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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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Last updated 4 years ago

Density plots are a commonly used tool to visualize the distribution of a continuous variable.

1. Simple Density Chart

2. Density Curves with Histogram

3. Bandwidth Control (Bins)

Just like in matplotlib, in seaborn, this is controlled using the bw argument of the kdeplot function.

sns.distplot(mpg.loc[mpg['origin'] == 'usa', 'horsepower'],  color='g', label='USA', hist_kws={'alpha':.6})
sns.distplot(mpg.loc[mpg['origin'] == 'europe', 'horsepower'],  color='deeppink', label='Europe', hist_kws={'alpha':.6})
sns.distplot(mpg.loc[mpg['origin'] == 'japan', 'horsepower'], color='dodgerblue', label='Japan', hist_kws={'alpha':.6})
plt.title('Density Plot of Horsepower by origins')
plt.legend()
bins
sns.kdeplot(mpg.loc[mpg["cylinders"] == 6, "mpg"], shade=True, color="g", label="bw: 0.1", alpha=.7,bw=.1)
sns.kdeplot(mpg.loc[mpg["cylinders"] == 6, "mpg"], shade=True, color="b", label="bw: 0.5", bw=.5)
plt.title("With  bandwidth control")
  • 1. Simple Density Chart
  • 2. Density Curves with Histogram
  • 3. Bandwidth Control (Bins)
Previous4.5.3 Histogram plotNext4.5.5 Joint plot
  1. 4. Seaborn
  2. 4.5 Distribution

4.5.4 Density plot

mpg = sns.load_dataset('mpg')   # load  the  embedded  dataset
# Draw Density Plot

sns.kdeplot(mpg.loc[mpg["cylinders"] == 4, "mpg"], shade=True, color="g", label="Cyl=4", alpha=.7)
sns.kdeplot(mpg.loc[mpg["cylinders"] == 5, "mpg"], shade=True, color="deeppink", label= "Cyl=5", alpha=.7)
sns.kdeplot(mpg.loc[mpg["cylinders"] == 6, "mpg"], shade=True, color="dodgerblue", label= "Cyl=6", alpha=.7)
sns.kdeplot(mpg.loc[mpg["cylinders"] == 8, "mpg"], shade=True, color="orange", label= "Cyl=8", alpha=.7)
plt.title("Density Plot of MPG by n_Cylinders")