A developer shares the rare joy of running Python code successfully on the first try and reflects on the experience.
Explains Higher Kinded Types (HKTs) in Python, their implementation, and how to use them via the dry-python/returns library.
A Python tutorial comparing methods to remove duplicates from a list, focusing on performance and code clarity.
A guide to using pandas and openpyxl to read and clean poorly structured Excel files, focusing on the usecols and header parameters.
Explains the theory behind linear regression models, a fundamental machine learning technique for predicting continuous numerical values.
Compares Python's type() and isinstance() functions, explaining their differences, performance, and when to use each.
The author starts a new Django project to build a social network with a capped number of connections to encourage thoughtful relationships.
A step-by-step guide to installing Google's ScaNN library for efficient vector similarity search on macOS, covering dependencies and troubleshooting.
A talk on implementing full-text search using only Django and PostgreSQL, following Pythonic principles for simplicity.
A case study on using Python to automate the collection, cleaning, and processing of gigabytes of historical weather data for analysis.
Learn how to configure VS Code to highlight unused Python variables and imports using the PyLance language server for cleaner code.
Explains Python membership testing using 'in' operator vs. for loops, with benchmarks showing performance differences.
Explains the YDNIY (You Don't Need It Yet) principle for shipping software faster by delaying non-essential features.
A developer shares lessons learned from creating simple games using Streamlit, focusing on managing game state within Streamlit's stateless paradigm.
Explains the best way to check for True/False in Python, comparing performance and recommending the PEP8-compliant 'if variable:' idiom.
Explains what a Django application is, its structure, and how it fits into a Django project.
An introduction to scientific computing in Python using NumPy for numerical arrays and Matplotlib for data visualization.
A tutorial on using NumPy for numerical arrays and Matplotlib for data visualization in Python, aimed at scientific computing and machine learning.
Explains the theory behind linear regression models, focusing on interpretability and use cases in fields like lending and medicine.
A technical comparison of Python's list.sort() and sorted() functions, analyzing their performance with benchmarks and correcting common testing mistakes.
