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Description: float-to-datetime conversion fixes
Avoid assuming that NaN casts to NaT (= fails on riscv64/hppa ?)
Don't round to int for the bounds check when we don't for the real
conversion (wrong near the bounds, and maybe also a waste of time)
Author: Rebecca N. Palmer <rebecca_palmer@zoho.com>
Forwarded: https://github.com/pandas-dev/pandas/pull/50183
--- a/pandas/_libs/tslib.pyx
+++ b/pandas/_libs/tslib.pyx
@@ -296,12 +296,18 @@ def array_with_unit_to_datetime(
# if we have nulls that are not type-compat
# then need to iterate
- if values.dtype.kind in ["i", "f", "u"]:
+ if values.dtype.kind in ["i", "u"]:
iresult = values.astype("i8", copy=False)
# fill missing values by comparing to NPY_NAT
mask = iresult == NPY_NAT
iresult[mask] = 0
- fvalues = iresult.astype("f8") * mult
+ fvalues = iresult.astype("f8") * mult # TODO as this is only used for bounds checking, would it be more efficient to divide the bounds by mult? (don't just use integer iresult * mult, we need arithmetic overflow to be an error not a wraparound)
+ need_to_iterate = False
+
+ if values.dtype.kind in ["f",]:
+ mask = (values != values) | (values == NPY_NAT) # first is NaNs
+ fvalues = (values * mult).astype("f8") # TODO would values.astype('f8')*mult have less rounding error? (or does this need to support longer-than-double floats where it's worse?) would copy=False be faster?
+ fvalues[mask] = 0
need_to_iterate = False
if not need_to_iterate:
@@ -315,11 +321,9 @@ def array_with_unit_to_datetime(
result = (iresult * mult).astype("M8[ns]")
elif values.dtype.kind == "f":
- fresult = (values * mult).astype("f8")
- fresult[mask] = 0
if prec:
- fresult = round(fresult, prec)
- result = fresult.astype("M8[ns]", copy=False)
+ fvalues = round(fvalues, prec) # TODO why does this exist, it looks like prec's always 0 (was p from precision_to_unit meant to be prec?)
+ result = fvalues.astype("M8[ns]", copy=False)
iresult = result.view("i8")
iresult[mask] = NPY_NAT
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