Python Tricks for Beginners: Simple Ways to Code Faster and Smarter

Ever typed the same line of code over and over in Python, only to wish there was a faster way? You’re not alone. Most beginners spend way too much time writing repetitive code because they don’t know the little tricks that make Python feel like magic. These aren’t advanced hacks or obscure libraries - just simple, practical moves that save time, reduce errors, and make your code cleaner. And the best part? You can start using them today, even if you’ve only written a few programs.

Use List Comprehensions Instead of Loops

Let’s say you need to create a list of squares from 1 to 10. Most beginners write this:

squares = []
for i in range(1, 11):
    squares.append(i ** 2)

It works. But it’s long. And messy. Here’s the Python way:

squares = [i ** 2 for i in range(1, 11)]

One line. Same result. List comprehensions aren’t just shorter - they’re faster and easier to read once you get used to them. You can even add conditions. Want only even squares?

squares = [i ** 2 for i in range(1, 11) if i % 2 == 0]

That’s it. No extra lines. No temporary variables. Just clean, readable code.

Swap Variables Without a Temporary One

In other languages, swapping two values takes three lines:

temp = a
a = b
b = temp

In Python? One line:

a, b = b, a

Python unpacks the right side and assigns values to the left side at the same time. This works with more than two variables too:

x, y, z = z, x, y

It’s not just a party trick - it’s useful when sorting data, reversing lists, or reordering function returns. You’ll use this constantly once you start writing real scripts.

Use enumerate() When You Need Both Index and Value

How often do you write code like this?

items = ['apple', 'banana', 'cherry']
for i in range(len(items)):
    print(i, items[i])

You’re manually tracking the index. That’s unnecessary. Python gives you enumerate():

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

It’s cleaner, less error-prone, and more readable. You can even start counting from 1:

for index, item in enumerate(items, 1):
    print(index, item)

Output:

1 apple
2 banana
3 cherry

No more forgetting to add +1 or mixing up index positions. This one change alone will cut bugs in half.

Use in Instead of Long if Chains

Checking if a variable equals one of several values? Don’t do this:

if color == 'red' or color == 'blue' or color == 'green':

It’s hard to read and easy to misspell. Use a set:

if color in {'red', 'blue', 'green'}:

Sets are faster than lists for membership tests, and this syntax is way easier to extend. Add another color? Just add it inside the braces. No extra or clauses. No typos. No headaches.

And if you’re checking against a long list of strings, like allowed file types, make it a constant at the top of your file:

ALLOWED_EXTENSIONS = {'jpg', 'png', 'gif', 'pdf'}


if file_extension in ALLOWED_EXTENSIONS:
    print('Valid file')

Use get() for Safe Dictionary Access

Accessing a key that doesn’t exist in a dictionary throws an error:

user = {'name': 'Alice', 'age': 30}
print(user['city'])  # KeyError: 'city'

You can avoid this with get():

city = user.get('city', 'Unknown')
print(city)  # Output: Unknown

If the key exists, it returns the value. If not, it returns the default you provide. No crashes. No try-except blocks. Just clean, safe code.

You can even use it to count occurrences:

word_count = {}
words = ['apple', 'banana', 'apple', 'cherry', 'banana', 'apple']

for word in words:
    word_count[word] = word_count.get(word, 0) + 1

Result:

{'apple': 3, 'banana': 2, 'cherry': 1}

No need to check if the key exists first. Python handles it for you.

Chain Comparisons Like a Pro

In math, you write: 1 < x < 10. In many languages, you’d have to write:

if x > 1 and x < 10:

In Python? You can write it exactly like math:

if 1 < x < 10:

It works with any number of comparisons:

if 0 <= age <= 120:

This isn’t just a shortcut - it’s more readable and less prone to logic errors. You’re not repeating variables. You’re not using and where it’s not needed. Python lets you think like a human, not a compiler.

Use join() to Combine Strings

Don’t do this:

result = ''
for word in words:
    result += word + ' '

That’s slow. Every time you use += on a string, Python creates a new string in memory. For a long list, that’s hundreds of copies.

Use join() instead:

result = ' '.join(words)

It’s faster, cleaner, and uses way less memory. You can join with any separator:

','.join(['a', 'b', 'c'])  # 'a,b,c'
' -- '.join(['first', 'second'])  # 'first -- second'

This is one of the most common performance fixes in beginner code. Use it every time you combine strings.

Use Underscores in Large Numbers

Reading 1000000 is hard. What about 1_000_000? Instantly clearer. Python lets you use underscores in numbers to make them readable:

population = 7_800_000_000
price = 19_999.99
binary = 0b_1101_0101

These underscores are ignored by Python. They’re only for you. No more counting zeros. No more mistakes. And it works with integers, floats, and even binary/hex values.

Use zip() to Pair Up Lists

Need to loop through two lists at the same time? Don’t use indexes:

names = ['Alice', 'Bob', 'Charlie']
scores = [85, 92, 78]

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

Use zip():

for name, score in zip(names, scores):
    print(name, score)

It pairs elements by position. Easy. Clean. No index math. And if the lists are different lengths, it stops at the shortest one - no surprises.

You can even turn two lists into a dictionary:

student_grades = dict(zip(names, scores))

Result:

{'Alice': 85, 'Bob': 92, 'Charlie': 78}

Use f-strings for String Formatting

Old-school formatting looks like this:

name = 'Alice'
age = 30
print('Hello, {}. You are {} years old.'.format(name, age))

Or worse:

print('Hello, ' + name + '. You are ' + str(age) + ' years old.')

Use f-strings:

print(f'Hello, {name}. You are {age} years old.')

It’s shorter, faster, and way easier to read. You can even put expressions inside:

print(f'Next year, you will be {age + 1}.')

And it works with functions, calculations, even nested variables. This is the standard in modern Python. Stop using .format() unless you have a very specific reason.

Use collections.defaultdict for Automatic Defaults

When you’re counting or grouping data, you often end up writing this:

groups = {}
for item in items:
    category = get_category(item)
    if category not in groups:
        groups[category] = []
    groups[category].append(item)

It’s tedious. Use defaultdict:

from collections import defaultdict

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

No more checking if the key exists. If it doesn’t, defaultdict creates an empty list automatically. Same works for integers (default 0), sets (empty set), or even custom functions.

Use pathlib for File Paths

Still using os.path.join()? Time to upgrade. Python 3.5+ introduced pathlib - a much cleaner way to handle files:

from pathlib import Path

# Instead of:
path = os.path.join('folder', 'subfolder', 'file.txt')

# Use:
path = Path('folder') / 'subfolder' / 'file.txt'

# Read a file:
content = path.read_text()

# Check if it exists:
if path.exists():
    print('Found it!')

# Get parent directory:
parent = path.parent

No more backslashes, no more escaping, no more os.path confusion. It’s object-oriented, readable, and works the same on Windows, Mac, and Linux.

Final Tip: Learn to Read the Error Messages

Most beginners panic when they see an error. But Python errors are actually super helpful. Look at the last line - it tells you exactly what went wrong. The lines above show you where it happened.

Example:

Traceback (most recent call last):
  File "script.py", line 10, in <module>
    result = data['key']
KeyError: 'key'

Line 10. Trying to access a key that doesn’t exist. You already know what to fix. No guessing. No stack overflow hunting. Just read the message. It’s written for you.

These tricks won’t turn you into a Python expert overnight. But they’ll make your code faster, cleaner, and easier to debug. And that’s half the battle. Start with one or two. Use them in your next project. Then add another. Before you know it, you’ll be writing Python the way experienced developers do - without even thinking about it.