I have a method that calls 4 other methods in sequence to check for specific conditions, and returns immediately (not checking the following ones) whenever one returns something Truthy.

def check_all_conditions():
    x = check_size()
    if x:
        return x

    x = check_color()
    if x:
        return x

    x = check_tone()
    if x:
        return x

    x = check_flavor()
    if x:
        return x
    return None

This seems like a lot of baggage code. Instead of each 2-line if statement, I’d rather do something like:

x and return x

But that is invalid Python. Am I missing a simple, elegant solution here? Incidentally, in this situation, those four check methods may be expensive, so I do not want to call them multiple times.

You could use a loop:

conditions = (check_size, check_color, check_tone, check_flavor)
for condition in conditions:
    result = condition()
    if result:
        return result

This has the added advantage that you can now make the number of conditions variable.

You could use map() + filter() (the Python 3 versions, use the future_builtins versions in Python 2) to get the first such matching value:

try:
    # Python 2
    from future_builtins import map, filter
except ImportError:
    # Python 3
    pass

conditions = (check_size, check_color, check_tone, check_flavor)
return next(filter(None, map(lambda f: f(), conditions)), None)

but if this is more readable is debatable.

Another option is to use a generator expression:

conditions = (check_size, check_color, check_tone, check_flavor)
checks = (condition() for condition in conditions)
return next((check for check in checks if check), None)

Alternatively to Martijn’s fine answer, you could chain or. This will return the first truthy value, or None if there’s no truthy value:

def check_all_conditions():
    return check_size() or check_color() or check_tone() or check_flavor() or None

Demo:

>>> x = [] or 0 or {} or -1 or None
>>> x
-1
>>> x = [] or 0 or {} or '' or None
>>> x is None
True

Don’t change it

There are other ways of doing this as the various other answers show. None are as clear as your original code.

In effectively the same answer as timgeb, but you could use parenthesis for nicer formatting:

def check_all_the_things():
    return (
        one()
        or two()
        or five()
        or three()
        or None
    )

According to Curly’s law, you can make this code more readable by splitting two concerns:

• What things do I check?
• Has one thing returned true?

into two functions:

def all_conditions():
    yield check_size()
    yield check_color()
    yield check_tone()
    yield check_flavor()

def check_all_conditions():
    for condition in all_conditions():
        if condition:
            return condition
    return None

This avoids:

• complicated logical structures
• really long lines
• repetition

…while preserving a linear, easy to read flow.

You can probably also come up with even better function names, according to your particular circumstance, which make it even more readable.

This is a variant of Martijns first example. It also uses the “collection of callables”-style in order to allow short-circuiting.

Instead of a loop you can use the builtin any.

conditions = (check_size, check_color, check_tone, check_flavor)
return any(condition() for condition in conditions) 

Note that any returns a boolean, so if you need the exact return value of the check, this solution will not work. any will not distinguish between 14, 'red', 'sharp', 'spicy' as return values, they will all be returned as True.

Have you considered just writing if x: return x all on one line?

def check_all_conditions():
    x = check_size()
    if x: return x

    x = check_color()
    if x: return x

    x = check_tone()
    if x: return x

    x = check_flavor()
    if x: return x

    return None

This isn’t any less repetitive than what you had, but IMNSHO it reads quite a bit smoother.

I’m quite surprised nobody mentioned the built-in any which is made for this purpose:

def check_all_conditions():
    return any([
        check_size(),
        check_color(),
        check_tone(),
        check_flavor()
    ])

Note that although this implementation is probably the clearest, it evaluates all the checks even if the first one is True.

If you really need to stop at the first failed check, consider using reduce which is made to convert a list to a simple value:

def check_all_conditions():
    checks = [check_size, check_color, check_tone, check_flavor]
    return reduce(lambda a, f: a or f(), checks, False)

reduce(function, iterable[, initializer]) : Apply function of two arguments cumulatively to the items of iterable, from left to right, so as to reduce the iterable to a single value. The left argument, x, is the accumulated value and the right argument, y, is the update value from the iterable. If the optional initializer is present, it is placed before the items of the iterable in the calculation

In your case:

• lambda a, f: a or f() is the function that checks that either the accumulator a or the current check f() is True. Note that if a is True, f() won’t be evaluated.
• checks contains check functions (the f item from the lambda)
• False is the initial value, otherwise no check would happen and the result would always be True

any and reduce are basic tools for functional programming. I strongly encourage you to train these out as well as map which is awesome too!

