PERF: is_list_like

asv_bench/benchmarks/libs.py

@@ -0,0 +1,42 @@
+"""
+Benchmarks for code in pandas/_libs, excluding pandas/_libs/tslibs,
+which has its own directory
+"""
+import numpy as np
+
+from pandas._libs.lib import (
+    is_list_like,
+    is_scalar,
+)
+
+from pandas import (
+    NA,
+    NaT,
+)
+
+# TODO: share with something in pd._testing?
+scalars = [
+    0,
+    1.0,
+    1 + 2j,
+    True,
+    "foo",
+    b"bar",
+    None,
+    np.datetime64(123, "ns"),
+    np.timedelta64(123, "ns"),
+    NaT,
+    NA,
+]
+zero_dims = [np.array("123")]
+listlikes = [np.array([1, 2, 3]), {0: 1}, {1, 2, 3}, [1, 2, 3], (1, 2, 3)]
+
+
+class ScalarListLike:
+    params = scalars + zero_dims + listlikes
+
+    def time_is_list_like(self, param):
+        is_list_like(param)
+
+    def time_is_scalar(self, param):
+        is_scalar(param)

pandas/_libs/lib.pyx

@@ -1059,11 +1059,12 @@ def is_list_like(obj: object, allow_sets: bool = True) -> bool:
 
 cdef inline bint c_is_list_like(object obj, bint allow_sets) except -1:
     return (
-        isinstance(obj, abc.Iterable)
+        # equiv: `isinstance(obj, abc.Iterable)`
+        hasattr(obj, "__iter__") and not isinstance(obj, type)
         # we do not count strings/unicode/bytes as list-like
         and not isinstance(obj, (str, bytes))
         # exclude zero-dimensional numpy arrays, effectively scalars
-        and not (util.is_array(obj) and obj.ndim == 0)
+        and not cnp.PyArray_IsZeroDim(obj)
         # exclude sets if allow_sets is False
         and not (allow_sets is False and isinstance(obj, abc.Set))
     )