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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
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  1. Preface

Who the book is written for

PreviousWelcomeNextHow the book is organized

Last updated 2 years ago

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We are living in an era of information overload. Excess amounts of information are overwhelming and we are even generating data by almost every daily behavior. Big data has become a buzz word for decade, however, are we getting better at handling data or benefiting more from data?

The answer is no.

Despite great progress in our ability to collect and store data, we’re still very weak in interpreting data. In 1954, Darrell Huff wrote his best-selling book about how people often intentionally use graphs to spread misinformation, especially in favor of their own products or causes. Today, vastly more misinformation is disseminated unintentionally because people don’t know how to use charts to communicate what they intend.

The book is for you:

  • If you have challenges dealing with numbers

  • If you need to present quantitative information

  • If you are suffering from the presentation, report, or dashboard

  • If you want to learn more about data exploration in Python.

How to use this book

  • The book is around 400 pages, with concepts, codes, and graphs. It's not necessary to use it like eating a fine dinner, but rather a practical tool.

  • Also, it's not necessary to learn all the methods to create the same graph. Just pick your preferred ones and code with me till you master them.

  • If you suddenly lose your way in the book, you can use the search button as a taxi that drives you to the possible destination.

  • Keeping your hands dirty is crucial for learning any applied knowledge and skills. The code in the book is 100% reproducible. Do not worry you will fail or can't make it. What you see is what you will get. Keep up!