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 `0`

and `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.

### Instructions

**1.**

The dataset containing information about the lengths of songs is stored in a variable named `songs`

.

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.