The plotting process begins with a call to the figure() function.

To experiment with the same numbers in the code, call np.ed(1). The code generates a series of random numbers to be used for the visualizations. X_range =(0, 11 ), y_range =(0,10 )) = 'First Example' = 'center' _color = 'red' _font_size = '25px' _fill_color = 'yellow' plot.line (x, y1, color = 'blue', legend_label = 'First' ) plot.line (x, y2, color = 'green', legend_label = 'Second' ) = 'bottom_right' _policy = 'hide' show (plot ) This section shows you how to install Bokeh using Pip and Anaconda or Miniconda. Bokeh is a very low-level product where you specify precisely how you want things drawn. This reduces the effort needed to apply special effects and eases the addition of labels to pie charts. Matplotlib provides additional drawing flexibility that allows you to make modifications directly to various axes. Matplotlib lacks such an extensive glyph library. In addition, Bokeh has an extensive library of glyphs that can be added to your visualizations. When you use Bokeh, you find that it produces beautiful interactive graphics with less code than Matplotlib requires.

However, given its age, Bokeh has a strong community. This may be because Bokeh has not been around as long as Matplotlib. This functionality is explored later in the guide.įrom a support perspective, Matplotlib enjoys far greater community support than Bokeh does. Bokeh can produce Jupyter Notebook output or send its output to a file. Matplotlib, on the other hand, provides Python visualizations that integrate well with Jupyter Notebook. If your focus is on website interactivity, then Bokeh is the better choice. While Bokeh and Matplotlib both help you plot data, these two libraries are different tools for different purposes. This guide introduces you to Bokeh with example code that creates line and bar graphs. Bokeh is an interactive visualization library that focuses on browser output.

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