Binning data with cut and qcut (pandas)

When working with continuous numerical data, it can often be helpful to split it into buckets or bins based on some cutoffs. There are two useful pandas methods for this: pd.cut and pd.qcut.

The key difference to remember between these two methods is that qcut, which is a quantile-based discretization function, splits the data into buckets of equal distribution. Each bucket will contain approximately the same number of data points. On the other hand, cut will divide the data into intervals of equal width. cut also can divide the data into discrete bins of nonuniform width if it is provided with a sequence of scalars representing the interval boundaries.

Both functions accept a labels argument to specify the names of the resulting bins. It must be an array of the same length as the number of resulting bins.

For an excellent description of how to use these methods, see this Practical Business Python blog post.