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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. Bubble Chart with Colorbar
  • 2. Bubble Charts with Colorscale
  • 3. Bubble chart
  • 4. Bubble Chart Animation

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  1. 6. Plotly
  2. 6.2 Advanced Charts

6.2.1 Advanced Scatter Chart

Previous6.2 Advanced ChartsNext6.2.2 Advanced Bar Chart

Last updated 4 years ago

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1. Bubble Chart with Colorbar

fig = go.Figure(data=go.Scatter(
    y = np.random.randn(500),mode='markers',
    marker=dict(
        size=24,
        color=np.random.randn(500), #set color equal to a variable
        colorscale='RdBu', # one of plotly colorscales
        showscale=True)
))
fig.update_layout(template= 'plotly_dark')
fig.show()

2. Bubble Charts with Colorscale

fig = go.Figure(data=[go.Scatter(
    x=[1, 3.2, 5.4, 7.6, 9.8, 12.5],
    y=[1, 3.2, 5.4, 7.6, 9.8, 12.5],
    mode='markers',
    marker=dict(
        color=[100, 110, 125, 155, 175, 210],
        size=[30, 40, 50, 70, 80, 100],
        colorscale='matter', # one of plotly colorscales
        showscale=True )
)])
fig.update_layout(template= 'none')  # let's try a new template

fig.show()

3. Bubble chart

df = px.data.gapminder()
fig = px.scatter(df.query("year==2007"), x="gdpPercap", y="lifeExp", size="pop", color="continent",
           hover_name="country", log_x=True, size_max=80)
fig.show()

4. Bubble Chart Animation

fig = px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", 
                      animation_group="country", size="pop", color="continent", 
                      hover_name="country", facet_col="continent",
           log_x=True, size_max=60, range_x=[100,100000], range_y=[40,90])
fig.show()

Bubble Chart with Colorbar
Bubble Chart with Colorscale
Sophiscated Bubble Chart
Bubble Chart with animation