STYLE autoupdate pre-commit

[MarcoGorelli]

Tomorrow, the autoupdate job will run, and there'll be a couple of updates which'll require some manual fixing:

• codespell
• cython-lint

The task is:

• run pre-commit autoupdate
• run pre-commit run cython-lint --all-files and pre-commit run codespell --all-files
• fixup the errors. e.g. if there's an error saying 'use_time' defined but unused, remove the definition of use_time from that line
• stage, commit, push, open pull request, celebrate

Please refer to the contributing guide, and feel free to ask if you run into anything unexpected (e.g. a false positive from the above tools)

[ramvikrams]

At what time tommorow will the autoupdate happen

[MarcoGorelli]

7 am UTC, but this PR can be done at any time

[ramvikrams]

I'll start it at 7 am then, can be done at any time after the autoupdate right

[MarcoGorelli]

it can be done before, the automated job will fail anyway so ideally we'd take your PR instead of the automated one

[ramvikrams]

Oh I'll start with it rightaway then

[ramvikrams]

While running the cython i found errors like some word defined but unused
for ex:- 'dts' defined but unused

def _from_value_and_reso(cls, int64_t value, NPY_DATETIMEUNIT reso, tzinfo tz):
        cdef:
            npy_datetimestruct dts
            _TSObject obj = _TSObject()

        if value == NPY_NAT:
            return NaT

So should I remove this dts word

[MarcoGorelli]

yeah looks like it's unused in that function, so you can just remove the npy_datetimestruct dts line

[ramvikrams]

Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "C:\Users\ramvi\.cache\pre-commit\repogmjseui2\py_env-python3.11\Scripts\cython-lint.EXE\__main__.py", line 7, in <module>
  File "C:\Users\ramvi\.cache\pre-commit\repogmjseui2\py_env-python3.11\Lib\site-packages\cython_lint.py", line 448, in main
    ret |= _main(
           ^^^^^^
  File "C:\Users\ramvi\.cache\pre-commit\repogmjseui2\py_env-python3.11\Lib\site-packages\cython_lint.py", line 308, in _main
    tokens = src_to_tokens(code)
           ^^^^^^
  File "C:\Users\ramvi\.cache\pre-commit\repogmjseui2\py_env-python3.11\Lib\site-packages\cython_lint.py", line 308, in _main
    tokens = src_to_tokens(code)
             ^^^^^^^^^^^^^^^^^^^
  File "C:\Users\ramvi\.cache\pre-commit\repogmjseui2\py_env-python3.11\Lib\site-packages\tokenize_rt.py", line 68, in src_to_tokens
    for tok_type, tok_text, (sline, scol), (eline, ecol), line in gen:
  File "C:\Users\ramvi\AppData\Local\Programs\Python\Python311\Lib\tokenize.py", line 516, in _tokenize
    raise IndentationError(
  File "<tokenize>", line 317
    cdef:
IndentationError: unindent does not match any outer indentation level

Getting this error after running this pre-commit run cython-lint --all-files

[MarcoGorelli]

could you please show me the output of git diff upstream/main?

[ramvikrams]

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 8ff7526b87..1893f57fc0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -18,16 +18,16 @@ repos:
         pass_filenames: true
         require_serial: false
 -   repo: https://github.com/python/black
-    rev: 22.8.0
+    rev: 22.10.0
     hooks:
     -   id: black
 -   repo: https://github.com/codespell-project/codespell
-    rev: v2.2.1
+    rev: v2.2.2
     hooks:
     -   id: codespell
         types_or: [python, rst, markdown]
 -   repo: https://github.com/MarcoGorelli/cython-lint
-    rev: v0.1.8
+    rev: v0.2.1
     hooks:
     -   id: cython-lint
 -   repo: https://github.com/pre-commit/pre-commit-hooks
@@ -60,7 +60,7 @@ repos:
         - flake8-bugbear==22.7.1
         - pandas-dev-flaker==0.5.0
 -   repo: https://github.com/pycqa/pylint
-    rev: v2.15.3
+    rev: v2.15.5
     hooks:
     -   id: pylint
 -   repo: https://github.com/PyCQA/isort
@@ -68,7 +68,7 @@ repos:
     hooks:
     -   id: isort
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v2.38.2
+    rev: v3.2.0
     hooks:
     -   id: pyupgrade
         args: [--py38-plus]
@@ -83,7 +83,7 @@ repos:
         types: [text]  # overwrite types: [rst]
         types_or: [python, rst]
 -   repo: https://github.com/sphinx-contrib/sphinx-lint
-    rev: v0.6.1
+    rev: v0.6.7
     hooks:
     - id: sphinx-lint
 -   repo: https://github.com/asottile/yesqa
diff --git a/pandas/_libs/algos.pyx b/pandas/_libs/algos.pyx
:

Here it is

[MarcoGorelli]

I can't see what you've modified in pandas/_libs/algos.pyx. if you open a draft pull request that might make it easier to tell what's going on

[ramvikrams]

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 8ff7526b87..1893f57fc0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -18,16 +18,16 @@ repos:
         pass_filenames: true
         require_serial: false
 -   repo: https://github.com/python/black
-    rev: 22.8.0
+    rev: 22.10.0
     hooks:
     -   id: black
 -   repo: https://github.com/codespell-project/codespell
-    rev: v2.2.1
+    rev: v2.2.2
     hooks:
     -   id: codespell
         types_or: [python, rst, markdown]
 -   repo: https://github.com/MarcoGorelli/cython-lint
-    rev: v0.1.8
+    rev: v0.2.1
     hooks:
     -   id: cython-lint
 -   repo: https://github.com/pre-commit/pre-commit-hooks
@@ -60,7 +60,7 @@ repos:
         - flake8-bugbear==22.7.1
         - pandas-dev-flaker==0.5.0
 -   repo: https://github.com/pycqa/pylint
-    rev: v2.15.3
+    rev: v2.15.5
     hooks:
     -   id: pylint
 -   repo: https://github.com/PyCQA/isort
@@ -68,7 +68,7 @@ repos:
     hooks:
     -   id: isort
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v2.38.2
+    rev: v3.2.0
     hooks:
     -   id: pyupgrade
         args: [--py38-plus]
@@ -83,7 +83,7 @@ repos:
         types: [text]  # overwrite types: [rst]
         types_or: [python, rst]
 -   repo: https://github.com/sphinx-contrib/sphinx-lint
-    rev: v0.6.1
+    rev: v0.6.7
     hooks:
     - id: sphinx-lint
 -   repo: https://github.com/asottile/yesqa
diff --git a/pandas/_libs/algos.pyx b/pandas/_libs/algos.pyx
index 96c47471aa..587e17e806 100644
--- a/pandas/_libs/algos.pyx
+++ b/pandas/_libs/algos.pyx
@@ -81,26 +81,26 @@ class Infinity:
     """
     Provide a positive Infinity comparison method for ranking.
     """
-    __lt__ = lambda self, other: False
-    __le__ = lambda self, other: isinstance(other, Infinity)
-    __eq__ = lambda self, other: isinstance(other, Infinity)
-    __ne__ = lambda self, other: not isinstance(other, Infinity)
-    __gt__ = lambda self, other: (not isinstance(other, Infinity) and
+    __lt__ = def self, other: False
+    __le__ = def self, other: isinstance(other, Infinity)
+    __eq__ = def self, other: isinstance(other, Infinity)
+    __ne__ = def self, other: not isinstance(other, Infinity)
+    __gt__ = def self, other: (not isinstance(other, Infinity) and
                                   not missing.checknull(other))
-    __ge__ = lambda self, other: not missing.checknull(other)
+    __ge__ = def self, other: not missing.checknull(other)


