- Skip two tests that fail with Numpy 2.1.
- Prepare for Python 3.13, by skipping it if we aren't building for it.
- Update to 2.2.3
* Bug in eval() on complex including division /
discards imaginary part. (GH 21374)
* Minor fixes for numpy 2.1 compatibility. (GH 59444)
* Missing licenses for 3rd party dependencies were
added back into the wheels. (GH 58632)
- Drop pandas-pr58269-pyarrow16xpass.patch, merged upstream
- Drop pandas-pr58484-matplotlib.patch, merged upstream
- Drop pandas-pr59175-matplotlib.patch, merged upstream
- Drop pandas-pr59353-np2eval.patch, merged upstream
- Drop tests-npdev.patch, merged upstream
- Drop tests-timedelta.patch, merged upstream
- Refresh tests-nomkl.patch
- Renumber remaining patches
OBS-URL: https://build.opensuse.org/request/show/1218705
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-pandas?expand=0&rev=71
* Bug in eval() on complex including division /
discards imaginary part. (GH 21374)
* Minor fixes for numpy 2.1 compatibility. (GH 59444)
* Missing licenses for 3rd party dependencies were
added back into the wheels. (GH 58632)
- Drop pandas-pr58269-pyarrow16xpass.patch, merged upstream
- Drop pandas-pr58484-matplotlib.patch, merged upstream
- Drop pandas-pr59175-matplotlib.patch, merged upstream
- Drop pandas-pr59353-np2eval.patch, merged upstream
- Drop tests-npdev.patch, merged upstream
- Drop tests-timedelta.patch, merged upstream
- Refresh tests-nomkl.patch
- Renumber remaining patches
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=131
- Add pandas-pr58269-pyarrow16xpass.patch
(gh#pandas-dev/pandas!58269)
- Add pandas-pr58720-xarray-dp.patch
(gh#pandas-dev/pandas!58720), which makes pandas compatible
with the modern xarray
- Add pandas-pr58484-matplotlib.patch
(gh#pandas-dev/pandas!58484), which makes pandas compatible
with the modern matplotlib
- Skip also test_plot_scatter_shape (gh#pandas-dev/pandas#58851)
OBS-URL: https://build.opensuse.org/request/show/1179390
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-pandas?expand=0&rev=66
- Update to 2.2.2
* Pandas 2.2.2 is now compatible with numpy 2.0
* Pandas 2.2.2 is the first version of pandas that is generally
compatible with the upcoming numpy 2.0 release, and wheels for
pandas 2.2.2 will work with both numpy 1.x and 2.x. One major
caveat is that arrays created with numpy 2.0’s new StringDtype
will convert to object dtyped arrays upon Series/DataFrame
creation. Full support for numpy 2.0’s StringDtype is expected
to land in pandas 3.0.
* As usual please report any bugs discovered to our issue tracker
## Fixed regressions
* DataFrame.__dataframe__() was producing incorrect data buffers
when the a column’s type was a pandas nullable on with missing
values (GH 56702)
* DataFrame.__dataframe__() was producing incorrect data buffers
when the a column’s type was a pyarrow nullable on with missing
values (GH 57664)
* Avoid issuing a spurious DeprecationWarning when a custom
DataFrame or Series subclass method is called (GH 57553)
* Fixed regression in precision of to_datetime() with string and
unit input (GH 57051)
## Bug fixes
* DataFrame.__dataframe__() was producing incorrect data buffers
when the column’s type was nullable boolean (GH 55332)
* DataFrame.__dataframe__() was showing bytemask instead of
bitmask for 'string[pyarrow]' validity buffer (GH 57762)
* DataFrame.__dataframe__() was showing non-null validity buffer
(instead of None) 'string[pyarrow]' without missing values (GH
57761)
* DataFrame.to_sql() was failing to find the right table when
using the schema argument (GH 57539)
- Remove obsolete python39 multibuild
- Add pandas-pr58269-pyarrow16xpass.patch
gh#pandas-dev/pandas#58269
OBS-URL: https://build.opensuse.org/request/show/1171775
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=114
- Update to 2.2.1
## Enhancements
* Added pyarrow pip extra so users can install pandas and pyarrow
with pip with pip install pandas[pyarrow] (#54466)
## Fixed regressions
* Fixed memory leak in `read_csv` (#57039)
* Fixed performance regression in `Series.combine_first` (#55845)
* Fixed regression causing overflow for near-minimum timestamps
(#57150)
* Fixed regression in `concat` changing long-standing behavior
that always sorted the non-concatenation axis when the axis was
a `DatetimeIndex` (#57006)
* Fixed regression in `merge_ordered` raising TypeError for
fill_method="ffill" and how="left" (#57010)
* Fixed regression in `pandas.testing.assert_series_equal`
defaulting to check_exact=True when checking the `Index`
(#57067)
* Fixed regression in `read_json` where an `Index` would be
returned instead of a `RangeIndex` (#57429)
* Fixed regression in `wide_to_long` raising an AttributeError
for string columns (#57066)
* Fixed regression in `.DataFrameGroupBy.idxmin`,
`.DataFrameGroupBy.idxmax`, `.SeriesGroupBy.idxmin`,
`.SeriesGroupBy.idxmax` ignoring the skipna argument (#57040)
* Fixed regression in `.DataFrameGroupBy.idxmin`,
`.DataFrameGroupBy.idxmax`, `.SeriesGroupBy.idxmin`,
`.SeriesGroupBy.idxmax` where values containing the minimum or
maximum value for the dtype could produce incorrect results
(#57040)
* Fixed regression in `CategoricalIndex.difference` raising
KeyError when other contains null values other than NaN
(#57318)
* Fixed regression in `DataFrame.groupby` raising ValueError when
grouping by a `Series` in some cases (#57276)
* Fixed regression in `DataFrame.loc` raising IndexError for
non-unique, masked dtype indexes where result has more than
10,000 rows (#57027)
* Fixed regression in `DataFrame.loc` which was unnecessarily
throwing "incompatible dtype warning" when expanding with
partial row indexer and multiple columns (see PDEP6) (#56503)
* Fixed regression in `DataFrame.map` with na_action="ignore" not
being respected for NumPy nullable and `ArrowDtypes` (#57316)
* Fixed regression in `DataFrame.merge` raising ValueError for
certain types of 3rd-party extension arrays (#57316)
* Fixed regression in `DataFrame.query` with all NaT column with
object dtype (#57068)
* Fixed regression in `DataFrame.shift` raising AssertionError
for axis=1 and empty `DataFrame` (#57301)
* Fixed regression in `DataFrame.sort_index` not producing a
stable sort for a index with duplicates (#57151)
* Fixed regression in `DataFrame.to_dict` with orient='list' and
datetime or timedelta types returning integers (#54824)
* Fixed regression in `DataFrame.to_json` converting nullable
integers to floats (#57224)
* Fixed regression in `DataFrame.to_sql` when method="multi" is
passed and the dialect type is not Oracle (#57310)
* Fixed regression in `DataFrame.transpose` with nullable
extension dtypes not having F-contiguous data potentially
causing exceptions when used (#57315)
* Fixed regression in `DataFrame.update` emitting incorrect
warnings about downcasting (#57124)
* Fixed regression in `DataFrameGroupBy.idxmin`,
`DataFrameGroupBy.idxmax`, `SeriesGroupBy.idxmin`,
`SeriesGroupBy.idxmax` ignoring the skipna argument (#57040)
* Fixed regression in `DataFrameGroupBy.idxmin`,
`DataFrameGroupBy.idxmax`, `SeriesGroupBy.idxmin`,
`SeriesGroupBy.idxmax` where values containing the minimum or
maximum value for the dtype could produce incorrect results
(#57040)
* Fixed regression in `ExtensionArray.to_numpy` raising for
non-numeric masked dtypes (#56991)
* Fixed regression in `Index.join` raising TypeError when joining
an empty index to a non-empty index containing mixed dtype
values (#57048)
* Fixed regression in `Series.astype` introducing decimals when
converting from integer with missing values to string dtype
(#57418)
* Fixed regression in `Series.pct_change` raising a ValueError
for an empty `Series` (#57056)
* Fixed regression in `Series.to_numpy` when dtype is given as
float and the data contains NaNs (#57121)
* Fixed regression in addition or subtraction of `DateOffset`
objects with millisecond components to datetime64 `Index`,
`Series`, or `DataFrame` (#57529)
## Bug fixes
* Fixed bug in `pandas.api.interchange.from_dataframe` which was
raising for Nullable integers (#55069)
* Fixed bug in `pandas.api.interchange.from_dataframe` which was
raising for empty inputs (#56700)
* Fixed bug in `pandas.api.interchange.from_dataframe` which
wasn't converting columns names to strings (#55069)
* Fixed bug in `DataFrame.__getitem__` for empty `DataFrame` with
Copy-on-Write enabled (#57130)
* Fixed bug in `PeriodIndex.asfreq` which was silently converting
frequencies which are not supported as period frequencies
instead of raising an error (#56945)
## Note
* The DeprecationWarning that was raised when pandas was imported
without PyArrow being installed has been removed. This decision
was made because the warning was too noisy for too many users
and a lot of feedback was collected about the decision to make
PyArrow a required dependency. Pandas is currently considering
the decision whether or not PyArrow should be added as a hard
dependency in 3.0. Interested users can follow the discussion
here.
