STYLE: upgrade ruff

.pre-commit-config.yaml

@@ -28,7 +28,7 @@ repos:
         types_or: [python, pyi]
         additional_dependencies: [black==23.1.0]
 -   repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.259
+    rev: v0.0.264
     hooks:
     -   id: ruff
         args: [--exit-non-zero-on-fix]
@@ -40,13 +40,13 @@ repos:
         pass_filenames: true
         require_serial: false
 -   repo: https://github.com/codespell-project/codespell
-    rev: v2.2.2
+    rev: v2.2.4
     hooks:
     -   id: codespell
         types_or: [python, rst, markdown, cython, c]
         additional_dependencies: [tomli]
 -   repo: https://github.com/MarcoGorelli/cython-lint
-    rev: v0.12.5
+    rev: v0.15.0
     hooks:
     -   id: cython-lint
     -   id: double-quote-cython-strings
@@ -104,7 +104,7 @@ repos:
     hooks:
     -   id: isort
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v3.3.1
+    rev: v3.3.2
     hooks:
     -   id: pyupgrade
         args: [--py38-plus]

pandas/_libs/lib.pyi

@@ -30,7 +30,7 @@ from enum import Enum
 class _NoDefault(Enum):
     no_default = ...
 
-no_default: Final = _NoDefault.no_default
+no_default: Final = _NoDefault.no_default  # noqa
 NoDefault = Literal[_NoDefault.no_default]
 
 i8max: int

pandas/core/frame.py

@@ -5139,7 +5139,7 @@ def drop(
 
         Drop columns and/or rows of MultiIndex DataFrame
 
-        >>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
+        >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
         ...                              ['speed', 'weight', 'length']],
         ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
         ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
@@ -5149,7 +5149,7 @@ def drop(
         ...                         [1, 0.8], [0.3, 0.2]])
         >>> df
                         big     small
-        lama    speed   45.0    30.0
+        llama   speed   45.0    30.0
                 weight  200.0   100.0
                 length  1.5     1.0
         cow     speed   30.0    20.0
@@ -5165,7 +5165,7 @@ def drop(
 
         >>> df.drop(index=('falcon', 'weight'))
                         big     small
-        lama    speed   45.0    30.0
+        llama   speed   45.0    30.0
                 weight  200.0   100.0
                 length  1.5     1.0
         cow     speed   30.0    20.0
@@ -5176,7 +5176,7 @@ def drop(
 
         >>> df.drop(index='cow', columns='small')
                         big
-        lama    speed   45.0
+        llama   speed   45.0
                 weight  200.0
                 length  1.5
         falcon  speed   320.0
@@ -5185,7 +5185,7 @@ def drop(
 
         >>> df.drop(index='length', level=1)
                         big     small
-        lama    speed   45.0    30.0
+        llama   speed   45.0    30.0
                 weight  200.0   100.0
         cow     speed   30.0    20.0
                 weight  250.0   150.0

pandas/core/series.py

@@ -2225,14 +2225,14 @@ def drop_duplicates(
         --------
         Generate a Series with duplicated entries.
 
-        >>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'],
+        >>> s = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama', 'hippo'],
         ...               name='animal')
         >>> s
-        0      lama
+        0     llama
         1       cow
-        2      lama
+        2     llama
         3    beetle
-        4      lama
+        4     llama
         5     hippo
         Name: animal, dtype: object
 
@@ -2241,7 +2241,7 @@ def drop_duplicates(
         set of duplicated entries. The default value of keep is 'first'.
 
         >>> s.drop_duplicates()
-        0      lama
+        0     llama
         1       cow
         3    beetle
         5     hippo
@@ -2253,7 +2253,7 @@ def drop_duplicates(
         >>> s.drop_duplicates(keep='last')
         1       cow
         3    beetle
-        4      lama
+        4     llama
         5     hippo
         Name: animal, dtype: object
 
@@ -2314,7 +2314,7 @@ def duplicated(self, keep: DropKeep = "first") -> Series:
         By default, for each set of duplicated values, the first occurrence is
         set on False and all others on True:
 
-        >>> animals = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama'])
+        >>> animals = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama'])
         >>> animals.duplicated()
         0    False
         1    False
@@ -4862,14 +4862,14 @@ def drop(
 
         Drop 2nd level label in MultiIndex Series
 
-        >>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
+        >>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
         ...                              ['speed', 'weight', 'length']],
         ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
         ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
         >>> s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
         ...               index=midx)
         >>> s
-        lama    speed      45.0
+        llama   speed      45.0
                 weight    200.0
                 length      1.2
         cow     speed      30.0
@@ -4881,7 +4881,7 @@ def drop(
         dtype: float64
 
         >>> s.drop(labels='weight', level=1)
-        lama    speed      45.0
+        llama   speed      45.0
                 length      1.2
         cow     speed      30.0
                 length      1.5
@@ -5048,9 +5048,9 @@ def isin(self, values) -> Series:
 
         Examples
         --------
-        >>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama',
+        >>> s = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama',
         ...                'hippo'], name='animal')
-        >>> s.isin(['cow', 'lama'])
+        >>> s.isin(['cow', 'llama'])
         0     True
         1     True
         2     True
@@ -5061,7 +5061,7 @@ def isin(self, values) -> Series:
 
         To invert the boolean values, use the ``~`` operator:
 
-        >>> ~s.isin(['cow', 'lama'])
+        >>> ~s.isin(['cow', 'llama'])
         0    False
         1    False
         2    False
@@ -5070,10 +5070,10 @@ def isin(self, values) -> Series:
         5     True
         Name: animal, dtype: bool
 
-        Passing a single string as ``s.isin('lama')`` will raise an error. Use
+        Passing a single string as ``s.isin('llama')`` will raise an error. Use
         a list of one element instead:
 
-        >>> s.isin(['lama'])
+        >>> s.isin(['llama'])
         0     True
         1    False
         2     True

pandas/tests/groupby/test_counting.py

@@ -257,10 +257,10 @@ def test_groupby_count_dateparseerror(self):
 
 def test_groupby_timedelta_cython_count():
     df = DataFrame(
-        {"g": list("ab" * 2), "delt": np.arange(4).astype("timedelta64[ns]")}
+        {"g": list("ab" * 2), "delta": np.arange(4).astype("timedelta64[ns]")}
     )
-    expected = Series([2, 2], index=Index(["a", "b"], name="g"), name="delt")
-    result = df.groupby("g").delt.count()
+    expected = Series([2, 2], index=Index(["a", "b"], name="g"), name="delta")
+    result = df.groupby("g").delta.count()
     tm.assert_series_equal(expected, result)
 
 

pandas/tests/io/excel/test_readers.py

@@ -843,7 +843,13 @@ def test_bad_sheetname_raises(self, read_ext, sheet_name):
     def test_missing_file_raises(self, read_ext):
         bad_file = f"foo{read_ext}"
         # CI tests with other languages, translates to "No such file or directory"
-        match = r"(No such file or directory|没有那个文件或目录|File o directory non esistente)"
+        match = "|".join(
+            [
+                "(No such file or directory",
+                "没有那个文件或目录",
+                "File o directory non esistente)",
+            ]
+        )
         with pytest.raises(FileNotFoundError, match=match):
             pd.read_excel(bad_file)