Learn Creating Joint Distribution Plots

JointGrid is the underlying figure-level object used to create bivariate plots with marginal univariate plots.

When you use sns.jointplot(), it creates a JointGrid for you automatically. However, using JointGrid directly gives you a blank canvas. You can explicitly decide what to draw in the center and what to draw on the sides.

The Workflow

Initialize: create the grid with your data and variables. At this point, it is empty;
g.plot_joint(): draws the bivariate plot in the center (e.g., scatter plot);
g.plot_marginals(): draws the univariate plots on the top and right axes (e.g., histogram or KDE).

Example

Here we create a custom grid with a regression plot in the center and KDE curves on the sides.


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import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
df = sns.load_dataset('penguins')

# 1. Initialize the grid
g = sns.JointGrid(data=df, x='bill_length_mm', y='bill_depth_mm')

# 2. Draw the plots
g.plot_joint(sns.regplot, scatter_kws={'alpha': 0.5})  # Center: Regression
g.plot_marginals(sns.kdeplot, fill=True)               # Sides: KDE

plt.show()

Task

Swipe to start coding

Analyze the relationship between bill length and depth, distinguishing by species.

Set the style to 'ticks'. Change the figure background color to 'lightcyan' ('figure.facecolor').
Initialize the JointGrid (g):
- Map 'bill_length_mm' to x and 'bill_depth_mm' to y.
- Color points by 'species' (hue).
- Use the 'viridis' palette.
Center plot (plot_joint):
- Draw a scatterplot.
- Make points semi-transparent (alpha=0.5).
- Set point border color (edgecolor) to 'pink'.
- Set border thickness (linewidth) to 1.
Side plots (plot_marginals):
- Draw a histplot.
- Add a KDE curve (kde=True).
Display the plot.

Solution

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Section 1. Chapter 20

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