Merge remote-tracking branch 'upstream/main' into string-dtype-predicates-nan-propagation

Author: jorisvandenbossche

.github/workflows/unit-tests.yml

@@ -380,7 +380,7 @@ jobs:
           fetch-depth: 0
 
       - name: Set up Python Free-threading Version
-        uses: deadsnakes/action@v3.1.0
+        uses: deadsnakes/action@v3.2.0
         with:
           python-version: 3.13-dev
           nogil: true

ci/code_checks.sh

@@ -70,15 +70,10 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         --format=actions \
         -i ES01 `# For now it is ok if docstrings are missing the extended summary` \
         -i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
-        -i "pandas.NA SA01" \
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
-        -i "pandas.Period.to_timestamp SA01" \
-        -i "pandas.PeriodDtype.freq SA01" \
         -i "pandas.RangeIndex.from_range PR01,SA01" \
-        -i "pandas.RangeIndex.start SA01" \
         -i "pandas.RangeIndex.step SA01" \
-        -i "pandas.RangeIndex.stop SA01" \
         -i "pandas.Series.cat.add_categories PR01,PR02" \
         -i "pandas.Series.cat.as_ordered PR01" \
         -i "pandas.Series.cat.as_unordered PR01" \
@@ -93,10 +88,8 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.dt.floor PR01,PR02" \
         -i "pandas.Series.dt.freq GL08" \
         -i "pandas.Series.dt.month_name PR01,PR02" \
-        -i "pandas.Series.dt.nanoseconds SA01" \
         -i "pandas.Series.dt.normalize PR01" \
         -i "pandas.Series.dt.round PR01,PR02" \
-        -i "pandas.Series.dt.seconds SA01" \
         -i "pandas.Series.dt.strftime PR01,PR02" \
         -i "pandas.Series.dt.to_period PR01,PR02" \
         -i "pandas.Series.dt.total_seconds PR01" \
@@ -108,39 +101,24 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.sparse.from_coo PR07,SA01" \
         -i "pandas.Series.sparse.npoints SA01" \
         -i "pandas.Series.sparse.sp_values SA01" \
-        -i "pandas.Timedelta.components SA01" \
         -i "pandas.Timedelta.max PR02" \
         -i "pandas.Timedelta.min PR02" \
         -i "pandas.Timedelta.resolution PR02" \
         -i "pandas.Timedelta.to_timedelta64 SA01" \
-        -i "pandas.Timedelta.total_seconds SA01" \
-        -i "pandas.Timedelta.view SA01" \
-        -i "pandas.TimedeltaIndex.nanoseconds SA01" \
-        -i "pandas.TimedeltaIndex.seconds SA01" \
         -i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \
         -i "pandas.Timestamp.max PR02" \
         -i "pandas.Timestamp.min PR02" \
         -i "pandas.Timestamp.nanosecond GL08" \
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.Timestamp.year GL08" \
-        -i "pandas.api.extensions.ExtensionArray.interpolate PR01,SA01" \
-        -i "pandas.api.types.is_bool PR01,SA01" \
-        -i "pandas.api.types.is_categorical_dtype SA01" \
-        -i "pandas.api.types.is_complex PR01,SA01" \
-        -i "pandas.api.types.is_complex_dtype SA01" \
-        -i "pandas.api.types.is_datetime64_dtype SA01" \
-        -i "pandas.api.types.is_datetime64_ns_dtype SA01" \
-        -i "pandas.api.types.is_datetime64tz_dtype SA01" \
         -i "pandas.api.types.is_dict_like PR07,SA01" \
-        -i "pandas.api.types.is_extension_array_dtype SA01" \
         -i "pandas.api.types.is_file_like PR07,SA01" \
         -i "pandas.api.types.is_float PR01,SA01" \
         -i "pandas.api.types.is_float_dtype SA01" \
         -i "pandas.api.types.is_hashable PR01,RT03,SA01" \
         -i "pandas.api.types.is_int64_dtype SA01" \
         -i "pandas.api.types.is_integer PR01,SA01" \
-        -i "pandas.api.types.is_integer_dtype SA01" \
         -i "pandas.api.types.is_interval_dtype SA01" \
         -i "pandas.api.types.is_iterator PR07,SA01" \
         -i "pandas.api.types.is_list_like SA01" \
@@ -152,7 +130,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
         -i "pandas.arrays.BooleanArray SA01" \
         -i "pandas.arrays.DatetimeArray SA01" \
-        -i "pandas.arrays.FloatingArray SA01" \
         -i "pandas.arrays.IntegerArray SA01" \
         -i "pandas.arrays.IntervalArray.left SA01" \
         -i "pandas.arrays.IntervalArray.length SA01" \
@@ -165,35 +142,27 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.groupby.DataFrameGroupBy.agg RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.aggregate RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.filter SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.groups SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.hist RT03" \
         -i "pandas.core.groupby.DataFrameGroupBy.indices SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.max SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.min SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.nth PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.sum SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.__iter__ RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.agg RT03" \
         -i "pandas.core.groupby.SeriesGroupBy.aggregate RT03" \
-        -i "pandas.core.groupby.SeriesGroupBy.filter PR01,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.groups 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.max SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.min SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.sum SA01" \
         -i "pandas.core.resample.Resampler.__iter__ RT03,SA01" \
         -i "pandas.core.resample.Resampler.ffill RT03" \
         -i "pandas.core.resample.Resampler.get_group RT03,SA01" \
@@ -222,7 +191,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.errors.IntCastingNaNError SA01" \
         -i "pandas.errors.InvalidIndexError SA01" \
         -i "pandas.errors.InvalidVersion SA01" \
-        -i "pandas.errors.MergeError SA01" \
         -i "pandas.errors.NullFrequencyError SA01" \
         -i "pandas.errors.NumExprClobberingError SA01" \
         -i "pandas.errors.NumbaUtilError SA01" \

