Learn Estimating Density with KDE

A kdeplot (kernel density estimation) is a method for visualizing the distribution of observations in a dataset. It is analogous to a histogram, but instead of using discrete bins, KDE represents the data using a continuous probability density curve.

This makes it excellent for seeing the "shape" of data and identifying peaks without the jaggedness of a histogram.

Visualizing Overlapping Distributions

When you have multiple categories (using hue), simple lines can become hard to distinguish. Seaborn offers parameters to fix this:

Stacking (multiple='stack'): instead of plotting lines over each other, this stacks them. It shows how different categories contribute to the total distribution;
Filling (fill=True): fills the area under the curve with color, making the visual weight of each category more apparent.

Example:


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import seaborn as sns
import matplotlib.pyplot as plt

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

# Create the stacked KDE plot
sns.kdeplot(
    data=df,
    x='flipper_length_mm',
    hue='species',
    multiple='stack', # Stack categories vertically
    fill=True         # Fill area with color
)

plt.show()

Task

Swipe to start coding

Visualize the distribution of maximum temperatures throughout the year:

Import pandas, seaborn, and matplotlib.pyplot.
Read the weather dataset.
Set the style to 'ticks' with a 'lightcyan' background color (already provided).
Create a KDE plot with the following parameters:
- Set x to 'max_temp';
- Group by 'month' using hue;
- Stack the distributions using multiple='stack';
- Fill the curves using fill=True;
- Disable the legend (legend=False) to avoid cluttering the plot.
Display the plot.

Solution

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Section 1. Chapter 5

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