The NumPy library has a function named
quantile() that will quickly calculate the quantiles of a dataset for you.
quantile() takes two parameters. The first is the dataset that you are using. The second parameter is a single number or a list of numbers between
1. These numbers represent the places in the data where you want to split.
For example, if you only wanted the value that split the first 10% of the data apart from the remaining 90%, you could use this code:
import numpy as np dataset = [5, 10, -20, 42, -9, 10] ten_percent = np.quantile(dataset, 0.10)
ten_percent now holds the value
-14.5. This result technically isn’t a quantile, because it isn’t splitting the dataset into groups of equal sizes — this value splits the data into one group with 10% of the data and another with 90%.
However, it would still be useful if you were curious about whether a data point was in the bottom 10% of the dataset.
The dataset containing information about the lengths of songs is stored in a variable named
Create a variable named
twenty_third_percentile that contains the value that splits the first 23% of the data from the rest of the data.