Gain insights into advanced Python 3, learn the Pythonic Way, explore data structures, functional and object-oriented paradigms, and master iterables, generators, and coroutines through practical projects.

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This highly interactive and advanced course will give you insight into the most common Python tricks and tips. This course is for practicing Python programmers who are aiming for proficiency in Python 3. If you want to switch from Python 2 to Python 3, this is your go-to course. 

This course will help you learn the Pythonic Way of writing code and leveraging advanced robust features to become more proficient than the average developer. You’ll start by covering Python’s built-in data structures, and then you’ll move on to different programming paradigms like functional programming and object-oriented programming. 

In the latter half of the course, you’ll focus on iterables, generators, and coroutines. You’ll also have the opportunity to work on two projects to help strengthen what you learn. In all, this course is perfect for anyone who wants to become a confident and competent Pythonista by picking up the idioms and practical tips.

Mastering the Art of Programming in Python 3

Generators can be easily created on the fly by using _generator expressions_. 
# Generator expression versus list comprehension
The syntax of a generator expression is similar to that of a list comprehension with just one difference: the _square brackets_ are replaced by _round parentheses_. For example:

orig = [1, 2, 3]

listcomp = [n+1 for n in orig]  # Designing list comprehennsion out of list
generator = (n+1 for n in orig) # Designing generator exp. out of list

print(listcomp)
print(generator)

We can see that a generator expression doesn't produce the output directly. It returns a generator object which will produce the items when explicitly told. Whereas, on printing `listcomp`, we immediately get the list elements: __[2, 3, 4]__.

Remember, list comprehension produces the entire list at a time but generator expression generates one item at a time. Generator expressions have _lazy execution_. For this reason, a generator expression is much more memory efficient technique than the equivalent list comprehension.

# Getting items from the generator
Here's how we start getting items from the generator:

Introduction

Built-in Data Structures

Functional Programming

Project I: Data Science with Data Structures

Data science with Data Structures

Project I Review

Object-Oriented Idioms

Iterables and Generators

Coroutines

Project II: Build Your Own Matrix Module

Build Your Own Matrix Library

Project II Review

Meta-Programming

The End

Generator Expressions