Merge branch 'pandas-dev:main' into main

Author: tilovashahrin

.github/workflows/wheels.yml

@@ -140,7 +140,7 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.18.1
+        uses: pypa/cibuildwheel@v2.19.1
         with:
          package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:

ci/code_checks.sh

@@ -91,7 +91,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.MultiIndex.to_frame RT03" \
         -i "pandas.NA SA01" \
         -i "pandas.NaT SA01" \
-        -i "pandas.NamedAgg SA01" \
         -i "pandas.Period.asfreq SA01" \
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.freqstr SA01" \
@@ -211,7 +210,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.to_frame SA01" \
         -i "pandas.Series.to_markdown SA01" \
         -i "pandas.Series.update PR07,SA01" \
-        -i "pandas.Timedelta.as_unit SA01" \
         -i "pandas.Timedelta.asm8 SA01" \
         -i "pandas.Timedelta.ceil SA01" \
         -i "pandas.Timedelta.components SA01" \
@@ -464,7 +462,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -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.merge_asof PR07,RT03" \
         -i "pandas.period_range RT03,SA01" \
         -i "pandas.plotting.andrews_curves RT03,SA01" \
         -i "pandas.plotting.lag_plot RT03,SA01" \

doc/source/user_guide/basics.rst

@@ -1606,7 +1606,7 @@ For instance:
 This method does not convert the row to a Series object; it merely
 returns the values inside a namedtuple. Therefore,
 :meth:`~DataFrame.itertuples` preserves the data type of the values
-and is generally faster as :meth:`~DataFrame.iterrows`.
+and is generally faster than :meth:`~DataFrame.iterrows`.
 
 .. note::
 

doc/source/user_guide/io.rst

@@ -3003,7 +3003,7 @@ However, if XPath does not reference node names such as default, ``/*``, then
 .. note::
 
    Since ``xpath`` identifies the parent of content to be parsed, only immediate
-   desendants which include child nodes or current attributes are parsed.
+   descendants which include child nodes or current attributes are parsed.
    Therefore, ``read_xml`` will not parse the text of grandchildren or other
    descendants and will not parse attributes of any descendant. To retrieve
    lower level content, adjust xpath to lower level. For example,
@@ -3535,7 +3535,7 @@ For example, to read in a ``MultiIndex`` index without names:
    df = pd.read_excel("path_to_file.xlsx", index_col=[0, 1])
    df
 
-If the index has level names, they will parsed as well, using the same
+If the index has level names, they will be parsed as well, using the same
 parameters.
 
 .. ipython:: python
@@ -5847,10 +5847,10 @@ You can check if a table exists using :func:`~pandas.io.sql.has_table`
 Schema support
 ''''''''''''''
 
-Reading from and writing to different schema's is supported through the ``schema``
+Reading from and writing to different schemas is supported through the ``schema``
 keyword in the :func:`~pandas.read_sql_table` and :func:`~pandas.DataFrame.to_sql`
 functions. Note however that this depends on the database flavor (sqlite does not
-have schema's). For example:
+have schemas). For example:
 
 .. code-block:: python
 

doc/source/user_guide/missing_data.rst

@@ -319,7 +319,7 @@ Missing values propagate through arithmetic operations between pandas objects.
 
 The descriptive statistics and computational methods discussed in the
 :ref:`data structure overview <basics.stats>` (and listed :ref:`here
-<api.series.stats>` and :ref:`here <api.dataframe.stats>`) are all
+<api.series.stats>` and :ref:`here <api.dataframe.stats>`) all
 account for missing data.
 
 When summing data, NA values or empty data will be treated as zero.

doc/source/user_guide/options.rst

@@ -8,7 +8,7 @@ Options and settings
 
 Overview
 --------
-pandas has an options API configure and customize global behavior related to
+pandas has an options API to configure and customize global behavior related to
 :class:`DataFrame` display, data behavior and more.
 
 Options have a full "dotted-style", case-insensitive name (e.g. ``display.max_rows``).

doc/source/user_guide/timeseries.rst

@@ -1479,7 +1479,7 @@ or some other non-observed day.  Defined observance rules are:
     "after_nearest_workday", "apply ``nearest_workday`` and then move to next workday after that day"
     "sunday_to_monday", "move Sunday to following Monday"
     "next_monday_or_tuesday", "move Saturday to Monday and Sunday/Monday to Tuesday"
-    "previous_friday", move Saturday and Sunday to previous Friday"
+    "previous_friday", "move Saturday and Sunday to previous Friday"
     "next_monday", "move Saturday and Sunday to following Monday"
     "weekend_to_monday", "same as ``next_monday``"
 

doc/source/whatsnew/v3.0.0.rst

@@ -503,8 +503,8 @@ Timezones
 
 Numeric
 ^^^^^^^
+- Bug in :meth:`DataFrame.quantile` where the column type was not preserved when ``numeric_only=True`` with a list-like ``q`` produced an empty result (:issue:`59035`)
 - Bug in ``np.matmul`` with :class:`Index` inputs raising a ``TypeError`` (:issue:`57079`)
--
 
 Conversion
 ^^^^^^^^^^
@@ -546,6 +546,7 @@ I/O
 - Bug in :meth:`DataFrame.to_excel` when writing empty :class:`DataFrame` with :class:`MultiIndex` on both axes (:issue:`57696`)
 - Bug in :meth:`DataFrame.to_stata` when writing :class:`DataFrame` and ``byteorder=`big```. (:issue:`58969`)
 - Bug in :meth:`DataFrame.to_string` that raised ``StopIteration`` with nested DataFrames. (:issue:`16098`)
+- Bug in :meth:`HDFStore.get` was failing to save data of dtype datetime64[s] correctly (:issue:`59004`)
 - 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_stata` raising ``KeyError`` when input file is stored in big-endian format and contains strL data. (:issue:`58638`)
 
@@ -608,6 +609,7 @@ Other
 - Bug in :meth:`DataFrame.where` where using a non-bool type array in the function would return a ``ValueError`` instead of a ``TypeError`` (:issue:`56330`)
 - Bug in :meth:`Index.sort_values` when passing a key function that turns values into tuples, e.g. ``key=natsort.natsort_key``, would raise ``TypeError`` (:issue:`56081`)
 - Bug in :meth:`Series.diff` allowing non-integer values for the ``periods`` argument. (:issue:`56607`)
+- 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 Dataframe Interchange Protocol implementation was returning incorrect results for data buffers' associated dtype, for string and datetime columns (:issue:`54781`)

pandas/_libs/groupby.pyx

@@ -398,11 +398,12 @@ def group_cumsum(
         for i in range(N):
             lab = labels[i]
 
-            if lab < 0:
+            if uses_mask and lab < 0:
                 # GH#58811
-                if uses_mask:
-                    result_mask[i, :] = True
-                    out[i, :] = 0
+                result_mask[i, :] = True
+                out[i, :] = 0
+                continue
+            elif lab < 0:
                 continue
 
             for j in range(K):

pandas/_libs/tslibs/timedeltas.pyx

@@ -1731,6 +1731,12 @@ cdef class _Timedelta(timedelta):
         -------
         Timedelta
 
+        See Also
+        --------
+        Timedelta : Represents a duration, the difference between two dates or times.
+        to_timedelta : Convert argument to timedelta.
+        Timedelta.asm8 : Return a numpy timedelta64 array scalar view.
+
         Examples
         --------
         >>> td = pd.Timedelta('1001ms')