The 14 Best Data Visualization Tools Nishith Sharma is the co-founder of frrole, a social intelligence startup. Raw data is boring and it’s difficult to make sense of it in its natural form. Add visualization to it and you get something that everybody can easily digest. Not only you can make sense of it faster, but you can also observe interesting patterns that wouldn’t be apparent from looking only at stats. All Killer, No Filler matplotlib: python plotting matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell (ala MATLAB®* or Mathematica®†), web application servers, and six graphical user interface toolkits. matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code. For a sampling, see the screenshots, thumbnail gallery, and examples directory For example, using "ipython --pylab" to provide an interactive environment, to generate 10,000 gaussian random numbers and plot a histogram with 100 bins, you simply need to type
Tutorials · mbostock/d3 Wiki Wiki ▸ Tutorials Please feel free to add links to your work! Tutorials may not be up-to-date with the latest version 4.0 of D3; consider reading them alongside the latest release notes, the 4.0 summary, and the 4.0 changes. Coffee Flavour Wheel Click to zoom! Sunburst Trees This is an example of using d3.layout.partition to generate a zoomable sunburst tree derived from hierarchical data. A sunburst tree is a radial space-filling visualisation, analagous to an icicle tree. Colours The original colours appear to have been selected by the designer, as opposed to matching any standard colour name palettes.
Quick scatterplot tutorial for d3.js When I code One of the many interesting things Github does are punchcards for repositories that can tell you when people work on their code. Unfortunately, they’re only per-repository and I was interested in per-user Github punchcards. So I made my own. Starting with Canvas for a D3.js addict - Visual Cinnamon In this blog I’d like to take you through my learnings from last week when I finally started with canvas. I hope that, after reading this blog, I will have convinced you that canvas is a really good visualization option if you need better performance than d3.js can give you and that it’s actually really not that difficult to learn canvas. Last September I made a data visualization project about the age distribution across all ~550 occupations in the US. I came up with the idea of combining the standard d3.js circle pack layout with mini bar charts, or ‘small multiple packing’ as I started calling it. The size of the circles encodes how many people are employed in that occupation and the bar chart within the circle gives another level of detail by showing you how these people are spread across 7 different age bins.