Merge branch 'main' into fix/dataframe-query

Author: miguelcsx

.github/workflows/package-checks.yml

@@ -67,7 +67,7 @@ jobs:
           fetch-depth: 0
 
       - name: Set up Python
-        uses: mamba-org/setup-micromamba@v1
+        uses: mamba-org/setup-micromamba@v2
         with:
           environment-name: recipe-test
           create-args: >-

.github/workflows/wheels.yml

@@ -165,7 +165,7 @@ jobs:
           CIBW_PLATFORM: ${{ matrix.buildplat[1] == 'pyodide_wasm32' && 'pyodide' || 'auto' }}
 
       - name: Set up Python
-        uses: mamba-org/setup-micromamba@v1
+        uses: mamba-org/setup-micromamba@v2
         with:
           environment-name: wheel-env
           # Use a fixed Python, since we might have an unreleased Python not

ci/code_checks.sh

@@ -96,7 +96,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.dt.tz_localize PR01,PR02" \
         -i "pandas.Series.dt.unit GL08" \
         -i "pandas.Series.pad PR01,SA01" \
-        -i "pandas.Series.sparse.from_coo PR07,SA01" \
         -i "pandas.Timedelta.max PR02" \
         -i "pandas.Timedelta.min PR02" \
         -i "pandas.Timedelta.resolution PR02" \
@@ -106,13 +105,11 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.Timestamp.year GL08" \
-        -i "pandas.api.types.is_float PR01,SA01" \
         -i "pandas.api.types.is_integer PR01,SA01" \
         -i "pandas.api.types.is_iterator PR07,SA01" \
         -i "pandas.api.types.is_re_compilable PR07,SA01" \
         -i "pandas.api.types.pandas_dtype PR07,RT03,SA01" \
         -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
-        -i "pandas.arrays.DatetimeArray SA01" \
         -i "pandas.arrays.IntegerArray SA01" \
         -i "pandas.arrays.IntervalArray.left SA01" \
         -i "pandas.arrays.IntervalArray.length SA01" \
@@ -163,7 +160,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.errors.DuplicateLabelError SA01" \
         -i "pandas.errors.IntCastingNaNError SA01" \
         -i "pandas.errors.InvalidIndexError SA01" \
-        -i "pandas.errors.InvalidVersion SA01" \
         -i "pandas.errors.NullFrequencyError SA01" \
         -i "pandas.errors.NumExprClobberingError SA01" \
         -i "pandas.errors.NumbaUtilError SA01" \
@@ -172,24 +168,18 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.errors.PerformanceWarning SA01" \
         -i "pandas.errors.PossibleDataLossError SA01" \
         -i "pandas.errors.PossiblePrecisionLoss SA01" \
-        -i "pandas.errors.SpecificationError SA01" \
         -i "pandas.errors.UndefinedVariableError PR01,SA01" \
         -i "pandas.errors.UnsortedIndexError SA01" \
         -i "pandas.errors.UnsupportedFunctionCall SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
         -i "pandas.infer_freq SA01" \
         -i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
-        -i "pandas.io.stata.StataReader.data_label SA01" \
-        -i "pandas.io.stata.StataReader.value_labels RT03,SA01" \
         -i "pandas.io.stata.StataReader.variable_labels RT03,SA01" \
         -i "pandas.io.stata.StataWriter.write_file SA01" \
         -i "pandas.json_normalize RT03,SA01" \
-        -i "pandas.period_range RT03,SA01" \
         -i "pandas.plotting.andrews_curves RT03,SA01" \
-        -i "pandas.plotting.lag_plot RT03,SA01" \
         -i "pandas.plotting.scatter_matrix PR07,SA01" \
         -i "pandas.set_eng_float_format RT03,SA01" \
-        -i "pandas.testing.assert_extension_array_equal SA01" \
         -i "pandas.tseries.offsets.BDay PR02,SA01" \
         -i "pandas.tseries.offsets.BQuarterBegin.is_on_offset GL08" \
         -i "pandas.tseries.offsets.BQuarterBegin.n GL08" \

doc/source/whatsnew/v3.0.0.rst

@@ -203,6 +203,67 @@ In cases with mixed-resolution inputs, the highest resolution is used:
     In [2]: pd.to_datetime([pd.Timestamp("2024-03-22 11:43:01"), "2024-03-22 11:43:01.002"]).dtype
     Out[2]: dtype('<M8[ns]')
 
