Python Crash Course

Python is a high-level open-source language. But the Python world is inhabited by many packages or libraries that provide useful things like array operations, plotting functions, and much more. We can import libraries of functions to expand the capabilities of Python in our programs.

OK! We'll start by importing a few libraries to help us out. First: our favorite library is NumPy, providing a bunch of useful array operations (similar to MATLAB). We will use it a lot! The second library we need is Matplotlib, a 2D plotting library which we will use to plot our results. The following code will be at the top of most of your programs, so execute this cell first:

We are importing one library named numpy and we are importing a module called pyplot of a big library called matplotlib. To use a function belonging to one of these libraries, we have to tell Python where to look for it. For that, each function name is written following the library name, with a dot in between. So if we want to use the NumPy function linspace(), which creates an array with equally spaced numbers between a start and end, we call it by writing:

If we don't preface the linspace() function with numpy, Python will throw an error.

The function linspace() is very useful. Try it changing the input parameters!

Import style:

You will often see code snippets that use the following lines

import numpy as np import matplotlib.pyplot as plt

What's all of this import-as business? It's a way of creating a 'shortcut' to the NumPy library and the pyplot module. You will see it frequently as it is in common usage, but we prefer to keep out imports explicit. We think it helps with code readability.

Pro tip:

Sometimes, you'll see people importing a whole library without assigning a shortcut for it (like from numpy import *). This saves typing but is sloppy and can get you in trouble. Best to get into good habits from the beginning!

To learn new functions available to you, visit the NumPy Reference page. If you are a proficient Matlab user, there is a wiki page that should prove helpful to you: NumPy for Matlab Users 

Python doesn't require explicitly declared variable types like C and other languages.

Note that if you divide an integer by an integer that yields a remainder, the result will be converted to a float. (This is different from the behavior in Python 2.7, beware!)

Python uses indents and whitespace to group statements together. To write a short loop in C, you might use:

for (i = 0, i < 5, i++){ printf("Hi! \n"); }

Python does not use curly braces like C, so the same program as above is written in Python as follows:

If you have nested for-loops, there is a further indent for the inner loop.

In NumPy, you can look at portions of arrays in the same way as in Matlab, with a few extra tricks thrown in. Let's take an array of values from 1 to 5.

Python uses a zero-based index, so let's look at the first and last element in the array myvals

There are 5 elements in the array myvals, but if we try to look at myvals[5], Python will be unhappy, as myvals[5] is actually calling the non-existant 6th element of that array.

Arrays can also be 'sliced', grabbing a range of values. Let's look at the first three elements

Note here, the slice is inclusive on the front end and exclusive on the back, so the above command gives us the values of myvals[0], myvals[1] and myvals[2], but not myvals[3].

One of the strange little quirks/features in Python that often confuses people comes up when assigning and comparing arrays of values. Here is a quick example. Let's start by defining a 1-D array called  $a$:

OK, so we have an array  $a$, with the values 1 through 5. I want to make a copy of that array, called  $b$, so I'll try the following:

Great. So  $a$ has the values 1 through 5 and now so does  $b$. Now that I have a backup of  $a$, I can change its values without worrying about losing data (or so I may think!).

Here, the 3rd element of  $a$ has been changed to 17. Now let's check on  $b$.

And that's how things go wrong! When you use a statement like  $a = b$, rather than copying all the values of  $a$ into a new array called  $b$, Python just creates an alias (or a pointer) called  $b$ and tells it to route us to  $a$. So if we change a value in  $a$ then  $b$ will reflect that change (technically, this is called assignment by reference). If you want to make a true copy of the array, you have to tell Python to copy every element of  $a$ into a new array. Let's call it  $c$.

Now, we can try again to change a value in  $a$ and see if the changes are also seen in  $c$.

OK, it worked! If the difference between a = b and a = b.copy() is unclear, you should read through this again. This issue will come back to haunt you otherwise.

There are a lot of resources online to learn more about using NumPy and other libraries. Just for kicks, here we use Jupyter's feature for embedding videos to point you to a short video on YouTube on using NumPy arrays.