 class NegInfinity:
     """
     Provide a negative Infinity comparison method for ranking.
     """
-    __lt__ = lambda self, other: (not isinstance(other, NegInfinity) and
+    __lt__ = def self, other: (not isinstance(other, NegInfinity) and
                                   not missing.checknull(other))
-    __le__ = lambda self, other: not missing.checknull(other)
-    __eq__ = lambda self, other: isinstance(other, NegInfinity)
-    __ne__ = lambda self, other: not isinstance(other, NegInfinity)
-    __gt__ = lambda self, other: False
-    __ge__ = lambda self, other: isinstance(other, NegInfinity)
+    __le__ = def self, other: not missing.checknull(other)
+    __eq__ = def self, other: isinstance(other, NegInfinity)
+    __ne__ = def self, other: not isinstance(other, NegInfinity)
+    __gt__ = def self, other: False
+    __ge__ = def self, other: isinstance(other, NegInfinity)


 @cython.wraparound(False)
@@ -321,7 +321,7 @@ def kth_smallest(numeric_t[::1] arr, Py_ssize_t k) -> numeric_t:
 @cython.cdivision(True)
 def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None):
     cdef:
-        Py_ssize_t i, j, xi, yi, N, K
+        Py_ssize_t i, xi, yi, N, K
         bint minpv
         float64_t[:, ::1] result
         ndarray[uint8_t, ndim=2] mask
@@ -377,7 +377,7 @@ def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None):
 @cython.wraparound(False)
 def nancorr_spearman(ndarray[float64_t, ndim=2] mat, Py_ssize_t minp=1) -> ndarray:
     cdef:
-        Py_ssize_t i, j, xi, yi, N, K
+        Py_ssize_t i, xi, yi, N, K
         ndarray[float64_t, ndim=2] result
         ndarray[float64_t, ndim=2] ranked_mat
         ndarray[float64_t, ndim=1] rankedx, rankedy
@@ -746,7 +746,8 @@ def is_monotonic(ndarray[numeric_object_t, ndim=1] arr, bint timelike):
     n = len(arr)

     if n == 1:
-        if arr[0] != arr[0] or (numeric_object_t is int64_t and timelike and arr[0] == NPY_NAT):
+        if arr[0] != arr[0] or (numeric_object_t is int64_t and timelike and 
+                                arr[0] == NPY_NAT):
             # single value is NaN
             return False, False, True
         else:
diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx
index f798655e9d..af2877b837 100644
--- a/pandas/_libs/groupby.pyx
+++ b/pandas/_libs/groupby.pyx
@@ -265,7 +265,7 @@ def group_cumprod(
     This method modifies the `out` parameter, rather than returning an object.
     """
     cdef:
-        Py_ssize_t i, j, N, K, size
+        Py_ssize_t i, j, N, K, 
         int64float_t val, na_val
         int64float_t[:, ::1] accum
         intp_t lab
@@ -356,7 +356,7 @@ def group_cumsum(
     This method modifies the `out` parameter, rather than returning an object.
     """
     cdef:
-        Py_ssize_t i, j, N, K, size
+        Py_ssize_t i, j, N, K, 
         int64float_t val, y, t, na_val
         int64float_t[:, ::1] accum, compensation
         uint8_t[:, ::1] accum_mask
@@ -441,7 +441,7 @@ def group_shift_indexer(
     int periods,
 ) -> None:
     cdef:
-        Py_ssize_t N, i, j, ii, lab
+        Py_ssize_t N, i, ii, lab
         int offset = 0, sign
         int64_t idxer, idxer_slot
         int64_t[::1] label_seen = np.zeros(ngroups, dtype=np.int64)
@@ -744,7 +744,7 @@ def group_sum(
                     if uses_mask:
                         isna_entry = mask[i, j]
                     elif (sum_t is float32_t or sum_t is float64_t
-                        or sum_t is complex64_t or sum_t is complex64_t):
+                          or sum_t is complex64_t or sum_t is complex64_t):
                         # avoid warnings because of equality comparison
                         isna_entry = not val == val
                     elif sum_t is int64_t and is_datetimelike and val == NPY_NAT:
@@ -771,7 +771,7 @@ def group_sum(
                         if uses_mask:
                             result_mask[i, j] = True
                         elif (sum_t is float32_t or sum_t is float64_t
-                            or sum_t is complex64_t or sum_t is complex64_t):
+                              or sum_t is complex64_t or sum_t is complex64_t):
                             out[i, j] = NAN
                         elif sum_t is int64_t:
                             out[i, j] = NPY_NAT
@@ -799,7 +799,7 @@ def group_prod(
     """
     cdef:
         Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
-        int64float_t val, count
+        int64float_t val, 
         int64float_t[:, ::1] prodx
         int64_t[:, ::1] nobs
         Py_ssize_t len_values = len(values), len_labels = len(labels)
@@ -872,7 +872,7 @@ def group_var(
         floating[:, ::1] mean
         int64_t[:, ::1] nobs
         Py_ssize_t len_values = len(values), len_labels = len(labels)
-        bint isna_entry, uses_mask = not mask is None
+        bint isna_entry, uses_mask = is not mask is None

     assert min_count == -1, "'min_count' only used in sum and prod"