doc/source/whatsnew/index.rst

@@ -32,6 +32,7 @@ Version 2.2
 .. toctree::
    :maxdepth: 2
 
+   v2.2.3
    v2.2.2
    v2.2.1
    v2.2.0

doc/source/whatsnew/v2.2.3.rst

@@ -0,0 +1,36 @@
+.. _whatsnew_223:
+
+What's new in 2.2.3 (September XX, 2024)
+----------------------------------------
+
+These are the changes in pandas 2.2.3. See :ref:`release` for a full changelog
+including other versions of pandas.
+
+{{ header }}
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_223.regressions:
+
+Fixed regressions
+~~~~~~~~~~~~~~~~~
+-
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_223.bug_fixes:
+
+Bug fixes
+~~~~~~~~~
+-
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_223.other:
+
+Other
+~~~~~
+-
+
+.. ---------------------------------------------------------------------------
+.. _whatsnew_223.contributors:
+
+Contributors
+~~~~~~~~~~~~

doc/source/whatsnew/v2.3.0.rst

@@ -102,9 +102,11 @@ Conversion
 
 Strings
 ^^^^^^^
+- Bug in :meth:`Series.rank` for :class:`StringDtype` with ``storage="pyarrow"`` incorrectly returning integer results in case of ``method="average"`` and raising an error if it would truncate results (:issue:`59768`)
 - Bug in :meth:`Series.str.replace` when ``n < 0`` for :class:`StringDtype` with ``storage="pyarrow"`` (:issue:`59628`)
+- Bug in ``ser.str.slice`` with negative ``step`` with :class:`ArrowDtype` and :class:`StringDtype` with ``storage="pyarrow"`` giving incorrect results (:issue:`59710`)
 - Bug in the ``center`` method on :class:`Series` and :class:`Index` object ``str`` accessors with pyarrow-backed dtype not matching the python behavior in corner cases with an odd number of fill characters (:issue:`54792`)
-
+-
 
