Lessons Learned with Asyncio - Look Ma, I Wrote a Distributed Hash Table
Explore asyncio for concurrent programming in Python, focusing on event loops, coroutines, futures, and networking. Learn through a distributed hash table implementation example.
Easy FullStack Deployments
Learn to manage full-stack development lifecycle using Python technologies and Docker, from project creation with Pyramid to consistent deployment infrastructure with Buildout and containers.
Decorators Demystified
Unravel the magic behind Python decorators, exploring scopes, namespaces, and closures. Learn to craft your own decorators and understand the '@' symbol's inner workings in this intermediate-level talk.
Machine Learning Under Test
Explore testing strategies for machine learning algorithms, focusing on code quality, domain-specific challenges, and practical implementation techniques for more robust and reliable ML models.
Python & Internet of Things
Explore Python's role in IoT, from powering embedded hardware to building end-to-end products. Learn about the possibilities and practical applications in this emerging field.
Activity Map from Space - Supporting Mine Clearance with Python
Satellite image processing with Python to detect ground activities and create maps for efficient UXO removal in minefields, using numpy, scipy, gdal, and IPython Notebook.
Distributed Locks with Python and Redis
Learn to implement distributed locks using Python and Redis for concurrent programming in distributed systems, with insights from building a modern contact center.
Big Data with Python and Hadoop
Introduction to big data processing using Apache Hadoop and Python. Explores Hadoop concepts, infrastructure, Python implementations, and available libraries for efficient big data handling and job acceleration.
What It's Really Like Building RESTful APIs with Django
Explore real-world experiences and lessons learned in building modern RESTful APIs with Django and Django REST framework, focusing on design decisions, best practices, and practical implementation tips.
12 Years of Pylint - Or How I Learned to Stop Worrying About Bugs
Explore Pylint's powerful static analysis capabilities for Python, from style checking to advanced bug detection and code understanding, with insights on its inner workings and future developments.
Sphinx Autodoc - Automated API Documentation
Learn to automate API documentation using Sphinx autodoc and related extensions. Discover techniques for efficient docstring writing, automated module listing, coverage checking, and doctest verification to create comprehensive Python library documentati…
Asyncio Stack & React.js or Development on the Edge
Explore modern web development with asyncio in Python and React.js in JavaScript. Learn about aiohttp, ES6 features, and Flux architecture for building efficient, scalable applications.
gitfs - Building a Filesystem in Python
Explore building a FUSE filesystem for Git repositories, enabling automatic commits and history snapshots. Learn crucial aspects, implementation steps, and testing methods.
The GIL is Dead - PyPy-STM
Explore PyPy's Software Transactional Memory, enabling GIL-free Python execution on multiple cores. Learn to identify and resolve STM conflicts for improved performance in large codebases.
How-To - Build a Local Python Community
Practical guide on starting and growing a local Python community, covering resources, expectations, and strategies for success. Ideal for enthusiasts wanting to foster a tech community.
RinohType - A Document Processor Inspired by LaTeX
Explore RinohType, a Python-based document processor inspired by LaTeX. Learn its features, design goals, and how to create PDFs from reStructuredText, with insights into its layout engine and style system.
PySpark - Data Processing in Python on Top of Apache Spark
Learn PySpark for large-scale data processing with Python on Apache Spark. Covers RDDs, DataFrame API, transformations, actions, and data sources. Ideal for those seeking efficient big data analysis techniques.
Everyone Can Do Data Science in Python
Learn essential data science techniques using Python, numpy, pandas, and sklearn. Explore real-world use cases with code snippets and live demos in IPython notebooks.
Building Nice Command Line Interfaces - A Look Beyond the Stdlib
Explore popular Python libraries for creating user-friendly command-line interfaces, comparing their concepts and features. Learn to build and test CLIs beyond standard library solutions.
Writing Quality Code
Learn techniques and tools for improving code quality in Python, focusing on readability, design, testing, and documentation for easier maintenance and extension.
Numba - A JIT Compiler for Fast Numerical Code
Explore Numba, a JIT compiler for Python that accelerates numerical algorithms beyond Numpy capabilities, with CPU and GPU support. Learn its use cases, performance expectations, and inner workings.