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BUG: astype with timedelta and datetime string (#22100) #22107

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BUG: astype with timedelta and datetime string (#22100) #22107

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8cb0654
BUG: astype with timedelta and datetime string (#22100)
fjdiod Jul 28, 2018
52257e3
fix style
fjdiod Jul 28, 2018
7196cf5
fix test
fjdiod Jul 28, 2018
e508d93
fix m8
fjdiod Jul 29, 2018
3a182a3
Merge remote-tracking branch 'upstream/master' into astype-time
Jul 30, 2018
7be8de9
add whatsnew entry
Jul 30, 2018
21c0520
fix test_astype_nansafe
Jul 31, 2018
74e670b
fix datetime64 in astype_nansafe
Jul 31, 2018
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3 changes: 2 additions & 1 deletion pandas/core/dtypes/cast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -712,7 +712,8 @@ def astype_nansafe(arr, dtype, copy=True):
elif is_object_dtype(arr):

# work around NumPy brokenness, #1987
if np.issubdtype(dtype.type, np.integer):
is_time = is_datetime64_dtype(dtype) or is_timedelta64_dtype(dtype)
if np.issubdtype(dtype.type, np.integer) and not is_time:
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)

# if we have a datetime/timedelta array of objects
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15 changes: 14 additions & 1 deletion pandas/tests/dtypes/test_cast.py
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Expand Up @@ -24,7 +24,8 @@
find_common_type,
construct_1d_object_array_from_listlike,
construct_1d_ndarray_preserving_na,
construct_1d_arraylike_from_scalar)
construct_1d_arraylike_from_scalar,
astype_nansafe)
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
Expand Down Expand Up @@ -456,3 +457,15 @@ def test_cast_1d_arraylike_from_scalar_categorical(self):
def test_construct_1d_ndarray_preserving_na(values, dtype, expected):
result = construct_1d_ndarray_preserving_na(values, dtype=dtype)
tm.assert_numpy_array_equal(result, expected)


@pytest.mark.parametrize('arr, dtype', [
(np.array(['0:0:1'], dtype='object'), 'timedelta64[ns]'),
(np.array(['0:0:1'], dtype='object'), 'timedelta64'),
(np.array(['2000'], dtype='object'), 'datetime64[ns]'),
(np.array(['2000'], dtype='object'), 'datetime64'),
])
def test_astype_nansafe(arr, dtype):
# GH #22100
result = astype_nansafe(arr, dtype)
is_dtype_equal(result.dtype, dtype)
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Should there be an assertion here?

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Piggybacking on this I think it would be better to explicitly parametrize the expected value and make assertions around that.

On a somewhat related note how do we want to handle datetime64[ns, tz]? Off the top of my head I can't think of how to do a conversion to that via astype so maybe should still raise for that?