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Comment by Lord_DeathMatch | Hacker News Reader

Specifically?

Not really a fan of the lambda syntax in Python. Comparison:

JavaScript:

  let arr = [1, 2, 3]
  let sumOfSquares = arr.map(n => n * n).reduce((a, b) => a + b) // 14

Python:

  arr = [1, 2, 3]
  sum_of_squares = reduce(lambda a, b: a + b, map(lambda n: n * n, arr)) # 14

Is there a more Pythonic way to do it? Lambdas are cool but usually not the first place you go in Python. I would think something like (my best guess, not a Python pro)...

    sum_of_squares = sum([x*x for x in arr])

Which I think is easier to read than either example post above.

Of course you will point out that this is less powerful than full map and reduce.. but meh... pros and cons to both styles

I would write it like this, to avoid constructing the immediate list:

    sum_of_squares = sum(x*x for x in arr)

This makes use of https://www.python.org/dev/peps/pep-0289/

Thanks for the link, this is good to know.

What's the advantage of list comprehension over lambdas (assuming the lambda syntax is decently lightweight)?

I feel like I come down hard on the side of lambdas, but I've never really spent enough time in a language with list comprehension, so there's a good chance I'm missing something.

I think it's just less to type really, and it's considered the more standard way to do it.

I find it a lot easier to read, part of which is that I'm used to the Scala way of sequence dot map function. When I see the python one I can't remember if the function comes first or the array.

I'm not positive, but I think it saves the need to create a new execution frame for each lambda call, since the whole loop executes in single frame used by the comprehension.

In theory I suppose the VM could have a map() implementation which opportunistically extracts the code from a lambda and inlines them when possible; but doubt CPython does that. OTOH, I'd be surprised if PyPy doesn't do something like that.

Since Python 3, both generators and lists create a new stack frame. [1] (2nd to last paragraph)

[1] http://python-history.blogspot.com/2010/06/from-list-compreh...

I'm not meaning when the comprehension is invoked, but during each iteration of the loop within the comprehension.

When doing something like `map(lambda x: 2+x, range(100))`, there will be 101 frames created: the outer frame, and 100 for each invocation of the lambda.

Whereas `[2+x for x in range(100)]` will only create 2: one for the outer frame, and one for the comprehension.

how can you come down hard on the side of one when you've never experienced the other?

I'm from a non-list-comprehension background too, but recently started working a lot in a large python codebase, and have found the dict/list comprehensions to be beautiful. I'm a huge fan. It's a shame lambda syntax is not the best and it's generally crippled, but comprehensions are a great 80/20 compromise for handling most cases very cleanly.

That example works only because the function sum is already defined in Python. If you wanted to do something less common than summing up elements you would have to either use reduce or implement a for loop.

Well, or write a static method somewhere that you call. Sum is used a lot, so handy it is written somewhere (vs having to do a lambda x + x thing.

That seems like an argument against lambda functions in general - why use lambdas when you can define a static function for every case? Well, the answer in my opinion is because it makes code more readable if you can define a simple lambda function instead of having to name every single function in the code base.

Well if you are going to reuse the function, name it. If it is a 1 time thing, use a lambda.

Sounds great in theory. Problem is if you need a lambda that isn't a single expression, you then have to name it. Welcome to the conversation.

True, but I've used python a lot, and I've used reduce maybe...twice? (well, twice that I can find on my github at least)

I just counted the number of reduce I used in my current python project (6k lines). reduce comes up 32 times. And by comparison, map is used 159 times and filter 125 times - for some reason I tend to use list comprehensions less than I should.

curious how often reduce is used with something else than operator.add?

One occurence was to calculate the GCD of a list of polynomials.

In fact I had "reduce" appearing in the names of some of my variables so I used it less than 32 times, about 20 times in that project.

I see nothing particularly inspiring in the examples posted on http://stackoverflow.com/questions/15995/useful-code-which-u...

Could you show your reduce calls?

They are very similar to this one from your link: http://stackoverflow.com/questions/15995/useful-code-which-u...

I also thing you use map not a ton if you get the list comprehension syntax down... it is a map with less cruft mostly...

In Python 3, reduce was intentionally moved into the functools library because it was argued that its two biggest use cases by far were sum and product, which were both added as builtins. In my experience, this has very much been the case. Reduce is still there if you need it, and isn't any more verbose. The only thing that is a little bit more gross about this example is the lambda syntax; I would argue that even that is a moot point, however, since Python supports first-class functions, so you can always just write your complicated reduce function out and then plug it in.

Worth noting that map() can be parallelized whereas a list comprehension can't necessarily (since it is an explicit loop). The multiprocessing module allows trivial map parallelization, but can't work on list comprehensions.

It's more than just stylistic.

I don't think this is the case, list comprehensions can be expressed as syntax sugar over list functions, it's how they work in Scala for example

http://docs.scala-lang.org/tutorials/FAQ/yield.html

So I have coded everything from dumb web servers (tm), to high performance trading engines (tm). I have toyed with doing the list in parallel thing... and used it in a toy GUI tool or two I wrote... but never really found it that useful in the real world. If you actually want high performance, doing a parallel map is not going to be fast enough. If you are a dumb web server, it's a waste of overhead 99% of the time.

