Stop Writing Python Like This (Please)

2025-12-29 · Source: Visually Explained · Field: Technology & Digital — Software Development & Engineering · Depth: Intermediate, medium

Summary

This content identifies five common "unpythonic" coding habits that can lead to slow, messy, and unmaintainable Python code, often stemming from programming mindsets adopted from languages like Java or C. It specifically addresses inefficient iteration over lists and dictionaries, advocating for Python's built-in `enumerate` and dictionary's `items()` method over index-based or key-only loops. The analysis also highlights the benefits of list, dict, and set comprehensions for creating new collections, replacing verbose loop-and-append patterns. Furthermore, it criticizes the use of `+` or `+=` for string concatenation, promoting the `join` method for performance and readability. Finally, it strongly recommends f-strings (formatted string literals) for string interpolation, citing their superior readability and conciseness compared to `+` concatenation or the `.format()` method.

Key takeaway

For Python developers aiming to improve code quality and performance, you should actively replace non-Pythonic patterns with idiomatic constructs. Prioritize using `enumerate()` for indexed iteration, `dict.items()` for dictionary loops, comprehensions for collection transformations, `str.join()` for string concatenation, and f-strings for string formatting to enhance readability and efficiency in your projects.

Key insights

Adopt Pythonic idioms to write cleaner, faster, and more maintainable code.

Principles

• Prioritize readability in Python code.
• Leverage built-in functions and methods.
• Avoid unnecessary object creation.

Method

Refactor common Python anti-patterns: replace `range()` loops with direct iteration or `enumerate()`, use `dict.items()` for dictionary iteration, employ comprehensions for collection creation, use `str.join()` for string concatenation, and prefer f-strings for formatting.

In practice

• Use `enumerate()` for indexed list iteration.
• Apply `dict.items()` for dictionary loops.
• Utilize list comprehensions for transformations.

Topics

• Pythonic Programming
• Efficient Iteration
• List Comprehensions
• String Formatting
• Code Optimization

Best for: Software Engineer, Data Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Visually Explained.