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An Introduction to Python

Overview

Teaching: 15 min
Exercises: 15 min
Questions

  • What are the key parts of Python we’ll need to know for the rest of the material?

Objectives

  • An introduction to Python, or a reminder of key features if you’ve used it before.

Python is one of the most popular languages in research computing, so even if it’s not the main language you use, it’s worth knowing about. This makes it a good choice of language for teaching purposes, so we’re going to use Python for most of the examples for the remainder of this course.

Before we can move on though, we’ll cover some of the core features of Python here. If you’re already familiar with Python, consider this a refresher. If not, then this section should cover everything you need to know for the remainder.

Data Types

An obvious place to start when learning a new language is with how it stores and represents data. In most languages, there are a range of types of data which can be represented. In Python, the most fundamental types are:

We create a variable and assign a value to it using the = operator.

int_variable = 5
float_variable = 3.142
bool_variable = True
str_variable = "Hello world!"

print(int_variable)
print(float_variable)
print(bool_variable)
print(str_variable)

Unlike some other languages (e.g. C, C++, Fortran, Java), we don’t need to say what data type a variable is going to hold - a variable can actually hold different types over its lifetime, e.g.:

my_var = 5
my_var = "Hello world!"

print(my_var)

Hello world!

Also unlike these languages, there’s no way in Python to declare a variable without assigning to it.

Data Structures

But what about when we need to store more than one value? That’s where the next set of types comes in, the collection types, representing common data structures. These data structures are:

Let’s start with lists. A list is a sequence of values - it has an order and particular values can be accessed based on their position within the list. Like many other languages, we start counting positions at 0, so the first item in a list is “item 0”, the second is “item 1”, and so on. If we need to add a new value to the end of an existing list we can use the append function / method.

odd_numbers = [1, 3, 5, 7, 9]
odd_numbers.append(11)

print(odd_numbers)
print(odd_numbers[3])

[1, 3, 5, 7, 9, 11]
7

For much more information on lists, see https://docs.python.org/3/tutorial/datastructures.html#more-on-lists.

Dictionaries are the second type of collection, typically used to associate a key and a value - like the index of a book:

index = {
    "python": 1,
    "c++": 5,
    "fortran": 5,
}

index["java"] = 12
print(index["c++"])

2

In a dictionary, a key can only be present once, so in the example above we couldn’t for example:

index = {
    "python": 1,
    "python": 2,
    "c++": 5,
    "fortran": 5,
}

In this case, the value at the key "python" will have the last value we provided for it: 2.

The final collection type we’ll introduce here is a set. Sets are similar to dictionaries, but

Looping and Branching

Now we know how to store and structure data, let’s move onto processing that data. Two important ideas in most programming languages you’ll encounter are branching and looping. In Python we use these two keywords:

number = 53

if number > 50:
    print("Number was greater than 50")

Number was greater than 50

In the example above, because number is greater than 50, the condition evaluates as True and the block of code within the if is executed. Code blocks are introduced in Python with a colon (:), which we see in the example above, but we’ll also see in a couple of other contexts soon. If the condition doesn’t evaluate as True (i.e. it evaluates as False), the block of code is skipped. The expression number > 50 evaluates to True if the value of number is less than 50, otherwise it evaluates to False.

We can also add more possible outcomes to the branching using elif (short for “else if”) and else. With elif we provide another condition and the block of code we provide executes if all of the previous conditions evaluate as False, but this one evaluates as True. If none of the conditions evaluate as True, then the else block executes.

Both elif and else are optional - you can have zero or more elif blocks and zero or one else blocks.

number = 94

if number > 100:
    print("Greater than 100")
elif number == 100:
    print("Equal to 100")
else:
    print("Less than 100")

Less than 100

In this example we see the == operator, used to check for equality. We use this instead of = because = is already used for assignment to a variable, so most languages use == to check for equality instead.

Loops are another important construct, which in Python use the keyword for:

fruit_bowl = ["apple", "banana", "cherry"]

for fruit in fruit_bowl:
    print(fruit)

apple
banana
cherry

In the example above, we loop over a collection. Each time the loop executes, the variable fruit takes the value of the next item from the collection.

Another common way to use loops is with the range() function. This function takes arguments for the bounds and produces a sequence of integers ranging from the lower bound to the upper bound.

for i in range(10):
    print(i)

0
1
2
3
4
5
6
7
8
9

In the above example, we just provide an upper bound, the lower bound takes the default value of 0. Notice that the values include the lower bound, but exclude the upper bound - this matches the zero-based indexing of lists.

So let’s take these and do something useful. One relatively simple numerical model is the approximation of pi, using a Monte Carlo method.

If we generate a large number of points random uniformly distributed within a square, the proportion of these points which are also within a circle of the same diameter gives us an approximation of pi, via the formula for the area of a circle:

import random

total = 1000000
inside = 0

for i in range(total):
    x = random.uniform(-1, 1)
    y = random.uniform(-1, 1)

    r2 = x**2 + y**2

    if r2 <= 1:
        inside += 1

pi = 4 * inside / total
print(pi)

3.138856

Key Points

  • We’ll be using Python for the following parts of the material, here’s an introduction / refresher.

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