### Question

In the context of this exercise, what is an axis in Numpy?

### Answer

An axis is similar to a dimension. For a 2-dimensional array, there are 2 axes: vertical and horizontal.

When applying certain Numpy functions like `np.mean()`

, we can specify what axis we want to calculate the values across.

For `axis=0`

, this means that we apply a function along each “column”, or all values that occur vertically.

For `axis=1`

, this means that we apply a function along each “row”, or all values horizontally.

#### Example

```
# Given the following 2-dimensional array
values = np.array([
[10, 20, 30, 40],
[50, 60, 70, 80],
])
# Axis=0
# along each "column"
print np.mean(values, axis=0)
# [30, 40, 50, 60]
# Axis=1
# along each "row"
print np.mean(values, axis=1)
# [25, 65]
```