Pythonic way to avoid "if x: return x" statements

Code structuring with multiline expressions

Avoid nested statements in complex expressions for better clarity. Split your code over several lines and use parentheses to improve visual readability:

return (
    # Sometimes life needs an expensive coffee
    (first_option := expensive_computation()) 
    or other_fallback 
    or default_value
)


The walrus operator := prevents multiple calls to expensive_computation(), thus enhancing performance.

Leveraging the beauty of generator expressions

Replace traditional if-else chains with a single, elegant generator expression:

return next((x for x in [compute_a(), compute_b(), 'default'] if x), None)


This generates a lazy iterator of expressions and yields the first truthy value, or None if all are falsy.

Clarity through function separation

Complex nested conditions can be hard to read. Divide and conquer via smaller functions to retain code readability:

def check_fruit_quality(a):
    # Everyone loves a good apple
    return a > 10

def make_apple_pie():
    return valid_apples if (valid_apples := check_fruit_quality(bag_of_apples)) else None


Straightforward function names combined with the walrus operator minimizes redundancy, keeping your code neat and efficient.

Embracing the power of Python 3.11

operator.call() in Python 3.11 provides cleaner execution within your code:

from operator import call

# Because two operators are better than one
return call(first, *args) or call(second) or default_value


Assignment expressions with := facilitate in-place evaluations, providing a simple, uncluttered structure.

Remembering Python's philosophy

The Zen of Python asserts that "Simple is better than complex." If the original way of doing things is the clearest, then stick to it.

Streamlined data handling with Map and Filter

Defog your complex data manipulations with the smart use of map() and filter():

# Like brewing a good cup of coffee, filter out the undesirables
return next(filter(None, map(lambda x: do_something(x), data)), default_value)


This snippet weaves together operations, executing functions across data and sifting out None values.

Standing by the classic

Sometimes, an explicit if-check is clearer, especially when x holds a complex expression:

if x is not None:
    # Because sometimes, old is gold
    return x


This retains an undeniably high readability.

Flexible condition handling with Loops

A for loop adds dynamism where a series of checks is involved:

for validator in [validate_a, validate_b, validate_c]:
    # Validator line-up, like superhero auditions
    if (result := validator(input)):
        return result


This code goes through the validator functions until a truthful result is found, eliminating redundancy.