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Revision 1.55 / (download) - annotate - [select for diffs], Sat Nov 11 10:04:38 2023 UTC (4 weeks, 1 day ago) by adam
Branch: MAIN
CVS Tags: HEAD
Changes since 1.54: +2 -6 lines
Diff to previous 1.54 (colored)

py-pandas: updated to 2.1.3

Pandas 2.1.3

This is a patch release in the 2.1.x series and includes some regression and bug fixes, and a security fix. We recommend that all users upgrade to this version.

Revision 1.54 / (download) - annotate - [select for diffs], Sun Oct 29 17:39:51 2023 UTC (6 weeks ago) by adam
Branch: MAIN
Changes since 1.53: +2 -3 lines
Diff to previous 1.53 (colored)

py-pandas: updated to 2.1.2

2.1.2

Deprecations

Reverted deprecation of fill_method=None in DataFrame.pct_change(), Series.pct_change(), DataFrameGroupBy.pct_change(), and SeriesGroupBy.pct_change(); the values 'backfill', 'bfill', 'pad', and 'ffill' are still deprecated (GH 53491)

Fixed regressions

Fixed regression in DataFrame.join() where result has missing values and dtype is arrow backed string (GH 55348)
Fixed regression in rolling() where non-nanosecond index or on column would produce incorrect results (GH 55026, GH 55106, GH 55299)
Fixed regression in DataFrame.resample() which was extrapolating back to origin when origin was outside its bounds (GH 55064)
Fixed regression in DataFrame.sort_index() which was not sorting correctly when the index was a sliced MultiIndex (GH 55379)
Fixed regression in DataFrameGroupBy.agg() and SeriesGroupBy.agg() where if the option compute.use_numba was set to True, groupby methods not supported by the numba engine would raise a TypeError (GH 55520)
Fixed performance regression with wide DataFrames, typically involving methods where all columns were accessed individually (GH 55256, GH 55245)
Fixed regression in merge_asof() raising TypeError for by with datetime and timedelta dtypes (GH 55453)
Fixed regression in read_parquet() when reading a file with a string column consisting of more than 2 GB of string data and using the "string" dtype (GH 55606)
Fixed regression in DataFrame.to_sql() not roundtripping datetime columns correctly for sqlite when using detect_types (GH 55554)
Fixed regression in construction of certain DataFrame or Series subclasses (GH 54922)

Bug fixes

Fixed bug in DataFrameGroupBy reductions not preserving object dtype when infer_string is set (GH 55620)
Fixed bug in SeriesGroupBy.value_counts() returning incorrect dtype for string columns (GH 55627)
Fixed bug in Categorical.equals() if other has arrow backed string dtype (GH 55364)
Fixed bug in DataFrame.__setitem__() not inferring string dtype for zero-dimensional array with infer_string=True (GH 55366)
Fixed bug in DataFrame.idxmin() and DataFrame.idxmax() raising for arrow dtypes (GH 55368)
Fixed bug in DataFrame.interpolate() raising incorrect error message (GH 55347)
Fixed bug in Index.insert() raising when inserting None into Index with dtype="string[pyarrow_numpy]" (GH 55365)
Fixed bug in Series.all() and Series.any() not treating missing values correctly for dtype="string[pyarrow_numpy]" (GH 55367)
Fixed bug in Series.floordiv() for ArrowDtype (GH 55561)
Fixed bug in Series.mode() not sorting values for arrow backed string dtype (GH 55621)
Fixed bug in Series.rank() for string[pyarrow_numpy] dtype (GH 55362)
Fixed bug in Series.str.extractall() for ArrowDtype dtype being converted to object (GH 53846)
Fixed bug where PDEP-6 warning about setting an item of an incompatible dtype was being shown when creating a new conditional column (GH 55025)
Silence Period[B] warnings introduced by GH 53446 during normal plotting activity (GH 55138)
Fixed bug in Series constructor not inferring string dtype when NA is the first value and infer_string is set (:issue:` 55655`)

Other

Fixed non-working installation of optional dependency group output_formatting. Replacing underscore _ with a dash - fixes broken dependency resolution. A correct way to use now is pip install pandas[output-formatting].

Revision 1.53 / (download) - annotate - [select for diffs], Sat Oct 28 19:57:11 2023 UTC (6 weeks, 1 day ago) by wiz
Branch: MAIN
Changes since 1.52: +2 -2 lines
Diff to previous 1.52 (colored)

python/wheel.mk: simplify a lot, and switch to 'installer' for installation

This follows the recommended bootstrap method (flit_core, build, installer).

However, installer installs different files than pip, so update PLISTs
for all packages using wheel.mk and bump their PKGREVISIONs.

Revision 1.52 / (download) - annotate - [select for diffs], Mon Oct 23 06:37:48 2023 UTC (7 weeks ago) by wiz
Branch: MAIN
Changes since 1.51: +3 -2 lines
Diff to previous 1.51 (colored)

*: update for Python base package change

Instead of depending on one of the removed packages (that are now included
in the base Python packages), include batteries-included.mk to require
a Python version that supplies them.

Remove now included packages.

Bump PKGREVISION.

Revision 1.51 / (download) - annotate - [select for diffs], Sun Oct 15 00:05:44 2023 UTC (8 weeks, 1 day ago) by gutteridge
Branch: MAIN
Changes since 1.50: +4 -2 lines
Diff to previous 1.50 (colored)

py-pandas: fix minimum meson dependency pattern

We need to force a minimum with the most recent Python multi-version
patching.

Revision 1.50 / (download) - annotate - [select for diffs], Thu Oct 5 04:46:05 2023 UTC (2 months ago) by gutteridge
Branch: MAIN
Changes since 1.49: +8 -1 lines
Diff to previous 1.49 (colored)

py-pandas: fix (sandboxed) non-default Python builds

Another issue where Meson isn't versioned in pkgsrc, so we end up with
it "helpfully" supplying the path to Python it believes is correct,
which is wrong for any non-default Python version. (The 2.1.0 version
of this package carried a similar fix, which was removed in the update
to 2.1.1; a variation of it is restored here.)

Separately, this package directly expresses a minimum Meson version, so
reflect that as well.

Revision 1.49 / (download) - annotate - [select for diffs], Thu Sep 28 16:01:24 2023 UTC (2 months, 1 week ago) by adam
Branch: MAIN
Changes since 1.48: +2 -8 lines
Diff to previous 1.48 (colored)

py-pandas: updated to 2.1.1

What„ŗ—‘ new in 2.1.1 (September 20, 2023)

These are the changes in pandas 2.1.1. See Release notes for a full changelog including other versions of pandas.

Fixed regressions

Fixed regression in concat() when DataFrame „ŗŌ‘ have two different extension dtypes (GH 54848)
Fixed regression in merge() when merging over a PyArrow string index (GH 54894)
Fixed regression in read_csv() when usecols is given and dtypes is a dict for engine="python" (GH 54868)
Fixed regression in read_csv() when delim_whitespace is True (GH 54918, GH 54931)
Fixed regression in GroupBy.get_group() raising for axis=1 (GH 54858)
Fixed regression in DataFrame.__setitem__() raising AssertionError when setting a Series with a partial MultiIndex (GH 54875)
Fixed regression in DataFrame.filter() not respecting the order of elements for filter (GH 54980)
Fixed regression in DataFrame.to_sql() not roundtripping datetime columns correctly for sqlite (GH 54877)
Fixed regression in DataFrameGroupBy.agg() when aggregating a DataFrame with duplicate column names using a dictionary (GH 55006)
Fixed regression in MultiIndex.append() raising when appending overlapping IntervalIndex levels (GH 54934)
Fixed regression in Series.drop_duplicates() for PyArrow strings (GH 54904)
Fixed regression in Series.interpolate() raising when fill_value was given (GH 54920)
Fixed regression in Series.value_counts() raising for numeric data if bins was specified (GH 54857)
Fixed regression in comparison operations for PyArrow backed columns not propagating exceptions correctly (GH 54944)
Fixed regression when comparing a Series with datetime64 dtype with None (GH 54870)

Bug fixes

Fixed bug for ArrowDtype raising NotImplementedError for fixed-size list (GH 55000)
Fixed bug in DataFrame.stack() with future_stack=True and columns a non-MultiIndex consisting of tuples (GH 54948)
Fixed bug in Series.dt.tz() with ArrowDtype where a string was returned instead of a tzinfo object (GH 55003)
Fixed bug in Series.pct_change() and DataFrame.pct_change() showing unnecessary FutureWarning (GH 54981)

Other

Reverted the deprecation that disallowed Series.apply() returning a DataFrame when the passed-in callable returns a Series object (GH 52116)

Revision 1.48 / (download) - annotate - [select for diffs], Sat Sep 2 07:19:56 2023 UTC (3 months, 1 week ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2023Q3-base, pkgsrc-2023Q3
Changes since 1.47: +15 -7 lines
Diff to previous 1.47 (colored)

py-pandas: updated to 2.1.0

https://pandas.pydata.org/docs/whatsnew/v2.1.0.html

Revision 1.47 / (download) - annotate - [select for diffs], Mon Aug 28 10:34:02 2023 UTC (3 months, 1 week ago) by adam
Branch: MAIN
Changes since 1.46: +12 -9 lines
Diff to previous 1.46 (colored)

py-pandas: updated to 2.0.3

2.0.3

Fixed regressions

Bug in Timestamp.weekday`() was returning incorrect results before '0000-02-29' (GH53738)
Fixed performance regression in merging on datetime-like columns (GH53231)
Fixed regression when DataFrame.to_string() creates extra space for string dtypes (GH52690)

Bug fixes

Bug in DataFrame.convert_dtype() and Series.convert_dtype() when trying to convert ArrowDtype with dtype_backend="nullable_numpy" (GH53648)
Bug in RangeIndex.union() when using sort=True with another RangeIndex (GH53490)
Bug in Series.reindex() when expanding a non-nanosecond datetime or timedelta Series would not fill with NaT correctly (GH53497)
Bug in read_csv() when defining dtype with bool[pyarrow] for the "c" and "python" engines (GH53390)
Bug in Series.str.split() and Series.str.rsplit() with expand=True for ArrowDtype with pyarrow.string (GH53532)
Bug in indexing methods (e.g. DataFrame.__getitem__()) where taking the entire DataFrame/Series would raise an OverflowError when Copy on Write was enabled and the length of the array was over the maximum size a 32-bit integer can hold (GH53616)
Bug when constructing a DataFrame with columns of an ArrowDtype with a pyarrow.dictionary type that reindexes the data (GH53617)
Bug when indexing a DataFrame or Series with an Index with a timestamp ArrowDtype would raise an AttributeError (GH53644)


2.0.2

Fixed regressions

Fixed performance regression in GroupBy.apply() (GH53195)
Fixed regression in merge() on Windows when dtype is np.intc (GH52451)
Fixed regression in read_sql() dropping columns with duplicated column names (GH53117)
Fixed regression in DataFrame.loc() losing MultiIndex name when enlarging object (GH53053)
Fixed regression in DataFrame.to_string() printing a backslash at the end of the first row of data, instead of headers, when the DataFrame doesn„ŗ—’ fit the line width (GH53054)
Fixed regression in MultiIndex.join() returning levels in wrong order (GH53093)

Bug fixes

Bug in arrays.ArrowExtensionArray incorrectly assigning dict instead of list for .type with pyarrow.map_ and raising a NotImplementedError with pyarrow.struct (GH53328)
Bug in api.interchange.from_dataframe() was raising IndexError on empty categorical data (GH53077)
Bug in api.interchange.from_dataframe() was returning DataFrame„ŗ—‘ of incorrect sizes when called on slices (GH52824)
Bug in api.interchange.from_dataframe() was unnecessarily raising on bitmasks (GH49888)
Bug in merge() when merging on datetime columns on different resolutions (GH53200)
Bug in read_csv() raising OverflowError for engine="pyarrow" and parse_dates set (GH53295)
Bug in to_datetime() was inferring format to contain "%H" instead of "%I" if date contained „ŗ◊ĘM„ŗ/ „ŗ◊ĪM„ŗtokens (GH53147)
Bug in DataFrame.convert_dtypes() ignores convert_* keywords when set to False dtype_backend="pyarrow" (GH52872)
Bug in DataFrame.convert_dtypes() losing timezone for tz-aware dtypes and dtype_backend="pyarrow" (GH53382)
Bug in DataFrame.sort_values() raising for PyArrow dictionary dtype (GH53232)
Bug in Series.describe() treating pyarrow-backed timestamps and timedeltas as categorical data (GH53001)
Bug in Series.rename() not making a lazy copy when Copy-on-Write is enabled when a scalar is passed to it (GH52450)
Bug in pd.array() raising for NumPy array and pa.large_string or pa.large_binary (GH52590)
Bug in DataFrame.__getitem__() not preserving dtypes for MultiIndex partial keys (GH51895)

2.0.1

Fixed regressions

Fixed regression for subclassed Series when constructing from a dictionary (GH52445)
Fixed regression in SeriesGroupBy.agg() failing when grouping with categorical data, multiple groupings, as_index=False, and a list of aggregations (GH52760)
Fixed regression in DataFrame.pivot() changing Index name of input object (GH52629)
Fixed regression in DataFrame.resample() raising on a DataFrame with no columns (GH52484)
Fixed regression in DataFrame.sort_values() not resetting index when DataFrame is already sorted and ignore_index=True (GH52553)
Fixed regression in MultiIndex.isin() raising TypeError for Generator (GH52568)
Fixed regression in Series.describe() showing RuntimeWarning for extension dtype Series with one element (GH52515)
Fixed regression when adding a new column to a DataFrame when the DataFrame.columns was a RangeIndex and the new key was hashable but not a scalar (GH52652)

Bug fixes

Bug in Series.dt.days that would overflow int32 number of days (GH52391)
Bug in arrays.DatetimeArray constructor returning an incorrect unit when passed a non-nanosecond numpy datetime array (GH52555)
Bug in ArrowExtensionArray with duration dtype overflowing when constructed from data containing numpy NaT (GH52843)
Bug in Series.dt.round() when passing a freq of equal or higher resolution compared to the Series would raise a ZeroDivisionError (GH52761)
Bug in Series.median() with ArrowDtype returning an approximate median (GH52679)
Bug in api.interchange.from_dataframe() was unnecessarily raising on categorical dtypes (GH49889)
Bug in api.interchange.from_dataframe() was unnecessarily raising on large string dtypes (GH52795)
Bug in pandas.testing.assert_series_equal() where check_dtype=False would still raise for datetime or timedelta types with different resolutions (GH52449)
Bug in read_csv() casting PyArrow datetimes to NumPy when dtype_backend="pyarrow" and parse_dates is set causing a performance bottleneck in the process (GH52546)
Bug in to_datetime() and to_timedelta() when trying to convert numeric data with a ArrowDtype (GH52425)
Bug in to_numeric() with errors='coerce' and dtype_backend='pyarrow' with ArrowDtype data (GH52588)
Bug in ArrowDtype.__from_arrow__() not respecting if dtype is explicitly given (GH52533)
Bug in DataFrame.describe() not respecting ArrowDtype in include and exclude (GH52570)
Bug in DataFrame.max() and related casting different Timestamp resolutions always to nanoseconds (GH52524)
Bug in Series.describe() not returning ArrowDtype with pyarrow.float64 type with numeric data (GH52427)
Bug in Series.dt.tz_localize() incorrectly localizing timestamps with ArrowDtype (GH52677)
Bug in arithmetic between np.datetime64 and np.timedelta64 NaT scalars with units always returning nanosecond resolution (GH52295)
Bug in logical and comparison operations between ArrowDtype and numpy masked types (e.g. "boolean") (GH52625)
Fixed bug in merge() when merging with ArrowDtype one one and a NumPy dtype on the other side (GH52406)
Fixed segfault in Series.to_numpy() with null[pyarrow] dtype (GH52443)

Other

DataFrame created from empty dicts had columns of dtype object. It is now a RangeIndex (GH52404)
Series created from empty dicts had index of dtype object. It is now a RangeIndex (GH52404)
Implemented Series.str.split() and Series.str.rsplit() for ArrowDtype with pyarrow.string (GH52401)
Implemented most str accessor methods for ArrowDtype with pyarrow.string (GH52401)
Supplying a non-integer hashable key that tests False in api.types.is_scalar() now raises a KeyError for RangeIndex.get_loc(), like it does for Index.get_loc(). Previously it raised an InvalidIndexError (GH52652).

Revision 1.46 / (download) - annotate - [select for diffs], Tue Aug 1 23:20:47 2023 UTC (4 months, 1 week ago) by wiz
Branch: MAIN
Changes since 1.45: +2 -2 lines
Diff to previous 1.45 (colored)

*: remove more references to Python 3.7

Revision 1.45 / (download) - annotate - [select for diffs], Sat Jul 1 08:37:40 2023 UTC (5 months, 1 week ago) by wiz
Branch: MAIN
Changes since 1.44: +2 -2 lines
Diff to previous 1.44 (colored)

*: restrict py-numpy users to 3.9+ in preparation for update

Revision 1.44 / (download) - annotate - [select for diffs], Tue Apr 25 13:51:49 2023 UTC (7 months, 2 weeks ago) by jperkin
Branch: MAIN
CVS Tags: pkgsrc-2023Q2-base, pkgsrc-2023Q2
Changes since 1.43: +2 -2 lines
Diff to previous 1.43 (colored)

*: GCC_REQD must always be appended to.

Revision 1.43 / (download) - annotate - [select for diffs], Sat Jan 28 19:47:54 2023 UTC (10 months, 1 week ago) by he
Branch: MAIN
CVS Tags: pkgsrc-2023Q1-base, pkgsrc-2023Q1
Changes since 1.42: +5 -1 lines
Diff to previous 1.42 (colored)

math/py-pandas: note upstream pull request, and remove .orig file.

The .orig file would otherwise be installed, cauisng a PLIST
mismatch.

Revision 1.42 / (download) - annotate - [select for diffs], Wed Jan 25 14:05:16 2023 UTC (10 months, 2 weeks ago) by adam
Branch: MAIN
Changes since 1.41: +2 -2 lines
Diff to previous 1.41 (colored)

py-pandas: updated to 1.5.3

What's new in 1.5.3 (January 18, 2023)
--------------------------------------

These are the changes in pandas 1.5.3. See :ref:`release` for a full changelog
including other versions of pandas.

Fixed regressions
~~~~~~~~~~~~~~~~~
- Fixed performance regression in :meth:`Series.isin` when ``values`` is empty (:issue:`49839`)
- Fixed regression in :meth:`DataFrame.memory_usage` showing unnecessary ``FutureWarning`` when :class:`DataFrame` is empty (:issue:`50066`)
- Fixed regression in :meth:`.DataFrameGroupBy.transform` when used with ``as_index=False`` (:issue:`49834`)
- Enforced reversion of ``color`` as an alias for ``c`` and ``size`` as an alias for ``s`` in function :meth:`DataFrame.plot.scatter` (:issue:`49732`)
- Fixed regression in :meth:`.SeriesGroupBy.apply` setting a ``name`` attribute on the result if the result was a :class:`DataFrame` (:issue:`49907`)
- Fixed performance regression in setting with the :meth:`~DataFrame.at` indexer (:issue:`49771`)
- Fixed regression in the methods ``apply``, ``agg``, and ``transform`` when used with NumPy functions that informed users to supply ``numeric_only=True`` if the operation failed on non-numeric dtypes; such columns must be dropped prior to using these methods (:issue:`50538`)
- Fixed regression in :func:`to_datetime` raising ``ValueError`` when parsing array of ``float`` containing ``np.nan`` (:issue:`50237`)


Bug fixes
~~~~~~~~~
- Bug in the Copy-on-Write implementation losing track of views when indexing a :class:`DataFrame` with another :class:`DataFrame` (:issue:`50630`)
- Bug in :meth:`.Styler.to_excel` leading to error when unrecognized ``border-style`` (e.g. ``"hair"``) provided to Excel writers (:issue:`48649`)
- Bug in :meth:`Series.quantile` emitting warning from NumPy when :class:`Series` has only ``NA`` values (:issue:`50681`)
- Bug when chaining several :meth:`.Styler.concat` calls, only the last styler was concatenated (:issue:`49207`)
- Fixed bug when instantiating a :class:`DataFrame` subclass inheriting from ``typing.Generic`` that triggered a ``UserWarning`` on python 3.11 (:issue:`49649`)
- Bug in :func:`pivot_table` with NumPy 1.24 or greater when the :class:`DataFrame` columns has nested elements (:issue:`50342`)
- Bug in :func:`pandas.testing.assert_series_equal` (and equivalent ``assert_`` functions) when having nested data and using numpy >= 1.25 (:issue:`50360`)


Other
~~~~~
    If you are using :meth:`DataFrame.to_sql`, :func:`read_sql`, :func:`read_sql_table`, or :func:`read_sql_query` with SQLAlchemy 1.4.46 or greater,
    you may see a ``sqlalchemy.exc.RemovedIn20Warning``. These warnings can be safely ignored for the SQLAlchemy 1.4.x releases
    as pandas works toward compatibility with SQLAlchemy 2.0.

- Reverted deprecation (:issue:`45324`) of behavior of :meth:`Series.__getitem__` and :meth:`Series.__setitem__` slicing with an integer :class:`Index`; this will remain positional (:issue:`49612`)
- A ``FutureWarning`` raised when attempting to set values inplace with :meth:`DataFrame.loc` or :meth:`DataFrame.iloc` has been changed to a ``DeprecationWarning`` (:issue:`48673`)

Revision 1.41 / (download) - annotate - [select for diffs], Mon Dec 5 22:42:54 2022 UTC (12 months ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2022Q4-base, pkgsrc-2022Q4
Changes since 1.40: +4 -2 lines
Diff to previous 1.40 (colored)

py-pandas: needs C++ and GCC >= 8

Revision 1.40 / (download) - annotate - [select for diffs], Mon Nov 28 21:46:51 2022 UTC (12 months, 1 week ago) by adam
Branch: MAIN
Changes since 1.39: +12 -17 lines
Diff to previous 1.39 (colored)

py-pandas: updated to 1.5.2

What's new in 1.5.2 (November 21, 2022)
---------------------------------------

These are the changes in pandas 1.5.2. See :ref:`release` for a full changelog
including other versions of pandas.

Fixed regressions
~~~~~~~~~~~~~~~~~
- Fixed regression in :meth:`MultiIndex.join` for extension array dtypes (:issue:`49277`)
- Fixed regression in :meth:`Series.replace` raising ``RecursionError`` with numeric dtype and when specifying ``value=None`` (:issue:`45725`)
- Fixed regression in arithmetic operations for :class:`DataFrame` with :class:`MultiIndex` columns with different dtypes (:issue:`49769`)
- Fixed regression in :meth:`DataFrame.plot` preventing :class:`~matplotlib.colors.Colormap` instance
  from being passed using the ``colormap`` argument if Matplotlib 3.6+ is used (:issue:`49374`)
- Fixed regression in :func:`date_range` returning an invalid set of periods for ``CustomBusinessDay`` frequency and ``start`` date with timezone (:issue:`49441`)
- Fixed performance regression in groupby operations (:issue:`49676`)
- Fixed regression in :class:`Timedelta` constructor returning object of wrong type when subclassing ``Timedelta`` (:issue:`49579`)

