As a data science organisation that self-styles itself as a “provider” for machine learning and artificial intelligence engines, Anaconda Inc. is understandably keen to sink its teeth into as many technology estates and deployment surfaces as possible. The company’s move to issue a public beta release of Anaconda Code within its Anaconda Toolbox for Excel is designed to enable developers to write Python code directly next to Excel and run it locally. So then, why do the two need to fuse quite so directly?
Let’s explain why we’re posing this question in the first place. Excel (love it or hate it) works pretty ubiquitously well and, equally, Python works effectively as an easy-to-learn language with a vibrant user community, plus it’s comparatively simple to debug and has a clean and logical syntax.
Taking the two technology entities in equal measure, Anaconda suggests that by running Python code locally in Excel, developers get additional flexibility and control over their Python environments; it’s a way for Python converts to quite literally get more juice out of their code by virtue of its local execution status which eliminates the need to wait for network communication to form the spreadsheet-to-software linkup, so to speak.
Why put code inside Excel?
To be clear, Anaconda’s distribution for Python in Excel has been around for almost a year, so this news relates to a more expanded Anaconda software toolbox. The validation for putting Python into such close proximity with Excel stems from the fact that (as we know) Python-based data science exploded in popularity over the last decade in line with the popularization of the Python programming language. The combined power of the Python ecosystem with the ubiquitous datasets and models that live within Excel are hoped to elevate business analytics and predictive modeling.
According to Peter Wang, co-founder and chief AI innovation officer at Anaconda, Python in Excel has enabled users to perform data manipulation, analysis and visualization, as well as advanced machine learning and AI tasks, directly within Excel spreadsheets. “Until now, the Python code would run on Microsoft Azure’s secure cloud servers. With Anaconda Code, Excel users gain the exclusive ability to run Python code on their local machines without relying on external compute services,” said Wang and team.
Excel users value security, shareability and long-term reproducibility in their spreadsheets. The company says that Anaconda Code addresses these challenges through its WebAssembly-based technology that enables local Python execution without requiring separate installations or complex environment management. By bridging the gap between traditional spreadsheet use and advanced coding practices, this solution grants users access to a wider Python ecosystem, enhancing data analysis capabilities while maintaining Excel’s core strengths.
“With Anaconda Code, we’re giving users the freedom to control the environment,” said Wang. “This release marks a significant step forward, enabling Excel users to harness Python’s vast ecosystem while maintaining the speed, reliability and accessibility that businesses and individuals have come to expect from their data tools.”
What do Python users get?
Users can access Anaconda Code via the Anaconda Toolbox in Excel, which democratizes Python use within Microsoft Excel. The toolbox is said to allow software engineers of all skill levels to generate code and create visualizations efficiently, while simultaneously learning Python. Additionally, the Toolbox facilitates collaboration between Excel users and Python experts through Anaconda cloud notebooks, enabling efficient data sharing and teamwork.
Excel users with Anaconda Toolbox have access to Anaconda Assistant to use AI to analyze tables and suggest data handling methods with history following users across workbooks for consistent code use. There’s also code snippet management to write, save and share Python code snippets directly within Excel.
Advanced visualizations enable users to create data visualizations using accessible templates (which will be available in the next few months) and libraries, which can be integrated into Excel worksheets. Streamlined data handling means teams can use data connectors to access, analyze and share data in Excel workbooks or Anaconda cloud notebooks with their improved versioning to ensure access to the most current datasets.
What do Excel users get?
Anaconda further notes that, using its technology, Excel users get access to data preparation functions to clean and prepare data with Python to save time and effort, focusing on the actual analysis. Custom logic and calculations features mean users can access Python expressions to create custom calculations against data and advanced statistical modeling means teams can use Python’s extensive statistical and mathematical libraries, such as StatsModels and SciPy, to perform complex analyses beyond Excel’s built-in capabilities.
Excel users also benefit from data visualization options which enable them to create sophisticated visualizations using Python’s matplotlib and seaborn libraries, going beyond traditional Excel charts. There’s also predictive analytics that makes use of machine learning models to predict future trends based on historical data within Excel directly.
Does the (closer) union of Python and Excel feel like a marriage made in heaven, or it more likely to represent one of convenience? In truth, it may be both and this may be a dovetailing that is conveniently heavenly given the widespread natue of generation-Z programmers who harbor a proclivity for Python and the generation-X C-suite who have grown up on Excel.