Four Time‑Saving Python Tricks to Speed Up Your Code

Today I want to share four time‑saving Python tricks that can reduce execution time by roughly 10–20%.

Reverse List

Python provides two common ways to reverse a list: slicing (e.g., mylist[::-1] ) and the reverse() method. The reverse() method mutates the original list, while slicing creates a new one.

Performance testing with python -m timeit shows that reverse() is faster (≈10.7 µs per loop) than slicing (≈15.6 µs per loop) for a list of 200 elements.

Swap Two Values

Python allows swapping two variables in a single line using tuple unpacking:

<code>variable_1, variable_2 = variable_2, variable_1</code>

The same technique works for dictionary entries:

<code>md[key_2], md[key_1] = md[key_1], md[key_2]</code>

This one‑liner avoids temporary variables and reduces execution time.

Loop Inside Function

Placing a for loop inside a function can be faster than calling a function repeatedly inside a loop because the loop overhead is incurred only once.

Example functions:

<code>def only_function(x):
    new_string = x.capitalize()
    output_string = x + " " + new_string
    print(output_string)</code>

<code>def for_in_function(list_of_strings):
    for x in list_of_strings:
        new_string = x.capitalize()
        output_string = x + " " + new_string
        print(output_string)</code>

Timing shows the in‑function loop is slightly faster.

Reduce Function Calls

When checking an object's type, prefer isinstance() (one call) over type() comparisons (two or three calls). Avoid placing repeated operations in loop conditions; compute them once before the loop.

<code># Avoid repeated len() calls
while i < len(a):
    ...
# Compute once
m = len(a)
while i < m:
    ...</code>

Import only the needed objects (e.g., from X import Y ) instead of importing the whole module and accessing attributes, which saves a lookup.

In summary, leveraging Python's built‑in functions and idioms can noticeably speed up programs while keeping the code clean and maintainable. For more details, consult the official Python documentation.