* Added the argument skipna to `DataFrameGroupBy.first`,
`DataFrameGroupBy.last`, `SeriesGroupBy.first`, and
`SeriesGroupBy.last`; achieving skipna=False used to be
available via `DataFrameGroupBy.nth`, but the behavior was
changed in pandas 2.0.0 (#57019)
* Added the argument skipna to `Resampler.first`,
`Resampler.last` (#57019)
- Release notes for 2.2.0
* For full changelog see
https://github.com/pandas-dev/pandas/blob/main/doc/source/whatsnew/v2.2.0.rst
## Enhancements
* ADBC Driver support in to_sql and read_sql
* Create a pandas Series based on one or more conditions
* to_numpy for NumPy nullable and Arrow types converts to
suitable NumPy dtype
* Series.struct accessor for PyArrow structured data
* Series.list accessor for PyArrow list data
* Calamine engine for `read_excel`
## Notable bug fixes
* `merge` and `DataFrame.join` now consistently follow documented
sort behavior
* `merge` and `DataFrame.join` no longer reorder levels when
levels differ
* Increased minimum versions for dependencies
## Deprecations
* Chained assignment
* Deprecate aliases M, Q, Y, etc. in favour of ME, QE, YE, etc.
for offsets
* Deprecated automatic downcasting
- Simplify flavor test setup: obs can evaluate %{shrink:} now
OBS-URL: https://build.opensuse.org/request/show/1152058
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=110
- Update to 2.1.4
## Fixed regressions
* Fixed regression when trying to read a pickled pandas DataFrame
from pandas 1.3 (GH 55137)
## Bug fixes
* Bug in Series constructor raising DeprecationWarning when index
is a list of Series (GH 55228)
* Bug in Series when trying to cast date-like string inputs to
ArrowDtype of pyarrow.timestamp (GH 56266)
* Bug in DataFrame.apply() where passing raw=True ignored args
passed to the applied function (GH 55753)
* Bug in Index.__getitem__() returning wrong result for Arrow
dtypes and negative stepsize (GH 55832)
* Fixed bug in to_numeric() converting to extension dtype for
string[pyarrow_numpy] dtype (GH 56179)
* Fixed bug in DataFrameGroupBy.min() and DataFrameGroupBy.max()
not preserving extension dtype for empty object (GH 55619)
* Fixed bug in DataFrame.__setitem__() casting Index with
object-dtype to PyArrow backed strings when infer_string option
is set (GH 55638)
* Fixed bug in DataFrame.to_hdf() raising when columns have
StringDtype (GH 55088)
* Fixed bug in Index.insert() casting object-dtype to PyArrow
backed strings when infer_string option is set (GH 55638)
* Fixed bug in Series.__ne__() resulting in False for comparison
between NA and string value for dtype="string[pyarrow_numpy]"
(GH 56122)
* Fixed bug in Series.mode() not keeping object dtype when
infer_string is set (GH 56183)
* Fixed bug in Series.reset_index() not preserving object dtype
when infer_string is set (GH 56160)
* Fixed bug in Series.str.split() and Series.str.rsplit() when
pat=None for ArrowDtype with pyarrow.string (GH 56271)
* Fixed bug in Series.str.translate() losing object dtype when
string option is set (GH 56152)
- Go back to Cython0, it has NOT been unpinned by upstream released
version
* https://github.com/pandas-dev/pandas/blob/v2.1.4/pyproject.toml#L8
* See also gh#jsonpickle/jsonpickle#460
OBS-URL: https://build.opensuse.org/request/show/1133481
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=104
- Update to 2.1.3:
* 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
* Fixed regressions
+ Fixed infinite recursion from operations that return a new object on
some DataFrame subclasses
+ Fixed regression in DataFrame.join() where result has missing values
and dtype is arrow backed string
+ Fixed regression in rolling() where non-nanosecond index or on column
would produce incorrect results
+ Fixed regression in DataFrame.resample() which was extrapolating back
to origin when origin was outside its bounds
+ Fixed regression in DataFrame.sort_index() which was not sorting
correctly when the index was a sliced MultiIndex
+ 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
+ Fixed performance regression with wide DataFrames, typically
involving methods where all columns were accessed individually
+ Fixed regression in merge_asof() raising TypeError for by with
datetime and timedelta dtypes
+ 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
+ Fixed regression in DataFrame.to_sql() not roundtripping datetime
columns correctly for sqlite when using detect_types
+ Fixed regression in construction of certain DataFrame or Series
subclasses
OBS-URL: https://build.opensuse.org/request/show/1130126
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-pandas?expand=0&rev=58
* 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
* Fixed regressions
+ Fixed infinite recursion from operations that return a new object on
some DataFrame subclasses
+ Fixed regression in DataFrame.join() where result has missing values
and dtype is arrow backed string
+ Fixed regression in rolling() where non-nanosecond index or on column
would produce incorrect results
+ Fixed regression in DataFrame.resample() which was extrapolating back
to origin when origin was outside its bounds
+ Fixed regression in DataFrame.sort_index() which was not sorting
correctly when the index was a sliced MultiIndex
+ 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
+ Fixed performance regression with wide DataFrames, typically
involving methods where all columns were accessed individually
+ Fixed regression in merge_asof() raising TypeError for by with
datetime and timedelta dtypes
+ 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
+ Fixed regression in DataFrame.to_sql() not roundtripping datetime
columns correctly for sqlite when using detect_types
+ Fixed regression in construction of certain DataFrame or Series
subclasses
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=102
- Update to 2.1.1
## Fixed regressions
* Fixed regression in concat() when DataFrame ‘s 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)
- Drop pandas-pr55073-pyarrow13.patch merged upstream
OBS-URL: https://build.opensuse.org/request/show/1116287
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=98
- Use git cloned archive gh#pandas-dev/pandas#54907
- Update to 2.1.0
* https://pandas.pydata.org/pandas-docs/version/2.1.0/whatsnew/v2.1.0.html
* Avoid NumPy object dtype for strings by default
* DataFrame reductions preserve extension dtypes
* Copy-on-Write improvements
* New DataFrame.map() method and support for ExtensionArrays
* New implementation of DataFrame.stack()
* Other minor enhancements (see link above)
## Backwards incompatible API changes
* pandas 2.1.0 supports Python 3.9 and higher
* Increased minimum versions for numpy 1.22.3 and some optional
dependencies
* arrays.PandasArray has been renamed NumpyExtensionArray and the
attached dtype name changed from PandasDtype to NumpyEADtype;
importing PandasArray still works until the next major version
(GH 53694)
## Deprecations
* Deprecated silent upcasting in setitem-like Series operations
* Deprecated parsing datetimes with mixed time zones
* Other Deprecation (see link above)
## More
* Performance Improvements (see link above)
* Bug fixes (see linkl above)
- Switch to meson build system
OBS-URL: https://build.opensuse.org/request/show/1109356
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=94
- update to 2.0.3:
* Bug in Timestamp.weekday`() was returning incorrect results
before '0000-02-29'
* Fixed performance regression in merging on datetime-like columns
* Fixed regression when DataFrame.to_string() creates extra space
for string dtypes
* Bug in DataFrame.convert_dtype() and Series.convert_dtype()
when trying to convert ArrowDtype with dtype_backend="nullable_numpy"
* Bug in RangeIndex.union() when using sort=True with another
RangeIndex
* Bug in Series.reindex() when expanding a non-nanosecond datetime
or timedelta
* Bug in read_csv() when defining dtype with bool[pyarrow] for
the "c" and "python" engines
* Bug in Series.str.split() and Series.str.rsplit() with expand=True
* Bug in indexing methods (e.g. DataFrame.__getitem__()) where
taking the entire DataFrame/Series would raise an OverflowError
when Copy on Write was enabled the length of the array was over
the maximum size a 32-bit integer can hold
* Bug when constructing a DataFrame with columns of an ArrowDtype
with a pyarrow.dictionary type that reindexes the data
* Bug when indexing a DataFrame or Series with an Index with a
timestamp ArrowDtype would raise an AttributeError
- drop pandas-fix-tests.patch (upstream)
OBS-URL: https://build.opensuse.org/request/show/1104661
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-pandas?expand=0&rev=53
* Bug in Timestamp.weekday`() was returning incorrect results
before '0000-02-29'
* Fixed performance regression in merging on datetime-like columns
* Fixed regression when DataFrame.to_string() creates extra space
for string dtypes
* Bug in DataFrame.convert_dtype() and Series.convert_dtype()
when trying to convert ArrowDtype with dtype_backend="nullable_numpy"
