``freq="C"`` (business day frequencies) (:issue:`16463`)
- Performance improvement in :func:`infer_freq` (:issue:`64463`)
- Performance improvement in :func:`merge` and :meth:`DataFrame.join` for many-to-many joins with ``sort=False`` (:issue:`56564`)
- Performance improvement in :func:`merge` with ``how="cross"`` (:issue:`38082`)
- Performance improvement in :func:`merge` with ``how="left"`` (:issue:`64370`)
- Performance improvement in :meth:`DataFrame.loc` with non-unique masked index (:issue:`56759`)
- Performance improvement in :meth:`DataFrame.to_stata` when writing object-dtype datetime columns with date formats that require year/month extraction (:issue:`64555`)
- Performance improvement in :meth:`GroupBy.any` and :meth:`GroupBy.all` for boolean-dtype columns (:issue:`37850`)
- Performance improvement in :meth:`GroupBy.first` and :meth:`GroupBy.last` for Extension Array dtypes, which no longer fall back to a slow ``apply``-based implementation (:issue:`57591`)
- Performance improvement in :meth:`GroupBy.quantile` (:issue:`64330`)
- Performance improvement in :meth:`Index.get_indexer` for large monotonic indexes, which now uses binary search instead of building a hash table when the number of targets is small (:issue:`14273`)
- Performance improvement in datetime/timedelta unit conversion (e.g. ``datetime64[s]`` to ``datetime64[ns]``) (:issue:`35025`)
- Performance improvement in indexing a :class:`DataFrame` with a :class:`CategoricalIndex` of :class:`Interval` categories (:issue:`61928`)
- Performance improvement in reading zip-compressed files (e.g. :func:`read_pickle`, :func:`read_csv`) on Python < 3.12 (:issue:`59279`)
- Performance improvement in repr of :class:`Series` and :class:`DataFrame` containing third-party array-like objects (e.g. xarray ``DataArray``) in object dtype columns (:issue:`61809`)

.. ---------------------------------------------------------------------------
.. _whatsnew_310.bug_fixes:

Bug fixes
~~~~~~~~~
- Bug in object-dtype hash table operations (``factorize``, ``unique``, ``duplicated``, ``isin``, ``value_counts``, ``groupby``, ``Index.get_loc``) not distinguishing between ``int`` and ``bool`` values, e.g. treating ``0`` and ``False`` as equal (:issue:`62888`)
- Fix bug in :func:`to_datetime` that could give an unnecessary ``RuntimeWarning`` when converting DataFrame containing missing values (:issue:`64141`)

Categorical
^^^^^^^^^^^
-
-

Datetimelike
^^^^^^^^^^^^
- Bug in :class:`Timestamp` constructor, :class:`Timedelta` constructor, :func:`to_datetime`, and :func:`to_timedelta` with non-round ``float`` input and ``unit`` failing to raise when the value is just outside the representable bounds (:issue:`57366`)
- Bug in :meth:`DatetimeArray.isin` and :meth:`TimedeltaArray.isin` where mismatched resolutions could silently truncate finer-resolution values, leading to false matches (:issue:`64545`)

Timedelta
^^^^^^^^^
-
-

Timezones
^^^^^^^^^
-
-

Numeric
^^^^^^^
- Fixed bug in :func:`read_excel` where having a column with mixture of numeric and boolean values will typecast the values based on the first appearance data type since 1==True and 0==False (:issue:`60088`)
- Fixed bug in :meth:`Series.clip` where passing a scalar numpy array (e.g. ``np.array(0)``) would raise a ``TypeError`` (:issue:`59053`)
- Fixed bug in :meth:`Series.skew` and :meth:`Series.kurt` (and their :class:`DataFrame` counterparts) returning ``0.0`` for degenerate distributions; these now return ``NaN`` (:issue:`62864`)

Conversion
^^^^^^^^^^
- Fixed :func:`pandas.array` to preserve mask information when converting NumPy masked arrays, converting masked values to missing values (:issue:`63879`).
- Fixed bug in :meth:`DataFrame.from_records` where ``exclude`` was ignored when ``data`` was an iterator and ``nrows=0`` (:issue:`63774`)
-

Strings
^^^^^^^
-
-

Interval
^^^^^^^^
-
-

Indexing
^^^^^^^^
- Bugs in setitem-with-expansion when adding new rows failing to keep the original dtype in some cases (:issue:`32346`, :issue:`15231`, :issue:`47503`, :issue:`6485`, :issue:`25383`, :issue:`52235`, :issue:`17026`, :issue:`56010`)
-

Missing
^^^^^^^
-
-

MultiIndex
^^^^^^^^^^
-
-

I/O
^^^
- Fixed bug in :func:`read_csv` with the ``c`` engine where an embedded ``\r`` followed by a space in an unquoted field could cause an infinite re-parsing loop, producing spurious rows or a buffer overflow (:issue:`51141`)
- Fixed bug in :func:`read_excel` where usage of ``skiprows`` could lead to an infinite loop (:issue:`64027`)
- Fixed :func:`read_json` with ``lines=True`` and ``chunksize`` to respect ``nrows``
  when the requested row count is not a multiple of the chunk size (:issue:`64025`)
- Fixed :func:`read_json` with ``lines=True`` and ``nrows=0`` to return an empty DataFrame (:issue:`64025`)

Period
^^^^^^
-
-

Plotting
^^^^^^^^
-
-

Groupby/resample/rolling
^^^^^^^^^^^^^^^^^^^^^^^^
- Bug in :meth:`.DataFrameGroupBy.agg` when there are no groups, multiple keys, and ``group_keys=False`` (:issue:`51445`)
- Bug in :meth:`.DataFrameGroupBy.agg` would operate on the group as a whole when ``args`` or ``kwargs`` are supplied for the provided ``func``; now this method only operates on each Series of the group (:issue:`39169`)
- Bug in :meth:`.Rolling.skew` and :meth:`.Rolling.kurt` (and their :class:`GroupBy` counterparts) returning ``0.0`` and ``-3.0`` respectively for degenerate windows or groups; these now return ``NaN`` (:issue:`62864`)
- Bug in :meth:`.Rolling.skew` and :meth:`.Rolling.kurt` returning ``NaN`` for low-variance windows (:issue:`62946`)

Reshaping
^^^^^^^^^
- Bug in :func:`merge` where merging on a :class:`MultiIndex` containing ``NaN`` values mapped ``NaN`` keys to the last level value instead of ``NaN`` (:issue:`64492`)
- In :func:`pivot_table`, when ``values`` is empty, the aggregation will be computed on a Series of all NA values (:issue:`46475`)
-

Sparse
^^^^^^
- Bug in indexing a :class:`SparseArray` with an out-of-bounds integer with the value of the length of the array returning the fill value instead of raising an ``IndexError`` (:issue:`64183`).
-

ExtensionArray
^^^^^^^^^^^^^^
- Fixed bug in :meth:`Series.apply` and :meth:`Series.map` where nullable integer dtypes were converted to float, causing precision loss for large integers; now the nullable dtype will be preserved (:issue:`63903`).
-
-

Styler
^^^^^^
-
-

Other
^^^^^

.. ***DO NOT USE THIS SECTION***

-
-

.. ---------------------------------------------------------------------------
.. _whatsnew_310.contributors:

Contributors
~~~~~~~~~~~~