### 5.2. The `arange()` function: Arithmetic progression

Use the `np.arange()` function to build a vector containing an arithmetic progression such as ```[0.0 0.1 0.2 0.3]```.

```>>> print np.arange(0.0, 0.4, 0.1)
[ 0.   0.1  0.2  0.3]
>>> print np.arange(5.0, -0.5, -0.5)
[ 5.   4.5  4.   3.5  3.   2.5  2.   1.5  1.   0.5  0. ]
```

Here is the general form:

```np.arange(start, stop=None, step=1, dtype=None)
```
`start`

The first value in the sequence.

`stop`

The limiting value: the last element of the sequence will never be greater than or equal to this value (assuming that the `step` value is positive; for negative `step` values, the last element of the sequence will always be greater than the `stop` value).

```>>> print np.arange(1.0, 4.0)
[ 1.  2.  3.]
```

If you omit the `stop` value, you will get a sequence starting at zero and using `start` as the limiting value.

```>>> print np.arange(4)
[0 1 2 3]
```
`step`

The common difference between successive values of the array. The default value is one.

`dtype`

Use this argument to force representation using a specific type.

```>>> print np.arange(10)
[0 1 2 3 4 5 6 7 8 9]
>>> print np.arange(10, dtype=np.float_)
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
```