Cythonize is_null_slice

pandas/_libs/lib.pyi

@@ -44,6 +44,7 @@ def is_iterator(obj: object) -> bool: ...
 def is_scalar(val: object) -> bool: ...
 def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ...
 def is_pyarrow_array(obj: object) -> bool: ...
+def is_null_slice(obj: object) -> bool: ...
 def is_period(val: object) -> TypeGuard[Period]: ...
 def is_interval(val: object) -> TypeGuard[Interval]: ...
 def is_decimal(val: object) -> TypeGuard[Decimal]: ...

pandas/_libs/lib.pyx

@@ -29,6 +29,7 @@ from cpython.object cimport (
 )
 from cpython.ref cimport Py_INCREF
 from cpython.sequence cimport PySequence_Check
+from cpython.slice cimport PySlice_Unpack
 from cpython.tuple cimport (
  PyTuple_New,
  PyTuple_SET_ITEM,
@@ -71,6 +72,7 @@ cdef extern from "Python.h":
  # Note: importing extern-style allows us to declare these as nogil
  # functions, whereas `from cpython cimport` does not.
  bint PyObject_TypeCheck(object obj, PyTypeObject* type) nogil
+ cdef Py_ssize_t PY_SSIZE_T_MAX
 
 cdef extern from "numpy/arrayobject.h":
  # cython's numpy.dtype specification is incorrect, which leads to
@@ -1250,6 +1252,20 @@ def is_pyarrow_array(obj):
  return False
 
 
+def is_null_slice(obj):
+ cdef Py_ssize_t start, stop, step
+ if isinstance(obj, slice):
+ try:
+ PySlice_Unpack(obj, &start, &stop, &step)
+ except TypeError:
+ return False
+
+ if start == 0 and stop == PY_SSIZE_T_MAX and step == 1:
+ return True
+
+ return False
+
+
 _TYPE_MAP = {
  "categorical": "categorical",
  "category": "categorical",

pandas/core/common.py

@@ -307,12 +307,7 @@ def is_null_slice(obj) -> bool:
  """
  We have a null slice.
  """
- return (
- isinstance(obj, slice)
- and obj.start is None
- and obj.stop is None
- and obj.step is None
- )
+ return lib.is_null_slice(obj)
 
 
 def is_empty_slice(obj) -> bool: