STYLE ruff: enable PIE

pandas/conftest.py

@@ -637,9 +637,7 @@ def index_flat(request):
         key
         for key, value in indices_dict.items()
         if not (
-            key.startswith("int")
-            or key.startswith("uint")
-            or key.startswith("float")
+            key.startswith(("int", "uint", "float"))
             or key in ["range", "empty", "repeats", "bool-dtype"]
         )
         and not isinstance(value, MultiIndex)

pandas/core/dtypes/dtypes.py

@@ -897,7 +897,7 @@ def freq(self):
     @classmethod
     def _parse_dtype_strict(cls, freq: str_type) -> BaseOffset:
         if isinstance(freq, str):  # note: freq is already of type str!
-            if freq.startswith("period[") or freq.startswith("Period["):
+            if freq.startswith(("Period[", "period[")):
                 m = cls._match.search(freq)
                 if m is not None:
                     freq = m.group("freq")
@@ -916,7 +916,7 @@ def construct_from_string(cls, string: str_type) -> PeriodDtype:
         """
         if (
             isinstance(string, str)
-            and (string.startswith("period[") or string.startswith("Period["))
+            and (string.startswith(("period[", "Period[")))
             or isinstance(string, BaseOffset)
         ):
             # do not parse string like U as period[U]
@@ -980,7 +980,7 @@ def is_dtype(cls, dtype: object) -> bool:
         if isinstance(dtype, str):
             # PeriodDtype can be instantiated from freq string like "U",
             # but doesn't regard freq str like "U" as dtype.
-            if dtype.startswith("period[") or dtype.startswith("Period["):
+            if dtype.startswith(("period[", "Period[")):
                 try:
                     return cls._parse_dtype_strict(dtype) is not None
                 except ValueError:

pandas/core/series.py

@@ -836,8 +836,6 @@ def view(self, dtype: Dtype | None = None) -> Series:
 
     # ----------------------------------------------------------------------
     # NDArray Compat
-    _HANDLED_TYPES = (Index, ExtensionArray, np.ndarray)
-
     def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray:
         """
         Return the values as a NumPy array.

pandas/io/formats/css.py

@@ -208,10 +208,8 @@ class CSSResolver:
             f"border-{prop}": _side_expander(f"border-{{:s}}-{prop}")
             for prop in ["color", "style", "width"]
         },
-        **{
-            "margin": _side_expander("margin-{:s}"),
-            "padding": _side_expander("padding-{:s}"),
-        },
+        "margin": _side_expander("margin-{:s}"),
+        "padding": _side_expander("padding-{:s}"),
     }
 
     def __call__(

pandas/io/json/_json.py

@@ -1415,8 +1415,7 @@ def is_ok(col) -> bool:
 
             col_lower = col.lower()
             if (
-                col_lower.endswith("_at")
-                or col_lower.endswith("_time")
+                col_lower.endswith(("_at", "_time"))
                 or col_lower == "modified"
                 or col_lower == "date"
                 or col_lower == "datetime"

pandas/io/pytables.py

@@ -5123,13 +5123,13 @@ def _dtype_to_kind(dtype_str: str) -> str:
     """
     dtype_str = _ensure_decoded(dtype_str)
 
-    if dtype_str.startswith("string") or dtype_str.startswith("bytes"):
+    if dtype_str.startswith(("string", "bytes")):
         kind = "string"
     elif dtype_str.startswith("float"):
         kind = "float"
     elif dtype_str.startswith("complex"):
         kind = "complex"
-    elif dtype_str.startswith("int") or dtype_str.startswith("uint"):
+    elif dtype_str.startswith(("int", "uint")):
         kind = "integer"
     elif dtype_str.startswith("datetime64"):
         kind = "datetime64"

pandas/io/stata.py

@@ -865,23 +865,23 @@ class StataMissingValue:
             MISSING_VALUES[i + b] = "." + chr(96 + i)
 
