👀
Crash Visualization
  • Welcome
  • Preface
    • Who the book is written for
    • How the book is organized
  • 1. Introduction of Data Visualization
    • 1.1 What is data visualization?
    • 1.2 Why does visualization matter?
  • 2. Tricks in Visualization
    • 2.1 Choose Appropriate Chart
    • 2.2 Features of Charts
      • 2.2.1 Table
      • 2.2.2 Column Chart
      • 2.2.3 Line Chart
      • 2.2.4 Pie Chart
      • 2.2.5 Scatter Chart
      • 2.2.6 Map Chart
    • 2.3 Misused Graph
    • 2.4 Tips in Visualization
  • 3. Matplotlib
    • 3.1 Basic Concepts
    • 3.2 Line Chart
    • 3.3 Area Chart
    • 3.4 Column Chart
    • 3.5 Histogram Chart
    • 3.6 Scatter Chart
    • 3.7 Lollipop Chart
    • 3.8 Pie Chart
    • 3.9 Venn Chart
    • 3.10 Waffle Chart
    • 3.11 Animation
  • 4. Seaborn
    • 4.1 Trends
    • 4.2 Ranking
      • 4.2.1 Barplot
      • 4.2.2 Boxplot
    • 4.3 Composition
      • 4.3.1 Stacked Chart
    • 4.4 Correlation
      • 4.4.1 Scatter Plot
      • 4.4.2 Linear Relationship
      • 4.4.3 Heatmap
      • 4.4.4 Pairplot
    • 4.5 Distribution
      • 4.5.1 Boxplot
      • 4.5.2 Violin plot
      • 4.5.3 Histogram plot
      • 4.5.4 Density plot
      • 4.5.5 Joint plot
  • 5. Bokeh
    • 5.1 Basic Plotting
    • 5.2 Data Sources
    • 5.3 Annotations
    • 5.4 Categorical Data
    • 5.5 Presentation and Layouts
    • 5.6 Linking and Interactions
    • 5.7 Network Graph
    • 5.8 Widgets
  • 6. Plotly
    • 6.1 Fundamental Concepts
      • 6.1.1 Plotly Express
      • 6.1.2 Plotly Graph Objects
    • 6.2 Advanced Charts
      • 6.2.1 Advanced Scatter Chart
      • 6.2.2 Advanced Bar Chart
      • 6.2.3 Advanced Pie Chart
      • 6.2.4 Advanced Heatmap
      • 6.2.5 Sankey Chart
      • 6.2.6 Tables
    • 6.3 Statistical Charts
      • 6.3.1 Common Statistical Charts
      • 6.3.2 Dendrograms
      • 6.3.3 Radar Chart
      • 6.3.4 Polar Chart
      • 6.3.5 Streamline Chart
    • 6.4 Financial Charts
      • 6.4.1 Funnel Chart
      • 6.4.2 Candlestick Chart
      • 6.4.3 Waterfall Chart
  • Support
    • Donation
Powered by GitBook
On this page

Was this helpful?

Welcome

NextWho the book is written for

Last updated 2 years ago

Was this helpful?

Visualization is a powerful tool for telling a story about datasets or analysis. If done correctly, visualization not only displays data but also helps the audience digest key information.

However, in the world we live in, bad communication is everywhere. One reason is that people do not understand the data behind the graph they create. Sometimes it is intended to mislead or shock. Sometimes it is well-meaning but the audience just plain misinterprets it. To avoid the above circumstances, no matter which field you are in, it’s better to know more about visualization.

We are not naturally good at dealing with data or representing it. We need to learn how to do it properly and how to make it better, just like we learn other knowledge and skills. It is my intention to write this book.

About The Author

Ivy is the founder of a big data consultancy firm that focuses on marketing and financial fields. She has 10 years of experience in data analysis and business intelligence. As CEO of VECCO INSIGHT, she has helped a number of clients who are stuck in a dilemma of decision making and gained deep insight into the typical pitfalls and issues in data interpretation and presentation.

__

My goal is to make this book as helpful and user-friendly as possible. Currently, it is on the preview stage and I'm still working on it. Any feedback is welcome and appreciated. Contact:

ivyvan.w@gmail.com
Book Cover