![]() ![]() The plotting libraries then vary in their level of abstraction from the data set. Plotting libraries in Python usually take care of the rendering part for you, either rendering the plot as SVG, PDF, PNG, or other formats, including interactive ones that use JavaScript and HTML Canvas that can be viewed in a browser. (I have actually done this before, writing a C program that hand-generated SVG, and it was paaaaainful.) That would be a very low-level way of generating a plot. One way you could build a plot is to hand-generate an SVG file that is a set of specifications for lines and circles and text and whatnot that comprises a plot. You can imagine that there are many many steps to building that. Now, say we want to make a plot of some data. Steps take place, but because we use Pandas’s high-level functionality, those details are invisible to us, and glad we are of that. We can loop over the rows in the data frame with a for loop, check to see what the value of the insomnia column is with an if statement, put the value in the percent correct field into an appropriate array based on whether or not the subject suffers from insomnia, and then, given those arrays, sort them and pull out the middle value. There are elementary tasks that go into it if we were to code it up without using Pandas’s delicious functionality. Exercise 7: MLE with variate-covariate modelsĭf.Exercise 6: Maximum likelihood estimation.Lesson 6: Maximum likelihood estimation.Lesson 5: Generative modeling and parametric inference.Lesson 4: Nonparametric inference with hacker stats. ![]() Lesson 3: Probability distributions and the plug-in principle.Styling Bokeh plots (omitted in live session).Bokeh’s grammar and our first plot with Bokeh.High-level and low-level plotting packages.Lesson 1: Pandas and split-apply-combine. ![]()
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