10 Python Tricks for a More Efficient Coding Experience

Ever feel like you’re writing the same lines of code over and over in Python? You’re not slow-you’re just not using the right tricks. Python is built for simplicity, but most developers miss out on powerful shortcuts that save hours every week. These aren’t obscure library hacks. These are real, everyday patterns used by teams at companies like Dropbox, Reddit, and Instagram to ship faster and debug less.

Use unpacking to swap variables without a temp

How many times have you written this?

temp = a
a = b
b = temp

Stop. Python lets you swap in one line:

a, b = b, a

This works with any number of variables. Need to reorder three values? Just do x, y, z = z, x, y. It’s not magic-it’s tuple unpacking under the hood. Every Python developer should know this. It’s cleaner, faster, and eliminates bugs from temporary variables.

Use list comprehensions instead of for loops

Instead of this:

squares = []
for x in range(10):
    squares.append(x ** 2)

Write this:

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

It’s not just shorter. It’s faster-up to 30% in real benchmarks. And it’s more readable once you get used to it. You can even add conditions:

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

Same logic, no extra lines. This applies to dictionaries too:

squared_dict = {x: x ** 2 for x in range(5)}

Use enumerate when you need both index and value

Don’t do this:

for i in range(len(items)):
    print(i, items[i])

Do this:

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

enumerate returns a tuple of index and value. It’s cleaner, avoids off-by-one errors, and works with any iterable-even generators. You can even start the count at 1:

for i, item in enumerate(items, 1):
    print(f"{i}. {item}")

Use set() for fast membership testing

Checking if an item exists in a list? That’s slow. For a list of 10,000 items, item in my_list can take seconds. Switch to a set:

my_set = set(my_list)
if item in my_set:
    # instant lookup

Sets use hash tables. Lookups are O(1), not O(n). Even if you have to convert the list once, it’s worth it for repeated checks. This is why Python’s own in operator is lightning-fast on sets but sluggish on lists.

Use get() with dictionaries to avoid KeyErrors

Instead of this risky code:

if key in my_dict:
    value = my_dict[key]
else:
    value = default

Just do this:

value = my_dict.get(key, default)

It’s one line. No conditionals. No exceptions. And you can chain it:

name = user_data.get('profile', {}).get('name', 'Anonymous')

This avoids crashes when nested keys are missing. You’ll thank yourself when your script doesn’t die in production because someone sent an empty JSON payload.

Use with for file handling

Never write this:

f = open('file.txt', 'r')
data = f.read()
f.close()

What if an error happens after open() but before close()? The file stays locked. Use a context manager:

with open('file.txt', 'r') as f:
    data = f.read()
# file automatically closed

This works for databases, network connections, locks-anything that needs cleanup. It’s not just safer. It’s more readable. You know exactly where the resource is active.

Use collections.defaultdict for cleaner data aggregation

Building a dictionary that counts things? This is painful:

counts = {}
for item in items:
    if item in counts:
        counts[item] += 1
    else:
        counts[item] = 1

Use defaultdict:

from collections import defaultdict

counts = defaultdict(int)
for item in items:
    counts[item] += 1

No checks. No conditionals. Just increment. You can use list for grouping too:

groups = defaultdict(list)
for item in items:
    groups[item.category].append(item)

It’s a game-changer for data processing.

Use itertools for lazy iteration

Need to loop over combinations, permutations, or infinite sequences? Don’t build lists in memory. Use itertools:

from itertools import permutations

for combo in permutations(['a', 'b', 'c'], 2):
    print(combo)

Output: ('a', 'b'), ('a', 'c'), ('b', 'a'), etc.

Or generate an infinite sequence:

from itertools import count

for i in count(1):  # 1, 2, 3, 4, ...
    if i > 10:
        break
    print(i)

These are memory-efficient. No giant lists. No slowdowns. Perfect for large datasets or real-time streams.

Use __slots__ to reduce memory usage in classes

If you’re creating thousands of objects with the same attributes, Python’s default dict-based storage is wasteful. Use __slots__:

class Point:
    __slots__ = ['x', 'y']
    def __init__(self, x, y):
        self.x = x
        self.y = y

Without __slots__, each instance uses 200+ bytes for the dict. With it? Around 40 bytes. That’s 80% less memory. It also makes attribute access faster. Use it when you have many instances and fixed attributes. Don’t use it if you need dynamic attributes.

Use typing to catch errors before runtime

Python is dynamic, but that doesn’t mean you should write without structure. Use type hints:

def process_data(items: list[str]) -> dict[str, int]:
    counts = {}
    for item in items:
        counts[item] = counts.get(item, 0) + 1
    return counts

Now tools like mypy or your IDE can warn you if you pass a list of integers to a function expecting strings. You’ll catch bugs before they hit production. It’s not required-but it’s the difference between a team that ships confidently and one that spends half their time debugging type mismatches.

These 10 tricks won’t turn you into a Python wizard overnight. But they’ll make your code faster, safer, and easier to maintain. Start with one. Then another. Soon, you’ll wonder how you ever coded without them.