In this Repository I have explored the JavaScript libraries available in Python for making highly interactive and customized plots.
D3 allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. For example, you can use D3 to generate an HTML table from an array of numbers. Or, use the same data to create an interactive SVG bar chart with smooth transitions and interaction.
D3 is not a monolithic framework that seeks to provide every conceivable feature. Instead, D3 solves the crux of the problem: efficient manipulation of documents based on data. This avoids proprietary representation and affords extraordinary flexibility, exposing the full capabilities of web standards such as HTML, SVG, and CSS. With minimal overhead, D3 is extremely fast, supporting large datasets and dynamic behaviors for interaction and animation. D3’s functional style allows code reuse through a diverse collection of official and community-developed modules.
Readers familiar with other DOM frameworks such as jQuery should immediately recognize similarities with D3. Yet styles, attributes, and other properties can be specified as functions of data in D3, not just simple constants. Despite their apparent simplicity, these functions can be surprisingly powerful; the d3.geoPath function, for example, projects geographic coordinates into SVG path data. D3 provides many built-in reusable functions and function factories, such as graphical primitives for area, line and pie charts.
Using D3’s enter and exit selections, you can create new nodes for incoming data and remove outgoing nodes that are no longer needed.
When data is bound to a selection, each element in the data array is paired with the corresponding node in the selection. If there are fewer nodes than data, the extra data elements form the enter selection, which you can instantiate by appending to the enter selection. For example
D3 does not introduce a new visual representation. Unlike Processing or Protovis, D3’s vocabulary of graphical marks comes directly from web standards: HTML, SVG, and CSS. For example, you can create SVG elements using D3 and style them with external stylesheets. You can use composite filter effects, dashed strokes and clipping. If browser vendors introduce new features tomorrow, you’ll be able to use them immediately—no toolkit update required. And, if you decide in the future to use a toolkit other than D3, you can take your knowledge of standards with you!
Best of all, D3 is easy to debug using the browser’s built-in element inspector: the nodes that you manipulate with D3 are exactly those that the browser understands natively.
D3’s focus on transformation extends naturally to animated transitions. Transitions gradually interpolate styles and attributes over time. Tweening can be controlled via easing functions such as “elastic”, “cubic-in-out” and “linear”. D3’s interpolators support both primitives, such as numbers and numbers embedded within strings (font sizes, path data, etc.), and compound values. You can even extend D3’s interpolator registry to support complex properties and data structures.
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