#82 Marginal plot with Seaborn

A marginal plot allows to study the relationship between 2 numeric variables. The central chart display their correlation. It is usually a scatterplot, a hexbin plot, a 2D histogram or a 2D density plot. The marginal charts, usually at the top and at the right, show the distribution of the 2 variables using histogram or density plot.

The seaborn library provides a joint plot function that is really handy to make this type of graphic. The top graph shows it default behaviour, and here are a few possible customizations. Seaborn has a nice documentation and some of these examples come from there.

• Central plot
• Marginal
• Ratio

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  #82 Default Marginal plot

  

  #82 Hexbin with marginal plot

  

  #82 2D contour with marginal plots

  
  

  Three main options are available for the central part: scatterplot (with possible variations), hexbin or 2D density plot. Here is the way to make them:

  
  # library & dataset
  import seaborn as sns
  df = sns.load_dataset('iris')
  
  # Custom the inside plot: options are: “scatter” | “reg” | “resid” | “kde” | “hex”
  sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='scatter')
  sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='hex')
  sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde')
  
  

  

  #82 Custom marginal plot

  

  #82 Custom color of marginal plot

  
  

  Note that you can custom these central graphics:

  
  # Then you can pass arguments to each type:
  sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='scatter', s=200, color='m', edgecolor="skyblue", linewidth=2)
  
  # Custom the color
  sns.set(style="white", color_codes=True)
  sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde', color="skyblue")
  

   

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  You can easily custom the marginal plots using the marginal_kws argument. Do not hesitate to visit the histogram and density section of the gallery to see which customisation are available.

   

   

   

   

   

  
  # library & dataset
  import seaborn as sns
  df = sns.load_dataset('iris')
  
  # Custom the histogram and add rug:
  sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='hex', marginal_kws=dict(bins=30, rug=True))
  
  

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  #82 Custom space in marginal plot

  

  #82 Custom space in marginal plot

  

  #82 Custom ratio in marginal plot

  
  

  You can control the space between main and marginal plots, and the size ratio between these 2 parts:

  
  # library & dataset
  import seaborn as sns
  df = sns.load_dataset('iris')
  
  # No space
  sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde', color="grey", space=0)
  
  # Huge space
  sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde', color="grey", space=3)
  
  # Make marginal bigger:
  sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde',ratio=1)