Bryan believes this has a lot to do with the project's early days, where patience, responsiveness, and receptiveness to the contributions from a community in its embryonic stages play a determining role in later successes. With 37,000+ public GitHub repositories declaring its use and 2.5 million monthly downloads, Bokeh has made a name for itself. Why is security important for open-source projects like Bokeh? Inspired by his previous contribution to Chaco (Python data visualization library) and the rise of JavaScript-heavy frameworks for frontend in the early 2010s, Bryan teamed up with Peter Wang to offer an alternative for Python developers, who were working on interactive data applications for the modern browser. He authored the conda package manager and worked full-time at Anaconda on its distribution, simplifying package management and deployment for more than 25 million users worldwide. Standalone examples of data plots made with the Bokeh libraryīefore starting his endeavor with Bokeh in 2012, Bryan was no stranger to open-source libraries. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications. ![]() It provides elegant and concise construction of plots while maintaining high-performance interactivity over large datasets. Bokeh, the interactive visualization library for the modern browserīokeh (pronounced /ˈboʊkeɪ/ BOH-kay) is an interactive visualization library for modern web browsers, written in Python. The goal of attackers is straightforward: introduce vulnerabilities downstream, and in turn, attack the software supply chains that depend on the same open-source packages and libraries. Bryan gave us an insider look at how open-source maintainers such as himself shield their projects against the attempts of malicious actors trying to exploit security gaps. We had the pleasure to exchange a few words with Bryan Van de Ven, co-creator and core maintainer of the Bokeh project, a Python library for data visualization. This time, we decided to go on the other side of the fence. Most discussions we are hearing today around security in this space are focused on the identification, fixing, and remediation of vulnerabilities - all seen from the “consumer” perspective. Its prevalence in commercial software is reaching unprecedented levels, to the extent that the European Commission has recently identified it as a public good, in a recent study assessing its impact on the region’s economy.īut the interstitial nature of open-source in modern software also makes it a subject of security and compliance concerns, as it is capable of exposing organizations that use it to a host of unknown risks and vulnerabilities. Save this file to your github pages directory.Open-source is everywhere, it is one of the driving forces of software innovation from the academic to the enterprise world (75% of codebases audited by Synopsys in the 2021 OSSRA report rely on open-source components). This command produces a single HTML file. ![]() circle ( 'petal_length', 'petal_width', color = 'colors', fill_alpha = 0.2, size = 10, source = ColumnDataSource ( flowers )) output_file ( 'flowers.html' ) show ( p ) Import pandas as pd from import flowers from otting import figure, show, output_file from bokeh.models import ColumnDataSource, HoverTool colormap = flowers = for x in flowers ] hover = HoverTool ( tooltips = ) p = figure ( title = "Iris Morphology", plot_height = 500, plot_width = 500, tools = ) p. Diagnosing possible errors Method 1: Embedding a single plot at a time with output_file()īy far the simplest option is to use the output_file() function to produce a single HTML document.įor this demo, I’ll use a slightly modified version of a classic example in the Bokeh documentation, Iris.py.Method 2: Embedding 1 or more plots with components().Method 1: Embedding a single plot at a time with output_file().This is a quick guide to embedding your visualizations in a Jekyll-hosted site, such as a Github Pages blog. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, and to extend this capability with high-performance interactivity over very large or streaming datasets. A quick how-to guide for embedding interactive Javascript plots, made with the Bokeh library in Python, on a Github Pages site.īokeh is a Python interactive visualization library that targets modern web browsers for presentation.
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