Writing Pythonic Code
Python’s true power lies in writing clear, simple code. As Tim Peters put it in The Zen of Python: “Simple is better than complex” and “Readability counts”. In practice, this means using Python’s expressive features and style guidelines (like PEP 8) to make code easy to read and understand. Remember: code is read far more often than it is written. When reviewers or future you look at your code, they should immediately grasp what it does without mental gymnastics.
Leverage Built-ins and Idioms
Rather than writing loops from scratch, Python provides many built-in functions and idioms that are concise, fast, and well-tested. For example, to sum a list of numbers, using sum(numbers) is not only shorter but often faster and less error-prone than a manual loop. Real Python notes that sum() “provides an elegant, readable, and efficient solution in a single line of code”. Similarly, functions like any(), all(), min(), max(), sorted(), and zip() capture common patterns. For instance, instead of writing:
found = False
for x in items:
if cond(x):
found = True
break
you can write:
found = any(cond(x) for x in items)
This one-line version is easy to scan and internally implemented in C. In general, Python’s built-ins are highly optimized, making code both readable and efficient. By using these tools, you focus on what you want to do, not on the low-level details of how to loop.
Embrace Comprehensions and Unpacking
List, set, and dict comprehensions are Pythonic ways to create collections that replace longer loops. Google’s style guide recommends them: list/dict comprehensions “provide a concise and efficient way to create container types and iterators without resorting to ... traditional loops”. They often make code shorter and clearer. For example, converting this loop:
squares = []
for n in numbers:
squares.append(n*n)
into a list comprehension gives:
squares = [n*n for n in numbers]
This single line is not only more concise but runs faster (list comprehensions are internally optimised compared to equivalent loops). However, avoid overly complex comprehensions. Google also cautions that “complicated comprehensions or generator expressions can be hard to read”, so prioritise clarity.
Tuple unpacking (multiple assignment) is another expressive idiom. You can unpack function results or swap values easily:
a, b = b, a # swap variables in one line
or
first, *rest = my_list # unpack list into variables
This replaces manual indexing and makes intentions clear. As Python trainer Trey Hunner notes, multiple assignment “can improve both the readability of your code and the correctness of your code” by making code more descriptive. It even “makes implicit assertions about the size and shape of the iterables you’re unpacking”. In short, learn to destructure values rather than index into sequences repeatedly.
Avoid Common Anti-Patterns
Being Pythonic also means not forcing Python to act like another language (such as Java). A common anti-pattern is using range(len(...)) or manual counters to loop over a sequence. In Python, you usually loop directly over items:
for i in range(len(names)):
print(names[i])
Pythonic version:
for name in names:
print(name)
This direct form is clearer and avoids off-by-one mistakes. If you need an index in addition to the item, use enumerate():
for idx, name in enumerate(names, start=1):
print(idx, name)
This is exactly what Real Python recommends: “Rather than creating and incrementing a variable yourself, you can use Python’s enumerate()”. Similarly, when iterating two lists in parallel, use zip() instead of indexing. These small changes may seem trivial but greatly improve maintainability.
Another anti-pattern is writing nested loops or manual filters where comprehensions or built-ins suffice. For example, instead of:
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new_list = []
for x in data:
if f(x):
new_list.append(g(x))
consider:
new_list = [g(x) for x in data if f(x)]
This is more concise and clearly shows that you’re filtering and transforming the sequence. Overusing explicit loops can clutter code and obscure intent. The mantra here is: “if the implementation is easy to explain, it may be a good idea”.
Use Context Managers (with) to Manage Resources Safely
In Python, the with statement automatically handles setup and teardown tasks, like closing files or network connections. It's clearer, safer, and eliminates easy-to-miss bugs.
Instead of this:
f = open('data.txt')
contents = f.read()
f.close()
Do this:
with open('data.txt') as f:
contents = f.read()
It's shorter, more readable, and guarantees the file closes properly even if an error occurs.
Whenever you're working with files, sockets, locks, or anything that needs cleanup — do this.
"It's Easier to Ask Forgiveness than Permission"
Pythonic code often tries an operation directly and handles exceptions, rather than checking conditions ahead of time. This leads to simpler, more direct code.
if key in my_dict:
value = my_dict[key]
else:
value = default_value
Prefer trying and handling:
try:
value = my_dict[key]
except KeyError:
value = default_value
This style keeps code flatter and easier to extend, especially when many things can go wrong.
Of course, use your judgment — this shines when exceptions are expected and rare, not for general control flow.
Clarity Over Cleverness
Ultimately, being Pythonic is about clarity and maintainability, not showing off clever tricks. Write code so that someone reading it can predict what it does without unraveling puzzles. Follow established style guides like PEP 8: it exists “to improve the readability of code and make it consistent”. Use descriptive names, follow indentation and line-length conventions, and add comments/docstrings where helpful. Remember that a senior engineer at Google or Dropbox, during a code review, will value code that is straightforward. As one epigram reminds us, “Errors should never pass silently” – meaning code should handle edge cases explicitly, not hide bugs.
Resist the urge to write one-liners or nested expressions that compress too much logic. It’s better to write a few more lines that are crystal-clear. After all, “explicit is better than implicit”. When your code is transparent, future-you (or your teammates) will thank you. Pythonic style means the code reads like Python, following its idioms and rhythms, rather than mimicking another language’s patterns.
Quick Tips to Write More Pythonic Code
Improving your Pythonic style is a gradual process. With each small change—leveraging built-ins, simplifying control flow, and following the Zen—you’ll write code that is faster to understand and easier to maintain. Practice these principles regularly, and soon they’ll become second nature. In next week's article, we will be discussing Advanced OOP in Python, stay tuned!