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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. Simple Bar
  • 2. Vertical barplot
  • 3. Horizontal Barplot
  • 4. Grouped Barplot
  • 5. Facet Barplot

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  1. 4. Seaborn
  2. 4.2 Ranking

4.2.1 Barplot

Previous4.2 RankingNext4.2.2 Boxplot

Last updated 4 years ago

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A barplot is one of the most common types of plot. It shows the relationship between a numerical variable and a categorical variable.

1. Simple Bar

x = np.array(list("ABCDEFGHIJ"))  # categorical variable 
y = np.arange(1, 11)
sns.barplot(x=x, y=y, palette="viridis")  

2. Vertical barplot

tips = sns.load_dataset("tips") # load embedded dataset "tips"

sns.barplot(x="day", y="total_bill", data=tips, color="royalblue")

3. Horizontal Barplot

# make a horizontal barplot
sns.barplot(x="total_bill", y="day", data=tips, color="royalblue")

# add a vertical line
plt.axvline(x = 19.8,color='r',linewidth = 2,linestyle = '--')

# add annotation
plt.text(19.8+0.2, 0.5, " Avg Bill: $20", size='small', color='r', weight='light')

4. Grouped Barplot

sns.barplot(x="day", y="total_bill", hue="sex", data=tips, palette = "Set1")
plt.legend(loc=2)  # set legend position 

5. Facet Barplot

plt.figure(figsize = (12,6))
sns.catplot(x="sex", y="total_bill",
                hue="time", col="day",
                data=tips, kind="bar",
                height=5, aspect=.7,palette = "Set1");
Simple Barplot
Vertical Barplot
Horizontal Barplot
Stacked Barplot
Facet Barplot