* Bug in RangeIndex.union() when using sort=True with another
RangeIndex
* Bug in Series.reindex() when expanding a non-nanosecond datetime
or timedelta
* Bug in read_csv() when defining dtype with bool[pyarrow] for
the "c" and "python" engines
* Bug in Series.str.split() and Series.str.rsplit() with expand=True
* Bug in indexing methods (e.g. DataFrame.__getitem__()) where
taking the entire DataFrame/Series would raise an OverflowError
when Copy on Write was enabled the length of the array was over
the maximum size a 32-bit integer can hold
* Bug when constructing a DataFrame with columns of an ArrowDtype
with a pyarrow.dictionary type that reindexes the data
* Bug when indexing a DataFrame or Series with an Index with a
timestamp ArrowDtype would raise an AttributeError
- drop pandas-fix-tests.patch (upstream)
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=92
- Update to 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’t 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’s 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 “AM” / “PM” tokens (GH53147)
OBS-URL: https://build.opensuse.org/request/show/1090040
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=84
- Update to version 1.5.2
## Fixed regressions
* Fixed regression in MultiIndex.join() for extension array
dtypes (GH49277)
* Fixed regression in Series.replace() raising RecursionError
with numeric dtype and when specifying value=None (GH45725)
* Fixed regression in arithmetic operations for DataFrame with
MultiIndex columns with different dtypes (GH49769)
* Fixed regression in DataFrame.plot() preventing Colormap
instance from being passed using the colormap argument if
Matplotlib 3.6+ is used (GH49374)
* Fixed regression in date_range() returning an invalid set of
periods for CustomBusinessDay frequency and start date with
timezone (GH49441)
* Fixed performance regression in groupby operations (GH49676)
* Fixed regression in Timedelta constructor returning object of
wrong type when subclassing Timedelta (GH49579)
## Bug fixes
* Bug in the Copy-on-Write implementation losing track of views
in certain chained indexing cases (GH48996)
* Fixed memory leak in Styler.to_excel() (GH49751)
## Other
* Reverted color as an alias for c and size as an alias for s in
function DataFrame.plot.scatter() (GH49732)
- Add pandas-pr49886-fix-numpy-deprecations.patch
* gh#pandas-dev/pandas#49887
- Move to PEP518 build
OBS-URL: https://build.opensuse.org/request/show/1045082
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=78
- Update to version 1.4.0
* https://pandas.pydata.org/docs/whatsnew/v1.4.0.html
* Enhancements
- Improved warning messages
- Index can hold arbitrary ExtensionArrays
- Enhancements in Styler
- Multi-threaded CSV reading with a new CSV Engine based on
pyarrow
- Rank function for rolling and expanding windows
- Groupby positional indexing
- DataFrame.from_dict and DataFrame.to_dict have new 'tight'
option
* Notable bug fixes
- Inconsistent date string parsing
- Ignoring dtypes in concat with empty or all-NA columns
- Null-values are no longer coerced to NaN-value in
value_counts and mode
- mangle_dupe_cols in read_csv no longer renames unique columns
conflicting with target names
- unstack and pivot_table no longer raises ValueError for
result that would exceed int32 limit
- groupby.apply consistent transform detection
* API changes
- Index.get_indexer_for() no longer accepts keyword arguments
(other than target); in the past these would be silently
ignored if the index was not unique (GH42310)
- Change in the position of the min_rows argument in
DataFrame.to_string() due to change in the docstring
(GH44304)
- Reduction operations for DataFrame or Series now raising a
ValueError when None is passed for skipna (GH44178)
- read_csv() and read_html() no longer raising an error when
one of the header rows consists only of Unnamed: columns
(GH13054)
- Changed the name attribute of several holidays in
USFederalHolidayCalendar to match official federal holiday
names.
* Deprecations
- Deprecated Int64Index, UInt64Index & Float64Index
- Deprecated Frame.append and Series.append
- Split out test runs into separate flavors, optimize memory usage
in pytest-xdist runs
OBS-URL: https://build.opensuse.org/request/show/948450
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=67
- Update to version 1.3.5
* Fixed regression in Series.equals() when comparing floats with
dtype object to None (GH44190)
* Fixed regression in merge_asof() raising error when array was
supplied as join key (GH42844)
* Fixed regression when resampling DataFrame with DateTimeIndex
with empty groups and uint8, uint16 or uint32 columns
incorrectly raising RuntimeError (GH43329)
* Fixed regression in creating a DataFrame from a timezone-aware
Timestamp scalar near a Daylight Savings Time transition
(GH42505)
* Fixed performance regression in read_csv() (GH44106)
* Fixed regression in Series.duplicated() and
Series.drop_duplicates() when Series has Categorical dtype with
boolean categories (GH44351)
* Fixed regression in GroupBy.sum() with timedelta64[ns] dtype
containing NaT failing to treat that value as NA (GH42659)
* Fixed regression in RollingGroupby.cov() and
RollingGroupby.corr() when other had the same shape as each
group would incorrectly return superfluous groups in the result
(GH42915)
OBS-URL: https://build.opensuse.org/request/show/943876
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=66
- Update to version 1.3.4
* 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)
* 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)
OBS-URL: https://build.opensuse.org/request/show/926551
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=65
- Update to version 1.3.3
* 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 improvement for DataFrame.__setitem__() when the
key or value is not a DataFrame, or key is not list-like
(GH43274)
* Fixed bug in DataFrameGroupBy.agg() and DataFrameGroupBy.
transform() with engine="numba" where index data was not being
correctly passed into func (GH43133)
- Release 1.3.2
* 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 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)
OBS-URL: https://build.opensuse.org/request/show/920383
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=64
- Update to version 1.3.1
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)
OBS-URL: https://build.opensuse.org/request/show/911851
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=62
- Update to version 1.2.2
* https://pandas.pydata.org/docs/whatsnew/v1.2.2.html
* fixed regressions and bugfixes
- Update to version 1.2.1
* https://pandas.pydata.org/docs/whatsnew/v1.2.1.html
* fixed regressions and bugfixes
* Calling NumPy ufuncs on non-aligned DataFrames
* The deprecated attributes _AXIS_NAMES and _AXIS_NUMBERS of
DataFrame and Series will no longer show up in dir or inspect.
getmembers calls (GH38740)
* Bumped minimum fastparquet version to 0.4.0 to avoid
AttributeError from numba (GH38344)
* Bumped minimum pymysql version to 0.8.1 to avoid test failures
(GH38344)
* Added reference to backwards incompatible check_freq arg of
testing.assert_frame_equal() and testing.assert_series_equal()
in pandas 1.1.0 whats new (GH34050)
- Update to version 1.2.0
* https://pandas.pydata.org/docs/whatsnew/v1.2.0.html
* WARNING:
The xlwt package for writing old-style .xls excel files is
no longer maintained. The xlrd package is now only for reading
old-style .xls files.
Previously, the default argument engine=None to read_excel()
would result in using the xlrd engine in many cases, including
new Excel 2007+ (.xlsx) files. If openpyxl is installed, many
of these cases will now default to using the openpyxl engine.
See the read_excel() documentation for more details.
Thus, it is strongly encouraged to install openpyxl to read
Excel 2007+ (.xlsx) files. Please do not report issues when
using ``xlrd`` to read ``.xlsx`` files. This is no longer
supported, switch to using openpyxl instead.
Attempting to use the xlwt engine will raise a FutureWarning
unless the option io.excel.xls.writer is set to "xlwt". While
this option is now deprecated and will also raise a
FutureWarning, it can be globally set and the warning
suppressed. Users are recommended to write .xlsx files using
the openpyxl engine instead.
Enhancements
* Optionally disallow duplicate labels
* Passing arguments to fsspec backends
* Support for binary file handles in to_csv
* Support for short caption and table position in to_latex
* Change in default floating precision for read_csv and
read_table
* Experimental nullable data types for float data
* Index/column name preservation when aggregating
* GroupBy supports EWM operations directly
Deprecations
* https://pandas.pydata.org/docs/whatsnew/v1.2.0.html#deprecations
- Skip python36 build: New minimum supported Python is 3.7.1
- Only Suggest instead of Recommend optional dependencies. Nobody
wants to pull in all of those packages by default.
