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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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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.24.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -500,7 +500,7 @@ Datetimelike
Timedelta
^^^^^^^^^

-
- Fixed bug where :meth:`DataFrame.astype` could not convert timedelta strings (:issue:`#22100`)
-
-

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3 changes: 2 additions & 1 deletion pandas/core/dtypes/cast.py
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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_timedelta64_dtype(dtype)
if np.issubdtype(dtype.type, np.integer) and is_time:
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use is_integer_dtype and also add a comment here (and can remove the existing work around comment)

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Ok, but it breaks:

pandas/pandas/tests/reshape/merge/test_merge.py

Lines 531 to 538 in f76a3f3

def test_other_timedelta_unit(self, unit):
# GH 13389
df1 = pd.DataFrame({'entity_id': [101, 102]})
s = pd.Series([None, None], index=[101, 102], name='days')
dtype = "m8[{}]".format(unit)
df2 = s.astype(dtype).to_frame('days')
assert df2['days'].dtype == 'm8[ns]'

Because of:

pandas/pandas/core/dtypes/cast.py

Lines 688 to 697 in f76a3f3

if ((PY3 and dtype not in [_INT64_DTYPE, _TD_DTYPE]) or
(not PY3 and dtype != _TD_DTYPE)):
# allow frequency conversions
# we return a float here!
if dtype.kind == 'm':
mask = isna(arr)
result = arr.astype(dtype).astype(np.float64)
result[mask] = np.nan
return result

Are this necessary? Here timedelta64 are turned to float64.
Should I change the test or this part of astype_nansafe?
Also, I've notices that conversions like pd.Series([None, 1, 2]).astype('timedelta64') do not work now. Should I open a new issue?

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Might be missing something but shouldn't it not be going down this branch? i.e. isn't the statement dtype not in [_INT64_DTYPE, _TD_DTYPE] supposed to be false?

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Is IntNADType handled correctly here?

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@WillAyd yah at this point I’m not sure if it’s tangential, but it looks like the TDDTYPE check should go above the (PY3 ...) check in line 688, after which that check is always-true.

return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)

# if we have a datetime/timedelta array of objects
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13 changes: 12 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 +457,13 @@ 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, expected', [
(np.array(['0:0:1'], dtype='O'), 'm8[ns]', 'm8[ns]'),
(np.array(['0:0:1'], dtype='O'), 'm8', 'float64'),
])
def test_astype_nansafe(arr, dtype, expected):
# GH #22100
result = astype_nansafe(arr, dtype)
assert is_dtype_equal(result.dtype, expected)