# python numpy vector math

## python numpy vector math

You can just use numpy arrays. Look at the numpy for matlab users page for a detailed overview of the pros and cons of arrays w.r.t. matrices.

As I mentioned in the comment, having to use the `dot()` function or method for mutiplication of vectors is the biggest pitfall. But then again, numpy arrays are consistent. All operations are element-wise. So adding or subtracting arrays and multiplication with a scalar all work as expected of vectors.

Edit2: Starting with Python 3.5 and numpy 1.10 you can use the `@` infix-operator for matrix multiplication, thanks to pep 465.

1. Yes. The whole of numpy is based on arrays.

2. Yes. `linalg.norm(v)` is a good way to get the length of a vector. But what you get depends on the possible second argument to norm! Read the docs.

3. To normalize a vector, just divide it by the length you calculated in (2). Division of arrays by a scalar is also element-wise.

An example in ipython:

``````In [1]: import math

In [2]: import numpy as np

In [3]: a = np.array([4,2,7])

In [4]: np.linalg.norm(a)
Out[4]: 8.3066238629180749

In [5]: math.sqrt(sum([n**2 for n in a]))
Out[5]: 8.306623862918075

In [6]: b = a/np.linalg.norm(a)

In [7]: np.linalg.norm(b)
Out[7]: 1.0
``````

Note that `In [5]` is an alternative way to calculate the length. `In [6]` shows normalizing the vector.