What is Bokeh?

Bokeh is a Python library that helps you create interactive, web-ready visualizations like charts and graphs. It turns data into pictures you can explore by zooming, panning, or clicking, all from a web browser.

Let's break it down

  • Python library: a collection of ready-made tools you can use in the Python programming language.
  • Interactive: you can move the mouse, zoom in/out, or click to see more details, instead of just a static picture.
  • Web-ready: the visualizations are built as HTML/JavaScript, so they work in any modern web browser without extra software.
  • Charts and graphs: visual ways to show numbers, like line plots, bar charts, scatter plots, etc.
  • Explore: you can change what you see on the fly, helping you discover patterns in the data.

Why does it matter?

Because visualizing data makes complex information easier to understand, and Bokeh lets you share those visuals online instantly. It empowers analysts, scientists, and developers to build dashboards that let users interact with data themselves, leading to better decisions and insights.

Where is it used?

  • Business dashboards: companies display sales trends, inventory levels, or KPI metrics that managers can filter and zoom.
  • Scientific research: researchers plot large datasets (e.g., climate data, genomics) and let peers explore the results interactively.
  • Education: teachers create live, manipulable graphs for students to experiment with math or statistics concepts.
  • Financial tools: traders build web apps to watch stock price movements and test strategies in real time.

Good things about it

  • Produces fully interactive plots that run in any browser.
  • Works well with other Python data tools like Pandas, NumPy, and Jupyter notebooks.
  • Supports large datasets and streaming data without huge performance loss.
  • Offers a high-level API for quick charts and a low-level API for fine-grained customization.
  • Open-source and actively maintained with a helpful community.

Not-so-good things

  • Learning curve can be steep if you need deep customizations; the low-level API is more complex.
  • Rendering very large numbers of glyphs (millions) may still be slower than specialized JavaScript libraries.
  • The default styling is functional but may require extra CSS/JS work for polished, brand-consistent looks.
  • Compared to some alternatives (e.g., Plotly), Bokeh’s built-in export to static images is less straightforward.