Week 2

Programming Languages and Python

Recap

Last time we discussed how we can use pseudocode to break down complex tasks into manageable chunks, before starting to issue exact instructions. We saw this in action during the jam sandwich demonstration.

And while pseudocode is nice, we ultimately need a consistent way of telling computers what to do - To this end, programming languages have been developed which allow allow programmers to write fairly readable code which is then converted into in machine-level binary code by a compiler (luckily, we don't need to worry about this).

Python

Why Python?

Easy to learn Python reads almost like English, just with a few extra rules thrown in - at least when compared to some other popular programming languages!

Fairly fast As a rule of thumb, one can usually assume that the more readable a language is, the slower and less customization it is. However, python manages can still be very fast when used sensibly, especially for routine data science and machine learning tasks.

Popular Python's popularity means that many libraries have been written for it - these allow us to do everything from data science through to interacting with spotify and twitter.

Additionally, python is an increasingly popular language in industry. It is used extensively for data science and machine learning work (especially at Google) as well as for prototyping new technologies (because it's quick and easy to get stuff working in it).

How Python?

For the purposes of this course we'll be using the Anaconda python distribution, with python version 3.7

This is because the Anaconda distribution comes with many packages pre-installed which will come in handy later, and because it comes with the Spyder IDE (integrated-development-environment), which makes writing and running python code a breeze.

Python - Baby Steps

Syntax and Comments

The syntax of a language describes the rules and instructions which can be used when coding.

All programming languages have specific ways of including normal English inside your scripts (i.e. files filled with code) which can be used to explain the function of the code. These are referred to as comments, because they allow us to comment on the code.

In python, single-line comments are started with hashtag (#) symbols and cause following text on the same line to be ignored by python. This allows the programmer to add explanations / notes to their code such that other programmers can understand it more easily. (Comments also serve as useful reminders for the programmers themselves)

Additionally, and text enclosed between a pair of triple-double (""") quotes forms a comment (which can be multi-line):

# Hashtags denote one-line comments - These are ignored by python
# Use comments to explain what the code is doing

""" Triple quotes can be used for multi-line comments
    These are usually used for file headers or when declaring fuctions 
"""

Hello World

In Python, the print statement allows us to output text to the console:

hello_world.py

# Say "Hello World!" to the user, via the terminal, when the code runs
print("Hello World!")

When specifying the string which is being printed, we need to be careful to ensure that the enclosing quotes are identical - otherwise python gets confused and doesn't think the string has ended.

string_quotes.py

print('I am a string')
print("So am I!")
print("Hey, can I join you guys?') # This wont work!

# You can have different quotes as part of your string
print("Gnomic as ever, Alex moaned 'Alea Iacta Est'")

Indentation

Other languages have lots of brackets ({[]}) all over the place - python only cares about indentation; I.e. things need to lined up nicely, or indented if they are nested in each other:

# Indentation needs to be consistent
print("Hello World!")
    print("Python won't run this!")

Variables

Variables are what we use to store information within our code, and can be thought of as labelled boxes containing data.

Variables can be thought of as labelled boxes containing data

Declaration

declaration_example.py

# Declare and assign the variables
my_string = "Hello World"
my_integer = 1239082
my_boolean = True

# Reassign a variable to different type
my_boolean = 47

pascal case vs camelcase

pascal_vs_camel.py

# Camel case and pascal case are the two most common variable naming schemes
# They don't affect how your code runs, but "code readability" is important!

aCamelCaseVariable = "This is not the python way"
a_pascal_case_variable = "This is the python way"

Maths

Operator	Description	Example	Output
+	Addition	17 + 3	20
-	Subtraction	14 - 21	-7
*	Multiplication	3 * 24	72
/	Division	10 / 4	2.5
**	Exponentiation	2 ** 4	16
%	Modulo	22 % 3	1

In the case of division, the output has a decimal point even though the operands are both integers - python is nice like that.

You will often need to be careful to not mix decimal and integer values when performing certain operations, as you may incur rounding error (we will discuss this more later on when we cover data types, and the difference between "int"s and "float"s).

Boolean Logic and Conditions

Mathematical logic essentially allows us to compare things in a well-defined, consistent way. The ability to do this is essential for allowing us to build useful computer programmes. The types of comparison operations that we can perform in python are outlines in the following table:

Operator	Description	Example	Output
<	less than	5 < 8	False
>	greater than	4.3 > 2	True
==	equals	"Hello" == "there"	False
<=	less than or equal to	5 <= 5	True
>=	greater than or equal to	8 >= 110	False
!=	not equal	"General" != "Kenobi"	True

For example

logic_example.py

# An example of using logic

is_this_true = 3 > 10 # Let us see if 3 is > 10; The result should be False
print(is_this_true) # Print out the result, hopefully it will say False

If and Else

If statements execute code provided that the specified condition is satisfied

if (True):
    print("Condition was met")
else:
    print("Condition wasn't met")

We can use conditional operators to determine when an if statement should trigger.

if (7 > 4):
    print("Computer is clever")
else:
    print("I thought 'it just works' steve?!")

Coupled with variables, this allows us to control the flow of our code.

Else if

In the script above, we used an "if-else" statement, but in some cases we might want to multiple possible specific answers differently. For this we can use "elif" (short for else if) :

elif_example.py

user_name = "Augustus" # We don't need humans!

if (user_name == "Alex"):
    print("I've been expecting you...")
elif (user_name == "Augustus"):
    print("Ad omnes Ave Caesar")
else:
    print("Nice to meet you " + user_name)

Input

Sometimes we want the user to give us some information, which we then use somehow in our code. Luckily, python has a built in function (aka method) which easily allows us to do this:

# Input returns a string
user_input = input()

Now we are ready to create programs which respond differently based on the user's input

if_else_example.py

user_answer = input("What is 7 + 3? ")

if (user_answer == "10"):
    print("Check you out!") 
else: # Catch all inputs which aren't 10
    print("Happens to the best of us") # Lie to the user

Finally, it is worth noting that we can concatenate string by using the + operator:

input_example.py

# This code will compliment our human user

name_of_user = input("Hi! What is your name? ") # Get user input

output_string = "Nice to meet you " + name_of_user + " - Looking good! ;)"
print(output_string) # Print our compliment string