 Interval
 ^^^^^^^^

doc/source/whatsnew/v3.0.0.rst

@@ -55,6 +55,7 @@ Other enhancements
 - :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`)
 - :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
+- :meth:`str.get_dummies` now accepts a  ``dtype`` parameter to specify the dtype of the resulting DataFrame (:issue:`47872`)
 - Multiplying two :class:`DateOffset` objects will now raise a ``TypeError`` instead of a ``RecursionError`` (:issue:`59442`)
 - Restore support for reading Stata 104-format and enable reading 103-format dta files (:issue:`58554`)
 - Support passing a :class:`Iterable[Hashable]` input to :meth:`DataFrame.drop_duplicates` (:issue:`59237`)
@@ -626,6 +627,7 @@ I/O
 - Bug in :meth:`read_csv` causing segmentation fault when ``encoding_errors`` is not a string. (:issue:`59059`)
 - Bug in :meth:`read_csv` raising ``TypeError`` when ``index_col`` is specified and ``na_values`` is a dict containing the key ``None``. (:issue:`57547`)
 - Bug in :meth:`read_csv` raising ``TypeError`` when ``nrows`` and ``iterator`` are specified without specifying a ``chunksize``. (:issue:`59079`)
+- Bug in :meth:`read_csv` where the order of the ``na_values`` makes an inconsistency when ``na_values`` is a list non-string values. (:issue:`59303`)
 - Bug in :meth:`read_excel` raising ``ValueError`` when passing array of boolean values when ``dtype="boolean"``. (:issue:`58159`)
 - Bug in :meth:`read_json` not validating the ``typ`` argument to not be exactly ``"frame"`` or ``"series"`` (:issue:`59124`)
 - Bug in :meth:`read_stata` raising ``KeyError`` when input file is stored in big-endian format and contains strL data. (:issue:`58638`)
@@ -668,6 +670,7 @@ Reshaping
 - Bug in :meth:`DataFrame.join` when a :class:`DataFrame` with a :class:`MultiIndex` would raise an ``AssertionError`` when :attr:`MultiIndex.names` contained ``None``. (:issue:`58721`)
 - Bug in :meth:`DataFrame.merge` where merging on a column containing only ``NaN`` values resulted in an out-of-bounds array access (:issue:`59421`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when ``sort=False`` (:issue:`54987`, :issue:`55516`)
+- Bug in :meth:`DataFrame.pivot_table` incorrectly subaggregating results when called without an ``index`` argument (:issue:`58722`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when manipulating empty :class:`DataFrame` with an :class:`ExtentionDtype` (:issue:`59123`)
 
 Sparse

pandas/_libs/lib.pyx

@@ -600,6 +600,8 @@ def array_equivalent_object(ndarray left, ndarray right) -> bool:
                     if not array_equivalent(x, y):
                         return False
 
+            elif PyArray_Check(x) or PyArray_Check(y):
+                return False
             elif (x is C_NA) ^ (y is C_NA):
                 return False
             elif not (
@@ -733,7 +735,9 @@ cpdef ndarray[object] ensure_string_array(
     convert_na_value : bool, default True
         If False, existing na values will be used unchanged in the new array.
     copy : bool, default True
-        Whether to ensure that a new array is returned.
+        Whether to ensure that a new array is returned. When True, a new array
+        is always returned. When False, a new array is only returned when needed
+        to avoid mutating the input array.
     skipna : bool, default True
         Whether or not to coerce nulls to their stringified form
         (e.g. if False, NaN becomes 'nan').
@@ -762,11 +766,15 @@ cpdef ndarray[object] ensure_string_array(
 
     result = np.asarray(arr, dtype="object")
 
-    if copy and (result is arr or np.shares_memory(arr, result)):
-        # GH#54654
-        result = result.copy()
-    elif not copy and result is arr:
-        already_copied = False
+    if result is arr or np.may_share_memory(arr, result):
+        # if np.asarray(..) did not make a copy of the input arr, we still need
+        #  to do that to avoid mutating the input array
+        # GH#54654: share_memory check is needed for rare cases where np.asarray
+        #  returns a new object without making a copy of the actual data
+        if copy:
+            result = result.copy()
+        else:
+            already_copied = False
     elif not copy and not result.flags.writeable:
         # Weird edge case where result is a view
         already_copied = False
@@ -1123,10 +1131,21 @@ def is_bool(obj: object) -> bool:
     """
     Return True if given object is boolean.
 
+    Parameters
+    ----------
+    obj : object
+        Object to check.
+
     Returns
     -------
     bool
 
+    See Also
+    --------
+    api.types.is_scalar : Check if the input is a scalar.
+    api.types.is_integer : Check if the input is an integer.
+    api.types.is_float : Check if the input is a float.
+
     Examples
     --------
     >>> pd.api.types.is_bool(True)
@@ -1142,10 +1161,22 @@ def is_complex(obj: object) -> bool:
     """
     Return True if given object is complex.
 
