[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -20,11 +20,11 @@ ci:
 repos:
 -   repo: https://github.com/hauntsaninja/black-pre-commit-mirror
     # black compiled with mypyc
-    rev: 23.9.1
+    rev: 23.10.1
     hooks:
       - id: black
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.291
+    rev: v0.1.4
     hooks:
     -   id: ruff
         args: [--exit-non-zero-on-fix]
@@ -34,14 +34,14 @@ repos:
         alias: ruff-selected-autofixes
         args: [--select, "ANN001,ANN204", --fix-only, --exit-non-zero-on-fix]
 -   repo: https://github.com/jendrikseipp/vulture
-    rev: 'v2.9.1'
+    rev: 'v2.10'
     hooks:
       - id: vulture
         entry: python scripts/run_vulture.py
         pass_filenames: true
         require_serial: false
 -   repo: https://github.com/codespell-project/codespell
-    rev: v2.2.5
+    rev: v2.2.6
     hooks:
     -   id: codespell
         types_or: [python, rst, markdown, cython, c]
@@ -52,7 +52,7 @@ repos:
     -   id: cython-lint
     -   id: double-quote-cython-strings
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
     -   id: check-ast
     -   id: check-case-conflict
@@ -71,7 +71,7 @@ repos:
         args: [--remove]
     -   id: trailing-whitespace
 -   repo: https://github.com/pylint-dev/pylint
-    rev: v3.0.0b0
+    rev: v3.0.1
     hooks:
     -   id: pylint
         stages: [manual]
@@ -94,7 +94,7 @@ repos:
     hooks:
     -   id: isort
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v3.13.0
+    rev: v3.15.0
     hooks:
     -   id: pyupgrade
         args: [--py39-plus]
@@ -111,11 +111,11 @@ repos:
         types: [text]  # overwrite types: [rst]
         types_or: [python, rst]
 -   repo: https://github.com/sphinx-contrib/sphinx-lint
-    rev: v0.6.8
+    rev: v0.8.1
     hooks:
     - id: sphinx-lint
 -   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: ea59a72
+    rev: v17.0.4
     hooks:
     - id: clang-format
       files: ^pandas/_libs/src|^pandas/_libs/include

doc/source/development/debugging_extensions.rst

@@ -23,7 +23,7 @@ By default building pandas from source will generate a release build. To generat
 
 .. note::
 
-   conda environements update CFLAGS/CPPFLAGS with flags that are geared towards generating releases. If using conda, you may need to set ``CFLAGS="$CFLAGS -O0"`` and ``CPPFLAGS="$CPPFLAGS -O0"`` to ensure optimizations are turned off for debugging
+   conda environments update CFLAGS/CPPFLAGS with flags that are geared towards generating releases. If using conda, you may need to set ``CFLAGS="$CFLAGS -O0"`` and ``CPPFLAGS="$CPPFLAGS -O0"`` to ensure optimizations are turned off for debugging
 
 By specifying ``builddir="debug"`` all of the targets will be built and placed in the debug directory relative to the project root. This helps to keep your debug and release artifacts separate; you are of course able to choose a different directory name or omit altogether if you do not care to separate build types.
 

doc/source/development/extending.rst

@@ -99,7 +99,7 @@ The interface consists of two classes.
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 A :class:`pandas.api.extensions.ExtensionDtype` is similar to a ``numpy.dtype`` object. It describes the
-data type. Implementors are responsible for a few unique items like the name.
+data type. Implementers are responsible for a few unique items like the name.
 
 One particularly important item is the ``type`` property. This should be the
 class that is the scalar type for your data. For example, if you were writing an

doc/source/getting_started/tutorials.rst

@@ -115,7 +115,6 @@ Various tutorials
 * `Statistical Data Analysis in Python, tutorial videos, by Christopher Fonnesbeck from SciPy 2013 <https://conference.scipy.org/scipy2013/tutorial_detail.php?id=109>`_
 * `Financial analysis in Python, by Thomas Wiecki <https://nbviewer.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb>`_
 * `Intro to pandas data structures, by Greg Reda <http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/>`_
-* `Pandas and Python: Top 10, by Manish Amde <https://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/>`_
 * `Pandas DataFrames Tutorial, by Karlijn Willems <https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python>`_
 * `A concise tutorial with real life examples <https://tutswiki.com/pandas-cookbook/chapter1/>`_
 * `430+ Searchable Pandas recipes by Isshin Inada <https://skytowner.com/explore/pandas_recipes_reference>`_

doc/source/user_guide/merging.rst

@@ -525,7 +525,7 @@ Performing an outer join with duplicate join keys in :class:`DataFrame`
 
 .. warning::
 
-  Merging on duplicate keys sigificantly increase the dimensions of the result
+  Merging on duplicate keys significantly increase the dimensions of the result
   and can cause a memory overflow.
 