Bug fixes
~~~~~~~~~
- Bug in the Copy-on-Write implementation losing track of views in certain chained indexing cases (:issue:`48996`)
- Fixed memory leak in :meth:`.Styler.to_excel` (:issue:`49751`)

Other
~~~~~
- Reverted ``color`` as an alias for ``c`` and ``size`` as an alias for ``s`` in function :meth:`DataFrame.plot.scatter` (:issue:`49732`)

Revision 1.39 / (download) - annotate - [select for diffs], Sun Apr 10 00:57:14 2022 UTC (20 months ago) by gutteridge
Branch: MAIN
CVS Tags: pkgsrc-2022Q3-base, pkgsrc-2022Q3, pkgsrc-2022Q2-base, pkgsrc-2022Q2
Changes since 1.38: +2 -2 lines
Diff to previous 1.38 (colored)

Fix build breakage from py-scipy now being Python >= 3.8

Revision 1.38 / (download) - annotate - [select for diffs], Sat Apr 9 21:33:50 2022 UTC (20 months ago) by gutteridge
Branch: MAIN
Changes since 1.37: +2 -2 lines
Diff to previous 1.37 (colored)

py-pandas: fix BUILDLINK_API_DEPENDS for py-numpy

Revision 1.37 / (download) - annotate - [select for diffs], Tue Jan 4 20:54:15 2022 UTC (23 months ago) by wiz
Branch: MAIN
CVS Tags: pkgsrc-2022Q1-base, pkgsrc-2022Q1
Changes since 1.36: +2 -1 lines
Diff to previous 1.36 (colored)

*: bump PKGREVISION for egg.mk users

They now have a tool dependency on py-setuptools instead of a DEPENDS

Revision 1.36 / (download) - annotate - [select for diffs], Thu Dec 30 13:05:37 2021 UTC (23 months, 1 week ago) by adam
Branch: MAIN
Changes since 1.35: +2 -2 lines
Diff to previous 1.35 (colored)

Forget about Python 3.6

Revision 1.35 / (download) - annotate - [select for diffs], Sun Dec 12 20:30:49 2021 UTC (23 months, 4 weeks ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2021Q4-base, pkgsrc-2021Q4
Changes since 1.34: +2 -2 lines
Diff to previous 1.34 (colored)

