Python Data Visualization with Matplotlib 2.x [Video]

More Information
Learn
  • Master with the latest features in Matplotlib 2.x
  • Create data visualizations on 2D and 3D charts in the form of bar charts, bubble charts, heat maps, histograms, scatter plots, stacked area charts, swarm plots, and many more.
  • Make clear and appealing figures for scientific publications.
  • Create interactive charts and animations.
  • Extend the functionalities of Matplotlib with third-party packages, such as Basemap, GeoPandas, Mplot3d, Pandas, Scikit-learn, and Seaborn.
  • Design intuitive infographics for effective storytelling.
About

Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization.Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts.By the end of this video, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This video will help you prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.

Style and Approach

Step–by-step comprehensive guide filled with real-world examples.

Features
  • Create and customize appealing and live graphs, by adding style, colors, and fonts.
  • Learn to represent data in the right way to engage readers
  • Explore the power of Python packages to design excellent graphs
  • A complete guide with insightful use cases and examples to perform data visualizations with Matplotlib's extensive toolkits.
  • Create timestamp data visualizations on 2D and 3D graphs in the form of plots, histogram, bar charts, scatter plots, and more.
Course Length 4 hours 12 minutes
ISBN 9781788839754
Date Of Publication 31 Oct 2017

Authors

Allen Chi Shing Yu

Allen Chi Shing Yu, PhD, is a Chevening Scholar, 2017-18, and an MSc student in computer science at the University of Oxford. He holds a PhD degree in Biochemistry from the Chinese University of Hong Kong, and he has used Python and Matplotlib extensively during his 10 years' experience in the field of bioinformatics and big data analysis. During his research career, Allen has published 12 international scientific research articles and presented at four international conferences, including on-stage presentations at the Congress On the Future of Engineering Software (COFES) 2011, USA, and Genome Informatics 2014, UK. Other research highlights include discovering the novel subtype of Spinocerebellar ataxia (SCA40), identifying the cause of pathogenesis for a family with Spastic paraparesis, leading the gold medalist team in 2011 International Genetically Engineered Machine (iGEM) competition, and co-developing a number of cancer genomics project.

Apart from academic research, Allen is also the co-founder of Codex Genetics Limited, which aims to provide personalized medicine services in Asia through the use of the latest genomics technology. With financial and business support from the HKSAR Innovation and Technology Commission, Hong Kong Science Park, and the Chinese University of Hong Kong, Codex Genetics has curated and transformed recent advances in cancer and neuro-genomics research into clinically actionable insights.

Claire Yik Lok Chung

Claire Yik Lok Chung is now a PhD student at the Chinese University of Hong Kong working on Bioinformatics, after receiving her BSc degree in Cell and Molecular Biology. With her passion for scientific research, she joined three labs during her college study, including the synthetic biology lab at the University of Edinburgh. Her current projects include soybean genomic analysis using optical mapping and the next-generation sequencing of data. Claire started programming 10 years ago, and uses Python and Matplotlib daily to tackle Bioinformatics problems and to bring convenience to life. Being interested in information technology in general, she leads the Campus Network Support Team in college and is constantly keeping up with the latest technological trends by participating in PyCon HK 2016. She is motivated to acquire new skills through self-learning and is keen to share her knowledge and experience. In addition to science, she has developed skills in multilingual translation and graphic design, and found these transferable skills useful at work.

Aldrin Kay Yuen Yim

Aldrin Kay Yuen Yim is a PhD student in computational and system biology at Washington University School of Medicine. Before joining the university, his research
primarily focused on big data analytics and bioinformatics, which led to multiple discoveries, including a novel major allergen class (designated as a Group 24th Major allergen by WHO/IUIS Allergen Nomenclature subcommittee) through a multi-omic approach analysis of dust mites (JACI 2015), as well as the identification of the salt-tolerance gene in soybeans through large-scale genomic analysis (Nat. Comm. 2014). He also loves to explore sci-fi ideas and put them into practice, such as the development of a DNA-based information storage system (iGEM 2010, Frontiers in Bioengineering and Biotechnology 2014). Aldrin's current research interest focuses on neuro-development and diseases, such as exploring the heterogeneity of cell types within the nervous system, as well as gender dimorphism in brain cancers (JCI Insight 2017).

Aldrin is also the founding CEO of Codex Genetics Limited, which is currently servicing two research hospitals and the cancer registry of Hong Kong.