Exploring Python in Excel
The Real Python Podcast
Exploring Possibilities and Tooling in Python with Rust
Exploring the potential of removing requirements in Python to enhance the speed of core Python packages, along with discussing the usage of the rough package as a linter and the advantages of type checkers in notebooks. Additionally, the role of Rust as a complementary tool to Python is examined.
Are you interested in using your Python skills within Excel? Would you like to share a data science project or visualization as a single Office file? This week on the show, we speak with Principal Architect John Lam and Sr. Cloud Developer Advocate Sarah Kaiser from Microsoft about Python in Excel.
John shares the multi-year journey of adding Python to Excel. He describes how the project moved beyond writing user functions in Python to something much more elaborate. He details assembling a team with diverse skills in interface design, languages, and security.
Sarah discusses the instant convenience of having familiar Python and pandas techniques at your fingertips inside Excel. We cover typical data science workflows and the potential of interactive visualizations within a spreadsheet. We also share multiple resources for you to learn more.
Note: Python in Excel is currently a preview accessible by joining the Microsoft 365 Insider Program and selecting the Beta Channel.
Course Spotlight: Data Cleaning With pandas and NumPy
In this video course, you’ll learn how to clean up messy data using pandas and NumPy. You’ll become equipped to deal with a range of problems, such as missing values, inconsistent formatting, malformed records, and nonsensical outliers.
Topics:
- 00:00:00 – Introduction
- 00:01:53 – Sr. Cloud Developer Advocate Sarah Kaiser
- 00:02:46 – Principal Architect John Lam
- 00:04:08 – What is Dev Div?
- 00:04:33 – Python data science inside Excel
- 00:09:05 – Designing features with a focus on sharing
- 00:14:28 – Moving between Excel and Python objects
- 00:18:20 – What libraries are imported by default?
- 00:23:11 – Sharing a workbook with others
- 00:26:12 – Recalculating data workflow
- 00:30:07 – Working in Jupyter Notebook vs Excel
- 00:33:03 – Creating a Python object
- 00:33:38 – Video Course Spotlight
- 00:35:02 – More history and project team
- 00:40:19 – Immediate wins of having Python in Excel
- 00:42:28 – Interactive visualizations
- 00:44:34 – Answering security concerns
- 00:49:17 – Limitations and potential
- 00:54:34 – Creating demo projects
- 01:00:25 – Resources to learn more
- 01:02:59 – What are you excited about in the world of Python?
- 01:10:41 – What do you want to learn next?
- 01:12:09 – How can people follow your work online?
- 01:13:26 – Thanks and goodbye
Show Links:
- Python in Excel – Python to Excel - Microsoft 365
- Get started with Python in Excel - Microsoft Support
- Python in Excel DataFrames - Microsoft Support
- Open-source libraries and Python in Excel - Microsoft Support
- User guide and tutorial - seaborn 0.13.1 documentation
- Assessing and Restoring Reproducibility of Jupyter Notebooks - IEEE Conference Publication - IEEE Xplore
- Book of Python in Excel - John Lam’s Website
- GitHub - microsoft/python-in-excel - Python in Microsoft Excel
- Use Python in Excel to enhance your data science - Python Day - YouTube
- Introducing Python in Excel: The Best of Both Worlds for Data Analysis and Visualization - Microsoft Community Hub
- PEP 703 – Making the Global Interpreter Lock Optional in CPython - peps.python.org
- Dr. Sarah Kaiser (@crazy4pi314@mathstodon.xyz) - Fosstodon
- John Lam (@john_lam) - X
Level up your Python skills with our expert-led courses:
The AI-powered Podcast Player