@@ -969,7 +969,7 @@ def group_mean(
         mean_t[:, ::1] sumx, compensation
         int64_t[:, ::1] nobs
         Py_ssize_t len_values = len(values), len_labels = len(labels)
-        bint isna_entry, uses_mask = not mask is None
+        bint isna_entry, uses_mask = is not mask is None

     assert min_count == -1, "'min_count' only used in sum and prod"

@@ -1042,10 +1042,10 @@ def group_ohlc(
     Only aggregates on axis=0
     """
     cdef:
-        Py_ssize_t i, j, N, K, lab
+        Py_ssize_t i, N, K, lab
         int64float_t val
         uint8_t[::1] first_element_set
-        bint isna_entry, uses_mask = not mask is None
+        bint isna_entry, uses_mask = is not mask is None

     assert min_count == -1, "'min_count' only used in sum and prod"

@@ -1240,7 +1240,8 @@ cdef inline bint _treat_as_na(numeric_object_t val, bint is_datetimelike) nogil:
         return False


-cdef numeric_object_t _get_min_or_max(numeric_object_t val, bint compute_max, bint is_datetimelike):
+cdef numeric_object_t _get_min_or_max(numeric_object_t val, bint compute_max, 
+                                      bint is_datetimelike):
     """
     Find either the min or the max supported by numeric_object_t; 'val' is a
     placeholder to effectively make numeric_object_t an argument.
@@ -1366,7 +1367,8 @@ def group_last(
                         #  set a placeholder value in out[i, j].
                         if uses_mask:
                             result_mask[i, j] = True
-                        elif numeric_object_t is float32_t or numeric_object_t is float64_t:
+                        elif numeric_object_t is float32_t or numeric_object_t 
+                            is float64_t:
                             out[i, j] = NAN
                         elif numeric_object_t is int64_t:
                             # Per above, this is a placeholder in
@@ -1486,7 +1488,8 @@ def group_nth(
                             #  it was initialized with np.empty. Also ensures
                             #  we can downcast out if appropriate.
                             out[i, j] = 0
-                        elif numeric_object_t is float32_t or numeric_object_t is float64_t:
+                        elif numeric_object_t is float32_t or numeric_object_t 
+                            is float64_t:
                             out[i, j] = NAN
                         elif numeric_object_t is int64_t:
                             # Per above, this is a placeholder in
diff --git a/pandas/_libs/internals.pyx b/pandas/_libs/internals.pyx
index 1a98633908..747f57e6ba 100644
--- a/pandas/_libs/internals.pyx
+++ b/pandas/_libs/internals.pyx
@@ -133,7 +133,7 @@ cdef class BlockPlacement:
     @property
     def as_array(self) -> np.ndarray:
         cdef:
-            Py_ssize_t start, stop, end, _
+            Py_ssize_t start, stop, _

         if not self._has_array:
             start, stop, step, _ = slice_get_indices_ex(self._as_slice)
@@ -259,7 +259,6 @@ cdef class BlockPlacement:
         """
         cdef:
             slice slc = self._ensure_has_slice()
-            slice new_slice
             ndarray[intp_t, ndim=1] new_placement

         if slc is not None and slc.step == 1:
diff --git a/pandas/_libs/join.pyx b/pandas/_libs/join.pyx
index e574aa10f6..1f2d717cab 100644
--- a/pandas/_libs/join.pyx
+++ b/pandas/_libs/join.pyx
@@ -275,7 +275,7 @@ def left_join_indexer_unique(
     cdef:
         Py_ssize_t i, j, nleft, nright
         ndarray[intp_t] indexer
-        numeric_object_t lval, rval
+        numeric_object_t, rval

     i = 0
     j = 0
@@ -324,7 +324,7 @@ def left_join_indexer(ndarray[numeric_object_t] left, ndarray[numeric_object_t]
     is non-unique (if both were unique we'd use left_join_indexer_unique).
     """
     cdef:
-        Py_ssize_t i, j, k, nright, nleft, count
+        Py_ssize_t i, j, nright, nleft, count
         numeric_object_t lval, rval
         ndarray[intp_t] lindexer, rindexer
         ndarray[numeric_object_t] result
@@ -434,7 +434,7 @@ def inner_join_indexer(ndarray[numeric_object_t] left, ndarray[numeric_object_t]
     Both left and right are monotonic increasing but not necessarily unique.
     """
     cdef:
-        Py_ssize_t i, j, k, nright, nleft, count
+        Py_ssize_t i, j, nright, nleft, count
         numeric_object_t lval, rval
         ndarray[intp_t] lindexer, rindexer
         ndarray[numeric_object_t] result
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 188b531b2b..914b33c01e 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -621,6 +621,7 @@ ctypedef fused ndarr_object:

 # TODO: get rid of this in StringArray and modify
 #  and go through ensure_string_array instead
+
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def convert_nans_to_NA(ndarr_object arr) -> ndarray:
@@ -765,9 +766,9 @@ def generate_bins_dt64(ndarray[int64_t, ndim=1] values, const int64_t[:] binner,
     Int64 (datetime64) version of generic python version in ``groupby.py``.
     """
     cdef:
-        Py_ssize_t lenidx, lenbin, i, j, bc, vc
+        Py_ssize_t lenidx, lenbin, i, j, bc, 
         ndarray[int64_t, ndim=1] bins
-        int64_t l_bin, r_bin, nat_count
+        int64_t, r_bin, nat_count
         bint right_closed = closed == 'right'

     nat_count = 0
@@ -2215,11 +2216,16 @@ def maybe_convert_numeric(
         int status, maybe_int
         Py_ssize_t i, n = values.size
         Seen seen = Seen(coerce_numeric)
-        ndarray[float64_t, ndim=1] floats = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_FLOAT64, 0)
-        ndarray[complex128_t, ndim=1] complexes = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_COMPLEX128, 0)
-        ndarray[int64_t, ndim=1] ints = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_INT64, 0)
-        ndarray[uint64_t, ndim=1] uints = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_UINT64, 0)
-        ndarray[uint8_t, ndim=1] bools = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_UINT8, 0)
+        ndarray[float64_t, ndim=1] floats = cnp.PyArray_EMPTY(1, values.shape, 
+                                                              cnp.NPY_FLOAT64, 0)
+        ndarray[complex128_t, ndim=1] complexes = cnp.PyArray_EMPTY(1, values.shape, 
+                                                                    cnp.NPY_COMPLEX128, 0)
+        ndarray[int64_t, ndim=1] ints = cnp.PyArray_EMPTY(1, values.shape,  
+                                                          cnp.NPY_INT64, 0)
+        ndarray[uint64_t, ndim=1] uints = cnp.PyArray_EMPTY(1, values.shape, 
+                                                            cnp.NPY_UINT64, 0)
+        ndarray[uint8_t, ndim=1] bools = cnp.PyArray_EMPTY(1, values.shape,  
+                                                           cnp.NPY_UINT8, 0)
         ndarray[uint8_t, ndim=1] mask = np.zeros(n, dtype="u1")
         float64_t fval
         bint allow_null_in_int = convert_to_masked_nullable
@@ -2298,7 +2304,7 @@ def maybe_convert_numeric(
             seen.float_ = True
         else:
             try:
-                status = floatify(val, &fval, &maybe_int)
+                # status = floatify(val, &fval, &maybe_int)

