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Handle NaN in array_with_unit_to_datetime #48705

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Merged
merged 3 commits into from
Dec 13, 2022
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Handle NaN in array_with_unit_to_datetime #48705

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Handle NaN in array_with_unit_to_datetime
andreas-schwab Sep 21, 2022
a4e921d
Merge branch 'main' into main
MarcoGorelli Dec 13, 2022
c4c8d20
Update pandas/_libs/tslib.pyx
MarcoGorelli Dec 13, 2022
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4 changes: 4 additions & 0 deletions pandas/_libs/tslib.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -298,6 +298,10 @@ def array_with_unit_to_datetime(
iresult = values.astype("i8", copy=False)
# fill missing values by comparing to NPY_NAT
mask = iresult == NPY_NAT
# Trying to Convert NaN to integer results in undefined
# behaviour, so handle it explicitly
if values.dtype.kind == "f":
mask |= values != values
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on the relevant hardware, what do you get for iresult when values is [np.nan]?

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$ python3
Python 3.10.7 (main, Sep 11 2022, 08:41:56) [GCC] on linux
Type "help", "copyright", "credits" or "license" for more information.

import numpy as np
np.array([np.nan]).astype("i8")
array([9223372036854775807])

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@seberg is getting 9223372036854775807 instead of 9223372036854775808 weird or is it just me?

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42 would also be a valid result.

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NumPy has no guarantees for NaN -> int64 casts currently! (Although they are better implemented, in a sense). So what you get is completely off-bets. For example on arm, you may get 0!
And that breaks mask = iresult == NPY_NAT? There should be tests failing here I would think?

However, NumPy does have guarantees for NaN -> datetime/timedelta casts, so going via i8 seems dubious in this case? The cast triggers undefined behavior in C.

I am not sure what you mean with:

42 would also be a valid result.

@andreas-schwab?

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No sure why you ask, you already gave the answer above.

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Misread the code. In any case, NumPy handles the cast correctly for datetime64, even if it uses a slower/weirder code path (currently, this is very much fixable!). So the question is wether it doesn't make more sense to cast to datetime64.

Further, the above may cause floating-point warnings in newer NumPy versions, which is something you probably don't want to see!

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@andreas-schwab i'd be OK with either a) investigating seberg's suggestions for a more in-depth "right way" to handle this, or b) adding a comment pointing back to this thread and being done with it

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this is fine. can you add a comment pointing back to this PR and a few words about why we're doing this

iresult[mask] = 0
fvalues = iresult.astype("f8") * mult
need_to_iterate = False
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