How do you make a Python loop faster?

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How do you make a Python loop faster?

Programmers are always looking for ways to make their code more efficient. One way to do this is to use a faster loop. Python is a high-level programming language that is widely used by developers and software engineers. It is known for its readability and ease of use. However, one downside of Python is that its loops can be slow. This can be a problem when you need to process large amounts of data. There are several ways to make Python loops faster. One way is to use a faster looping construct, such as C. Another way is to use an optimized library, such as NumPy. Finally, you can vectorize your code, which means converting it into a format that can be run on a GPU or other parallel computing platform. By using these techniques, you can significantly speed up your Python code.

According to Vladislav Zorov, If not talking about NumPy or something, try to use list comprehension expressions where possible. Those are handled by the C code of the Python interpreter, instead of looping in Python. Basically same idea like the NumPy solution, you just don’t want code running in Python.

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Example: (Python 3.0)

lst = [n for n in range(1000000)]
def loops():
    newlst = []
    for n in lst:
        newlst.append(n * 2)
    return newlst
def lstcomp():
    return [n * 2 for n in lst]
from timeit import timeit
print(timeit(loops, number=100))
#18.953254899999592 seconds
print(timeit(lstcomp, number=100))
#11.669047399991541 seconds
Or Do this in Python 2.0
How do you make a Python loop faster?
How do you make a Python loop faster?

Python list traversing tip:

Instead of this: for i in range(len(l)): x = l[i]

Use this for i, x in enumerate(l): …

TO keep track of indices and values inside a loop.

Twice faster, and the code looks better.

Another option is to write loops in C instead of Python. This can be done by using a Python extension module such as pyximport. By doing this, programmers can take advantage of the speed of C while still using the convenient syntax of Python.

Finally, developers can also improve the performance of their code by making use of caching. By caching values that are computed inside a loop, programmers can avoid having to recalculate them each time through the loop. By taking these steps, programmers can make their Python code more efficient and faster.

Very Important: Don’t worry about code efficiency until you find yourself needing to worry about code efficiency.

The place where you think about efficiency is within the logic of your implementations.

This is where “big O” discussions come in to play. If you aren’t familiar, here is a link on the topic

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