v3.1.0.rst

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

These are the changes in pandas 3.1.0. See :ref:`release` for a full changelog including other versions of pandas.

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Enhancements

enhancement1

enhancement2

Other enhancements

• :class:`Period` now supports f-string formatting via __format__, e.g. f"{period:%Y-%m}" (:issue:`48536`)
• :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`)

Notable bug fixes

These are bug fixes that might have notable behavior changes.

notable_bug_fix1

notable_bug_fix2

Backwards incompatible API changes

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 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.

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`).

Deprecations

• Deprecated :attr:`Timestamp.dayofweek`, :attr:`Timestamp.dayofyear`, :attr:`Timestamp.daysinmonth` in favor of :attr:`Timestamp.day_of_week`, :attr:`Timestamp.day_of_year`, :attr:`Timestamp.days_in_month`, respectively. The same deprecation applies to the corresponding attributes on :class:`Period`, :class:`DatetimeIndex`, :class:`PeriodIndex`, and :attr:`Series.dt` (:issue:`46768`)
• 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 :meth:`ExcelFile.parse`, use :func:`read_excel` instead (:issue:`58247`)
• Deprecated engine="fastparquet" and engine="auto" in :func:`read_parquet` and :meth:`DataFrame.to_parquet`. The fastparquet library has been retired; use engine="pyarrow" or do not pass engine to use the default. (:issue:`64597`)
• 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 passing a dict to :meth:`DataFrame.from_records`, use the :class:`DataFrame` constructor or :meth:`DataFrame.from_dict` instead (:issue:`22025`)
• Deprecated passing a non-dict (e.g. a list of dicts) to :meth:`DataFrame.from_dict`. Use the :class:`DataFrame` constructor instead (:issue:`58862`)
• Deprecated passing unnecessary *args and **kwargs to :meth:`.GroupBy.cumsum`, :meth:`.GroupBy.cumprod`, :meth:`.GroupBy.cummin`, :meth:`.GroupBy.cummax`, :meth:`.SeriesGroupBy.skew`, :meth:`.DataFrameGroupBy.skew`, :meth:`.SeriesGroupBy.take`, and :meth:`.DataFrameGroupBy.take`. The skipna parameter for the cum* methods is now an explicit keyword argument (:issue:`50407`)
• Deprecated setting values with :meth:`DataFrame.at` and :meth:`Series.at` when the key does not exist in the index, which previously expanded the object. Use .loc instead (:issue:`48323`)
• 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`)
• Deprecated the dropna keyword in :meth:`DataFrame.to_hdf`, :meth:`HDFStore.put`, :meth:`HDFStore.append`, and :meth:`HDFStore.append_to_multiple`, and the io.hdf.dropna_table option. Use :meth:`DataFrame.dropna` before writing instead (:issue:`32038`)
• Deprecated the float_precision argument in :func:`read_csv`, :func:`read_table`, and :func:`read_fwf`. All float precision modes now use the same converter (:issue:`64395`)
• Deprecated the weekday property on :class:`DatetimeIndex`, :class:`.DatetimeArray`, :class:`PeriodIndex`, :class:`.PeriodArray`, and :class:`Period`. Use day_of_week instead. Timestamp.weekday() remains a method consistent with :meth:`datetime.datetime.weekday` (:issue:`12816`)
• Deprecated the xlrd and pyxlsb engines in :func:`read_excel`. Use engine="calamine" instead (:issue:`56542`)
• Deprecated the default value of exact in :func:`assert_index_equal`; in a future version this will default to True instead of "equiv" (:issue:`57436`)
• Deprecated the default value of track_times in :meth:`HDFStore.put`. In a future version, the default will change from True to False so that HDF5 files are deterministic by default (:issue:`51456`)

