map() function is everywhere in Python. It's a built in, it's part of the
concurrent.futures.Executor, and also
multiprocessing.Pool; but... it's limited!
It's limited because you cannot pass multiple arguments to it. However, what if I told you that there's some easy ways you can do that?
In this post, I’m going to show what you can do to map a function that expects multiple arguments. By the end of this article, you'll know:
- what is a map function and the problem with it
- how to map two or more arguments with
- how to use
functools.partialto "freeze" and pass multiple arguments to map
- the way to map multiple arguments by "repeating" them
- how to pass multiple args to multiprocessing
- how to pass multiple arguments to a concurrent futures ProcessPoolExecutor (or ThreadPoolExecutor)?
What Is a Map Function and the Problem With It
map() is a function that expects one or more iterables and a function as arguments.
For each item in these iterables,
map applies the function passed as argument. The result is an iterator where each element is produced by the function you provided as argument. If you pass multiple iterables, you must pass a function that accepts that many arguments.
Let’s imagine that you have a function called
sum_four that takes 4 arguments and returns their sum.
def sum_four(a, b, c, d): return a + b + c + d
Let’s also suppose that you are solving a very specific problem that requires the first 3 arguments to be fixed. In this problem, you want to compare how the function behaves when you vary only the last parameter.
1, 2, 3 sum_four(a=a, b=b, c=c, d=1) 7 sum_four(a=a, b=b, c=c, d=2) 8 sum_four(a=a, b=b, c=c, d=3) 9 sum_four(a=a, b=b, c=c, d=4) 10a, b, c =
Now, say that you want to use
map, because you like functional programming, or maybe because you come from a language that encourages this paradigm.
d varies, we could store all potential values for
d we want to test in a list like this
all_d_values = [1, 2, 3, 4].
The issue is, given a function and a list of single elements, if you want to pass that list to a
map function and it takes only one element, what can you do?
Solution 1 - Mapping Multiple Arguments with
The first solution is to not adopt the
map function but use
itertools.starmap instead. This function will take a function as arguments and an iterable of tuples. Then,
starmap will iterate over each tuple
t and call the function by unpacking the arguments, like this
for t in tuples: function(*t).
To make things more clear, consider the following example.
import itertools all_d_values = [1, 2, 3, 4] items = ((a, b, c, d) for d in all_d_values) list(items) [(1, 2, 3, 1), (1, 2, 3, 2), (1, 2, 3, 3), (1, 2, 3, 4)] list(itertools.starmap(sum_four, items)) [7, 8, 9, 10]
As you can see, there’s a lot of repetition, which may inevitably consume a lot of memory if the list is big. To improve that I made
items as a generator, this way we only hold in memory the element we’ll be processing.
Solution 2 - Using
functools.partial to “Freeze” the Arguments
The second solution is to use currying and create a new partial function. According to the docs,
partial() will "freeze" some portion of a function’s arguments and/or keywords resulting in a new function with a simplified signature.
import functools partial_sum_four = functools.partial(sum_four, a, b, c) partial_sum_four(3) 9 list(map(partial_sum_four, all_d_values)) [7, 8, 9, 10]
Solution 3 - Mapping Multiple Arguments by "Repeating" Them
The third alternative is to use the
This function produces an iterator that returns object over and over again. It will run indefinitely if you don’t specify the times argument.
If we take a closer look at
map()'s signature, it accepts a function and multiple iterables,
map(function, iterable, ...).
According to its description,
If additional iterable arguments are passed, function must take that many arguments and is applied to the items from all iterables in parallel. With multiple iterables, the iterator stops when the shortest iterable is exhausted.
Bingo! We can make
c infitnite iterables by using
itertools.repeat(). As soon as
all_d_values is exhausted, which is the shortest iterable,
map() will stop.
import itertools list(map(sum_four, itertools.repeat(a), itertools.repeat(b), itertools.repeat(c), all_d_values)) [7, 8, 9, 10]
To put it another way, using
repeat() is roughly equivalent to:
1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], all_d_values)) [7, 8, 9, 10]list(map(sum_four, [
You don't need to worry too much about memory as
repeat produces the elements on the go. In fact, it returns a
list [ref] .
Problem 2: Passing Multiple Parameters to multiprocessing
This problem is very similar to using the regular
map(). The only difference is that we need to pass multiple arguments to the multiprocessing's pool map.
Suppose that we want to speed up our code and run
sum_four in parallel using processes.
The good news is, you can use the solutions above, with one exception:
Pool.map only accepts one iterable. This means we cannot use
repeat() here. Let's see the alternatives.
Pool class from
multiprocessing module implements a
starmap function that works the same way as its counterpart from the
from multiprocessing import Pool import itertools def sum_four(a, b, c, d): return a + b + c + d a, b, c = 1, 2, 3 all_d_values = [1, 2, 3, 4] items = [(a, b, c, d) for d in all_d_values] items [(1, 2, 3, 1), (1, 2, 3, 2), (1, 2, 3, 3), (1, 2, 3, 4)] with Pool(processes=4) as pool: res = pool.starmap(sum_four, items) res [7, 8, 9, 10]
As alternative, we can also rely on the good
import functools partial_sum_four = functools.partial(sum_four, a, b, c) with Pool(processes=4) as pool: res = pool.map(partial_sum_four, all_d_values) res [7, 8, 9, 10]
Problem 3: How to Pass Multiple Arguments to concurrent futures
concurrent.futures module provides a high-level interface called
Executor to run callables asynchronously.
There are two different implementations available, a
ThreadPoolExecutor and a
Executor does not have a
startmap() function. However, its
map() implementation supports multiple iterables, which allow us to use
repeat(). Another difference is that
Executor.map returns a generator, not a list.
partial() With a ProcessPoolExecutor (or ThreadPoolExecutor)
By "freezing" the arguments using
partial we use the
map method from
ProcessPoolExecutor like a regular map function. Since they both share the same interface, you can do the same interchangeably with a
from concurrent.futures import ProcessPoolExecutor import functools def sum_four(a, b, c, d): return a + b + c + d a, b, c = 1, 2, 3 all_d_values = [1, 2, 3, 4] partial_sum_four = functools.partial(sum_four, a, b, c) with ProcessPoolExecutor(max_workers=4) as pool: res = list(pool.map(partial_sum_four, all_d_values)) res [7, 8, 9, 10]
Again, we can just use
itertools.repeat to get the job done like the previous solutions.
from concurrent.futures import ProcessPoolExecutor from itertools import repeat def sum_four(a, b, c, d): return a + b + c + d a, b, c = 1, 2, 3 all_d_values = [1, 2, 3, 4] with ProcessPoolExecutor(max_workers=4) as pool: res = list(pool.map(sum_four, repeat(a), repeat(b), repeat(c), all_d_values)) res [7, 8, 9, 10]
That’s it for today, folks! I hope you’ve learned something different and useful. The
map() function makes Python feel like a functional programming language.
map() is available not only as a built-in function but also as methods in the
concurrent.futures module. In this article, I showed what I do to map functions that take several arguments.
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