- Remove pandas-pytest.ini
- Rework test deselection
- Limit to 4 pytest-xdist workers, as collection consumes a lot of
memory
OBS-URL: https://build.opensuse.org/request/show/872216
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=56
- specfile:
* updated versions of some requirements, require numpy during build
* removed pandas-pr34991-npconstructor.patch, included upstream
* removed sed commands that are not needed anymore
* skip test to see if pandas is installed
- update to version 1.1.1:
* Fixed regressions
+ Fixed regression in CategoricalIndex.format() where, when
stringified scalars had different lengths, the shorter string
would be right-filled with spaces, so it had the same length as
the longest string (GH35439)
+ Fixed regression in Series.truncate() when trying to truncate a
single-element series (GH35544)
+ Fixed regression where DataFrame.to_numpy() would raise a
RuntimeError for mixed dtypes when converting to str (GH35455)
+ Fixed regression where read_csv() would raise a ValueError when
pandas.options.mode.use_inf_as_na was set to True (GH35493)
+ Fixed regression where pandas.testing.assert_series_equal()
would raise an error when non-numeric dtypes were passed with
check_exact=True (GH35446)
+ Fixed regression in .groupby(..).rolling(..) where column
selection was ignored (GH35486)
+ Fixed regression where DataFrame.interpolate() would raise a
TypeError when the DataFrame was empty (GH35598)
+ Fixed regression in DataFrame.shift() with axis=1 and
heterogeneous dtypes (GH35488)
+ Fixed regression in DataFrame.diff() with read-only data
(GH35559)
+ Fixed regression in .groupby(..).rolling(..) where a segfault
would occur with center=True and an odd number of values
OBS-URL: https://build.opensuse.org/request/show/832629
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-pandas?expand=0&rev=29
- Skip test_raw_roundtrip on i586
- Update to version 1.0.5
* Fixed regressions
+ Fix regression in read_parquet() when reading from file-like objects (GH34467).
+ Fix regression in reading from public S3 buckets (GH34626).
Note this disables the ability to read Parquet files from
directories on S3 again (GH26388, GH34632), which was added
in the 1.0.4 release, but is now targeted for pandas 1.1.0.
+ Fixed regression in replace() raising an AssertionError when replacing values in an extension dtype with values of a different dtype (GH34530)
* Bug fixes
+ Fixed building from source with Python 3.8 fetching the wrong version of NumPy
OBS-URL: https://build.opensuse.org/request/show/817948
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-pandas?expand=0&rev=27
- Update to version 1.0.5
* Fixed regressions
+ Fix regression in read_parquet() when reading from file-like objects (GH34467).
+ Fix regression in reading from public S3 buckets (GH34626).
Note this disables the ability to read Parquet files from
directories on S3 again (GH26388, GH34632), which was added
in the 1.0.4 release, but is now targeted for pandas 1.1.0.
+ Fixed regression in replace() raising an AssertionError when replacing values in an extension dtype with values of a different dtype (GH34530)
* Bug fixes
+ Fixed building from source with Python 3.8 fetching the wrong version of NumPy
OBS-URL: https://build.opensuse.org/request/show/816736
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=43
+ Enhancements
* Using Numba in rolling.apply and expanding.apply
* Defining custom windows for rolling operations
* Converting to Markdown
+ Experimental new features
* Experimental NA scalar to denote missing values
* Dedicated string data type
* Boolean data type with missing values support
* convert_dtypes method to ease use of supported extension dtypes
+ Backwards incompatible API changes
* Avoid using names from MultiIndex.levels
* New repr for IntervalArray
* DataFrame.rename now only accepts one positional argument
* Extended verbose info output for DataFrame
* pandas.array() inference changes
* arrays.IntegerArray now uses pandas.NA
* arrays.IntegerArray comparisons return arrays.BooleanArray
* By default Categorical.min() now returns the minimum instead of np.nan
* Default dtype of empty pandas.Series
* Result dtype inference changes for resample operations
+ Many, many fixed regressions
- Add Jinja2 and xsel to BuildRequires
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=27
- Update to version 0.25.3
+ Support Python 3.8
+ Bug fixes
> Indexing
* Fix regression in DataFrame.reindex() not following the limit argument
* Fix regression in RangeIndex.get_indexer() for decreasing RangeIndex
where target values may be improperly identified as missing/present
> I/O
* Fix regression in notebook display where <th> tags were missing for
DataFrame.index values
* Regression in to_csv() where writing a Series or DataFrame indexed by
an IntervalIndex would incorrectly raise a TypeError
* Fix to_csv() with ExtensionArray with list-like values
> Groupby/resample/rolling
* Bug incorrectly raising an IndexError when passing a list of quantiles
to pandas.core.groupby.DataFrameGroupBy.quantile()
* Bug in pandas.core.groupby.GroupBy.shift(),
pandas.core.groupby.GroupBy.bfill() and
pandas.core.groupby.GroupBy.ffill() where timezone information would
be dropped
* Bug in DataFrameGroupBy.quantile() where NA values in the grouping
could cause segfaults or incorrect results
OBS-URL: https://build.opensuse.org/request/show/747290
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-pandas?expand=0&rev=21
+ Support Python 3.8
+ Bug fixes
> Indexing
* Fix regression in DataFrame.reindex() not following the limit argument
* Fix regression in RangeIndex.get_indexer() for decreasing RangeIndex
where target values may be improperly identified as missing/present
> I/O
* Fix regression in notebook display where <th> tags were missing for
DataFrame.index values
* Regression in to_csv() where writing a Series or DataFrame indexed by
an IntervalIndex would incorrectly raise a TypeError
* Fix to_csv() with ExtensionArray with list-like values
> Groupby/resample/rolling
* Bug incorrectly raising an IndexError when passing a list of quantiles
to pandas.core.groupby.DataFrameGroupBy.quantile()
* Bug in pandas.core.groupby.GroupBy.shift(),
pandas.core.groupby.GroupBy.bfill() and
pandas.core.groupby.GroupBy.ffill() where timezone information would
be dropped
* Bug in DataFrameGroupBy.quantile() where NA values in the grouping
could cause segfaults or incorrect results
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=23
- Update to Version 0.25.0
+ Warning
* Starting with the 0.25.x series of releases, pandas only supports Python 3.5.3 and higher.
* The minimum supported Python version will be bumped to 3.6 in a future release.
* Panel has been fully removed. For N-D labeled data structures, please
use xarray
* read_pickle read_msgpack are only guaranteed backwards compatible back to
pandas version 0.20.3
+ Enhancements
* Groupby aggregation with relabeling
Pandas has added special groupby behavior, known as "named aggregation", for naming the
output columns when applying multiple aggregation functions to specific columns.
* Groupby Aggregation with multiple lambdas
You can now provide multiple lambda functions to a list-like aggregation in
pandas.core.groupby.GroupBy.agg.
* Better repr for MultiIndex
Printing of MultiIndex instances now shows tuples of each row and ensures
that the tuple items are vertically aligned, so it's now easier to understand
the structure of the MultiIndex.
* Shorter truncated repr for Series and DataFrame
Currently, the default display options of pandas ensure that when a Series
or DataFrame has more than 60 rows, its repr gets truncated to this maximum
of 60 rows (the display.max_rows option). However, this still gives
a repr that takes up a large part of the vertical screen estate. Therefore,
a new option display.min_rows is introduced with a default of 10 which
determines the number of rows showed in the truncated repr:
* Json normalize with max_level param support
json_normalize normalizes the provided input dict to all
nested levels. The new max_level parameter provides more control over
which level to end normalization.
* Series.explode to split list-like values to rows
Series and DataFrame have gained the DataFrame.explode methods to transform
list-likes to individual rows.
* DataFrame.plot keywords logy, logx and loglog can now accept the value 'sym' for symlog scaling.
* Added support for ISO week year format ('%G-%V-%u') when parsing datetimes using to_datetime
* Indexing of DataFrame and Series now accepts zerodim np.ndarray
* Timestamp.replace now supports the fold argument to disambiguate DST transition times
* DataFrame.at_time and Series.at_time now support datetime.time objects with timezones
* DataFrame.pivot_table now accepts an observed parameter which is passed to underlying calls to DataFrame.groupby to speed up grouping categorical data.
