Plotting and visualization with python using matplotlib
Course content: A brief matplotlib API primer: Colors, Markers, and Line styles. Figures and Subplots. Ticks, labels, legends, and saving plots to file. Different plots using matplotlib, pandas, and seaborn: Line & bar plots. Histograms & density plots. Scatter or point plots. Facet grids and categorical data. Overview: Making informative visualizations (sometimes called plots) is one of the most important tasks in data analysis. It may be a part of the exploratory process—for example, to help identify outliers or needed data transformations, or as a way of generating ideas for models. Python has many add-on libraries for making static or dynamic visualizations, we will mainly be focused on matplotlib and libraries that build on top of it. matplotlib is a desktop plotting package designed for creating (mostly two dimension...