News Analysis
The Gist
- Python integration. Python in Excel bridges the gap between spreadsheet utility and programming capabilities, offering a diverse set of analytical tools.
- User accessibility. The plugin allows users to access Python functionalities directly within Excel, making advanced analysis less daunting for those unused to IDEs.
- Longevity assured. The integration of Python cements Excel's position in data analytics, suggesting that Excel's utility in the workplace is far from diminishing.
When it comes to spreadsheets, no software has outshone Microsoft's Excel. Since its debut in 1987, Excel has reigned supreme as a workplace staple, maintaining its status as the king of spreadsheets.
Microsoft has continually defended Excel's crown, refreshing its features over the years with new functions and integrations. The latest jewel in this crown is a public beta of an Excel plugin called Python in Excel, which taps into the popular open-source programming language, Python. This plugin stands as one of the most significant updates in analytics, allowing analysts to leverage a diverse skill set and deepen their Python understanding, all within the familiar office setting of Excel.
What Is The Skinny on Python In Excel?
Python in Excel is an in-spreadsheet plugin that natively combines Python syntax into the cell functions of Excel. It benefits data analysts and marketers through permitting advanced data analytics and statistics tactics within a given Excel workbook.
How to Use Python in Excel
To use the plugin, users have to sign up for Microsoft 365 Insider, a program where users can download preview versions of Microsoft software. The program allows users to test the downloads version, called a build, to identify potential user issues and provide feedback about new features still in development.
Once installed, users can access Python in Excel in two ways. They can either type Python syntax directly into a cell or use the "Insert Python" button located in the ribbon menu, which is adorned with Python symbols, similar to other Excel ribbon options.
The Python commands are entered in the spreadsheet cells, with the results returned to the referenced worksheet cell. The user sees the commands written as they would in a terminal or integrated development environment (IDE).
The access to Python opens the door to using Python objects within cell functions and calculations. Imagine a Python object referenced, or its data used in a PivotTable. Even prolific libraries like Scikit-learn, Seaborn and Matplotlib can can be used within Excel. This opens up Python-created visualizations, data models and statistical calculations to be used alongside Excel functions and plugins.
Python in Excel uses Anaconda, a prominent repository notable for allowing developers to run multiple Python environments. Python in Excel uses Anaconda and Azure Container Instances to run Python commands within an isolated container in the cloud. This is done to establish a secure software supply chain for source-built packages from Anaconda.
Related Article: Excel, SQL, Python: What's Your Data Flavor for Customer Experience?
What Is the Significance of Python In Excel?
The Python In Excel features arrive as analysts debate about the appropriateness of spreadsheets in an advanced analysis workflow. A traditional weakness lies in how it calculates large numbers. Excel cells can display a number precision up to 15 digits, so it is beyond appropriate for numbers requiring low precisions such as integers and monetary amounts. But Excel calculates quickly at the expense of accuracy for high-precision numbers, introducing minor assumptions that scale into accuracy errors or returning error warnings from high-precision values imported from outside sources.
Python in Excel Will Equip Analysts for High-Precision Observations
New tools and expanded database access equip analysts to work with datasets featuring high-precision observations. These solutions highlight intriguing capabilities, from data visualizations to integrations with various data sources. This ranges from the cloud-based accessibility of Google Sheets and its extensive plugins to Python and R libraries, which offer user-friendly table functions and document creation features. Even a cloud-AI alternative to Excel, Rows.com, has entered the marketplace to build on the current AI buzz. Despite the advances, each new entry revives debates about the limitations of spreadsheets as analytics tools.
Managers should consider debates as an understandable part of the tech lifecycle because such discussions touch upon the concepts of technical debt — making a solution choice that solves an immediate problem but has a long-term cost that has been deferred. How do you decide the best tabular software for the long-term workflow needs? The topic of Excel’s viability often comes up when people discuss new Excel features, such as this post on AI in Excel.
Yet Excel’s demise due to any shortcomings, perceived or real, is highly unlikely. Microsoft met each tech generation of advances with changes in Excel to stay in step during the product’s 37-year run, such as increased row and column capacity in the spreadsheets — over 1 million rows and 16,000 columns in the current edition — as well as adding sophisticated plugins like Solver for optimization analysis. Each useful feature expanded Excel’s calculation and data integration capacities, keeping customers highly accustomed to Excel satisfied.
Moreover, analysts and managers in a variety of industries rely on Excel’s internal functions to calculate metrics, audit dataset for desired observations and make decisions. The arrival of other options such as Google Sheets only confirms how spreadsheet usage has evolved with data, data storage and the quality of programming options.
Related Article: The Evolution of Excel: When to Ditch the Spreadsheet
The Benefits of Python in Excel For Marketers Working With Data & Analytics
Excel users have traditionally depended on statistical functions for creating tables. However, most of Excel's built-in functions make assumptions about the data's standard qualities, such as a column being normally distributed. Open-source languages like R and Python have long provided advanced libraries that include functions for complex data distributions and statistical techniques not found in Excel. Python in Excel eliminates a barrier, allowing users to integrate these advanced library functions even with smaller datasets that don't necessitate database storage.
The plugin also serves as a low-code solution for analysts seeking advanced analysis and visualization but find setting up an IDE like Microsoft Visual Code too daunting and time-consuming. IDEs enable Python scripts to run outside a terminal, merging the ease of a word processing software with the ability to view program output. This is particularly useful since many programming languages rely on compilers to execute a program.
Consider Data Tech Stack With Care
Despite the expansion of functions and visualization with Python in Excel, marketers should consider the features of their data tech stack with care.
I covered when to use a spreadsheet for an analysis in this 2021 post and explained differences between SQL, Python and Excel relative to delivering customer experience in a post earlier this year. Use these posts as a guide to determine when spreadsheets are a sound technical choice for your data, versus when their use incurs analytical technical debt.
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Final Thoughts on Python in Excel
Marketers are poised for a renaissance in data analysis with Excel. The incorporation of Python into data modeling has reached a significant milestone, marked by integrations in various tech platforms, including R through the RStudio IDE. With this latest plugin, it appears the digital sun isn't setting on the Excel empire anytime soon.