If you want the same code structure, you could use ternary statements!

def check_all_conditions():
    x = check_size()
    x = x if x else check_color()
    x = x if x else check_tone()
    x = x if x else check_flavor()

    return x if x else None

I think this looks nice and clear if you look at it.

Demo:

For me, the best answer is that from @phil-frost, followed by @wayne-werner’s.

What I find interesting is that no one has said anything about the fact that a function will be returning many different data types, which will make then mandatory to do checks on the type of x itself to do any further work.

So I would mix @PhilFrost’s response with the idea of keeping a single type:

def all_conditions(x):
    yield check_size(x)
    yield check_color(x)
    yield check_tone(x)
    yield check_flavor(x)

def assessed_x(x,func=all_conditions):
    for condition in func(x):
        if condition:
            return x
    return None

Notice that x is passed as an argument, but also all_conditions is used as a passed generator of checking functions where all of them get an x to be checked, and return True or False. By using func with all_conditions as default value, you can use assessed_x(x), or you can pass a further personalised generator via func.

That way, you get x as soon as one check passes, but it will always be the same type.

Ideally, I would re-write the check_ functions to return True or False rather than a value. Your checks then become

if check_size(x):
    return x
#etc

Assuming your x is not immutable, your function can still modify it (although they can’t reassign it) – but a function called check shouldn’t really be modifying it anyway.

A slight variation on Martijns first example above, that avoids the if inside the loop:

Status = None
for c in [check_size, check_color, check_tone, check_flavor]:
  Status = Status or c();
return Status

I like @timgeb’s. In the meantime I would like to add that expressing None in the return statement is not needed as the collection of or separated statements are evaluated and the first none-zero, none-empty, none-None is returned and if there isn’t any then None is returned whether there is a None or not!

So my check_all_conditions() function looks like this:

def check_all_conditions():
    return check_size() or check_color() or check_tone() or check_flavor()

Using timeit with number=10**7 I looked at the running time of a number of the suggestions. For the sake of comparison I just used the random.random() function to return a string or None based on random numbers. Here is the whole code:

import random
import timeit

def check_size():
    if random.random() < 0.25: return "BIG"

def check_color():
    if random.random() < 0.25: return "RED"

def check_tone():
    if random.random() < 0.25: return "SOFT"

def check_flavor():
    if random.random() < 0.25: return "SWEET"

def check_all_conditions_Bernard():
    x = check_size()
    if x:
        return x

    x = check_color()
    if x:
        return x

    x = check_tone()
    if x:
        return x

    x = check_flavor()
    if x:
        return x
    return None

def check_all_Martijn_Pieters():
    conditions = (check_size, check_color, check_tone, check_flavor)
    for condition in conditions:
        result = condition()
        if result:
            return result

def check_all_conditions_timgeb():
    return check_size() or check_color() or check_tone() or check_flavor() or None

def check_all_conditions_Reza():
    return check_size() or check_color() or check_tone() or check_flavor()

def check_all_conditions_Phinet():
    x = check_size()
    x = x if x else check_color()
    x = x if x else check_tone()
    x = x if x else check_flavor()

    return x if x else None

def all_conditions():
    yield check_size()
    yield check_color()
    yield check_tone()
    yield check_flavor()

def check_all_conditions_Phil_Frost():
    for condition in all_conditions():
        if condition:
            return condition

def main():
    num = 10000000
    random.seed(20)
    print("Bernard:", timeit.timeit('check_all_conditions_Bernard()', 'from __main__ import check_all_conditions_Bernard', number=num))
    random.seed(20)
    print("Martijn Pieters:", timeit.timeit('check_all_Martijn_Pieters()', 'from __main__ import check_all_Martijn_Pieters', number=num))
    random.seed(20)
    print("timgeb:", timeit.timeit('check_all_conditions_timgeb()', 'from __main__ import check_all_conditions_timgeb', number=num))
    random.seed(20)
    print("Reza:", timeit.timeit('check_all_conditions_Reza()', 'from __main__ import check_all_conditions_Reza', number=num))
    random.seed(20)
    print("Phinet:", timeit.timeit('check_all_conditions_Phinet()', 'from __main__ import check_all_conditions_Phinet', number=num))
    random.seed(20)
    print("Phil Frost:", timeit.timeit('check_all_conditions_Phil_Frost()', 'from __main__ import check_all_conditions_Phil_Frost', number=num))

if __name__ == '__main__':
    main()

And here are the results:

Bernard: 7.398444877040768
Martijn Pieters: 8.506569201346597
timgeb: 7.244275416364456
Reza: 6.982133448743038
Phinet: 7.925932800076634
Phil Frost: 11.924794811353031

This way is a little bit outside of the box, but I think the end result is simple, readable, and looks nice.

