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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. Basic Annotated Heatmap
  • 2. Heatmap with defined color-scale
  • 3. Customized Heatmap

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  1. 6. Plotly
  2. 6.2 Advanced Charts

6.2.4 Advanced Heatmap

Previous6.2.3 Advanced Pie ChartNext6.2.5 Sankey Chart

Last updated 4 years ago

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1. Basic Annotated Heatmap

## Annotated heatmap
import plotly.figure_factory as ff

z = [[.1, .3, .5, .7, .9],
     [1, .8, .6, .4, .2],
     [.2, 0, .5, .7, .9],
     [.9, .8, .4, .2, 0],
     [.3, .4, .5, .7, 1]]

fig = ff.create_annotated_heatmap(z)
fig.show()

2. Heatmap with defined color-scale

z = [[.1, .3, .5, .7],
     [1.0, .8, .6, .4],
     [.6, .4, .2, 0.0],
     [.9, .7, .5, .3]]

colorscale = [[0, 'lightblue'], [1, 'navy']]
font_colors = ['white']
fig = ff.create_annotated_heatmap(z, colorscale=colorscale, font_colors=font_colors)

# Make text size larger
for i in range(len(fig.layout.annotations)):
    fig.layout.annotations[i].font.size = 16
fig.show()

3. Customized Heatmap

z = [[.1, .3, .5],
     [1.0, .8, .6],
     [.6, .4, .2]]

x = ['Product A', 'Product B', 'Product C']
y = ['Game Three', 'Game Two', 'Game One']

z_text = [['Good', 'Bad', 'Good'],
          ['Good', 'Good', 'Bad'],
          ['Bad', 'Bad', 'Good']]

fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_text, colorscale='hot')
# Make text size larger
for i in range(len(fig.layout.annotations)):
    fig.layout.annotations[i].font.size = 16
fig.show()