ECDF - empirical cumulative distribution function

seaborn.ecdfplot() plots the Empirical Cumulative Distribution Function (ECDF) of one or more numeric variables.

An ECDF shows, for each value, the proportion (or count) of data points less than or equal to that value.

It’s especially useful for comparing distributions and seeing how data accumulates across values — without the binning of histograms.

Syntax:

Parameters:

• data = DataFrame containing your data
• x, y = Variables for ECDF axes
• hue = Adds subgroups (coloured lines)
• stat = "proportion" (default) or "count" — what the y-axis represents
• complementary = If True, shows 1 - ECDF(x) (reverse cumulative)
• log_scale = Apply logarithmic scale to axes
• palette = Colour palette for hue categories
• linewidth, linestyle, marker = Line appearance controls
• alpha = Transparency (0–1)

Basic example

Shows how the fraction of observations increases as total_bill grows. The ECDF starts at 0 and rises smoothly to 1.

Show counts instead of proportions

The y-axis now shows number of observations, not proportion.

Add hue for group comparison

Each colour shows a separate ECDF for that group (e.g., Male vs. Female).

Compare multiple categories

Easy way to compare how different days accumulate totals - the steeper the curve, the more concentrated the data.

Complementary ECDF (1 − ECDF)

Shows 1 - ECDF(x), which represents, “the proportion of data points greater than x”.

Horizontal orientation

Simply swap x for y to flip the plot orientation.

Add markers or lines

Makes the plot more visually distinctive, especially for overlapping curves.

Logarithmic scale

Useful when values span several orders of magnitude.

Custom style example