Learn By Example: Matplotlib

Matplotlib is the most popular Python library to visualize data. Any exploratory data analysis performed by an analyst or a scientists first starts off with data visualization which allows them to understand relationships that might exist in the data. Matplotlib is highly customizable and offers powerful features to build graphs and plots which are production-ready.

Here is what this course covers:

Basic figure components: The anatomy of a Matplotlib figure and its customizable parts

Figures, axes, subplots: Components that allow you to layout and customize your graphs

Lines, markers, watermarks: Customize the look at feel of plot components

Shapes, curves, annotations: Highlight significant insights

Boxplots, Violinplots: Draw statistical insights from data

Histograms, pie charts and stacked plots: View different ways to convey statistical information

Color maps and palettes: Get plot aesthetics in the colors of your choice

Autocorrelation: View interesting insights with autocorrelation plots

This course is built around hands on demos, built to explicitly explain concepts underpinning each topic.

Software used: Python 3, Matplotlib