 .. _merging.validation:

doc/source/user_guide/reshaping.rst

@@ -480,7 +480,7 @@ The values can be cast to a different type using the ``dtype`` argument.
 
 .. versionadded:: 1.5.0
 
-:func:`~pandas.from_dummies` coverts the output of :func:`~pandas.get_dummies` back into
+:func:`~pandas.from_dummies` converts the output of :func:`~pandas.get_dummies` back into
 a :class:`Series` of categorical values from indicator values.
 
 .. ipython:: python

doc/source/whatsnew/v1.0.0.rst

@@ -1079,7 +1079,7 @@ Datetimelike
 - Bug in masking datetime-like arrays with a boolean mask of an incorrect length not raising an ``IndexError`` (:issue:`30308`)
 - Bug in :attr:`Timestamp.resolution` being a property instead of a class attribute (:issue:`29910`)
 - Bug in :func:`pandas.to_datetime` when called with ``None`` raising ``TypeError`` instead of returning ``NaT`` (:issue:`30011`)
-- Bug in :func:`pandas.to_datetime` failing for ``deques`` when using ``cache=True`` (the default) (:issue:`29403`)
+- Bug in :func:`pandas.to_datetime` failing for ``dequeues`` when using ``cache=True`` (the default) (:issue:`29403`)
 - Bug in :meth:`Series.item` with ``datetime64`` or ``timedelta64`` dtype, :meth:`DatetimeIndex.item`, and :meth:`TimedeltaIndex.item` returning an integer instead of a :class:`Timestamp` or :class:`Timedelta` (:issue:`30175`)
 - Bug in :class:`DatetimeIndex` addition when adding a non-optimized :class:`DateOffset` incorrectly dropping timezone information (:issue:`30336`)
 - Bug in :meth:`DataFrame.drop` where attempting to drop non-existent values from a DatetimeIndex would yield a confusing error message (:issue:`30399`)

pandas/__init__.py

@@ -24,7 +24,7 @@
 try:
     # numpy compat
     from pandas.compat import (
-        is_numpy_dev as _is_numpy_dev,  # pyright: ignore[reportUnusedImport] # noqa: F401,E501
+        is_numpy_dev as _is_numpy_dev,  # pyright: ignore[reportUnusedImport] # noqa: F401
     )
 except ImportError as _err:  # pragma: no cover
     _module = _err.name

pandas/_libs/__init__.py

@@ -13,8 +13,8 @@
 # Below imports needs to happen first to ensure pandas top level
 # module gets monkeypatched with the pandas_datetime_CAPI
 # see pandas_datetime_exec in pd_datetime.c
-import pandas._libs.pandas_parser  # noqa: E501 # isort: skip # type: ignore[reportUnusedImport]
-import pandas._libs.pandas_datetime  # noqa: F401,E501 # isort: skip # type: ignore[reportUnusedImport]
+import pandas._libs.pandas_parser  # isort: skip # type: ignore[reportUnusedImport]
+import pandas._libs.pandas_datetime  # noqa: F401 # isort: skip # type: ignore[reportUnusedImport]
 from pandas._libs.interval import Interval
 from pandas._libs.tslibs import (
     NaT,

pandas/_libs/include/pandas/portable.h

@@ -21,5 +21,5 @@ The full license is in the LICENSE file, distributed with this software.
 #define getdigit_ascii(c, default)                                             \
   (isdigit_ascii(c) ? ((int)((c) - '0')) : default)
 #define isspace_ascii(c) (((c) == ' ') || (((unsigned)(c) - '\t') < 5))
-#define toupper_ascii(c) ((((unsigned)(c) - 'a') < 26) ? ((c)&0x5f) : (c))
+#define toupper_ascii(c) ((((unsigned)(c) - 'a') < 26) ? ((c) & 0x5f) : (c))
 #define tolower_ascii(c) ((((unsigned)(c) - 'A') < 26) ? ((c) | 0x20) : (c))

pandas/_libs/include/pandas/vendored/ujson/lib/ultrajson.h

@@ -189,13 +189,13 @@ typedef struct __JSONObjectEncoder {
 
   /*
   Begin iteration of an iterable object (JS_ARRAY or JS_OBJECT)
-  Implementor should setup iteration state in ti->prv
+  Implementer should setup iteration state in ti->prv
   */
   JSPFN_ITERBEGIN iterBegin;
 
