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

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

Author: pre-commit-ci[bot]

pandas/core/frame.py

@@ -7235,7 +7235,7 @@ def sort_values(
             indexer = lexsort_indexer(
                 keys_data, orders=ascending, na_position=na_position, key=key
             )
-        elif len(by):
+        elif by:
             # len(by) == 1
 
             k = self._get_label_or_level_values(by[0], axis=axis)

pandas/core/indexes/base.py

@@ -7635,7 +7635,7 @@ def ensure_index(index_like: Axes, copy: bool = False) -> Index:
             # check in clean_index_list
             index_like = list(index_like)
 
-        if len(index_like) and lib.is_all_arraylike(index_like):
+        if index_like and lib.is_all_arraylike(index_like):
             from pandas.core.indexes.multi import MultiIndex
 
             return MultiIndex.from_arrays(index_like)

pandas/io/parsers/base_parser.py

@@ -243,7 +243,7 @@ def extract(r):
             names.insert(single_ic, single_ic)
 
         # Clean the column names (if we have an index_col).
-        if len(ic):
+        if ic:
             col_names = [
                 r[ic[0]]
                 if ((r[ic[0]] is not None) and r[ic[0]] not in self.unnamed_cols)

pandas/io/parsers/python_parser.py

@@ -281,7 +281,7 @@ def read(
 
         index: Index | None
         columns: Sequence[Hashable] = list(self.orig_names)
-        if not len(content):  # pragma: no cover
+        if not content:  # pragma: no cover
             # DataFrame with the right metadata, even though it's length 0
             # error: Cannot determine type of 'index_col'
             names = dedup_names(

pandas/tests/arithmetic/test_datetime64.py

@@ -9,7 +9,6 @@
 )
 from itertools import (
     product,
-    starmap,
 )
 import operator
 
@@ -2211,7 +2210,7 @@ def test_timedelta64_equal_timedelta_supported_ops(self, op, box_with_array):
 
         def timedelta64(*args):
             # see casting notes in NumPy gh-12927
-            return np.sum(list(starmap(np.timedelta64, zip(args, intervals))))
+            return np.sum(list(map(np.timedelta64, args, intervals)))
 
         for d, h, m, s, us in product(*([range(2)] * 5)):
             nptd = timedelta64(d, h, m, s, us)