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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. Area Chart and Highlight Line
  • 2. Stacked Area Chart

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  1. 3. Matplotlib

3.3 Area Chart

An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures, and hatchings.

1. Area Chart and Highlight Line

First, let us create a simple dataset and customize the figure set.

x = range(1,13)
y1 = [4,6,8,4,5,3,7,4,1,5,6,8]
y2 = [9,5,4,8,5,6,2,4,1,3,7,7]

# customize setting
plt.style.use('seaborn-whitegrid')
plt.rcParams.update({'font.size': 18})

Then, Here are two examples. One is a simple area chart, the other is an area chart with a highlight line.

fig,(ax1,ax2) = plt.subplots(1,2,sharey=True, figsize= (16,6))

ax1.fill_between(x,y1,color= 'salmon',alpha = 0.4)
ax1.set_title('Simple Area Chart')   #  set title of  ax1

ax2.fill_between(x,y2,color= 'skyblue',alpha = 0.4)   #  create area  chart
ax2.plot(x, y2, color="blue",linewidth = 2)   # create highlight line
ax2.set_title('Area + Hightlight Line')

Example 2

Suppose You want to know the sales performance of your two kinds of goods in one year. As they share the same period, it's better to overlap the two sales performance together.

plt.style.use('seaborn-white')  # let  us try another style
plt.figure(figsize= (12,6))     # set the figure size
plt.fill_between(x,y1,color= 'salmon',alpha = 0.5,label = 'Salmon')
plt.fill_between(x,y2,color= 'skyblue',alpha = 0.8,label = 'Tuna')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.legend()

2. Stacked Area Chart

If you want to represent an evolution for several groups in the same time, you are probably interested by stacked area chart, where every groups are displayed one of top of each other.

Let us create a simple financial dataset.

x = range(1,7)   #  the list x has  6 items, not 7
y3 =[ [10,12,14,18,18,16], [30,34,28,26,30,27], [5,6,6,6,4,4],[18,22,14,17,21,30] ]
plt.stackplot(x,y3, labels=['Inventory','Sales','Credit','Cash'], alpha=0.7 )
plt.xlabel('Month')
plt.ylabel('Million/USD')

# Put a legend to the right of the current axis
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))

Although a stacked area chart may not be the best way to illustrate a financial report, we still can figure out that the company's financial situation in the last 6 months was very healthy.

Previous3.2 Line ChartNext3.4 Column Chart

Last updated 4 years ago

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Stacked Area Chart