This is an excellent blog post - I'd never heard of Great Tables before, and I'm a newly minted fan!
> confronted with an all-too-familiar dilemma: copy your data into a tool like Excel to make the table, or, display an otherwise unpolished table.
One add-on (coming from the past 4 years of working on a tabular-data from Pythons startup [1]) is that users aren't just copying data into Excel because if it's good formatting capability: very often, there are organizational constraints that mean that Excel _needs_ to be where this data ends up.
The most common reasons I've seen for data ending up in Excel:
1. Other parts of the report rely on Excel features - you want to build pivot tables or graphs in Excel (often, these are much easier to build in Excel than in Python for anyone who isn't a real Pythonista)
2. The report you're sending out for display is _expected_ in an Excel format. The two main reasons for this are just organizational momentum, or that you want to let the receiver conduct additional ad-hoc analysis (Excel is best for this in almost every org).
The way we've sliced this problem space is by improving the interfaces that users can use to export formatting to Excel. You can see some of our (open-core) code here [2]. TL;DR: Mito gives you an interface in Jupyter that looks like a spreadsheet, where you can apply formatting like Excel (number formatting, conditional formatting, color formatting) - and then Mito automatically generates code that exports this formatting to an Excel. This is one of our more compelling enterprise features, for decision makers that work with non-expert Python programmers - getting formatting into Excel is a big hassle.
Of course, for folks who can ditch Excel entirely, this is entirely unnecessary. Great Tables seems excellent in this case (and anyone writing blog posts this good is probably writing good code too... :) )
Playing nice with Excel (and PowerPoint) is an underrated feature. The next step I see from business users is taking the formatted Excel table and pasting it into a PowerPoint slide. The hacker mindset often says the Microsoft Office suite is the wrong tool for the job, so we should use X tool and Y process instead. That may be true, but there's so much institutional inertia at established organizations that it's hard to completely abandon the Office suite. Anything that lets a technical user do something programmatically, but allows the output to be easily manipulated by a non-expert is invaluable.
I've had success generating svg visuals and placing them in slides, which PPT treats as a "shape" (the Graphics Format ribbon appears), and business users like that they can modify the shapes (for example, change the color). Great Tables supports pdf export, but not svg. I just tested a pdf vector in the current version of PPT, and while it maintains the vector, PPT won't let me convert it to a shape (only the Picture Format ribbon is available). Great Tables doesn't seem to support svg export directly, so there needs to be an additional pdf -> svg conversion.
> confronted with an all-too-familiar dilemma: copy your data into a tool like Excel to make the table, or, display an otherwise unpolished table.
One add-on (coming from the past 4 years of working on a tabular-data from Pythons startup [1]) is that users aren't just copying data into Excel because if it's good formatting capability: very often, there are organizational constraints that mean that Excel _needs_ to be where this data ends up.
The most common reasons I've seen for data ending up in Excel: 1. Other parts of the report rely on Excel features - you want to build pivot tables or graphs in Excel (often, these are much easier to build in Excel than in Python for anyone who isn't a real Pythonista) 2. The report you're sending out for display is _expected_ in an Excel format. The two main reasons for this are just organizational momentum, or that you want to let the receiver conduct additional ad-hoc analysis (Excel is best for this in almost every org).
The way we've sliced this problem space is by improving the interfaces that users can use to export formatting to Excel. You can see some of our (open-core) code here [2]. TL;DR: Mito gives you an interface in Jupyter that looks like a spreadsheet, where you can apply formatting like Excel (number formatting, conditional formatting, color formatting) - and then Mito automatically generates code that exports this formatting to an Excel. This is one of our more compelling enterprise features, for decision makers that work with non-expert Python programmers - getting formatting into Excel is a big hassle.
Of course, for folks who can ditch Excel entirely, this is entirely unnecessary. Great Tables seems excellent in this case (and anyone writing blog posts this good is probably writing good code too... :) )
[1] https://trymito.io
[2] https://github.com/mito-ds/mito/blob/dev/mitosheet/mitosheet...