Python Tricks: Practical Tips to Speed Up Your Coding

A single line can replace ten — and that's not clickbait. These Python tricks aren't flashy; they save time, cut bugs, and make your code easier to read. I'll show you concise, practical habits and small snippets you can apply today.

Write less, do more: unpacking, f-strings, and comprehensions

Stop writing boilerplate. Unpack tuples directly:

# instead of indexing
x, y = point

Use f-strings for clear formatting:

name = 'Alex'
print(f'Hello, {name}!')

Comprehensions turn loops into readable one-liners and often run faster than manual loops:

squares = [x*x for x in range(10) if x % 2 == 0]

Prefer generator expressions when you only need iteration and want lower memory use:

total = sum(x for x in large_iterable)

Small tools that make a big difference

Use enumerate() to avoid index bugs:

for i, val in enumerate(items):
    print(i, val)

Zip pairs values cleanly:

for a, b in zip(list_a, list_b):
    process(a, b)

defaultdict and Counter cut down conditional code for grouped counts:

from collections import defaultdict, Counter
freq = Counter(words)
by_key = defaultdict(list)
for k, v in pairs:
    by_key[k].append(v)

Use pathlib for file paths — it's cross-platform and cleaner than os.path:

from pathlib import Path
p = Path('data') / 'file.txt'
text = p.read_text()

Cache expensive calls with functools.lru_cache to skip repeated work:

from functools import lru_cache
@lru_cache()
def fib(n):
    return n if n < 2 else fib(n-1) + fib(n-2)

The walrus operator (:=) helps when you want to both test and keep a value:

if (line := f.readline().strip()):
    process(line)

Prefer context managers to keep resources tidy:

with open('data.csv') as f:
    for line in f:
        handle(line)

Type hints improve readability and help tools catch mistakes early. They don’t slow you down and often pay off in team projects:

def greet(name: str) -> str:
    return f'Hi {name}'

Finally, lean on tests and small scripts. Writing one focused unit test for a tricky function saves far more time than repeatedly debugging later.

Try one trick this week. Replace a loop with a comprehension, or add a tiny test. Small changes stack quickly and make your Python code cleaner, faster, and easier to maintain.