handle generic list-like

Author: jreback

pandas/_libs/lib.pyx

@@ -886,6 +886,22 @@ def is_period(val: object) -> bool:
  return util.is_period_object(val)
 
 
+cpdef inline bint is_list_like(object obj):
+ """
+ Parameters
+ ----------
+ obj : object
+
+ Returns
+ -------
+ bool : if this is a list-like object
+ """
+ return (hasattr(obj, '__iter__') and
+ not isinstance(obj, (str, bytes))
+ # exclude zero-dimensional numpy arrays, effectively scalars
+ and not (isinstance(obj, ndarray) and obj.ndim == 0))
+
+
 _TYPE_MAP = {
  'categorical': 'categorical',
  'category': 'categorical',

pandas/_libs/reshape.pyx

@@ -4,6 +4,7 @@ from cython import Py_ssize_t
 from numpy cimport (int8_t, int16_t, int32_t, int64_t, uint8_t, uint16_t,
  uint32_t, uint64_t, float32_t, float64_t, ndarray)
 import numpy
+from pandas._libs.lib import is_list_like
 
 
 ctypedef fused reshape_t:
@@ -120,7 +121,7 @@ def explode(ndarray[object] values):
  counts = numpy.zeros(n, dtype='uint8')
  for i in range(n):
  v = values[i]
- if isinstance(v, (list, tuple)):
+ if is_list_like(v):
  if len(v):
  counts[i] += len(v)
  else:
@@ -134,7 +135,7 @@ def explode(ndarray[object] values):
  for i in range(n):
  v = values[i]
 
- if isinstance(v, (list, tuple)):
+ if is_list_like(v):
  if len(v):
  for j in range(len(v)):
  result[count] = v[j]

pandas/tests/series/test_explode.py

@@ -17,7 +17,9 @@ def test_basic():
 
 
 def test_mixed_type():
- s = pd.Series([[0, 1, 2], np.nan, None, [], ("a", "b")], name="foo")
+ s = pd.Series(
+ [[0, 1, 2], np.nan, None, np.array([]), pd.Series(["a", "b"])], name="foo"
+ )
  result = s.explode()
  expected = pd.Series(
  [0, 1, 2, np.nan, None, np.nan, "a", "b"],