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D3.js Tutorials, Screencasts and a Newsletter

D3.js Tutorials, Screencasts and a Newsletter

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Related:  d3.jsData-VisualizationD3web 2.0 tools (2)code

A fascination with xml maps Fig 1 - MapD Twitter Map 80M tweets Oct 19 - Oct 30 Visualizing large data sets with maps is an ongoing concern these days. Just ask the NSA, or note this federal vehicle tracking initiative reported at the LA Times. Or, this SPD mesh network for tracking any MAC address wandering by. Using D3, backbone and tornado to visualize histograms of a csv file After being procrastinating for weeks the learning of D3.js and backbone.js I have finally made my first example using both libraries to explore (via histograms) a pandas DataFrame. The reason of the procrastination is very simple: I love python to much, because is probably the only language who is great in all areas (that I am interested at least): Great web frameworks as Django and Tornado - "fighting" with ruby (rails)Great Data Analysis packages such as pandas - "fighting" with RGreat machine-learning libraries such as scikit-learnProbably not the most successful but has a good gaming library pyGameIs a great general purpose language - I use it to program a robot for a NASA competition using a PS3 controller, serial-ports, web-server, cameras, and all in one languageAnd the list could go for hours The Javascript community is in a similar condition everyday new libraries come up and change how people do stuff, such as D3.js changed how people do interactive visualizations. Conclusion

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.

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