srowen commented on a change in pull request #30745:
URL: https://github.com/apache/spark/pull/30745#discussion_r579821551
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File path: python/pyspark/sql/functions.py
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@@ -222,6 +222,45 @@ def sum_distinct(col):
return _invoke_function_over_column("sum_distinct", col)
+def product(col, scale=1.0):
+ """
+ Aggregate function: returns the product of the values in a group.
+
+ .. versionadded:: 3.2.0
+
+ Parameters
+ ----------
+ col : str, :class:`Column`
+ column containing values to be multiplied together
+ scale : float
Review comment:
I get that a very large float loses some precision as it only has so
many significant digits to play with. But does scaling them make it more
precise? each multiplication by 0.01 or whatever also introduces some error. It
won't stop the final result from overflowing a double if in fact it's so large
right? or is the idea that you don't want the actual product, but the scaled
product? In that case can you compute `product(0.01 * $"col")` anyway, that
kind of thing?
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