                 if fval in na_values:
                     seen.saw_null()
@@ -2437,7 +2443,7 @@ def maybe_convert_objects(ndarray[object] objects,
         int64_t[::1] itimedeltas
         Seen seen = Seen()
         object val
-        float64_t fval, fnan = np.nan
+        float64_t, fnan = np.nan

     n = len(objects)

@@ -2917,7 +2923,7 @@ def to_object_array(rows: object, min_width: int = 0) -> ndarray:

 def tuples_to_object_array(ndarray[object] tuples):
     cdef:
-        Py_ssize_t i, j, n, k, tmp
+        Py_ssize_t i, j, n, k, 
         ndarray[object, ndim=2] result
         tuple tup

@@ -3045,7 +3051,8 @@ cpdef ndarray eq_NA_compat(ndarray[object] arr, object key):
     key is assumed to have `not isna(key)`
     """
     cdef:
-        ndarray[uint8_t, cast=True] result = cnp.PyArray_EMPTY(arr.ndim, arr.shape, cnp.NPY_BOOL, 0)
+        ndarray[uint8_t, cast=True] result = cnp.PyArray_EMPTY(arr.ndim, arr.shape, 
+                                                               cnp.NPY_BOOL, 0)
         Py_ssize_t i
         object item

diff --git a/pandas/_libs/testing.pyx b/pandas/_libs/testing.pyx
index 679cde9932..678ed54fdc 100644
--- a/pandas/_libs/testing.pyx
+++ b/pandas/_libs/testing.pyx
@@ -161,13 +161,15 @@ cpdef assert_almost_equal(a, b,
                 is_unequal = True
                 diff += 1
                 if not first_diff:
-                    first_diff = f"At positional index {i}, first diff: {a[i]} != {b[i]}"
+                    first_diff = f"At positional index {i}, 
+                                  first diff: {a[i]} != {b[i]}"

         if is_unequal:
             from pandas._testing import raise_assert_detail
             msg = (f"{obj} values are different "
                    f"({np.round(diff * 100.0 / na, 5)} %)")
-            raise_assert_detail(obj, msg, lobj, robj, first_diff=first_diff, index_values=index_values)
+            raise_assert_detail(obj, msg, lobj, robj, 
+                                first_diff=first_diff, index_values=index_values)

         return True

diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx
index d7c0c91332..699c0255dc 100644
--- a/pandas/_libs/tslib.pyx
+++ b/pandas/_libs/tslib.pyx
@@ -260,7 +260,7 @@ def array_with_unit_to_datetime(
     tz : parsed timezone offset or None
     """
     cdef:
-        Py_ssize_t i, j, n=len(values)
+        Py_ssize_t i, n=len(values)
         int64_t mult
         int prec = 0
         ndarray[float64_t] fvalues
@@ -417,6 +417,7 @@ def array_with_unit_to_datetime(

     return oresult, tz

+
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def first_non_null(values: ndarray) -> int:
@@ -424,7 +425,6 @@ def first_non_null(values: ndarray) -> int:
     cdef:
         Py_ssize_t n = len(values)
         Py_ssize_t i
-        int result
     for i in range(n):
         val = values[i]
         if checknull_with_nat_and_na(val):
@@ -435,6 +435,7 @@ def first_non_null(values: ndarray) -> int:
     else:
         return -1

+
 @cython.wraparound(False)
 @cython.boundscheck(False)
 cpdef array_to_datetime(
@@ -609,7 +610,8 @@ cpdef array_to_datetime(
                                 continue
                             elif is_raise:
                                 raise ValueError(
-                                    f"time data \"{val}\" at position {i} doesn't match format specified"
+                                    f"time data \"{val}\" at position {i} doesn't 
+                                      match format specified"
                                 )
                             return values, tz_out

@@ -625,7 +627,8 @@ cpdef array_to_datetime(
                             if is_coerce:
                                 iresult[i] = NPY_NAT
                                 continue
-                            raise TypeError(f"invalid string coercion to datetime for \"{val}\" at position {i}")
+                            raise TypeError(f"invalid string coercion to datetime for \"{val}\" 
+                                             at position {i}")

                         if tz is not None:
                             seen_datetime_offset = True
diff --git a/pandas/_libs/tslibs/dtypes.pyx b/pandas/_libs/tslibs/dtypes.pyx
index 9478137429..0693a142ec 100644
--- a/pandas/_libs/tslibs/dtypes.pyx
+++ b/pandas/_libs/tslibs/dtypes.pyx
@@ -396,7 +396,8 @@ cdef NPY_DATETIMEUNIT freq_group_code_to_npy_unit(int freq) nogil:


 # TODO: use in _matplotlib.converter?
-cpdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=NPY_DATETIMEUNIT.NPY_FR_ns) except? -1:
+cpdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=NPY_DATETIMEUNIT.NPY_FR_ns) 
+    except? -1:
     """
     How many of the given time units fit into a single day?
     """
diff --git a/pandas/_libs/tslibs/fields.pyx b/pandas/_libs/tslibs/fields.pyx
index 3c7406d231..e8f256d1dc 100644
--- a/pandas/_libs/tslibs/fields.pyx
+++ b/pandas/_libs/tslibs/fields.pyx
@@ -325,7 +325,8 @@ def get_start_end_field(

 @cython.wraparound(False)
 @cython.boundscheck(False)
-def get_date_field(const int64_t[:] dtindex, str field, NPY_DATETIMEUNIT reso=NPY_FR_ns):
+def get_date_field(const int64_t[:] dtindex, str field, NPY_DATETIMEUNIT  
+                   reso=NPY_FR_ns):
     """
     Given a int64-based datetime index, extract the year, month, etc.,
     field and return an array of these values.
diff --git a/pandas/_libs/tslibs/nattype.pyx b/pandas/_libs/tslibs/nattype.pyx
index 79299ec38e..a51f3a4b7b 100644
--- a/pandas/_libs/tslibs/nattype.pyx
+++ b/pandas/_libs/tslibs/nattype.pyx
@@ -204,7 +204,8 @@ cdef class _NaT(datetime):
                     return result

