CLN: tighten noqas (pandas-dev#44529)

doc/source/user_guide/io.rst

@@ -3023,6 +3023,7 @@ Read in the content of the "books.xml" as instance of ``StringIO`` or
 Even read XML from AWS S3 buckets such as Python Software Foundation's IRS 990 Form:
 
 .. ipython:: python
+   :okwarning:
 
    df = pd.read_xml(
        "s3://irs-form-990/201923199349319487_public.xml",

pandas/api/__init__.py

@@ -1,5 +1,5 @@
 """ public toolkit API """
-from pandas.api import (  # noqa
+from pandas.api import (  # noqa:F401
     extensions,
     indexers,
     types,

pandas/core/algorithms.py

@@ -1849,5 +1849,5 @@ def union_with_duplicates(lvals: ArrayLike, rvals: ArrayLike) -> ArrayLike:
     unique_array = ensure_wrapped_if_datetimelike(unique_array)
 
     for i, value in enumerate(unique_array):
-        indexer += [i] * int(max(l_count[value], r_count[value]))
+        indexer += [i] * int(max(l_count.at[value], r_count.at[value]))
     return unique_array.take(indexer)

pandas/core/arrays/floating.py

@@ -176,9 +176,7 @@ def coerce_to_array(
     if mask.any():
         values = values.copy()
         values[mask] = np.nan
-        values = values.astype(dtype, copy=False)  # , casting="safe")
-    else:
-        values = values.astype(dtype, copy=False)  # , casting="safe")
+    values = values.astype(dtype, copy=False)  # , casting="safe")
 
     return values, mask
 

pandas/core/arrays/integer.py

@@ -214,9 +214,9 @@ def coerce_to_array(
     else:
         assert len(mask) == len(values)
 
-    if not values.ndim == 1:
+    if values.ndim != 1:
         raise TypeError("values must be a 1D list-like")
-    if not mask.ndim == 1:
+    if mask.ndim != 1:
         raise TypeError("mask must be a 1D list-like")
 
     # infer dtype if needed

pandas/core/dtypes/missing.py

@@ -162,7 +162,6 @@ def _isna(obj, inf_as_na: bool = False):
             return libmissing.checknull_old(obj)
         else:
             return libmissing.checknull(obj)
-    # hack (for now) because MI registers as ndarray
     elif isinstance(obj, ABCMultiIndex):
         raise NotImplementedError("isna is not defined for MultiIndex")
     elif isinstance(obj, type):

pandas/core/internals/blocks.py

@@ -309,7 +309,7 @@ def _slice(self, slicer) -> ArrayLike:
         return self.values[slicer]
 
     @final
-    def getitem_block(self, slicer) -> Block:
+    def getitem_block(self, slicer: slice | npt.NDArray[np.intp]) -> Block:
         """
         Perform __getitem__-like, return result as block.
 
@@ -326,7 +326,9 @@ def getitem_block(self, slicer) -> Block:
         return type(self)(new_values, new_mgr_locs, self.ndim)
 
     @final
-    def getitem_block_columns(self, slicer, new_mgr_locs: BlockPlacement) -> Block:
+    def getitem_block_columns(
+        self, slicer: slice, new_mgr_locs: BlockPlacement
+    ) -> Block:
         """
         Perform __getitem__-like, return result as block.
 

pandas/core/reshape/tile.py

@@ -443,7 +443,8 @@ def _bins_to_cuts(
             )
         elif ordered and len(set(labels)) != len(labels):
             raise ValueError(
-                "labels must be unique if ordered=True; pass ordered=False for duplicate labels"  # noqa
+                "labels must be unique if ordered=True; pass ordered=False "
+                "for duplicate labels"
             )
         else:
             if len(labels) != len(bins) - 1:

pandas/io/sas/__init__.py

@@ -1 +1 @@
-from pandas.io.sas.sasreader import read_sas  # noqa
+from pandas.io.sas.sasreader import read_sas  # noqa:F401

pandas/tests/arrays/boolean/test_function.py

@@ -59,8 +59,8 @@ def test_ufuncs_unary(ufunc):
     expected[a._mask] = np.nan
     tm.assert_extension_array_equal(result, expected)
 
-    s = pd.Series(a)
-    result = ufunc(s)
+    ser = pd.Series(a)
+    result = ufunc(ser)
     expected = pd.Series(ufunc(a._data), dtype="boolean")
     expected[a._mask] = np.nan
     tm.assert_series_equal(result, expected)
@@ -86,8 +86,8 @@ def test_value_counts_na():
 
 
 def test_value_counts_with_normalize():
-    s = pd.Series([True, False, pd.NA], dtype="boolean")
-    result = s.value_counts(normalize=True)
+    ser = pd.Series([True, False, pd.NA], dtype="boolean")
+    result = ser.value_counts(normalize=True)
     expected = pd.Series([1, 1], index=[True, False], dtype="Float64") / 2
     tm.assert_series_equal(result, expected)
 
@@ -102,7 +102,7 @@ def test_diff():
     )
     tm.assert_extension_array_equal(result, expected)
 
-    s = pd.Series(a)
-    result = s.diff()
+    ser = pd.Series(a)
+    result = ser.diff()
     expected = pd.Series(expected)
     tm.assert_series_equal(result, expected)

pandas/tests/arrays/categorical/test_constructors.py

@@ -248,9 +248,7 @@ def test_constructor(self):
 
         # this is a legitimate constructor
         with tm.assert_produces_warning(None):
-            c = Categorical(  # noqa
-                np.array([], dtype="int64"), categories=[3, 2, 1], ordered=True
-            )
+            Categorical(np.array([], dtype="int64"), categories=[3, 2, 1], ordered=True)
 
     def test_constructor_with_existing_categories(self):
         # GH25318: constructing with pd.Series used to bogusly skip recoding