Learn Summarizing Data with Box Plots

A boxplot is a standardized way of displaying the distribution of data based on a five-number summary:

Minimum (lowest value excluding outliers);
First quartile (Q1) (25th percentile);
Median (50th percentile);
Third quartile (Q3) (75th percentile);
Maximum (highest value excluding outliers).

Why use a Boxplot?

It is the best tool for comparing distributions between groups. It immediately tells you:

Central tendency: where is the median line?;
Spread: how tall is the box? (the interquartile range);
Symmetry: is the median in the center of the box?;
Outliers: are there dots outside the whiskers?

Key Parameters

saturation: controls the intensity of the colors (0 to 1). Lower values make the colors more muted;
linewidth: controls the thickness of the box outlines and whiskers;
width: controls the width of the box itself.

Example

Here is a boxplot analyzing the "Tips" dataset. Notice how the dots representing outliers appear above the whiskers.


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import seaborn as sns
import matplotlib.pyplot as plt

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

# Create a boxplot
sns.boxplot(
    data=df,
    hue='day',
    x='day',
    y='total_bill',
    palette='coolwarm',
    linewidth=2,     # Thicker lines
    saturation=0.7   # Slightly muted colors
)

plt.show()

Task

Swipe to start coding

Visualize the distance of planets discovered by different methods.

Set the style to 'ticks'. Customize the theme by passing a dictionary to change the background to 'grey' ('figure.facecolor') and the tick colors to 'white' ('xtick.color' and 'ytick.color').
Create a boxplot using the planets dataset (df):
- Map 'distance' to the x axis and 'method' to the y axis.
- Set the box width to 0.6.
- Make the lines thicker using linewidth=2.
- Mute the colors significantly by setting saturation to 0.4.
- Use the 'vlag' palette.
Display the plot.

Solution

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Section 1. Chapter 11

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