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Laszlo Sragner - Code Smells in Data Science: What can we do about them? | PyData London 2023
Improve code quality and reduce technical debt by recognizing and addressing common code smells in data science, with practical refactoring tips and strategies for success.
- Refactoring is essential to improve code quality, but it requires careful planning and testing.
- Code smells are patterns that indicate poor design or implementation, leading to code rot and maintenance nightmares.
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Examples of code smells include:
- Long parameter lists
- Data clusters (unnecessary variables)
- Long and complicated composition of code
- Improper variable scoping
- Primitive obsession (overuse of basic types instead of meaningful classes)
- Feature envy (processing data in a class that doesn’t own it)
- Code review is essential to identify and fix code smells, but it should be a positive and collaborative process.
- The Happy Path is the main part of the code, while the error handling is secondary.
- Dependency injection is a design pattern that helps decouple code from specific implementations.
- Code quality is crucial in high-velocity environments, where rapid changes require maintainable code.
- Code smells can lead to technical debt, which is often difficult to pay back.
- Writing readable code is essential for communication and collaboration, and it takes practice to develop good coding skills.
- Code review should focus on making the code better, not just on checking for errors.
- Improving code quality is important for reducing technical debt and increasing productivity.
- The goal of code review is to improve the code, not to criticize the author.