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BUG: GH3416 properly convert np.datetime64 objects in the _possibily_convert_datetimes #3496

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BUG: GH3416 properly convert np.datetime64 objects in the _possibily_convert_datetimes #3496

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2 changes: 2 additions & 0 deletions RELEASE.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -58,8 +58,10 @@ pandas 0.11.1
- Duplicate indexes with getitem will return items in the correct order (GH3455_, GH3457_)
- Fix sorting in a frame with a list of columns which contains datetime64[ns] dtypes (GH3461_)
- DataFrames fetched via FRED now handle '.' as a NaN. (GH3469_)
- Properly convert np.datetime64 objects in a Series (GH3416_)

.. _GH3164: https://github.com/pydata/pandas/issues/3164
.. _GH3416: https://github.com/pydata/pandas/issues/3416
.. _GH3251: https://github.com/pydata/pandas/issues/3251
.. _GH3379: https://github.com/pydata/pandas/issues/3379
.. _GH3454: https://github.com/pydata/pandas/issues/3454
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4 changes: 2 additions & 2 deletions pandas/core/common.py
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Expand Up @@ -1119,12 +1119,12 @@ def _possibly_cast_to_datetime(value, dtype, coerce = False):
v = [ v ]
if len(v):
inferred_type = lib.infer_dtype(v)
if inferred_type == 'datetime':
if inferred_type in ['datetime','datetime64']:
try:
value = tslib.array_to_datetime(np.array(v))
except:
pass
elif inferred_type == 'timedelta':
elif inferred_type in ['timedelta','timedelta64']:
value = _possibly_cast_to_timedelta(value)

return value
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14 changes: 14 additions & 0 deletions pandas/tests/test_series.py
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Expand Up @@ -469,6 +469,20 @@ def test_constructor_dtype_datetime64(self):
self.assert_(isnull(s[1]) == True)
self.assert_(s.dtype == 'M8[ns]')

# GH3416
import pdb; pdb.set_trace()
dates = [
np.datetime64(datetime(2013, 1, 1)),
np.datetime64(datetime(2013, 1, 2)),
np.datetime64(datetime(2013, 1, 3)),
]

s = Series(dates)
self.assert_(s.dtype == 'M8[ns]')

s.ix[0] = np.nan
self.assert_(s.dtype == 'M8[ns]')

def test_constructor_dict(self):
d = {'a': 0., 'b': 1., 'c': 2.}
result = Series(d, index=['b', 'c', 'd', 'a'])
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