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Python Data Visualization with Matplotlib 2.x

Video Description

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

About This Video

  • 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.

In Detail

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.

Table of Contents

  1. Chapter 1 : Hello Plotting World!
    1. The Course Overview 00:05:14
    2. Getting Started with Matplotlib 00:07:50
    3. Setting Up the Plotting Environment 00:08:46
    4. Editing and Running Code 00:05:11
    5. Loading Data for Plotting 00:08:05
    6. Plotting Our First Graph 00:05:56
  2. Chapter 2 : Figure Aesthetics
    1. Basic Structure of a Matplotlib Figure 00:04:49
    2. Setting Colors in Matplotlib 00:09:16
    3. Adjusting Text Formats 00:06:49
    4. Customizing Lines and Markers 00:07:44
    5. Customizing Grids and Ticks 00:08:52
    6. Customizing Axes 00:09:16
    7. Using Style Sheets 00:03:27
    8. Title and Legend 00:03:42
  3. Chapter 3 : Figure Layout and Annotations
    1. Adjusting Layout 00:06:31
    2. Adding Subplots 00:03:29
    3. Adjusting Margins 00:03:21
    4. Drawing Inset Plots 00:04:27
    5. Adding Text Annotations 00:04:14
    6. Adding Graphical Annotations 00:05:45
  4. Chapter 4 : Visualizing Online Data
    1. Typical API Data Formats 00:03:17
    2. Introducing Pandas 00:07:08
    3. Visualizing the Trend of Data 00:04:41
    4. Visualizing Univariate Distribution 00:06:45
    5. Visualizing a Bivariate Distribution 00:07:05
    6. Visualizing Categorical Data 00:06:02
    7. Controlling SeabornFigure Aesthetics 00:06:18
    8. More About Colors 00:04:37
  5. Chapter 5 : Visualizing Multivariate Data
    1. Getting End-of-Day (EOD) Stock Data from Quandl 00:03:51
    2. Two-Dimensional Faceted Plots 00:07:11
    3. Other Two-Dimensional Multivariate Plots 00:08:21
    4. Three-Dimensional (3D) plots 00:06:01
  6. Chapter 6 : Adding Interactivity and Animating Plots
    1. Scraping Information from Websites 00:04:37
    2. Non-Interactive Backends 00:02:06
    3. Interactive Backends 00:05:13
    4. Creating Animated Plots 00:03:03
  7. Chapter 7 : A Practical Guide to Scientific Plotting
    1. Effective Visualization – Planning Your Figure 00:02:29
    2. Effective Visualization – Crafting Your Figure 00:04:56
    3. Visualizing Statistical Data More Intuitively 00:05:41
    4. Methods for Dimension Reduction 00:09:17
  8. Chapter 8 : Exploratory Data Analysis Analytics and Infographics
    1. Visualizing Population Health Information 00:05:02
    2. Map-Based Visualization for Geographical Data 00:06:01
    3. Combining Geographical and Population Health Data 00:09:31
    4. Survival Data Analysis on Cancer 00:04:14