# How to Pass Multiple Arguments to a map Function in Python

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In this post, I’m going to show what I do to map a function that expects multiple arguments. The solutions work not only for the regular `map` function, you can also use the trick to pass multiple parameters to `concurrent.futures.Executor.map` and `multiprocessing.Pool`.

## Problem

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 we want to test in a list `ds = [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

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

>>> ds = [1, 2, 3, 4]

>>> items = ((a, b, c, d) for d in ds)

>>> 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

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.

``````In [29]: import functools

In [30]: partial_sum_four = functools.partial(sum_four, a, b, c)

In [31]: partial_sum_four(3)
Out[31]: 9

In [32]: list(map(partial_sum_four, ds))
Out[32]: [7, 8, 9, 10]
``````

## Solution 3

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 `ds` 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), ds))
[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], ds))
[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: How to Pass Multiple Parameters to `multiprocessing.Pool.map`?

This is very similar to using the regular `map()` with multiple parameters. 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 `starmap`

``````>>> from multiprocessing import Pool

>>> import itertools

>>> def sum_four(a, b, c, d):
return a + b + c + d

>>> a, b, c = 1, 2, 3

>>> ds = [1, 2, 3, 4]

>>> items = [(a, b, c, d) for d in ds]

>>> 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()`

``````
>>> import functools

>>> partial_sum_four = functools.partial(sum_four, a, b, c)

>>> with Pool(processes=4) as pool:
res = pool.map(partial_sum_four, ds)

>>> 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()`

``````>>> 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

>>> ds = [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, ds))

>>> res
[7, 8, 9, 10]
``````

### Using `repeat()`

``````>>> 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

>>> ds = [1, 2, 3, 4]

>>> with ProcessPoolExecutor(max_workers=4) as pool:
res = list(pool.map(sum_four, repeat(a), repeat(b), repeat(c), ds))

>>> 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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