py-pandas: updated to 1.3.5

What's new in 1.3.5 (December 12, 2021)
---------------------------------------

Fixed regressions
~~~~~~~~~~~~~~~~~
- Fixed regression in :meth:`Series.equals` when comparing floats with dtype object to None (:issue:`44190`)
- Fixed regression in :func:`merge_asof` raising error when array was supplied as join key (:issue:`42844`)
- Fixed regression when resampling :class:`DataFrame` with :class:`DateTimeIndex` with empty groups and ``uint8``, ``uint16`` or ``uint32`` columns incorrectly raising ``RuntimeError`` (:issue:`43329`)
- Fixed regression in creating a :class:`DataFrame` from a timezone-aware :class:`Timestamp` scalar near a Daylight Savings Time transition (:issue:`42505`)
- Fixed performance regression in :func:`read_csv` (:issue:`44106`)
- Fixed regression in :meth:`Series.duplicated` and :meth:`Series.drop_duplicates` when Series has :class:`Categorical` dtype with boolean categories (:issue:`44351`)
- Fixed regression in :meth:`.GroupBy.sum` with ``timedelta64[ns]`` dtype containing ``NaT`` failing to treat that value as NA (:issue:`42659`)
- Fixed regression in :meth:`.RollingGroupby.cov` and :meth:`.RollingGroupby.corr` when ``other`` had the same shape as each group would incorrectly return superfluous groups in the result (:issue:`42915`)

Revision 1.34 / (download) - annotate - [select for diffs], Sun Nov 21 16:31:26 2021 UTC (2 years ago) by ryoon
Branch: MAIN
Changes since 1.33: +2 -2 lines
Diff to previous 1.33 (colored)

py-pandas: Update to 1.3.4

Changelog:
What's new in 1.3.4 (October 17, 2021)

These are the changes in pandas 1.3.4. See Release notes for a full changelog
including other versions of pandas.

-------------------------------------------------------------------------------

Fixed regressions

  * Fixed regression in DataFrame.convert_dtypes() incorrectly converts byte
    strings to strings (GH43183)

  * Fixed regression in GroupBy.agg() where it was failing silently with mixed
    data types along axis=1 and MultiIndex (GH43209)

  * Fixed regression in merge() with integer and NaN keys failing with outer
    merge (GH43550)

  * Fixed regression in DataFrame.corr() raising ValueError with method=
    "spearman" on 32-bit platforms (GH43588)

  * Fixed performance regression in MultiIndex.equals() (GH43549)

  * Fixed performance regression in GroupBy.first() and GroupBy.last() with
    StringDtype (GH41596)

  * Fixed regression in Series.cat.reorder_categories() failing to update the
    categories on the Series (GH43232)

  * Fixed regression in Series.cat.categories() setter failing to update the
    categories on the Series (GH43334)

  * Fixed regression in read_csv() raising UnicodeDecodeError exception when
    memory_map=True (GH43540)

  * Fixed regression in DataFrame.explode() raising AssertionError when column
    is any scalar which is not a string (GH43314)

  * Fixed regression in Series.aggregate() attempting to pass args and kwargs
    multiple times to the user supplied func in certain cases (GH43357)

  * Fixed regression when iterating over a DataFrame.groupby.rolling object
    causing the resulting DataFrames to have an incorrect index if the input
    groupings were not sorted (GH43386)

  * Fixed regression in DataFrame.groupby.rolling.cov() and
    DataFrame.groupby.rolling.corr() computing incorrect results if the input
    groupings were not sorted (GH43386)

-------------------------------------------------------------------------------

Bug fixes

  * Fixed bug in pandas.DataFrame.groupby.rolling() and
    pandas.api.indexers.FixedForwardWindowIndexer leading to segfaults and
    window endpoints being mixed across groups (GH43267)

  * Fixed bug in GroupBy.mean() with datetimelike values including NaT values
    returning incorrect results (GH43132)

  * Fixed bug in Series.aggregate() not passing the first args to the user
    supplied func in certain cases (GH43357)

  * Fixed memory leaks in Series.rolling.quantile() and Series.rolling.median()
    (GH43339)

-------------------------------------------------------------------------------

Other

  * The minimum version of Cython needed to compile pandas is now 0.29.24 (
    GH43729)


What's new in 1.3.3 (September 12, 2021)

These are the changes in pandas 1.3.3. See Release notes for a full changelog
including other versions of pandas.

-------------------------------------------------------------------------------

Fixed regressions

  * Fixed regression in DataFrame constructor failing to broadcast for defined
    Index and len one list of Timestamp (GH42810)

  * Fixed regression in GroupBy.agg() incorrectly raising in some cases (
    GH42390)

  * Fixed regression in GroupBy.apply() where nan values were dropped even with
    dropna=False (GH43205)

  * Fixed regression in GroupBy.quantile() which was failing with pandas.NA (
    GH42849)

  * Fixed regression in merge() where on columns with ExtensionDtype or bool
    data types were cast to object in right and outer merge (GH40073)

  * Fixed regression in RangeIndex.where() and RangeIndex.putmask() raising
    AssertionError when result did not represent a RangeIndex (GH43240)

  * Fixed regression in read_parquet() where the fastparquet engine would not
    work properly with fastparquet 0.7.0 (GH43075)

  * Fixed regression in DataFrame.loc.__setitem__() raising ValueError when
    setting array as cell value (GH43422)

  * Fixed regression in is_list_like() where objects with __iter__ set to None
    would be identified as iterable (GH43373)

  * Fixed regression in DataFrame.__getitem__() raising error for slice of
    DatetimeIndex when index is non monotonic (GH43223)

  * Fixed regression in Resampler.aggregate() when used after column selection
    would raise if func is a list of aggregation functions (GH42905)

  * Fixed regression in DataFrame.corr() where Kendall correlation would
    produce incorrect results for columns with repeated values (GH43401)

  * Fixed regression in DataFrame.groupby() where aggregation on columns with
    object types dropped results on those columns (GH42395, GH43108)

  * Fixed regression in Series.fillna() raising TypeError when filling float
    Series with list-like fill value having a dtype which couldn't cast
    lostlessly (like float32 filled with float64) (GH43424)

  * Fixed regression in read_csv() raising AttributeError when the file handle
    is an tempfile.SpooledTemporaryFile object (GH43439)

  * Fixed performance regression in
    core.window.ewm.ExponentialMovingWindow.mean() (GH42333)

-------------------------------------------------------------------------------

Performance improvements

  * Performance improvement for DataFrame.__setitem__() when the key or value
    is not a DataFrame, or key is not list-like (GH43274)

-------------------------------------------------------------------------------

Bug fixes

  * Fixed bug in DataFrameGroupBy.agg() and DataFrameGroupBy.transform() with
    engine="numba" where index data was not being correctly passed into func (
    GH43133)


What's new in 1.3.2 (August 15, 2021)

These are the changes in pandas 1.3.2. See Release notes for a full changelog
including other versions of pandas.

-------------------------------------------------------------------------------

Fixed regressions

  * Performance regression in DataFrame.isin() and Series.isin() for nullable
    data types (GH42714)

  * Regression in updating values of Series using boolean index, created by
    using DataFrame.pop() (GH42530)

  * Regression in DataFrame.from_records() with empty records (GH42456)

  * Fixed regression in DataFrame.shift() where TypeError occurred when
    shifting DataFrame created by concatenation of slices and fills with values
    (GH42719)

  * Regression in DataFrame.agg() when the func argument returned lists and
    axis=1 (GH42727)

  * Regression in DataFrame.drop() does nothing if MultiIndex has duplicates
    and indexer is a tuple or list of tuples (GH42771)

  * Fixed regression where read_csv() raised a ValueError when parameters names
    and prefix were both set to None (GH42387)

  * Fixed regression in comparisons between Timestamp object and datetime64
    objects outside the implementation bounds for nanosecond datetime64 (
    GH42794)

  * Fixed regression in Styler.highlight_min() and Styler.highlight_max() where
    pandas.NA was not successfully ignored (GH42650)

  * Fixed regression in concat() where copy=False was not honored in axis=1
    Series concatenation (GH42501)

  * Regression in Series.nlargest() and Series.nsmallest() with nullable
    integer or float dtype (GH42816)

  * Fixed regression in Series.quantile() with Int64Dtype (GH42626)

  * Fixed regression in Series.groupby() and DataFrame.groupby() where
    supplying the by argument with a Series named with a tuple would
    incorrectly raise (GH42731)

-------------------------------------------------------------------------------

Bug fixes

  * Bug in read_excel() modifies the dtypes dictionary when reading a file with
    duplicate columns (GH42462)

  * 1D slices over extension types turn into N-dimensional slices over
    ExtensionArrays (GH42430)

  * Fixed bug in Series.rolling() and DataFrame.rolling() not calculating
    window bounds correctly for the first row when center=True and window is an
    offset that covers all the rows (GH42753)

  * Styler.hide_columns() now hides the index name header row as well as column
    headers (GH42101)

  * Styler.set_sticky() has amended CSS to control the column/index names and
    ensure the correct sticky positions (GH42537)

  * Bug in de-serializing datetime indexes in PYTHONOPTIMIZED mode (GH42866)


What's new in 1.3.1 (July 25, 2021)

These are the changes in pandas 1.3.1. See Release notes for a full changelog
including other versions of pandas.

-------------------------------------------------------------------------------

Fixed regressions

  * Pandas could not be built on PyPy (GH42355)

  * DataFrame constructed with an older version of pandas could not be
    unpickled (GH42345)

  * Performance regression in constructing a DataFrame from a dictionary of
    dictionaries (GH42248)

  * Fixed regression in DataFrame.agg() dropping values when the DataFrame had
    an Extension Array dtype, a duplicate index, and axis=1 (GH42380)

  * Fixed regression in DataFrame.astype() changing the order of noncontiguous
    data (GH42396)

  * Performance regression in DataFrame in reduction operations requiring
    casting such as DataFrame.mean() on integer data (GH38592)

  * Performance regression in DataFrame.to_dict() and Series.to_dict() when
    orient argument one of 'records', 'dict', or 'split' (GH42352)

  * Fixed regression in indexing with a list subclass incorrectly raising
    TypeError (GH42433, GH42461)

  * Fixed regression in DataFrame.isin() and Series.isin() raising TypeError
    with nullable data containing at least one missing value (GH42405)

  * Regression in concat() between objects with bool dtype and integer dtype
    casting to object instead of to integer (GH42092)

  * Bug in Series constructor not accepting a dask.Array (GH38645)

  * Fixed regression for SettingWithCopyWarning displaying incorrect stacklevel
    (GH42570)

  * Fixed regression for merge_asof() raising KeyError when one of the by
    columns is in the index (GH34488)

  * Fixed regression in to_datetime() returning pd.NaT for inputs that produce
    duplicated values, when cache=True (GH42259)

  * Fixed regression in SeriesGroupBy.value_counts() that resulted in an
    IndexError when called on a Series with one row (GH42618)

-------------------------------------------------------------------------------

Bug fixes

  * Fixed bug in DataFrame.transpose() dropping values when the DataFrame had
    an Extension Array dtype and a duplicate index (GH42380)

  * Fixed bug in DataFrame.to_xml() raising KeyError when called with index=
    False and an offset index (GH42458)

  * Fixed bug in Styler.set_sticky() not handling index names correctly for
    single index columns case (GH42537)

  * Fixed bug in DataFrame.copy() failing to consolidate blocks in the result (
    GH42579)


What's new in 1.3.0 (July 2, 2021)

These are the changes in pandas 1.3.0. See Release notes for a full changelog
including other versions of pandas.

Warning

When reading new Excel 2007+ (.xlsx) files, the default argument engine=None to
read_excel() will now result in using the openpyxl engine in all cases when the
option io.excel.xlsx.reader is set to "auto". Previously, some cases would use
the xlrd engine instead. See What's new 1.2.0 for background on this change.

-------------------------------------------------------------------------------

Enhancements

-------------------------------------------------------------------------------

Custom HTTP(s) headers when reading csv or json files

When reading from a remote URL that is not handled by fsspec (e.g. HTTP and
HTTPS) the dictionary passed to storage_options will be used to create the
headers included in the request. This can be used to control the User-Agent
header or send other custom headers (GH36688). For example:

In [1]: headers = {"User-Agent": "pandas"}

In [2]: df = pd.read_csv(
   ...:     "https://download.bls.gov/pub/time.series/cu/cu.item",
   ...:     sep="\t",
   ...:     storage_options=headers
   ...: )
   ...:

-------------------------------------------------------------------------------

Read and write XML documents

We added I/O support to read and render shallow versions of XML documents with
read_xml() and DataFrame.to_xml(). Using lxml as parser, both XPath 1.0 and
XSLT 1.0 are available. (GH27554)

In [1]: xml = """<?xml version='1.0' encoding='utf-8'?>
   ...: <data>
   ...:  <row>
   ...:     <shape>square</shape>
   ...:     <degrees>360</degrees>
   ...:     <sides>4.0</sides>
   ...:  </row>
   ...:  <row>
   ...:     <shape>circle</shape>
   ...:     <degrees>360</degrees>
   ...:     <sides/>
   ...:  </row>
   ...:  <row>
   ...:     <shape>triangle</shape>
   ...:     <degrees>180</degrees>
   ...:     <sides>3.0</sides>
   ...:  </row>
   ...:  </data>"""

In [2]: df = pd.read_xml(xml)
In [3]: df
Out[3]:
      shape  degrees  sides
0    square      360    4.0
1    circle      360    NaN
2  triangle      180    3.0

In [4]: df.to_xml()
Out[4]:
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row>
    <index>0</index>
    <shape>square</shape>
    <degrees>360</degrees>
    <sides>4.0</sides>
  </row>
  <row>
    <index>1</index>
    <shape>circle</shape>
    <degrees>360</degrees>
    <sides/>
  </row>
  <row>
    <index>2</index>
    <shape>triangle</shape>
    <degrees>180</degrees>
    <sides>3.0</sides>
  </row>
</data>

For more, see Writing XML in the user guide on IO tools.

-------------------------------------------------------------------------------

Styler enhancements

We provided some focused development on Styler. See also the Styler
documentation which has been revised and improved (GH39720, GH39317, GH40493).

      + The method Styler.set_table_styles() can now accept more natural CSS
        language for arguments, such as 'color:red;' instead of [('color',
        'red')] (GH39563)

      + The methods Styler.highlight_null(), Styler.highlight_min(), and
        Styler.highlight_max() now allow custom CSS highlighting instead of the
        default background coloring (GH40242)

      + Styler.apply() now accepts functions that return an ndarray when axis=
        None, making it now consistent with the axis=0 and axis=1 behavior (
        GH39359)

      + When incorrectly formatted CSS is given via Styler.apply() or
        Styler.applymap(), an error is now raised upon rendering (GH39660)

      + Styler.format() now accepts the keyword argument escape for optional
        HTML and LaTeX escaping (GH40388, GH41619)

      + Styler.background_gradient() has gained the argument gmap to supply a
        specific gradient map for shading (GH22727)

      + Styler.clear() now clears Styler.hidden_index and Styler.hidden_columns
        as well (GH40484)

      + Added the method Styler.highlight_between() (GH39821)

      + Added the method Styler.highlight_quantile() (GH40926)

      + Added the method Styler.text_gradient() (GH41098)

      + Added the method Styler.set_tooltips() to allow hover tooltips; this
        can be used enhance interactive displays (GH21266, GH40284)

      + Added the parameter precision to the method Styler.format() to control
        the display of floating point numbers (GH40134)

      + Styler rendered HTML output now follows the w3 HTML Style Guide (
        GH39626)

      + Many features of the Styler class are now either partially or fully
        usable on a DataFrame with a non-unique indexes or columns (GH41143)

      + One has greater control of the display through separate sparsification
        of the index or columns using the new styler options, which are also
        usable via option_context() (GH41142)

      + Added the option styler.render.max_elements to avoid browser overload
        when styling large DataFrames (GH40712)

      + Added the method Styler.to_latex() (GH21673, GH42320), which also
        allows some limited CSS conversion (GH40731)

      + Added the method Styler.to_html() (GH13379)

      + Added the method Styler.set_sticky() to make index and column headers
        permanently visible in scrolling HTML frames (GH29072)

-------------------------------------------------------------------------------

DataFrame constructor honors copy=False with dict

When passing a dictionary to DataFrame with copy=False, a copy will no longer
be made (GH32960).

In [3]: arr = np.array([1, 2, 3])

In [4]: df = pd.DataFrame({"A": arr, "B": arr.copy()}, copy=False)

In [5]: df
Out[5]:
   A  B
0  1  1
1  2  2
2  3  3

df["A"] remains a view on arr:

In [6]: arr[0] = 0

In [7]: assert df.iloc[0, 0] == 0

The default behavior when not passing copy will remain unchanged, i.e. a copy
will be made.

-------------------------------------------------------------------------------

PyArrow backed string data type

We've enhanced the StringDtype, an extension type dedicated to string data. (
GH39908)

It is now possible to specify a storage keyword option to StringDtype. Use
pandas options or specify the dtype using dtype='string[pyarrow]' to allow the
StringArray to be backed by a PyArrow array instead of a NumPy array of Python
objects.

The PyArrow backed StringArray requires pyarrow 1.0.0 or greater to be
installed.

Warning

string[pyarrow] is currently considered experimental. The implementation and
parts of the API may change without warning.

In [8]: pd.Series(['abc', None, 'def'], dtype=pd.StringDtype(storage="pyarrow"))
Out[8]:
0     abc
1    <NA>
2     def
dtype: string

You can use the alias "string[pyarrow]" as well.

In [9]: s = pd.Series(['abc', None, 'def'], dtype="string[pyarrow]")

In [10]: s
Out[10]:
0     abc
1    <NA>
2     def
dtype: string

You can also create a PyArrow backed string array using pandas options.

In [11]: with pd.option_context("string_storage", "pyarrow"):
   ....:     s = pd.Series(['abc', None, 'def'], dtype="string")
   ....:

In [12]: s
Out[12]:
0     abc
1    <NA>
2     def
dtype: string

The usual string accessor methods work. Where appropriate, the return type of
the Series or columns of a DataFrame will also have string dtype.

In [13]: s.str.upper()
Out[13]:
0     ABC
1    <NA>
2     DEF
dtype: string

In [14]: s.str.split('b', expand=True).dtypes
Out[14]:
0    string
1    string
dtype: object

String accessor methods returning integers will return a value with Int64Dtype

In [15]: s.str.count("a")
Out[15]:
0       1
1    <NA>
2       0
dtype: Int64

-------------------------------------------------------------------------------

Centered datetime-like rolling windows

When performing rolling calculations on DataFrame and Series objects with a
datetime-like index, a centered datetime-like window can now be used (GH38780).
For example:

In [16]: df = pd.DataFrame(
   ....:     {"A": [0, 1, 2, 3, 4]}, index=pd.date_range("2020", periods=5, freq="1D")
   ....: )
   ....:

In [17]: df
Out[17]:
            A
2020-01-01  0
2020-01-02  1
2020-01-03  2
2020-01-04  3
2020-01-05  4

In [18]: df.rolling("2D", center=True).mean()
Out[18]:
              A
2020-01-01  0.5
2020-01-02  1.5
2020-01-03  2.5
2020-01-04  3.5
2020-01-05  4.0

-------------------------------------------------------------------------------

Other enhancements

  * DataFrame.rolling(), Series.rolling(), DataFrame.expanding(), and
    Series.expanding() now support a method argument with a 'table' option that
    performs the windowing operation over an entire DataFrame. See Window
    Overview for performance and functional benefits (GH15095, GH38995)

  * ExponentialMovingWindow now support a online method that can perform mean
    calculations in an online fashion. See Window Overview (GH41673)

  * Added MultiIndex.dtypes() (GH37062)

  * Added end and end_day options for the origin argument in DataFrame.resample
    () (GH37804)

  * Improved error message when usecols and names do not match for read_csv()
    and engine="c" (GH29042)

  * Improved consistency of error messages when passing an invalid win_type
    argument in Window methods (GH15969)

  * read_sql_query() now accepts a dtype argument to cast the columnar data
    from the SQL database based on user input (GH10285)

  * read_csv() now raising ParserWarning if length of header or given names
    does not match length of data when usecols is not specified (GH21768)

  * Improved integer type mapping from pandas to SQLAlchemy when using
    DataFrame.to_sql() (GH35076)

  * to_numeric() now supports downcasting of nullable ExtensionDtype objects (
    GH33013)

  * Added support for dict-like names in MultiIndex.set_names and
    MultiIndex.rename (GH20421)

  * read_excel() can now auto-detect .xlsb files and older .xls files (GH35416,
    GH41225)

  * ExcelWriter now accepts an if_sheet_exists parameter to control the
    behavior of append mode when writing to existing sheets (GH40230)

  * Rolling.sum(), Expanding.sum(), Rolling.mean(), Expanding.mean(),
    ExponentialMovingWindow.mean(), Rolling.median(), Expanding.median(),
    Rolling.max(), Expanding.max(), Rolling.min(), and Expanding.min() now
    support Numba execution with the engine keyword (GH38895, GH41267)

  * DataFrame.apply() can now accept NumPy unary operators as strings, e.g.
    df.apply("sqrt"), which was already the case for Series.apply() (GH39116)

  * DataFrame.apply() can now accept non-callable DataFrame properties as
    strings, e.g. df.apply("size"), which was already the case for Series.apply
    () (GH39116)

  * DataFrame.applymap() can now accept kwargs to pass on to the user-provided
    func (GH39987)

  * Passing a DataFrame indexer to iloc is now disallowed for
    Series.__getitem__() and DataFrame.__getitem__() (GH39004)

  * Series.apply() can now accept list-like or dictionary-like arguments that
    aren't lists or dictionaries, e.g. ser.apply(np.array(["sum", "mean"])),
    which was already the case for DataFrame.apply() (GH39140)

  * DataFrame.plot.scatter() can now accept a categorical column for the
    argument c (GH12380, GH31357)

  * Series.loc() now raises a helpful error message when the Series has a
    MultiIndex and the indexer has too many dimensions (GH35349)

  * read_stata() now supports reading data from compressed files (GH26599)

  * Added support for parsing ISO 8601-like timestamps with negative signs to
    Timedelta (GH37172)

  * Added support for unary operators in FloatingArray (GH38749)

  * RangeIndex can now be constructed by passing a range object directly e.g.
    pd.RangeIndex(range(3)) (GH12067)

  * Series.round() and DataFrame.round() now work with nullable integer and
    floating dtypes (GH38844)

  * read_csv() and read_json() expose the argument encoding_errors to control
    how encoding errors are handled (GH39450)

  * GroupBy.any() and GroupBy.all() use Kleene logic with nullable data types (
    GH37506)

  * GroupBy.any() and GroupBy.all() return a BooleanDtype for columns with
    nullable data types (GH33449)

  * GroupBy.any() and GroupBy.all() raising with object data containing pd.NA
    even when skipna=True (GH37501)

  * GroupBy.rank() now supports object-dtype data (GH38278)

  * Constructing a DataFrame or Series with the data argument being a Python
    iterable that is not a NumPy ndarray consisting of NumPy scalars will now
    result in a dtype with a precision the maximum of the NumPy scalars; this
    was already the case when data is a NumPy ndarray (GH40908)

  * Add keyword sort to pivot_table() to allow non-sorting of the result (
    GH39143)

  * Add keyword dropna to DataFrame.value_counts() to allow counting rows that
    include NA values (GH41325)

  * Series.replace() will now cast results to PeriodDtype where possible
    instead of object dtype (GH41526)

  * Improved error message in corr and cov methods on Rolling, Expanding, and
    ExponentialMovingWindow when other is not a DataFrame or Series (GH41741)

  * Series.between() can now accept left or right as arguments to inclusive to
    include only the left or right boundary (GH40245)

  * DataFrame.explode() now supports exploding multiple columns. Its column
    argument now also accepts a list of str or tuples for exploding on multiple
    columns at the same time (GH39240)

  * DataFrame.sample() now accepts the ignore_index argument to reset the index
    after sampling, similar to DataFrame.drop_duplicates() and
    DataFrame.sort_values() (GH38581)

-------------------------------------------------------------------------------

Notable bug fixes

These are bug fixes that might have notable behavior changes.

-------------------------------------------------------------------------------

Categorical.unique now always maintains same dtype as original

Previously, when calling Categorical.unique() with categorical data, unused
categories in the new array would be removed, making the dtype of the new array
different than the original (GH18291)

As an example of this, given:

In [19]: dtype = pd.CategoricalDtype(['bad', 'neutral', 'good'], ordered=True)

In [20]: cat = pd.Categorical(['good', 'good', 'bad', 'bad'], dtype=dtype)

In [21]: original = pd.Series(cat)

In [22]: unique = original.unique()

Previous behavior:

In [1]: unique
['good', 'bad']
Categories (2, object): ['bad' < 'good']
In [2]: original.dtype == unique.dtype
False

New behavior:

In [23]: unique
Out[23]:
['good', 'bad']
Categories (3, object): ['bad' < 'neutral' < 'good']

In [24]: original.dtype == unique.dtype
Out[24]: True

-------------------------------------------------------------------------------

Preserve dtypes in DataFrame.combine_first()

DataFrame.combine_first() will now preserve dtypes (GH7509)

In [25]: df1 = pd.DataFrame({"A": [1, 2, 3], "B": [1, 2, 3]}, index=[0, 1, 2])

In [26]: df1
Out[26]:
   A  B
0  1  1
1  2  2
2  3  3

In [27]: df2 = pd.DataFrame({"B": [4, 5, 6], "C": [1, 2, 3]}, index=[2, 3, 4])

In [28]: df2
Out[28]:
   B  C
2  4  1
3  5  2
4  6  3

In [29]: combined = df1.combine_first(df2)

Previous behavior:

In [1]: combined.dtypes
Out[2]:
A    float64
B    float64
C    float64
dtype: object

New behavior:

In [30]: combined.dtypes
Out[30]:
A    float64
B      int64
C    float64
dtype: object

-------------------------------------------------------------------------------

Groupby methods agg and transform no longer changes return dtype for callables


Previously the methods DataFrameGroupBy.aggregate(), SeriesGroupBy.aggregate(),
DataFrameGroupBy.transform(), and SeriesGroupBy.transform() might cast the
result dtype when the argument func is callable, possibly leading to
undesirable results (GH21240). The cast would occur if the result is numeric
and casting back to the input dtype does not change any values as measured by
np.allclose. Now no such casting occurs.

In [31]: df = pd.DataFrame({'key': [1, 1], 'a': [True, False], 'b': [True, True]})

In [32]: df
Out[32]:
   key      a     b
0    1   True  True
1    1  False  True

Previous behavior:

In [5]: df.groupby('key').agg(lambda x: x.sum())
Out[5]:
        a  b
key
1    True  2

New behavior:

In [33]: df.groupby('key').agg(lambda x: x.sum())
Out[33]:
     a  b
key
1    1  2

-------------------------------------------------------------------------------

float result for GroupBy.mean(), GroupBy.median(), and GroupBy.var()

Previously, these methods could result in different dtypes depending on the
input values. Now, these methods will always return a float dtype. (GH41137)

In [34]: df = pd.DataFrame({'a': [True], 'b': [1], 'c': [1.0]})

Previous behavior:

In [5]: df.groupby(df.index).mean()
Out[5]:
        a  b    c
0    True  1  1.0

New behavior:

In [35]: df.groupby(df.index).mean()
Out[35]:
     a    b    c
0  1.0  1.0  1.0

-------------------------------------------------------------------------------

Try operating inplace when setting values with loc and iloc

When setting an entire column using loc or iloc, pandas will try to insert the
values into the existing data rather than create an entirely new array.

In [36]: df = pd.DataFrame(range(3), columns=["A"], dtype="float64")

In [37]: values = df.values

In [38]: new = np.array([5, 6, 7], dtype="int64")

In [39]: df.loc[[0, 1, 2], "A"] = new

In both the new and old behavior, the data in values is overwritten, but in the
old behavior the dtype of df["A"] changed to int64.

Previous behavior:

In [1]: df.dtypes
Out[1]:
A    int64
dtype: object
In [2]: np.shares_memory(df["A"].values, new)
Out[2]: False
In [3]: np.shares_memory(df["A"].values, values)
Out[3]: False

In pandas 1.3.0, df continues to share data with values

New behavior:

In [40]: df.dtypes
Out[40]:
A    float64
dtype: object

In [41]: np.shares_memory(df["A"], new)
Out[41]: False

In [42]: np.shares_memory(df["A"], values)
Out[42]: True

-------------------------------------------------------------------------------

Never operate inplace when setting frame[keys] = values

When setting multiple columns using frame[keys] = values new arrays will
replace pre-existing arrays for these keys, which will not be over-written (
GH39510). As a result, the columns will retain the dtype(s) of values, never
casting to the dtypes of the existing arrays.

In [43]: df = pd.DataFrame(range(3), columns=["A"], dtype="float64")

In [44]: df[["A"]] = 5

In the old behavior, 5 was cast to float64 and inserted into the existing array
backing df:

Previous behavior:

In [1]: df.dtypes
Out[1]:
A    float64

In the new behavior, we get a new array, and retain an integer-dtyped 5:

New behavior:

In [45]: df.dtypes
Out[45]:
A    int64
dtype: object

-------------------------------------------------------------------------------

Consistent casting with setting into Boolean Series

Setting non-boolean values into a Series with dtype=bool now consistently casts
to dtype=object (GH38709)

In [46]: orig = pd.Series([True, False])

In [47]: ser = orig.copy()

In [48]: ser.iloc[1] = np.nan

In [49]: ser2 = orig.copy()

In [50]: ser2.iloc[1] = 2.0

Previous behavior:

In [1]: ser
Out [1]:
0    1.0
1    NaN
dtype: float64

In [2]:ser2
Out [2]:
0    True
1     2.0
dtype: object

New behavior:

In [51]: ser
Out[51]:
0    True
1     NaN
dtype: object

In [52]: ser2
Out[52]:
0    True
1     2.0
dtype: object

-------------------------------------------------------------------------------

GroupBy.rolling no longer returns grouped-by column in values

The group-by column will now be dropped from the result of a groupby.rolling
operation (GH32262)

In [53]: df = pd.DataFrame({"A": [1, 1, 2, 3], "B": [0, 1, 2, 3]})

In [54]: df
Out[54]:
   A  B
0  1  0
1  1  1
2  2  2
3  3  3

Previous behavior:

In [1]: df.groupby("A").rolling(2).sum()
Out[1]:
       A    B
A
1 0  NaN  NaN
1    2.0  1.0
2 2  NaN  NaN
3 3  NaN  NaN

New behavior:

In [55]: df.groupby("A").rolling(2).sum()
Out[55]:
       B
A
1 0  NaN
  1  1.0
2 2  NaN
3 3  NaN

-------------------------------------------------------------------------------

Removed artificial truncation in rolling variance and standard deviation

Rolling.std() and Rolling.var() will no longer artificially truncate results
that are less than ~1e-8 and ~1e-15 respectively to zero (GH37051, GH40448,
GH39872).

However, floating point artifacts may now exist in the results when rolling
over larger values.

In [56]: s = pd.Series([7, 5, 5, 5])

In [57]: s.rolling(3).var()
Out[57]:
0             NaN
1             NaN
2    1.333333e+00
3    4.440892e-16
dtype: float64

-------------------------------------------------------------------------------

GroupBy.rolling with MultiIndex no longer drops levels in the result

GroupBy.rolling() will no longer drop levels of a DataFrame with a MultiIndex
in the result. This can lead to a perceived duplication of levels in the
resulting MultiIndex, but this change restores the behavior that was present in
version 1.1.3 (GH38787, GH38523).

In [58]: index = pd.MultiIndex.from_tuples([('idx1', 'idx2')], names=['label1', 'label2'])

In [59]: df = pd.DataFrame({'a': [1], 'b': [2]}, index=index)

In [60]: df
Out[60]:
               a  b
label1 label2
idx1   idx2    1  2

Previous behavior:

In [1]: df.groupby('label1').rolling(1).sum()
Out[1]:
          a    b
label1
idx1    1.0  2.0

New behavior:

In [61]: df.groupby('label1').rolling(1).sum()
Out[61]:
                        a    b
label1 label1 label2
idx1   idx1   idx2    1.0  2.0

-------------------------------------------------------------------------------

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

numpy           1.17.3          X        X

pytz            2017.3          X

python-dateutil 2.7.3           X

bottleneck      1.2.1

numexpr         2.7.0                    X

pytest (dev)    6.0                      X

mypy (dev)      0.812                    X

setuptools      38.6.0                   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

beautifulsoup4 4.6.0

fastparquet    0.4.0           X

fsspec         0.7.4

gcsfs          0.6.0

lxml           4.3.0

matplotlib     2.2.3

numba          0.46.0

openpyxl       3.0.0           X

pyarrow        0.17.0          X

pymysql        0.8.1           X

pytables       3.5.1

s3fs           0.4.0

scipy          1.2.0

sqlalchemy     1.3.0           X

tabulate       0.8.7           X

xarray         0.12.0

xlrd           1.2.0

xlsxwriter     1.0.2

xlwt           1.3.0

pandas-gbq     0.12.0

See Dependencies and Optional dependencies for more.

-------------------------------------------------------------------------------

Other API changes

  * Partially initialized CategoricalDtype objects (i.e. those with categories=
    None) will no longer compare as equal to fully initialized dtype objects (
    GH38516)

  * Accessing _constructor_expanddim on a DataFrame and _constructor_sliced on
    a Series now raise an AttributeError. Previously a NotImplementedError was
    raised (GH38782)

  * Added new engine and **engine_kwargs parameters to DataFrame.to_sql() to
    support other future 'SQL engines'. Currently we still only use
    SQLAlchemy under the hood, but more engines are planned to be supported
    such as turbodbc (GH36893)

  * Removed redundant freq from PeriodIndex string representation (GH41653)

  * ExtensionDtype.construct_array_type() is now a required method instead of
    an optional one for ExtensionDtype subclasses (GH24860)

  * Calling hash on non-hashable pandas objects will now raise TypeError with
    the built-in error message (e.g. unhashable type: 'Series'). Previously it
    would raise a custom message such as 'Series' objects are mutable, thus
    they cannot be hashed. Furthermore, isinstance(<Series>,
    abc.collections.Hashable) will now return False (GH40013)

  * Styler.from_custom_template() now has two new arguments for template names,
    and removed the old name, due to template inheritance having been
    introducing for better parsing (GH42053). Subclassing modifications to
    Styler attributes are also needed.

-------------------------------------------------------------------------------

Build

  * Documentation in .pptx and .pdf formats are no longer included in wheels or
    source distributions. (GH30741)

-------------------------------------------------------------------------------

Deprecations

-------------------------------------------------------------------------------

Deprecated dropping nuisance columns in DataFrame reductions and
DataFrameGroupBy operations

Calling a reduction (e.g. .min, .max, .sum) on a DataFrame with numeric_only=
None (the default), columns where the reduction raises a TypeError are silently
ignored and dropped from the result.

This behavior is deprecated. In a future version, the TypeError will be raised,
and users will need to select only valid columns before calling the function.

For example:

In [62]: df = pd.DataFrame({"A": [1, 2, 3, 4], "B": pd.date_range("2016-01-01", periods=4)})

In [63]: df
Out[63]:
   A          B
0  1 2016-01-01
1  2 2016-01-02
2  3 2016-01-03
3  4 2016-01-04

Old behavior:

In [3]: df.prod()
Out[3]:
Out[3]:
A    24
dtype: int64

Future behavior:

In [4]: df.prod()
...
TypeError: 'DatetimeArray' does not implement reduction 'prod'

In [5]: df[["A"]].prod()
Out[5]:
A    24
dtype: int64

Similarly, when applying a function to DataFrameGroupBy, columns on which the
function raises TypeError are currently silently ignored and dropped from the
result.

This behavior is deprecated. In a future version, the TypeError will be raised,
and users will need to select only valid columns before calling the function.

For example:

In [64]: df = pd.DataFrame({"A": [1, 2, 3, 4], "B": pd.date_range("2016-01-01", periods=4)})

In [65]: gb = df.groupby([1, 1, 2, 2])

Old behavior:

In [4]: gb.prod(numeric_only=False)
Out[4]:
A
1   2
2  12

Future behavior:

In [5]: gb.prod(numeric_only=False)
...
TypeError: datetime64 type does not support prod operations

In [6]: gb[["A"]].prod(numeric_only=False)
Out[6]:
    A
1   2
2  12

-------------------------------------------------------------------------------

Other Deprecations

  * Deprecated allowing scalars to be passed to the Categorical constructor (
    GH38433)

  * Deprecated constructing CategoricalIndex without passing list-like data (
    GH38944)

  * Deprecated allowing subclass-specific keyword arguments in the Index
    constructor, use the specific subclass directly instead (GH14093, GH21311,
    GH22315, GH26974)

  * Deprecated the astype() method of datetimelike (timedelta64[ns], datetime64
    [ns], Datetime64TZDtype, PeriodDtype) to convert to integer dtypes, use
    values.view(...) instead (GH38544)

  * Deprecated MultiIndex.is_lexsorted() and MultiIndex.lexsort_depth(), use
    MultiIndex.is_monotonic_increasing() instead (GH32259)

  * Deprecated keyword try_cast in Series.where(), Series.mask(),
    DataFrame.where(), DataFrame.mask(); cast results manually if desired (
    GH38836)

  * Deprecated comparison of Timestamp objects with datetime.date objects.
    Instead of e.g. ts <= mydate use ts <= pd.Timestamp(mydate) or ts.date() <=
    mydate (GH36131)

  * Deprecated Rolling.win_type returning "freq" (GH38963)

  * Deprecated Rolling.is_datetimelike (GH38963)

  * Deprecated DataFrame indexer for Series.__setitem__() and
    DataFrame.__setitem__() (GH39004)

  * Deprecated ExponentialMovingWindow.vol() (GH39220)

  * Using .astype to convert between datetime64[ns] dtype and DatetimeTZDtype
    is deprecated and will raise in a future version, use obj.tz_localize or
    obj.dt.tz_localize instead (GH38622)

  * Deprecated casting datetime.date objects to datetime64 when used as
    fill_value in DataFrame.unstack(), DataFrame.shift(), Series.shift(), and
    DataFrame.reindex(), pass pd.Timestamp(dateobj) instead (GH39767)

  * Deprecated Styler.set_na_rep() and Styler.set_precision() in favor of
    Styler.format() with na_rep and precision as existing and new input
    arguments respectively (GH40134, GH40425)

  * Deprecated Styler.where() in favor of using an alternative formulation with
    Styler.applymap() (GH40821)

  * Deprecated allowing partial failure in Series.transform() and
    DataFrame.transform() when func is list-like or dict-like and raises
    anything but TypeError; func raising anything but a TypeError will raise in
    a future version (GH40211)

  * Deprecated arguments error_bad_lines and warn_bad_lines in read_csv() and
    read_table() in favor of argument on_bad_lines (GH15122)

  * Deprecated support for np.ma.mrecords.MaskedRecords in the DataFrame
    constructor, pass {name: data[name] for name in data.dtype.names} instead (
    GH40363)

  * Deprecated using merge(), DataFrame.merge(), and DataFrame.join() on a
    different number of levels (GH34862)

  * Deprecated the use of **kwargs in ExcelWriter; use the keyword argument
    engine_kwargs instead (GH40430)

  * Deprecated the level keyword for DataFrame and Series aggregations; use
    groupby instead (GH39983)

  * Deprecated the inplace parameter of Categorical.remove_categories(),
    Categorical.add_categories(), Categorical.reorder_categories(),
    Categorical.rename_categories(), Categorical.set_categories() and will be
    removed in a future version (GH37643)

  * Deprecated merge() producing duplicated columns through the suffixes
    keyword and already existing columns (GH22818)

  * Deprecated setting Categorical._codes, create a new Categorical with the
    desired codes instead (GH40606)

  * Deprecated the convert_float optional argument in read_excel() and
    ExcelFile.parse() (GH41127)

  * Deprecated behavior of DatetimeIndex.union() with mixed timezones; in a
    future version both will be cast to UTC instead of object dtype (GH39328)

  * Deprecated using usecols with out of bounds indices for read_csv() with
    engine="c" (GH25623)

  * Deprecated special treatment of lists with first element a Categorical in
    the DataFrame constructor; pass as pd.DataFrame({col: categorical, ...})
    instead (GH38845)

  * Deprecated behavior of DataFrame constructor when a dtype is passed and the
    data cannot be cast to that dtype. In a future version, this will raise
    instead of being silently ignored (GH24435)

  * Deprecated the Timestamp.freq attribute. For the properties that use it (
    is_month_start, is_month_end, is_quarter_start, is_quarter_end,
    is_year_start, is_year_end), when you have a freq, use e.g.
    freq.is_month_start(ts) (GH15146)

  * Deprecated construction of Series or DataFrame with DatetimeTZDtype data
    and datetime64[ns] dtype. Use Series(data).dt.tz_localize(None) instead (
    GH41555, GH33401)

  * Deprecated behavior of Series construction with large-integer values and
    small-integer dtype silently overflowing; use Series(data).astype(dtype)
    instead (GH41734)

  * Deprecated behavior of DataFrame construction with floating data and
    integer dtype casting even when lossy; in a future version this will remain
    floating, matching Series behavior (GH41770)

  * Deprecated inference of timedelta64[ns], datetime64[ns], or DatetimeTZDtype
    dtypes in Series construction when data containing strings is passed and no
    dtype is passed (GH33558)

  * In a future version, constructing Series or DataFrame with datetime64[ns]
    data and DatetimeTZDtype will treat the data as wall-times instead of as
    UTC times (matching DatetimeIndex behavior). To treat the data as UTC
    times, use pd.Series(data).dt.tz_localize("UTC").dt.tz_convert(dtype.tz) or
    pd.Series(data.view("int64"), dtype=dtype) (GH33401)

  * Deprecated passing lists as key to DataFrame.xs() and Series.xs() (GH41760)

  * Deprecated boolean arguments of inclusive in Series.between() to have
    {"left", "right", "neither", "both"} as standard argument values (GH40628)

  * Deprecated passing arguments as positional for all of the following, with
    exceptions noted (GH41485):

      + concat() (other than objs)

      + read_csv() (other than filepath_or_buffer)

      + read_table() (other than filepath_or_buffer)

      + DataFrame.clip() and Series.clip() (other than upper and lower)

      + DataFrame.drop_duplicates() (except for subset), Series.drop_duplicates
        (), Index.drop_duplicates() and MultiIndex.drop_duplicates()

      + DataFrame.drop() (other than labels) and Series.drop()

      + DataFrame.dropna() and Series.dropna()

      + DataFrame.ffill(), Series.ffill(), DataFrame.bfill(), and Series.bfill
        ()

      + DataFrame.fillna() and Series.fillna() (apart from value)

      + DataFrame.interpolate() and Series.interpolate() (other than method)

      + DataFrame.mask() and Series.mask() (other than cond and other)

      + DataFrame.reset_index() (other than level) and Series.reset_index()

      + DataFrame.set_axis() and Series.set_axis() (other than labels)

      + DataFrame.set_index() (other than keys)

      + DataFrame.sort_index() and Series.sort_index()

      + DataFrame.sort_values() (other than by) and Series.sort_values()

      + DataFrame.where() and Series.where() (other than cond and other)

      + Index.set_names() and MultiIndex.set_names() (except for names)

      + MultiIndex.codes() (except for codes)

      + MultiIndex.set_levels() (except for levels)

      + Resampler.interpolate() (other than method)

-------------------------------------------------------------------------------

Performance improvements

  * Performance improvement in IntervalIndex.isin() (GH38353)

  * Performance improvement in Series.mean() for nullable data types (GH34814)

  * Performance improvement in Series.isin() for nullable data types (GH38340)

  * Performance improvement in DataFrame.fillna() with method="pad" or method=
    "backfill" for nullable floating and nullable integer dtypes (GH39953)

  * Performance improvement in DataFrame.corr() for method=kendall (GH28329)

  * Performance improvement in DataFrame.corr() for method=spearman (GH40956,
    GH41885)

  * Performance improvement in Rolling.corr() and Rolling.cov() (GH39388)

  * Performance improvement in RollingGroupby.corr(), ExpandingGroupby.corr(),
    ExpandingGroupby.corr() and ExpandingGroupby.cov() (GH39591)

  * Performance improvement in unique() for object data type (GH37615)

  * Performance improvement in json_normalize() for basic cases (including
    separators) (GH40035 GH15621)

  * Performance improvement in ExpandingGroupby aggregation methods (GH39664)

  * Performance improvement in Styler where render times are more than 50%
    reduced and now matches DataFrame.to_html() (GH39972 GH39952, GH40425)

  * The method Styler.set_td_classes() is now as performant as Styler.apply()
    and Styler.applymap(), and even more so in some cases (GH40453)

  * Performance improvement in ExponentialMovingWindow.mean() with times (
    GH39784)

  * Performance improvement in GroupBy.apply() when requiring the Python
    fallback implementation (GH40176)

  * Performance improvement in the conversion of a PyArrow Boolean array to a
    pandas nullable Boolean array (GH41051)

  * Performance improvement for concatenation of data with type
    CategoricalDtype (GH40193)

  * Performance improvement in GroupBy.cummin() and GroupBy.cummax() with
    nullable data types (GH37493)

  * Performance improvement in Series.nunique() with nan values (GH40865)

  * Performance improvement in DataFrame.transpose(), Series.unstack() with
    DatetimeTZDtype (GH40149)

  * Performance improvement in Series.plot() and DataFrame.plot() with entry
    point lazy loading (GH41492)

-------------------------------------------------------------------------------

Bug fixes

-------------------------------------------------------------------------------

Categorical

  * Bug in CategoricalIndex incorrectly failing to raise TypeError when scalar
    data is passed (GH38614)

  * Bug in CategoricalIndex.reindex failed when the Index passed was not
    categorical but whose values were all labels in the category (GH28690)

  * Bug where constructing a Categorical from an object-dtype array of date
    objects did not round-trip correctly with astype (GH38552)

  * Bug in constructing a DataFrame from an ndarray and a CategoricalDtype (
    GH38857)

  * Bug in setting categorical values into an object-dtype column in a
    DataFrame (GH39136)

  * Bug in DataFrame.reindex() was raising an IndexError when the new index
    contained duplicates and the old index was a CategoricalIndex (GH38906)

  * Bug in Categorical.fillna() with a tuple-like category raising
    NotImplementedError instead of ValueError when filling with a non-category
    tuple (GH41914)

-------------------------------------------------------------------------------

Datetimelike

  * Bug in DataFrame and Series constructors sometimes dropping nanoseconds
    from Timestamp (resp. Timedelta) data, with dtype=datetime64[ns] (resp.
    timedelta64[ns]) (GH38032)

  * Bug in DataFrame.first() and Series.first() with an offset of one month
    returning an incorrect result when the first day is the last day of a month
    (GH29623)

  * Bug in constructing a DataFrame or Series with mismatched datetime64 data
    and timedelta64 dtype, or vice-versa, failing to raise a TypeError (GH38575
    , GH38764, GH38792)

  * Bug in constructing a Series or DataFrame with a datetime object out of
    bounds for datetime64[ns] dtype or a timedelta object out of bounds for
    timedelta64[ns] dtype (GH38792, GH38965)

  * Bug in DatetimeIndex.intersection(), DatetimeIndex.symmetric_difference(),
    PeriodIndex.intersection(), PeriodIndex.symmetric_difference() always
    returning object-dtype when operating with CategoricalIndex (GH38741)

  * Bug in DatetimeIndex.intersection() giving incorrect results with non-Tick
    frequencies with n != 1 (GH42104)

  * Bug in Series.where() incorrectly casting datetime64 values to int64 (
    GH37682)

  * Bug in Categorical incorrectly typecasting datetime object to Timestamp (
    GH38878)

  * Bug in comparisons between Timestamp object and datetime64 objects just
    outside the implementation bounds for nanosecond datetime64 (GH39221)

  * Bug in Timestamp.round(), Timestamp.floor(), Timestamp.ceil() for values
    near the implementation bounds of Timestamp (GH39244)

  * Bug in Timedelta.round(), Timedelta.floor(), Timedelta.ceil() for values
    near the implementation bounds of Timedelta (GH38964)

  * Bug in date_range() incorrectly creating DatetimeIndex containing NaT
    instead of raising OutOfBoundsDatetime in corner cases (GH24124)

  * Bug in infer_freq() incorrectly fails to infer 'H' frequency of
    DatetimeIndex if the latter has a timezone and crosses DST boundaries (
    GH39556)

  * Bug in Series backed by DatetimeArray or TimedeltaArray sometimes failing
    to set the array's freq to None (GH41425)

-------------------------------------------------------------------------------

Timedelta

  * Bug in constructing Timedelta from np.timedelta64 objects with
    non-nanosecond units that are out of bounds for timedelta64[ns] (GH38965)

  * Bug in constructing a TimedeltaIndex incorrectly accepting np.datetime64
    ("NaT") objects (GH39462)

  * Bug in constructing Timedelta from an input string with only symbols and no
    digits failed to raise an error (GH39710)

  * Bug in TimedeltaIndex and to_timedelta() failing to raise when passed
    non-nanosecond timedelta64 arrays that overflow when converting to
    timedelta64[ns] (GH40008)

-------------------------------------------------------------------------------

Timezones

  * Bug in different tzinfo objects representing UTC not being treated as
    equivalent (GH39216)

  * Bug in dateutil.tz.gettz("UTC") not being recognized as equivalent to other
    UTC-representing tzinfos (GH39276)

-------------------------------------------------------------------------------

Numeric

  * Bug in DataFrame.quantile(), DataFrame.sort_values() causing incorrect
    subsequent indexing behavior (GH38351)

  * Bug in DataFrame.sort_values() raising an IndexError for empty by (GH40258)

  * Bug in DataFrame.select_dtypes() with include=np.number would drop numeric
    ExtensionDtype columns (GH35340)

  * Bug in DataFrame.mode() and Series.mode() not keeping consistent integer
    Index for empty input (GH33321)

  * Bug in DataFrame.rank() when the DataFrame contained np.inf (GH32593)

  * Bug in DataFrame.rank() with axis=0 and columns holding incomparable types
    raising an IndexError (GH38932)

  * Bug in Series.rank(), DataFrame.rank(), and GroupBy.rank() treating the
    most negative int64 value as missing (GH32859)

  * Bug in DataFrame.select_dtypes() different behavior between Windows and
    Linux with include="int" (GH36596)

  * Bug in DataFrame.apply() and DataFrame.agg() when passed the argument func=
    "size" would operate on the entire DataFrame instead of rows or columns (
    GH39934)

  * Bug in DataFrame.transform() would raise a SpecificationError when passed a
    dictionary and columns were missing; will now raise a KeyError instead (
    GH40004)

  * Bug in GroupBy.rank() giving incorrect results with pct=True and equal
    values between consecutive groups (GH40518)

  * Bug in Series.count() would result in an int32 result on 32-bit platforms
    when argument level=None (GH40908)

  * Bug in Series and DataFrame reductions with methods any and all not
    returning Boolean results for object data (GH12863, GH35450, GH27709)

  * Bug in Series.clip() would fail if the Series contains NA values and has
    nullable int or float as a data type (GH40851)

  * Bug in UInt64Index.where() and UInt64Index.putmask() with an np.int64 dtype
    other incorrectly raising TypeError (GH41974)

  * Bug in DataFrame.agg() not sorting the aggregated axis in the order of the
    provided aggregation functions when one or more aggregation function fails
    to produce results (GH33634)

  * Bug in DataFrame.clip() not interpreting missing values as no threshold (
    GH40420)

-------------------------------------------------------------------------------

Conversion

  * Bug in Series.to_dict() with orient='records' now returns Python native
    types (GH25969)

  * Bug in Series.view() and Index.view() when converting between datetime-like
    (datetime64[ns], datetime64[ns, tz], timedelta64, period) dtypes (GH39788)

  * Bug in creating a DataFrame from an empty np.recarray not retaining the
    original dtypes (GH40121)

  * Bug in DataFrame failing to raise a TypeError when constructing from a
    frozenset (GH40163)

  * Bug in Index construction silently ignoring a passed dtype when the data
    cannot be cast to that dtype (GH21311)

  * Bug in StringArray.astype() falling back to NumPy and raising when
    converting to dtype='categorical' (GH40450)

  * Bug in factorize() where, when given an array with a numeric NumPy dtype
    lower than int64, uint64 and float64, the unique values did not keep their
    original dtype (GH41132)

  * Bug in DataFrame construction with a dictionary containing an array-like
    with ExtensionDtype and copy=True failing to make a copy (GH38939)

  * Bug in qcut() raising error when taking Float64DType as input (GH40730)

  * Bug in DataFrame and Series construction with datetime64[ns] data and dtype
    =object resulting in datetime objects instead of Timestamp objects (GH41599
    )

  * Bug in DataFrame and Series construction with timedelta64[ns] data and
    dtype=object resulting in np.timedelta64 objects instead of Timedelta
    objects (GH41599)

  * Bug in DataFrame construction when given a two-dimensional object-dtype
    np.ndarray of Period or Interval objects failing to cast to PeriodDtype or
    IntervalDtype, respectively (GH41812)

  * Bug in constructing a Series from a list and a PandasDtype (GH39357)

  * Bug in creating a Series from a range object that does not fit in the
    bounds of int64 dtype (GH30173)

  * Bug in creating a Series from a dict with all-tuple keys and an Index that
    requires reindexing (GH41707)

  * Bug in infer_dtype() not recognizing Series, Index, or array with a Period
    dtype (GH23553)

  * Bug in infer_dtype() raising an error for general ExtensionArray objects.
    It will now return "unknown-array" instead of raising (GH37367)

  * Bug in DataFrame.convert_dtypes() incorrectly raised a ValueError when
    called on an empty DataFrame (GH40393)

-------------------------------------------------------------------------------

Strings

  * Bug in the conversion from pyarrow.ChunkedArray to StringArray when the
    original had zero chunks (GH41040)

  * Bug in Series.replace() and DataFrame.replace() ignoring replacements with
    regex=True for StringDType data (GH41333, GH35977)

  * Bug in Series.str.extract() with StringArray returning object dtype for an
    empty DataFrame (GH41441)

  * Bug in Series.str.replace() where the case argument was ignored when regex=
    False (GH41602)

-------------------------------------------------------------------------------

Interval

  * Bug in IntervalIndex.intersection() and IntervalIndex.symmetric_difference
    () always returning object-dtype when operating with CategoricalIndex (
    GH38653, GH38741)

  * Bug in IntervalIndex.intersection() returning duplicates when at least one
    of the Index objects have duplicates which are present in the other (
    GH38743)

  * IntervalIndex.union(), IntervalIndex.intersection(),
    IntervalIndex.difference(), and IntervalIndex.symmetric_difference() now
    cast to the appropriate dtype instead of raising a TypeError when operating
    with another IntervalIndex with incompatible dtype (GH39267)

  * PeriodIndex.union(), PeriodIndex.intersection(),
    PeriodIndex.symmetric_difference(), PeriodIndex.difference() now cast to
    object dtype instead of raising IncompatibleFrequency when operating with
    another PeriodIndex with incompatible dtype (GH39306)

  * Bug in IntervalIndex.is_monotonic(), IntervalIndex.get_loc(),
    IntervalIndex.get_indexer_for(), and IntervalIndex.__contains__() when NA
    values are present (GH41831)

-------------------------------------------------------------------------------

Indexing

  * Bug in Index.union() and MultiIndex.union() dropping duplicate Index values
    when Index was not monotonic or sort was set to False (GH36289, GH31326,
    GH40862)

  * Bug in CategoricalIndex.get_indexer() failing to raise InvalidIndexError
    when non-unique (GH38372)

  * Bug in IntervalIndex.get_indexer() when target has CategoricalDtype and
    both the index and the target contain NA values (GH41934)

  * Bug in Series.loc() raising a ValueError when input was filtered with a
    Boolean list and values to set were a list with lower dimension (GH20438)

  * Bug in inserting many new columns into a DataFrame causing incorrect
    subsequent indexing behavior (GH38380)

  * Bug in DataFrame.__setitem__() raising a ValueError when setting multiple
    values to duplicate columns (GH15695)

  * Bug in DataFrame.loc(), Series.loc(), DataFrame.__getitem__() and
    Series.__getitem__() returning incorrect elements for non-monotonic
    DatetimeIndex for string slices (GH33146)

  * Bug in DataFrame.reindex() and Series.reindex() with timezone aware indexes
    raising a TypeError for method="ffill" and method="bfill" and specified
    tolerance (GH38566)

  * Bug in DataFrame.reindex() with datetime64[ns] or timedelta64[ns]
    incorrectly casting to integers when the fill_value requires casting to
    object dtype (GH39755)

  * Bug in DataFrame.__setitem__() raising a ValueError when setting on an
    empty DataFrame using specified columns and a nonempty DataFrame value (
    GH38831)

  * Bug in DataFrame.loc.__setitem__() raising a ValueError when operating on a
    unique column when the DataFrame has duplicate columns (GH38521)

  * Bug in DataFrame.iloc.__setitem__() and DataFrame.loc.__setitem__() with
    mixed dtypes when setting with a dictionary value (GH38335)

  * Bug in Series.loc.__setitem__() and DataFrame.loc.__setitem__() raising
    KeyError when provided a Boolean generator (GH39614)

  * Bug in Series.iloc() and DataFrame.iloc() raising a KeyError when provided
    a generator (GH39614)

  * Bug in DataFrame.__setitem__() not raising a ValueError when the right hand
    side is a DataFrame with wrong number of columns (GH38604)

  * Bug in Series.__setitem__() raising a ValueError when setting a Series with
    a scalar indexer (GH38303)

  * Bug in DataFrame.loc() dropping levels of a MultiIndex when the DataFrame
    used as input has only one row (GH10521)

  * Bug in DataFrame.__getitem__() and Series.__getitem__() always raising
    KeyError when slicing with existing strings where the Index has
    milliseconds (GH33589)

  * Bug in setting timedelta64 or datetime64 values into numeric Series failing
    to cast to object dtype (GH39086, GH39619)

  * Bug in setting Interval values into a Series or DataFrame with mismatched
    IntervalDtype incorrectly casting the new values to the existing dtype (
    GH39120)

  * Bug in setting datetime64 values into a Series with integer-dtype
    incorrectly casting the datetime64 values to integers (GH39266)

  * Bug in setting np.datetime64("NaT") into a Series with Datetime64TZDtype
    incorrectly treating the timezone-naive value as timezone-aware (GH39769)

  * Bug in Index.get_loc() not raising KeyError when key=NaN and method is
    specified but NaN is not in the Index (GH39382)

  * Bug in DatetimeIndex.insert() when inserting np.datetime64("NaT") into a
    timezone-aware index incorrectly treating the timezone-naive value as
    timezone-aware (GH39769)

  * Bug in incorrectly raising in Index.insert(), when setting a new column
    that cannot be held in the existing frame.columns, or in Series.reset_index
    () or DataFrame.reset_index() instead of casting to a compatible dtype (
    GH39068)

  * Bug in RangeIndex.append() where a single object of length 1 was
    concatenated incorrectly (GH39401)

  * Bug in RangeIndex.astype() where when converting to CategoricalIndex, the
    categories became a Int64Index instead of a RangeIndex (GH41263)

  * Bug in setting numpy.timedelta64 values into an object-dtype Series using a
    Boolean indexer (GH39488)

  * Bug in setting numeric values into a into a boolean-dtypes Series using at
    or iat failing to cast to object-dtype (GH39582)

  * Bug in DataFrame.__setitem__() and DataFrame.iloc.__setitem__() raising
    ValueError when trying to index with a row-slice and setting a list as
    values (GH40440)

  * Bug in DataFrame.loc() not raising KeyError when the key was not found in
    MultiIndex and the levels were not fully specified (GH41170)

  * Bug in DataFrame.loc.__setitem__() when setting-with-expansion incorrectly
    raising when the index in the expanding axis contained duplicates (GH40096)

  * Bug in DataFrame.loc.__getitem__() with MultiIndex casting to float when at
    least one index column has float dtype and we retrieve a scalar (GH41369)

  * Bug in DataFrame.loc() incorrectly matching non-Boolean index elements (
    GH20432)

  * Bug in indexing with np.nan on a Series or DataFrame with a
    CategoricalIndex incorrectly raising KeyError when np.nan keys are present
    (GH41933)

  * Bug in Series.__delitem__() with ExtensionDtype incorrectly casting to
    ndarray (GH40386)

  * Bug in DataFrame.at() with a CategoricalIndex returning incorrect results
    when passed integer keys (GH41846)

  * Bug in DataFrame.loc() returning a MultiIndex in the wrong order if an
    indexer has duplicates (GH40978)

  * Bug in DataFrame.__setitem__() raising a TypeError when using a str
    subclass as the column name with a DatetimeIndex (GH37366)

  * Bug in PeriodIndex.get_loc() failing to raise a KeyError when given a
    Period with a mismatched freq (GH41670)

  * Bug .loc.__getitem__ with a UInt64Index and negative-integer keys raising
    OverflowError instead of KeyError in some cases, wrapping around to
    positive integers in others (GH41777)

  * Bug in Index.get_indexer() failing to raise ValueError in some cases with
    invalid method, limit, or tolerance arguments (GH41918)

  * Bug when slicing a Series or DataFrame with a TimedeltaIndex when passing
    an invalid string raising ValueError instead of a TypeError (GH41821)

  * Bug in Index constructor sometimes silently ignoring a specified dtype (
    GH38879)

  * Index.where() behavior now mirrors Index.putmask() behavior, i.e.
    index.where(mask, other) matches index.putmask(~mask, other) (GH39412)

-------------------------------------------------------------------------------

Missing

  * Bug in Grouper did not correctly propagate the dropna argument;
    DataFrameGroupBy.transform() now correctly handles missing values for
    dropna=True (GH35612)

  * Bug in isna(), Series.isna(), Index.isna(), DataFrame.isna(), and the
    corresponding notna functions not recognizing Decimal("NaN") objects (
    GH39409)

  * Bug in DataFrame.fillna() not accepting a dictionary for the downcast
    keyword (GH40809)

  * Bug in isna() not returning a copy of the mask for nullable types, causing
    any subsequent mask modification to change the original array (GH40935)

  * Bug in DataFrame construction with float data containing NaN and an integer
    dtype casting instead of retaining the NaN (GH26919)

  * Bug in Series.isin() and MultiIndex.isin() didn't treat all nans as
    equivalent if they were in tuples (GH41836)

-------------------------------------------------------------------------------

MultiIndex

  * Bug in DataFrame.drop() raising a TypeError when the MultiIndex is
    non-unique and level is not provided (GH36293)

  * Bug in MultiIndex.intersection() duplicating NaN in the result (GH38623)

  * Bug in MultiIndex.equals() incorrectly returning True when the MultiIndex
    contained NaN even when they are differently ordered (GH38439)

  * Bug in MultiIndex.intersection() always returning an empty result when
    intersecting with CategoricalIndex (GH38653)

  * Bug in MultiIndex.difference() incorrectly raising TypeError when indexes
    contain non-sortable entries (GH41915)

  * Bug in MultiIndex.reindex() raising a ValueError when used on an empty
    MultiIndex and indexing only a specific level (GH41170)

  * Bug in MultiIndex.reindex() raising TypeError when reindexing against a
    flat Index (GH41707)

-------------------------------------------------------------------------------

I/O

  * Bug in Index.__repr__() when display.max_seq_items=1 (GH38415)

  * Bug in read_csv() not recognizing scientific notation if the argument
    decimal is set and engine="python" (GH31920)

  * Bug in read_csv() interpreting NA value as comment, when NA does contain
    the comment string fixed for engine="python" (GH34002)

  * Bug in read_csv() raising an IndexError with multiple header columns and
    index_col is specified when the file has no data rows (GH38292)

  * Bug in read_csv() not accepting usecols with a different length than names
    for engine="python" (GH16469)

  * Bug in read_csv() returning object dtype when delimiter="," with usecols
    and parse_dates specified for engine="python" (GH35873)

  * Bug in read_csv() raising a TypeError when names and parse_dates is
    specified for engine="c" (GH33699)

  * Bug in read_clipboard() and DataFrame.to_clipboard() not working in WSL (
    GH38527)

  * Allow custom error values for the parse_dates argument of read_sql(),
    read_sql_query() and read_sql_table() (GH35185)

  * Bug in DataFrame.to_hdf() and Series.to_hdf() raising a KeyError when
    trying to apply for subclasses of DataFrame or Series (GH33748)

  * Bug in HDFStore.put() raising a wrong TypeError when saving a DataFrame
    with non-string dtype (GH34274)

  * Bug in json_normalize() resulting in the first element of a generator
    object not being included in the returned DataFrame (GH35923)

  * Bug in read_csv() applying the thousands separator to date columns when the
    column should be parsed for dates and usecols is specified for engine=
    "python" (GH39365)

  * Bug in read_excel() forward filling MultiIndex names when multiple header
    and index columns are specified (GH34673)

  * Bug in read_excel() not respecting set_option() (GH34252)

  * Bug in read_csv() not switching true_values and false_values for nullable
    Boolean dtype (GH34655)

  * Bug in read_json() when orient="split" not maintaining a numeric string
    index (GH28556)

  * read_sql() returned an empty generator if chunksize was non-zero and the
    query returned no results. Now returns a generator with a single empty
    DataFrame (GH34411)

  * Bug in read_hdf() returning unexpected records when filtering on
    categorical string columns using the where parameter (GH39189)

  * Bug in read_sas() raising a ValueError when datetimes were null (GH39725)

  * Bug in read_excel() dropping empty values from single-column spreadsheets (
    GH39808)

  * Bug in read_excel() loading trailing empty rows/columns for some filetypes
    (GH41167)

  * Bug in read_excel() raising an AttributeError when the excel file had a
    MultiIndex header followed by two empty rows and no index (GH40442)

  * Bug in read_excel(), read_csv(), read_table(), read_fwf(), and
    read_clipboard() where one blank row after a MultiIndex header with no
    index would be dropped (GH40442)

  * Bug in DataFrame.to_string() misplacing the truncation column when index=
    False (GH40904)

  * Bug in DataFrame.to_string() adding an extra dot and misaligning the
    truncation row when index=False (GH40904)

  * Bug in read_orc() always raising an AttributeError (GH40918)

  * Bug in read_csv() and read_table() silently ignoring prefix if names and
    prefix are defined, now raising a ValueError (GH39123)

  * Bug in read_csv() and read_excel() not respecting the dtype for a
    duplicated column name when mangle_dupe_cols is set to True (GH35211)

  * Bug in read_csv() silently ignoring sep if delimiter and sep are defined,
    now raising a ValueError (GH39823)

  * Bug in read_csv() and read_table() misinterpreting arguments when
    sys.setprofile had been previously called (GH41069)

  * Bug in the conversion from PyArrow to pandas (e.g. for reading Parquet)
    with nullable dtypes and a PyArrow array whose data buffer size is not a
    multiple of the dtype size (GH40896)

  * Bug in read_excel() would raise an error when pandas could not determine
    the file type even though the user specified the engine argument (GH41225)

  * Bug in read_clipboard() copying from an excel file shifts values into the
    wrong column if there are null values in first column (GH41108)

  * Bug in DataFrame.to_hdf() and Series.to_hdf() raising a TypeError when
    trying to append a string column to an incompatible column (GH41897)

-------------------------------------------------------------------------------

Period

  * Comparisons of Period objects or Index, Series, or DataFrame with
    mismatched PeriodDtype now behave like other mismatched-type comparisons,
    returning False for equals, True for not-equal, and raising TypeError for
    inequality checks (GH39274)

-------------------------------------------------------------------------------

Plotting

  * Bug in plotting.scatter_matrix() raising when 2d ax argument passed (
    GH16253)

  * Prevent warnings when Matplotlib's constrained_layout is enabled (GH25261)

  * Bug in DataFrame.plot() was showing the wrong colors in the legend if the
    function was called repeatedly and some calls used yerr while others didn
    t (GH39522)

  * Bug in DataFrame.plot() was showing the wrong colors in the legend if the
    function was called repeatedly and some calls used secondary_y and others
    use legend=False (GH40044)

  * Bug in DataFrame.plot.box() when dark_background theme was selected, caps
    or min/max markers for the plot were not visible (GH40769)

-------------------------------------------------------------------------------

Groupby/resample/rolling

  * Bug in GroupBy.agg() with PeriodDtype columns incorrectly casting results
    too aggressively (GH38254)

  * Bug in SeriesGroupBy.value_counts() where unobserved categories in a
    grouped categorical Series were not tallied (GH38672)

  * Bug in SeriesGroupBy.value_counts() where an error was raised on an empty
    Series (GH39172)

  * Bug in GroupBy.indices() would contain non-existent indices when null
    values were present in the groupby keys (GH9304)

  * Fixed bug in GroupBy.sum() causing a loss of precision by now using Kahan
    summation (GH38778)

  * Fixed bug in GroupBy.cumsum() and GroupBy.mean() causing loss of precision
    through using Kahan summation (GH38934)

  * Bug in Resampler.aggregate() and DataFrame.transform() raising a TypeError
    instead of SpecificationError when missing keys had mixed dtypes (GH39025)

  * Bug in DataFrameGroupBy.idxmin() and DataFrameGroupBy.idxmax() with
    ExtensionDtype columns (GH38733)

  * Bug in Series.resample() would raise when the index was a PeriodIndex
    consisting of NaT (GH39227)

  * Bug in RollingGroupby.corr() and ExpandingGroupby.corr() where the groupby
    column would return 0 instead of np.nan when providing other that was
    longer than each group (GH39591)

  * Bug in ExpandingGroupby.corr() and ExpandingGroupby.cov() where 1 would be
    returned instead of np.nan when providing other that was longer than each
    group (GH39591)

  * Bug in GroupBy.mean(), GroupBy.median() and DataFrame.pivot_table() not
    propagating metadata (GH28283)

  * Bug in Series.rolling() and DataFrame.rolling() not calculating window
    bounds correctly when window is an offset and dates are in descending order
    (GH40002)

  * Bug in Series.groupby() and DataFrame.groupby() on an empty Series or
    DataFrame would lose index, columns, and/or data types when directly using
    the methods idxmax, idxmin, mad, min, max, sum, prod, and skew or using
    them through apply, aggregate, or resample (GH26411)

  * Bug in GroupBy.apply() where a MultiIndex would be created instead of an
    Index when used on a RollingGroupby object (GH39732)

  * Bug in DataFrameGroupBy.sample() where an error was raised when weights was
    specified and the index was an Int64Index (GH39927)

  * Bug in DataFrameGroupBy.aggregate() and Resampler.aggregate() would
    sometimes raise a SpecificationError when passed a dictionary and columns
    were missing; will now always raise a KeyError instead (GH40004)

  * Bug in DataFrameGroupBy.sample() where column selection was not applied
    before computing the result (GH39928)

  * Bug in ExponentialMovingWindow when calling __getitem__ would incorrectly
    raise a ValueError when providing times (GH40164)

  * Bug in ExponentialMovingWindow when calling __getitem__ would not retain
    com, span, alpha or halflife attributes (GH40164)

  * ExponentialMovingWindow now raises a NotImplementedError when specifying
    times with adjust=False due to an incorrect calculation (GH40098)

  * Bug in ExponentialMovingWindowGroupby.mean() where the times argument was
    ignored when engine='numba' (GH40951)

  * Bug in ExponentialMovingWindowGroupby.mean() where the wrong times were
    used the in case of multiple groups (GH40951)

  * Bug in ExponentialMovingWindowGroupby where the times vector and values
    became out of sync for non-trivial groups (GH40951)

  * Bug in Series.asfreq() and DataFrame.asfreq() dropping rows when the index
    was not sorted (GH39805)

  * Bug in aggregation functions for DataFrame not respecting numeric_only
    argument when level keyword was given (GH40660)

  * Bug in SeriesGroupBy.aggregate() where using a user-defined function to
    aggregate a Series with an object-typed Index causes an incorrect Index
    shape (GH40014)

  * Bug in RollingGroupby where as_index=False argument in groupby was ignored
    (GH39433)

  * Bug in GroupBy.any() and GroupBy.all() raising a ValueError when using with
    nullable type columns holding NA even with skipna=True (GH40585)

  * Bug in GroupBy.cummin() and GroupBy.cummax() incorrectly rounding integer
    values near the int64 implementations bounds (GH40767)

  * Bug in GroupBy.rank() with nullable dtypes incorrectly raising a TypeError
    (GH41010)

  * Bug in GroupBy.cummin() and GroupBy.cummax() computing wrong result with
    nullable data types too large to roundtrip when casting to float (GH37493)

  * Bug in DataFrame.rolling() returning mean zero for all NaN window with
    min_periods=0 if calculation is not numerical stable (GH41053)

  * Bug in DataFrame.rolling() returning sum not zero for all NaN window with
    min_periods=0 if calculation is not numerical stable (GH41053)

  * Bug in SeriesGroupBy.agg() failing to retain ordered CategoricalDtype on
    order-preserving aggregations (GH41147)

  * Bug in GroupBy.min() and GroupBy.max() with multiple object-dtype columns
    and numeric_only=False incorrectly raising a ValueError (GH41111)

  * Bug in DataFrameGroupBy.rank() with the GroupBy object's axis=0 and the
    rank method's keyword axis=1 (GH41320)

  * Bug in DataFrameGroupBy.__getitem__() with non-unique columns incorrectly
    returning a malformed SeriesGroupBy instead of DataFrameGroupBy (GH41427)

  * Bug in DataFrameGroupBy.transform() with non-unique columns incorrectly
    raising an AttributeError (GH41427)

  * Bug in Resampler.apply() with non-unique columns incorrectly dropping
    duplicated columns (GH41445)

  * Bug in Series.groupby() aggregations incorrectly returning empty Series
    instead of raising TypeError on aggregations that are invalid for its
    dtype, e.g. .prod with datetime64[ns] dtype (GH41342)

  * Bug in DataFrameGroupBy aggregations incorrectly failing to drop columns
    with invalid dtypes for that aggregation when there are no valid columns (
    GH41291)

  * Bug in DataFrame.rolling.__iter__() where on was not assigned to the index
    of the resulting objects (GH40373)

  * Bug in DataFrameGroupBy.transform() and DataFrameGroupBy.agg() with engine=
    "numba" where *args were being cached with the user passed function (
    GH41647)

  * Bug in DataFrameGroupBy methods agg, transform, sum, bfill, ffill, pad,
    pct_change, shift, ohlc dropping .columns.names (GH41497)

-------------------------------------------------------------------------------

Reshaping

  * Bug in merge() raising error when performing an inner join with partial
    index and right_index=True when there was no overlap between indices (
    GH33814)

  * Bug in DataFrame.unstack() with missing levels led to incorrect index names
    (GH37510)

  * Bug in merge_asof() propagating the right Index with left_index=True and
    right_on specification instead of left Index (GH33463)

  * Bug in DataFrame.join() on a DataFrame with a MultiIndex returned the wrong
    result when one of both indexes had only one level (GH36909)

  * merge_asof() now raises a ValueError instead of a cryptic TypeError in case
    of non-numerical merge columns (GH29130)

  * Bug in DataFrame.join() not assigning values correctly when the DataFrame
    had a MultiIndex where at least one dimension had dtype Categorical with
    non-alphabetically sorted categories (GH38502)

  * Series.value_counts() and Series.mode() now return consistent keys in
    original order (GH12679, GH11227 and GH39007)

  * Bug in DataFrame.stack() not handling NaN in MultiIndex columns correctly (
    GH39481)

  * Bug in DataFrame.apply() would give incorrect results when the argument
    func was a string, axis=1, and the axis argument was not supported; now
    raises a ValueError instead (GH39211)

  * Bug in DataFrame.sort_values() not reshaping the index correctly after
    sorting on columns when ignore_index=True (GH39464)

  * Bug in DataFrame.append() returning incorrect dtypes with combinations of
    ExtensionDtype dtypes (GH39454)

  * Bug in DataFrame.append() returning incorrect dtypes when used with
    combinations of datetime64 and timedelta64 dtypes (GH39574)

  * Bug in DataFrame.append() with a DataFrame with a MultiIndex and appending
    a Series whose Index is not a MultiIndex (GH41707)

  * Bug in DataFrame.pivot_table() returning a MultiIndex for a single value
    when operating on an empty DataFrame (GH13483)

  * Index can now be passed to the numpy.all() function (GH40180)

  * Bug in DataFrame.stack() not preserving CategoricalDtype in a MultiIndex (
    GH36991)

  * Bug in to_datetime() raising an error when the input sequence contained
    unhashable items (GH39756)

  * Bug in Series.explode() preserving the index when ignore_index was True and
    values were scalars (GH40487)

  * Bug in to_datetime() raising a ValueError when Series contains None and NaT
    and has more than 50 elements (GH39882)

  * Bug in Series.unstack() and DataFrame.unstack() with object-dtype values
    containing timezone-aware datetime objects incorrectly raising TypeError (
    GH41875)

  * Bug in DataFrame.melt() raising InvalidIndexError when DataFrame has
    duplicate columns used as value_vars (GH41951)

-------------------------------------------------------------------------------

Sparse

  * Bug in DataFrame.sparse.to_coo() raising a KeyError with columns that are a
    numeric Index without a 0 (GH18414)

  * Bug in SparseArray.astype() with copy=False producing incorrect results
    when going from integer dtype to floating dtype (GH34456)

  * Bug in SparseArray.max() and SparseArray.min() would always return an empty
    result (GH40921)

-------------------------------------------------------------------------------

ExtensionArray

  * Bug in DataFrame.where() when other is a Series with an ExtensionDtype (
    GH38729)

  * Fixed bug where Series.idxmax(), Series.idxmin(), Series.argmax(), and
    Series.argmin() would fail when the underlying data is an ExtensionArray (
    GH32749, GH33719, GH36566)

  * Fixed bug where some properties of subclasses of PandasExtensionDtype where
    improperly cached (GH40329)

  * Bug in DataFrame.mask() where masking a DataFrame with an ExtensionDtype
    raises a ValueError (GH40941)

-------------------------------------------------------------------------------

Styler

  * Bug in Styler where the subset argument in methods raised an error for some
    valid MultiIndex slices (GH33562)

  * Styler rendered HTML output has seen minor alterations to support w3 good
    code standards (GH39626)

  * Bug in Styler where rendered HTML was missing a column class identifier for
    certain header cells (GH39716)

  * Bug in Styler.background_gradient() where text-color was not determined
    correctly (GH39888)

  * Bug in Styler.set_table_styles() where multiple elements in CSS-selectors
    of the table_styles argument were not correctly added (GH34061)

  * Bug in Styler where copying from Jupyter dropped the top left cell and
    misaligned headers (GH12147)

  * Bug in Styler.where where kwargs were not passed to the applicable callable
    (GH40845)

  * Bug in Styler causing CSS to duplicate on multiple renders (GH39395,
    GH40334)

-------------------------------------------------------------------------------

Other

  * inspect.getmembers(Series) no longer raises an AbstractMethodError (GH38782
    )

  * Bug in Series.where() with numeric dtype and other=None not casting to nan
    (GH39761)

  * Bug in assert_series_equal(), assert_frame_equal(), assert_index_equal()
    and assert_extension_array_equal() incorrectly raising when an attribute
    has an unrecognized NA type (GH39461)

  * Bug in assert_index_equal() with exact=True not raising when comparing
    CategoricalIndex instances with Int64Index and RangeIndex categories (
    GH41263)

  * Bug in DataFrame.equals(), Series.equals(), and Index.equals() with
    object-dtype containing np.datetime64("NaT") or np.timedelta64("NaT") (
    GH39650)

  * Bug in show_versions() where console JSON output was not proper JSON (
    GH39701)

  * pandas can now compile on z/OS when using xlc (GH35826)

  * Bug in pandas.util.hash_pandas_object() not recognizing hash_key, encoding
    and categorize when the input object type is a DataFrame (GH41404)


What's new in 1.2.5 (June 22, 2021)

These are the changes in pandas 1.2.5. See Release notes for a full changelog
including other versions of pandas.

-------------------------------------------------------------------------------

Fixed regressions

  * Fixed regression in concat() between two DataFrame where one has an Index
    that is all-None and the other is DatetimeIndex incorrectly raising (
    GH40841)

  * Fixed regression in DataFrame.sum() and DataFrame.prod() when min_count and
    numeric_only are both given (GH41074)

  * Fixed regression in read_csv() when using memory_map=True with an non-UTF8
    encoding (GH40986)

  * Fixed regression in DataFrame.replace() and Series.replace() when the
    values to replace is a NumPy float array (GH40371)

  * Fixed regression in ExcelFile() when a corrupt file is opened but not
    closed (GH41778)

  * Fixed regression in DataFrame.astype() with dtype=str failing to convert
    NaN in categorical columns (GH41797)

Revision 1.33 / (download) - annotate - [select for diffs], Thu May 6 04:39:03 2021 UTC (2 years, 7 months ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2021Q3-base, pkgsrc-2021Q3, pkgsrc-2021Q2-base, pkgsrc-2021Q2
Changes since 1.32: +8 -12 lines
Diff to previous 1.32 (colored)

py-pandas: updated to 1.2.4

What's new in 1.2.4 (April 12, 2021)

Fixed regressions
- Fixed regression in :meth:`DataFrame.sum` when ``min_count`` greater than the :class:`DataFrame` shape was passed resulted in a ``ValueError`` (:issue:`39738`)
- Fixed regression in :meth:`DataFrame.to_json` raising ``AttributeError`` when run on PyPy (:issue:`39837`)
- Fixed regression in (in)equality comparison of ``pd.NaT`` with a non-datetimelike numpy array returning a scalar instead of an array (:issue:`40722`)
- Fixed regression in :meth:`DataFrame.where` not returning a copy in the case of an all True condition (:issue:`39595`)
- Fixed regression in :meth:`DataFrame.replace` raising ``IndexError`` when ``regex`` was a multi-key dictionary (:issue:`39338`)
- Fixed regression in repr of floats in an ``object`` column not respecting ``float_format`` when printed in the console or outputted through :meth:`DataFrame.to_string`, :meth:`DataFrame.to_html`, and :meth:`DataFrame.to_latex` (:issue:`40024`)
- Fixed regression in NumPy ufuncs such as ``np.add`` not passing through all arguments for :class:`DataFrame`


What's new in 1.2.3 (March 02, 2021)

Fixed regressions
- Fixed regression in :meth:`~DataFrame.to_excel` raising ``KeyError`` when giving duplicate columns with ``columns`` attribute (:issue:`39695`)
- Fixed regression in nullable integer unary ops propagating mask on assignment (:issue:`39943`)
- Fixed regression in :meth:`DataFrame.__setitem__` not aligning :class:`DataFrame` on right-hand side for boolean indexer (:issue:`39931`)
- Fixed regression in :meth:`~DataFrame.to_json` failing to use ``compression`` with URL-like paths that are internally opened in binary mode or with user-provided file objects that are opened in binary mode (:issue:`39985`)
- Fixed regression in :meth:`Series.sort_index` and :meth:`DataFrame.sort_index`, which exited with an ungraceful error when having kwarg ``ascending=None`` passed. Passing ``ascending=None`` is still considered invalid, and the improved error message suggests a proper usage (``ascending`` must be a boolean or a list-like of boolean) (:issue:`39434`)
- Fixed regression in :meth:`DataFrame.transform` and :meth:`Series.transform` giving incorrect column labels when passed a dictionary with a mix of list and non-list values (:issue:`40018`)

What's new in 1.2.2 (February 09, 2021)
---------------------------------------

These are the changes in pandas 1.2.2. See :ref:`release` for a full changelog
including other versions of pandas.

{{ header }}

.. ---------------------------------------------------------------------------

.. _whatsnew_122.regressions:

Fixed regressions
~~~~~~~~~~~~~~~~~