Performance improvements

• Performance improvement in casting integer and boolean dtypes to string[pyarrow] by using PyArrow's native cast instead of element-wise conversion (:issue:`56505`)
• 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 :attr:`Series.is_monotonic_increasing` and :attr:`Series.is_monotonic_decreasing` for :class:`ArrowDtype` and masked dtypes by dispatching to the :class:`ExtensionArray` (:issue:`56619`)
• Performance improvement in :class:`GroupBy` reductions and transformations for :class:`SparseDtype` columns, which now use Cython instead of falling back to slow Python aggregation (:issue:`36123`)
• 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 :func:`merge` with sort=False for single-key how="left"/how="right" joins when the opposite join key is sorted, unique, and range-like (:issue:`64146`)
• Performance improvement in :func:`read_csv` with engine="c" when reading from binary file-like objects (e.g. PyArrow S3 file handles) by avoiding unnecessary TextIOWrapper wrapping (:issue:`46823`)
• Performance improvement in :func:`read_html` and the Python CSV parser when thousands is set, fixing catastrophic regex backtracking on cells with many comma-separated digit groups followed by non-numeric text (:issue:`52619`)
• Performance improvement in :func:`read_sas` by reading page header fields directly in Cython instead of falling back to Python (:issue:`47339`)
• Performance improvement in :func:`read_sas` for SAS7BDAT files by pre-computing date/datetime column classification once during metadata parsing instead of per chunk (:issue:`47339`)
• Performance improvement in :func:`read_sas` for compressed SAS7BDAT files by reusing the decompression buffer instead of allocating per row (:issue:`47339`)
• Performance improvement in :func:`read_sas` when decoding strings (:issue:`47339`)
• Performance improvement in :func:`util.hash_pandas_object` for PyArrow-backed string and binary types by using PyArrow's dictionary_encode instead of converting to NumPy for factorization (:issue:`48964`)
• Performance improvement in :meth:`DataFrame.corr` and :meth:`DataFrame.cov` when data contains no NaN values (:issue:`64857`)
• Performance improvement in :meth:`DataFrame.fillna` and :meth:`Series.fillna` with scalar fill value for float, object, nullable, and datetime-like dtypes (:issue:`42147`)
• Performance improvement in :meth:`DataFrame.from_records` when passing a 2D :class:`numpy.ndarray` (:issue:`22025`)
• Performance improvement in :meth:`DataFrame.insert` when the number of blocks is small (:issue:`57641`)
• Performance improvement in :meth:`DataFrame.loc` with non-unique masked index (:issue:`56759`)
• Performance improvement in :meth:`DataFrame.query` and :meth:`DataFrame.eval` when the :class:`DataFrame` contains :class:`PeriodDtype` or :class:`IntervalDtype` columns (:issue:`35247`)
• Performance improvement in :meth:`DataFrame.rank` and :meth:`Series.rank` by skipping unnecessary putmask for non-nullable dtypes (:issue:`64857`)
• Performance improvement in :meth:`DataFrame.sort_values` with multiple numeric columns by avoiding unnecessary :class:`Categorical` conversion (:issue:`15389`)
• 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 :meth:`Index.join` and :meth:`Index.union` for :class:`RangeIndex` by avoiding unnecessary memory allocation in the libjoin fastpath (:issue:`54646`)
• Performance improvement in :meth:`IntervalIndex.get_indexer` for monotonic non-overlapping indexes, which now uses binary search instead of the interval tree (:issue:`47614`)
• Performance improvement in :meth:`NDFrame.__finalize__`, :meth:`Series.to_numpy`, :attr:`DataFrame.dtypes`, and :meth:`DataFrame.__getitem__` by reducing overhead from metadata propagation, memory sharing checks, and attribute setting (:issue:`57431`)
• Performance improvement in :meth:`arrays.SparseArray.isna` by avoiding a dense-then-resparsify round-trip (:issue:`41023`)
• 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 indexing a :class:`MultiIndex` with a list-like indexer (:issue:`55786`)
• Performance improvement in partial-string indexing on a monotonic decreasing :class:`DatetimeIndex` or :class:`PeriodIndex` (:issue:`64811`)
• Performance improvement in plotting :class:`DatetimeIndex` with multiplied frequencies (e.g. "1000ms", "100s") (:issue:`50355`)
• 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`)
• Performance improvement in :meth:`DataFrame.loc` and :meth:`DataFrame.iloc` setitem with a 2D list-of-lists value by avoiding a wasteful round-trip through an intermediate object array (:issue:`64229`).

