[pkgsrc/trunk]: pkgsrc/math/py-pandas py-pandas: updated to 1.2.4

To: pkgsrc-changes-hg%NetBSD.org@localhost
Subject: [pkgsrc/trunk]: pkgsrc/math/py-pandas py-pandas: updated to 1.2.4
From: adam <adam%pkgsrc.org@localhost>
Date: Thu, 06 May 2021 05:41:13 +0000
details:   https://anonhg.NetBSD.org/pkgsrc/rev/aeb8245f5e85
branches:  trunk
changeset: 452169:aeb8245f5e85
user:      adam <adam%pkgsrc.org@localhost>
date:      Thu May 06 04:39:03 2021 +0000

description:
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`)

diffstat:

 math/py-pandas/Makefile |    20 +-
 math/py-pandas/PLIST    |  1918 +++++++++++++++++++++++++++++++++++-----------
 math/py-pandas/distinfo |    11 +-
 3 files changed, 1460 insertions(+), 489 deletions(-)

diffs (truncated from 2968 to 300 lines):

diff -r d920a76e758e -r aeb8245f5e85 math/py-pandas/Makefile
--- a/math/py-pandas/Makefile   Thu May 06 04:34:47 2021 +0000
+++ b/math/py-pandas/Makefile   Thu May 06 04:39:03 2021 +0000
@@ -1,8 +1,7 @@
-# $NetBSD: Makefile,v 1.32 2021/04/09 14:41:35 tnn Exp $
+# $NetBSD: Makefile,v 1.33 2021/05/06 04:39:03 adam Exp $
 
-DISTNAME=      pandas-0.25.3
+DISTNAME=      pandas-1.2.4
 PKGNAME=       ${PYPKGPREFIX}-${DISTNAME}
-PKGREVISION=   1
 CATEGORIES=    math graphics python
 MASTER_SITES=  ${MASTER_SITE_PYPI:=p/pandas/}
 
@@ -12,28 +11,25 @@
 LICENSE=       modified-bsd
 
 DEPENDS+=      ${PYPKGPREFIX}-bottleneck-[0-9]*:../../math/py-bottleneck
-DEPENDS+=      ${PYPKGPREFIX}-dateutil>=2.6.1:../../time/py-dateutil
+DEPENDS+=      ${PYPKGPREFIX}-dateutil>=2.7.3:../../time/py-dateutil
 DEPENDS+=      ${PYPKGPREFIX}-matplotlib-[0-9]*:../../graphics/py-matplotlib
 DEPENDS+=      ${PYPKGPREFIX}-numexpr-[0-9]*:../../math/py-numexpr
-DEPENDS+=      ${PYPKGPREFIX}-pytz>=2017.2:../../time/py-pytz
+DEPENDS+=      ${PYPKGPREFIX}-pytz>=2017.3:../../time/py-pytz
 DEPENDS+=      ${PYPKGPREFIX}-scipy>=0.7:../../math/py-scipy
 DEPENDS+=      ${PYPKGPREFIX}-sqlite3-[0-9]*:../../databases/py-sqlite3
 DEPENDS+=      ${PYPKGPREFIX}-tables>=2.2:../../math/py-tables
 BUILD_DEPENDS+=        ${PYPKGPREFIX}-test-runner-[0-9]*:../../devel/py-test-runner
 TEST_DEPENDS+= ${PYPKGPREFIX}-hypothesis>=3.58:../../devel/py-hypothesis
-TEST_DEPENDS+= ${PYPKGPREFIX}-test>=4.0.2:../../devel/py-test
+TEST_DEPENDS+= ${PYPKGPREFIX}-test>=5.0.1:../../devel/py-test
 TEST_DEPENDS+= ${PYPKGPREFIX}-test-xdist-[0-9]*:../../devel/py-test-xdist
-# 20 test failures as of 0.18.1, see
-# https://github.com/pydata/pandas/issues/12337
-# https://github.com/pydata/pandas/issues/14043
 
 USE_LANGUAGES= c c++
 
-PYTHON_VERSIONS_INCOMPATIBLE=  36 27   # py-scipy
-
 PYSETUPTESTTARGET=     pytest
 
