Merge branch 'main' into 60237

Author: road-1

.github/workflows/wheels.yml

@@ -152,7 +152,7 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.21.3
+        uses: pypa/cibuildwheel@v2.22.0
         with:
          package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:

ci/code_checks.sh

@@ -73,7 +73,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
         -i "pandas.RangeIndex.from_range PR01,SA01" \
-        -i "pandas.Series.dt.freq GL08" \
         -i "pandas.Series.dt.unit GL08" \
         -i "pandas.Series.pad PR01,SA01" \
         -i "pandas.Timedelta.max PR02" \
@@ -83,28 +82,20 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.min PR02" \
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
-        -i "pandas.api.types.is_re_compilable PR07,SA01" \
         -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
         -i "pandas.arrays.IntegerArray SA01" \
         -i "pandas.arrays.IntervalArray.length SA01" \
         -i "pandas.arrays.NumpyExtensionArray SA01" \
         -i "pandas.arrays.TimedeltaArray PR07,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.indices SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.nth PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.indices SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
         -i "pandas.core.resample.Resampler.get_group RT03,SA01" \
-        -i "pandas.core.resample.Resampler.indices SA01" \
         -i "pandas.core.resample.Resampler.max PR01,RT03,SA01" \
         -i "pandas.core.resample.Resampler.mean SA01" \
         -i "pandas.core.resample.Resampler.min PR01,RT03,SA01" \
@@ -114,19 +105,11 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.resample.Resampler.std SA01" \
         -i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
         -i "pandas.core.resample.Resampler.var SA01" \
-        -i "pandas.errors.AttributeConflictWarning SA01" \
-        -i "pandas.errors.ChainedAssignmentError SA01" \
-        -i "pandas.errors.DuplicateLabelError SA01" \
         -i "pandas.errors.IntCastingNaNError SA01" \
-        -i "pandas.errors.InvalidIndexError SA01" \
         -i "pandas.errors.NullFrequencyError SA01" \
-        -i "pandas.errors.NumExprClobberingError SA01" \
         -i "pandas.errors.NumbaUtilError SA01" \
-        -i "pandas.errors.OutOfBoundsTimedelta SA01" \
         -i "pandas.errors.PerformanceWarning SA01" \
-        -i "pandas.errors.PossibleDataLossError SA01" \
         -i "pandas.errors.UndefinedVariableError PR01,SA01" \
-        -i "pandas.errors.UnsortedIndexError SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
         -i "pandas.infer_freq SA01" \
         -i "pandas.io.json.build_table_schema PR07,RT03,SA01" \

doc/source/conf.py

@@ -242,7 +242,6 @@
     "external_links": [],
     "footer_start": ["pandas_footer", "sphinx-version"],
     "github_url": "https://github.com/pandas-dev/pandas",
-    "twitter_url": "https://twitter.com/pandas_dev",
     "analytics": {
         "plausible_analytics_domain": "pandas.pydata.org",
         "plausible_analytics_url": "https://views.scientific-python.org/js/script.js",
@@ -258,6 +257,11 @@
     # patch version doesn't compare as equal (e.g. 2.2.1 != 2.2.0 but it should be)
     "show_version_warning_banner": False,
     "icon_links": [
+        {
+            "name": "X",
+            "url": "https://x.com/pandas_dev",
+            "icon": "fa-brands fa-square-x-twitter",
+        },
         {
             "name": "Mastodon",
             "url": "https://fosstodon.org/@pandas_dev",

doc/source/user_guide/reshaping.rst

@@ -321,7 +321,7 @@ The missing value can be filled with a specific value with the ``fill_value`` ar
 .. image:: ../_static/reshaping_melt.png
 
 The top-level :func:`~pandas.melt` function and the corresponding :meth:`DataFrame.melt`
-are useful to massage a :class:`DataFrame` into a format where one or more columns
+are useful to reshape a :class:`DataFrame` into a format where one or more columns
 are *identifier variables*, while all other columns, considered *measured
 variables*, are "unpivoted" to the row axis, leaving just two non-identifier
 columns, "variable" and "value". The names of those columns can be customized

doc/source/user_guide/window.rst

@@ -567,9 +567,9 @@ One must have :math:`0 < \alpha \leq 1`, and while it is possible to pass
 
