
Efficient CSV parsing and conversion to NumPy arrays with full dtype support and optional type discovery, outperforming Pandas for various DataFrame shapes and heterogeneity.

Efficient techniques for querying and joining data across distant machines using Python, addressing slow network transfers and large datasets that exceed local memory.

Explore advanced Python instrumentation techniques, challenging real-world cases, and design patterns. Learn to hook into import systems, instrument popular libraries, and make your code instrumentation-friendly.

Explore Python's role in optimizing transportation networks through real-world examples, including driver-load matching, network optimization, and decision-making using graph analytics and data science concepts.

Explore Polars, a fast data manipulation library, through hands-on exercises with real-world datasets. Learn key operations, compare with Pandas, and optimize data pipelines.

Learn to create a FastAPI application that serves real-time predictions from a trained scikit-learn model, bridging the gap between model development and production deployment.

Learn to create CLI apps that cater to both human users and automated tools. Explore Command Line Interface Guidelines and build a PyCon talk catalogue using Python, typer, and Rich.

Learn to apply Python skills to pressing environmental issues, using tools like numpy, pandas, and matplotlib to analyze deforestation, wildlife trade, and carbon pollution data.

Learn to create, generate, host, and automate up-to-date API documentation using Sphinx and GitHub Actions, integrating it seamlessly into your software development lifecycle.

Discover how to optimize Python code performance using Numba, a JIT compiler for numerical calculations. Learn when and how to apply Numba effectively to speed up math-heavy code in data science and development projects.

Learn to enhance Python functions using decorators. Explore their inner workings, create custom decorators, and discover how they simplify code and improve readability in popular libraries and frameworks.

Comprehensive guide to object-oriented programming in Python, covering design, inheritance, special methods, and converting procedural code to OOP through practical examples.

Aprenda a criar um pacote Python usando programação literária, combinando explicações em linguagem natural com código. Explore ferramentas como Jupyter Notebooks e Nbdev para desenvolvimento eficiente e documentação integrada.

Learn to create serverless Python web apps using PyScript. Explore project setup, data handling, DOM manipulation, JavaScript integration, and optimization techniques for browser-based Python development.

Learn to create interactive GUIs and web apps using streamlit in Python. Hands-on exercises cover basics, data visualization, custom layouts, and integration with popular libraries for effective data science projects.

Learn techniques for analyzing and visualizing massive datasets using Python libraries like Dask and hvPlot. Explore efficient data formats, parallel computation, cloud resources, and interactive visualization for large-scale data science projects.

Learn to build modern web apps using Python, HTMX, and Tailwind CSS. Simplify development with hypermedia-driven architecture and streamlined design processes.

Learn to create a robust GraphQL API with Python and Strawberry, focusing on authentication, performance, and type safety. Explore real-time APIs and frontend integration using Django.

Hands-on tutorial for Python users to master SQLite, covering advanced SQL features, data manipulation with sqlite-utils, and exploration using Datasette. Learn to build and deploy data applications.

Explore Python comprehensions: learn to write and utilize list, set, and dict comprehensions, nested comprehensions, and generator expressions. Understand their advantages over regular loops.

Explore advanced pickling techniques using Stackless Python and sPickle, enhancing data serialization and deserialization for improved performance and functionality.