   /*
   Retrieve next object in an iteration. Should return 0 to indicate iteration
-  has reached end or 1 if there are more items. Implementor is responsible for
+  has reached end or 1 if there are more items. Implementer is responsible for
   keeping state of the iteration. Use ti->prv fields for this
   */
   JSPFN_ITERNEXT iterNext;

pandas/_libs/src/vendored/ujson/lib/ultrajsonenc.c

@@ -72,7 +72,7 @@ or UTF-16 surrogate pairs
 The extra 2 bytes are for the quotes around the string
 
 */
-#define RESERVE_STRING(_len) (2 + ((_len)*6))
+#define RESERVE_STRING(_len) (2 + ((_len) * 6))
 
 static const double g_pow10[] = {1,
                                  10,

pandas/_libs/tslibs/parsing.pyx

@@ -950,7 +950,7 @@ def guess_datetime_format(dt_str: str, bint dayfirst=False) -> str | None:
             # the offset is separated into two tokens, ex. ['+', '0900’].
             # This separation will prevent subsequent processing
             # from correctly parsing the time zone format.
-            # So in addition to the format nomalization, we rejoin them here.
+            # So in addition to the format normalization, we rejoin them here.
             try:
                 tokens[offset_index] = parsed_datetime.strftime("%z")
             except ValueError:

pandas/_libs/tslibs/timedeltas.pyx

@@ -1229,7 +1229,7 @@ cdef class _Timedelta(timedelta):
             return cmp_scalar(self._value, ots._value, op)
         return self._compare_mismatched_resos(ots, op)
 
-    # TODO: re-use/share with Timestamp
+    # TODO: reuse/share with Timestamp
     cdef bint _compare_mismatched_resos(self, _Timedelta other, op):
         # Can't just dispatch to numpy as they silently overflow and get it wrong
         cdef:

pandas/_testing/__init__.py

@@ -1059,14 +1059,14 @@ def shares_memory(left, right) -> bool:
     if (
         isinstance(left, ExtensionArray)
         and is_string_dtype(left.dtype)
-        and left.dtype.storage in ("pyarrow", "pyarrow_numpy")  # type: ignore[attr-defined]  # noqa: E501
+        and left.dtype.storage in ("pyarrow", "pyarrow_numpy")  # type: ignore[attr-defined]
     ):
         # https://github.com/pandas-dev/pandas/pull/43930#discussion_r736862669
         left = cast("ArrowExtensionArray", left)
         if (
             isinstance(right, ExtensionArray)
             and is_string_dtype(right.dtype)
-            and right.dtype.storage in ("pyarrow", "pyarrow_numpy")  # type: ignore[attr-defined]  # noqa: E501
+            and right.dtype.storage in ("pyarrow", "pyarrow_numpy")  # type: ignore[attr-defined]
         ):
             right = cast("ArrowExtensionArray", right)
             left_pa_data = left._pa_array

pandas/core/arrays/base.py

@@ -455,7 +455,7 @@ def __setitem__(self, key, value) -> None:
         -------
         None
         """
-        # Some notes to the ExtensionArray implementor who may have ended up
+        # Some notes to the ExtensionArray implementer who may have ended up
         # here. While this method is not required for the interface, if you
         # *do* choose to implement __setitem__, then some semantics should be
         # observed:
@@ -775,7 +775,7 @@ def _values_for_argsort(self) -> np.ndarray:
         Notes
         -----
         The caller is responsible for *not* modifying these values in-place, so
-        it is safe for implementors to give views on ``self``.
+        it is safe for implementers to give views on ``self``.
 