- Fixed regression in :func:`read_excel` that caused it to raise ``AttributeError`` when checking version of older xlrd versions (:issue:`38955`)
- Fixed regression in :class:`DataFrame` constructor reordering element when construction from datetime ndarray with dtype not ``"datetime64[ns]"`` (:issue:`39422`)
- Fixed regression in :meth:`DataFrame.astype` and :meth:`Series.astype` not casting to bytes dtype (:issue:`39474`)
- Fixed regression in :meth:`~DataFrame.to_pickle` failing to create bz2/xz compressed pickle files with ``protocol=5`` (:issue:`39002`)
- Fixed regression in :func:`pandas.testing.assert_series_equal` and :func:`pandas.testing.assert_frame_equal` always raising ``AssertionError`` when comparing extension dtypes (:issue:`39410`)
- Fixed regression in :meth:`~DataFrame.to_csv` opening ``codecs.StreamWriter`` in binary mode instead of in text mode and ignoring user-provided ``mode`` (:issue:`39247`)
- Fixed regression in :meth:`Categorical.astype` casting to incorrect dtype when ``np.int32`` is passed to dtype argument (:issue:`39402`)
- Fixed regression in :meth:`~DataFrame.to_excel` creating corrupt files when appending (``mode="a"``) to an existing file (:issue:`39576`)
- Fixed regression in :meth:`DataFrame.transform` failing in case of an empty DataFrame or Series (:issue:`39636`)
- Fixed regression in :meth:`~DataFrame.groupby` or :meth:`~DataFrame.resample` when aggregating an all-NaN or numeric object dtype column (:issue:`39329`)
- Fixed regression in :meth:`.Rolling.count` where the ``min_periods`` argument would be set to ``0`` after the operation (:issue:`39554`)
- Fixed regression in :func:`read_excel` that incorrectly raised when the argument ``io`` was a non-path and non-buffer and the ``engine`` argument was specified (:issue:`39528`)

