Merge branch 'pandas-dev:main' into series-sum-attrs

Author: fbourgey

.pre-commit-config.yaml

@@ -19,7 +19,7 @@ ci:
     skip: [pyright, mypy]
 repos:
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.6
+    rev: v0.9.4
     hooks:
     -   id: ruff
         args: [--exit-non-zero-on-fix]
@@ -41,7 +41,7 @@ repos:
         pass_filenames: true
         require_serial: false
 -   repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.1
     hooks:
     -   id: codespell
         types_or: [python, rst, markdown, cython, c]
@@ -70,7 +70,7 @@ repos:
     -   id: trailing-whitespace
         args: [--markdown-linebreak-ext=md]
 -   repo: https://github.com/PyCQA/isort
-    rev: 5.13.2
+    rev: 6.0.0
     hooks:
     -   id: isort
 -   repo: https://github.com/asottile/pyupgrade
@@ -95,14 +95,14 @@ repos:
     - id: sphinx-lint
       args: ["--enable", "all", "--disable", "line-too-long"]
 -   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v19.1.6
+    rev: v19.1.7
     hooks:
     - id: clang-format
       files: ^pandas/_libs/src|^pandas/_libs/include
       args: [-i]
       types_or: [c, c++]
 -   repo: https://github.com/trim21/pre-commit-mirror-meson
-    rev: v1.6.1
+    rev: v1.7.0
     hooks:
     - id: meson-fmt
       args: ['--inplace']

asv_bench/benchmarks/io/style.py

@@ -13,8 +13,8 @@ class Render:
     def setup(self, cols, rows):
         self.df = DataFrame(
             np.random.randn(rows, cols),
-            columns=[f"float_{i+1}" for i in range(cols)],
-            index=[f"row_{i+1}" for i in range(rows)],
+            columns=[f"float_{i + 1}" for i in range(cols)],
+            index=[f"row_{i + 1}" for i in range(rows)],
         )
 
     def time_apply_render(self, cols, rows):

doc/make.py

@@ -260,8 +260,7 @@ def latex(self, force=False):
                 for i in range(3):
                     self._run_os("pdflatex", "-interaction=nonstopmode", "pandas.tex")
                 raise SystemExit(
-                    "You should check the file "
-                    '"build/latex/pandas.pdf" for problems.'
+                    'You should check the file "build/latex/pandas.pdf" for problems.'
                 )
             self._run_os("make")
             return ret_code
@@ -343,8 +342,7 @@ def main():
         dest="verbosity",
         default=0,
         help=(
-            "increase verbosity (can be repeated), "
-            "passed to the sphinx build command"
+            "increase verbosity (can be repeated), passed to the sphinx build command"
         ),
     )
     argparser.add_argument(

doc/source/user_guide/cookbook.rst

@@ -874,7 +874,7 @@ Timeseries
 <https://stackoverflow.com/questions/13893227/vectorized-look-up-of-values-in-pandas-dataframe>`__
 
 `Aggregation and plotting time series
-<https://nipunbatra.github.io/blog/visualisation/2013/05/01/aggregation-timeseries.html>`__
+<https://nipunbatra.github.io/blog/posts/2013-05-01-aggregation-timeseries.html>`__
 
 Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series.
 `How to rearrange a Python pandas DataFrame?
@@ -1043,7 +1043,7 @@ CSV
 
 The :ref:`CSV <io.read_csv_table>` docs
 
-`read_csv in action <https://wesmckinney.com/blog/update-on-upcoming-pandas-v0-10-new-file-parser-other-performance-wins/>`__
+`read_csv in action <https://www.datacamp.com/tutorial/pandas-read-csv>`__
 
 `appending to a csv
 <https://stackoverflow.com/questions/17134942/pandas-dataframe-output-end-of-csv>`__

doc/source/user_guide/enhancingperf.rst

@@ -427,7 +427,7 @@ prefer that Numba throw an error if it cannot compile a function in a way that
 speeds up your code, pass Numba the argument
 ``nopython=True`` (e.g.  ``@jit(nopython=True)``). For more on
 troubleshooting Numba modes, see the `Numba troubleshooting page
-<https://numba.pydata.org/numba-doc/latest/user/troubleshoot.html#the-compiled-code-is-too-slow>`__.
+<https://numba.readthedocs.io/en/stable/user/troubleshoot.html>`__.
 
 Using ``parallel=True`` (e.g. ``@jit(parallel=True)``) may result in a ``SIGABRT`` if the threading layer leads to unsafe
 behavior. You can first `specify a safe threading layer <https://numba.readthedocs.io/en/stable/user/threading-layer.html#selecting-a-threading-layer-for-safe-parallel-execution>`__

doc/source/user_guide/groupby.rst

@@ -418,7 +418,7 @@ You can also include the grouping columns if you want to operate on them.
 
 .. note::
 
-   The ``groupby`` operation in Pandas drops the ``name`` field of the columns Index object
+   The ``groupby`` operation in pandas drops the ``name`` field of the columns Index object
    after the operation. This change ensures consistency in syntax between different
    column selection methods within groupby operations.
 

doc/source/user_guide/io.rst

@@ -990,7 +990,7 @@ Thousand separators
 
 For large numbers that have been written with a thousands separator, you can
 set the ``thousands`` keyword to a string of length 1 so that integers will be parsed
-correctly:
+correctly.
 
 By default, numbers with a thousands separator will be parsed as strings:
 

doc/source/user_guide/merging.rst

@@ -586,7 +586,7 @@ A string argument to ``indicator`` will use the value as the name for the indica
 Overlapping value columns
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The merge ``suffixes`` argument takes a tuple of list of strings to append to
+The merge ``suffixes`` argument takes a tuple or list of strings to append to
 overlapping column names in the input :class:`DataFrame` to disambiguate the result
 columns:
 
@@ -979,7 +979,7 @@ nearest key rather than equal keys. For each row in the ``left`` :class:`DataFra
 the last row in the ``right`` :class:`DataFrame` are selected where the ``on`` key is less
 than the left's key. Both :class:`DataFrame` must be sorted by the key.
 
-Optionally an :func:`merge_asof` can perform a group-wise merge by matching the
+Optionally :func:`merge_asof` can perform a group-wise merge by matching the
 ``by`` key in addition to the nearest match on the ``on`` key.
 
 .. ipython:: python

doc/source/user_guide/pyarrow.rst

@@ -22,7 +22,7 @@ Data Structure Integration
 
 A :class:`Series`, :class:`Index`, or the columns of a :class:`DataFrame` can be directly backed by a :external+pyarrow:py:class:`pyarrow.ChunkedArray`
 which is similar to a NumPy array. To construct these from the main pandas data structures, you can pass in a string of the type followed by
-``[pyarrow]``, e.g. ``"int64[pyarrow]""`` into the ``dtype`` parameter
+``[pyarrow]``, e.g. ``"int64[pyarrow]"`` into the ``dtype`` parameter
 
 .. ipython:: python
 

doc/source/user_guide/scale.rst

@@ -5,7 +5,7 @@ Scaling to large datasets
 *************************
 
 pandas provides data structures for in-memory analytics, which makes using pandas
-to analyze datasets that are larger than memory datasets somewhat tricky. Even datasets
+to analyze datasets that are larger than memory somewhat tricky. Even datasets
 that are a sizable fraction of memory become unwieldy, as some pandas operations need
 to make intermediate copies.