In 2023, Microsoft introduced Python in Excel, a feature combining the power of Python and Excel analytics within the same workbook. Notably, users don"t need any setup on their PCs; Python calculations run in the Microsoft Cloud, with results, including plots and visualizations, returned to the worksheet. Previously, Python in Excel utilized the Anaconda Distribution for Python running in Azure.
Today, Anaconda announced a new way for Excel users to run Python code locally on their PCs without relying on the Microsoft Cloud. The public beta release of Anaconda Code within the Anaconda Toolbox for Excel includes this new local Python processing capability. This enhancement offers Excel users flexibility and control over their Python environments. Anaconda Code uses WebAssembly-based technology to enable Python execution without separate installations or complex environment setups.
Excel users with Anaconda Toolbox will have the following features:
- Anaconda Assistant: Utilize AI to analyze tables and suggest data handling methods with history following users across workbooks for consistent code use.
- Code Snippet Management: Write, save, and share Python code snippets directly within Excel, enhancing productivity and collaboration.
- Advanced Visualizations: Create powerful data visualizations using accessible templates and libraries, easily integrated into Excel worksheets.
- Streamlined Data Handling: Use data connectors to access, analyze, and share data in Excel workbooks or Anaconda.cloud notebooks with improved versioning to ensure access to the most current datasets.
Anaconda Toolbox is free for users while in public beta, and you can download the Anaconda Toolbox in Excel from Microsoft"s add-in marketplace.
Anaconda"s Co-Founder and Chief AI Innovation Officer, Peter Wang, emphasized the significance of the Anaconda Code release, stating it empowers users with control over their environment. He highlighted the release as a major advancement, allowing Excel users to leverage Python"s extensive capabilities while maintaining the performance, dependability, and ease of use expected in modern data tools.
Source: Anaconda