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Are you a Python developer who already knows the basics and wants to brush up on some less obvious but useful skills to add to your repertoire?
Perhaps you already know how to use lists. Maybe you’re now adept at creating features. You may be able to easily control the execution flow of your program. You probably have the necessary knowledge of Python’s type system and when to use types. At this point, you just want some more advanced Python tricks.
If this sounds like you, you can find a free e-book 10 practical tricks of Python programming. Increase your productivity and code quality to be helpful.
Take these tips to improve your Python programming skills and stand out as a skilled developer who can easily create high-quality, high-performance applications.
A product of Data Science Horizons, this free e-book covers the following topics in an effort to increase reader productivity and code quality:
- List comprehensions
- Lambda functions
- The Walrus Operator (Task Phrases)
- Itertools module
- F-strings (formatted string literals)
- Context Managers and the “with” statement
- Generators and generator expressions
- Decorators
- Type hints and static type checking
- Python One-Liners
As the title suggests, along with demonstrating the skills listed above, this e-book focuses on both efficiency and code quality;
Writing efficient code means optimizing the execution speed of your code and minimizing resource consumption, such as memory usage. Clean coding, on the other hand, focuses on readability, storage, and organization. Both aspects go hand-in-hand, as efficient code is easier to understand, debug, and modify, while clean code inherently leads to better performance. By adopting the best practices outlined in this e-book, you’ll be better equipped to write high-quality Python code that’s not only fast and resource-efficient, but also easy to understand and modify.
Check out the free e-book 10 practical tricks of Python programming. Increase your productivity and code quality today if you’re ready to take your Python programming to the next level. It might be just what you’re looking for.
Matthew Mayo (@mattmayo13) is a data scientist and editor-in-chief of KDnuggets, the premier online resource for data science and machine learning. His interests are in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew has a Masters in Computer Science and a Postgraduate Diploma in Data Mining. He can be reached at editor1: kdnuggets[dot]com.