To get a better sense of the data in the `iris.data`

matrix, let’s visualize it!

With Matplotlib, we can create a 2D scatter plot of the Iris dataset using two of its features (sepal length vs. petal length). The sepal length measurements are stored in column `0`

of the matrix, and the petal length measurements are stored in column `2`

of the matrix.

But how do we get these values?

Suppose we only want to retrieve the values that are in column `0`

of a matrix, we can use the NumPy/pandas notation `[:,0]`

like so:

`matrix[:,0]`

`[:,0]`

can be translated to `[all_rows , column_0]`

Once you have the measurements we need, we can make a scatter plot like this:

`plt.scatter(x, y)`

To show the plot:

`plt.show()`

Let’s try this! But this time, plot the sepal length (column `0`

) vs. sepal width (column `1`

) instead.

### Instructions

**1.**

Store `iris.data`

in a variable named `samples`

.

**2.**

Create a list named `x`

that contains the column `0`

values of `samples`

.

Create a list named `y`

that contains the column `1`

values of `samples`

.

**3.**

Use the `.scatter()`

function to create a scatter plot of `x`

and `y`

.

Because some of the data samples have the exact same features, let’s add `alpha=0.5`

:

`plt.scatter(x, y, alpha=0.5)`

**4.**

Call the `.show()`

function to display the graph.

*If you didn’t know there are three species of the Iris plant, would you have known just by looking at the visualization?*