quantiles
- quantiles(data, *, n=4, method='exclusive')
Divide *data* into *n* continuous intervals with equal probability. Returns a list of (n - 1) cut points separating the intervals. Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles. Set *n* to 100 for percentiles which gives the 99 cuts points that separate *data* in to 100 equal sized groups. The *data* can be any iterable containing sample. The cut points are linearly interpolated between data points. If *method* is set to *inclusive*, *data* is treated as population data. The minimum value is treated as the 0th percentile and the maximum value is treated as the 100th percentile.
© Python Software Foundation. PSF License Version 2 · Official docs ↗
Changed in 3.13
- 3.13: No longer raises an exception for an input with only a single data point. This allows quantile estimates to be built up one sample point at a time becoming gradually more refined with each new data point.
1 Example
# Decile cut points for empirically sampled data >>> data = [105, 129, 87, 86, 111, 111, 89, 81, 108, 92, 110, ... 100, 75, 105, 103, 109, 76, 119, 99, 91, 103, 129, ... 106, 101, 84, 111, 74, 87, 86, 103, 103, 106, 86, ... 111, 75, 87, 102, 121, 111, 88, 89, 101, 106, 95, ... 103, 107, 101, 81, 109, 104] >>> [round(q, 1) for q in quantiles(data, n=10)] [81.0, 86.2, 89.0, 99.4, 102.5, 103.6, 106.0, 109.8, 111.0]
from the Python docs