     float32_base: bytes = b"\x00\x00\x00\x7f"
-    increment: int = struct.unpack("<i", b"\x00\x08\x00\x00")[0]
+    increment_32: int = struct.unpack("<i", b"\x00\x08\x00\x00")[0]
     for i in range(27):
         key = struct.unpack("<f", float32_base)[0]
         MISSING_VALUES[key] = "."
         if i > 0:
             MISSING_VALUES[key] += chr(96 + i)
-        int_value = struct.unpack("<i", struct.pack("<f", key))[0] + increment
+        int_value = struct.unpack("<i", struct.pack("<f", key))[0] + increment_32
         float32_base = struct.pack("<i", int_value)
 
     float64_base: bytes = b"\x00\x00\x00\x00\x00\x00\xe0\x7f"
-    increment = struct.unpack("q", b"\x00\x00\x00\x00\x00\x01\x00\x00")[0]
+    increment_64 = struct.unpack("q", b"\x00\x00\x00\x00\x00\x01\x00\x00")[0]
     for i in range(27):
         key = struct.unpack("<d", float64_base)[0]
         MISSING_VALUES[key] = "."
         if i > 0:
             MISSING_VALUES[key] += chr(96 + i)
-        int_value = struct.unpack("q", struct.pack("<d", key))[0] + increment
+        int_value = struct.unpack("q", struct.pack("<d", key))[0] + increment_64
         float64_base = struct.pack("q", int_value)
 
     BASE_MISSING_VALUES: Final = {

pandas/tests/copy_view/test_indexing.py

@@ -583,8 +583,8 @@ def test_subset_chained_getitem(
         # with ArrayManager, it doesn't matter whether we have
         # single vs mixed block or numpy vs nullable dtypes
         subset_is_view = test_callspec.endswith(
-            "column-iloc-slice"
-        ) or test_callspec.endswith("column-loc-slice")
+            ("column-iloc-slice", "column-loc-slice")
+        )
 
     # modify subset -> don't modify parent
     subset = method(df)

pandas/tests/frame/methods/test_to_records.py

@@ -489,7 +489,7 @@ def keys(self):
         df = DataFrame({"A": [1, 2], "B": [0.2, 1.5], "C": ["a", "bc"]})
 
         dtype_mappings = {
-            "column_dtypes": DictLike(**{"A": np.int8, "B": np.float32}),
+            "column_dtypes": DictLike(A=np.int8, B=np.float32),
             "index_dtypes": f"{tm.ENDIAN}U2",
         }
 

pandas/tests/io/formats/style/test_style.py

@@ -46,7 +46,7 @@ def mi_styler(mi_df):
 def mi_styler_comp(mi_styler):
     # comprehensively add features to mi_styler
     mi_styler = mi_styler._copy(deepcopy=True)
-    mi_styler.css = {**mi_styler.css, **{"row": "ROW", "col": "COL"}}
+    mi_styler.css = {**mi_styler.css, "row": "ROW", "col": "COL"}
     mi_styler.uuid_len = 5
     mi_styler.uuid = "abcde"
     mi_styler.set_caption("capt")

pandas/tseries/frequencies.py

@@ -596,7 +596,7 @@ def _is_annual(rule: str) -> bool:
 
 def _is_quarterly(rule: str) -> bool:
     rule = rule.upper()
-    return rule == "Q" or rule.startswith("Q-") or rule.startswith("BQ")
+    return rule == "Q" or rule.startswith(("Q-", "BQ"))
 
 
 def _is_monthly(rule: str) -> bool:

pyproject.toml

@@ -212,6 +212,8 @@ select = [
   "Q",
   # pylint
   "PLE", "PLR", "PLW",
+  # misc lints
+  "PIE",
   # tidy imports
   "TID",
 ]

scripts/validate_exception_location.py

@@ -51,11 +51,7 @@ def __init__(self, path: str, exception_set: set[str]) -> None:
 
     def visit_ClassDef(self, node) -> None:
         def is_an_exception_subclass(base_id: str) -> bool:
-            return (
-                base_id == "Exception"
-                or base_id.endswith("Warning")
-                or base_id.endswith("Error")
-            )
+            return base_id == "Exception" or base_id.endswith(("Warning", "Error"))
 
         exception_classes = []