Python Tutorial: Plotting with glyphs
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AI Productivity Tools60%
Key Takeaways
Plotting with glyphs using Bokeh in Python
Original Description
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In this first chapter we will take a look the bokeh.plotting interface. This is a simple but flexible interface that is a good place to get started with using Bokeh. This interface gives you a basic empty plot with sensible (but customizable) defaults for things like axes, grids, and tools. Into this plot you can add glyphs that connect visual properties directly to your data.
So what are glyphs?
Glyphs are visual shapes that can be drawn on the screen.
These can be simple point-like markers such as circles, squares, triangles
Or more sophisticated shapes such as rectangles, lines, wedges and others
In every case, these shapes have visual properties that can include things like:
position or (x,y) coordinates for locating a shape in the plot
size or radius, fill and outline colors, or transparency (also called alpha)
Let's see what this actually looks like in typical usage.
First, in order to use bokeh.plotting and also to see our results, there are some standard Python imports.
Here we see "from bokeh.plotting import output_file, show". These two functions make it easy to save the plots we make in an HTML file, and to open up a browser to display the file. It's also worth mentioning that we could instead import "output_notebook" in order to display plots inline in a Jupyter notebook. Although we will not be using Jupyter Notebooks in this course, they are very common in the world of Data science.
We also see "from bokeh.plotting import figure". The "figure" function is what creates the basic empty plot with sensible defaults I mentioned earlier, and is all that is required to start using the bokeh.plotting interface.
Next we call the figure function with some arguments that control general properties of a plot. In this ca
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