python

Almost all python programmers know that there are list comprehensions PEP 202.

# create list of odd numbers with cycle
a = []
for i in range(10):
    if i % 2:
        a.append(i)

# create same collection using list comprehension
a = [i for i in range(10) if i % 2]

It isn’t only syntax sugar, comprehensions have CPython code optimisations and work faster. But what benefits do they provide? Let’s try to evaluate this.

I wrote a simple python program to compare execution for operation create collections using cycles and comprehensions. We will check dict, list and set. Base class for benchmarking has methods

from typing import Dict, List, Set

class BaseBench:

    def __init__(self, n: int = 1) -> None:
        self.n = n

    def get_new_dict(self) -> Dict[int, str]:
        raise NotImplementedError

    def get_filtered_dict(self, data: Dict[int, str]) -> Dict[int, str]:
        raise NotImplementedError

    def get_new_set(self) -> Set[int]:
        raise NotImplementedError

    def get_filtered_set(self, data: Set[int]) -> Set[int]:
        raise NotImplementedError

    def get_new_list(self) -> List[int]:
        raise NotImplementedError

    def get_filtered_list(self, data: List[int]) -> List[int]:
        raise NotImplementedError

I write here a part of code, example only for dict. Other listings you can find in github:

class CycleBench(BaseBench):

    def get_new_dict(self) -> Dict[int, str]:
        result = {}
        for i in range(self.n):
            if i % 2:
                result[i] = 'odd'
            else:
                result[i] = 'even'
        return result

    def get_filtered_dict(self, data: Dict[int, str]) -> Dict[int, str]:
        result = {}
        for key, value in data.items():
            if key % 2:
                result[key] = value
        return result


class ComprehensionBench(BaseBench):

    def get_new_dict(self) -> Dict[int, str]:
        return {i: 'odd' if i % 2 else 'even' for i in range(self.n)}

    def get_filtered_dict(self, data: Dict[int, str]) -> Dict[int, str]:
        return {key: value for key, value in data.items() if key % 2}

It’s interesting to check comprehension efficiency with different sizes of collections, small [10-1000] and big [10k-100k].

We see that more efficiency of comprehension is for list, and there is minimal time difference for dict.

Conclusion

Yes, comprehension is faster and often does code more readable. But it is not a rule «always use comprehension». For some cases cycles are better. For example sometimes maintainability may be more important than performance.

Python comprehensions vs cycles

Conclusion