* Series.str has gained Series.str.casefold method to removes all case distinctions present in a string
* DataFrame.set_index now works for instances of abc.Iterator, provided their output is of the same length as the calling frame
* DatetimeIndex.union now supports the sort argument. The behavior of the sort parameter matches that of Index.union
* RangeIndex.union now supports the sort argument. If sort=False an unsorted Int64Index is always returned. sort=None is the default and returns a monotonically increasing RangeIndex if possible or a sorted Int64Index if not
* TimedeltaIndex.intersection now also supports the sort keyword
* DataFrame.rename now supports the errors argument to raise errors when attempting to rename nonexistent keys
* Added api.frame.sparse for working with a DataFrame whose values are sparse
* RangeIndex has gained ~RangeIndex.start, ~RangeIndex.stop, and ~RangeIndex.step attributes
* datetime.timezone objects are now supported as arguments to timezone methods and constructors
* DataFrame.query and DataFrame.eval now supports quoting column names with backticks to refer to names with spaces
* merge_asof now gives a more clear error message when merge keys are categoricals that are not equal
* pandas.core.window.Rolling supports exponential (or Poisson) window type
* Error message for missing required imports now includes the original import error's text
* DatetimeIndex and TimedeltaIndex now have a mean method
* DataFrame.describe now formats integer percentiles without decimal point
* Added support for reading SPSS .sav files using read_spss
* Added new option plotting.backend to be able to select a plotting backend different than the existing matplotlib one. Use pandas.set_option('plotting.backend', '<backend-module>') where <backend-module is a library implementing the pandas plotting API
* pandas.offsets.BusinessHour supports multiple opening hours intervals
* read_excel can now use openpyxl to read Excel files via the engine='openpyxl' argument. This will become the default in a future release
* pandas.io.excel.read_excel supports reading OpenDocument tables. Specify engine='odf' to enable. Consult the IO User Guide <io.ods> for more details
* Interval, IntervalIndex, and ~arrays.IntervalArray have gained an ~Interval.is_empty attribute denoting if the given interval(s) are empty
+ Backwards incompatible API changes
* Indexing with date strings with UTC offsets
Indexing a DataFrame or Series with a DatetimeIndex with a
date string with a UTC offset would previously ignore the UTC offset. Now, the UTC offset
is respected in indexing.
* MultiIndex constructed from levels and codes
Constructing a MultiIndex with NaN levels or codes value < -1 was allowed previously.
Now, construction with codes value < -1 is not allowed and NaN levels' corresponding codes
would be reassigned as -1.
* Groupby.apply on DataFrame evaluates first group only once
The implementation of DataFrameGroupBy.apply()
previously evaluated the supplied function consistently twice on the first group
to infer if it is safe to use a fast code path. Particularly for functions with
side effects, this was an undesired behavior and may have led to surprises.
* Concatenating sparse values
When passed DataFrames whose values are sparse, concat will now return a
Series or DataFrame with sparse values, rather than a SparseDataFrame .
* The .str-accessor performs stricter type checks
Due to the lack of more fine-grained dtypes, Series.str so far only checked whether the data was
of object dtype. Series.str will now infer the dtype data *within* the Series; in particular,
'bytes'-only data will raise an exception (except for Series.str.decode, Series.str.get,
Series.str.len, Series.str.slice).
* Categorical dtypes are preserved during groupby
Previously, columns that were categorical, but not the groupby key(s) would be converted to object dtype during groupby operations. Pandas now will preserve these dtypes.
* Incompatible Index type unions
When performing Index.union operations between objects of incompatible dtypes,
the result will be a base Index of dtype object. This behavior holds true for
unions between Index objects that previously would have been prohibited. The dtype
of empty Index objects will now be evaluated before performing union operations
rather than simply returning the other Index object. Index.union can now be
considered commutative, such that A.union(B) == B.union(A) .
* DataFrame groupby ffill/bfill no longer return group labels
The methods ffill, bfill, pad and backfill of
DataFrameGroupBy <pandas.core.groupby.DataFrameGroupBy>
previously included the group labels in the return value, which was
inconsistent with other groupby transforms. Now only the filled values
are returned.
* DataFrame describe on an empty categorical / object column will return top and freq
When calling DataFrame.describe with an empty categorical / object
column, the 'top' and 'freq' columns were previously omitted, which was inconsistent with
the output for non-empty columns. Now the 'top' and 'freq' columns will always be included,
with numpy.nan in the case of an empty DataFrame
* __str__ methods now call __repr__ rather than vice versa
Pandas has until now mostly defined string representations in a Pandas objects's
__str__/__unicode__/__bytes__ methods, and called __str__ from the __repr__
method, if a specific __repr__ method is not found. This is not needed for Python3.
In Pandas 0.25, the string representations of Pandas objects are now generally
defined in __repr__, and calls to __str__ in general now pass the call on to
the __repr__, if a specific __str__ method doesn't exist, as is standard for Python.
This change is backward compatible for direct usage of Pandas, but if you subclass
Pandas objects *and* give your subclasses specific __str__/__repr__ methods,
you may have to adjust your __str__/__repr__ methods .
* Indexing an IntervalIndex with Interval objects
Indexing methods for IntervalIndex have been modified to require exact matches only for Interval queries.
IntervalIndex methods previously matched on any overlapping Interval. Behavior with scalar points, e.g. querying
with an integer, is unchanged .
* Binary ufuncs on Series now align
Applying a binary ufunc like numpy.power now aligns the inputs
when both are Series .
* Categorical.argsort now places missing values at the end
Categorical.argsort now places missing values at the end of the array, making it
consistent with NumPy and the rest of pandas .
* Column order is preserved when passing a list of dicts to DataFrame
Starting with Python 3.7 the key-order of dict is guaranteed <https://mail.python.org/pipermail/python-dev/2017-December/151283.html>_. In practice, this has been true since
Python 3.6. The DataFrame constructor now treats a list of dicts in the same way as
it does a list of OrderedDict, i.e. preserving the order of the dicts.
This change applies only when pandas is running on Python>=3.6 .
* Increased minimum versions for dependencies
* DatetimeTZDtype will now standardize pytz timezones to a common timezone instance
* Timestamp and Timedelta scalars now implement the to_numpy method as aliases to Timestamp.to_datetime64 and Timedelta.to_timedelta64, respectively.
* Timestamp.strptime will now rise a NotImplementedError
* Comparing Timestamp with unsupported objects now returns :pyNotImplemented instead of raising TypeError. This implies that unsupported rich comparisons are delegated to the other object, and are now consistent with Python 3 behavior for datetime objects
* Bug in DatetimeIndex.snap which didn't preserving the name of the input Index
* The arg argument in pandas.core.groupby.DataFrameGroupBy.agg has been renamed to func
* The arg argument in pandas.core.window._Window.aggregate has been renamed to func
* Most Pandas classes had a __bytes__ method, which was used for getting a python2-style bytestring representation of the object. This method has been removed as a part of dropping Python2
* The .str-accessor has been disabled for 1-level MultiIndex, use MultiIndex.to_flat_index if necessary
* Removed support of gtk package for clipboards
* Using an unsupported version of Beautiful Soup 4 will now raise an ImportError instead of a ValueError
* Series.to_excel and DataFrame.to_excel will now raise a ValueError when saving timezone aware data.
* ExtensionArray.argsort places NA values at the end of the sorted array.
* DataFrame.to_hdf and Series.to_hdf will now raise a NotImplementedError when saving a MultiIndex with extention data types for a fixed format.
* Passing duplicate names in read_csv will now raise a ValueError
+ Deprecations
* Sparse subclasses
The SparseSeries and SparseDataFrame subclasses are deprecated. Their functionality is better-provided
by a Series or DataFrame with sparse values.
* msgpack format
The msgpack format is deprecated as of 0.25 and will be removed in a future version. It is recommended to use pyarrow for on-the-wire transmission of pandas objects.
* The deprecated .ix[] indexer now raises a more visible FutureWarning instead of DeprecationWarning .
* Deprecated the units=M (months) and units=Y (year) parameters for units of pandas.to_timedelta, pandas.Timedelta and pandas.TimedeltaIndex
* pandas.concat has deprecated the join_axes-keyword. Instead, use DataFrame.reindex or DataFrame.reindex_like on the result or on the inputs
* The SparseArray.values attribute is deprecated. You can use np.asarray(...) or
the SparseArray.to_dense method instead .
* The functions pandas.to_datetime and pandas.to_timedelta have deprecated the box keyword. Instead, use to_numpy or Timestamp.to_datetime64 or Timedelta.to_timedelta64.
* The DataFrame.compound and Series.compound methods are deprecated and will be removed in a future version .
* The internal attributes _start, _stop and _step attributes of RangeIndex have been deprecated.
Use the public attributes ~RangeIndex.start, ~RangeIndex.stop and ~RangeIndex.step instead .
* The Series.ftype, Series.ftypes and DataFrame.ftypes methods are deprecated and will be removed in a future version.
Instead, use Series.dtype and DataFrame.dtypes .