    \alpha =
     \begin{cases}
-        \frac{2}{s + 1},               & \text{for span}\ s \geq 1\\
-        \frac{1}{1 + c},               & \text{for center of mass}\ c \geq 0\\
-        1 - \exp^{\frac{\log 0.5}{h}}, & \text{for half-life}\ h > 0
+        \frac{2}{s + 1},            & \text{for span}\ s \geq 1\\
+        \frac{1}{1 + c},            & \text{for center of mass}\ c \geq 0\\
+        1 - e^{\frac{\log 0.5}{h}}, & \text{for half-life}\ h > 0
     \end{cases}
 
 One must specify precisely one of **span**, **center of mass**, **half-life**

doc/source/whatsnew/v3.0.0.rst

@@ -54,6 +54,7 @@ Other enhancements
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)
+- :func:`read_parquet` accepts ``to_pandas_kwargs`` which are forwarded to :meth:`pyarrow.Table.to_pandas` which enables passing additional keywords to customize the conversion to pandas, such as ``maps_as_pydicts`` to read the Parquet map data type as python dictionaries (:issue:`56842`)
 - :meth:`DataFrameGroupBy.transform`, :meth:`SeriesGroupBy.transform`, :meth:`DataFrameGroupBy.agg`, :meth:`SeriesGroupBy.agg`, :meth:`RollingGroupby.apply`, :meth:`ExpandingGroupby.apply`, :meth:`Rolling.apply`, :meth:`Expanding.apply`, :meth:`DataFrame.apply` with ``engine="numba"`` now supports positional arguments passed as kwargs (:issue:`58995`)
 - :meth:`Series.map` can now accept kwargs to pass on to func (:issue:`59814`)
 - :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
@@ -667,7 +668,7 @@ Indexing
 ^^^^^^^^
 - Bug in :meth:`DataFrame.__getitem__` returning modified columns when called with ``slice`` in Python 3.12 (:issue:`57500`)
 - Bug in :meth:`DataFrame.from_records` throwing a ``ValueError`` when passed an empty list in ``index`` (:issue:`58594`)
--
+- Bug in printing :attr:`Index.names` and :attr:`MultiIndex.levels` would not escape single quotes (:issue:`60190`)
 
 Missing
 ^^^^^^^
@@ -690,6 +691,7 @@ I/O
 - Bug in :meth:`DataFrame.from_records` where ``columns`` parameter with numpy structured array was not reordering and filtering out the columns (:issue:`59717`)
 - Bug in :meth:`DataFrame.to_dict` raises unnecessary ``UserWarning`` when columns are not unique and ``orient='tight'``. (:issue:`58281`)
 - Bug in :meth:`DataFrame.to_excel` when writing empty :class:`DataFrame` with :class:`MultiIndex` on both axes (:issue:`57696`)
+- Bug in :meth:`DataFrame.to_excel` where the :class:`MultiIndex` index with a period level was not a date (:issue:`60099`)
 - Bug in :meth:`DataFrame.to_stata` when writing :class:`DataFrame` and ``byteorder=`big```. (:issue:`58969`)
 - Bug in :meth:`DataFrame.to_stata` when writing more than 32,000 value labels. (:issue:`60107`)
 - Bug in :meth:`DataFrame.to_string` that raised ``StopIteration`` with nested DataFrames. (:issue:`16098`)
@@ -760,11 +762,12 @@ ExtensionArray
 - Bug in :meth:`.arrays.ArrowExtensionArray.__setitem__` which caused wrong behavior when using an integer array with repeated values as a key (:issue:`58530`)
 - Bug in :meth:`api.types.is_datetime64_any_dtype` where a custom :class:`ExtensionDtype` would return ``False`` for array-likes (:issue:`57055`)
 - Bug in comparison between object with :class:`ArrowDtype` and incompatible-dtyped (e.g. string vs bool) incorrectly raising instead of returning all-``False`` (for ``==``) or all-``True`` (for ``!=``) (:issue:`59505`)
+- Bug in constructing pandas data structures when passing into ``dtype`` a string of the type followed by ``[pyarrow]`` while PyArrow is not installed would raise ``NameError`` rather than ``ImportError`` (:issue:`57928`)
 - Bug in various :class:`DataFrame` reductions for pyarrow temporal dtypes returning incorrect dtype when result was null (:issue:`59234`)
 
