A technical tutorial on building a data product using Python, Markov chains, and a dataset of science questions to generate random quiz questions.
A technical guide on processing millions of small text files using GNU Parallel and stream processing, without needing Hadoop or a database.
A guide on using the moto library to mock AWS S3 interactions in Python tests, replacing complex boto mocks.
A technical guide on using Python to scrape public data, including answers to questions, from the European Parliament website.
A guide to using Python's tempfile.NamedTemporaryFile() for creating and managing temporary files with control over deletion.
Explains why Python's hasattr() function is dangerous and misleading, especially in Python 2, and recommends using try/except or getattr() instead.
Explains how Python's Mock objects handle non-existent attributes and methods, focusing on special behavior for 'assert' prefixes.
Argues that Python is the best programming language for scientists to learn, highlighting its community, learning resources, and scientific packages.
A tutorial on building a Twitter bot using Python 2.7 and AWS Lambda, covering setup, dependencies, and deployment.
Nilearn 0.2 release enhances machine learning for neuroimaging with new spatial regularizations, dictionary learning, and improved visualization tools.
A post-doc position in computational neuroscience using Python and machine learning to find biomarkers from fMRI brain connectivity data.
A developer's journey from avoiding virtualenvs to adopting 'pew', a tool that simplifies Python virtual environment management.
Analyzing Twitch chat data from Bob Ross painting marathons on Twitch Creative, including scraping methods and message volume statistics.
Explains how PYTHONHASHSEED affects test reliability and demonstrates robust testing strategies for non-deterministic dictionary ordering.
A guide to profiling Python code using cProfile to identify performance bottlenecks and optimize execution time.
A blog owner is seeking guest bloggers to write about Python and related topics due to their busy schedule.
Explores Visual Question Answering (VQA) as an alternative Turing Test, detailing neural network approaches using Python and Keras.
Explains the benefits of using a `src` directory in Python projects for accurate testing and packaging, and how to measure combined test coverage across multiple Python versions.
A tutorial on analyzing Seattle's Pronto CycleShare data using Python, Pandas, and the PyData stack for data science.
Announcing Hyper-h2 v1.0.0, a new Python library project providing foundational tools for building HTTP/2 implementations.