But hey, if you want to use map when you actually need to do a parallel map, cool. But seems very very uncommon. ~ 1 in 10,000 maps I write.

map() can only be parallelized if the function has no side effects. If there are no side effects, list comprehensions can be parallelized just as well

For simple mathematical operations you can import them as functions:

    from operator import mul, add
    arr = [1, 2, 3]
    sum_of_squares = reduce(add, map(mul, arr, arr))

It's even more concise in Clojure:

    (defn sum-of-squares [a] (reduce + (map #(* % %) a)))
    (sum-of-squares [1, 2, 3]) ; => 14

I think lambda syntax can be a bit cumbersome, but that aside what I really miss is a clean syntax for chaining functional operations. So often I find myself thinking about data in terms of 'pipelines'. i.e. in JS:

  _.chain(values)
   .map(() => {})
   .flatten()
   .compact()
   .uniq()
   .value()

vs Python where doing the same thing becomes either a nested mess of function calls or comprehensions or a for loop.

But that's a function of API, not the language itself. Django (and most ORMs, I believe) support that kind of behavior:

    MyTable.objects.
        filter(some_row__gt=5).
        exlude(other_row='q').
        order_by('other_row')

The Python iterable APIs have decided to use nesting rather than chaining, but you can still have an underscore-like API: https://github.com/serkanyersen/underscore.py

The bigger problem remains: lambda functions are hideous in Python. map() will forever be ugly if you try to use it in the same way it is used in most functional languages.

This sort of API is hard to implement in Python though, because there's no formal notion of interfaces, so you cannot extend all iterables generically. So you need to use free functions (which don't read well when chained) or a wrapper object (ick).

You don't like using pytoolz? Pseudocode:

    result = pytoolz.pipe(values, map, flatten, compact, uniq, value)
    # or
    func = pytoolz.compose(value, uniq, compact, flatten, map)
    results = func(values)

Looks interesting. How do you tell map what function to use?

You probably need `compose(foo, partial(map, fn), bar)`.

Yeah, you do this or use curried version:

    pytoolz.curried.map(fn)

I've been thinking that it might be nice to use chaining (though I didn't know it had a name) in ordinary mathematical notation too, writing "x f g" instead of "g(f(x))".

Elixir and F# have my favorite syntax for that:

    values |> map(&({})) |> flatten |> compact |> uniq

Although, the closure syntax is a little clunky before you get used to it.

I think a big thing for many is the lambda: syntax, as well as the lack of full anonymous functions.

I agree that typing out the word lambda is annoying, but you can use them as fully anonymous functions.

> you can use them as fully anonymous functions.

A lambda can only contain a single expression, by "full anonymous function" I'm guessing hexane360 means multiple statements. You can't put a for loop or a context manager in a lambda for instance.

You can nest lambdas to get the equivalent of several expressions. I once wrote a Runge-Kutta example on Rosetta Code showing this:

http://rosettacode.org/wiki/Runge-Kutta_method#using_lambda

It does not look as bad as one might expect, though the nesting of parenthesis makes things messy.

You can but you still can't get statements in there.

You can hack together multiple expressions chaining them with and http://sigusr2.net/one-line-echo-server-using-let-python.htm...

That still doesn't get you statements.

You can't get context managers or exception handling (although you can raise exceptions) into lambdas, I've tried.

Well you might be able to if you add a bunch of named function combinators wrapping these, but definitely not with only lambdas, unless you define your combinators using `ast`, which I think would let you define statements via expressions.

That's an awful lot of effort to go to to avoid naming a function.

Well sure, you could do something like

    def apply_ctx(ctx, func, *func_args, **func_kwargs):
        with ctx as __ctx:
            func(*func_args, **func_kwargs, ctx=__ctx)

but you can't do that itself as a lambda. And I consider modifying the ast cheating :P

> but you can't do that itself as a lambda. And I consider modifying the ast cheating :P

No disagreement, really depends whether you're a "rules" or "spirit" kind of person though.

The latter can't really happen given Python is a statements- and indentations-based language. You'd need some really weird meta-magical syntax which really isn't going to happen in Python. Although you can cheat by fucking around with decorators e.g.

    def postfix(fn, *args):
        return lambda arg: fn(arg, *args)

    @postfix(map, range(5))
    def result(i):
        return i * i

    print result
    # [0, 1, 4, 9, 16]

(`postfix` is necessary because `map` takes its argument positionally so it's not possible to pass in the sequence with functools.partial)

> The latter can't really happen given Python is a statements- and indentations-based language.

Yeah, though I suppose you could hack around that and get nearly-full functionality in lambdas if you built a library that either wrapped non-expression statements in functions or provided equivalent functions. There are obviously some statements that there aren't good solutions for in that direction.

OTOH, using named functions is in many cases more readable -- in the context of what is otherwise a normal Python codebase -- than the kind of lambdas that you can't easily write in Python. But I like the Coconut approach of but providing a more concise syntax for the kind of lambdas Python already supports.

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