.. ---------------------------------------------------------------------------

.. _whatsnew_122.bug_fixes:

Bug fixes
~~~~~~~~~

- :func:`pandas.read_excel` error message when a specified ``sheetname`` does not exist is now uniform across engines (:issue:`39250`)
- Fixed bug in :func:`pandas.read_excel` producing incorrect results when the engine ``openpyxl`` is used and the excel file is missing or has incorrect dimension information; the fix requires ``openpyxl`` >= 3.0.0, prior versions may still fail (:issue:`38956`, :issue:`39001`)
- Fixed bug in :func:`pandas.read_excel` sometimes producing a ``DataFrame`` with trailing rows of ``np.nan`` when the engine ``openpyxl`` is used (:issue:`39181`)


What's new in 1.2.1 (January 20, 2021)
--------------------------------------

These are the changes in pandas 1.2.1. See :ref:`release` for a full changelog
including other versions of pandas.

{{ header }}

.. ---------------------------------------------------------------------------

.. _whatsnew_121.regressions:

Fixed regressions
~~~~~~~~~~~~~~~~~
- Fixed regression in :meth:`~DataFrame.to_csv` that created corrupted zip files when there were more rows than ``chunksize`` (:issue:`38714`)
- Fixed regression in :meth:`~DataFrame.to_csv` opening ``codecs.StreamReaderWriter`` in binary mode instead of in text mode (:issue:`39247`)
- Fixed regression in :meth:`read_csv` and other read functions were the encoding error policy (``errors``) did not default to ``"replace"`` when no encoding was specified (:issue:`38989`)
- Fixed regression in :func:`read_excel` with non-rawbyte file handles (:issue:`38788`)
- Fixed regression in :meth:`DataFrame.to_stata` not removing the created file when an error occured (:issue:`39202`)
- Fixed regression in ``DataFrame.__setitem__`` raising ``ValueError`` when expanding :class:`DataFrame` and new column is from type ``"0 - name"`` (:issue:`39010`)
- Fixed regression in setting with :meth:`DataFrame.loc`  raising ``ValueError`` when :class:`DataFrame` has unsorted :class:`MultiIndex` columns and indexer is a scalar (:issue:`38601`)
- Fixed regression in setting with :meth:`DataFrame.loc` raising ``KeyError`` with :class:`MultiIndex` and list-like columns indexer enlarging :class:`DataFrame` (:issue:`39147`)
- Fixed regression in :meth:`~DataFrame.groupby()` with :class:`Categorical` grouping column not showing unused categories for ``grouped.indices`` (:issue:`38642`)
- Fixed regression in :meth:`.GroupBy.sem` where the presence of non-numeric columns would cause an error instead of being dropped (:issue:`38774`)
- Fixed regression in :meth:`.DataFrameGroupBy.diff` raising for ``int8`` and ``int16`` columns (:issue:`39050`)
- Fixed regression in :meth:`DataFrame.groupby` when aggregating an ``ExtensionDType`` that could fail for non-numeric values (:issue:`38980`)
- Fixed regression in :meth:`.Rolling.skew` and :meth:`.Rolling.kurt` modifying the object inplace (:issue:`38908`)
- Fixed regression in :meth:`DataFrame.any` and :meth:`DataFrame.all` not returning a result for tz-aware ``datetime64`` columns (:issue:`38723`)
- Fixed regression in :meth:`DataFrame.apply` with ``axis=1`` using str accessor in apply function (:issue:`38979`)
- Fixed regression in :meth:`DataFrame.replace` raising ``ValueError`` when :class:`DataFrame` has dtype ``bytes`` (:issue:`38900`)
- Fixed regression in :meth:`Series.fillna` that raised ``RecursionError`` with ``datetime64[ns, UTC]`` dtype (:issue:`38851`)
- Fixed regression in comparisons between ``NaT`` and ``datetime.date`` objects incorrectly returning ``True`` (:issue:`39151`)
- Fixed regression in calling NumPy :func:`~numpy.ufunc.accumulate` ufuncs on DataFrames, e.g. ``np.maximum.accumulate(df)`` (:issue:`39259`)
- Fixed regression in repr of float-like strings of an ``object`` dtype having trailing 0's truncated after the decimal (:issue:`38708`)
- Fixed regression that raised ``AttributeError`` with PyArrow versions [0.16.0, 1.0.0) (:issue:`38801`)
- Fixed regression in :func:`pandas.testing.assert_frame_equal` raising ``TypeError`` with ``check_like=True`` when :class:`Index` or columns have mixed dtype (:issue:`39168`)

We have reverted a commit that resulted in several plotting related regressions in pandas 1.2.0 (:issue:`38969`, :issue:`38736`, :issue:`38865`, :issue:`38947` and :issue:`39126`).
As a result, bugs reported as fixed in pandas 1.2.0 related to inconsistent tick labeling in bar plots are again present (:issue:`26186` and :issue:`11465`)