+.. _whatsnew_300.api_breaking.value_counts_sorting:
+
+Changed behavior in :meth:`DataFrame.value_counts` and :meth:`DataFrameGroupBy.value_counts` when ``sort=False``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In previous versions of pandas, :meth:`DataFrame.value_counts` with ``sort=False`` would sort the result by row labels (as was documented). This was nonintuitive and inconsistent with :meth:`Series.value_counts` which would maintain the order of the input. Now :meth:`DataFrame.value_counts` will maintain the order of the input.
+
+.. ipython:: python
+
+    df = pd.DataFrame(
+        {
+            "a": [2, 2, 2, 2, 1, 1, 1, 1],
+            "b": [2, 1, 3, 1, 2, 3, 1, 1],
+        }
+    )
+    df
+
+*Old behavior*
+
+.. code-block:: ipython
+
+    In [3]: df.value_counts(sort=False)
+    Out[3]:
+    a  b
+    1  1    2
+       2    1
+       3    1
+    2  1    2
+       2    1
+       3    1
+    Name: count, dtype: int64
+
+*New behavior*
+
+.. ipython:: python
+
+    df.value_counts(sort=False)
+
+This change also applies to :meth:`.DataFrameGroupBy.value_counts`. Here, there are two options for sorting: one ``sort`` passed to :meth:`DataFrame.groupby` and one passed directly to :meth:`.DataFrameGroupBy.value_counts`. The former will determine whether to sort the groups, the latter whether to sort the counts. All non-grouping columns will maintain the order of the input *within groups*.
+
+*Old behavior*
+
+.. code-block:: ipython
+
+    In [5]: df.groupby("a", sort=True).value_counts(sort=False)
+    Out[5]:
+    a  b
+    1  1    2
+       2    1
+       3    1
+    2  1    2
+       2    1
+       3    1
+    dtype: int64
+
+*New behavior*
+
+.. ipython:: python
+
+    df.groupby("a", sort=True).value_counts(sort=False)
+
 .. _whatsnew_300.api_breaking.deps:
 
 Increased minimum version for Python
@@ -701,6 +762,7 @@ Other
 - Bug in :func:`eval` on :class:`ExtensionArray` on including division ``/`` failed with a ``TypeError``. (:issue:`58748`)
 - Bug in :func:`eval` where the names of the :class:`Series` were not preserved when using ``engine="numexpr"``. (:issue:`10239`)
 - Bug in :func:`eval` with ``engine="numexpr"`` returning unexpected result for float division. (:issue:`59736`)
+- Bug in :func:`to_numeric` raising ``TypeError`` when ``arg`` is a :class:`Timedelta` or :class:`Timestamp` scalar. (:issue:`59944`)
 - Bug in :func:`unique` on :class:`Index` not always returning :class:`Index` (:issue:`57043`)
 - Bug in :meth:`DataFrame.apply` where passing ``engine="numba"`` ignored ``args`` passed to the applied function (:issue:`58712`)
 - Bug in :meth:`DataFrame.eval` and :meth:`DataFrame.query` which caused an exception when using NumPy attributes via ``@`` notation, e.g., ``df.eval("@np.floor(a)")``. (:issue:`58041`)

pandas/_libs/lib.pyx

@@ -1089,9 +1089,23 @@ def is_float(obj: object) -> bool:
     """
     Return True if given object is float.
 
+    This method checks whether the passed object is a float type. It
+    returns `True` if the object is a float, and `False` otherwise.
+
+    Parameters
+    ----------
+    obj : object
+        The object to check for float type.
+
     Returns
     -------
     bool
+        `True` if the object is of float type, otherwise `False`.
+
+    See Also
+    --------
+    api.types.is_integer : Check if an object is of integer type.
+    api.types.is_numeric_dtype : Check if an object is of numeric type.
 
     Examples
     --------

pandas/_libs/tslibs/nattype.pyi

@@ -9,6 +9,7 @@ from typing import (
     Literal,
     NoReturn,
     TypeAlias,
+    overload,
 )
 
