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TST: Add astype_nansafe datetime tests #28533

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TST: Add astype_nansafe datetime tests #28533

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TST: Add astype_nansafe datetime tests
Sep 19, 2019
e8b324d
Add some uint types
Sep 19, 2019
2b598f5
Edit test name
Sep 20, 2019
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22 changes: 22 additions & 0 deletions pandas/tests/dtypes/test_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@

import pandas.util._test_decorators as td

from pandas.core.dtypes.cast import astype_nansafe
import pandas.core.dtypes.common as com
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
Expand Down Expand Up @@ -706,3 +707,24 @@ def test__get_dtype_fails(input_param, expected_error_message):
)
def test__is_dtype_type(input_param, result):
assert com._is_dtype_type(input_param, lambda tipo: tipo == result)


@pytest.mark.parametrize("from_type", [np.datetime64, np.timedelta64])
@pytest.mark.parametrize(
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Could this get used elsewhere? If so might be good to make a shared fixture for numeric_dtype or something to the effect

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This particular setup might be a little specific in that all the to_types have lesser precision than the from_types, but did you have something in mind?

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this is ok

"to_type",
[
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can you add a test for the object path (for dtype=) when its a datetime/timedelta

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What would the expected output be for that case? I would think it'd agree with astype from numpy when we don't have any NA values but the result is a bit odd:

[ins] In [3]: arr = np.array([np.datetime64("2018")])                                          

[ins] In [4]: arr.astype("object")                                                             
Out[4]: array([datetime.date(2018, 1, 1)], dtype=object)

[ins] In [5]: astype_nansafe(arr, dtype="object")                                              
Out[5]: array([datetime.datetime(1970, 1, 1, 0, 0)], dtype=object)

np.uint8,
np.uint16,
np.uint32,
np.int8,
np.int16,
np.int32,
np.float16,
np.float32,
],
)
def test_astype_datetime64_bad_dtype_raises(from_type, to_type):
arr = np.array([from_type("2018")])

with pytest.raises(TypeError, match="cannot astype"):
astype_nansafe(arr, dtype=to_type)