The basic idea is to raise an exception when one of the functions evaluates as truthy, and return the result. Here’s how it might look:

def check_conditions():
    try:
        assertFalsey(
            check_size,
            check_color,
            check_tone,
            check_flavor)
    except TruthyException as e:
        return e.trigger
    else:
        return None

You’ll need a assertFalsey function that raises an exception when one of the called function arguments evaluates as truthy:

def assertFalsey(*funcs):
    for f in funcs:
        o = f()
        if o:
            raise TruthyException(o)

The above could be modified so as to also provide arguments for the functions to be evaluated.

And of course you’ll need the TruthyException itself. This exception provides the object that triggered the exception:

class TruthyException(Exception):
    def __init__(self, obj, *args):
        super().__init__(*args)
        self.trigger = obj

You can turn the original function into something more general, of course:

def get_truthy_condition(*conditions):
    try:
        assertFalsey(*conditions)
    except TruthyException as e:
        return e.trigger
    else:
        return None

result = get_truthy_condition(check_size, check_color, check_tone, check_flavor)

This might be a bit slower because you are using both an if statement and handling an exception. However, the exception is only handled a maximum of one time, so the hit to performance should be minor unless you expect to run the check and get a True value many many thousands of times.

The pythonic way is either using reduce (as someone already mentioned) or itertools (as shown below), but it seems to me that simply using short circuiting of the or operator produces clearer code

from itertools import imap, dropwhile

def check_all_conditions():
    conditions = (check_size,\
        check_color,\
        check_tone,\
        check_flavor)
    results_gen = dropwhile(lambda x:not x, imap(lambda check:check(), conditions))
    try:
        return results_gen.next()
    except StopIteration:
        return None

I’m going to jump in here and have never written a single line of Python, but I assume if x = check_something(): return x is valid?

if so:

def check_all_conditions():

    if (x := check_size()): return x
    if (x := check_color()): return x
    if (x := check_tone()): return x
    if (x := check_flavor()): return x

    return None

Or use max:

def check_all_conditions():
    return max(check_size(), check_color(), check_tone(), check_flavor()) or None

I have seen some interesting implementations of switch/case statements with dicts in the past that led me to this answer. Using the example you’ve provided you would get the following. (It’s madness using_complete_sentences_for_function_names, so check_all_conditions is renamed to status. See (1))

def status(k = 'a', s = {'a':'b','b':'c','c':'d','d':None}) :
  select = lambda next, test : test if test else next
  d = {'a': lambda : select(s['a'], check_size()  ),
       'b': lambda : select(s['b'], check_color() ),
       'c': lambda : select(s['c'], check_tone()  ),
       'd': lambda : select(s['d'], check_flavor())}
  while k in d : k = d[k]()
  return k

The select function eliminates the need to call each check_FUNCTION twice i.e. you avoid check_FUNCTION() if check_FUNCTION() else next by adding another function layer. This is useful for long running functions. The lambdas in the dict delay execution of it’s values until the while loop.

As a bonus you may modify the execution order and even skip some of the tests by altering k and s e.g. k='c',s={'c':'b','b':None} reduces the number of tests and reverses the original processing order.

The timeit fellows might haggle over the cost of adding an extra layer or two to the stack and the cost for the dict look up but you seem more concerned with the prettiness of the code.

Alternatively a simpler implementation might be the following :

def status(k=check_size) :
  select = lambda next, test : test if test else next
  d = {check_size  : lambda : select(check_color,  check_size()  ),
       check_color : lambda : select(check_tone,   check_color() ),
       check_tone  : lambda : select(check_flavor, check_tone()  ),
       check_flavor: lambda : select(None,         check_flavor())}
  while k in d : k = d[k]()
  return k

1. I mean this not in terms of pep8 but in terms of using one concise descriptive word in place of a sentence. Granted the OP may be following some coding convention, working one some existing code base or not care for terse terms in their codebase.