         Functions that use this (e.g. ``ExtensionArray.argsort``) should ignore
         entries with missing values in the original array (according to
@@ -833,7 +833,7 @@ def argsort(
         >>> arr.argsort()
         array([1, 2, 0, 4, 3])
         """
-        # Implementor note: You have two places to override the behavior of
+        # Implementer note: You have two places to override the behavior of
         # argsort.
         # 1. _values_for_argsort : construct the values passed to np.argsort
         # 2. argsort : total control over sorting. In case of overriding this,
@@ -874,7 +874,7 @@ def argmin(self, skipna: bool = True) -> int:
         >>> arr.argmin()
         1
         """
-        # Implementor note: You have two places to override the behavior of
+        # Implementer note: You have two places to override the behavior of
         # argmin.
         # 1. _values_for_argsort : construct the values used in nargminmax
         # 2. argmin itself : total control over sorting.
@@ -908,7 +908,7 @@ def argmax(self, skipna: bool = True) -> int:
         >>> arr.argmax()
         3
         """
-        # Implementor note: You have two places to override the behavior of
+        # Implementer note: You have two places to override the behavior of
         # argmax.
         # 1. _values_for_argsort : construct the values used in nargminmax
         # 2. argmax itself : total control over sorting.

pandas/core/arrays/datetimes.py

@@ -559,7 +559,7 @@ def _box_func(self, x: np.datetime64) -> Timestamp | NaTType:
     # error: Return type "Union[dtype, DatetimeTZDtype]" of "dtype"
     # incompatible with return type "ExtensionDtype" in supertype
     # "ExtensionArray"
-    def dtype(self) -> np.dtype[np.datetime64] | DatetimeTZDtype:  # type: ignore[override]  # noqa: E501
+    def dtype(self) -> np.dtype[np.datetime64] | DatetimeTZDtype:  # type: ignore[override]
         """
         The dtype for the DatetimeArray.
 

pandas/core/arrays/interval.py

@@ -850,7 +850,7 @@ def argsort(
         ascending = nv.validate_argsort_with_ascending(ascending, (), kwargs)
 
         if ascending and kind == "quicksort" and na_position == "last":
-            # TODO: in an IntervalIndex we can re-use the cached
+            # TODO: in an IntervalIndex we can reuse the cached
             #  IntervalTree.left_sorter
             return np.lexsort((self.right, self.left))
 

pandas/core/arrays/timedeltas.py

@@ -92,7 +92,7 @@ def f(self) -> np.ndarray:
             # error: Incompatible types in assignment (
             # expression has type "ndarray[Any, dtype[signedinteger[_32Bit]]]",
             # variable has type "ndarray[Any, dtype[signedinteger[_64Bit]]]
-            result = get_timedelta_field(values, alias, reso=self._creso)  # type: ignore[assignment]  # noqa: E501
+            result = get_timedelta_field(values, alias, reso=self._creso)  # type: ignore[assignment]
         if self._hasna:
             result = self._maybe_mask_results(
                 result, fill_value=None, convert="float64"

pandas/core/groupby/groupby.py

@@ -962,7 +962,7 @@ def _selected_obj(self):
                 return self.obj[self._selection]
 
             # Otherwise _selection is equivalent to _selection_list, so
-            #  _selected_obj matches _obj_with_exclusions, so we can re-use
+            #  _selected_obj matches _obj_with_exclusions, so we can reuse
             #  that and avoid making a copy.
             return self._obj_with_exclusions
 
@@ -1466,7 +1466,7 @@ def _concat_objects(
             # when the ax has duplicates
             # so we resort to this
             # GH 14776, 30667
-            # TODO: can we re-use e.g. _reindex_non_unique?
+            # TODO: can we reuse e.g. _reindex_non_unique?
             if ax.has_duplicates and not result.axes[self.axis].equals(ax):
                 # e.g. test_category_order_transformer
                 target = algorithms.unique1d(ax._values)
@@ -2864,7 +2864,7 @@ def _value_counts(
             result_series.name = name
             result_series.index = index.set_names(range(len(columns)))
             result_frame = result_series.reset_index()
-            orig_dtype = self.grouper.groupings[0].obj.columns.dtype  # type: ignore[union-attr]  # noqa: E501
+            orig_dtype = self.grouper.groupings[0].obj.columns.dtype  # type: ignore[union-attr]
             cols = Index(columns, dtype=orig_dtype).insert(len(columns), name)
             result_frame.columns = cols
             result = result_frame

pandas/core/indexes/base.py

@@ -5369,7 +5369,7 @@ def _getitem_slice(self, slobj: slice) -> Self:
         result = type(self)._simple_new(res, name=self._name, refs=self._references)
         if "_engine" in self._cache:
             reverse = slobj.step is not None and slobj.step < 0
-            result._engine._update_from_sliced(self._engine, reverse=reverse)  # type: ignore[union-attr]  # noqa: E501
+            result._engine._update_from_sliced(self._engine, reverse=reverse)  # type: ignore[union-attr]
 
