Bokeh is a Python interactive visualization library for large datasets. It features high-performance interactivity and uses the latest web technologies. Bokeh aims at providing concise graphics construction that bases on the ideas of the Grammar of Graphics, Protovis and D3. Its primary output backend is HTML5 Canvas. Bokeh uses a syntax familiar to R/ggplot users.
The library's name origins from the Japanese word and photography term “bokeh” that indicates the blurring of the out-of-focus parts of an image. It is a great method to alter the photo in order to draw attention to subjects of interest. In the same way Bokeh as a Python library focuses on important and challengeable issues of large dataset visualization.
Bokeh has several dependencies:
BokehJS is the in-browser client-side runtime library that serves as interaction tool between users and Bokeh. It is written in Coffeescript and is built as a standalone, first-class Javascript plotting library. BokehJS aims at dynamic and interactive visualization in the browser. Communication between the web client and backend application is accomplished via a redis-database persistence layer.
Bokeh differs from other Python plotting packages by concentrating on the needs of statistical plotting and multidimensional datasets. This functionality is implemented via declarative data transformation scheme and is engineered to operate in a client/server model for the modern web. Bokeh achieves cross-platform ubiquity through IPython’s new model of in-browser, client-side rendering.
This modular and open-source Python package can be used to deliver high-performance interactive visualization over large datasets and easily generate pages with multiple plots.