This is problematic, as it binds a name to a lambda, so needs re-work.

I think this is also referenced in the introduction. We'll need to remove all of them.

…I now see I also left a temporarily inserted list comprehension in there.

Would you rather have

def sum_of_multiples(limit, factors):
    return sum(filter(
        lambda n: any(n % f == 0 for f in factors if f != 0),
        range(limit)
    ))

and keep the explanation of lambda, or

def sum_of_multiples(limit, factors):
    def is_multiple (n):
        return any(n % f == 0 for f in factors if f != 0)

    return sum(filter(is_multiple, range(limit)))

Either work for me. The first might be preferable, considering that you've already done a good explanation of lambda. The second one would need an explanation of nested functions. Absolutely not opposed to doing that - but it is extra work for you.

lambda can sometimes be slower in filter or map, since it opens another stack frame. But that's sorta irrelevant to this particular exercise, methinks.

lambda can sometimes be slower in filter or map, since it opens another stack frame.

Can you elaborate or link to a source on this? I had no idea.

The TL;DR is that lambda has all the overhead/execution of any other function call (here's a link to python tutor for the two examples above), so in any situation where you are using a lambda over a built-in, or in place of a generator expression that does the same/similar operation, you will incur the 'extra' overhead of the function call. Trivial in small/medium cases -- but it can add up for larger data sets.

And where you're converting that filter + lambda into a list or other 'realized' structure, it will be a lot slower than the corresponding comprehension or generator, due to the overhead of calling an additional function for every item in the list.

But this varies widely (since python 3.x returns iterators instead of lists) - if you don't need to realize the values and can consume them lazily (like in a call to sum()), then filter/map/reduce outperform comprehensions, and are mostly even for generators. But again, a generator that doesn't call an extra function will be faster than filter + lambda (because of the lambda function call).

Here are some articles - but many of them assume that values need to be realized, and so aren't really comparing apples to apples. The finxter blog (apologies for the aggressive ads there!) does do the comparisons for both realized and un-realized data - and you can really see the difference.

• finxter: Which is Faster?
• Towards Data Science: List Comprehensions vs built-in functions
• Stack Overflow: Comprehension vs Lambda + Filter
• Stack Overflow: Generators vs Filter
• Stack Overflow: Do lambda expressions help reduce execution time?
• (this one is a bit of a digression, and shows function call overhead)How not to be slow using Python: functions
• (more and wider digression on function cacheing)Python caching functions

And I'll provide this for completeness, although its really mostly a rant about personal preference with readability, and not on performance: Trey Hunner: Stop Writing Lambda Expressions

Thanks. I haven't read all the links yet, but the gist certainly makes sense. I thought previously you meant that

def f(x): return f_body(x)

map(f, xs)
# be faster than
map(lambda x: f_body(x), xs)

That in-lined functions in comprehensions are faster than mapped functions I already expected.

I really like this explanation. Both from a cursor for typing ... and a cursor from a DB. Both can only be gone through once, and you can't back up. 😄