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ENH: check for __array__ instead of ndarray subclasses when creating an Index #7270

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Merged
merged 1 commit into from
May 29, 2014
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ENH: check for __array__ instead of ndarray subclasses when creating an Index #7270

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3 changes: 2 additions & 1 deletion pandas/core/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2038,7 +2038,8 @@ def intersection(*seqs):
def _asarray_tuplesafe(values, dtype=None):
from pandas.core.index import Index

if not isinstance(values, (list, tuple, np.ndarray)):
if not (isinstance(values, (list, tuple))
or hasattr(values, '__array__')):
values = list(values)
elif isinstance(values, Index):
return values.values
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12 changes: 12 additions & 0 deletions pandas/tests/test_index.py
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Expand Up @@ -173,6 +173,18 @@ def test_constructor_from_series(self):
result = pd.infer_freq(df['date'])
self.assertEqual(result,'MS')

def test_constructor_ndarray_like(self):
# GH 5460#issuecomment-44474502
# it should be possible to convert any object that satisfies the numpy
# ndarray interface directly into an Index
class ArrayLike(object):
def __array__(self, dtype=None):
return np.arange(5)

expected = pd.Index(np.arange(5))
result = pd.Index(ArrayLike())
self.assertTrue(result.equals(expected))

def test_index_ctor_infer_periodindex(self):
from pandas import period_range, PeriodIndex
xp = period_range('2012-1-1', freq='M', periods=3)
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