                 # __rsub__ logic here
-                # TODO(cython3): remove this, move above code out of ``if not is_rsub`` block
+                # TODO(cython3): remove this, move above code out of   
+                # ``if not is_rsub`` block
                 # timedelta64 - NaT we have to treat NaT as timedelta64
                 #  for this to be meaningful, and the result is timedelta64
                 result = np.empty(other.shape, dtype="timedelta64[ns]")
@@ -240,7 +241,8 @@ cdef class _NaT(datetime):
                 result = np.empty(other.shape, dtype="timedelta64[ns]")
                 result.fill("NaT")
                 return result
-        # other cases are same, swap operands is allowed even though we subtract because this is NaT
+        #  other cases are same, swap operands is allowed even though we subtract 
+        # because this is NaT  
         return self.__sub__(other)

     def __pos__(self):
@@ -1201,6 +1203,7 @@ default 'raise'
         NaT
         """,
     )
+
     @property
     def tz(self) -> None:
         return None
diff --git a/pandas/_libs/tslibs/np_datetime.pyx b/pandas/_libs/tslibs/np_datetime.pyx
index 07872050dc..bf5cdd4a0d 100644
--- a/pandas/_libs/tslibs/np_datetime.pyx
+++ b/pandas/_libs/tslibs/np_datetime.pyx
@@ -46,7 +46,7 @@ cdef extern from "src/datetime/np_datetime.h":
     npy_datetimestruct _S_MIN_DTS, _S_MAX_DTS
     npy_datetimestruct _M_MIN_DTS, _M_MAX_DTS

-    PyArray_DatetimeMetaData get_datetime_metadata_from_dtype(cnp.PyArray_Descr *dtype);
+    PyArray_DatetimeMetaData get_datetime_metadata_from_dtype(cnp.PyArray_Descr *dtype)

 cdef extern from "src/datetime/np_datetime_strings.h":
     int parse_iso_8601_datetime(const char *str, int len, int want_exc,
@@ -171,7 +171,8 @@ class OutOfBoundsTimedelta(ValueError):
     pass
 

-cdef get_implementation_bounds(NPY_DATETIMEUNIT reso, npy_datetimestruct *lower, npy_datetimestruct *upper):
+cdef get_implementation_bounds(NPY_DATETIMEUNIT reso, npy_datetimestruct *lower, 
+                               npy_datetimestruct *upper):
     if reso == NPY_FR_ns:
         upper[0] = _NS_MAX_DTS
         lower[0] = _NS_MIN_DTS
@@ -420,7 +421,6 @@ def compare_mismatched_resolutions(ndarray left, ndarray right, op):
         Py_ssize_t i, N = left.size
         npy_datetimestruct ldts, rdts

-
     for i in range(N):
         # Analogous to: lval = lvalues[i]
         lval = (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
@@ -511,7 +511,8 @@ cdef ndarray astype_round_check(


 @cython.overflowcheck(True)
-cdef int64_t get_conversion_factor(NPY_DATETIMEUNIT from_unit, NPY_DATETIMEUNIT to_unit) except? -1:
+cdef int64_t get_conversion_factor(NPY_DATETIMEUNIT from_unit, NPY_DATETIMEUNIT to_unit)
+    except? -1:
     """
     Find the factor by which we need to multiply to convert from from_unit to to_unit.
     """
diff --git a/pandas/_libs/tslibs/offsets.pyx b/pandas/_libs/tslibs/offsets.pyx
index 37b87f9297..700d8574cf 100644
--- a/pandas/_libs/tslibs/offsets.pyx
+++ b/pandas/_libs/tslibs/offsets.pyx
@@ -2268,7 +2268,8 @@ cdef class QuarterOffset(SingleConstructorOffset):
     def _apply_array(self, dtarr):
         reso = get_unit_from_dtype(dtarr.dtype)
         shifted = shift_quarters(
-            dtarr.view("i8"), self.n, self.startingMonth, self._day_opt, modby=3, reso=reso
+            dtarr.view("i8"), self.n, self.startingMonth, self._day_opt, modby=3, 
+            reso=reso
         )
         return shifted

@@ -2548,7 +2549,8 @@ cdef class SemiMonthOffset(SingleConstructorOffset):
             ndarray i8other = dtarr.view("i8")
             Py_ssize_t i, count = dtarr.size
             int64_t val, res_val
-            ndarray out = cnp.PyArray_EMPTY(i8other.ndim, i8other.shape, cnp.NPY_INT64, 0)
+            ndarray out = cnp.PyArray_EMPTY(i8other.ndim, i8other.shape, cnp.NPY_INT64,
+                                            0)
             npy_datetimestruct dts
             int months, to_day, nadj, n = self.n
             int days_in_month, day, anchor_dom = self.day_of_month
@@ -2756,7 +2758,8 @@ cdef class Week(SingleConstructorOffset):
         cdef:
             Py_ssize_t i, count = i8other.size
             int64_t val, res_val
-            ndarray out = cnp.PyArray_EMPTY(i8other.ndim, i8other.shape, cnp.NPY_INT64, 0)
+            ndarray out = cnp.PyArray_EMPTY(i8other.ndim, i8other.shape, cnp.NPY_INT64,
+                                            0)
             npy_datetimestruct dts
             int wday, days, weeks, n = self.n
             int anchor_weekday = self.weekday
diff --git a/pandas/_libs/tslibs/parsing.pyx b/pandas/_libs/tslibs/parsing.pyx
index 1312124cfb..c65d678c08 100644
--- a/pandas/_libs/tslibs/parsing.pyx
+++ b/pandas/_libs/tslibs/parsing.pyx
@@ -418,7 +418,8 @@ cdef parse_datetime_string_with_reso(
             from pandas import Timestamp
             parsed = Timestamp(date_string)
         else:
-            parsed = datetime(dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us)
+            parsed = datetime(dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, 
+                              dts.us)
         reso = {
             NPY_DATETIMEUNIT.NPY_FR_Y: "year",
             NPY_DATETIMEUNIT.NPY_FR_M: "month",
@@ -717,7 +718,7 @@ def try_parse_dates(
             date = datetime.now()
             default = datetime(date.year, date.month, 1)

-        parse_date = lambda x: du_parse(x, dayfirst=dayfirst, default=default)
+        parse_date = def x: du_parse(x, dayfirst=dayfirst, default=default)

         # EAFP here
         try:
@@ -1050,6 +1051,7 @@ def guess_datetime_format(dt_str: str, bint dayfirst=False) -> str | None:
     else:
         return None

+
 cdef str _fill_token(token: str, padding: int):
     cdef str token_filled
     if '.' not in token:
@@ -1064,6 +1066,7 @@ cdef str _fill_token(token: str, padding: int):
         token_filled = f'{seconds}.{nanoseconds}'
     return token_filled

+
 @cython.wraparound(False)
 @cython.boundscheck(False)
 cdef inline object convert_to_unicode(object item, bint keep_trivial_numbers):
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index be6f877912..d50fd9ade1 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -1053,7 +1053,8 @@ def period_asfreq_arr(ndarray[int64_t] arr, int freq1, int freq2, bint end):
     cdef:
         Py_ssize_t n = len(arr)
         Py_ssize_t increment = arr.strides[0] // 8
-        ndarray[int64_t] result = cnp.PyArray_EMPTY(arr.ndim, arr.shape, cnp.NPY_INT64, 0)
+        ndarray[int64_t] result = cnp.PyArray_EMPTY(arr.ndim, arr.shape, cnp.NPY_INT64,  
+                                                    0)

     _period_asfreq(
         <int64_t*>cnp.PyArray_DATA(arr),
@@ -1362,7 +1363,6 @@ def get_period_field_arr(str field, const int64_t[:] arr, int freq):
     cdef:
         Py_ssize_t i, sz
         int64_t[::1] out
-        accessor f

     func = _get_accessor_func(field)
     if func is NULL:
@@ -1439,7 +1439,7 @@ def extract_ordinals(ndarray values, freq) -> np.ndarray:
         Py_ssize_t i, n = values.size
         int64_t ordinal
         ndarray ordinals = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_INT64, 0)
-        cnp.broadcast mi = cnp.PyArray_MultiIterNew2(ordinals, values)
+        cnp.broadcast mi = cnp.PyArray_MultiIterNew2(ordinals, values) 
         object p

     if values.descr.type_num != cnp.NPY_OBJECT:
@@ -2478,7 +2478,8 @@ class Period(_Period):
         the start or the end of the period, but rather the entire period itself.
     freq : str, default None
         One of pandas period strings or corresponding objects. Accepted
-        strings are listed in the :ref:`offset alias section <timeseries.offset_aliases>` in the user docs.
+        strings are listed in the :ref:`offset alias section 
+        <timeseries.offset_aliases>` in the user docs. 
     ordinal : int, default None
         The period offset from the proleptic Gregorian epoch.
     year : int, default None
@@ -2511,7 +2512,6 @@ class Period(_Period):
         # ('T', 5) but may be passed in as a string like '5T'

         # ordinal is the period offset from the gregorian proleptic epoch
-        cdef _Period self

         if freq is not None:
             freq = cls._maybe_convert_freq(freq)
diff --git a/pandas/_libs/tslibs/strptime.pyx b/pandas/_libs/tslibs/strptime.pyx
index 6287c2fbc5..f540ad19c4 100644
--- a/pandas/_libs/tslibs/strptime.pyx
+++ b/pandas/_libs/tslibs/strptime.pyx
@@ -75,7 +75,6 @@ def array_strptime(ndarray[object] values, str fmt, bint exact=True, errors='rai
         int iso_week, iso_year
         int64_t us, ns
         object val, group_key, ampm, found, timezone
-        dict found_key
         bint is_raise = errors=='raise'
         bint is_ignore = errors=='ignore'
         bint is_coerce = errors=='coerce'
diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index f3de67b705..62b30855a9 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -176,7 +176,8 @@ def ints_to_pytimedelta(ndarray m8values, box=False):
         #  `it` iterates C-order as well, so the iteration matches
         #  See discussion at
         #  github.com/pandas-dev/pandas/pull/46886#discussion_r860261305
-        ndarray result = cnp.PyArray_EMPTY(m8values.ndim, m8values.shape, cnp.NPY_OBJECT, 0)
+        ndarray result = cnp.PyArray_EMPTY(m8values.ndim, m8values.shape, 
+                                           cnp.NPY_OBJECT, 0)
         object[::1] res_flat = result.ravel()     # should NOT be a copy

         ndarray arr = m8values.view("i8")
@@ -468,7 +469,8 @@ cdef inline int64_t _item_to_timedelta64_fastpath(object item) except? -1:
         return parse_timedelta_string(item)


-cdef inline int64_t _item_to_timedelta64(object item, str parsed_unit, str errors) except? -1:
+cdef inline int64_t _item_to_timedelta64(object item, str parsed_unit, str errors) 
+    except? -1:
     """
     See array_to_timedelta64.
     """
@@ -967,7 +969,6 @@ cdef _timedelta_from_value_and_reso(int64_t value, NPY_DATETIMEUNIT reso):
             "Only resolutions 's', 'ms', 'us', 'ns' are supported."
         )

-
     td_base.value = value
     td_base._is_populated = 0
     td_base._creso = reso
@@ -1570,7 +1571,7 @@ class Timedelta(_Timedelta):
                            "milliseconds", "microseconds", "nanoseconds"}

     def __new__(cls, object value=_no_input, unit=None, **kwargs):
-        cdef _Timedelta td_base
+        cdef _Timedelta 

         if value is _no_input:
             if not len(kwargs):
@@ -1625,7 +1626,8 @@ class Timedelta(_Timedelta):
             if len(kwargs):
                 # GH#48898
                 raise ValueError(
-                    "Cannot pass both a Timedelta input and timedelta keyword arguments, got "
+                    "Cannot pass both a Timedelta input and timedelta keyword 
+                     arguments, got "
                     f"{list(kwargs.keys())}"
                 )
             return value
@@ -1712,7 +1714,7 @@ class Timedelta(_Timedelta):
     @cython.cdivision(True)
     def _round(self, freq, mode):
         cdef:
-            int64_t result, unit, remainder
+            int64_t result, unit,
             ndarray[int64_t] arr

         from pandas._libs.tslibs.offsets import to_offset
@@ -1802,7 +1804,7 @@ class Timedelta(_Timedelta):

     def __truediv__(self, other):
         cdef:
-            int64_t new_value
+            int64_t 

