The Complete Python/PostgreSQL Course 2.0

This is a short introductory video to this section. I'm really excited to guide you through this Python refresher course!

This lecture has a link to all the Python code we'll write in this section. Use it to check your code as you write it, or to refresh your memory!

In this lecture we look at three essential data structures in Python: lists, tuples, and sets.

A list is an ordered collection of items.

A tuple is an immutable ordered collection of items.

A set is an unordered collection of unique items.

In this fascinating video, we look at advanced set operations: calculating items which are in two sets, or items which are in one set but not another.

This video explores how to create programs which can change depending on some input. For example, we might ask the user if they want to continue or not.

This makes use of boolean comparisons, such as:

• 1 == 1 (which is True)

• 5 > 5 (which is False)

The boolean comparisons we have available in Python are many:

• ==

• !=

• >, <, <=, >=

• is

• is not

• in

• not in

Loops allow us to repeat things over and over. This video explores two different types of loop in Python: for loop and while loop.

List comprehension is a relatively unique thing to Python.

It allows us to succinctly use a for loop inside a list to generate values. These values then end up in the list.

For example, [x for x in range(10)] generates a list [0, 1, 2, 3, 4, 5, 6, 7, 8, 9].

Dictionaries are an extremely useful thing in Python.

They are akin to sets, but instead of being a set of unique values, they are a set of unique keys, and each has a value associated with it.

In this video, let's look at methods in Python by creating some examples. Creating methods is simple, you just need the one keyword: def.

*args and **kwargs are truly fascinatingly confusing. For eons, they have annoyed Python learners.

To this I say no more!

They're just a way of passing arguments.

Objects are the natural progression from dictionaries. Instead of just holding data, objects hold another special type of data: methods.

A method is a function which operates on the object calling it. Thus, an object can use its own values to calculate outputs of methods. Very cool.

In many instances, we don't want our methods to be solely referencing the object which calls them. Sometimes, we want to reference the class of the object. Other times, we don't need either the object or the class.

@classmethod and @staticmethod are two decorators (looking at that shortly!) which extend the capabilities of methods.

Classes in Python can also inherit from one another. This essentially means that a class contains all of the properties and methods of the class it inherits from—but with the added bonus that it can have more.

Not only we can pass values from one method to another, but we can also pass functions.

This is not used very often, but it can sometimes yield very powerful methods in very few lines of code.

One of the most confusing aspects of Python for learners is the concept of decorators.

These are things we can place on top of function definitions which allow us to extend the function by executing code before and after the function.

They are extremely powerful when used well!

In this video we look at advanced decorators in Python, which is decorators that take arguments.

This amplifies the decorator's usefulness, although also makes them slightly more contrived.