Learning Path PythonData Visualization with Matplotlib 2x

Perform data visualization and represent data in the right way with Matplotlib 2.x
Learning Path PythonData Visualization with Matplotlib 2x
File Size :
1.36 GB
Total length :
10h 24m



Packt Publishing


Last update




Learning Path PythonData Visualization with Matplotlib 2x

What you’ll learn

Use Matplotlib for data visualization with the Python programming language
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
Master with the latest features in Matplotlib 2.x
Make clear and appealing figures for scientific publications
Extend the functionalities of Matplotlib with third-party packages, such as Basemap, GeoPandas, Mplot3d, Pandas, Scikit-learn, and Seaborn.
Design interactive plots using Jupyter Notebook

Learning Path PythonData Visualization with Matplotlib 2x


Working knowledge on Python is needed.


Are you looking forward to learn powerful data visualization techniques to make your data more presentable and informative? If yes, then this Learning Path is for you.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Matplotlib is a multi-platform data visualization tool built upon the NumPy and SciPy frameworks. One of the most important features of Matplotlib is its ability to work well with many operating systems and graphics backends. 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 supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish.

The highlights of this Learning Path are:
Construct different types of plot such as lines and scatters, bar plots, and histograms
Customize and represent data in 3D
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 much more
Leverage the various aspects of data visualization and plots

In this Learning Path, you’ll hit the ground running and quickly learn how to make beautiful, illuminating figures with Matplotlib and a handful of other Python tools. You’ll understand data dimensionality and set up an environment by beginning with basic plots. You’ll 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 and contours 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 backend and transformations. You’ll then explore the most important companions for Matplotlib, Pandas and Jupyter, used widely for data manipulation, analysis, and visualization. You’ll acquire the basic knowledge on how to create and customize plots by Matplotlib.

Further, you’ll learn 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’ll learn to visualize geographical data on maps and implement interactive charts. You’ll learn to create intuitive infographics.

You’ll explore 3D plotting, one of the best features when it comes to 3D data visualization, along with Jupyter Notebook, widgets, and creating movies for enhanced data representation. Geospatial plotting will be also be explored. Finally, you’ll learn how to create interactive plots with the help of Jupyter.

By the end of this Learning Path, you’ll be well versed with Matplotlib and construct advanced plots with additional customization techniques to perform advanced data visualization using the Matplotlib library.

Meet Your Experts:

We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:

Benjamin Keller is a postdoctoral researcher in the MUSTANG group at Universität Heidelberg’s Astronomisches Rechen-Institut. He obtained his PhD at McMaster University and got his BSc in Physics with a minor in Computer Science from the University of Calgary in 2011. His current research involves numerical modeling of the interstellar medium over cosmological timescales. As an undergraduate at the U of C, he worked with Dr. Jeroen Stil 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. At McMaster, he worked with Dr. James Wadsley in the Physics & Astronomy department. His current research was focused around understanding how the energy released from supernovae explosions regulated the flow of gas through galaxies, and how that gas is converted into stars.

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.

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 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.


Section 1: Matplotlib for Python Developers

Lecture 1 The Course Overview

Lecture 2 Understanding Data, Dimensionality, and Why We Plot

Lecture 3 Setting Up Your Environment

Lecture 4 Beginning with the Most Basic Plots

Lecture 5 Differentiating Line and Scatter Plots

Lecture 6 Constructing Bar Plots and Histograms

Lecture 7 Exploring Images and Contours

Lecture 8 Working on Plots with Uncertainties

Lecture 9 Looking at Other Useful Plot Types

Lecture 10 Making Multiple Panel Plots

Lecture 11 Using Color Bars and Legends

Lecture 12 Working with the Components of a Matplotlib Plot

Lecture 13 Figure and Axes – How Do They Work?

