Data Visualization in Python by Examples

Data Visualization in Python by Examples

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 17m | 318 MB

Data visualization with matplotlib, ggplot, and seaborn in Python

Data visualization is just a wise investment in your future big-data needs. You will learn how to deploy maps and networks to display geographic and network data. To do this, we will focus on the following very popular libraries in Python: matplotlib, ggplot, seaborn, and plotly.

In this course, you will walk through some of the fundamentals of data visualization, sharing many examples of how to handle different types of data and how best to present your insights. We’ll take a look at chart types, such as Matplotlib for visualizing the impact of tornadoes in the US, North Korean nuke tests on global stocks, and analyze forex performances using charts. You will see how ggplot can be used to analyze trends in BRICS economies and crude oil price trends. You will see how to level up your data visualization skills using Python’s advanced plotting libraries: matplotlib and Seaborn, and how you can present the data from the most unstable regions in the world through data visualization.

You will then carry out a visual analysis of the performance of various Hollywood releases. Finally, you will use Plotly to plot comparative graphs of Apple iPhone version releases and compare the performance of gaming consoles such as Xbox and PlayStation.

This friendly course takes you through data visualization in Python using matplotlib, ggplot, seaborn, and plotly. It is packed with step-by-step instructions and working examples. This comprehensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you.

What You Will Learn

  • Set up various data visualization tools available in Python
  • Learn the best ways to visualize data on the most interesting data sets
  • Create your own plots to show the impact of events on different trends
  • Visualize relationships, patterns between various activities
  • Identify and act on emerging trends more rapidly
  • Manipulate and interact directly with data
  • Foster a new business language for your board meetings
  • Absorb information in new and more constructive ways
  • Add impact to data analysis by visualizing the interpretation
Table of Contents

Programming Data Visualizations Using Python’s Matplotlib
1 The Course Overview
2 Setting Up and Getting Started with Python Data Visualization
3 Analyzing Effects of Tornadoes in the US – Most Affected States
4 Analyzing Effects of Tornadoes in the US – Least Affected States
5 Plots – Impact of North Korean Atomic Test on Global Stock Markets
6 Analyzing Forex Performance Using Custom Charts

Data Visualization with ggplot Python Library
7 Setting Up and Getting Started with ggplot
8 Plotting a Comparison of BRICS Market Economies – GDP Numbers
9 Plotting a Comparison of BRICS Market Economies – GDP Growth Trends
10 Crude Prices Representation Through Plots with ggplot
11 Customizing Representation of Crude Prices with ggplot

Programming Advanced Visualizations with Seaborn
12 Setting Up and Getting Started with Seaborn Python Library
13 Plotting the Most Unstable Areas in the World Using Seaborn
14 Plotting the Most Unstable Areas – Advanced Customizations
15 Visualizing Performance of Recent Hollywood Releases in Seaborn
16 Visualizing Performance of Hollywood Releases in Seaborn Using Custom Plots

Data Visualization Using Plotly
17 Setting Up and Getting Started with Plotly
18 Plotting the Data for Apple iPhone Launches with Plotly
19 Plotting the Data for Apple iPhone Launches – Customizations
20 Various Plots Showing Performance of Game Consoles Sales
21 Performance of Game Consoles Sales – Building Online Dashboards