+PYTHON_VERSIONS_INCOMPATIBLE=  27 36
+
 .include "../../lang/python/egg.mk"
-BUILDLINK_API_DEPENDS.pynumpy+= ${PYPKGPREFIX}-numpy>=1.13.3
+BUILDLINK_API_DEPENDS.pynumpy+= ${PYPKGPREFIX}-numpy>=1.16.5
 .include "../../math/py-numpy/buildlink3.mk"
 .include "../../mk/bsd.pkg.mk"
diff -r d920a76e758e -r aeb8245f5e85 math/py-pandas/PLIST
--- a/math/py-pandas/PLIST      Thu May 06 04:34:47 2021 +0000
+++ b/math/py-pandas/PLIST      Thu May 06 04:39:03 2021 +0000
@@ -1,4 +1,4 @@
-@comment $NetBSD: PLIST,v 1.18 2020/02/14 16:21:55 minskim Exp $
+@comment $NetBSD: PLIST,v 1.19 2021/05/06 04:39:03 adam Exp $
 ${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
 ${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
 ${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
@@ -40,35 +40,42 @@
 ${PYSITELIB}/pandas/_libs/lib.so
 ${PYSITELIB}/pandas/_libs/missing.so
 ${PYSITELIB}/pandas/_libs/ops.so
+${PYSITELIB}/pandas/_libs/ops_dispatch.so
 ${PYSITELIB}/pandas/_libs/parsers.so
 ${PYSITELIB}/pandas/_libs/properties.so
 ${PYSITELIB}/pandas/_libs/reduction.so
 ${PYSITELIB}/pandas/_libs/reshape.so
-${PYSITELIB}/pandas/_libs/skiplist.so
 ${PYSITELIB}/pandas/_libs/sparse.so
 ${PYSITELIB}/pandas/_libs/testing.so
 ${PYSITELIB}/pandas/_libs/tslib.so
 ${PYSITELIB}/pandas/_libs/tslibs/__init__.py
 ${PYSITELIB}/pandas/_libs/tslibs/__init__.pyc
 ${PYSITELIB}/pandas/_libs/tslibs/__init__.pyo
-${PYSITELIB}/pandas/_libs/tslibs/c_timestamp.so
+${PYSITELIB}/pandas/_libs/tslibs/base.so
 ${PYSITELIB}/pandas/_libs/tslibs/ccalendar.so
 ${PYSITELIB}/pandas/_libs/tslibs/conversion.so
+${PYSITELIB}/pandas/_libs/tslibs/dtypes.so
 ${PYSITELIB}/pandas/_libs/tslibs/fields.so
-${PYSITELIB}/pandas/_libs/tslibs/frequencies.so
 ${PYSITELIB}/pandas/_libs/tslibs/nattype.so
 ${PYSITELIB}/pandas/_libs/tslibs/np_datetime.so
 ${PYSITELIB}/pandas/_libs/tslibs/offsets.so
 ${PYSITELIB}/pandas/_libs/tslibs/parsing.so
 ${PYSITELIB}/pandas/_libs/tslibs/period.so
-${PYSITELIB}/pandas/_libs/tslibs/resolution.so
 ${PYSITELIB}/pandas/_libs/tslibs/strptime.so
 ${PYSITELIB}/pandas/_libs/tslibs/timedeltas.so
 ${PYSITELIB}/pandas/_libs/tslibs/timestamps.so
 ${PYSITELIB}/pandas/_libs/tslibs/timezones.so
 ${PYSITELIB}/pandas/_libs/tslibs/tzconversion.so
-${PYSITELIB}/pandas/_libs/window.so
+${PYSITELIB}/pandas/_libs/tslibs/vectorized.so
+${PYSITELIB}/pandas/_libs/window/__init__.py
+${PYSITELIB}/pandas/_libs/window/__init__.pyc
+${PYSITELIB}/pandas/_libs/window/__init__.pyo
+${PYSITELIB}/pandas/_libs/window/aggregations.so
+${PYSITELIB}/pandas/_libs/window/indexers.so
 ${PYSITELIB}/pandas/_libs/writers.so
+${PYSITELIB}/pandas/_testing.py
+${PYSITELIB}/pandas/_testing.pyc
+${PYSITELIB}/pandas/_testing.pyo
 ${PYSITELIB}/pandas/_typing.py
 ${PYSITELIB}/pandas/_typing.pyc
 ${PYSITELIB}/pandas/_typing.pyo
@@ -81,6 +88,9 @@
 ${PYSITELIB}/pandas/api/extensions/__init__.py