Lecture 14 Working with Transformations

Lecture 15 Controlling Axes and Ticks

Lecture 16 Ticker Formatting

Lecture 17 Working on Back Ends

Lecture 18 The Jupyter Notebook

Lecture 19 Using Pandas to Manipulate Tabular Data

Lecture 20 Slicing and Dicing Pandas Data

Lecture 21 Pandas Built-in Plotting

Section 2: Python Data Visualization with Matplotlib 2.x

Lecture 22 The Course Overview

Lecture 23 Getting Started with Matplotlib

Lecture 24 Setting Up the Plotting Environment

Lecture 25 Editing and Running Code

Lecture 26 Loading Data for Plotting

Lecture 27 Plotting Our First Graph

Lecture 28 Basic Structure of a Matplotlib Figure

Lecture 29 Setting Colors in Matplotlib

Lecture 30 Adjusting Text Formats

Lecture 31 Customizing Lines and Markers

Lecture 32 Customizing Grids and Ticks

Lecture 33 Customizing Axes

Lecture 34 Using Style Sheets

Lecture 35 Title and Legend

Lecture 36 Adjusting Layout

Lecture 37 Adding Subplots

Lecture 38 Adjusting Margins

Lecture 39 Drawing Inset Plots

Lecture 40 Adding Text Annotations

Lecture 41 Adding Graphical Annotations

Lecture 42 Typical API Data Formats

Lecture 43 Introducing Pandas

Lecture 44 Visualizing the Trend of Data

Lecture 45 Visualizing Univariate Distribution

Lecture 46 Visualizing a Bivariate Distribution

Lecture 47 Visualizing Categorical Data

Lecture 48 Controlling SeabornFigure Aesthetics

Lecture 49 More About Colors

Lecture 50 Getting End-of-Day (EOD) Stock Data from Quandl

Lecture 51 Two-Dimensional Faceted Plots

Lecture 52 Other Two-Dimensional Multivariate Plots

Lecture 53 Three-Dimensional (3D) plots

Lecture 54 Scraping Information from Websites

Lecture 55 Non-Interactive Backends

Lecture 56 Interactive Backends

Lecture 57 Creating Animated Plots

Lecture 58 Effective Visualization – Planning Your Figure

Lecture 59 Effective Visualization – Crafting Your Figure

Lecture 60 Visualizing Statistical Data More Intuitively

Lecture 61 Methods for Dimension Reduction

Lecture 62 Visualizing Population Health Information

Lecture 63 Map-Based Visualization for Geographical Data

Lecture 64 Combining Geographical and Population Health Data

Lecture 65 Survival Data Analysis on Cancer

Section 3: Developing Advanced Plots with Matplotlib

Lecture 66 The Course Overview

Lecture 67 Customizing Pylab in Style

Lecture 68 Color Deep Dive

Lecture 69 Working on Non-Trivial Layouts

Lecture 70 The Matplotlib Configuration Files

Lecture 71 Putting Lines in Place

Lecture 72 Adding Text on Your Plots

Lecture 73 Playing with Polygons and Shapes

Lecture 74 Versatile Annotating

Lecture 75 Non-Cartesian Plots

Lecture 76 Plotting Vector Fields

Lecture 77 Statistics with Boxes and Violins

Lecture 78 Visualizing Ordinal and Tabular Data

Lecture 79 Plotting with 3D Axes

Lecture 80 Looking at Various 3D Plot Types

Lecture 81 The Basemap Methods

Lecture 82 Plotting on Map Projections

Lecture 83 Adding Geography

Lecture 84 Interactive Plots in the Jupyter Notebook

Lecture 85 Event Handling with Plot Callbacks

Lecture 86 GUI Neutral Widgets

Lecture 87 Making Movies

If you want to visualize data in truly beautiful plots, this Learning Path is for you,This Learning Path will help anyone interested in data visualization get insights from big data with Python and Matplotlib 2.x

Course Information:

Udemy | English | 10h 24m | 1.36 GB
Created by: Packt Publishing

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