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The downside to this is that you will have to prefix all Numpy functions with
`np.`, like so:
```
e = np.array([1, 2, 3, 4, 5])
z = np.zeros((100, 100))
d = np.diag([2, 3, 4, 5])
print(e)
print(z)
print(d)
```
There is a big upside, however, in that other people who have to read/use your
code will like you a lot more. This is because it will be easier for them to
figure out what the hell your code is doing. Namespaces are your friend - use
them!
<a class="anchor" id="appendix-vectors-in-numpy"></a>
One aspect of Numpy which might trip you up, and which can be quite
frustrating at times, is that Numpy has no understanding of row or column
vectors. __An array with only one dimension is neither a row, nor a column
vector - it is just a 1D array__. If you have a 1D array, and you want to use
it as a row vector, you need to reshape it to a shape of `(1, N)`. Similarly,
to use a 1D array as a column vector, you must reshape it to have shape
`(N, 1)`.
In general, when you are mixing 1D arrays with 2- or N-dimensional arrays, you
need to make sure that your arrays have the correct shape. For example:
r = np.random.randint(1, 10, 3)
print('r is a row: ', r)
print('r.T should be a column: ', r.T, ' ... huh?')
print('Ok, make n a 2D array with one row: ', r.reshape(1, -1))
print('We could also use the np.atleast_2d function:', np.atleast_2d(r))
print('Now we can transpose r to get a column:')
print(np.atleast_2d(r).T)
<a class="anchor" id="useful-references"></a>
## Useful references
* [The Numpy manual](https://docs.scipy.org/doc/numpy/)
* [Linear algebra in `numpy.linalg`](https://docs.scipy.org/doc/numpy/reference/routines.linalg.html)
* [Broadcasting in Numpy](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
* [Indexing in Numpy](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html)
* [Random sampling in `numpy.random`](https://docs.scipy.org/doc/numpy/reference/routines.random.html)
* [Python slicing](https://www.pythoncentral.io/how-to-slice-listsarrays-and-tuples-in-python/)