 ${PYSITELIB}/pandas/api/extensions/__init__.pyc
 ${PYSITELIB}/pandas/api/extensions/__init__.pyo
+${PYSITELIB}/pandas/api/indexers/__init__.py
+${PYSITELIB}/pandas/api/indexers/__init__.pyc
+${PYSITELIB}/pandas/api/indexers/__init__.pyo
 ${PYSITELIB}/pandas/api/types/__init__.py
 ${PYSITELIB}/pandas/api/types/__init__.pyc
 ${PYSITELIB}/pandas/api/types/__init__.pyo
@@ -114,6 +124,9 @@
 ${PYSITELIB}/pandas/core/accessor.py
 ${PYSITELIB}/pandas/core/accessor.pyc
 ${PYSITELIB}/pandas/core/accessor.pyo
+${PYSITELIB}/pandas/core/aggregation.py
+${PYSITELIB}/pandas/core/aggregation.pyc
+${PYSITELIB}/pandas/core/aggregation.pyo
 ${PYSITELIB}/pandas/core/algorithms.py
 ${PYSITELIB}/pandas/core/algorithms.pyc
 ${PYSITELIB}/pandas/core/algorithms.pyo
@@ -123,18 +136,39 @@
 ${PYSITELIB}/pandas/core/apply.py
 ${PYSITELIB}/pandas/core/apply.pyc
 ${PYSITELIB}/pandas/core/apply.pyo
+${PYSITELIB}/pandas/core/array_algos/__init__.py
+${PYSITELIB}/pandas/core/array_algos/__init__.pyc
+${PYSITELIB}/pandas/core/array_algos/__init__.pyo
+${PYSITELIB}/pandas/core/array_algos/masked_reductions.py
+${PYSITELIB}/pandas/core/array_algos/masked_reductions.pyc
+${PYSITELIB}/pandas/core/array_algos/masked_reductions.pyo
+${PYSITELIB}/pandas/core/array_algos/replace.py
+${PYSITELIB}/pandas/core/array_algos/replace.pyc
+${PYSITELIB}/pandas/core/array_algos/replace.pyo
+${PYSITELIB}/pandas/core/array_algos/transforms.py
+${PYSITELIB}/pandas/core/array_algos/transforms.pyc
+${PYSITELIB}/pandas/core/array_algos/transforms.pyo
+${PYSITELIB}/pandas/core/arraylike.py
+${PYSITELIB}/pandas/core/arraylike.pyc
+${PYSITELIB}/pandas/core/arraylike.pyo
 ${PYSITELIB}/pandas/core/arrays/__init__.py
 ${PYSITELIB}/pandas/core/arrays/__init__.pyc
 ${PYSITELIB}/pandas/core/arrays/__init__.pyo
+${PYSITELIB}/pandas/core/arrays/_arrow_utils.py
+${PYSITELIB}/pandas/core/arrays/_arrow_utils.pyc
+${PYSITELIB}/pandas/core/arrays/_arrow_utils.pyo
+${PYSITELIB}/pandas/core/arrays/_mixins.py
+${PYSITELIB}/pandas/core/arrays/_mixins.pyc
+${PYSITELIB}/pandas/core/arrays/_mixins.pyo
 ${PYSITELIB}/pandas/core/arrays/_ranges.py
 ${PYSITELIB}/pandas/core/arrays/_ranges.pyc
 ${PYSITELIB}/pandas/core/arrays/_ranges.pyo
-${PYSITELIB}/pandas/core/arrays/array_.py
-${PYSITELIB}/pandas/core/arrays/array_.pyc
-${PYSITELIB}/pandas/core/arrays/array_.pyo
 ${PYSITELIB}/pandas/core/arrays/base.py
 ${PYSITELIB}/pandas/core/arrays/base.pyc
 ${PYSITELIB}/pandas/core/arrays/base.pyo
+${PYSITELIB}/pandas/core/arrays/boolean.py
+${PYSITELIB}/pandas/core/arrays/boolean.pyc
+${PYSITELIB}/pandas/core/arrays/boolean.pyo
 ${PYSITELIB}/pandas/core/arrays/categorical.py
 ${PYSITELIB}/pandas/core/arrays/categorical.pyc
 ${PYSITELIB}/pandas/core/arrays/categorical.pyo
@@ -144,21 +178,48 @@
 ${PYSITELIB}/pandas/core/arrays/datetimes.py
 ${PYSITELIB}/pandas/core/arrays/datetimes.pyc
