Artwork

内容由Real Python提供。所有播客内容(包括剧集、图形和播客描述)均由 Real Python 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal
Player FM -播客应用
使用Player FM应用程序离线!

Preparing Data Science Projects for Production

59:12
 
分享
 

Manage episode 519418766 series 2637014
内容由Real Python提供。所有播客内容(包括剧集、图形和播客描述)均由 Real Python 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

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:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

  continue reading

277集单集

Artwork
icon分享
 
Manage episode 519418766 series 2637014
内容由Real Python提供。所有播客内容(包括剧集、图形和播客描述)均由 Real Python 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

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:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

  continue reading

277集单集

Все серии

×
 
Loading …

欢迎使用Player FM

Player FM正在网上搜索高质量的播客,以便您现在享受。它是最好的播客应用程序,适用于安卓、iPhone和网络。注册以跨设备同步订阅。

 

快速参考指南

版权2025 | 隐私政策 | 服务条款 | | 版权
边探索边听这个节目
播放