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BUG: DataFrame constructor reordering elements with ndarray from datetime dtype not datetime64[ns] #39442

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Merged
merged 5 commits into from
Jan 28, 2021
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BUG: DataFrame constructor reordering elements with ndarray from datetime dtype not datetime64[ns] #39442

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c4570b1
BUG: DataFrame constructor reordering elements with ndarray from date…
phofl Jan 27, 2021
3cdd101
Parametrize over contiguity
phofl Jan 27, 2021
6238485
Add order
phofl Jan 27, 2021
6b39f01
Change empty construction
phofl Jan 27, 2021
85f0bd4
Add timedelta tests
phofl Jan 28, 2021
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.3.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -233,6 +233,7 @@ Datetimelike
- Bug in constructing a :class:`DataFrame` or :class:`Series` with mismatched ``datetime64`` data and ``timedelta64`` dtype, or vice-versa, failing to raise ``TypeError`` (:issue:`38575`, :issue:`38764`, :issue:`38792`)
- Bug in constructing a :class:`Series` or :class:`DataFrame` with a ``datetime`` object out of bounds for ``datetime64[ns]`` dtype or a ``timedelta`` object out of bounds for ``timedelta64[ns]`` dtype (:issue:`38792`, :issue:`38965`)
- Bug in :meth:`DatetimeIndex.intersection`, :meth:`DatetimeIndex.symmetric_difference`, :meth:`PeriodIndex.intersection`, :meth:`PeriodIndex.symmetric_difference` always returning object-dtype when operating with :class:`CategoricalIndex` (:issue:`38741`)
- Bug in :class:`DataFrame` constructor reordering element when construction from datetime ndarray with dtype not ``"datetime64[ns]"`` (:issue:`39422`)
- Bug in :meth:`Series.where` incorrectly casting ``datetime64`` values to ``int64`` (:issue:`37682`)
- Bug in :class:`Categorical` incorrectly typecasting ``datetime`` object to ``Timestamp`` (:issue:`38878`)
- Bug in comparisons between :class:`Timestamp` object and ``datetime64`` objects just outside the implementation bounds for nanosecond ``datetime64`` (:issue:`39221`)
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2 changes: 1 addition & 1 deletion pandas/_libs/tslibs/conversion.pyx
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Expand Up @@ -224,7 +224,7 @@ def ensure_datetime64ns(arr: ndarray, copy: bool=True):

ivalues = arr.view(np.int64).ravel("K")

result = np.empty(shape, dtype=DT64NS_DTYPE)
result = np.empty_like(arr, dtype=DT64NS_DTYPE)
iresult = result.ravel("K").view(np.int64)

if len(iresult) == 0:
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64 changes: 64 additions & 0 deletions pandas/tests/frame/test_constructors.py
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Expand Up @@ -1762,6 +1762,70 @@ def test_constructor_datetimes_with_nulls(self, arr):
expected = Series([np.dtype("datetime64[ns]")])
tm.assert_series_equal(result, expected)

@pytest.mark.parametrize("order", ["K", "A", "C", "F"])
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can you do the same with timedelta? not sure if that is broken as well.

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This uses numpy astype under the hood, is fine. Added tests.

Is there a way to create a df without a numpy or pandas array which has a dtype other than timedelta64[ns]?
I casted back to compare them

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umm not sure i understand what you are asking

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Sorry, here an example:

expected = DataFrame(
    [
        [Timedelta(days=1), Timedelta(days=2)],
        [Timedelta(days=3), Timedelta(days=4)]
    ], dtype="timedelta64[ms]"
)

returns

             0            1
0   86400000.0  172800000.0
1  259200000.0  345600000.0

with dtype float
while

na = np.array(
    [
        [np.timedelta64(1, 'D'), np.timedelta64(2, 'D')],
        [np.timedelta64(4, 'D'), np.timedelta64(5, 'D')]
    ],
    dtype="timedelta64[ms]",
)
df = DataFrame(na)

returns

       0      1
0 1 days 2 days
1 4 days 5 days

where dtype is timedelta64[ms]

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Question is if we can create the expected in a way to have timedelta64[ms] without using numpy or pandas array

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i dont think so, no. if it were float or int we could use a memoryview directly

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Casting to timedelta64[ns] may seem a bit odd, but does what we want here, so I would keep the current layout. Thx

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yeah agree, i think what you did is fine for the tests.

@pytest.mark.parametrize(
"dtype",
[
"datetime64[M]",
"datetime64[D]",
"datetime64[h]",
"datetime64[m]",
"datetime64[s]",
"datetime64[ms]",
"datetime64[us]",
"datetime64[ns]",
],
)
def test_constructor_datetimes_non_ns(self, order, dtype):
na = np.array(
[
["2015-01-01", "2015-01-02", "2015-01-03"],
["2017-01-01", "2017-01-02", "2017-02-03"],
],
dtype=dtype,
order=order,
)
df = DataFrame(na)
expected = DataFrame(
[
["2015-01-01", "2015-01-02", "2015-01-03"],
["2017-01-01", "2017-01-02", "2017-02-03"],
]
)
expected = expected.astype(dtype=dtype)
tm.assert_frame_equal(df, expected)

@pytest.mark.parametrize("order", ["K", "A", "C", "F"])
@pytest.mark.parametrize(
"dtype",
[
"timedelta64[D]",
"timedelta64[h]",
"timedelta64[m]",
"timedelta64[s]",
"timedelta64[ms]",
"timedelta64[us]",
"timedelta64[ns]",
],
)
def test_constructor_timedelta_non_ns(self, order, dtype):
na = np.array(
[
[np.timedelta64(1, "D"), np.timedelta64(2, "D")],
[np.timedelta64(4, "D"), np.timedelta64(5, "D")],
],
dtype=dtype,
order=order,
)
df = DataFrame(na).astype("timedelta64[ns]")
expected = DataFrame(
[
[Timedelta(1, "D"), Timedelta(2, "D")],
[Timedelta(4, "D"), Timedelta(5, "D")],
],
)
tm.assert_frame_equal(df, expected)

def test_constructor_for_list_with_dtypes(self):
# test list of lists/ndarrays
df = DataFrame([np.arange(5) for x in range(5)])
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