Learn Plotting Cumulative Distributions

An ecdfplot represents the proportion or count of observations falling below each unique value in a dataset.

Compared to a histogram or density plot, it has a significant advantage: each observation is visualized directly. This means there are no bins to adjust and no smoothing parameters that might distort the data. It is often considered the most "honest" way to visualize a distribution.

Key Parameters

By default, the plot shows the proportion (0 to 1) of data smaller than X. You can change this behavior:

stat='count': instead of a percentage, the Y-axis shows the number of observations;
complementary=True: reverses the logic. Instead of showing values below the threshold, it shows values above it. This is essentially a "survival curve" (e.g., "How many penguins have a beak longer than 50mm?").

Example

Here is how complementary changes the visualization. The curve goes down instead of up.


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import seaborn as sns
import matplotlib.pyplot as plt

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

# Create a Complementary ECDF
# This answers: "How many penguins have a flipper length GREATER than X?"
sns.ecdfplot(
    data=df,
    x='flipper_length_mm',
    hue='species',
    stat='count',       # Show exact number of penguins
    complementary=True  # Curve descends from Total to 0
)

plt.show()

Task

Swipe to start coding

Analyze the bill lengths of penguins to see how many of them exceed a certain length.

Import pandas, seaborn, and matplotlib.pyplot.
Read the penguins dataset.
Create an ECDF plot:
- Set x to 'bill_length_mm'.
- Group by 'island' using hue.
- Enable the "survival" mode by setting complementary=True.
- Show absolute numbers by setting stat='count'.
- Use the 'mako' palette.
- Use the df variable as data.
Display the plot.

Solution

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Section 1. Chapter 7

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