In the last exercise, we found a single “quantile” — we split the first 23% of the data away from the remaining 77%.

However, quantiles are usually a set of values that split the data into groups of equal size. For example, you wanted to get the 5-quantiles, or the four values that split the data into five groups of equal size, you could use this code:

import numpy as np dataset = [5, 10, -20, 42, -9, 10] ten_percent = np.quantile(dataset, [0.2, 0.4, 0.6, 0.8])

Note that we had to do a little math in our head to make sure that the values `[0.2, 0.4, 0.6, 0.8]`

split the data into groups of equal size. Each group has 20% of the data.

If we used the values `[0.2, 0.4, 0.7, 0.8]`

, the function would return the four values at those split points. However, those values wouldn’t split the data into five equally sized groups. One group would only have 10% of the data and another group would have 30% of the data!

### Instructions

**1.**

Create a variable named `quartiles`

that contains the quartiles of the `songs`

dataset.

The quartiles of a dataset split the data into four groups of equal size. Each group should have 25% of the data, so you’ll want to use `[0.25, 0.5, 0.75]`

as the second parameter to the `quantile()`

function.

**2.**

Create a variable named `deciles`

. `deciles`

should store the values that split the dataset into ten groups of equal size. Each group should have 10% of the data.

The first value should be at 10% of the data. The next value should be at 20% of the data. The final value should be at 90% of the data.

**3.**

Look at the printout of the deciles. If you had a song that was `170`

seconds long, what tenth of the dataset would it fall in?

Create a variable named `tenth`

and set it equal to the `1`

if you think the `170`

second song would fall in the first tenth of the data. Set it equal to `2`

if you think the song would fall in the second tenth of the data. If you think the song would fall in the final tenth of the data, set `tenth`

equal to `10`

.