More Information
  • Use Matplotlib for data visualization with the Python programming language
  • Construct different types of plot such as lines and scatters, bar plots, and histograms
  • Make use of various aspects of data visualization with Matplotlib
  • Work on transformation and back-ends, and change fonts and colors 
  • Use Pandas and Jupyter to manipulate your tabular data

Matplotlib is a multi-platform data visualization tool built upon the Numpy and Scipy framework. One of matplotlib's most important features is its ability to play well with many operating systems and graphics backends.

In this course, we hit the ground running and quickly learn how to make beautiful, illuminating figures with Matplotlib and a handful of other Python tools. We understand data dimensionality and set up an environment by beginning with basic plots. We enter into the exciting world of data visualization and plotting. You'll work with line and scatter plots and construct bar plots and histograms. You'll also explore images, contours, and histograms in depth. Plot scaffolding is a very interesting topic wherein you'll be taken through axes and figures to help you design excellent plots. You'll learn how to control axes and ticks, and change fonts and colors. You'll work on backends and transformations. Then lastly you'll explore the most important companions for Matplotlib, Pandas and Jupyter, used widely for data manipulation, analysis, and visualization.

By the end of this course you'll be able to construct effective and beautiful data plots using the Matplotlib library for the Python programming language.

Style and Approach

A step-by-step practical guide filled with real-world use cases and examples that will help developers and data scientists create elaborate visualizations (graphs & data plots) for their projects using the Matplotlib library.

  • Crisp and clear content that puts the theory into practice and delves into the core of Matplotlib
  • Leverage the various aspects of data visualization and plots
  • Explore Matplotlib further using Pandas and Jupyter 
Course Length 2 hours 56 minutes
ISBN 9781787281998
Date Of Publication 31 Oct 2017


Benjamin Walter Keller

Benjamin Walter Keller is currently a PhD candidate at McMaster University and gained his BSc in physics with a minor in computer science from the University of Calgary in 2011. His current research involves numerical modeling of galaxy evolution over cosmological timescales. As an undergraduate at the U of C, he worked on stacking radio polarization to examine faint extragalactic sources. He also worked in the POSSUM Working Group 2 to determine the requirements for stacking applications for the Australian SKA Pathfinder (ASKAP) radio telescope. He is particularly interested in questions involving stellar feedback (supernovae, stellar winds, and so on) and its impact on galaxies and their surrounding intergalactic medium.