 ${PYSITELIB}/pandas/core/arrays/datetimes.pyo
+${PYSITELIB}/pandas/core/arrays/floating.py
+${PYSITELIB}/pandas/core/arrays/floating.pyc
+${PYSITELIB}/pandas/core/arrays/floating.pyo
 ${PYSITELIB}/pandas/core/arrays/integer.py
 ${PYSITELIB}/pandas/core/arrays/integer.pyc
 ${PYSITELIB}/pandas/core/arrays/integer.pyo
 ${PYSITELIB}/pandas/core/arrays/interval.py
 ${PYSITELIB}/pandas/core/arrays/interval.pyc
 ${PYSITELIB}/pandas/core/arrays/interval.pyo
+${PYSITELIB}/pandas/core/arrays/masked.py
+${PYSITELIB}/pandas/core/arrays/masked.pyc
+${PYSITELIB}/pandas/core/arrays/masked.pyo
+${PYSITELIB}/pandas/core/arrays/numeric.py
+${PYSITELIB}/pandas/core/arrays/numeric.pyc
+${PYSITELIB}/pandas/core/arrays/numeric.pyo
 ${PYSITELIB}/pandas/core/arrays/numpy_.py
 ${PYSITELIB}/pandas/core/arrays/numpy_.pyc
 ${PYSITELIB}/pandas/core/arrays/numpy_.pyo
 ${PYSITELIB}/pandas/core/arrays/period.py
 ${PYSITELIB}/pandas/core/arrays/period.pyc
 ${PYSITELIB}/pandas/core/arrays/period.pyo
-${PYSITELIB}/pandas/core/arrays/sparse.py
-${PYSITELIB}/pandas/core/arrays/sparse.pyc
-${PYSITELIB}/pandas/core/arrays/sparse.pyo
+${PYSITELIB}/pandas/core/arrays/sparse/__init__.py
+${PYSITELIB}/pandas/core/arrays/sparse/__init__.pyc
+${PYSITELIB}/pandas/core/arrays/sparse/__init__.pyo
+${PYSITELIB}/pandas/core/arrays/sparse/accessor.py
+${PYSITELIB}/pandas/core/arrays/sparse/accessor.pyc
+${PYSITELIB}/pandas/core/arrays/sparse/accessor.pyo
+${PYSITELIB}/pandas/core/arrays/sparse/array.py
+${PYSITELIB}/pandas/core/arrays/sparse/array.pyc
+${PYSITELIB}/pandas/core/arrays/sparse/array.pyo
+${PYSITELIB}/pandas/core/arrays/sparse/dtype.py
+${PYSITELIB}/pandas/core/arrays/sparse/dtype.pyc
+${PYSITELIB}/pandas/core/arrays/sparse/dtype.pyo
+${PYSITELIB}/pandas/core/arrays/sparse/scipy_sparse.py
+${PYSITELIB}/pandas/core/arrays/sparse/scipy_sparse.pyc
+${PYSITELIB}/pandas/core/arrays/sparse/scipy_sparse.pyo
+${PYSITELIB}/pandas/core/arrays/string_.py
+${PYSITELIB}/pandas/core/arrays/string_.pyc
+${PYSITELIB}/pandas/core/arrays/string_.pyo
+${PYSITELIB}/pandas/core/arrays/string_arrow.py
+${PYSITELIB}/pandas/core/arrays/string_arrow.pyc
+${PYSITELIB}/pandas/core/arrays/string_arrow.pyo
 ${PYSITELIB}/pandas/core/arrays/timedeltas.py
 ${PYSITELIB}/pandas/core/arrays/timedeltas.pyc
 ${PYSITELIB}/pandas/core/arrays/timedeltas.pyo
@@ -198,6 +259,9 @@
 ${PYSITELIB}/pandas/core/computation/ops.py
 ${PYSITELIB}/pandas/core/computation/ops.pyc
 ${PYSITELIB}/pandas/core/computation/ops.pyo
+${PYSITELIB}/pandas/core/computation/parsing.py
+${PYSITELIB}/pandas/core/computation/parsing.pyc
+${PYSITELIB}/pandas/core/computation/parsing.pyo
 ${PYSITELIB}/pandas/core/computation/pytables.py
 ${PYSITELIB}/pandas/core/computation/pytables.pyc
 ${PYSITELIB}/pandas/core/computation/pytables.pyo
@@ -207,6 +271,9 @@
 ${PYSITELIB}/pandas/core/config_init.py
