Python Tips You Can Use Right Now

Ever feel stuck on a simple Python problem and wish there was a shortcut? You’re not alone. Below are bite‑size tips that cut the guesswork and help you write better code faster. No fluff, just things you can copy‑paste or try in your next project.

Make Lists Work for You

Lists are the go‑to data type, but they can get messy fast. Use list comprehensions to replace multi‑line loops. Instead of:

result = []
for i in range(10):
    if i % 2 == 0:
        result.append(i * i)

When you need a copy of a list without the original reference, remember new_list = old_list[:] or list(old_list). This avoids the dreaded "list changed during iteration" bugs.

Debug Faster with Built‑in Tools

Skip printing every variable. The pdb module lets you pause execution and inspect state. Add import pdb; pdb.set_trace() where you want to stop, then use n for next line, c to continue, and p variable to print a value.

If you’re on Python 3.7+ you can also use the breakpoint() shortcut – it calls the default debugger without extra imports.

For quick one‑liners, the print() function supports the file argument. Send debug output to a file instead of the console: print('debug', file=open('log.txt','a')).

Another tip: use enumerate() instead of managing an index manually. It saves you from off‑by‑one errors and makes the loop easier to read.

When working with dictionaries, dict.get(key, default) prevents KeyError and lets you provide a fallback value in one line.

Speed Up Common Operations

String concatenation in a loop is slow because strings are immutable. Build a list of pieces and ''.join(pieces) at the end. The same idea works for building large text blocks.

Use collections.defaultdict for counting or grouping without checking if a key exists first. Example:

from collections import defaultdict
counts = defaultdict(int)
for word in words:
    counts[word] += 1

If you need to sort a large list of objects, the key argument is your friend. Instead of writing a custom compare function, provide a lightweight key extractor: sorted(items, key=lambda x: x.price). It runs faster and reads better.

Write Cleaner Functions

Keep functions short – aim for one responsibility. If a function grows past 20 lines, ask yourself if part of it could be a helper.

Use type hints to make your code self‑documenting. Adding def add(a: int, b: int) -> int: doesn’t change runtime, but IDEs and linters can catch mismatches early.

When a function returns multiple values, return a namedtuple or a dataclass. It gives each piece a name and avoids a confusing tuple index.

Leverage the Standard Library

Before reaching for a third‑party package, check the standard library. itertools has tools for chunking, flattening, and combinatorial tasks. pathlib replaces os.path with an object‑oriented approach, making file paths easier to read.

Want to parse command‑line arguments? argparse handles help messages, default values, and type conversion without extra code.

Finally, remember that Python’s json module works with both strings and files. Use json.dump(data, f, indent=2) for pretty‑printed files you can open in a text editor.

These tips aren’t exhaustive, but they’re enough to give you a noticeable boost. Try one today, see how it changes your workflow, then add another. Small tweaks add up fast, and before you know it you’ll be writing Python that feels natural and painless.