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.
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 | 🔗 | 🔗 | |
| Speed-up Tools | Writing Safer PySpark Queries with Parameters | 🔗 | 🔗 | |
| Speed-up Tools | Narwhals: Unified DataFrame Functions for pandas, Polars, and PySpark | 🔗 | 🔗 | |
| Speed-up Tools | Scaling Pandas Workflows with PySpark's Pandas API | 🔗 | 🔗 |
If you're passionate about data science and want to share your knowledge about open-source tools for data processing and LLM applications in Python, we'd love to have you contribute!
To contribute:
- Create a GitHub issue:
- Click on the "Issues" tab
- Click "New issue"
- Select "Article Topic Suggestion" template
- Fill in the template with your article proposal
- Read our contribution guidelines
