Data Science Articles from CodeCut

About CodeCut

CodeCut is the platform that helps data scientists stay productive and current by delivering short, practical code examples that highlight modern tools in action.

It's the resource you wish you had when learning a new library—clean, concise, and instantly applicable.

Article Collection

This repository is a curated collection of data science articles from CodeCut, covering topics like MLOps, data management, testing, visualization, and more. Each article comes with practical examples, code repositories, and video tutorials to help you quickly implement these tools and practices in your own projects.

Category	Title	Article	Repository	Video
MLOps	Goodbye Pip and Poetry. Why UV Might Be All You Need	🔗
MLOps	Stop Hard Coding in a Data Science Project – Use Configuration Files Instead	🔗	🔗	🔗
MLOps	Poetry: A Better Way to Manage Python Dependencies	🔗		🔗
MLOps	Git for Data Scientists: Learn Git through Practical Examples	🔗		🔗
MLOps	4 pre-commit Plugins to Automate Code Reviewing and Formatting in Python	🔗	🔗	🔗
MLOps	How to Structure a Data Science Project for Maintainability	🔗	🔗	🔗
MLOps	Build Reliable Machine Learning Pipelines with Continuous Integration	🔗	🔗	🔗
MLOps	Automate Machine Learning Deployment with GitHub Actions	🔗	🔗	🔗
MLOps	How to Build a Fully Automated Data Drift Detection Pipeline	🔗	🔗	🔗
Data Management Tools	Version Control for Data and Models Using DVC	🔗	🔗	🔗
Data Management Tools	What is dbt (data build tool) and When should you use it?	🔗	🔗	🔗
Data Management Tools	Streamline dbt Model Development with Notebook-Style Workspace	🔗	🔗	🔗
Testing	Pytest for Data Scientists	🔗	🔗	🔗
Python Helper Tools	Write Clean Python Code Using Pipes	🔗	🔗	🔗
Python Helper Tools	Introducing FugueSQL — SQL for Pandas, Spark, and Dask DataFrames	🔗	🔗
Python Helper Tools	Fugue and DuckDB: Fast SQL Code in Python	🔗	🔗
Python Helper Tools	Marimo: A Modern Notebook for Reproducible Data Science	🔗	🔗
Feature Engineering	Polars vs. Pandas: A Fast, Multi-Core Alternative for DataFrames	🔗	🔗
Visualization	Top 6 Python Libraries for Visualization: Which one to Use?	🔗	🔗
Python	Python Clean Code: 6 Best Practices to Make Your Python Functions More Readable	🔗	🔗	🔗
Logging and Debugging	Loguru: Simple as Print, Flexible as Logging	🔗	🔗	🔗
LLM	Enforce Structured Outputs from LLMs with PydanticAI	🔗	🔗
LLM	Run Private AI Workflows with LangChain and Ollama	🔗	🔗
Speed-up Tools	Writing Safer PySpark Queries with Parameters	🔗	🔗
Speed-up Tools	Narwhals: Unified DataFrame Functions for pandas, Polars, and PySpark	🔗	🔗
Speed-up Tools	Eager to Lazy DataFrames with Narwhals	🔗	🔗
Speed-up Tools	Scaling Pandas Workflows with PySpark's Pandas API	🔗	🔗

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