New Python-Based Data Visualization Tool

I've only had to deal with multi-dimensional data analysis once in my career. That was in the early 1990s, when I was working for a now-defunct software company called Alacrity. At the time, I was their office manager, which meant that I was their bookkeeper, technical writer, inside salesperson, and general jack of all trades.

The company got a contract to build a sales analysis tool for the local office of one of the big drug companies and I got roped in to help because I was a spreadsheet jockey. We built it in Supercalc, using pivot tables, to analyse five different dimensions of sales data (something like region, salesperson, product, product category, and profit). It wasn't a huge data set and even an early PC spreadsheet could handle the data. 

Since then I've been interested in seeing how people work with large data sets, which is something that scientists have to do all the time. Nature has an article about a new Python-based data visualization tool called napari. 

napari — the name refers to a Pacific island village midway between the developers’ bases in San Francisco and Melbourne — features a simple graphical interface with a built-in Python console in which images can be rendered, rotated and manipulated in 2D or 3D, with additional dimensions, such as the succession of temporal ‘slices’ in a time series, accessible using sliders beneath the image window. If available, graphics-processing units can be used to accelerate the software. “We make sure that we actually use the computer to its full capacity,” Royer explains. (ImageJ users can also work in Python using PyImageJ; see pypi.org/project/pyimagej).

Adobe Photoshop-like layers allow users to overlay points, vectors, tracks, surfaces, polygons, annotations or other images. A researcher could, for instance, open an image of a tissue in napari, identify cell nuclei with a click of the mouse, retrieve those points in Python and use them to ‘seed’ a cell-segmentation algorithm, which identifies cell boundaries. By then pushing the results to napari as a new layer on the original image, they can assess how well the segmentation process worked.

I'm going to pass this along to a couple of my former co-workers who are doing data analysis. I'm not sure you could apply it to visualizing stock market data but you never know.