Update: You can find a newer version of this article on Python Morsels.

In this article, we’re going to look at a common programming pattern and discuss how we can refactor our code when we notice this pattern. 🏗

We’ll be discussing how to make code with this shape a little more descriptive:

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all_good = True
for item in iterable:
    if not condition(item):
        all_good = False
        break

An Example: Primality

Here’s a function that checks whether a given number is prime by trying to divide it by all numbers below it:

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def is_prime(candidate):
    for n in range(2, candidate):
        if candidate % n == 0:
            return False
    return True

Note: a square root makes this faster and our code breaks below 2 but we’ll ignore those issues here

This function:

1. loops from 2 to the given number
2. returns False as soon as a divisor is found
3. returns True if no divisor was found

This primality check is asking “do any numbers evenly divide the candidate number”.

Note that this function returns as soon as it finds a divisor, so it only iterates all the way through the number range when the candidate number is prime.

Let’s take a look at how we can rewrite this function using all.

What’s all?

Python has a built-in function all that returns True if all items are truthy

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>>> all(['hello, 'there'])
True
>>> all(['hello, 'there', ''])
False
>>> all([1, 2, 3])
True
>>> all([0, 1, 2, 3])
False

You can think of truthy as meaning non-empty or non-zero. For our purposes, we’ll treat it as pretty much the same as True.

The all built-in function is equivalent to this:

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def all(iterable):
    for element in iterable:
        if not element:
            return False
    return True

Notice the similarity between all and our is_prime function? Our is_prime function is similar, but they’re not quite the same structure.

The all function checks for the truthiness of element, but we need something a little more than that: we need to check a condition on each element (whether it’s a divsior).

Using all

Our original is_prime function looks like this:

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def is_prime(candidate):
    for n in range(2, candidate):
        if candidate % n == 0:
            return False
    return True

If we want to use all in this function, we need an iterable (like a list) to pass to all.

If we wanted to be really silly, we could make such a list of boolean values like this:

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def is_prime(candidate):
  divisibility = []
    for n in range(2, candidate):
        if candidate % n == 0:
            divisibility.append(False)
        else:
            divisibility.append(True)
  return all(divisibility)

We could simplify this function like this:

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def is_prime(candidate):
  divisibility = []
    for n in range(2, candidate):
      divisibility.append(candidate % n != 0)
  return all(divisibility)

I know this is probably doesn’t seem like progress, but bear with me for a few more steps…

List comprehensions

If you’re familiar with list comprehensions, this code structure might look a little familiar. We’re creating one iterable from another which is exactly what list comprehensions are good for.

Let’s copy-paste our way into a list comprehension (see my article on how to write list comprehensions):

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def is_prime(candidate):
  divisibility = [
      candidate % n != 0
      for n in range(2, candidate)
  ]
  return all(divisibility)

That’s quite a bit shorter, but there’s a problem: we’re building up an entire list just to loop over it once!

This is less efficient than our original approach, which only looped all the way when candidate was prime.

Let’s fix this inefficiency by turning our list comprehension into a generator expression.

Generator expressions

A generator expression is like a list comprehension, but instead of making a list it makes a generator object.

A generator is an iterator: generators don’t compute the items they contain until you loop over them. We’ll see what that means in a moment.

We can turn our list comprehension into a generator expression by changing the brackets to parentheses:

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def is_prime(candidate):
  divisibility = (
      candidate % n != 0
      for n in range(2, candidate)
  )
  return all(divisibility)

Now our code doesn’t create a list to loop over. Instead it provides us with a generator that allows us to compute the divisibility of each number one-by-one.

We can make this code even more readable by putting that generator expression inside the function call (notice that we can drop the second set of parentheses):

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def is_prime(candidate):
  return all(
      candidate % n != 0
      for n in range(2, candidate)
    )

Note that because our generator is lazy, we stop computing divisibilities as soon as our all function finds a divisible number. So we end up calculating candidate % n != 0 only as many times as we did in our original function.

Recap

So we started with a for loop, an if statement, a return statement for stopping once we find a divisor, and a return statement for the case where our number had no divisors (when it’s prime).

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def is_prime(candidate):
    for n in range(2, candidate):
        if candidate % n == 0:
            return False
    return True

We turned all that into a generator expression passed to the all function.

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def is_prime(candidate):
    return all(
        candidate % n != 0
        for n in range(2, candidate)
    )

I prefer this second approach (a generator expression with all) because I find it more descriptive.

We’re checking to see whether “all numbers in a range are not divisors of our candidate number”. That sounds quite a bit more like English to me than “loop over all numbers in a range and return False if a divisor is found otherwise return True”.

If you don’t find the behavior of all intuitive, you might find it easier to understand (and more English-like) when used with if:

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if all(condition(item) for item in iterable):
    message = "All good"
else:
    message = "Bad value found"

You can always reformat your code to use an if statement if you find it more readable.

any or all

We’ve been working with the all function, but I haven’t mentioned it’s counterpart: the any function. Let’s take a look at how all and any compare.

These two expressions:

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all_good = all(
    condition(x)
    for x in things
)
some_bad = not all(
    condition(x)
    for x in things
)

Are equivalent to these two expressions (because of DeMorgan’s Laws):

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all_good = not any(
    not condition(x)
    for x in things
)
some_bad = any(
    not condition(x)
    for x in things
)

So this code:

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def is_prime(candidate):
    return all(
        candidate % n != 0
        for n in range(2, candidate)
    )

Is feature-identical to this code:

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def is_prime(candidate):
    return not any(
        candidate % n == 0
        for n in range(2, candidate)
    )

Both of them stop as soon as they find a divisor.

I find the use of all more readable here, but I wanted to mention that any would work just as well.

Cheat sheet for refactoring with any and all

All that explanation above was valuable, but how can we use this new knowledge to refactor our own code? Here’s a cheat sheet for you.

Anytime you see code like this:

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all_good = True
for item in iterable:
    if not condition(item):
        all_good = False
        break

You can replace that code with this:

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all_good = all(
    condition(item)
    for item in iterable
)

Anytime you see code like this:

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any_good = False
for item in iterable:
    if condition(item):
        any_good = True
        break

You can replace it with this:

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any_good = any(
    condition(item)
    for item in iterable
)

Note that break is used in the code above because we’re not returning from a function. Using return (like we did in is_prime) is another way to stop our loop early.

Python’s any and all functions were made for use with generator expressions (discussion here and here). You can use any and all without generator expressions, but I don’t find a need for that as often.

Quick note: any(item == 'something' for item in iterable) is the same as 'something' in iterable. Don’t use all/any for checking containment, use in.

Code style is a process

As you discover new Python idioms and new language features are invented, your code style will evolve. Your preferred code style may never stop evolving. Code style is not concrete: it’s a process.

I hope I’ve inspired you to embrace the use of any/all with generator expressions for improved readability and code clarity.

If you’d like to get practice with the any and all functions right now, sign up for Python Morsels using the form below to get an exercise that benefits from using one of these two functions.

I made Python Morsels to help experienced programmers level up their Python skills every week. For more details on it, [see the Python Morsels][python morsels] website.