How do you prepare your Python data science projects for production? What are the essential tools and techniques to make your code reproducible, organized, and testable? This week on the show, Khuyen Tran from CodeCut discusses her new book, “Production Ready Data Science.”

Khuyen shares how she got into blogging and what motivated her to write a book. She shares tips on how to create repeatable workflows. We delve into modern Python tools that will help you bring your projects to production.

Topics:

00:00:00 – Introduction
00:01:27 – Recent article about top six visualization libraries
00:02:19 – How long have you been blogging?
00:03:55 – What do you cover in your book?
00:07:07 – Potential issues with notebooks
00:11:40 – Structuring data science projects
00:15:12 – Reproducibility and sharing notebooks
00:20:33 – Using Polars
00:26:03 – Advantages of marimo notebooks
00:34:21 – Video Course Spotlight
00:35:44 – Shipping a project in data science
00:42:10 – Advice on testing
00:49:50 – Creating importable parameter values
00:53:55 – Seeing the commit diff of a notebook
00:55:12 – What are you excited about in the world of Python?
00:56:04 – What do you want to learn next?
00:56:52 – What’s the best way to follow your work online?
00:58:28 – Thanks and goodbye

Show Links:

Production Ready Data Science by Khuyen Tran - CodeCut
CodeCut
Top 6 Python Libraries for Visualization: Which One to Use? - CodeCut
Ruff
uv
Cookiecutter
marimo - a next-generation Python notebook
Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python
Polars — DataFrames for the new era
Episode #260: Harnessing the Power of Python Polars
Narwhals
Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries
pytest documentation
nbdime: Tools for diffing and merging of Jupyter notebooks.
LangChain
Build Production-Ready LLM Agents with LangChain 1.0 Middleware - CodeCut
Build an LLM RAG Chatbot With LangChain
Khuyen Tran - LinkedIn
Khuyen Tran (@KhuyenTran16) - X

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