Bug fixes

• Fixed bug in :class:`Index` repr where attributes were not wrapped to respect display.width (:issue:`11552`)
• Fixed bug in :func:`to_timedelta` and :class:`Timedelta` not accepting Day offsets (:issue:`64240`)

Categorical

• Bug in :meth:`Categorical.__repr__` where the values and categories lines could exceed display.width (:issue:`12066`)
• Bug in :meth:`CategoricalIndex.union` and :meth:`CategoricalIndex.intersection` giving incorrect results when the two indexes have the same unordered categories in different orders (:issue:`55335`)
• Bug in :meth:`Index.fillna` raising TypeError when filling with a tuple value (e.g. on object-dtype or :class:`CategoricalIndex` with tuple categories) (:issue:`37681`)

Datetimelike

• Bug in :class:`DatetimeIndex` constructor raising ValueError when passing equivalent but not equal frequencies (e.g. QS-FEB vs QS-MAY) (:issue:`61086`)
• Bug in :class:`DatetimeIndex` raising AttributeError when comparing against Arrow date types (date32, date64) (:issue:`62051`)
• Bug in :class:`Timestamp` constructor where passing np.str_ objects would fail in Cython string parsing (:issue:`48974`)
• 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 :func:`date_range` where inclusive="left" and inclusive="right" returned a single-element result instead of empty when start equals end (:issue:`55293`)
• Bug in :func:`date_range` where inclusive parameter failed to filter endpoints when only start and periods or end and periods were specified (:issue:`46331`)
• Bug in :func:`date_range` where periods=1 with offsets that disallow n=0 (e.g. :class:`offsets.LastWeekOfMonth`, :class:`offsets.FY5253`) raised ValueError (:issue:`41563`)
• Bug in :func:`date_range` where calendar-based offsets (e.g. MS, ME, QS, YS) could exclude the last offset boundary when end's time-of-day was earlier than start's (:issue:`35342`)
• Bug in :func:`to_datetime` and :func:`to_timedelta` on ARM platforms where round float values outside the int64 domain (e.g. float(2**63)) could silently produce incorrect results instead of raising (:issue:`64619`)
• Bug in :func:`to_datetime` and :func:`to_timedelta` where uint64 values greater than int64 max silently overflowed instead of raising :class:`OutOfBoundsDatetime` or :class:`OutOfBoundsTimedelta` (:issue:`60677`)
• Bug in :meth:`DataFrame.replace` and :meth:`Series.replace` raising AssertionError instead of :class:`OutOfBoundsDatetime` when replacing with a datetime value outside the datetime64[ns] range (:issue:`61671`)
• Bug in :meth:`DatetimeArray.isin` and :meth:`TimedeltaArray.isin` where mismatched resolutions could silently truncate finer-resolution values, leading to false matches (:issue:`64545`)
• Bug in adding non-nano :class:`DatetimeIndex` with non-vectorized offsets (e.g. :class:`CustomBusinessDay`, :class:`CustomBusinessMonthEnd`) having a sub-unit offset parameter incorrectly truncating the result or raising AttributeError (:issue:`56586`)
• Bug in subtracting :class:`BusinessHour` (or :class:`CustomBusinessHour`) from a :class:`Timestamp` giving incorrect results when the subtraction would land exactly on the business-hour opening time (:issue:`33682`)

Timedelta

• Bug in :class:`DateOffset` where DateOffset(1) and DateOffset(days=1) returned different results near daylight saving time transitions (:issue:`61862`)
• Bug in :func:`to_timedelta` where passing np.str_ objects would fail in Cython string parsing (:issue:`48974`)
• Fixed regression in :meth:`Timedelta.round`, :meth:`Timedelta.floor`, and :meth:`Timedelta.ceil` raising ZeroDivisionError for sub-second freq (:issue:`64828`)

Timezones

• Bug in :class:`DatetimeIndex` addition with a :class:`DateOffset` that has only timedelta components (e.g. DateOffset(hours=-2)) raising ValueError near DST transitions, while scalar :class:`Timestamp` addition worked correctly (:issue:`28610`)

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.mean` and :meth:`Series.sum` (and their :class:`DataFrame` counterparts) overflowing for float16 dtypes instead of upcasting to float64 (:issue:`43929`)
• 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`)
• Fixed bug where :class:`DataFrame` arithmetic operations with :class:`Series` did not support the fill_value parameter(:issue:`61581`)

Conversion

• Bug in :class:`DataFrame` constructor where NaT in a :class:`TimedeltaIndex` row was incorrectly inferred as datetime64 instead of timedelta64 (:issue:`23985`)
• Bug in :class:`DataFrame` constructor where constructing from a list of uniform-dtype arrays (e.g. pyarrow, :class:`CategoricalDtype`, nullable dtypes) lost the dtype (:issue:`49593`)
• Bug in :func:`pd.array` silently converting NaN to a nonsensical integer when given float data containing NaN and a NumPy integer dtype (:issue:`41724`)
• 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

