Percentiles
In this lesson, we will learn about percentiles.
Representing data through percentiles
Another useful description of a dataset is by using percentiles.
For this we consider ordered data, meaning data that is sorted in ascending order. The percentile marks a data point in the ordered data such that of the data is below this data point and thus is above this data point. If we say that the percentile score on an exam was 85%, then of the candidates scored less than on the exam.
The percentiles of a dataset are commonly referred to as the ‘empirical percentiles’ as they are the percentiles of the dataset, not of the underlying distribution. The empirical percentile is equivalent to the median of the data. Common intervals to look at are the region around the median, also called the interquartile range or IQR.
IQR runs from the empirical percentile to the empirical percentile. The region, which runs from the empirical percentile to the empirical percentile. Percentiles of a dataset may be computed with the percentile()
function in the numpy
package. The first argument is the data, the second argument is a list of percentiles:
Get hands-on with 1200+ tech skills courses.