Now that you’ve written these three distance formulas yourself, let’s look at how to use them using Python’s SciPy library:

- Euclidean Distance
`.euclidean()`

- Manhattan Distance
`.cityblock()`

- Hamming Distance
`.hamming()`

There are a few noteworthy details to talk about:

First, the `scipy`

implementation of Manhattan distance is called `cityblock()`

. Remember, computing Manhattan distance is like asking how many blocks away you are from a point.

Second, the `scipy`

implementation of Hamming distance will always return a number between `0`

an `1`

. Rather than summing the number of differences in dimensions, this implementation sums those differences and then divides by the total number of dimensions. For example, in your implementation, the Hamming distance between `[1, 2, 3]`

and `[7, 2, -10]`

would be `2`

. In `scipy`

‘s version, it would be `2/3`

.

### Instructions

**1.**

Call `distance.euclidean()`

using the points `[1, 2]`

and `[4, 0]`

as parameters.

Print the result.

**2.**

Call `distance.cityblock()`

using the points `[1, 2]`

and `[4, 0]`

as parameters.

Print the result.

**3.**

Call `distance.hamming()`

using `[5, 4, 9]`

and `[1, 7, 9]`

as parameters and print the results.

Your answer *shouldn’t* match your function’s results. Remember, `scipy`

divides by the number of dimensions.