Tooling for Static Analysis of Python Programs
Explore static analysis tools for Python, including gast and beniget modules, to enhance linting, type inference, and IDE capabilities despite Python's dynamic nature.
Data Ingestion and Big Data
Learn web scraping, crawling, and API usage for data retrieval. Explore technical and ethical considerations in simulating user navigation and leveraging data APIs for analysis and business opportunities.
Pydon'ts
Explore idiomatic Python coding practices to write more elegant and efficient code, regardless of skill level. Learn forgotten vanilla features and refactor a function using Pythonic idioms.
Taming Nondeterminism with Dependency Injection
Learn to control nondeterminism in Python using dependency injection. Explore challenges, identify issues, and implement solutions to improve code reliability and testability.
Continuous Documentation for Your Code
Learn effective strategies for continuous code documentation, improving readability, maintainability, and knowledge sharing within development teams.
Powerful Tests and Reproducible Benchmarks with `pytest-cases`
Explore advanced pytest techniques using pytest-cases for complex parametrization, elegant test organization, and reproducible benchmarks in scientific research and data science projects.
Sentry as a Way Not to Be Afraid
Descubre cómo Sentry mejora el seguimiento de errores en aplicaciones web en tiempo real. Aprende a implementarlo, leer informes y optimizar su uso tanto en backend como frontend.
Leveraging Linked Data Using Python and SPARQL
Explore Python and SPARQL to extract structured knowledge from Wikipedia, creating datasets and corpora for data science projects without traditional web scraping techniques.
Type Check Your Django App
Learn to implement type hints in Django projects, covering views, models, and querysets. Explore tools, limitations, and mypy configuration for effective type checking in Python 3.9.4+.
Create Beautiful and Localized Documentations and Websites Using MkDocs + Github
Learn to create stunning, multilingual documentation and websites using MkDocs and Github Pages. Discover new features, localization support, and best practices for building feature-rich content.
Sweeter Debugging and Benchmarking with Ycecream
Explore ycecream for enhanced Python debugging and benchmarking, featuring simplified variable printing, location tracking, and advanced timing functionalities.
Python Anti-Patterns
Explore common Python anti-patterns and learn how to avoid them, improving code quality and maintainability in your projects.
Writing Better Documentation for Developers
Learn to create effective developer documentation that serves as marketing, product definition, and user interface, improving discoverability and usability of your tools.
From Research Project to PyPI Release
Learn to transform a research project into a publicly released Python package, covering testing, CI, documentation, and citation on Zenodo. Ideal for researchers seeking to increase impact and visibility of their work.
Stop Writing Tests
Explore next-generation tools for automating test case selection, test writing, and continuous testing. Learn about property-based testing, code introspection, and adaptive fuzzing to enhance your testing workflow.
Driving 3D Printers with Python
Explore challenges and solutions in developing OctoPrint, an open-source web interface for 3D printers, focusing on end-user software maintenance and Python implementation insights.
Automatic Testing of Python Functions Based on Contracts
Explore automatic testing of Python functions using contracts, reducing manual effort and improving code quality through tools like crosshair and icontract-hypothesis.
Developing Flask Applications for Google Cloud
Learn to build Flask apps for Cloud Run, using Cloud Code for faster development. Explore integration with Cloud SQL, Pub/Sub, and Secret Manager on Google Cloud's serverless platform.
Code From Nothing - Procedural Generation of Python Source Code
Explore novel techniques for testing Python linters and fixers using Hypothesis, focusing on procedural generation of source code to uncover edge cases and improve tool reliability.
Speeding Up the Deep Learning Development Life Cycle for Cancer Diagnostics
Optimizing deep learning development for cancer diagnostics: strategies to accelerate iteration, from task prioritization to GPU parallelization, enhancing model robustness across diverse laboratory settings.
Reproducible and Deployable Data Science with Open-Source Python
Aprenda a criar código de ciência de dados escalável, reproduzível e confiável usando Kedro, Apache Airflow e Great Expectations. Descubra como combinar iteração rápida com manutenibilidade e qualidade de dados consistente.