         if _should_cast_to_timedelta(other):
             # We interpret NaT as timedelta64("NaT")
diff --git a/pandas/_libs/tslibs/timestamps.pyx b/pandas/_libs/tslibs/timestamps.pyx
index 3c3bb8496a..95fc683ed4 100644
--- a/pandas/_libs/tslibs/timestamps.pyx
+++ b/pandas/_libs/tslibs/timestamps.pyx
@@ -267,7 +267,6 @@ cdef class _Timestamp(ABCTimestamp):
     @classmethod
     def _from_value_and_reso(cls, int64_t value, NPY_DATETIMEUNIT reso, tzinfo tz):
         cdef:
-            npy_datetimestruct dts
             _TSObject obj = _TSObject()

         if value == NPY_NAT:
@@ -294,8 +293,8 @@ cdef class _Timestamp(ABCTimestamp):
         # This is herely mainly so we can incrementally implement non-nano
         #  (e.g. only tznaive at first)
         cdef:
-            npy_datetimestruct dts
-            int64_t value
+            npy_datetimestruct 
+            int64_t value 
             NPY_DATETIMEUNIT reso

         reso = get_datetime64_unit(dt64)
@@ -317,7 +316,6 @@ cdef class _Timestamp(ABCTimestamp):
     def __richcmp__(_Timestamp self, object other, int op):
         cdef:
             _Timestamp ots
-            int ndim

         if isinstance(other, _Timestamp):
             ots = other
@@ -1532,7 +1530,7 @@ class Timestamp(_Timestamp):
                 if (is_integer_object(tz)
                     and is_integer_object(ts_input)
                     and is_integer_object(freq)
-                ):
+                     ):
                     # GH#31929 e.g. Timestamp(2019, 3, 4, 5, 6, tzinfo=foo)
                     # TODO(GH#45307): this will still be fragile to
                     #  mixed-and-matched positional/keyword arguments
@@ -1675,7 +1673,8 @@ class Timestamp(_Timestamp):
             if not is_offset_object(freq):
                 freq = to_offset(freq)

-        return create_timestamp_from_ts(ts.value, ts.dts, ts.tzinfo, freq, ts.fold, ts.creso)
+        return create_timestamp_from_ts(ts.value, ts.dts, ts.tzinfo, freq, ts.fold,
+                                        ts.creso)

     def _round(self, freq, mode, ambiguous='raise', nonexistent='raise'):
         cdef:
diff --git a/pandas/_libs/tslibs/tzconversion.pyx b/pandas/_libs/tslibs/tzconversion.pyx
index e2812178a2..030113df86 100644
--- a/pandas/_libs/tslibs/tzconversion.pyx
+++ b/pandas/_libs/tslibs/tzconversion.pyx
@@ -224,14 +224,13 @@ timedelta-like}
     """
     cdef:
         ndarray[uint8_t, cast=True] ambiguous_array
-        Py_ssize_t i, idx, pos, n = vals.shape[0]
-        Py_ssize_t delta_idx_offset, delta_idx, pos_left, pos_right
+        Py_ssize_t i, n = vals.shape[0]
+        Py_ssize_t delta_idx_offset, delta_idx,  
         int64_t v, left, right, val, new_local, remaining_mins
         int64_t first_delta, delta
         int64_t shift_delta = 0
         ndarray[int64_t] result_a, result_b, dst_hours
-        int64_t[::1] result
-        npy_datetimestruct dts
+        int64_t[::1] result 
         bint infer_dst = False, is_dst = False, fill = False
         bint shift_forward = False, shift_backward = False
         bint fill_nonexist = False
diff --git a/pandas/_libs/tslibs/vectorized.pyx b/pandas/_libs/tslibs/vectorized.pyx
index 6a6b156af3..0a16cf38ee 100644
--- a/pandas/_libs/tslibs/vectorized.pyx
+++ b/pandas/_libs/tslibs/vectorized.pyx
@@ -155,7 +155,7 @@ def ints_to_pydatetime(
     elif box == "timestamp":
         use_ts = True
     elif box == "time":
-        use_time = True
+        # use_time = True
     elif box == "datetime":
         use_pydt = True
     else:
diff --git a/pandas/_libs/window/aggregations.pyx b/pandas/_libs/window/aggregations.pyx
index 68c05f2bb2..8e08d63477 100644
--- a/pandas/_libs/window/aggregations.pyx
+++ b/pandas/_libs/window/aggregations.pyx
@@ -172,7 +172,8 @@ def roll_sum(const float64_t[:] values, ndarray[int64_t] start,
                     add_sum(values[j], &nobs, &sum_x, &compensation_add,
                             &num_consecutive_same_value, &prev_value)

-            output[i] = calc_sum(minp, nobs, sum_x, num_consecutive_same_value, prev_value)
+            output[i] = calc_sum(minp, nobs, sum_x, num_consecutive_same_value, 
+                                 prev_value)

             if not is_monotonic_increasing_bounds:
                 nobs = 0
@@ -296,7 +297,8 @@ def roll_mean(const float64_t[:] values, ndarray[int64_t] start,
                     add_mean(val, &nobs, &sum_x, &neg_ct, &compensation_add,
                              &num_consecutive_same_value, &prev_value)

-            output[i] = calc_mean(minp, nobs, neg_ct, sum_x, num_consecutive_same_value, prev_value)
+            output[i] = calc_mean(minp, nobs, neg_ct, sum_x, num_consecutive_same_value, 
+                                  prev_value)

             if not is_monotonic_increasing_bounds:
                 nobs = 0
@@ -310,7 +312,8 @@ def roll_mean(const float64_t[:] values, ndarray[int64_t] start,


 cdef inline float64_t calc_var(int64_t minp, int ddof, float64_t nobs,
-                               float64_t ssqdm_x, int64_t num_consecutive_same_value) nogil:
+                               float64_t ssqdm_x, int64_t num_consecutive_same_value) 
+                               nogil:
     cdef:
         float64_t result

