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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?