Learn Using the Distribution Interface

The displot (distribution plot) is the "parent" function for all the plots we have learned so far. It provides a single interface to create histograms, KDE plots, and ECDFs.

Why Use Displot?

While histplot and kdeplot are great for single plots, displot has a superpower: faceting.

Using the col (column) or row parameter, displot can automatically split your dataset into multiple side-by-side subplots based on a category.

Key Parameters

kind: determines the type of plot;
- 'hist' (default);
- 'kde';
- 'ecdf'.
col / row: splits the data into separate subplots arranged in columns or rows.

Example

Here is how you can instantly create 3 separate histograms for different species using just one line of code.


              12345678910111213141516
            
import seaborn as sns
import matplotlib.pyplot as plt

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

# Create a grid of plots
sns.displot(
    data=df,
    x='bill_length_mm',
    col='species',     # Creates 3 subplots (one per species)
    kind='hist',       # Draw histograms
    element='step'
)

plt.show()

Task

Swipe to start coding

Visualize the distribution of diamond carats using the diamonds dataset.

Set the style to 'darkgrid'.
Create a KDE plot using the displot function:
- Set x to 'carat'.
- Color the curves by 'cut' using hue.
- Split the plot into separate columns based on the 'color' of the diamond using col.
- Set the type of plot to 'kde' using the kind parameter.
- Normalize the data using multiple='fill' to show relative proportions.
- Use the 'viridis' palette.
- Use the df variable as data.
Display the plot.

Solution

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 8

single

Ask AI

Ask anything or try one of the suggested questions to begin our chat

Swipe to show menu