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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.