@@ -330,7 +333,8 @@ cdef inline float64_t calc_var(int64_t minp, int ddof, float64_t nobs,

 cdef inline void add_var(float64_t val, float64_t *nobs, float64_t *mean_x,
                          float64_t *ssqdm_x, float64_t *compensation,
-                         int64_t *num_consecutive_same_value, float64_t *prev_value) nogil:
+                         int64_t *num_consecutive_same_value, float64_t *prev_value) 
+                         nogil:
     """ add a value from the var calc """
     cdef:
         float64_t delta, prev_mean, y, t
@@ -566,7 +570,7 @@ def roll_skew(ndarray[float64_t] values, ndarray[int64_t] start,
               ndarray[int64_t] end, int64_t minp) -> np.ndarray:
     cdef:
         Py_ssize_t i, j
-        float64_t val, prev, min_val, mean_val, sum_val = 0
+        float64_t val, min_val, mean_val, sum_val = 0
         float64_t compensation_xxx_add, compensation_xxx_remove
         float64_t compensation_xx_add, compensation_xx_remove
         float64_t compensation_x_add, compensation_x_remove
@@ -574,7 +578,7 @@ def roll_skew(ndarray[float64_t] values, ndarray[int64_t] start,
         float64_t prev_value
         int64_t nobs = 0, N = len(start), V = len(values), nobs_mean = 0
         int64_t s, e, num_consecutive_same_value
-        ndarray[float64_t] output, mean_array, values_copy
+        ndarray[float64_t] output, values_copy
         bint is_monotonic_increasing_bounds

     minp = max(minp, 3)
@@ -779,7 +783,7 @@ def roll_kurt(ndarray[float64_t] values, ndarray[int64_t] start,
               ndarray[int64_t] end, int64_t minp) -> np.ndarray:
     cdef:
         Py_ssize_t i, j
-        float64_t val, prev, mean_val, min_val, sum_val = 0
+        float64_t val, mean_val, min_val, sum_val = 0
         float64_t compensation_xxxx_add, compensation_xxxx_remove
         float64_t compensation_xxx_remove, compensation_xxx_add
         float64_t compensation_xx_remove, compensation_xx_add
@@ -853,7 +857,8 @@ def roll_kurt(ndarray[float64_t] values, ndarray[int64_t] start,
                              &compensation_xxx_add, &compensation_xxxx_add,
                              &num_consecutive_same_value, &prev_value)

-            output[i] = calc_kurt(minp, nobs, x, xx, xxx, xxxx, num_consecutive_same_value)
+            output[i] = calc_kurt(minp, nobs, x, xx, xxx, xxxx, 
+                                  num_consecutive_same_value)

             if not is_monotonic_increasing_bounds:
                 nobs = 0
@@ -876,7 +881,7 @@ def roll_median_c(const float64_t[:] values, ndarray[int64_t] start,
         bint err = False, is_monotonic_increasing_bounds
         int midpoint, ret = 0
         int64_t nobs = 0, N = len(start), s, e, win
-        float64_t val, res, prev
+        float64_t val, res,
         skiplist_t *sl
         ndarray[float64_t] output

@@ -1149,7 +1154,7 @@ def roll_quantile(const float64_t[:] values, ndarray[int64_t] start,
         Py_ssize_t i, j, s, e, N = len(start), idx
         int ret = 0
         int64_t nobs = 0, win
-        float64_t val, prev, midpoint, idx_with_fraction
+        float64_t val, idx_with_fraction
         float64_t vlow, vhigh
         skiplist_t *skiplist
         InterpolationType interpolation_type
@@ -1275,7 +1280,7 @@ def roll_rank(const float64_t[:] values, ndarray[int64_t] start,
     derived from roll_quantile
     """
     cdef:
-        Py_ssize_t i, j, s, e, N = len(start), idx
+        Py_ssize_t i, j, s, e, N = len(start),
         float64_t rank_min = 0, rank = 0
         int64_t nobs = 0, win
         float64_t val
diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py
index 3e4f116953..89ac1c1025 100644
--- a/pandas/errors/__init__.py
+++ b/pandas/errors/__init__.py
@@ -283,7 +283,7 @@ class SettingWithCopyError(ValueError):
     The ``mode.chained_assignment`` needs to be set to set to 'raise.' This can
     happen unintentionally when chained indexing.

-    For more information on eveluation order,
+    For more information on evaluation order,
     see :ref:`the user guide<indexing.evaluation_order>`.

     For more information on view vs. copy,
@@ -306,7 +306,7 @@ class SettingWithCopyWarning(Warning):
     'Warn' is the default option. This can happen unintentionally when
     chained indexing.

-    For more information on eveluation order,
+    For more information on evaluation order,
     see :ref:`the user guide<indexing.evaluation_order>`.

     For more information on view vs. copy,
diff --git a/pandas/io/sas/byteswap.pyx b/pandas/io/sas/byteswap.pyx
index 4620403910..a83419b15b 100644
--- a/pandas/io/sas/byteswap.pyx
+++ b/pandas/io/sas/byteswap.pyx
@@ -1,5 +1,6 @@
 """
-The following are faster versions of struct.unpack that avoid the overhead of Python function calls.
+The following are faster versions of struct.unpack that avoid the overhead of Python  
+function calls.

 In the SAS7BDAT parser, they may be called up to (n_rows * n_cols) times.
 """
diff --git a/pandas/io/sas/sas.pyx b/pandas/io/sas/sas.pyx
index 9406900b69..3e8471907f 100644
--- a/pandas/io/sas/sas.pyx
+++ b/pandas/io/sas/sas.pyx
@@ -253,8 +253,10 @@ cdef:


 def _init_subheader_signatures():
-    subheaders_32bit = [(sig, idx) for sig, idx in const.subheader_signature_to_index.items() if len(sig) == 4]
-    subheaders_64bit  = [(sig, idx) for sig, idx in const.subheader_signature_to_index.items() if len(sig) == 8]
+    subheaders_32bit = [(sig, idx) for sig, idx 
+                                in const.subheader_signature_to_index.items() if len(sig) == 4]
+    subheaders_64bit  = [(sig, idx) for sig, idx 
+                                in const.subheader_signature_to_index.items() if len(sig) == 8]
     assert len(subheaders_32bit) == 13
     assert len(subheaders_64bit) == 17
     assert len(const.subheader_signature_to_index) == 13 + 17
@@ -366,7 +368,6 @@ cdef class Parser:
     def read(self, int nrows):
         cdef:
             bint done
-            int i

         for _ in range(nrows):
             done = self.readline()
@@ -490,7 +491,8 @@ cdef class Parser:
             rpos = self.decompress(source, decompressed_source)
             if rpos != self.row_length:
                 raise ValueError(
-                    f"Expected decompressed line of length {self.row_length} bytes but decompressed {rpos} bytes"
+                    f"Expected decompressed line of length {self.row_length} bytes but 
+                      decompressed {rpos} bytes"
                 )
             source = decompressed_source

(END)

Sorry here is the full output

[ramvikrams]

I can't see what you've modified in pandas/_libs/algos.pyx. if you open a draft pull request that might make it easier to tell what's going on

Should I open it or the full output is fine