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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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On this page
  • 1. Installation
  • 2. 2-Group Venn Chart
  • 3. 3-Group Venn Chart

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  1. 3. Matplotlib

3.9 Venn Chart

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Last updated 4 years ago

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A Venn diagram is an illustration that uses circles to show the relationships among things or finite groups of things. Venn diagrams help to visually represent the similarities and differences between the two concepts.

We can usethe library to draw a Venn chart. It contains four main functions: venn2, venn2_circles, venn3 and venn3_circles.

1. Installation

pip install matplotlib-venn

2. 2-Group Venn Chart

There are two ways to draw a Venn Chart. One is to give directly the size of your group and their intersection. The other is to give 2 sets of values, python will calculate itself the length of each set (each group) and the number of common values (their intersection).

The functions venn2 and venn2_circles accept as their only required argument a 3-element list (Ab, aB, AB) of subset sizes

# import library to draw a  2-group Venn chart
from matplotlib_venn import venn2

Option 1

plt.figure(figsize = (6,6))
venn2(subsets = (3, 2, 1),set_labels = ('Group A', 'Group B'))
plt.show()

Option 2

plt.figure(figsize = (6,6))
venn2([set(['A', 'B', 'C', 'D']), set(['D', 'E', 'F'])])
plt.show()

3. 3-Group Venn Chart

Similarly, the functions venn3 and venn3_circles take a 7-element list of subset sizes (Abc, aBc, ABc, abC, AbC, aBC, ABC)

from matplotlib_venn import venn3, venn3_circles
#  make a basic 3 groups Venn chart

plt.figure(figsize = (8,8))
plt.title("Three Groups Venn diagram")

v=venn3(subsets = (10, 6, 20, 7,9,1,3), 
        set_labels = ('Group A', 'Group B', 'Group C'))
# add some customization
c=venn3_circles(subsets = (10, 6, 20, 7,9,1,3), 
                linestyle='dashed', linewidth=3, color="darkblue")
matplotlib-venn