Multiply Your Testing Effectiveness with Parameterized Testing
Explore pytest parametrization techniques to enhance testing efficiency, covering multiple test cases with a single function and simplifying test maintenance.
Type Hints - Putting More Buzz in Your Fizz
Explore type hints in Python, from basics to complex structures. Learn debugging techniques, class definitions, and library stubs. Enhance code quality and confidence through practical examples.
We Need to Talk About Windows
Insider's look at building Python from source across platforms, focusing on Windows challenges and ActiveState's efforts to contribute to the Python community.
Stop Using Mocks - for a While
Explore alternatives to mocks in testing, focusing on design patterns and techniques for better code structure, maintainable tests, and a healthy test pyramid.
Introduction to Sprinting Workshop
Learn essential Git concepts and commands for effective collaboration in software development projects, including cloning, inspecting, and comparing repositories.
Introduction to Property-Based Testing
Discover property-based testing with Hypothesis, learn to write robust tests, and find real bugs. Hands-on experience in creating generalized tests for diverse scenarios.
From Spreadsheets to DataFrames
Learn to transition from spreadsheets to Python DataFrames, exploring web scraping, data processing, analysis, and visualization using popular libraries like Pandas and Matplotlib. Gain skills for efficient data management and problem-solving.
Python Unit Testing with Pytest and Mock
Learn to write high-quality Python unit tests using Pytest and mock. Master best practices for effective testing, enhancing code reliability and maintainability.
When Is an Exception Not an Exception? Using Warnings in Python
Explore Python's warning system for handling minor issues without raising exceptions. Learn to generate, filter, and customize warnings for more nuanced error handling in your code.
Gradual Typing in Practice
Explore gradual typing in Python, its benefits for code quality, and strategies for implementing types in large codebases, focusing on Instagram's experience with Pyre.
PyTesting the Limits of Machine Learning
Explore techniques and tools for testing machine learning components in Python, addressing challenges of nondeterminism and ensuring reliability in ML-driven applications.
Testing Stochastic AI Models with Hypothesis
Explore property-based testing for AI models using Hypothesis library, focusing on generating random edge cases and defining system properties and specifications.
Packaging Python in 2021
Explore the evolving Python packaging landscape, focusing on pyproject.toml's rise and setup.py's decline. Learn about modern alternatives and best practices for efficient project setup.
Reproducible and Maintainable Data Science Code with Kedro
Discover how Kedro, an open-source Python framework, transforms experimental data science code into production-ready ML pipelines, emphasizing reproducibility, maintainability, and modularity.
Writing Performant Code for Modern Python Interpreters
Explore modern Python optimization techniques, learn new performance guidelines, and discover how to leverage language improvements for faster code execution.
Writing Functional Code in Python
Explore functional programming concepts and their application in Python, enhancing code quality and efficiency through practical examples and key principles.
Open Source on Easy Mode
Streamline open source project maintenance with automation tools and best practices for packaging, testing, CI/CD, documentation, and community engagement. Focus on the fun parts of development.
Distributed Web Scraping in Python
Learn to build a distributed web scraper in Python, reducing batch job times and improving code durability. Explore techniques, lessons, and stories for efficient large-scale web scraping.
In House to Open Source - Stitching the Past to the Future
Explore the transition from legacy code to open-source solutions, focusing on Python's advantages in software migrations and the implementation of robust frameworks like Apache Airflow.
Building a Python Code Completer
Uncover the secrets behind Python code completion, from parsing to AST, and learn how to build a simple completer in just minutes.
Software Development for Machine Learning in Python
Explore best practices for developing ML software in Python, focusing on abstraction, optimization, and production-ready code. Learn to design efficient, trackable, and easily maintainable ML systems.