ENH: Add is_array_like method

Used for abstracting checks in DataFrame.merge,
but the function itself can be quite useful.

Author: gfyoung

pandas/core/dtypes/api.py

@@ -55,6 +55,7 @@
                      is_dict_like,
                      is_iterator,
                      is_file_like,
+                     is_array_like,
                      is_list_like,
                      is_hashable,
                      is_named_tuple)

pandas/core/dtypes/inference.py

@@ -267,6 +267,39 @@ def is_list_like(obj):
             not isinstance(obj, string_and_binary_types))
 
 
+def is_array_like(obj):
+    """
+    Check if the object is array-like.
+
+    For an object to be considered array-like, it must be list-like and
+    have a `dtype` attribute.
+
+    Parameters
+    ----------
+    obj : The object to check.
+
+    Returns
+    -------
+    is_array_like : bool
+        Whether `obj` has array-like properties.
+
+    Examples
+    --------
+    >>> is_array_like(np.array([1, 2, 3]))
+    True
+    >>> is_array_like(pd.Series(["a", "b"]))
+    True
+    >>> is_array_like(pd.Index(["2016-01-01"]))
+    True
+    >>> is_array_like([1, 2, 3])
+    False
+    >>> is_array_like(("a", "b"))
+    False
+    """
+
+    return is_list_like(obj) and hasattr(obj, "dtype")
+
+
 def is_nested_list_like(obj):
     """
     Check if the object is list-like, and that all of its elements

pandas/core/reshape/merge.py

@@ -10,14 +10,15 @@
 from pandas.compat import range, lzip, zip, map, filter
 import pandas.compat as compat
 
-from pandas import (Categorical, Series, DataFrame,
+from pandas import (Categorical, DataFrame,
                     Index, MultiIndex, Timedelta)
 from pandas.core.frame import _merge_doc
 from pandas.core.dtypes.common import (
     is_datetime64tz_dtype,
     is_datetime64_dtype,
     needs_i8_conversion,
     is_int64_dtype,
+    is_array_like,
     is_categorical_dtype,
     is_integer_dtype,
     is_float_dtype,
@@ -817,10 +818,9 @@ def _get_merge_keys(self):
 
         left, right = self.left, self.right
         stacklevel = 5  # Number of stack levels from df.merge
-        list_types = (np.ndarray, Series, Index)
 
-        is_lkey = lambda x: isinstance(x, list_types) and len(x) == len(left)
-        is_rkey = lambda x: isinstance(x, list_types) and len(x) == len(right)
+        is_lkey = lambda x: is_array_like(x) and len(x) == len(left)
+        is_rkey = lambda x: is_array_like(x) and len(x) == len(right)
 
         # Note that pd.merge_asof() has separate 'on' and 'by' parameters. A
         # user could, for example, request 'left_index' and 'left_by'. In a

pandas/tests/api/test_types.py

@@ -30,7 +30,7 @@ class TestTypes(Base):
                'is_period_dtype', 'is_interval', 'is_interval_dtype',
                'is_re', 'is_re_compilable',
                'is_dict_like', 'is_iterator', 'is_file_like',
-               'is_list_like', 'is_hashable',
+               'is_list_like', 'is_hashable', 'is_array_like',
                'is_named_tuple',
                'pandas_dtype', 'union_categoricals', 'infer_dtype']
     deprecated = ['is_any_int_dtype', 'is_floating_dtype', 'is_sequence']

pandas/tests/dtypes/test_inference.py

@@ -78,6 +78,23 @@ def test_is_list_like_fails(ll):
     assert not inference.is_list_like(ll)
 
 
+def test_is_array_like():
+    assert inference.is_array_like(Series([]))
+    assert inference.is_array_like(Series([1, 2]))
+    assert inference.is_array_like(np.array(["a", "b"]))
+    assert inference.is_array_like(Index(["2016-01-01"]))
+
+    class DtypeList(list):
+        dtype = "special"
+
+    assert inference.is_array_like(DtypeList())
+
+    assert not inference.is_array_like([1, 2, 3])
+    assert not inference.is_array_like(tuple())
+    assert not inference.is_array_like("foo")
+    assert not inference.is_array_like(123)
+
+
 @pytest.mark.parametrize('inner', [
     [], [1], (1, ), (1, 2), {'a': 1}, set([1, 'a']), Series([1]),
     Series([]), Series(['a']).str, (x for x in range(5))