* The Series.get_values, DataFrame.get_values, Index.get_values,
SparseArray.get_values and Categorical.get_values methods are deprecated.
One of np.asarray(..) or ~Series.to_numpy can be used instead .
* The 'outer' method on NumPy ufuncs, e.g. np.subtract.outer has been deprecated on Series objects. Convert the input to an array with Series.array first
* Timedelta.resolution is deprecated and replaced with Timedelta.resolution_string. In a future version, Timedelta.resolution will be changed to behave like the standard library datetime.timedelta.resolution
* read_table has been undeprecated.
* Index.dtype_str is deprecated.
* Series.imag and Series.real are deprecated.
* Series.put is deprecated.
* Index.item and Series.item is deprecated.
* The default value ordered=None in ~pandas.api.types.CategoricalDtype has been deprecated in favor of ordered=False. When converting between categorical types ordered=True must be explicitly passed in order to be preserved.
* Index.contains is deprecated. Use key in index (__contains__) instead .
* DataFrame.get_dtype_counts is deprecated.
* Categorical.ravel will return a Categorical instead of a np.ndarray
+ Removal of prior version deprecations/changes
* Removed Panel
* Removed the previously deprecated sheetname keyword in read_excel
* Removed the previously deprecated TimeGrouper
* Removed the previously deprecated parse_cols keyword in read_excel
* Removed the previously deprecated pd.options.html.border
* Removed the previously deprecated convert_objects
* Removed the previously deprecated select method of DataFrame and Series
* Removed the previously deprecated behavior of Series treated as list-like in ~Series.cat.rename_categories
* Removed the previously deprecated DataFrame.reindex_axis and Series.reindex_axis
* Removed the previously deprecated behavior of altering column or index labels with Series.rename_axis or DataFrame.rename_axis
* Removed the previously deprecated tupleize_cols keyword argument in read_html, read_csv, and DataFrame.to_csv
* Removed the previously deprecated DataFrame.from.csv and Series.from_csv
* Removed the previously deprecated raise_on_error keyword argument in DataFrame.where and DataFrame.mask
* Removed the previously deprecated ordered and categories keyword arguments in astype
* Removed the previously deprecated cdate_range
* Removed the previously deprecated True option for the dropna keyword argument in SeriesGroupBy.nth
* Removed the previously deprecated convert keyword argument in Series.take and DataFrame.take
+ Performance improvements
* Significant speedup in SparseArray initialization that benefits most operations, fixing performance regression introduced in v0.20.0
* DataFrame.to_stata() is now faster when outputting data with any string or non-native endian columns
* Improved performance of Series.searchsorted. The speedup is especially large when the dtype is
int8/int16/int32 and the searched key is within the integer bounds for the dtype
* Improved performance of pandas.core.groupby.GroupBy.quantile
* Improved performance of slicing and other selected operation on a RangeIndex
* RangeIndex now performs standard lookup without instantiating an actual hashtable, hence saving memory
* Improved performance of read_csv by faster tokenizing and faster parsing of small float numbers
* Improved performance of read_csv by faster parsing of N/A and boolean values
* Improved performance of IntervalIndex.is_monotonic, IntervalIndex.is_monotonic_increasing and IntervalIndex.is_monotonic_decreasing by removing conversion to MultiIndex
* Improved performance of DataFrame.to_csv when writing datetime dtypes
* Improved performance of read_csv by much faster parsing of MM/YYYY and DD/MM/YYYY datetime formats
* Improved performance of nanops for dtypes that cannot store NaNs. Speedup is particularly prominent for Series.all and Series.any
* Improved performance of Series.map for dictionary mappers on categorical series by mapping the categories instead of mapping all values
* Improved performance of IntervalIndex.intersection
* Improved performance of read_csv by faster concatenating date columns without extra conversion to string for integer/float zero and float NaN; by faster checking the string for the possibility of being a date
* Improved performance of IntervalIndex.is_unique by removing conversion to MultiIndex
* Restored performance of DatetimeIndex.__iter__ by re-enabling specialized code path
* Improved performance when building MultiIndex with at least one CategoricalIndex level
* Improved performance by removing the need for a garbage collect when checking for SettingWithCopyWarning
* For to_datetime changed default value of cache parameter to True
* Improved performance of DatetimeIndex and PeriodIndex slicing given non-unique, monotonic data .
* Improved performance of pd.read_json for index-oriented data.
* Improved performance of MultiIndex.shape .
+ Bug fixes
> Categorical
* Bug in DataFrame.at and Series.at that would raise exception if the index was a CategoricalIndex
* Fixed bug in comparison of ordered Categorical that contained missing values with a scalar which sometimes incorrectly resulted in True
* Bug in DataFrame.dropna when the DataFrame has a CategoricalIndex containing Interval objects incorrectly raised a TypeError
> Datetimelike
* Bug in to_datetime which would raise an (incorrect) ValueError when called with a date far into the future and the format argument specified instead of raising OutOfBoundsDatetime
* Bug in to_datetime which would raise InvalidIndexError: Reindexing only valid with uniquely valued Index objects when called with cache=True, with arg including at least two different elements from the set {None, numpy.nan, pandas.NaT}
* Bug in DataFrame and Series where timezone aware data with dtype='datetime64[ns] was not cast to naive
* Improved Timestamp type checking in various datetime functions to prevent exceptions when using a subclassed datetime
* Bug in Series and DataFrame repr where np.datetime64('NaT') and np.timedelta64('NaT') with dtype=object would be represented as NaN
* Bug in to_datetime which does not replace the invalid argument with NaT when error is set to coerce
* Bug in adding DateOffset with nonzero month to DatetimeIndex would raise ValueError
* Bug in to_datetime which raises unhandled OverflowError when called with mix of invalid dates and NaN values with format='%Y%m%d' and error='coerce'
* Bug in isin for datetimelike indexes; DatetimeIndex, TimedeltaIndex and PeriodIndex where the levels parameter was ignored.
* Bug in to_datetime which raises TypeError for format='%Y%m%d' when called for invalid integer dates with length >= 6 digits with errors='ignore'
* Bug when comparing a PeriodIndex against a zero-dimensional numpy array
* Bug in constructing a Series or DataFrame from a numpy datetime64 array with a non-ns unit and out-of-bound timestamps generating rubbish data, which will now correctly raise an OutOfBoundsDatetime error .
* Bug in date_range with unnecessary OverflowError being raised for very large or very small dates
* Bug where adding Timestamp to a np.timedelta64 object would raise instead of returning a Timestamp
* Bug where comparing a zero-dimensional numpy array containing a np.datetime64 object to a Timestamp would incorrect raise TypeError
* Bug in to_datetime which would raise ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True when called with cache=True, with arg including datetime strings with different offset
> Timedelta
* Bug in TimedeltaIndex.intersection where for non-monotonic indices in some cases an empty Index was returned when in fact an intersection existed
* Bug with comparisons between Timedelta and NaT raising TypeError
* Bug when adding or subtracting a BusinessHour to a Timestamp with the resulting time landing in a following or prior day respectively
* Bug when comparing a TimedeltaIndex against a zero-dimensional numpy array
> Timezones
* Bug in DatetimeIndex.to_frame where timezone aware data would be converted to timezone naive data
* Bug in to_datetime with utc=True and datetime strings that would apply previously parsed UTC offsets to subsequent arguments
* Bug in Timestamp.tz_localize and Timestamp.tz_convert does not propagate freq
* Bug in Series.at where setting Timestamp with timezone raises TypeError
* Bug in DataFrame.update when updating with timezone aware data would return timezone naive data
* Bug in to_datetime where an uninformative RuntimeError was raised when passing a naive Timestamp with datetime strings with mixed UTC offsets
* Bug in to_datetime with unit='ns' would drop timezone information from the parsed argument
* Bug in DataFrame.join where joining a timezone aware index with a timezone aware column would result in a column of NaN
* Bug in date_range where ambiguous or nonexistent start or end times were not handled by the ambiguous or nonexistent keywords respectively
* Bug in DatetimeIndex.union when combining a timezone aware and timezone unaware DatetimeIndex
* Bug when applying a numpy reduction function (e.g. numpy.minimum) to a timezone aware Series
> Numeric
* Bug in to_numeric in which large negative numbers were being improperly handled
* Bug in to_numeric in which numbers were being coerced to float, even though errors was not coerce
* Bug in to_numeric in which invalid values for errors were being allowed
* Bug in format in which floating point complex numbers were not being formatted to proper display precision and trimming
* Bug in error messages in DataFrame.corr and Series.corr. Added the possibility of using a callable.