+    Parameters
+    ----------
+    obj : object
+        Object to check.
+
     Returns
     -------
     bool
 
+    See Also
+    --------
+    api.types.is_complex_dtype: Check whether the provided array or
+                                dtype is of a complex dtype.
+    api.types.is_number: Check if the object is a number.
+    api.types.is_integer: Return True if given object is integer.
+
     Examples
     --------
     >>> pd.api.types.is_complex(1 + 1j)

pandas/_libs/missing.pyx

@@ -347,6 +347,14 @@ class NAType(C_NAType):
     The NA singleton is a missing value indicator defined by pandas. It is
     used in certain new extension dtypes (currently the "string" dtype).
 
+    See Also
+    --------
+    numpy.nan : Floating point representation of Not a Number (NaN) for numerical data.
+    isna : Detect missing values for an array-like object.
+    notna : Detect non-missing values for an array-like object.
+    DataFrame.fillna : Fill missing values in a DataFrame.
+    Series.fillna : Fill missing values in a Series.
+
     Examples
     --------
     >>> pd.NA

pandas/_libs/tslibs/nattype.pyx

@@ -493,6 +493,14 @@ class NaTType(_NaT):
         """
         Total seconds in the duration.
 
+        This method calculates the total duration in seconds by combining
+        the days, seconds, and microseconds of the `Timedelta` object.
+
+        See Also
+        --------
+        to_timedelta : Convert argument to timedelta.
+        Timedelta : Represents a duration, the difference between two dates or times.
+
         Examples
         --------
         >>> td = pd.Timedelta('1min')

pandas/_libs/tslibs/period.pyx

@@ -2001,6 +2001,12 @@ cdef class _Period(PeriodMixin):
         -------
         Timestamp
 
+        See Also
+        --------
+        Timestamp : A class representing a single point in time.
+        Period : Represents a span of time with a fixed frequency.
+        PeriodIndex.to_timestamp : Convert a `PeriodIndex` to a `DatetimeIndex`.
+
         Examples
         --------
         >>> period = pd.Period('2023-1-1', freq='D')

pandas/_libs/tslibs/timedeltas.pyx

@@ -1189,6 +1189,14 @@ cdef class _Timedelta(timedelta):
         """
         Total seconds in the duration.
 
+        This method calculates the total duration in seconds by combining
+        the days, seconds, and microseconds of the `Timedelta` object.
+
+        See Also
+        --------
+        to_timedelta : Convert argument to timedelta.
+        Timedelta : Represents a duration, the difference between two dates or times.
+
         Examples
         --------
         >>> td = pd.Timedelta('1min')
@@ -1458,11 +1466,26 @@ cdef class _Timedelta(timedelta):
         """
         Array view compatibility.
 
+        This method allows you to reinterpret the underlying data of a Timedelta
+        object as a different dtype. The `view` method provides a way to reinterpret
+        the internal representation of the `Timedelta` object without modifying its
+        data. This is particularly useful when you need to work with the underlying
+        data directly, such as for performance optimizations or interfacing with
+        low-level APIs. The returned value is typically the number of nanoseconds
+        since the epoch, represented as an integer or another specified dtype.
+
         Parameters
         ----------
         dtype : str or dtype
             The dtype to view the underlying data as.
 
+        See Also
+        --------
+        numpy.ndarray.view : Returns a view of an array with the same data.
+        Timedelta.to_numpy : Converts the Timedelta to a NumPy timedelta64.
+        Timedelta.total_seconds : Returns the total duration of the Timedelta
+            object in seconds.
+
         Examples
         --------
         >>> td = pd.Timedelta('3D')
@@ -1478,6 +1501,17 @@ cdef class _Timedelta(timedelta):
         """
         Return a components namedtuple-like.
 
+        Each component represents a different time unit, allowing you to access the
+        breakdown of the total duration in terms of days, hours, minutes, seconds,
+        milliseconds, microseconds, and nanoseconds.
+
+        See Also
+        --------
+        Timedelta.total_seconds : Returns the total duration of the Timedelta in
+            seconds.
+        to_timedelta : Convert argument to Timedelta.
+        Timedelta : Represents a duration, the difference between two dates or times.
+
         Examples
         --------
         >>> td = pd.Timedelta('2 day 4 min 3 us 42 ns')