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Sometimes a raw number like age tells you less than a label like young or senior. Binning turns a continuous value into tidy groups. 🪣

Models and charts often read categories more clearly than exact figures. Grouping prices into low, mid, and high can surface trends you would otherwise miss.

The cut function slices a column into bins you define by edges. You give it the boundaries, and it labels each value for you.

Pass labels so each bin has a friendly name instead of an interval. Now your output reads like a real category, not a math range.

By default cut includes the right edge of each bin and excludes the left. So a value of 18 lands in the first bin, not the second.

Want the left edge included instead? Set right=False and the boundary behavior flips, so 18 moves into the next bin up.

Give cut a single number instead of edges and it creates that many equal-width bins automatically across the value range.

The qcut function splits by quantiles, so each bin holds roughly the same number of rows. Great when your data is lopsided.

Use cut for fixed, meaningful edges like price tiers. Use qcut when you want balanced groups such as quartiles of income.

The result is a categorical column you can drop straight into your DataFrame and feed to grouping, plotting, or a model.

Values outside your bin edges become NaN. Always set edges that cover your real minimum and maximum, or you will silently lose rows.

You want four groups that each hold about the same number of rows. Which tool fits?

You learned to group numbers into labels: cut for chosen edges and equal widths, qcut for equal counts. Mind the edges and you turn raw values into useful features. 🎉