* Bug in Series.divmod and Series.rdivmod which would raise an (incorrect) ValueError rather than return a pair of Series objects as result
* Raises a helpful exception when a non-numeric index is sent to interpolate with methods which require numeric index.
* Bug in ~pandas.eval when comparing floats with scalar operators, for example: x < -0.1
* Fixed bug where casting all-boolean array to integer extension array failed
* Bug in divmod with a Series object containing zeros incorrectly raising AttributeError
* Inconsistency in Series floor-division (//) and divmod filling positive//zero with NaN instead of Inf
> Conversion
* Bug in DataFrame.astype() when passing a dict of columns and types the errors parameter was ignored.
> Strings
* Bug in the __name__ attribute of several methods of Series.str, which were set incorrectly
* Improved error message when passing Series of wrong dtype to Series.str.cat
> Interval
* Construction of Interval is restricted to numeric, Timestamp and Timedelta endpoints
* Fixed bug in Series/DataFrame not displaying NaN in IntervalIndex with missing values
* Bug in IntervalIndex.get_loc where a KeyError would be incorrectly raised for a decreasing IntervalIndex
* Bug in Index constructor where passing mixed closed Interval objects would result in a ValueError instead of an object dtype Index
> Indexing
* Improved exception message when calling DataFrame.iloc with a list of non-numeric objects .
* Improved exception message when calling .iloc or .loc with a boolean indexer with different length .
* Bug in KeyError exception message when indexing a MultiIndex with a non-existant key not displaying the original key .
* Bug in .iloc and .loc with a boolean indexer not raising an IndexError when too few items are passed .
* Bug in DataFrame.loc and Series.loc where KeyError was not raised for a MultiIndex when the key was less than or equal to the number of levels in the MultiIndex .
* Bug in which DataFrame.append produced an erroneous warning indicating that a KeyError will be thrown in the future when the data to be appended contains new columns .
* Bug in which DataFrame.to_csv caused a segfault for a reindexed data frame, when the indices were single-level MultiIndex .
* Fixed bug where assigning a arrays.PandasArray to a pandas.core.frame.DataFrame would raise error
* Allow keyword arguments for callable local reference used in the DataFrame.query string
* Fixed a KeyError when indexing a MultiIndex` level with a list containing exactly one label, which is missing
* Bug which produced AttributeError on partial matching Timestamp in a MultiIndex
* Bug in Categorical and CategoricalIndex with Interval values when using the in operator (__contains) with objects that are not comparable to the values in the Interval
* Bug in DataFrame.loc and DataFrame.iloc on a DataFrame with a single timezone-aware datetime64[ns] column incorrectly returning a scalar instead of a Series
* Bug in CategoricalIndex and Categorical incorrectly raising ValueError instead of TypeError when a list is passed using the in operator (__contains__)
* Bug in setting a new value in a Series with a Timedelta object incorrectly casting the value to an integer
* Bug in Series setting a new key (__setitem__) with a timezone-aware datetime incorrectly raising ValueError
* Bug in DataFrame.iloc when indexing with a read-only indexer
* Bug in Series setting an existing tuple key (__setitem__) with timezone-aware datetime values incorrectly raising TypeError
> Missing
* Fixed misleading exception message in Series.interpolate if argument order is required, but omitted .
* Fixed class type displayed in exception message in DataFrame.dropna if invalid axis parameter passed
* A ValueError will now be thrown by DataFrame.fillna when limit is not a positive integer
> MultiIndex
* Bug in which incorrect exception raised by Timedelta when testing the membership of MultiIndex
> I/O
* Bug in DataFrame.to_html() where values were truncated using display options instead of outputting the full content
* Fixed bug in missing text when using to_clipboard if copying utf-16 characters in Python 3 on Windows
* Bug in read_json for orient='table' when it tries to infer dtypes by default, which is not applicable as dtypes are already defined in the JSON schema
* Bug in read_json for orient='table' and float index, as it infers index dtype by default, which is not applicable because index dtype is already defined in the JSON schema
* Bug in read_json for orient='table' and string of float column names, as it makes a column name type conversion to Timestamp, which is not applicable because column names are already defined in the JSON schema
* Bug in json_normalize for errors='ignore' where missing values in the input data, were filled in resulting DataFrame with the string "nan" instead of numpy.nan
* DataFrame.to_html now raises TypeError when using an invalid type for the classes parameter instead of AssertionError
* Bug in DataFrame.to_string and DataFrame.to_latex that would lead to incorrect output when the header keyword is used
* Bug in read_csv not properly interpreting the UTF8 encoded filenames on Windows on Python 3.6+
* Improved performance in pandas.read_stata and pandas.io.stata.StataReader when converting columns that have missing values
* Bug in DataFrame.to_html where header numbers would ignore display options when rounding
* Bug in read_hdf where reading a table from an HDF5 file written directly with PyTables fails with a ValueError when using a sub-selection via the start or stop arguments
* Bug in read_hdf not properly closing store after a KeyError is raised
* Improved the explanation for the failure when value labels are repeated in Stata dta files and suggested work-arounds
* Improved pandas.read_stata and pandas.io.stata.StataReader to read incorrectly formatted 118 format files saved by Stata
* Improved the col_space parameter in DataFrame.to_html to accept a string so CSS length values can be set correctly
* Fixed bug in loading objects from S3 that contain # characters in the URL
* Adds use_bqstorage_api parameter to read_gbq to speed up downloads of large data frames. This feature requires version 0.10.0 of the pandas-gbq library as well as the google-cloud-bigquery-storage and fastavro libraries.
* Fixed memory leak in DataFrame.to_json when dealing with numeric data
* Bug in read_json where date strings with Z were not converted to a UTC timezone
* Added cache_dates=True parameter to read_csv, which allows to cache unique dates when they are parsed
* DataFrame.to_excel now raises a ValueError when the caller's dimensions exceed the limitations of Excel
* Fixed bug in pandas.read_csv where a BOM would result in incorrect parsing using engine='python'
* read_excel now raises a ValueError when input is of type pandas.io.excel.ExcelFile and engine param is passed since pandas.io.excel.ExcelFile has an engine defined
* Bug while selecting from HDFStore with where='' specified .
* Fixed bug in DataFrame.to_excel() where custom objects (i.e. PeriodIndex) inside merged cells were not being converted into types safe for the Excel writer
* Bug in read_hdf where reading a timezone aware DatetimeIndex would raise a TypeError
* Bug in to_msgpack and read_msgpack which would raise a ValueError rather than a FileNotFoundError for an invalid path
* Fixed bug in DataFrame.to_parquet which would raise a ValueError when the dataframe had no columns
* Allow parsing of PeriodDtype columns when using read_csv
> Plotting
* Fixed bug where api.extensions.ExtensionArray could not be used in matplotlib plotting
* Bug in an error message in DataFrame.plot. Improved the error message if non-numerics are passed to DataFrame.plot
* Bug in incorrect ticklabel positions when plotting an index that are non-numeric / non-datetime
* Fixed bug causing plots of PeriodIndex timeseries to fail if the frequency is a multiple of the frequency rule code
* Fixed bug when plotting a DatetimeIndex with datetime.timezone.utc timezone
> Groupby/resample/rolling
* Bug in pandas.core.resample.Resampler.agg with a timezone aware index where OverflowError would raise when passing a list of functions
* Bug in pandas.core.groupby.DataFrameGroupBy.nunique in which the names of column levels were lost
* Bug in pandas.core.groupby.GroupBy.agg when applying an aggregation function to timezone aware data
* Bug in pandas.core.groupby.GroupBy.first and pandas.core.groupby.GroupBy.last where timezone information would be dropped
* Bug in pandas.core.groupby.GroupBy.size when grouping only NA values
* Bug in Series.groupby where observed kwarg was previously ignored
* Bug in Series.groupby where using groupby with a MultiIndex Series with a list of labels equal to the length of the series caused incorrect grouping
* Ensured that ordering of outputs in groupby aggregation functions is consistent across all versions of Python
* Ensured that result group order is correct when grouping on an ordered Categorical and specifying observed=True
* Bug in pandas.core.window.Rolling.min and pandas.core.window.Rolling.max that caused a memory leak
* Bug in pandas.core.window.Rolling.count and pandas.core.window.Expanding.count was previously ignoring the axis keyword
* Bug in pandas.core.groupby.GroupBy.idxmax and pandas.core.groupby.GroupBy.idxmin with datetime column would return incorrect dtype
* Bug in pandas.core.groupby.GroupBy.cumsum, pandas.core.groupby.GroupBy.cumprod, pandas.core.groupby.GroupBy.cummin and pandas.core.groupby.GroupBy.cummax with categorical column having absent categories, would return incorrect result or segfault
* Bug in pandas.core.groupby.GroupBy.nth where NA values in the grouping would return incorrect results
* Bug in pandas.core.groupby.SeriesGroupBy.transform where transforming an empty group would raise a ValueError
* Bug in pandas.core.frame.DataFrame.groupby where passing a pandas.core.groupby.grouper.Grouper would return incorrect groups when using the .groups accessor
* Bug in pandas.core.groupby.GroupBy.agg where incorrect results are returned for uint64 columns.