What's new in 1.2.0 (December 26, 2020)

Performance improvements
- Performance improvements when creating DataFrame or Series with dtype ``str`` or :class:`StringDtype` from array with many string elements (:issue:`36304`, :issue:`36317`, :issue:`36325`, :issue:`36432`, :issue:`37371`)
- Performance improvement in :meth:`.GroupBy.agg` with the ``numba`` engine (:issue:`35759`)
- Performance improvements when creating :meth:`Series.map` from a huge dictionary (:issue:`34717`)
- Performance improvement in :meth:`.GroupBy.transform` with the ``numba`` engine (:issue:`36240`)
- :class:`.Styler` uuid method altered to compress data transmission over web whilst maintaining reasonably low table collision probability (:issue:`36345`)
- Performance improvement in :func:`to_datetime` with non-ns time unit for ``float`` ``dtype`` columns (:issue:`20445`)
- Performance improvement in setting values on an :class:`IntervalArray` (:issue:`36310`)
- The internal index method :meth:`~Index._shallow_copy` now makes the new index and original index share cached attributes, avoiding creating these again, if created on either. This can speed up operations that depend on creating copies of existing indexes (:issue:`36840`)
- Performance improvement in :meth:`.RollingGroupby.count` (:issue:`35625`)
- Small performance decrease to :meth:`.Rolling.min` and :meth:`.Rolling.max` for fixed windows (:issue:`36567`)
- Reduced peak memory usage in :meth:`DataFrame.to_pickle` when using ``protocol=5`` in python 3.8+ (:issue:`34244`)
- Faster ``dir`` calls when the object has many index labels, e.g. ``dir(ser)`` (:issue:`37450`)
- Performance improvement in :class:`ExpandingGroupby` (:issue:`37064`)
- Performance improvement in :meth:`Series.astype` and :meth:`DataFrame.astype` for :class:`Categorical` (:issue:`8628`)
- Performance improvement in :meth:`DataFrame.groupby` for ``float`` ``dtype`` (:issue:`28303`), changes of the underlying hash-function can lead to changes in float based indexes sort ordering for ties (e.g. :meth:`Index.value_counts`)
- Performance improvement in :meth:`pd.isin` for inputs with more than 1e6 elements (:issue:`36611`)
- Performance improvement for :meth:`DataFrame.__setitem__` with list-like indexers (:issue:`37954`)
- :meth:`read_json` now avoids reading entire file into memory when chunksize is specified (:issue:`34548`)

Bug fixes

Categorical
- :meth:`Categorical.fillna` will always return a copy, validate a passed fill value regardless of whether there are any NAs to fill, and disallow an ``NaT`` as a fill value for numeric categories (:issue:`36530`)
- Bug in :meth:`Categorical.__setitem__` that incorrectly raised when trying to set a tuple value (:issue:`20439`)
- Bug in :meth:`CategoricalIndex.equals` incorrectly casting non-category entries to ``np.nan`` (:issue:`37667`)
- Bug in :meth:`CategoricalIndex.where` incorrectly setting non-category entries to ``np.nan`` instead of raising ``TypeError`` (:issue:`37977`)
- Bug in :meth:`Categorical.to_numpy` and ``np.array(categorical)`` with tz-aware ``datetime64`` categories incorrectly dropping the time zone information instead of casting to object dtype (:issue:`38136`)

Datetime-like
- Bug in :meth:`DataFrame.combine_first` that would convert datetime-like column on other :class:`DataFrame` to integer when the column is not present in original :class:`DataFrame` (:issue:`28481`)
- Bug in :attr:`.DatetimeArray.date` where a ``ValueError`` would be raised with a read-only backing array (:issue:`33530`)
- Bug in ``NaT`` comparisons failing to raise ``TypeError`` on invalid inequality comparisons (:issue:`35046`)
- Bug in :class:`.DateOffset` where attributes reconstructed from pickle files differ from original objects when input values exceed normal ranges (e.g. months=12) (:issue:`34511`)
- Bug in :meth:`.DatetimeIndex.get_slice_bound` where ``datetime.date`` objects were not accepted or naive :class:`Timestamp` with a tz-aware :class:`.DatetimeIndex` (:issue:`35690`)
- Bug in :meth:`.DatetimeIndex.slice_locs` where ``datetime.date`` objects were not accepted (:issue:`34077`)
- Bug in :meth:`.DatetimeIndex.searchsorted`, :meth:`.TimedeltaIndex.searchsorted`, :meth:`PeriodIndex.searchsorted`, and :meth:`Series.searchsorted` with ``datetime64``, ``timedelta64`` or :class:`Period` dtype placement of ``NaT`` values being inconsistent with NumPy (:issue:`36176`, :issue:`36254`)
- Inconsistency in :class:`.DatetimeArray`, :class:`.TimedeltaArray`, and :class:`.PeriodArray` method ``__setitem__`` casting arrays of strings to datetime-like scalars but not scalar strings (:issue:`36261`)
- Bug in :meth:`.DatetimeArray.take` incorrectly allowing ``fill_value`` with a mismatched time zone (:issue:`37356`)
- Bug in :class:`.DatetimeIndex.shift` incorrectly raising when shifting empty indexes (:issue:`14811`)
- :class:`Timestamp` and :class:`.DatetimeIndex` comparisons between tz-aware and tz-naive objects now follow the standard library ``datetime`` behavior, returning ``True``/``False`` for ``!=``/``==`` and raising for inequality comparisons (:issue:`28507`)
- Bug in :meth:`.DatetimeIndex.equals` and :meth:`.TimedeltaIndex.equals` incorrectly considering ``int64`` indexes as equal (:issue:`36744`)
- :meth:`Series.to_json`, :meth:`DataFrame.to_json`, and :meth:`read_json` now implement time zone parsing when orient structure is ``table`` (:issue:`35973`)
- :meth:`astype` now attempts to convert to ``datetime64[ns, tz]`` directly from ``object`` with inferred time zone from string (:issue:`35973`)
- Bug in :meth:`.TimedeltaIndex.sum` and :meth:`Series.sum` with ``timedelta64`` dtype on an empty index or series returning ``NaT`` instead of ``Timedelta(0)`` (:issue:`31751`)
- Bug in :meth:`.DatetimeArray.shift` incorrectly allowing ``fill_value`` with a mismatched time zone (:issue:`37299`)
- Bug in adding a :class:`.BusinessDay` with nonzero ``offset`` to a non-scalar other (:issue:`37457`)
- Bug in :func:`to_datetime` with a read-only array incorrectly raising (:issue:`34857`)
- Bug in :meth:`Series.isin` with ``datetime64[ns]`` dtype and :meth:`.DatetimeIndex.isin` incorrectly casting integers to datetimes (:issue:`36621`)
- Bug in :meth:`Series.isin` with ``datetime64[ns]`` dtype and :meth:`.DatetimeIndex.isin` failing to consider tz-aware and tz-naive datetimes as always different (:issue:`35728`)
- Bug in :meth:`Series.isin` with ``PeriodDtype`` dtype and :meth:`PeriodIndex.isin` failing to consider arguments with different ``PeriodDtype`` as always different (:issue:`37528`)
- Bug in :class:`Period` constructor now correctly handles nanoseconds in the ``value`` argument (:issue:`34621` and :issue:`17053`)

Timedelta
- Bug in :class:`.TimedeltaIndex`, :class:`Series`, and :class:`DataFrame` floor-division with ``timedelta64`` dtypes and ``NaT`` in the denominator (:issue:`35529`)
- Bug in parsing of ISO 8601 durations in :class:`Timedelta` and :func:`to_datetime` (:issue:`29773`, :issue:`36204`)
- Bug in :func:`to_timedelta` with a read-only array incorrectly raising (:issue:`34857`)
- Bug in :class:`Timedelta` incorrectly truncating to sub-second portion of a string input when it has precision higher than nanoseconds (:issue:`36738`)

Timezones
- Bug in :func:`date_range` was raising ``AmbiguousTimeError`` for valid input with ``ambiguous=False`` (:issue:`35297`)
- Bug in :meth:`Timestamp.replace` was losing fold information (:issue:`37610`)

Numeric
- Bug in :func:`to_numeric` where float precision was incorrect (:issue:`31364`)
- Bug in :meth:`DataFrame.any` with ``axis=1`` and ``bool_only=True`` ignoring the ``bool_only`` keyword (:issue:`32432`)
- Bug in :meth:`Series.equals` where a ``ValueError`` was raised when NumPy arrays were compared to scalars (:issue:`35267`)
- Bug in :class:`Series` where two Series each have a :class:`.DatetimeIndex` with different time zones having those indexes incorrectly changed when performing arithmetic operations (:issue:`33671`)
- Bug in :mod:`pandas.testing` module functions when used with ``check_exact=False`` on complex numeric types (:issue:`28235`)
- Bug in :meth:`DataFrame.__rmatmul__` error handling reporting transposed shapes (:issue:`21581`)
- Bug in :class:`Series` flex arithmetic methods where the result when operating with a ``list``, ``tuple`` or ``np.ndarray`` would have an incorrect name (:issue:`36760`)
- Bug in :class:`.IntegerArray` multiplication with ``timedelta`` and ``np.timedelta64`` objects (:issue:`36870`)
- Bug in :class:`MultiIndex` comparison with tuple incorrectly treating tuple as array-like (:issue:`21517`)
- Bug in :meth:`DataFrame.diff` with ``datetime64`` dtypes including ``NaT`` values failing to fill ``NaT`` results correctly (:issue:`32441`)
- Bug in :class:`DataFrame` arithmetic ops incorrectly accepting keyword arguments (:issue:`36843`)
- Bug in :class:`.IntervalArray` comparisons with :class:`Series` not returning Series (:issue:`36908`)
- Bug in :class:`DataFrame` allowing arithmetic operations with list of array-likes with undefined results. Behavior changed to raising ``ValueError`` (:issue:`36702`)
- Bug in :meth:`DataFrame.std` with ``timedelta64`` dtype and ``skipna=False`` (:issue:`37392`)
- Bug in :meth:`DataFrame.min` and :meth:`DataFrame.max` with ``datetime64`` dtype and ``skipna=False`` (:issue:`36907`)
- Bug in :meth:`DataFrame.idxmax` and :meth:`DataFrame.idxmin` with mixed dtypes incorrectly raising ``TypeError`` (:issue:`38195`)

Conversion
- Bug in :meth:`DataFrame.to_dict` with ``orient='records'`` now returns python native datetime objects for datetime-like columns (:issue:`21256`)
- Bug in :meth:`Series.astype` conversion from ``string`` to ``float`` raised in presence of ``pd.NA`` values (:issue:`37626`)

Strings
- Bug in :meth:`Series.to_string`, :meth:`DataFrame.to_string`, and :meth:`DataFrame.to_latex` adding a leading space when ``index=False`` (:issue:`24980`)
- Bug in :func:`to_numeric` raising a ``TypeError`` when attempting to convert a string dtype Series containing only numeric strings and ``NA`` (:issue:`37262`)

Interval
- Bug in :meth:`DataFrame.replace` and :meth:`Series.replace` where :class:`Interval` dtypes would be converted to object dtypes (:issue:`34871`)
- Bug in :meth:`IntervalIndex.take` with negative indices and ``fill_value=None`` (:issue:`37330`)
- Bug in :meth:`IntervalIndex.putmask` with datetime-like dtype incorrectly casting to object dtype (:issue:`37968`)
- Bug in :meth:`IntervalArray.astype` incorrectly dropping dtype information with a :class:`CategoricalDtype` object (:issue:`37984`)

Indexing
- Bug in :meth:`PeriodIndex.get_loc` incorrectly raising ``ValueError`` on non-datelike strings instead of ``KeyError``, causing similar errors in :meth:`Series.__getitem__`, :meth:`Series.__contains__`, and :meth:`Series.loc.__getitem__` (:issue:`34240`)
- Bug in :meth:`Index.sort_values` where, when empty values were passed, the method would break by trying to compare missing values instead of pushing them to the end of the sort order (:issue:`35584`)
- Bug in :meth:`Index.get_indexer` and :meth:`Index.get_indexer_non_unique` where ``int64`` arrays are returned instead of ``intp`` (:issue:`36359`)
- Bug in :meth:`DataFrame.sort_index` where parameter ascending passed as a list on a single level index gives wrong result (:issue:`32334`)
- Bug in :meth:`DataFrame.reset_index` was incorrectly raising a ``ValueError`` for input with a :class:`MultiIndex` with missing values in a level with ``Categorical`` dtype (:issue:`24206`)
- Bug in indexing with boolean masks on datetime-like values sometimes returning a view instead of a copy (:issue:`36210`)
- Bug in :meth:`DataFrame.__getitem__` and :meth:`DataFrame.loc.__getitem__` with :class:`IntervalIndex` columns and a numeric indexer (:issue:`26490`)
- Bug in :meth:`Series.loc.__getitem__` with a non-unique :class:`MultiIndex` and an empty-list indexer (:issue:`13691`)
- Bug in indexing on a :class:`Series` or :class:`DataFrame` with a :class:`MultiIndex` and a level named ``"0"`` (:issue:`37194`)
- Bug in :meth:`Series.__getitem__` when using an unsigned integer array as an indexer giving incorrect results or segfaulting instead of raising ``KeyError`` (:issue:`37218`)
- Bug in :meth:`Index.where` incorrectly casting numeric values to strings (:issue:`37591`)
- Bug in :meth:`DataFrame.loc` returning empty result when indexer is a slice with negative step size (:issue:`38071`)
- Bug in :meth:`Series.loc` and :meth:`DataFrame.loc` raises when the index was of ``object`` dtype and the given numeric label was in the index (:issue:`26491`)
- Bug in :meth:`DataFrame.loc` returned requested key plus missing values when ``loc`` was applied to single level from a :class:`MultiIndex` (:issue:`27104`)
- Bug in indexing on a :class:`Series` or :class:`DataFrame` with a :class:`CategoricalIndex` using a list-like indexer containing NA values (:issue:`37722`)
- Bug in :meth:`DataFrame.loc.__setitem__` expanding an empty :class:`DataFrame` with mixed dtypes (:issue:`37932`)
- Bug in :meth:`DataFrame.xs` ignored ``droplevel=False`` for columns (:issue:`19056`)
- Bug in :meth:`DataFrame.reindex` raising ``IndexingError`` wrongly for empty DataFrame with ``tolerance`` not ``None`` or ``method="nearest"`` (:issue:`27315`)
- Bug in indexing on a :class:`Series` or :class:`DataFrame` with a :class:`CategoricalIndex` using list-like indexer that contains elements that are in the index's ``categories`` but not in the index itself failing to raise ``KeyError`` (:issue:`37901`)
- Bug on inserting a boolean label into a :class:`DataFrame` with a numeric :class:`Index` columns incorrectly casting to integer (:issue:`36319`)
- Bug in :meth:`DataFrame.iloc` and :meth:`Series.iloc` aligning objects in ``__setitem__`` (:issue:`22046`)
- Bug in :meth:`MultiIndex.drop` does not raise if labels are partially found (:issue:`37820`)
- Bug in :meth:`DataFrame.loc` did not raise ``KeyError`` when missing combination was given with ``slice(None)`` for remaining levels (:issue:`19556`)
- Bug in :meth:`DataFrame.loc` raising ``TypeError`` when non-integer slice was given to select values from :class:`MultiIndex` (:issue:`25165`, :issue:`24263`)
- Bug in :meth:`Series.at` returning :class:`Series` with one element instead of scalar when index is a :class:`MultiIndex` with one level (:issue:`38053`)
- Bug in :meth:`DataFrame.loc` returning and assigning elements in wrong order when indexer is differently ordered than the :class:`MultiIndex` to filter (:issue:`31330`, :issue:`34603`)
- Bug in :meth:`DataFrame.loc` and :meth:`DataFrame.__getitem__`  raising ``KeyError`` when columns were :class:`MultiIndex` with only one level (:issue:`29749`)
- Bug in :meth:`Series.__getitem__` and :meth:`DataFrame.__getitem__` raising blank ``KeyError`` without missing keys for :class:`IntervalIndex` (:issue:`27365`)
- Bug in setting a new label on a :class:`DataFrame` or :class:`Series` with a :class:`CategoricalIndex` incorrectly raising ``TypeError`` when the new label is not among the index's categories (:issue:`38098`)
- Bug in :meth:`Series.loc` and :meth:`Series.iloc` raising ``ValueError`` when inserting a list-like ``np.array``, ``list`` or ``tuple`` in an ``object`` Series of equal length (:issue:`37748`, :issue:`37486`)
- Bug in :meth:`Series.loc` and :meth:`Series.iloc` setting all the values of an ``object`` Series with those of a list-like ``ExtensionArray`` instead of inserting it (:issue:`38271`)

Missing
- Bug in :meth:`.SeriesGroupBy.transform` now correctly handles missing values for ``dropna=False`` (:issue:`35014`)
- Bug in :meth:`Series.nunique` with ``dropna=True`` was returning incorrect results when both ``NA`` and ``None`` missing values were present (:issue:`37566`)
- Bug in :meth:`Series.interpolate` where kwarg ``limit_area`` and ``limit_direction`` had no effect when using methods ``pad`` and ``backfill`` (:issue:`31048`)

MultiIndex
- Bug in :meth:`DataFrame.xs` when used with :class:`IndexSlice` raises ``TypeError`` with message ``"Expected label or tuple of labels"`` (:issue:`35301`)
- Bug in :meth:`DataFrame.reset_index` with ``NaT`` values in index raises ``ValueError`` with message ``"cannot convert float NaN to integer"`` (:issue:`36541`)
- Bug in :meth:`DataFrame.combine_first` when used with :class:`MultiIndex` containing string and ``NaN`` values raises ``TypeError`` (:issue:`36562`)
- Bug in :meth:`MultiIndex.drop` dropped ``NaN`` values when non existing key was given as input (:issue:`18853`)
- Bug in :meth:`MultiIndex.drop` dropping more values than expected when index has duplicates and is not sorted (:issue:`33494`)

I/O
- :func:`read_sas` no longer leaks resources on failure (:issue:`35566`)
- Bug in :meth:`DataFrame.to_csv` and :meth:`Series.to_csv` caused a ``ValueError`` when it was called with a filename in combination with ``mode`` containing a ``b`` (:issue:`35058`)
- Bug in :meth:`read_csv` with ``float_precision='round_trip'`` did not handle ``decimal`` and ``thousands`` parameters (:issue:`35365`)
- :meth:`to_pickle` and :meth:`read_pickle` were closing user-provided file objects (:issue:`35679`)
- :meth:`to_csv` passes compression arguments for ``'gzip'`` always to ``gzip.GzipFile`` (:issue:`28103`)
- :meth:`to_csv` did not support zip compression for binary file object not having a filename (:issue:`35058`)
- :meth:`to_csv` and :meth:`read_csv` did not honor ``compression`` and ``encoding`` for path-like objects that are internally converted to file-like objects (:issue:`35677`, :issue:`26124`, :issue:`32392`)
- :meth:`DataFrame.to_pickle`, :meth:`Series.to_pickle`, and :meth:`read_pickle` did not support compression for file-objects (:issue:`26237`, :issue:`29054`, :issue:`29570`)
- Bug in :func:`LongTableBuilder.middle_separator` was duplicating LaTeX longtable entries in the List of Tables of a LaTeX document (:issue:`34360`)
- Bug in :meth:`read_csv` with ``engine='python'`` truncating data if multiple items present in first row and first element started with BOM (:issue:`36343`)
- Removed ``private_key`` and ``verbose`` from :func:`read_gbq` as they are no longer supported in ``pandas-gbq`` (:issue:`34654`, :issue:`30200`)
- Bumped minimum pytables version to 3.5.1 to avoid a ``ValueError`` in :meth:`read_hdf` (:issue:`24839`)
- Bug in :func:`read_table` and :func:`read_csv` when ``delim_whitespace=True`` and ``sep=default`` (:issue:`36583`)
- Bug in :meth:`DataFrame.to_json` and :meth:`Series.to_json` when used with ``lines=True`` and ``orient='records'`` the last line of the record is not appended with 'new line character' (:issue:`36888`)
- Bug in :meth:`read_parquet` with fixed offset time zones. String representation of time zones was not recognized (:issue:`35997`, :issue:`36004`)
- Bug in :meth:`DataFrame.to_html`, :meth:`DataFrame.to_string`, and :meth:`DataFrame.to_latex` ignoring the ``na_rep`` argument when ``float_format`` was also specified (:issue:`9046`, :issue:`13828`)
- Bug in output rendering of complex numbers showing too many trailing zeros (:issue:`36799`)
- Bug in :class:`HDFStore` threw a ``TypeError`` when exporting an empty DataFrame with ``datetime64[ns, tz]`` dtypes with a fixed HDF5 store (:issue:`20594`)
- Bug in :class:`HDFStore` was dropping time zone information when exporting a Series with ``datetime64[ns, tz]`` dtypes with a fixed HDF5 store (:issue:`20594`)
- :func:`read_csv` was closing user-provided binary file handles when ``engine="c"`` and an ``encoding`` was requested (:issue:`36980`)
- Bug in :meth:`DataFrame.to_hdf` was not dropping missing rows with ``dropna=True`` (:issue:`35719`)
- Bug in :func:`read_html` was raising a ``TypeError`` when supplying a ``pathlib.Path`` argument to the ``io`` parameter (:issue:`37705`)
- :meth:`DataFrame.to_excel`, :meth:`Series.to_excel`, :meth:`DataFrame.to_markdown`, and :meth:`Series.to_markdown` now support writing to fsspec URLs such as S3 and Google Cloud Storage (:issue:`33987`)
- Bug in :func:`read_fwf` with ``skip_blank_lines=True`` was not skipping blank lines (:issue:`37758`)
- Parse missing values using :func:`read_json` with ``dtype=False`` to ``NaN`` instead of ``None`` (:issue:`28501`)
- :meth:`read_fwf` was inferring compression with ``compression=None`` which was not consistent with the other ``read_*`` functions (:issue:`37909`)
- :meth:`DataFrame.to_html` was ignoring ``formatters`` argument for ``ExtensionDtype`` columns (:issue:`36525`)
- Bumped minimum xarray version to 0.12.3 to avoid reference to the removed ``Panel`` class (:issue:`27101`, :issue:`37983`)
- :meth:`DataFrame.to_csv` was re-opening file-like handles that also implement ``os.PathLike`` (:issue:`38125`)
- Bug in the conversion of a sliced ``pyarrow.Table`` with missing values to a DataFrame (:issue:`38525`)
- Bug in :func:`read_sql_table` raising a ``sqlalchemy.exc.OperationalError`` when column names contained a percentage sign (:issue:`37517`)

Period
- Bug in :meth:`DataFrame.replace` and :meth:`Series.replace` where :class:`Period` dtypes would be converted to object dtypes (:issue:`34871`)

