Plot a univariate distribution along the x axis:
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import seaborn as sns; sns.set()
penguins = sns.load_dataset("penguins")
sns.ecdfplot(data=penguins, x="flipper_length_mm")
Flip the plot by assigning the data variable to the y axis:
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sns.ecdfplot(data=penguins, y="flipper_length_mm")
If neither x nor y is assigned, the dataset is treated as wide-form, and a histogram is drawn for each numeric column:
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sns.ecdfplot(data=penguins.filter(like="culmen_", axis="columns"))
You can also draw multiple histograms from a long-form dataset with hue mapping:
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sns.ecdfplot(data=penguins, x="culmen_length_mm", hue="species")
The default distribution statistic is normalized to show a proportion, but you can show absolute counts instead:
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sns.ecdfplot(data=penguins, x="culmen_length_mm", hue="species", stat="count")
It's also possible to plot the empirical complementary CDF (1 - CDF):
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sns.ecdfplot(data=penguins, x="culmen_length_mm", hue="species", complementary=True)
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