Archive for the 'PyXLL' category

PyXLL: Deploy Python to Excel Easily

Feb 06 2014 Published by under Canopy, Enthought Canopy, Finance, News, Python, PyXLL

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Today Enthought announced that it is now the worldwide distributor for PyXLL, and we’re excited to offer this key product for deploying Python models, algorithms and code to Excel. Technical teams can use the full power of Enthought Canopy, or another Python distro, and end-users can access the results in their familiar Excel environment. And it’s pretty straightforward to set up and use.

PyXLL is free for non-commercial and evaluation purposes, and in Canopy you can simply grab it from the Enthought repo via the Package Manager as shown in the screenshots below (note that at this time PyXLL is only available for Windows users). The rest of the configuration instructions are in the Quick Start portion of the documentation. PyXLL itself is a plug-in to Excel. When you start Excel, PyXLL loads into Excel and reads in Python modules that you have created for PyXLL. This makes PyXLL especially useful for organizations that want to manage their code centrally and deploy to multiple Excel users.

Enthought Canopy Package Manager   Install PyXLL from Enthought Canopy's Package Manager

To create a PyXLL Python Excel function, you use the @xl_func decorator to tell PyXLL the following function should be registered with Excel, what its argument types are, and optionally what its return type is. PyXLL also reads the function’s docstring and provides that in the Excel function description. As an example, I created a module and registered it with PyXLL via the PyXLL config file. In that module I put a simple function pyfib(): a naive Fibonacci implementation.

When I start Excel, I can access the Excel function wizard and find my pyfib() function and use it. The function documentation in Excel comes from my docstring. PyXLL parses the “n: integer input” portion as the variable documentation.

If I go back and make a change to the function, I can reload PyXLL without restarting Excel and update the cells. If I add another function to my module, it too will get loaded and be available to use in my worksheet.

So if you are developing Python models or functions for a large number of distributed Excel users, you can manage the code centrally. PyXLL will load new versions and new functions from the central repository whenever a user starts Excel. Deployment is very straightforward, and central management of all the code reduces the risk of Excel macros and functions proliferating uncontrolled.

I can also create menu functions using the decorator @xl_menu. PyXLL ships with several examples that you can start with. The one below adds a menu item to the Excel Add-in menu, and pops up a message box when selected.


As I said earlier, PyXLL is free to download for non-commercial and evaluation purposes. In Canopy it’s available in the Package Manager (as long as you upgrade to Canopy v1.3 first), and for other Python distros it’s available from our PyXLL store page. You can also find more details and documentation on the PyXLL product pages.

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Avoiding “Excel Hell!” using a Python-based Toolchain

May 31 2013 Published by under Canopy, Conferences, Enthought Canopy, Finance, General, PyXLL

Update (Feb 6, 2014):  Enthought is now the exclusive distributor of PyXLL, a solution that helps users avoid “Excel Hell” by making it easy to develop add-ins for Excel in Python. Learn more here.

Didrik Pinte gave an informative, provocatively-titled presentation at the second, in-person New York Quantitative Python User’s Group (NY QPUG) meeting earlier this month.

There are a lot of examples in the press of Excel workflow mess-ups and spreadsheet errors contributing to some eye-popping mishaps in the finance world (e.g. JP Morgan’s spreadsheet issues may have led to the 2012 massive loss at “the London Whale”). Most of these can be traced to similar fundamental issues:

  • Data referencing/traceability

  • Numerical errors

  • Error-prone manual operations (cut & paste, …)

  • Tracing IO’s in libraries/API’s

  • Missing version control

  • Toolchain that doesn’t meet the needs of researchers, analysts, IT, etc.

Python, the coding language and its tool ecosystem, can provide a nice solution to these challenges, and many organizations are already turning to Python-based workflows in response. And with integration tools like PyXLL (to execute Python functions within Excel) and others, organizations can adopt Python-based workflows incrementally and start improving their current “Excel Hell” situation quickly.

For the details check out the video of Didrik’s NY QPUG presentation.  He demonstrates a an example solution using PyXLL and Enthought Canopy.
Vimeo -

And grab the PDF of his slides here.


It would be great to hear your stories about “Excel Hell”. Let us know below.

–Brett Murphy

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