* Bug in pandas.core.window.Rolling.median and pandas.core.window.Rolling.quantile where MemoryError is raised with empty window
* Bug in pandas.core.window.Rolling.median and pandas.core.window.Rolling.quantile where incorrect results are returned with closed='left' and closed='neither'
* Improved pandas.core.window.Rolling, pandas.core.window.Window and pandas.core.window.EWM functions to exclude nuisance columns from results instead of raising errors and raise a DataError only if all columns are nuisance
* Bug in pandas.core.window.Rolling.max and pandas.core.window.Rolling.min where incorrect results are returned with an empty variable window
* Raise a helpful exception when an unsupported weighted window function is used as an argument of pandas.core.window.Window.aggregate
> Reshaping
* Bug in pandas.merge adds a string of None, if None is assigned in suffixes instead of remain the column name as-is .
* Bug in merge when merging by index name would sometimes result in an incorrectly numbered index (missing index values are now assigned NA)
* to_records now accepts dtypes to its column_dtypes parameter
* Bug in concat where order of OrderedDict (and dict in Python 3.6+) is not respected, when passed in as objs argument
* Bug in pivot_table where columns with NaN values are dropped even if dropna argument is False, when the aggfunc argument contains a list
* Bug in concat where the resulting freq of two DatetimeIndex with the same freq would be dropped .
* Bug in merge where merging with equivalent Categorical dtypes was raising an error
* bug in DataFrame instantiating with a dict of iterators or generators (e.g. pd.DataFrame({'A': reversed(range(3))})) raised an error .
* Bug in DataFrame instantiating with a range (e.g. pd.DataFrame(range(3))) raised an error .
* Bug in DataFrame constructor when passing non-empty tuples would cause a segmentation fault
* Bug in Series.apply failed when the series is a timezone aware DatetimeIndex
* Bug in pandas.cut where large bins could incorrectly raise an error due to an integer overflow
* Bug in DataFrame.sort_index where an error is thrown when a multi-indexed DataFrame is sorted on all levels with the initial level sorted last
* Bug in Series.nlargest treats True as smaller than False
* Bug in DataFrame.pivot_table with a IntervalIndex as pivot index would raise TypeError
* Bug in which DataFrame.from_dict ignored order of OrderedDict when orient='index' .
* Bug in DataFrame.transpose where transposing a DataFrame with a timezone-aware datetime column would incorrectly raise ValueError
* Bug in pivot_table when pivoting a timezone aware column as the values would remove timezone information
* Bug in merge_asof when specifying multiple by columns where one is datetime64[ns, tz] dtype
> Sparse
* Significant speedup in SparseArray initialization that benefits most operations, fixing performance regression introduced in v0.20.0
* Bug in SparseFrame constructor where passing None as the data would cause default_fill_value to be ignored
* Bug in SparseDataFrame when adding a column in which the length of values does not match length of index, AssertionError is raised instead of raising ValueError
* Introduce a better error message in Series.sparse.from_coo so it returns a TypeError for inputs that are not coo matrices
* Bug in numpy.modf on a SparseArray. Now a tuple of SparseArray is returned .
> Build Changes
* Fix install error with PyPy on macOS
> ExtensionArray
* Bug in factorize when passing an ExtensionArray with a custom na_sentinel .
* Series.count miscounts NA values in ExtensionArrays
* Added Series.__array_ufunc__ to better handle NumPy ufuncs applied to Series backed by extension arrays .
* Keyword argument deep has been removed from ExtensionArray.copy
> Other
* Removed unused C functions from vendored UltraJSON implementation
* Allow Index and RangeIndex to be passed to numpy min and max functions
* Use actual class name in repr of empty objects of a Series subclass .
* Bug in DataFrame where passing an object array of timezone-aware datetime objects would incorrectly raise ValueError
- Remove upstream-included pandas-tests-memory.patch
OBS-URL: https://build.opensuse.org/request/show/717704
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=17
- Add requirement for at least 4 GB of physical memory
- Do not delete tests, they are used even by other inheriting packages
for their testing
- Execute tests
- Update to 0.24.1
* The default ``sort`` value for :meth:`Index.union` has changed from ``True`` to ``None`` (:issue:`24959`).
The default *behavior*, however, remains the same
* Fixed regression in :meth:`DataFrame.to_dict` with ``records`` orient raising an
``AttributeError`` when the ``DataFrame`` contained more than 255 columns, or
wrongly converting column names that were not valid python identifiers (:issue:`24939`, :issue:`24940`).
* Fixed regression in :func:`read_sql` when passing certain queries with MySQL/pymysql (:issue:`24988`).
* Fixed regression in :class:`Index.intersection` incorrectly sorting the values by default (:issue:`24959`).
* Fixed regression in :func:`merge` when merging an empty ``DataFrame`` with multiple timezone-aware columns on one of the timezone-aware columns (:issue:`25014`).
* Fixed regression in :meth:`Series.rename_axis` and :meth:`DataFrame.rename_axis` where passing ``None`` failed to remove the axis name (:issue:`25034`)
* Fixed regression in :func:`to_timedelta` with `box=False` incorrectly returning a ``datetime64`` object instead of a ``timedelta64`` object (:issue:`24961`)
* Fixed regression where custom hashable types could not be used as column keys in :meth:`DataFrame.set_index` (:issue:`24969`)
* Bug in :meth:`DataFrame.groupby` with :class:`Grouper` when there is a time change (DST) and grouping frequency is ``'1d'`` (:issue:`24972`)
* Fixed the warning for implicitly registered matplotlib converters not showing. See :ref:`whatsnew_0211.converters` for more (:issue:`24963`).
* Fixed AttributeError when printing a DataFrame's HTML repr after accessing the IPython config object (:issue:`25036`)
- Update to 0.24.0
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
OBS-URL: https://build.opensuse.org/request/show/677956
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-pandas?expand=0&rev=16
- Update to 0.24.1
* The default ``sort`` value for :meth:`Index.union` has changed from ``True`` to ``None`` (:issue:`24959`).
The default *behavior*, however, remains the same
* Fixed regression in :meth:`DataFrame.to_dict` with ``records`` orient raising an
``AttributeError`` when the ``DataFrame`` contained more than 255 columns, or
wrongly converting column names that were not valid python identifiers (:issue:`24939`, :issue:`24940`).
* Fixed regression in :func:`read_sql` when passing certain queries with MySQL/pymysql (:issue:`24988`).
* Fixed regression in :class:`Index.intersection` incorrectly sorting the values by default (:issue:`24959`).
* Fixed regression in :func:`merge` when merging an empty ``DataFrame`` with multiple timezone-aware columns on one of the timezone-aware columns (:issue:`25014`).
* Fixed regression in :meth:`Series.rename_axis` and :meth:`DataFrame.rename_axis` where passing ``None`` failed to remove the axis name (:issue:`25034`)
* Fixed regression in :func:`to_timedelta` with `box=False` incorrectly returning a ``datetime64`` object instead of a ``timedelta64`` object (:issue:`24961`)
* Fixed regression where custom hashable types could not be used as column keys in :meth:`DataFrame.set_index` (:issue:`24969`)
* Bug in :meth:`DataFrame.groupby` with :class:`Grouper` when there is a time change (DST) and grouping frequency is ``'1d'`` (:issue:`24972`)
* Fixed the warning for implicitly registered matplotlib converters not showing. See :ref:`whatsnew_0211.converters` for more (:issue:`24963`).
* Fixed AttributeError when printing a DataFrame's HTML repr after accessing the IPython config object (:issue:`25036`)
OBS-URL: https://build.opensuse.org/request/show/672192
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-pandas?expand=0&rev=10
- Update to 0.23.0:
* 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.
- Do not bother generating pandas doc if it is already in both
html and pdf provided by upstream, just point to the URL
OBS-URL: https://build.opensuse.org/request/show/610084
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-pandas?expand=0&rev=10
# PATCH-FIX-UPSTREAM pandas-pr59353-np2eval.patch -- gh#pandas-dev/pandas#59144 backported to 2.2, no new tests, see gh#pandas-dev/pandas#58548, gh#pandas-dev/pandas#59353
# https://github.com/pandas-dev/pandas/pull/55901, not gonna merge this huge patch to fix one test failing with new timezone, will be included in new release
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