• Bug in :meth:`DataFrame.replace` with regex=True mutating the underlying :class:`StringArray` when the replacement value was not a string (:issue:`57733`)

Interval

Indexing

• Bug in :meth:`DataFrame.loc` returning incorrect dtype when the column key is a slice (:issue:`63071`)
• Bug in :meth:`Index.get_indexer` where method="pad", "backfill", or "nearest" returned incorrect results when the target contained NaT or NaN instead of -1 (:issue:`32572`)
• 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`)
• Bug in :meth:`DataFrame.__getitem__` raising InvalidIndexError when indexing with a tuple containing a slice on a :class:`DataFrame` with :class:`MultiIndex` columns (e.g., df[:, "t1"]) (:issue:`26511`)
• Bug in :meth:`DataFrame.iloc` setitem raising AttributeError when assigning a :class:`Series` or :class:`Index` with a nullable EA dtype (e.g. Int64, Float64, boolean) into a column with a NumPy dtype (:issue:`47776`)
• Bug in :meth:`DataFrame.mask` with inplace=True where incorrect values were produced when other was a :class:`Series` with :class:`ExtensionArray` values (:issue:`64635`)
• Bug in :meth:`DataFrame.where` and :meth:`DataFrame.mask` raising TypeError when cond is a :class:`Series` and axis=1 (:issue:`58190`)
• Bug in :meth:`DataFrame.xs` where drop_level=False was ignored for fully specified :class:`MultiIndex` keys when level was not explicitly provided (:issue:`6507`)
• Bug in :meth:`Index.get_level_values` mishandling boolean, NA-like (np.nan, pd.NA, pd.NaT) and integer index names (:issue:`62169`)
• Bug in :meth:`Index.get_loc` raising KeyError when looking up a tuple in an object-dtype :class:`Index` with duplicates (:issue:`37800`)
• Bug in :meth:`Index.insert` silently casting booleans to numeric when used with nullable numeric dtypes like Float64 or Int64 (:issue:`61709`)

Missing

• Bug in :meth:`DataFrame.fillna` with a dict value raising RecursionError when columns are a :class:`MultiIndex` with duplicate entries (:issue:`53498`)

MultiIndex

• Bug in :meth:`DataFrame.loc` with a :class:`MultiIndex` where using a tuple indexer with a scalar and a list (e.g., (scalar, list)) did not drop the scalar-indexed level (:issue:`18631`)
• Bug in :meth:`MultiIndex.sortlevel` not raising TypeError when sorting a level with incomparable types (e.g., Timestamp and str) (:issue:`21136`)

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`)
• Bug in :meth:`DataFrame.__repr__` where horizontally truncated output could exceed the terminal width by up to 4 characters because the " ..." separator column was not accounted for in the width budget (:issue:`32461`)
• Bug in :meth:`DataFrame.to_stata` raising KeyError when column names require renaming and convert_dates is specified for a different column (:issue:`60536`)
• Fixed :func:`read_json` with lines=True and nrows=0 to return an empty DataFrame (:issue:`64025`)
• Fixed bug in :meth:`HDFStore.select` where passing where as a list of conditions referencing caller-scope variables failed on Python 3.12+ due to PEP 709 inlining list comprehension stack frames (:issue:`64881`)

Period

• Bug in :class:`Period` constructor where passing np.str_ objects would fail in Cython string parsing (:issue:`48974`)

Plotting

• Bug in :meth:`DataFrame.plot.hexbin` ignoring rcParams["image.cmap"] and always defaulting to "BuGn" when no colormap was specified (:issue:`31871`)

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`)
• Bug in :meth:`DataFrame.groupby` with a :class:`Grouper` with freq raising AttributeError when all grouping keys are NaT (:issue:`43486`)
• Bug in :meth:`Series.resample` and :meth:`DataFrame.resample` where same-frequency resampling with monthly, quarterly, or annual frequencies bypassed aggregation, returning the original values instead of the aggregation result (:issue:`18553`)

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`)
• Bug in :meth:`Index.union` where the result could be unsorted when both inputs were monotonic increasing but disjoint, when sort was not False (:issue:`54646`)
• 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 :meth:`SparseArray.astype` where converting a datetime64 :class:`SparseArray` with NaT fill value to "Sparse[int64]" silently replaced the fill value with 0 instead of iNaT (:issue:`49631`)
• 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`).

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