 import numpy as np
@@ -159,15 +160,31 @@ class NaTType:
     # inject Period properties
     @property
     def qyear(self) -> float: ...
+    # comparisons
     def __eq__(self, other: object) -> bool: ...
     def __ne__(self, other: object) -> bool: ...
     __lt__: _NatComparison
     __le__: _NatComparison
     __gt__: _NatComparison
     __ge__: _NatComparison
+    # unary operators
+    def __pos__(self) -> Self: ...
+    def __neg__(self) -> Self: ...
+    # binary operators
     def __sub__(self, other: Self | timedelta | datetime) -> Self: ...
     def __rsub__(self, other: Self | timedelta | datetime) -> Self: ...
     def __add__(self, other: Self | timedelta | datetime) -> Self: ...
     def __radd__(self, other: Self | timedelta | datetime) -> Self: ...
+    def __mul__(self, other: float) -> Self: ...  # analogous to timedelta
+    def __rmul__(self, other: float) -> Self: ...
+    @overload  # analogous to timedelta
+    def __truediv__(self, other: Self | timedelta) -> float: ...  # Literal[NaN]
+    @overload
+    def __truediv__(self, other: float) -> Self: ...
+    @overload  # analogous to timedelta
+    def __floordiv__(self, other: Self | timedelta) -> float: ...  # Literal[NaN]
+    @overload
+    def __floordiv__(self, other: float) -> Self: ...
+    # other
     def __hash__(self) -> int: ...
     def as_unit(self, unit: str, round_ok: bool = ...) -> NaTType: ...

pandas/_testing/asserters.py

@@ -701,6 +701,10 @@ def assert_extension_array_equal(
     """
     Check that left and right ExtensionArrays are equal.
 
+    This method compares two ``ExtensionArray`` instances for equality,
+    including checks for missing values, the dtype of the arrays, and
+    the exactness of the comparison (or tolerance when comparing floats).
+
     Parameters
     ----------
     left, right : ExtensionArray
@@ -726,6 +730,12 @@ def assert_extension_array_equal(
 
         .. versionadded:: 2.0.0
 
+    See Also
+    --------
+    testing.assert_series_equal : Check that left and right ``Series`` are equal.
+    testing.assert_frame_equal : Check that left and right ``DataFrame`` are equal.
+    testing.assert_index_equal : Check that left and right ``Index`` are equal.
+
     Notes
     -----
     Missing values are checked separately from valid values.

pandas/core/arrays/datetimes.py

@@ -205,6 +205,14 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):  # type: ignore[misc]
     -------
     None
 
+    See Also
+    --------
+    DatetimeIndex : Immutable Index for datetime-like data.
+    Series : One-dimensional labeled array capable of holding datetime-like data.
+    Timestamp : Pandas replacement for python datetime.datetime object.
+    to_datetime : Convert argument to datetime.
+    period_range : Return a fixed frequency PeriodIndex.
+
     Examples
     --------
     >>> pd.arrays.DatetimeArray._from_sequence(

pandas/core/arrays/sparse/accessor.py

@@ -88,9 +88,17 @@ def from_coo(cls, A, dense_index: bool = False) -> Series:
         """
         Create a Series with sparse values from a scipy.sparse.coo_matrix.
 
+        This method takes a ``scipy.sparse.coo_matrix`` (coordinate format) as input and
+        returns a pandas ``Series`` where the non-zero elements are represented as
+        sparse values. The index of the Series can either include only the coordinates
+        of non-zero elements (default behavior) or the full sorted set of coordinates
+        from the matrix if ``dense_index`` is set to `True`.
+
         Parameters
         ----------
         A : scipy.sparse.coo_matrix
+            The sparse matrix in coordinate format from which the sparse Series
+            will be created.
         dense_index : bool, default False
             If False (default), the index consists of only the
             coords of the non-null entries of the original coo_matrix.
@@ -102,6 +110,12 @@ def from_coo(cls, A, dense_index: bool = False) -> Series:
         s : Series
             A Series with sparse values.
 
+        See Also
+        --------
+        DataFrame.sparse.from_spmatrix : Create a new DataFrame from a scipy sparse
+            matrix.
+        scipy.sparse.coo_matrix : A sparse matrix in COOrdinate format.
+
         Examples
         --------
         >>> from scipy import sparse

pandas/core/frame.py

@@ -7266,7 +7266,11 @@ def value_counts(
         normalize : bool, default False
             Return proportions rather than frequencies.
         sort : bool, default True
-            Sort by frequencies when True. Sort by DataFrame column values when False.
+            Sort by frequencies when True. Preserve the order of the data when False.
+
+            .. versionchanged:: 3.0.0
+
+                Prior to 3.0.0, ``sort=False`` would sort by the columns values.
         ascending : bool, default False
             Sort in ascending order.
         dropna : bool, default True
@@ -7372,7 +7376,9 @@ def value_counts(
             subset = self.columns.tolist()
 
         name = "proportion" if normalize else "count"
-        counts = self.groupby(subset, dropna=dropna, observed=False)._grouper.size()
+        counts = self.groupby(
+            subset, sort=False, dropna=dropna, observed=False
+        )._grouper.size()
         counts.name = name
 
         if sort: