[pre-commit.ci] pre-commit autoupdate (#54450)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/hauntsaninja/black-pre-commit-mirror: 23.3.0 → 23.7.0](psf/black-pre-commit-mirror@23.3.0...23.7.0)
- [github.com/astral-sh/ruff-pre-commit: v0.0.277 → v0.0.282](astral-sh/ruff-pre-commit@v0.0.277...v0.0.282)
- [github.com/asottile/pyupgrade: v3.7.0 → v3.10.1](asottile/pyupgrade@v3.7.0...v3.10.1)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update black, address ruff

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Matthew Roeschke <[email protected]>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -20,11 +20,11 @@ ci:
 repos:
 -   repo: https://github.com/hauntsaninja/black-pre-commit-mirror
     # black compiled with mypyc
-    rev: 23.3.0
+    rev: 23.7.0
     hooks:
       - id: black
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.277
+    rev: v0.0.282
     hooks:
     -   id: ruff
         args: [--exit-non-zero-on-fix]
@@ -107,7 +107,7 @@ repos:
     hooks:
     -   id: isort
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v3.7.0
+    rev: v3.10.1
     hooks:
     -   id: pyupgrade
         args: [--py39-plus]

pandas/_config/config.py

@@ -160,7 +160,7 @@ def _set_option(*args, **kwargs) -> None:
     silent = kwargs.pop("silent", False)
 
     if kwargs:
-        kwarg = list(kwargs.keys())[0]
+        kwarg = next(iter(kwargs.keys()))
         raise TypeError(f'_set_option() got an unexpected keyword argument "{kwarg}"')
 
     for k, v in zip(args[::2], args[1::2]):

pandas/_libs/lib.pyi

@@ -8,6 +8,7 @@ from typing import (
     Generator,
     Hashable,
     Literal,
+    TypeAlias,
     overload,
 )
 
@@ -31,7 +32,7 @@ class _NoDefault(Enum):
     no_default = ...
 
 no_default: Final = _NoDefault.no_default
-NoDefault = Literal[_NoDefault.no_default]
+NoDefault: TypeAlias = Literal[_NoDefault.no_default]
 
 i8max: int
 u8max: int

pandas/_libs/ops.pyi

@@ -3,15 +3,16 @@ from typing import (
     Callable,
     Iterable,
     Literal,
+    TypeAlias,
     overload,
 )
 
 import numpy as np
 
 from pandas._typing import npt
 
-_BinOp = Callable[[Any, Any], Any]
-_BoolOp = Callable[[Any, Any], bool]
+_BinOp: TypeAlias = Callable[[Any, Any], Any]
+_BoolOp: TypeAlias = Callable[[Any, Any], bool]
 
 def scalar_compare(
     values: np.ndarray,  # object[:]

pandas/_libs/tslibs/nattype.pyi

@@ -3,6 +3,7 @@ from datetime import (
     timedelta,
     tzinfo as _tzinfo,
 )
+import typing
 
 import numpy as np
 
@@ -12,7 +13,9 @@ NaT: NaTType
 iNaT: int
 nat_strings: set[str]
 
-_NaTComparisonTypes = datetime | timedelta | Period | np.datetime64 | np.timedelta64
+_NaTComparisonTypes: typing.TypeAlias = (
+    datetime | timedelta | Period | np.datetime64 | np.timedelta64
+)
 
 class _NatComparison:
     def __call__(self, other: _NaTComparisonTypes) -> bool: ...

pandas/_libs/tslibs/timedeltas.pyi

@@ -2,6 +2,7 @@ from datetime import timedelta
 from typing import (
     ClassVar,
     Literal,
+    TypeAlias,
     TypeVar,
     overload,
 )
@@ -19,7 +20,7 @@ from pandas._typing import (
 
 # This should be kept consistent with the keys in the dict timedelta_abbrevs
 # in pandas/_libs/tslibs/timedeltas.pyx
-UnitChoices = Literal[
+UnitChoices: TypeAlias = Literal[
     "Y",
     "y",
     "M",

pandas/core/base.py

@@ -636,7 +636,7 @@ def to_numpy(
         if isinstance(self.dtype, ExtensionDtype):
             return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
         elif kwargs:
-            bad_keys = list(kwargs.keys())[0]
+            bad_keys = next(iter(kwargs.keys()))
             raise TypeError(
                 f"to_numpy() got an unexpected keyword argument '{bad_keys}'"
             )

pandas/core/frame.py

@@ -1781,7 +1781,7 @@ def from_dict(
         if orient == "index":
             if len(data) > 0:
                 # TODO speed up Series case
-                if isinstance(list(data.values())[0], (Series, dict)):
+                if isinstance(next(iter(data.values())), (Series, dict)):
                     data = _from_nested_dict(data)
                 else:
                     index = list(data.keys())

pandas/core/groupby/generic.py

@@ -1668,7 +1668,7 @@ def _wrap_applied_output_series(
                 # GH6124 - propagate name of Series when it's consistent
                 names = {v.name for v in values}
                 if len(names) == 1:
-                    columns.name = list(names)[0]
+                    columns.name = next(iter(names))
         else:
             index = first_not_none.index
             columns = key_index

pandas/core/indexes/base.py

@@ -4788,7 +4788,7 @@ def _join_multi(self, other: Index, how: JoinHow):
 
             return multi_join_idx, lidx, ridx
 
-        jl = list(overlap)[0]
+        jl = next(iter(overlap))
 
         # Case where only one index is multi
         # make the indices into mi's that match

pandas/core/indexes/period.py

@@ -230,7 +230,7 @@ def __new__(
             refs = data._references
 
         if not set(fields).issubset(valid_field_set):
-            argument = list(set(fields) - valid_field_set)[0]
+            argument = next(iter(set(fields) - valid_field_set))
             raise TypeError(f"__new__() got an unexpected keyword argument {argument}")
 
         name = maybe_extract_name(name, data, cls)

pandas/io/pytables.py

@@ -1380,7 +1380,7 @@ def append_to_multiple(
             )
 
         # figure out the splitting axis (the non_index_axis)
-        axis = list(set(range(value.ndim)) - set(_AXES_MAP[type(value)]))[0]
+        axis = next(iter(set(range(value.ndim)) - set(_AXES_MAP[type(value)])))
 
         # figure out how to split the value
         remain_key = None
@@ -3936,7 +3936,7 @@ def _create_axes(
             nan_rep = "nan"
 
         # We construct the non-index-axis first, since that alters new_info
-        idx = [x for x in [0, 1] if x not in axes][0]
+        idx = next(x for x in [0, 1] if x not in axes)
 
         a = obj.axes[idx]
         # we might be able to change the axes on the appending data if necessary

pandas/tests/arrays/categorical/test_dtypes.py

@@ -120,12 +120,12 @@ def test_codes_dtypes(self):
     def test_iter_python_types(self):
         # GH-19909
         cat = Categorical([1, 2])
-        assert isinstance(list(cat)[0], int)
+        assert isinstance(next(iter(cat)), int)
         assert isinstance(cat.tolist()[0], int)
 
     def test_iter_python_types_datetime(self):
         cat = Categorical([Timestamp("2017-01-01"), Timestamp("2017-01-02")])
-        assert isinstance(list(cat)[0], Timestamp)
+        assert isinstance(next(iter(cat)), Timestamp)
         assert isinstance(cat.tolist()[0], Timestamp)
 
     def test_interval_index_category(self):

pandas/tests/base/test_conversion.py

@@ -102,10 +102,10 @@ def test_iterable_items(self, dtype, rdtype):
         # test if items yields the correct boxed scalars
         # this only applies to series
         s = Series([1], dtype=dtype)
-        _, result = list(s.items())[0]
+        _, result = next(iter(s.items()))
         assert isinstance(result, rdtype)
 
-        _, result = list(s.items())[0]
+        _, result = next(iter(s.items()))
         assert isinstance(result, rdtype)
 
     @pytest.mark.parametrize(

pandas/tests/groupby/test_grouping.py

@@ -845,7 +845,7 @@ def test_get_group(self):
         )
 
         g = df.groupby("DATE")
-        key = list(g.groups)[0]
+        key = next(iter(g.groups))
         result1 = g.get_group(key)
         result2 = g.get_group(Timestamp(key).to_pydatetime())
         result3 = g.get_group(str(Timestamp(key)))
@@ -854,7 +854,7 @@ def test_get_group(self):
 
         g = df.groupby(["DATE", "label"])
 
-        key = list(g.groups)[0]
+        key = next(iter(g.groups))
         result1 = g.get_group(key)
         result2 = g.get_group((Timestamp(key[0]).to_pydatetime(), key[1]))
         result3 = g.get_group((str(Timestamp(key[0])), key[1]))
@@ -916,8 +916,8 @@ def test_get_group_grouped_by_tuple_with_lambda(self):
         gb = df.groupby("Tuples")
         gb_lambda = df.groupby(lambda x: df.iloc[x, 0])
 
-        expected = gb.get_group(list(gb.groups.keys())[0])
-        result = gb_lambda.get_group(list(gb_lambda.groups.keys())[0])
+        expected = gb.get_group(next(iter(gb.groups.keys())))
+        result = gb_lambda.get_group(next(iter(gb_lambda.groups.keys())))
 
         tm.assert_frame_equal(result, expected)
 

pandas/tests/groupby/test_timegrouper.py

@@ -504,7 +504,7 @@ def test_groupby_groups_datetimeindex(self):
 
         # it works!
         groups = grouped.groups
-        assert isinstance(list(groups.keys())[0], datetime)
+        assert isinstance(next(iter(groups.keys())), datetime)
 
         # GH#11442
         index = date_range("2015/01/01", periods=5, name="date")

pandas/tests/indexes/datetimes/test_datetime.py

@@ -135,7 +135,7 @@ def test_iteration_over_chunksize(self, periods):
     def test_misc_coverage(self):
         rng = date_range("1/1/2000", periods=5)
         result = rng.groupby(rng.day)
-        assert isinstance(list(result.values())[0][0], Timestamp)
+        assert isinstance(next(iter(result.values()))[0], Timestamp)
 
     def test_groupby_function_tuple_1677(self):
         df = DataFrame(

pandas/tests/indexes/timedeltas/test_timedelta.py

@@ -22,7 +22,7 @@ def index(self):
     def test_misc_coverage(self):
         rng = timedelta_range("1 day", periods=5)
         result = rng.groupby(rng.days)
-        assert isinstance(list(result.values())[0][0], Timedelta)
+        assert isinstance(next(iter(result.values()))[0], Timedelta)
 
     def test_map(self):
         # test_map_dictlike generally tests

pandas/tests/io/excel/test_readers.py

@@ -160,7 +160,7 @@ def test_engine_kwargs(self, read_ext, engine):
             "ods": {"foo": "abcd"},
         }
 
-        if read_ext[1:] == "xls" or read_ext[1:] == "xlsb":
+        if read_ext[1:] in {"xls", "xlsb"}:
             msg = re.escape(r"open_workbook() got an unexpected keyword argument 'foo'")
         elif read_ext[1:] == "ods":
             msg = re.escape(r"load() got an unexpected keyword argument 'foo'")

pandas/tests/plotting/frame/test_frame.py

@@ -1866,7 +1866,7 @@ def test_errorbar_scatter(self):
     def test_errorbar_scatter_color(self):
         def _check_errorbar_color(containers, expected, has_err="has_xerr"):
             lines = []
-            errs = [c.lines for c in ax.containers if getattr(c, has_err, False)][0]
+            errs = next(c.lines for c in ax.containers if getattr(c, has_err, False))
             for el in errs:
                 if is_list_like(el):
                     lines.extend(el)

pandas/tests/plotting/test_datetimelike.py

@@ -52,7 +52,7 @@ def test_ts_plot_with_tz(self, tz_aware_fixture):
         ts = Series([188.5, 328.25], index=index)
         _check_plot_works(ts.plot)
         ax = ts.plot()
-        xdata = list(ax.get_lines())[0].get_xdata()
+        xdata = next(iter(ax.get_lines())).get_xdata()
         # Check first and last points' labels are correct
         assert (xdata[0].hour, xdata[0].minute) == (0, 0)
         assert (xdata[-1].hour, xdata[-1].minute) == (1, 0)

pandas/tests/reshape/concat/test_append_common.py

@@ -205,12 +205,10 @@ def test_concatlike_dtypes_coercion(self, item, item2, request):
             exp_series_dtype = typ1
             mark = pytest.mark.xfail(reason="GH#39187 casting to object")
             request.node.add_marker(mark)
-        elif (
-            typ1 == "datetime64[ns, US/Eastern]"
-            or typ2 == "datetime64[ns, US/Eastern]"
-            or typ1 == "timedelta64[ns]"
-            or typ2 == "timedelta64[ns]"
-        ):
+        elif typ1 in {"datetime64[ns, US/Eastern]", "timedelta64[ns]"} or typ2 in {
+            "datetime64[ns, US/Eastern]",
+            "timedelta64[ns]",
+        }:
             exp_index_dtype = object
             exp_series_dtype = object
 

pandas/tests/series/test_reductions.py

@@ -84,7 +84,7 @@ def test_prod_numpy16_bug():
 @pytest.mark.parametrize("kwargs", [{"keepdims": True}, {"out": object()}])
 def test_validate_any_all_out_keepdims_raises(kwargs, func):
     ser = Series([1, 2])
-    param = list(kwargs)[0]
+    param = next(iter(kwargs))
     name = func.__name__
 
     msg = (

pandas/util/_validators.py

@@ -134,7 +134,7 @@ def _check_for_invalid_keys(fname, kwargs, compat_args):
     diff = set(kwargs) - set(compat_args)
 
     if diff:
-        bad_arg = list(diff)[0]
+        bad_arg = next(iter(diff))
         raise TypeError(f"{fname}() got an unexpected keyword argument '{bad_arg}'")
 
 

pyproject.toml

@@ -185,7 +185,7 @@ environment = {CFLAGS="-g0"}
 
 [tool.black]
 target-version = ['py39', 'py310']
-required-version = '23.3.0'
+required-version = '23.7.0'
 exclude = '''
 (
     asv_bench/env