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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. Scatter Plot
  • 2. Markers

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  1. 3. Matplotlib

3.6 Scatter Chart

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Last updated 4 years ago

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1. Scatter Plot

In the session, we discussed how to use plt.plot() to draw the line chart. It also can be used to produce a scatter chart. Since it is a scatter plot, we can use plt.scatter() as well.

x = np.linspace(0, 10, 50)
y = np.sin(x)

fig,(ax1,ax2) =plt.subplots(1, 2, figsize = (12,6))

ax1.plot(x, y, 'o', color='firebrick')  # plt.plot()
ax1.set_title('plt.plot way')

ax2.scatter(x, y, marker='o', color='m'); # plt.scatter()
ax2.set_title('plt.scatter way')

2. Markers

We can use Markers to customize a scatter plot, make it more stylish.

For example, we use "star" to draw the same curve.

plt.scatter(x, y, marker='*',color='firebrick');

More Marker Types

plt.figure(1,figsize = (12,6))
plt.plot( x, y, marker='s', markersize=8,label = 'with line')
plt.plot( x+1, y+1, marker='o', markersize=18,linestyle='none',
          label = 'without line')
plt.legend()

x = [3,6,5,7,9,10]
y =[4,2,14, 6,7,3]

sizes = [3080,7000,5000,1000,3200,440]
colors = ['r','y','orange','g','b','c']
plt.figure(figsize =(8,8))
plt.scatter(x, y, c = colors, s = sizes, alpha = 0.7)
plt.title('Marker Colors')

Most Used Colors

  • The color is controlled by the markerfacecolor and markeredgecolor arguments.

  • The edge size is controlled by markeredgewidth

plt.figure(1, figsize =(12,6))

plt.plot( x, y, markerfacecolor='skyblue', marker="D", 
         markeredgecolor="black", markersize=16, markeredgewidth =3,
         label = 'thick edge')
         
plt.plot( x+1, y+1, linestyle='none', marker="*", 
         markerfacecolor='yellow', markeredgecolor="black", 
         markersize=24, label = 'thin edge')
plt.legend()

Line Chart Plot
Figure 1.5.1 Simple Scatter Chart
Figure 1.5.3 Star Scatter Plot
Figure 1.5.4 More Marker Types
Figure 1.5.5 Marker Size Example
Figure 1.5.6 Marker Colors
Figure 1.5.7 Base Colors
Figure 1.5.8 Tableau Palette Colors
Figure 1.5.9 Marker Edge