 ${PYSITELIB}/pandas/core/config_init.pyc
 ${PYSITELIB}/pandas/core/config_init.pyo
+${PYSITELIB}/pandas/core/construction.py
+${PYSITELIB}/pandas/core/construction.pyc
+${PYSITELIB}/pandas/core/construction.pyo
 ${PYSITELIB}/pandas/core/dtypes/__init__.py
 ${PYSITELIB}/pandas/core/dtypes/__init__.pyc
 ${PYSITELIB}/pandas/core/dtypes/__init__.pyo
@@ -237,6 +304,9 @@
 ${PYSITELIB}/pandas/core/dtypes/missing.py
 ${PYSITELIB}/pandas/core/dtypes/missing.pyc
 ${PYSITELIB}/pandas/core/dtypes/missing.pyo
+${PYSITELIB}/pandas/core/flags.py
+${PYSITELIB}/pandas/core/flags.pyc
+${PYSITELIB}/pandas/core/flags.pyo
 ${PYSITELIB}/pandas/core/frame.py
 ${PYSITELIB}/pandas/core/frame.pyc
 ${PYSITELIB}/pandas/core/frame.pyo
@@ -261,6 +331,9 @@
 ${PYSITELIB}/pandas/core/groupby/grouper.py
 ${PYSITELIB}/pandas/core/groupby/grouper.pyc
 ${PYSITELIB}/pandas/core/groupby/grouper.pyo
+${PYSITELIB}/pandas/core/groupby/numba_.py
+${PYSITELIB}/pandas/core/groupby/numba_.pyc
+${PYSITELIB}/pandas/core/groupby/numba_.pyo
 ${PYSITELIB}/pandas/core/groupby/ops.py
 ${PYSITELIB}/pandas/core/groupby/ops.pyc
 ${PYSITELIB}/pandas/core/groupby/ops.pyo
@@ -291,6 +364,9 @@
 ${PYSITELIB}/pandas/core/indexes/datetimes.py
 ${PYSITELIB}/pandas/core/indexes/datetimes.pyc
 ${PYSITELIB}/pandas/core/indexes/datetimes.pyo
+${PYSITELIB}/pandas/core/indexes/extension.py
+${PYSITELIB}/pandas/core/indexes/extension.pyc
+${PYSITELIB}/pandas/core/indexes/extension.pyo
 ${PYSITELIB}/pandas/core/indexes/frozen.py
 ${PYSITELIB}/pandas/core/indexes/frozen.pyc
 ${PYSITELIB}/pandas/core/indexes/frozen.pyo
@@ -318,9 +394,6 @@
 ${PYSITELIB}/pandas/core/internals/__init__.py
 ${PYSITELIB}/pandas/core/internals/__init__.pyc
 ${PYSITELIB}/pandas/core/internals/__init__.pyo
-${PYSITELIB}/pandas/core/internals/arrays.py
-${PYSITELIB}/pandas/core/internals/arrays.pyc
-${PYSITELIB}/pandas/core/internals/arrays.pyo
 ${PYSITELIB}/pandas/core/internals/blocks.py
 ${PYSITELIB}/pandas/core/internals/blocks.pyc
 ${PYSITELIB}/pandas/core/internals/blocks.pyo
@@ -333,6 +406,9 @@
 ${PYSITELIB}/pandas/core/internals/managers.py
 ${PYSITELIB}/pandas/core/internals/managers.pyc
 ${PYSITELIB}/pandas/core/internals/managers.pyo
+${PYSITELIB}/pandas/core/internals/ops.py
+${PYSITELIB}/pandas/core/internals/ops.pyc
+${PYSITELIB}/pandas/core/internals/ops.pyo
 ${PYSITELIB}/pandas/core/missing.py
 ${PYSITELIB}/pandas/core/missing.pyc
 ${PYSITELIB}/pandas/core/missing.pyo
@@ -342,9 +418,27 @@
 ${PYSITELIB}/pandas/core/ops/__init__.py
 ${PYSITELIB}/pandas/core/ops/__init__.pyc
 ${PYSITELIB}/pandas/core/ops/__init__.pyo
+${PYSITELIB}/pandas/core/ops/array_ops.py
+${PYSITELIB}/pandas/core/ops/array_ops.pyc
+${PYSITELIB}/pandas/core/ops/array_ops.pyo
+${PYSITELIB}/pandas/core/ops/common.py
+${PYSITELIB}/pandas/core/ops/common.pyc
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