Python Data Visualization with Matplotlib 2.x [Video]

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Python Data Visualization with Matplotlib 2.x [Video]

Aldrin Kay Yuen Yim, Allen Chi Shing Yu, Claire Yik Lok Chung

Explore the world of amazing and efficient graphs with Matplotlib 2.x to make your data more presentable and informative

Quick links: > What will you learn?> Table of content

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Video Details

ISBN 139781788839754
Course Length4 hours and 12 minutes

Video Description

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.

Table of Contents

Hello Plotting World!
The Course Overview
Getting Started with Matplotlib
Setting Up the Plotting Environment
Editing and Running Code
Loading Data for Plotting
Plotting Our First Graph
Figure Aesthetics
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
Figure Layout and Annotations
Adjusting Layout
Adding Subplots
Adjusting Margins
Drawing Inset Plots
Adding Text Annotations
Adding Graphical Annotations
Visualizing Online Data
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
Visualizing Multivariate Data
Getting End-of-Day (EOD) Stock Data from Quandl
Two-Dimensional Faceted Plots
Other Two-Dimensional Multivariate Plots
Three-Dimensional (3D) plots
Adding Interactivity and Animating Plots
Scraping Information from Websites
Non-Interactive Backends
Interactive Backends
Creating Animated Plots
A Practical Guide to Scientific Plotting
Effective Visualization – Planning Your Figure
Effective Visualization – Crafting Your Figure
Visualizing Statistical Data More Intuitively
Methods for Dimension Reduction
Exploratory Data Analysis Analytics and Infographics
Visualizing Population Health Information
Map-Based Visualization for Geographical Data
Combining Geographical and Population Health Data
Survival Data Analysis on Cancer

What You Will 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.

Authors

Table of Contents

Hello Plotting World!
The Course Overview
Getting Started with Matplotlib
Setting Up the Plotting Environment
Editing and Running Code
Loading Data for Plotting
Plotting Our First Graph
Figure Aesthetics
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
Figure Layout and Annotations
Adjusting Layout
Adding Subplots
Adjusting Margins
Drawing Inset Plots
Adding Text Annotations
Adding Graphical Annotations
Visualizing Online Data
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
Visualizing Multivariate Data
Getting End-of-Day (EOD) Stock Data from Quandl
Two-Dimensional Faceted Plots
Other Two-Dimensional Multivariate Plots
Three-Dimensional (3D) plots
Adding Interactivity and Animating Plots
Scraping Information from Websites
Non-Interactive Backends
Interactive Backends
Creating Animated Plots
A Practical Guide to Scientific Plotting
Effective Visualization – Planning Your Figure
Effective Visualization – Crafting Your Figure
Visualizing Statistical Data More Intuitively
Methods for Dimension Reduction
Exploratory Data Analysis Analytics and Infographics
Visualizing Population Health Information
Map-Based Visualization for Geographical Data
Combining Geographical and Population Health Data
Survival Data Analysis on Cancer

Video Details

ISBN 139781788839754
Course Length4 hours and 12 minutes
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