Plotting
- Bug in :meth:`DataFrame.plot` was rotating xticklabels when ``subplots=True``, even if the x-axis wasn't an irregular time series (:issue:`29460`)
- Bug in :meth:`DataFrame.plot` where a marker letter in the ``style`` keyword sometimes caused a ``ValueError`` (:issue:`21003`)
- Bug in :meth:`DataFrame.plot.bar` and :meth:`Series.plot.bar` where ticks positions were assigned by value order instead of using the actual value for numeric or a smart ordering for string (:issue:`26186`, :issue:`11465`). This fix has been reverted in pandas 1.2.1, see :doc:`v1.2.1`
- Twinned axes were losing their tick labels which should only happen to all but the last row or column of 'externally' shared axes (:issue:`33819`)
- Bug in :meth:`Series.plot` and :meth:`DataFrame.plot` was throwing a :exc:`ValueError` when the Series or DataFrame was
  indexed by a :class:`.TimedeltaIndex` with a fixed frequency and the x-axis lower limit was greater than the upper limit (:issue:`37454`)
- Bug in :meth:`.DataFrameGroupBy.boxplot` when ``subplots=False`` would raise a ``KeyError`` (:issue:`16748`)
- Bug in :meth:`DataFrame.plot` and :meth:`Series.plot` was overwriting matplotlib's shared y axes behavior when no ``sharey`` parameter was passed (:issue:`37942`)
- Bug in :meth:`DataFrame.plot` was raising a ``TypeError`` with ``ExtensionDtype`` columns (:issue:`32073`)

Styler
- Bug in :meth:`Styler.render` HTML was generated incorrectly because of formatting error in ``rowspan`` attribute, it now matches with w3 syntax (:issue:`38234`)

Groupby/resample/rolling
- Bug in :meth:`.DataFrameGroupBy.count` and :meth:`SeriesGroupBy.sum` returning ``NaN`` for missing categories when grouped on multiple ``Categoricals``. Now returning ``0`` (:issue:`35028`)
- Bug in :meth:`.DataFrameGroupBy.apply` that would sometimes throw an erroneous ``ValueError`` if the grouping axis had duplicate entries (:issue:`16646`)
- Bug in :meth:`DataFrame.resample` that would throw a ``ValueError`` when resampling from ``"D"`` to ``"24H"`` over a transition into daylight savings time (DST) (:issue:`35219`)
- Bug when combining methods :meth:`DataFrame.groupby` with :meth:`DataFrame.resample` and :meth:`DataFrame.interpolate` raising a ``TypeError`` (:issue:`35325`)
- Bug in :meth:`.DataFrameGroupBy.apply` where a non-nuisance grouping column would be dropped from the output columns if another groupby method was called before ``.apply`` (:issue:`34656`)
- Bug when subsetting columns on a :class:`~pandas.core.groupby.DataFrameGroupBy` (e.g. ``df.groupby('a')[['b']])``) would reset the attributes ``axis``, ``dropna``, ``group_keys``, ``level``, ``mutated``, ``sort``, and ``squeeze`` to their default values (:issue:`9959`)
- Bug in :meth:`.DataFrameGroupBy.tshift` failing to raise ``ValueError`` when a frequency cannot be inferred for the index of a group (:issue:`35937`)
- Bug in :meth:`DataFrame.groupby` does not always maintain column index name for ``any``, ``all``, ``bfill``, ``ffill``, ``shift`` (:issue:`29764`)
- Bug in :meth:`.DataFrameGroupBy.apply` raising error with ``np.nan`` group(s) when ``dropna=False`` (:issue:`35889`)
- Bug in :meth:`.Rolling.sum` returned wrong values when dtypes where mixed between float and integer and ``axis=1`` (:issue:`20649`, :issue:`35596`)
- Bug in :meth:`.Rolling.count` returned ``np.nan`` with :class:`~pandas.api.indexers.FixedForwardWindowIndexer` as window, ``min_periods=0`` and only missing values in the window (:issue:`35579`)
- Bug where :class:`pandas.core.window.Rolling` produces incorrect window sizes when using a ``PeriodIndex`` (:issue:`34225`)
- Bug in :meth:`.DataFrameGroupBy.ffill` and :meth:`.DataFrameGroupBy.bfill` where a ``NaN`` group would return filled values instead of ``NaN`` when ``dropna=True`` (:issue:`34725`)
- Bug in :meth:`.RollingGroupby.count` where a ``ValueError`` was raised when specifying the ``closed`` parameter (:issue:`35869`)
- Bug in :meth:`.DataFrameGroupBy.rolling` returning wrong values with partial centered window (:issue:`36040`)
- Bug in :meth:`.DataFrameGroupBy.rolling` returned wrong values with time aware window containing ``NaN``. Raises ``ValueError`` because windows are not monotonic now (:issue:`34617`)
- Bug in :meth:`.Rolling.__iter__` where a ``ValueError`` was not raised when ``min_periods`` was larger than ``window`` (:issue:`37156`)
- Using :meth:`.Rolling.var` instead of :meth:`.Rolling.std` avoids numerical issues for :meth:`.Rolling.corr` when :meth:`.Rolling.var` is still within floating point precision while :meth:`.Rolling.std` is not (:issue:`31286`)
- Bug in :meth:`.DataFrameGroupBy.quantile` and :meth:`.Resampler.quantile` raised ``TypeError`` when values were of type ``Timedelta`` (:issue:`29485`)
- Bug in :meth:`.Rolling.median` and :meth:`.Rolling.quantile` returned wrong values for :class:`.BaseIndexer` subclasses with non-monotonic starting or ending points for windows (:issue:`37153`)
- Bug in :meth:`DataFrame.groupby` dropped ``nan`` groups from result with ``dropna=False`` when grouping over a single column (:issue:`35646`, :issue:`35542`)
- Bug in :meth:`.DataFrameGroupBy.head`, :meth:`DataFrameGroupBy.tail`, :meth:`SeriesGroupBy.head`, and :meth:`SeriesGroupBy.tail` would raise when used with ``axis=1`` (:issue:`9772`)
- Bug in :meth:`.DataFrameGroupBy.transform` would raise when used with ``axis=1`` and a transformation kernel (e.g. "shift") (:issue:`36308`)
- Bug in :meth:`.DataFrameGroupBy.resample` using ``.agg`` with sum produced different result than just calling ``.sum`` (:issue:`33548`)
- Bug in :meth:`.DataFrameGroupBy.apply` dropped values on ``nan`` group when returning the same axes with the original frame (:issue:`38227`)
- Bug in :meth:`.DataFrameGroupBy.quantile` couldn't handle with arraylike ``q`` when grouping by columns (:issue:`33795`)
- Bug in :meth:`DataFrameGroupBy.rank` with ``datetime64tz`` or period dtype incorrectly casting results to those dtypes instead of returning ``float64`` dtype (:issue:`38187`)

Reshaping
- Bug in :meth:`DataFrame.crosstab` was returning incorrect results on inputs with duplicate row names, duplicate column names or duplicate names between row and column labels (:issue:`22529`)
- Bug in :meth:`DataFrame.pivot_table` with ``aggfunc='count'`` or ``aggfunc='sum'`` returning ``NaN`` for missing categories when pivoted on a ``Categorical``. Now returning ``0`` (:issue:`31422`)
- Bug in :func:`concat` and :class:`DataFrame` constructor where input index names are not preserved in some cases (:issue:`13475`)
- Bug in func :meth:`crosstab` when using multiple columns with ``margins=True`` and ``normalize=True`` (:issue:`35144`)
- Bug in :meth:`DataFrame.stack` where an empty DataFrame.stack would raise an error (:issue:`36113`). Now returning an empty Series with empty MultiIndex.
- Bug in :meth:`Series.unstack`. Now a Series with single level of Index trying to unstack would raise a ``ValueError`` (:issue:`36113`)
- Bug in :meth:`DataFrame.agg` with ``func={'name':<FUNC>}`` incorrectly raising ``TypeError`` when ``DataFrame.columns==['Name']`` (:issue:`36212`)
- Bug in :meth:`Series.transform` would give incorrect results or raise when the argument ``func`` was a dictionary (:issue:`35811`)
- Bug in :meth:`DataFrame.pivot` did not preserve :class:`MultiIndex` level names for columns when rows and columns are both multiindexed (:issue:`36360`)
- Bug in :meth:`DataFrame.pivot` modified ``index`` argument when ``columns`` was passed but ``values`` was not (:issue:`37635`)
- Bug in :meth:`DataFrame.join` returned a non deterministic level-order for the resulting :class:`MultiIndex` (:issue:`36910`)
- Bug in :meth:`DataFrame.combine_first` caused wrong alignment with dtype ``string`` and one level of ``MultiIndex`` containing only ``NA`` (:issue:`37591`)
- Fixed regression in :func:`merge` on merging :class:`.DatetimeIndex` with empty DataFrame (:issue:`36895`)
- Bug in :meth:`DataFrame.apply` not setting index of return value when ``func`` return type is ``dict`` (:issue:`37544`)
- Bug in :meth:`DataFrame.merge` and :meth:`pandas.merge` returning inconsistent ordering in result for ``how=right`` and ``how=left`` (:issue:`35382`)
- Bug in :func:`merge_ordered` couldn't handle list-like ``left_by`` or ``right_by`` (:issue:`35269`)
- Bug in :func:`merge_ordered` returned wrong join result when length of ``left_by`` or ``right_by`` equals to the rows of ``left`` or ``right`` (:issue:`38166`)
- Bug in :func:`merge_ordered` didn't raise when elements in ``left_by`` or ``right_by`` not exist in ``left`` columns or ``right`` columns (:issue:`38167`)
- Bug in :func:`DataFrame.drop_duplicates` not validating bool dtype for ``ignore_index`` keyword (:issue:`38274`)

ExtensionArray
- Fixed bug where :class:`DataFrame` column set to scalar extension type via a dict instantiation was considered an object type rather than the extension type (:issue:`35965`)
- Fixed bug where ``astype()`` with equal dtype and ``copy=False`` would return a new object (:issue:`28488`)
- Fixed bug when applying a NumPy ufunc with multiple outputs to an :class:`.IntegerArray` returning ``None`` (:issue:`36913`)
- Fixed an inconsistency in :class:`.PeriodArray`'s ``__init__`` signature to those of :class:`.DatetimeArray` and :class:`.TimedeltaArray` (:issue:`37289`)
- Reductions for :class:`.BooleanArray`, :class:`.Categorical`, :class:`.DatetimeArray`, :class:`.FloatingArray`, :class:`.IntegerArray`, :class:`.PeriodArray`, :class:`.TimedeltaArray`, and :class:`.PandasArray` are now keyword-only methods (:issue:`37541`)
- Fixed a bug where a  ``TypeError`` was wrongly raised if a membership check was made on an ``ExtensionArray`` containing nan-like values (:issue:`37867`)

Other
- Bug in :meth:`DataFrame.replace` and :meth:`Series.replace` incorrectly raising an ``AssertionError`` instead of a ``ValueError`` when invalid parameter combinations are passed (:issue:`36045`)
- Bug in :meth:`DataFrame.replace` and :meth:`Series.replace` with numeric values and string ``to_replace`` (:issue:`34789`)
- Fixed metadata propagation in :meth:`Series.abs` and ufuncs called on Series and DataFrames (:issue:`28283`)
- Bug in :meth:`DataFrame.replace` and :meth:`Series.replace` incorrectly casting from ``PeriodDtype`` to object dtype (:issue:`34871`)
- Fixed bug in metadata propagation incorrectly copying DataFrame columns as metadata when the column name overlaps with the metadata name (:issue:`37037`)
- Fixed metadata propagation in the :class:`Series.dt`, :class:`Series.str` accessors, :class:`DataFrame.duplicated`, :class:`DataFrame.stack`, :class:`DataFrame.unstack`, :class:`DataFrame.pivot`, :class:`DataFrame.append`, :class:`DataFrame.diff`, :class:`DataFrame.applymap` and :class:`DataFrame.update` methods (:issue:`28283`, :issue:`37381`)
- Fixed metadata propagation when selecting columns with ``DataFrame.__getitem__`` (:issue:`28283`)
- Bug in :meth:`Index.intersection` with non-:class:`Index` failing to set the correct name on the returned :class:`Index` (:issue:`38111`)
- Bug in :meth:`RangeIndex.intersection` failing to set the correct name on the returned :class:`Index` in some corner cases (:issue:`38197`)
- Bug in :meth:`Index.difference` failing to set the correct name on the returned :class:`Index` in some corner cases (:issue:`38268`)
- Bug in :meth:`Index.union` behaving differently depending on whether operand is an :class:`Index` or other list-like (:issue:`36384`)
- Bug in :meth:`Index.intersection` with non-matching numeric dtypes casting to ``object`` dtype instead of minimal common dtype (:issue:`38122`)
- Bug in :meth:`IntervalIndex.union` returning an incorrectly-typed :class:`Index` when empty (:issue:`38282`)
- Passing an array with 2 or more dimensions to the :class:`Series` constructor now raises the more specific ``ValueError`` rather than a bare ``Exception`` (:issue:`35744`)
- Bug in ``dir`` where ``dir(obj)`` wouldn't show attributes defined on the instance for pandas objects (:issue:`37173`)
- Bug in :meth:`Index.drop` raising ``InvalidIndexError`` when index has duplicates (:issue:`38051`)
- Bug in :meth:`RangeIndex.difference` returning :class:`Int64Index` in some cases where it should return :class:`RangeIndex` (:issue:`38028`)
- Fixed bug in :func:`assert_series_equal` when comparing a datetime-like array with an equivalent non extension dtype array (:issue:`37609`)
- Bug in :func:`.is_bool_dtype` would raise when passed a valid string such as ``"boolean"`` (:issue:`38386`)
- Fixed regression in logical operators raising ``ValueError`` when columns of :class:`DataFrame` are a :class:`CategoricalIndex` with unused categories (:issue:`38367`)

Revision 1.32 / (download) - annotate - [select for diffs], Fri Apr 9 14:41:35 2021 UTC (2 years, 8 months ago) by tnn
Branch: MAIN
Changes since 1.31: +2 -2 lines
Diff to previous 1.31 (colored)

revert wrong fix for py-scipy python 3.6 deprecation, fix properly

Revision 1.31 / (download) - annotate - [select for diffs], Mon Oct 12 21:52:03 2020 UTC (3 years, 1 month ago) by bacon
Branch: MAIN
CVS Tags: pkgsrc-2021Q1-base, pkgsrc-2021Q1, pkgsrc-2020Q4-base, pkgsrc-2020Q4
Changes since 1.30: +2 -1 lines
Diff to previous 1.30 (colored)

math/blas, math/lapack: Install interchangeable BLAS system

Install the new interchangeable BLAS system created by Thomas Orgis,
currently supporting Netlib BLAS/LAPACK, OpenBLAS, cblas, lapacke, and
Apple's Accelerate.framework.  This system allows the user to select any
BLAS implementation without modifying packages or using package options, by
setting PKGSRC_BLAS_TYPES in mk.conf. See mk/blas.buildlink3.mk for details.

This commit should not alter behavior of existing packages as the system
defaults to Netlib BLAS/LAPACK, which until now has been the only supported
implementation.

Details:

Add new mk/blas.buildlink3.mk for inclusion in dependent packages
Install compatible Netlib math/blas and math/lapack packages
Update math/blas and math/lapack MAINTAINER approved by adam@
OpenBLAS, cblas, and lapacke will follow in separate commits
Update direct dependents to use mk/blas.buildlink3.mk
Perform recursive revbump

Revision 1.30 / (download) - annotate - [select for diffs], Fri Feb 14 16:21:55 2020 UTC (3 years, 9 months ago) by minskim
Branch: MAIN
CVS Tags: pkgsrc-2020Q3-base, pkgsrc-2020Q3, pkgsrc-2020Q2-base, pkgsrc-2020Q2, pkgsrc-2020Q1-base, pkgsrc-2020Q1
Changes since 1.29: +9 -8 lines
Diff to previous 1.29 (colored)

math/py-pandas: Update to 0.25.3

Highlights:

- Groupby aggregation with relabeling
- Better repr for MultiIndex
- Better truncated repr for Series and DataFrame
- Series.explode to split list-like values to rows

Revision 1.29 / (download) - annotate - [select for diffs], Sun Jan 26 17:31:40 2020 UTC (3 years, 10 months ago) by rillig
Branch: MAIN
Changes since 1.28: +2 -2 lines
Diff to previous 1.28 (colored)

all: migrate homepages from http to https

pkglint -r --network --only "migrate"

As a side-effect of migrating the homepages, pkglint also fixed a few
indentations in unrelated lines. These and the new homepages have been
checked manually.

Revision 1.28 / (download) - annotate - [select for diffs], Sun Jun 16 19:14:52 2019 UTC (4 years, 5 months ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2019Q4-base, pkgsrc-2019Q4, pkgsrc-2019Q3-base, pkgsrc-2019Q3, pkgsrc-2019Q2-base, pkgsrc-2019Q2
Changes since 1.27: +11 -5 lines
Diff to previous 1.27 (colored)

py-pandas: updated to 0.24.2

Whats New in 0.24.2
Fixed Regressions
Bug Fixes

Whats New in 0.24.1
Changing the sort parameter for Index set operations
Fixed Regressions
Bug Fixes

What„ŗ—‘ New in 0.24.0

This is a major release from 0.23.4 and includes a number of API changes, new features, enhancements, and performance improvements along with a large number of bug fixes.

Highlights include:
* Optional Integer NA Support
* New APIs for accessing the array backing a Series or Index
* A new top-level method for creating arrays
* Store Interval and Period data in a Series or DataFrame
* Support for joining on two MultiIndexes

Revision 1.27 / (download) - annotate - [select for diffs], Fri Aug 10 09:00:36 2018 UTC (5 years, 4 months ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2019Q1-base, pkgsrc-2019Q1, pkgsrc-2018Q4-base, pkgsrc-2018Q4, pkgsrc-2018Q3-base, pkgsrc-2018Q3
Changes since 1.26: +2 -3 lines
Diff to previous 1.26 (colored)

py-pandas: updated to 0.23.4

v0.23.4:
This is a minor bug-fix release in the 0.23.x series and includes some regression fixes, bug fixes, and performance improvements. We recommend that all users upgrade to this version.

Revision 1.26 / (download) - annotate - [select for diffs], Mon Jul 9 08:22:45 2018 UTC (5 years, 5 months ago) by adam
Branch: MAIN
Changes since 1.25: +2 -3 lines
Diff to previous 1.25 (colored)

py-pandas: updated to 0.23.3

0.23.3:
This is a minor bug-fix release in the 0.23.x series and includes a fix for the source distribution on Python 3.7. We recommend that all users upgrade to this version.

Revision 1.25 / (download) - annotate - [select for diffs], Thu Jul 5 01:21:05 2018 UTC (5 years, 5 months ago) by minskim
Branch: MAIN
Changes since 1.24: +2 -2 lines
Diff to previous 1.24 (colored)

Update path to math/py-tables

Revision 1.24 / (download) - annotate - [select for diffs], Wed Jul 4 06:50:04 2018 UTC (5 years, 5 months ago) by adam
Branch: MAIN
Changes since 1.23: +3 -2 lines
Diff to previous 1.23 (colored)

py-pandas: revbump for py-tables

Revision 1.23 / (download) - annotate - [select for diffs], Mon Jun 18 07:08:23 2018 UTC (5 years, 5 months ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2018Q2-base, pkgsrc-2018Q2
Changes since 1.22: +3 -3 lines
Diff to previous 1.22 (colored)

py-pandas: updated to 0.23.1

pandas 0.23.1
This is a minor release from 0.23.0 and includes a number of bug fixes and
performance improvements.

Revision 1.22 / (download) - annotate - [select for diffs], Wed May 30 07:56:30 2018 UTC (5 years, 6 months ago) by adam
Branch: MAIN
Changes since 1.21: +3 -4 lines
Diff to previous 1.21 (colored)

py-pandas: updated to 0.23.0

v0.23.0:

This is a major release from 0.22.0 and includes a number of API changes,
deprecations, new features, enhancements, and performance improvements along
with a large number of bug fixes. We recommend that all users upgrade to this
version.

Highlights include:
- Round-trippable JSON format with 'table' orient
- Instantiation from dicts respects order for Python 3.6+
- Dependent column arguments for assign
- Merging / sorting on a combination of columns and index levels
- Extending Pandas with custom types
- Excluding unobserved categories from groupby
- Changes to make output shape of DataFrame.apply consistent

Revision 1.21 / (download) - annotate - [select for diffs], Tue Jan 30 09:21:44 2018 UTC (5 years, 10 months ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2018Q1-base, pkgsrc-2018Q1
Changes since 1.20: +2 -2 lines
Diff to previous 1.20 (colored)

Now DEPENDS on py-matplotlib rather than buildlinking

Revision 1.20 / (download) - annotate - [select for diffs], Fri Jan 5 16:13:51 2018 UTC (5 years, 11 months ago) by adam
Branch: MAIN
Changes since 1.19: +2 -2 lines
Diff to previous 1.19 (colored)

py-pandas: updated to 0.22.0

v0.22.0:

This is a major release from 0.21.1 and includes a single, API-breaking change. We recommend that all users upgrade to this version after carefully reading the release note.

The only changes are:
* The sum of an empty or all-NA Series is now 0
* The product of an empty or all-NA Series is now 1
* We„ŗ—◊e added a min_count parameter to .sum() and .prod() controlling the minimum number of valid values for the result to be valid. If fewer than min_count non-NA values are present, the result is NA. The default is 0. To return NaN, the 0.21 behavior, use min_count=1.

Revision 1.19 / (download) - annotate - [select for diffs], Thu Dec 14 13:37:59 2017 UTC (5 years, 11 months ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2017Q4-base, pkgsrc-2017Q4
Changes since 1.18: +2 -2 lines
Diff to previous 1.18 (colored)

py-pandas: updated to 0.21.1

v0.21.1:
Restore Matplotlib datetime Converter Registration
New features
- Improvements to the Parquet IO functionality
- Other Enhancements
Deprecations
Performance Improvements
Bug Fixes
- Conversion
- Indexing
- I/O
- Plotting
- Groupby/Resample/Rolling
- Reshaping
- Numeric
- Categorical
- String

Revision 1.18 / (download) - annotate - [select for diffs], Thu Nov 2 09:41:38 2017 UTC (6 years, 1 month ago) by adam
Branch: MAIN
Changes since 1.17: +2 -2 lines
Diff to previous 1.17 (colored)

py-pandas: updated to 0.21.0

v0.21.0 Final:

This is a major release from 0.20.3 and includes a number of API changes, deprecations, new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

Highlights include:
* Integration with Apache Parquet, including a new top-level read_parquet function and DataFrame.to_parquet method, see here.
* New user-facing dtype pandas.api.types.CategoricalDtype for specifying categoricals independent of the data, see here.
* The behavior of sum and prod on all-NaN Series/DataFrames is now consistent and no longer depends on whether bottleneck is installed, see here.
* Compatibility fixes for pypy, see here.
* Additions to the drop, reindex and rename API to make them more consistent, see here.
* Addition of the new methods DataFrame.infer_objects (see here) and GroupBy.pipe (see here).
* Indexing with a list of labels, where one or more of the labels is missing, is deprecated and will raise a KeyError in a future version

Revision 1.17 / (download) - annotate - [select for diffs], Fri Jul 14 10:17:02 2017 UTC (6 years, 4 months ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2017Q3-base, pkgsrc-2017Q3
Changes since 1.16: +2 -2 lines
Diff to previous 1.16 (colored)

0.20.3

Bug Fixes
* Fixed a bug in failing to compute rolling computations of a column-MultiIndexed DataFrame
* Fixed a pytest marker failing downstream packages„ŗtests suites

Conversion
* Bug in pickle compat prior to the v0.20.x series, when UTC is a timezone in a Series/DataFrame/Index
* Bug in Series construction when passing a Series with dtype='category'.
* Bug in DataFrame.astype() when passing a Series as the dtype kwarg..

