We’ve spent this lesson building a boxplot by hand. Let’s now look at how Python’s Matplotlib library does it!

The `matplotlib.pyplot`

module has a function named `boxplot()`

. `boxplot()`

takes a dataset as a parameter. This dataset could be something like a list of numbers, or a Pandas DataFrame.

import matplotlib.pyplot as plt data = [1, 2, 3, 4, 5] plt.boxplot(data) plt.show()

One of the strengths of Matplotlib is the ease of plotting two boxplots side by side. If you pass `boxplot()`

a list of datasets, Matplotlib will make a boxplot for each, allowing you to compare their spread and central tendencies,

import matplotlib.pyplot as plt dataset_one = [1, 2, 3, 4, 5] dataset_two = [3, 4, 5, 6, 7] plt.boxplot([dataset_one, dataset_two]) plt.show()

### Instructions

**1.**

We’ve imported the dataset of song lengths, but this time, we’ve split the data into three groups — songs that were released in the year 2000 (`two_thousand`

), songs that were released in the year 2001 (`two_thousand_one`

), and songs that were released in the year 2002 (`two_thousand_two`

).

Plot all three datasets as three separate boxplots in the order described above.

Make sure to call `plt.show()`

after calling the `plt.boxplot()`

function.

**2.**

Let’s add labels to our graph so we know which box plot is which.

Add the parameter `labels = ["2000 Songs", "2001 Songs", "2002 Songs"]`

to your call to the `plt.boxplot()`

function.

**3.**

Let’s think about what the boxplot is showing us. What can you say about this data that would be hard to know without a boxplot?

Look at the hint to see our thoughts. Hit the “Run” button when you’re ready to move on.