         return result
 

pandas/core/indexing.py

@@ -985,7 +985,7 @@ def _getitem_tuple_same_dim(self, tup: tuple):
         This is only called after a failed call to _getitem_lowerdim.
         """
         retval = self.obj
-        # Selecting columns before rows is signficiantly faster
+        # Selecting columns before rows is significantly faster
         start_val = (self.ndim - len(tup)) + 1
         for i, key in enumerate(reversed(tup)):
             i = self.ndim - i - start_val

pandas/core/interchange/dataframe.py

@@ -87,7 +87,7 @@ def select_columns(self, indices: Sequence[int]) -> PandasDataFrameXchg:
             self._df.iloc[:, indices], self._nan_as_null, self._allow_copy
         )
 
-    def select_columns_by_name(self, names: list[str]) -> PandasDataFrameXchg:  # type: ignore[override]  # noqa: E501
+    def select_columns_by_name(self, names: list[str]) -> PandasDataFrameXchg:  # type: ignore[override]
         if not isinstance(names, abc.Sequence):
             raise ValueError("`names` is not a sequence")
         if not isinstance(names, list):

pandas/core/internals/blocks.py

@@ -1415,7 +1415,7 @@ def where(
 
         try:
             # try/except here is equivalent to a self._can_hold_element check,
-            #  but this gets us back 'casted' which we will re-use below;
+            #  but this gets us back 'casted' which we will reuse below;
             #  without using 'casted', expressions.where may do unwanted upcasts.
             casted = np_can_hold_element(values.dtype, other)
         except (ValueError, TypeError, LossySetitemError):
@@ -1786,7 +1786,7 @@ def delete(self, loc) -> list[Block]:
             else:
                 # No overload variant of "__getitem__" of "ExtensionArray" matches
                 # argument type "Tuple[slice, slice]"
-                values = self.values[previous_loc + 1 : idx, :]  # type: ignore[call-overload]  # noqa: E501
+                values = self.values[previous_loc + 1 : idx, :]  # type: ignore[call-overload]
                 locs = mgr_locs_arr[previous_loc + 1 : idx]
                 nb = type(self)(
                     values, placement=BlockPlacement(locs), ndim=self.ndim, refs=refs

pandas/core/internals/construction.py

@@ -550,7 +550,7 @@ def _prep_ndarraylike(values, copy: bool = True) -> np.ndarray:
 
     if len(values) == 0:
         # TODO: check for length-zero range, in which case return int64 dtype?
-        # TODO: re-use anything in try_cast?
+        # TODO: reuse anything in try_cast?
         return np.empty((0, 0), dtype=object)
     elif isinstance(values, range):
         arr = range_to_ndarray(values)

pandas/core/reshape/merge.py

@@ -1390,13 +1390,13 @@ def _maybe_coerce_merge_keys(self) -> None:
                 ):
                     ct = find_common_type([lk.dtype, rk.dtype])
                     if is_extension_array_dtype(ct):
-                        rk = ct.construct_array_type()._from_sequence(rk)  # type: ignore[union-attr]  # noqa: E501
+                        rk = ct.construct_array_type()._from_sequence(rk)  # type: ignore[union-attr]
                     else:
                         rk = rk.astype(ct)  # type: ignore[arg-type]
                 elif is_extension_array_dtype(rk.dtype):
                     ct = find_common_type([lk.dtype, rk.dtype])
                     if is_extension_array_dtype(ct):
-                        lk = ct.construct_array_type()._from_sequence(lk)  # type: ignore[union-attr]  # noqa: E501
+                        lk = ct.construct_array_type()._from_sequence(lk)  # type: ignore[union-attr]
                     else:
                         lk = lk.astype(ct)  # type: ignore[arg-type]
 

pandas/core/reshape/reshape.py

@@ -222,7 +222,7 @@ def mask_all(self) -> bool:
 
     @cache_readonly
     def arange_result(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.bool_]]:
-        # We cache this for re-use in ExtensionBlock._unstack
+        # We cache this for reuse in ExtensionBlock._unstack
         dummy_arr = np.arange(len(self.index), dtype=np.intp)
         new_values, mask = self.get_new_values(dummy_arr, fill_value=-1)
         return new_values, mask.any(0)