Indexing
* Bug in Float64Index causing an empty array instead of None to be returned from .get(np.nan) on a Series whose index did not contain any NaN s
* Bug in MultiIndex.isin causing an error when passing an empty iterable
* Fixed a bug in a slicing DataFrame/Series that have a TimedeltaIndex

I/O
* Bug in read_csv() in which files weren„ŗ—’ opened as binary files by the C engine on Windows, causing EOF characters mid-field, which would fail
* Bug in read_hdf() in which reading a Series saved to an HDF file in „ŗŌ«ixed„ŗformat fails when an explicit mode='r' argument is supplied
* Bug in DataFrame.to_latex() where bold_rows was wrongly specified to be True by default, whereas in reality row labels remained non-bold whatever parameter provided.
* Fixed an issue with DataFrame.style() where generated element ids were not unique
* Fixed loading a DataFrame with a PeriodIndex, from a format='fixed' HDFStore, in Python 3, that was written in Python 2

Plotting
* Fixed regression that prevented RGB and RGBA tuples from being used as color arguments
* Fixed an issue with DataFrame.plot.scatter() that incorrectly raised a KeyError when categorical data is used for plotting

Reshaping
* PeriodIndex / TimedeltaIndex.join was missing the sort= kwarg
* Bug in joining on a MultiIndex with a category dtype for a level.
* Bug in merge() when merging/joining with multiple categorical columns

Categorical
* Bug in DataFrame.sort_values not respecting the kind parameter with categorical data

Revision 1.16 / (download) - annotate - [select for diffs], Wed Jun 7 08:13:56 2017 UTC (6 years, 6 months ago) by adam
Branch: MAIN
CVS Tags: pkgsrc-2017Q2-base, pkgsrc-2017Q2
Changes since 1.15: +6 -9 lines
Diff to previous 1.15 (colored)

v0.20.2:
This is a minor bug-fix release in the 0.20.x series and includes some small regression fixes, bug fixes and performance improvements. We recommend that all users upgrade to this version.

Revision 1.15 / (download) - annotate - [select for diffs], Sun May 21 08:54:33 2017 UTC (6 years, 6 months ago) by adam
Branch: MAIN
Changes since 1.14: +2 -3 lines
Diff to previous 1.14 (colored)

Changes 0.20.1:
New .agg() API for Series/DataFrame similar to the groupby-rolling-resample API„ŗ—‘, see here
Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather() method, see here.
The .ix indexer has been deprecated, see here
Panel has been deprecated, see here
Addition of an IntervalIndex and Interval scalar type, see here
Improved user API when grouping by index levels in .groupby(), see here
Improved support for UInt64 dtypes, see here
A new orient for JSON serialization, orient='table', that uses the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here
Experimental support for exporting styled DataFrames (DataFrame.style) to Excel, see here
Window binary corr/cov operations now return a MultiIndexed DataFrame rather than a Panel, as Panel is now deprecated, see here
Support for S3 handling now uses s3fs, see here
Google BigQuery support now uses the pandas-gbq library, see here

Revision 1.14 / (download) - annotate - [select for diffs], Mon Feb 20 17:00:36 2017 UTC (6 years, 9 months ago) by wiz
Branch: MAIN
CVS Tags: pkgsrc-2017Q1-base, pkgsrc-2017Q1
Changes since 1.13: +2 -4 lines
Diff to previous 1.13 (colored)

Switch py-dateutils to plain DEPENDS.

It supports both python 2 and 3 nowadays.

Revision 1.13 / (download) - annotate - [select for diffs], Fri Aug 19 07:57:26 2016 UTC (7 years, 3 months ago) by wiz
Branch: MAIN
CVS Tags: pkgsrc-2016Q4-base, pkgsrc-2016Q4, pkgsrc-2016Q3-base, pkgsrc-2016Q3
Changes since 1.12: +9 -3 lines
Diff to previous 1.12 (colored)

Prefer egg.mk to distutils.mk. Clean up. Add missing dependency on
py-sqlite3.  Add missing test dependency on py-nose.
Add comments with links to bug reports about test failures.

Bump PKGREVISION for dependency change.

Revision 1.12 / (download) - annotate - [select for diffs], Tue Aug 16 03:22:12 2016 UTC (7 years, 3 months ago) by maya
Branch: MAIN
Changes since 1.11: +3 -3 lines
Diff to previous 1.11 (colored)

Update py-pandas to 0.18.1

Highlights in changelog:

v0.18.1:
    .groupby(...) has been enhanced to provide convenient syntax when working with .rolling(..), .expanding(..) and .resample(..) per group, see here
    pd.to_datetime() has gained the ability to assemble dates from a DataFrame, see here
    Method chaining improvements, see here.
    Custom business hour offset, see here.
    Many bug fixes in the handling of sparse, see here
    Expanded the Tutorials section with a feature on modern pandas, courtesy of @TomAugsburger. (GH13045).

v0.18.0:
    Moving and expanding window functions are now methods on Series and DataFrame, similar to .groupby, see here.
    Adding support for a RangeIndex as a specialized form of the Int64Index for memory savings, see here.
    API breaking change to the .resample method to make it more .groupby like, see here.
    Removal of support for positional indexing with floats, which was deprecated since 0.14.0. This will now raise a TypeError, see here.
    The .to_xarray() function has been added for compatibility with the xarray package, see here.
    The read_sas function has been enhanced to read sas7bdat files, see here.
    Addition of the .str.extractall() method, and API changes to the .str.extract() method and .str.cat() method.
    pd.test() top-level nose test runner is available (GH4327).

Update by K.I.A.Derouiche in PR pkg/51272
Slightly modified.

Revision 1.11 / (download) - annotate - [select for diffs], Fri Jul 15 07:24:06 2016 UTC (7 years, 4 months ago) by wiz
Branch: MAIN
Changes since 1.10: +2 -2 lines
Diff to previous 1.10 (colored)

Do not include py-numexpr/bl3.mk, just DEPEND on it.

Revision 1.10 / (download) - annotate - [select for diffs], Wed Jun 8 17:43:35 2016 UTC (7 years, 6 months ago) by wiz
Branch: MAIN
CVS Tags: pkgsrc-2016Q2-base, pkgsrc-2016Q2
Changes since 1.9: +2 -2 lines
Diff to previous 1.9 (colored)

Switch to MASTER_SITES_PYPI.

Revision 1.9 / (download) - annotate - [select for diffs], Mon Dec 28 14:35:02 2015 UTC (7 years, 11 months ago) by wiz
Branch: MAIN
CVS Tags: pkgsrc-2016Q1-base, pkgsrc-2016Q1
Changes since 1.8: +2 -2 lines
Diff to previous 1.8 (colored)

Update py-pandas to 0.17.1.

0.17.1

This is a minor bug-fix release from 0.17.0 and includes a large
number of bug fixes along several new features, enhancements, and
performance improvements. We recommend that all users upgrade to
this version.

Highlights include:

    Support for Conditional HTML Formatting, see here
    Releasing the GIL on the csv reader & other ops, see here
    Fixed regression in DataFrame.drop_duplicates from 0.16.2,
    causing incorrect results on integer values (GH11376)

0.17.0

This is a major release from 0.16.2 and includes a small number of
API changes, several new features, enhancements, and performance
improvements along with a large number of bug fixes. We recommend
that all users upgrade to this version.


Highlights include:

    Release the Global Interpreter Lock (GIL) on some cython
    operations, see here
    Plotting methods are now available as attributes of the .plot
    accessor, see here
    The sorting API has been revamped to remove some long-time
    inconsistencies, see here
    Support for a datetime64[ns] with timezones as a first-class
    dtype, see here
    The default for to_datetime will now be to raise when presented
    with unparseable formats, previously this would return the
    original input. Also, date parse functions now return consistent
    results. See here
    The default for dropna in HDFStore has changed to False, to
    store by default all rows even if they are all NaN, see here
    Datetime accessor (dt) now supports Series.dt.strftime to
    generate formatted strings for datetime-likes, and
    Series.dt.total_seconds to generate each duration of the
    timedelta in seconds. See here
    Period and PeriodIndex can handle multiplied freq like 3D,
    which corresponding to 3 days span. See here
    Development installed versions of pandas will now have PEP440
    compliant version strings (GH9518)
    Development support for benchmarking with the Air Speed Velocity
    library (GH8361)
    Support for reading SAS xport files, see here
    Documentation comparing SAS to pandas, see here
    Removal of the automatic TimeSeries broadcasting, deprecated
    since 0.8.0, see here
    Display format with plain text can optionally align with Unicode
    East Asian Width, see here
    Compatibility with Python 3.5 (GH11097)
    Compatibility with matplotlib 1.5.0 (GH11111)

Revision 1.8 / (download) - annotate - [select for diffs], Tue Jul 21 19:44:45 2015 UTC (8 years, 4 months ago) by bad
Branch: MAIN
CVS Tags: pkgsrc-2015Q4-base, pkgsrc-2015Q4, pkgsrc-2015Q3-base, pkgsrc-2015Q3
Changes since 1.7: +5 -5 lines
Diff to previous 1.7 (colored)

Update py-pandas to 0.16.2.

Closes PR pkg/49958 by matthewd.

Changes since 0.14.1 for a full list see
http://pandas.pydata.org/pandas-docs/stable/whatsnew.html:

v 0.16.2

This is a minor bug-fix release from 0.16.1 and includes a a large
number of bug fixes along some new features (pipe() method),
enhancements, and performance improvements.

We recommend that all users upgrade to this version.

Highlights include:

    A new pipe method
    Documentation on how to use numba with pandas,

v 0.16.1
This is a minor bug-fix release from 0.16.0 and includes a a large
number of bug fixes along several new features, enhancements, and
performance improvements. We recommend that all users upgrade to this
version.

Highlights include:

    Support for a CategoricalIndex, a category based index
    New section on how-to-contribute to pandas
    Revised „ŗ◊ģerge, join, and concatenate„ŗdocumentation, including
    graphical examples to make it easier to understand each operations
    New method sample for drawing random samples from Series, DataFrames
    and Panels.
    The default Index printing has changed to a more uniform format
    BusinessHour datetime-offset is now supported
    Further enhancement to the .str accessor to make string operations easier

v0.16.0 (March 22, 2015)

This is a major release from 0.15.2 and includes a small number of
API changes, several new features, enhancements, and performance
improvements along with a large number of bug fixes. We recommend that
all users upgrade to this version.

Highlights include:

    DataFrame.assign method
    Series.to_coo/from_coo methods to interact with scipy.sparse
    Backwards incompatible change to Timedelta to conform the .seconds
    attribute with datetime.timedelta
    Changes to the .loc slicing API to conform with the behavior of .ix
    Changes to the default for ordering in the Categorical constructor
    Enhancement to the .str accessor to make string operations easier
    The pandas.tools.rplot, pandas.sandbox.qtpandas and pandas.rpy
    modules are deprecated. We refer users to external packages like
    seaborn, pandas-qt and rpy2 for similar or equivalent functionality,
    see here
v0.15.0 (October 18, 2014)

This is a major release from 0.14.1 and includes a small number of
API changes, several new features, enhancements, and performance
improvements along with a large number of bug fixes. We recommend that
all users upgrade to this version.

Warning

pandas >= 0.15.0 will no longer support compatibility with NumPy
versions < 1.7.0. If you want to use the latest versions of pandas,
please upgrade to NumPy >= 1.7.0 (GH7711)

    Highlights include:
        The Categorical type was integrated as a first-class pandas type
        New scalar type Timedelta, and a new index type TimedeltaIndex
        New datetimelike properties accessor .dt for Series, see
        Datetimelike Properties
        New DataFrame default display for df.info() to include memory
        usage, see Memory Usage
        read_csv will now by default ignore blank lines when parsing
        API change in using Indexes in set operations
        Enhancements in the handling of timezones
        A lot of improvements to the rolling and expanding moment funtions
        Internal refactoring of the Index class to no longer sub-class
        ndarray, see Internal Refactoring
        dropping support for PyTables less than version 3.0.0, and
        numexpr less than version 2.1 (GH7990)
        Split indexing documentation into Indexing and Selecting Data
        and MultiIndex / Advanced Indexing
        Split out string methods documentation into Working with Text Data

Revision 1.7 / (download) - annotate - [select for diffs], Sat Jul 19 13:17:46 2014 UTC (9 years, 4 months ago) by bad
Branch: MAIN
CVS Tags: pkgsrc-2015Q2-base, pkgsrc-2015Q2, pkgsrc-2015Q1-base, pkgsrc-2015Q1, pkgsrc-2014Q4-base, pkgsrc-2014Q4, pkgsrc-2014Q3-base, pkgsrc-2014Q3
Changes since 1.6: +2 -2 lines
Diff to previous 1.6 (colored)

Update math/py-pandas to 0.14.1.

This is two major releases since 0.12.0.  Changes include API changes, new
features, enhancements, and performance improvements along with a large
number of bug fixes.

For the detailed list of changes see
http://pandas.pydata.org/pandas-docs/stable/whatsnew.html

Revision 1.6 / (download) - annotate - [select for diffs], Thu Jan 16 10:41:53 2014 UTC (9 years, 10 months ago) by wiz
Branch: MAIN
CVS Tags: pkgsrc-2014Q2-base, pkgsrc-2014Q2, pkgsrc-2014Q1-base, pkgsrc-2014Q1
Changes since 1.5: +4 -2 lines
Diff to previous 1.5 (colored)

Convert to use versioned_dependencies.mk.

Revision 1.5 / (download) - annotate - [select for diffs], Tue Dec 10 13:00:30 2013 UTC (10 years ago) by bad
Branch: MAIN
CVS Tags: pkgsrc-2013Q4-base, pkgsrc-2013Q4
Changes since 1.4: +3 -2 lines
Diff to previous 1.4 (colored)

Update pandas to 0.12.0.

This is a major release from 0.11.0 and includes several new features
and enhancements along with a large number of bug fixes.

Highlites include a consistent I/O API naming scheme, routines to read
html, write multi-indexes to csv files, read & write STATA data files,
read & write JSON format files, Python 3 support for HDFStore, filtering
of groupby expressions via filter, and a revamped replace routine that
accepts regular expressions.

For detailed changes see:
http://pandas.pydata.org/pandas-docs/stable/whatsnew.html

Revision 1.4 / (download) - annotate - [select for diffs], Thu May 16 23:10:16 2013 UTC (10 years, 6 months ago) by bad
Branch: MAIN
CVS Tags: pkgsrc-2013Q3-base, pkgsrc-2013Q3, pkgsrc-2013Q2-base, pkgsrc-2013Q2
Changes since 1.3: +4 -2 lines
Diff to previous 1.3 (colored)

Update py-pandas to 0.11.0.

Summary of changes since 0.10.1:

This is a major release from 0.10.1 and includes many new features and
enhancements along with a large number of bug fixes. The methods of
Selecting Data have had quite a number of additions, and Dtype support
is now full-fledged. There are also a number of important API changes
that long-time pandas users should pay close attention to.

* New precision indexing fields loc, iloc, at, and iat, to reduce
  occasional ambiguity in the catch-all hitherto ix method.

* Expanded support for NumPy data types in DataFrame.

* NumExpr integration to accelerate various operator evaluation.

* Improved DataFrame to CSV exporting performance.

For a full list refer to the "what's new" page.

Also fixes PLIST errors introduced in last update.

Revision 1.3 / (download) - annotate - [select for diffs], Sat Feb 16 00:02:19 2013 UTC (10 years, 9 months ago) by bad
Branch: MAIN
CVS Tags: pkgsrc-2013Q1-base, pkgsrc-2013Q1
Changes since 1.2: +2 -2 lines
Diff to previous 1.2 (colored)

Update pandas to 0.10.1.

Release date: 2013-01-22

New features:

        Add data inferface to World Bank WDI pandas.io.wb (GH2592)

API Changes:

        Restored inplace=True behavior returning self (same object) with
	  deprecation warning until 0.11 (GH1893)
        HDFStore
            refactored HFDStore to deal with non-table stores as objects, will
	      allow future enhancements
            removed keyword compression from put (replaced by keyword complib
	      to be consistent across library)
            warn PerformanceWarning if you are attempting to store types that
	      will be pickled by PyTables

Improvements to existing features:

        HDFStore
            enables storing of multi-index dataframes (closes GH1277)
            support data column indexing and selection, via data_columns
	      keyword in append
            support write chunking to reduce memory footprint, via chunksize
	      keyword to append
            support automagic indexing via index keyword to append
            support expectedrows keyword in append to inform PyTables about
	      the expected tablesize
            support start and stop keywords in select to limit the row
	      selection space
            added get_store context manager to automatically import with pandas
            added column filtering via columns keyword in select
            added methods append_to_multiple/select_as_multiple/
	      select_as_coordinates to do multiple-table append/selection
            added support for datetime64 in columns
            added method unique to select the unique values in an indexable
	      or data column
            added method copy to copy an existing store (and possibly upgrade)
            show the shape of the data on disk for non-table stores when
	      printing the store
            added ability to read PyTables flavor tables (allows compatiblity
	      to other HDF5 systems)
        Add logx option to DataFrame/Series.plot (GH2327, GH2565)
        Support reading gzipped data from file-like object
        pivot_table aggfunc can be anything used in GroupBy.aggregate (GH2643)
        Implement DataFrame merges in case where set cardinalities might
	  overflow 64-bit integer (GH2690)
        Raise exception in C file parser if integer dtype specified and have
	  NA values. (GH2631)
        Attempt to parse ISO8601 format dates when parse_dates=True in read_csv
	  for major performance boost in such cases (GH2698)
        Add methods neg and inv to Series
        Implement kind option in ExcelFile to indicate whether it's an XLS
	  or XLSX file (GH2613)

Bug fixes:

        Fix read_csv/read_table multithreading issues (GH2608)
        HDFStore
            correctly handle nan elements in string columns; serialize via the
	      nan_rep keyword to append
            raise correctly on non-implemented column types (unicode/date)
            handle correctly Term passed types (e.g. index<1000, when index is
	      Int64), (closes GH512)
            handle Timestamp correctly in data_columns (closes GH2637)
            contains correctly matches on non-natural names
            correctly store float32 dtypes in tables (if not other float types
	      in the same table)
        Fix DataFrame.info bug with UTF8-encoded columns. (GH2576)
        Fix DatetimeIndex handling of FixedOffset tz (GH2604)
        More robust detection of being in IPython session for wide DataFrame
	  console formatting (GH2585)
        Fix platform issues with file:/// in unit test (GH2564)
        Fix bug and possible segfault when grouping by hierarchical level that
	  contains NA values (GH2616)
        Ensure that MultiIndex tuples can be constructed with NAs (GH2616)
        Fix int64 overflow issue when unstacking MultiIndex with many levels
	  (GH2616)
        Exclude non-numeric data from DataFrame.quantile by default (GH2625)
        Fix a Cython C int64 boxing issue causing read_csv to return incorrect
	  results (GH2599)
        Fix groupby summing performance issue on boolean data (GH2692)
        Don't bork Series containing datetime64 values with to_datetime (GH2699)
        Fix DataFrame.from_records corner case when passed columns, index
	  column, but empty record list (GH2633)
        Fix C parser-tokenizer bug with trailing fields. (GH2668)
        Don't exclude non-numeric data from GroupBy.max/min (GH2700)
        Don't lose time zone when calling DatetimeIndex.drop (GH2621)
        Fix setitem on a Series with a boolean key and a non-scalar as value
	  (GH2686)
        Box datetime64 values in Series.apply/map (GH2627, GH2689)
        Upconvert datetime + datetime64 values when concatenating frames
	  (GH2624)
        Raise a more helpful error message in merge operations when one
	  DataFrame has duplicate columns (GH2649)
        Fix partial date parsing issue occuring only when code is run at EOM
	  (GH2618)
        Prevent MemoryError when using counting sort in sortlevel with
	  high-cardinality MultiIndex objects (GH2684)
        Fix Period resampling bug when all values fall into a single bin
	  (GH2070)
        Fix buggy interaction with usecols argument in read_csv when there is
	  an implicit first index column (GH2654)

Revision 1.2 / (download) - annotate - [select for diffs], Mon Jan 7 23:18:35 2013 UTC (10 years, 11 months ago) by bad
Branch: MAIN
Changes since 1.1: +3 -2 lines
Diff to previous 1.1 (colored)

Update pandas to 0.10.0.

pkgsrc change: depend on math/py-pytables.

Changes since 0.9.1:

* Delimited file parsing engine rewritten to use a fraction of memory while
  being 40%+ faster.
- Much-improved Unicode handling via the encoding option.
- Column filtering (usecols)
- Dtype specification (dtype argument)
- Ability to specify strings to be recognized as True/False
- Ability to yield NumPy record arrays (as_recarray)
- High performance delim_whitespace option
- Decimal format (e.g. European format) specification
- Easier CSV dialect options: escapechar, lineterminator, quotechar, etc.
- More robust handling of many exceptional kinds of files observed in the wild

* API changes
- Deprecated DataFrame BINOP TimeSeries special case behavior
- Altered resample default behavior
- Infinity and negative infinity are no longer treated as NA by isnull and
  notnull.
- Methods with the inplace option now all return None instead of the calling
  object.
- pandas.merge no longer sorts the group keys (sort=False) by default.
- The default column names for a file with no header have been changed.
- Values like 'Yes' and 'No' are not interpreted as boolean by default.
- The file parsers will not recognize non-string values arising from a
  converter function as NA.
- Calling fillna on Series or DataFrame with no arguments is no longer valid
  code.
- Series.apply will now operate on a returned value from the applied function.
- New API functions for working with pandas options.

* New features
- Wide DataFrame Printing.
- Updated PyTables Support.

* Enhancements
- added ability to hierarchical keys.
- added mixed-dtype support!
- performance improvments on table writing.
- support for arbitrarily indexed dimensions.
- SparseSeries now has a density property.

* Bug fixes
- added Term method of specifying where conditions.
- del store['df'] now call store.remove('df') for store deletion.
- deleting of consecutive rows is much faster than before.
- in_itemsize parameter can be specified in table creation to force a minimum
  size for indexing columns.
- indexing support via create_table_index (requires PyTables >= 2.3)
- appending on a store would fail if the table was not first created via put.
- fixed issue with missing attributes after loading a pickled dataframe.
- minor change to select and remove: require a table ONLY if where is also
  provided.

* Compatibility
- 0.10 of HDFStore is backwards compatible for reading tables created
  in a prior version of pandas, however, query terms using the prior
  (undocumented) methodology are unsupported.

* N Dimensional Panels (Experimental)

Revision 1.1 / (download) - annotate - [select for diffs], Thu Nov 22 00:15:13 2012 UTC (11 years ago) by bad
Branch: MAIN
CVS Tags: pkgsrc-2012Q4-base, pkgsrc-2012Q4

Initial import of pandas 0.9.1.

pandas is an open source, BSD-licensed library providing
high-performance, easy-to-use data structures and data analysis tools
for the Python programming language.

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