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I find the canvas approach to data analysis very intuitive, and it facilitates deep-dives into of insights questions while presenting a clear picture of the steps to my work. Count's ability to quickly import and match static csv data alongside data warehouse queries makes it particularly valuable when working with a range of inputs.
I have also found Count to be an effective way to collaborate and share insights with stakeholders, both in a classic report format and in a canvas that allows for them to explore further themselves. The speed at which I can build and edit visuals in the canvas is incredibly useful when I need to give clear answers to ad-hoc queries.
The Count team are also very responsive to requests for support. Review collected by and hosted on G2.com.
There are a couple of limitations - Count can only import packages that are either in pyodide or a pure python package.
Formatting can sometimes be an issue - in the past, I've built a canvas to be shared, then found certain visual elements have changed slightly with an update.
The main problem I had previously was that Count slowed down a lot for very large canvases, but I think they now have a fix for this in which DuckDB runs on the server instead. Review collected by and hosted on G2.com.
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