 Styler
 ^^^^^^
--
+- Bug in :meth:`Styler.to_latex` where styling column headers when combined with a hidden index or hidden index-levels is fixed.
 
 Other
 ^^^^^
@@ -788,6 +791,7 @@ Other
 - Bug in :meth:`Series.dt` methods in :class:`ArrowDtype` that were returning incorrect values. (:issue:`57355`)
 - Bug in :meth:`Series.rank` that doesn't preserve missing values for nullable integers when ``na_option='keep'``. (:issue:`56976`)
 - Bug in :meth:`Series.replace` and :meth:`DataFrame.replace` inconsistently replacing matching instances when ``regex=True`` and missing values are present. (:issue:`56599`)
+- Bug in :meth:`Series.to_string` when series contains complex floats with exponents (:issue:`60405`)
 - Bug in :meth:`read_csv` where chained fsspec TAR file and ``compression="infer"`` fails with ``tarfile.ReadError`` (:issue:`60028`)
 - Bug in Dataframe Interchange Protocol implementation was returning incorrect results for data buffers' associated dtype, for string and datetime columns (:issue:`54781`)
 - Bug in ``Series.list`` methods not preserving the original :class:`Index`. (:issue:`58425`)

pandas/_libs/index.pyi

@@ -72,6 +72,9 @@ class MaskedUInt16Engine(MaskedIndexEngine): ...
 class MaskedUInt8Engine(MaskedIndexEngine): ...
 class MaskedBoolEngine(MaskedUInt8Engine): ...
 
+class StringObjectEngine(ObjectEngine):
+    def __init__(self, values: object, na_value) -> None: ...
+
 class BaseMultiIndexCodesEngine:
     levels: list[np.ndarray]
     offsets: np.ndarray  # np.ndarray[..., ndim=1]

pandas/_libs/index.pyx

@@ -557,6 +557,31 @@ cdef class StringEngine(IndexEngine):
             raise KeyError(val)
         return str(val)
 
+cdef class StringObjectEngine(ObjectEngine):
+
+    cdef:
+        object na_value
+        bint uses_na
+
+    def __init__(self, ndarray values, na_value):
+        super().__init__(values)
+        self.na_value = na_value
+        self.uses_na = na_value is C_NA
+
+    cdef bint _checknull(self, object val):
+        if self.uses_na:
+            return val is C_NA
+        else:
+            return util.is_nan(val)
+
+    cdef _check_type(self, object val):
+        if isinstance(val, str):
+            return val
+        elif self._checknull(val):
+            return self.na_value
+        else:
+            raise KeyError(val)
+
 
 cdef class DatetimeEngine(Int64Engine):
 

pandas/_libs/tslibs/np_datetime.pyx

@@ -201,6 +201,10 @@ class OutOfBoundsTimedelta(ValueError):
 
     Representation should be within a timedelta64[ns].
 
+    See Also
+    --------
+    date_range : Return a fixed frequency DatetimeIndex.
+
     Examples
     --------
     >>> pd.date_range(start="1/1/1700", freq="B", periods=100000)

pandas/core/arrays/interval.py

@@ -1055,7 +1055,9 @@ def shift(self, periods: int = 1, fill_value: object = None) -> IntervalArray:
             from pandas import Index
 
             fill_value = Index(self._left, copy=False)._na_value
-            empty = IntervalArray.from_breaks([fill_value] * (empty_len + 1))
+            empty = IntervalArray.from_breaks(
+                [fill_value] * (empty_len + 1), closed=self.closed
+            )
         else:
             empty = self._from_sequence([fill_value] * empty_len, dtype=self.dtype)