Random Python benchmarks. Is Python fast yet 🐍?
Keyword Args of Partial Functions
TL;DR
Binding keyword arguments in functools.partial is slower than raw calls.
def f(a1, a2, a3, a4, a5, a6, a7):
return sum([a1, a2, a3, a4, a5, a6, a7])
# Raw
f(1, a2=2, a3=3, a4=4, a5=5, a6=6, a7=7)
# Partial
f_partial = functools.partial(f, a2=2, a3=3, a4=4, a5=5, a6=6, a7=7)
f_partial(1)
# Spread
kwargs = {"a2": 2, "a3": 3, "a4": 4, "a5": 5, "a6": 6, "a7": 7}
f(1, **kwargs)| Impl | Time (s) |
|---|---|
| Python (Raw) | 0.135 |
| Python (Partial) | 0.237 |
| Python (Spread) | 0.213 |
In Python, keyword arguments **kwargs are potentially mutable within the function body.
def why_you_have_to_do_this(a, **kwargs):
kwargs["a"] += aTherefore, each call to the function has to create a new dict to store all keyword arguments.
This copy also happens when using functools.partial.
There is no performance gain compared to simply spreading a dict of arguments in the call,
yet you have to pay for the overhead of merging arguments when using partial.