Learning Path: Python:Data Visualization with Matplotlib 2.x

"Matplotlib for Python Developers
The Course Overview
Understanding Data, Dimensionality, and Why We Plot
Setting Up Your Environment
Beginning with the Most Basic Plots
Differentiating Line and Scatter Plots
Constructing Bar Plots and Histograms
Exploring Images and Contours
Working on Plots with Uncertainties
Looking at Other Useful Plot Types
Making Multiple Panel Plots
Using Color Bars and Legends
Working with the Components of a Matplotlib Plot
Figure and Axes – How Do They Work?
Working with Transformations
Controlling Axes and Ticks
Ticker Formatting
Working on Back Ends
The Jupyter Notebook
Using Pandas to Manipulate Tabular Data
Slicing and Dicing Pandas Data
Pandas Built-in Plotting
Test Your Knowledge
Python Data Visualization with Matplotlib 2.x
The Course Overview
Getting Started with Matplotlib
Setting Up the Plotting Environment
Editing and Running Code
Loading Data for Plotting
Plotting Our First Graph
Basic Structure of a Matplotlib Figure
Setting Colors in Matplotlib
Adjusting Text Formats
Customizing Lines and Markers
Customizing Grids and Ticks
Customizing Axes
Using Style Sheets
Title and Legend
Adjusting Layout
Adding Subplots
Adjusting Margins
Drawing Inset Plots
Adding Text Annotations
Adding Graphical Annotations
Typical API Data Formats
Introducing Pandas
Visualizing the Trend of Data
Visualizing Univariate Distribution
Visualizing a Bivariate Distribution
Visualizing Categorical Data
Controlling SeabornFigure Aesthetics
More About Colors
Getting End-of-Day (EOD) Stock Data from Quandl
Two-Dimensional Faceted Plots
Other Two-Dimensional Multivariate Plots
Three-Dimensional (3D) plots
Scraping Information from Websites
Non-Interactive Backends
Interactive Backends
Creating Animated Plots
Effective Visualization – Planning Your Figure
Effective Visualization – Crafting Your Figure
Visualizing Statistical Data More Intuitively
Methods for Dimension Reduction
Visualizing Population Health Information
Map-Based Visualization for Geographical Data
Combining Geographical and Population Health Data
Survival Data Analysis on Cancer
Test Your Knowledge
Developing Advanced Plots with Matplotlib
The Course Overview
Customizing Pylab in Style
Color Deep Dive
Working on Non-Trivial Layouts
The Matplotlib Configuration Files
Putting Lines in Place
Adding Text on Your Plots
Playing with Polygons and Shapes
Versatile Annotating
Non-Cartesian Plots
Plotting Vector Fields
Statistics with Boxes and Violins
Visualizing Ordinal and Tabular Data
Plotting with 3D Axes
Looking at Various 3D Plot Types
The Basemap Methods
Plotting on Map Projections
Adding Geography
Interactive Plots in the Jupyter Notebook
Event Handling with Plot Callbacks
GUI Neutral Widgets
Making Movies
Test Your Knowledge"