Spenserrrr commented on code in PR #58319:
URL: https://github.com/apache/spark/pull/58319#discussion_r3876682978
##########
python/pyspark/pandas/numpy_compat.py:
##########
@@ -152,17 +152,29 @@ def _copysign_func(c1: Column, c2: Column) -> Column:
def _fmod_func(c1: Column, c2: Column) -> Column:
c1_double = c1.cast("double")
c2_double = c2.cast("double")
+ integral_types = ["tinyint", "smallint", "int", "bigint"]
+ # Dispatched on type twice: floating operands need a NaN for a zero
divisor, and among the
+ # rest, at the end of the branch below, only integral operands can take
the remainder in
+ # integer space.
return F.when(
F.typeof(c1).isin("float", "double") | F.typeof(c2).isin("float",
"double"),
F.when(c1.isNull() | F.isnan(c1), c1_double)
.when(c2.isNull() | F.isnan(c2), c2_double)
.when(c2_double == 0, F.lit(float("nan")))
.otherwise(F.try_mod(c1_double, c2_double)),
).otherwise(
+ # Non-floating operands, where NumPy normalizes a zero divisor to 0
instead of a NaN.
Review Comment:
I also flattened the dispatch here to match your suggestion on #58306, with
one branch per operand type.
One thing I noticed while doing it: NumPy raises on Decimal objects too, so
the decimal branch has no NumPy behavior to match. I kept your original answer
there, which is 0 for a zero divisor and the remainder in double. I think
fixing it properly needs the same dtype check as the string case.
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