Most Excel files are already applications in disguise. They have inputs, rules, exceptions, and repeated handoffs. What they usually do not have is an interface built for the job people are actually trying to do.

Tool Builder is a way to add that interface without leaving Excel. You ask an AI tool to generate a small single-file web app, paste it into Tool Builder, and run it directly in the workbook. The workbook stays the source of truth. The app becomes the simpler surface around it.

This comprehensive post compares Excel's newly introduced native regex functions (REGEXTEST, REGEXEXTRACT, and REGEXREPLACE) with Python's long-established re module. We explore their respective capabilities, syntax variations, supported regex constructs, and advanced features to help you understand which tool is best suited for different text manipulation scenarios. Whether you're working within a spreadsheet for interactive data exploration or building automated text processing pipelines in Python, this detailed analysis will highlight the strengths and limitations of each approach.

We are thrilled to announce the availability of Python-powered statistical functions for Excel users. This release brings the full power of Python's scipy.stats library directly into your spreadsheets with comprehensive distribution support, advanced statistical methods, and capabilities that go far beyond what native Excel functions provide. Our Python functions enable you to access full distribution support (PDF, CDF, quantiles, and more) for distributions not available in Excel, perform advanced analytics, and leverage the complete toolkit of scipy.stats directly from your spreadsheets.

We've launched a repository of Python Functions that can be used in Excel as custom functions through the Python for Excel add-in and as web apps via Gradio.

Spreadsheets have long been the workhorse of data organization, calculation, and analysis. From simple budgets to complex financial models, their grid-based structure provides a powerful way to manage information. However, dealing with unstructured text, complex data cleaning, pattern recognition beyond simple formulas, and generating insights often required manual effort or specialized tools outside the spreadsheet environment.

The integration of Artificial Intelligence (AI) directly into spreadsheet functions is changing this paradigm. Now, users can leverage the power of large language models (LLMs) and other AI techniques to perform tasks that were previously difficult or impossible within the confines of rows and columns. This document explores the diverse applications of AI in spreadsheets, transforming them from static data containers into dynamic tools for insight generation, automation, and enhanced productivity.

Discover how AI-powered functions like AI_ASK, AI_EXTRACT, AI_FORMAT, and others are revolutionizing spreadsheet workflows by enabling natural language interaction, intelligent data extraction, and automated pattern recognition. These capabilities transform spreadsheets into powerful tools for insight generation, data processing, and workflow automation across diverse business domains.

We're excited to announce a major update to our Python for Excel add-in that makes it easier than ever to create Excel LAMBDA functions from Python code. The new code editor provides a familiar VS Code-like experience right inside Excel, with features like syntax highlighting, function management, and integrated testing.

Fuzzy matching is a technique used to find strings that are approximately equal, e.g. "Microsoft" and "Microsoft Inc". This is particularly useful in scenarios where exact matches are not possible due to typographical errors, variations in spelling, or other inconsistencies. In this post, we will explore how to perform fuzzy matching with Python in Excel using the NLTK library.

Our text_distance function for fuzzy matching is similar to that used in our Fuzzy Match app, but supports many more algorithms and can be readily modified to your specific needs. Actually, we've recently updated our Fuzzy Match tutorial with more details.

Azure Sentiment Analysis is a powerful cloud-based service that goes beyond simple sentiment scoring by providing aspect-based sentiment analysis, allowing you to extract sentiment for specific aspects of text (like "food" and "service" in customer reviews). This post demonstrates how to integrate this service with Excel using Python to analyze text data and return results in a tabular format.

Since we launched Boardflare's Python for Excel add-in over a month ago, we've heard from users that they would like to see more pre-made Python functions that they can use as-is or modify to suit their needs. As we've started buiding out a collection of Python functions, we realized that we could align our API with Microsoft's Python in Excel so that the same code could be used in both with minimal modification.

Microsoft's Python in Excel has now reached GA. It enables the user to input Python code and it will return the result as either a Python object or Excel values. You can read in more detail how to use this function in the official documentation.

The Python in Excel feature is only available to Microsoft 365 subscribers, and access to the higher performance runtime requires a separate add-on license.

The purpose of this post is to focus on the strengths and potential weaknesses of this implementation and to explore alternatives.