Introduction

In this post, I’m going to show what you can do to map a function that expects multiple arguments.

There are multiple ways of doing that and they work not only for the regular map function, you can also use the trick to pass multiple parameters to Executor.map from the concurrent.futures and multiprocessing.Pool.

Table of Contents

1. The Problem With the map() Function

   1.1. Solution 1 - Mapping Multiple Arguments with itertools.starmap()

   1.2. Solution 2 - Using functools.partial to “Freeze” the Arguments

   1.3. Solution 3 - Mapping Multiple Arguments by "Repeating" Them

2. Problem 2: Passing Multiple Parameters to multiprocessing Pool.map

   2.1. Using pool.starmap

   2.2. Using partial()

3. Problem 3: How to Pass Multiple Arguments to a concurrent futures ProcessPoolExecutor (or ThreadPoolExecutor)?

   3.1. Using partial()

   3.2. Using repeat()

4. Conclusion

The Problem With the map() Function

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.

>>> a, b, c = 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)
 10

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.

Since only 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 itertools.starmap()

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 itertools.repeat().

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 a, b and 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:

>>> list(map(sum_four, [1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], all_d_values))
 [7, 8, 9, 10]

You don't need to worry too much about memory as repeat produces the elements on the go. In fact, it returns a repeatobject, not list [ref] .

Problem 2: Passing Multiple Parameters to multiprocessing Pool.map

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.

Using pool.starmap

The Pool class from multiprocessing module implements a starmap function that works the same way as its counterpart from the itertools module.

>>> 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]

Using partial()

As alternative, we can also rely on the good partial function.

>>> 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 Executor.map?

The concurrent.futures module provides a high-level interface called Executor to run callables asynchronously.

There are two different implementations available, a ThreadPoolExecutor and a ProcessPoolExecutor.

Contrary to multiprocessing.Pool, 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.

Using 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 ThreadPoolExecutor

>>> 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]

Using repeat()

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]

Conclusion

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 multiprocessing and concurrent.futures module. In this article, I showed what I do to map functions that take several arguments.

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This post was originally published at https://miguendes.me/how-to-pass-multiple-arguments-to-a-map-function-in-python

See you next time!