Python 3: Deep Dive (Part 1 - Functional)

44h 40m 37s
English
Paid

Course description

This is Part 1 of a series of courses intended to dive into the inner mechanics and more complicated aspects of Python 3. This is not a beginner course - if you've been coding Python for a week or a couple of months, you probably should keep writing Python for a bit more before tackling this series.

Read more about the course

On the other hand, if you're now starting to ask yourself questions like:

  • I wonder how this works?

  • is there another way of doing this?

  • what's a closure? is that the same as a lambda?

  • I know how to use a decorator someone else wrote, but how does it work? Can I write my own?

  • why isn't this boolean expression returning a boolean value?

  • what does an import actually do, and why am I getting side effects?

  • and similar types of question...

then this course is for you.

Please make sure you review the pre-requisites for this course - although I give a brief refresh of basic concepts at the beginning of the course, those are concepts you should already be very comfortable with as you being this course.

In this course series, I will give you a much more fundamental and deeper understanding of the Python language and the standard library.

Python is called a "batteries-included" language for good reason - there is a ton of functionality in base Python that remains to be explored and studied.

So this course is not about explaining my favorite 3rd party libraries - it's about Python, as a language, and the standard library.

In particular this course is based on the canonical CPython. You will also need Jupyter Notebooks to view the downloadable fully-annotated Python notebooks.

It's about helping you explore Python and answer questions you are asking yourself as you develop more and more with the language.

In Python 3: Deep Dive (Part 1) we will take a much closer look at:

  • Variables - in particular that they are just symbols pointing to objects in memory

  • Namespaces and scope

  • Python's numeric types

  • Python boolean type - there's more to a simple or statement than you might think!

  • Run-time vs compile-time and how that affects function defaults, decorators, importing modules, etc

  • Functions in general (including lambdas)

  • Functional programming techniques (such as map, reduce, filter, zip, etc)

  • Closures

  • Decorators

  • Imports, modules and packages

  • Tuples as data structures

  • Named tuples

To get the most out of this course, you should be prepared to pause the coding videos, and attempt to write code before I do! Sit back during the concept videos, but lean in for the code videos!

And after you have seen a code video, pause the course, and try things out yourself - explore, experiment, play with code, and see how things work (or don't work! - that's also a great way to learn!)

Requirements:
  • Basic introductory knowledge of Python programming (variables, conditional statements, loops, functions, lists, tuples, dictionaries, classes).
  • You will need Python 3.6 or above, and a development environment of your choice (command line, PyCharm, Jupyter, etc.)
Who this course is for:
  • Anyone with a basic understanding of Python that wants to take it to the next level and get a really deep understanding of the Python language and its data structures.
  • Anyone preparing for an in-depth Python technical interview.

What you'll learn:

  • An in-depth look at variables, memory, namespaces and scopes
  • A deep dive into Python's memory management and optimizations
  • In-depth understanding and advanced usage of Python's numerical data types (Booleans, Integers, Floats, Decimals, Fractions, Complex Numbers)
  • Advanced Boolean expressions and operators
  • Advanced usage of callables including functions, lambdas and closures
  • Functional programming techniques such as map, reduce, filter, and partials
  • Create advanced decorators, including parametrized decorators, class decorators, and decorator classes
  • Advanced decorator applications such as memoization and single dispatch generic functions
  • Use and understand Python's complex Module and Package system
  • Idiomatic Python and best practices
  • Understand Python's compile-time and run-time and how this affects your code
  • Avoid common pitfalls

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#1: Course Overview

All Course Lessons (156)

#Lesson TitleDurationAccess
1
Course Overview Demo
18:09
2
Introduction
01:44
3
The Python Type Hierarchy
05:52
4
Multi-Line Statements and Strings
21:50
5
Variable Names
11:01
6
Conditionals
07:39
7
Functions
12:28
8
The While Loop
14:26
9
Break, Continue and the Try Statement
10:26
10
The For Loop
17:22
11
Classes
40:18
12
Introduction
02:55
13
Variables are Memory References
08:22
14
Reference Counting
14:22
15
Garbage Collection
26:40
16
Dynamic vs Static Typing
05:29
17
Variable Re-Assignment
04:49
18
Object Mutability
15:23
19
Function Arguments and Mutability
17:30
20
Shared References and Mutability
09:37
21
Variable Equality
14:23
22
Everything is an Object
13:59
23
Python Optimizations: Interning
09:15
24
Python Optimizations: String Interning
19:12
25
Python Optimizations: Peephole
20:10
26
Introduction
02:59
27
Integers: Data Types
18:08
28
Integers: Operations
24:26
29
Integers: Constructors and Bases - Lecture
29:35
30
Integers: Constructors and Bases - Coding
20:24
31
Rational Numbers - Lecture
14:28
32
Rationals Numbers - Coding
12:34
33
Floats: Internal Representations - Lecture
19:53
34
Floats: Internal Representations - Coding
04:57
35
Floats: Equality Testing - Lecture
18:43
36
Floats: Equality Testing - Coding
14:41
37
Floats: Coercing to Integers - Lecture
09:40
38
Floats: Coercing to Integers - Coding
05:04
39
Floats: Rounding - Lecture
25:23
40
Floats: Rounding - Coding
13:34
41
Decimals - Lecture
16:50
42
Decimals - Coding
10:28
43
Decimals: Constructors and Contexts - Lecture
10:07
44
Decimals: Constructors and Contexts - Coding
10:29
45
Decimals: Math Operations - Lecture
09:33
46
Decimals: Math Operations - Coding
13:31
47
Decimals: Performance Considerations
10:30
48
Complex Numbers - Lecture
11:29
49
Complex Numbers - Coding
14:17
50
Booleans
21:01
51
Booleans: Truth Values - Lecture
09:09
52
Booleans: Truth Values - Coding
14:48
53
Booleans: Precedence and Short-Circuiting - Lecture
21:11
54
Booleans: Precedence and Short-Circuiting - Coding
13:39
55
Booleans: Boolean Operators - Lecture
18:01
56
Booleans: Boolean Operators - Coding
14:46
57
Comparison Operators
20:54
58
Introduction
01:06
59
Argument vs Parameter
03:44
60
Positional and Keyword Arguments - Lecture
13:06
61
Positional and Keyword Arguments - Coding
06:22
62
Unpacking Iterables - Lecture
13:02
63
Unpacking Iterables - Coding
21:10
64
Extended Unpacking - Lecture
17:51
65
Extended Unpacking - Coding
29:05
66
*args - Lecture
06:01
67
*args - Coding
11:48
68
Keyword Arguments - Lecture
09:24
69
Keyword Arguments - Coding
14:19
70
**kwargs
10:29
71
Putting it all Together - Lecture
13:26
72
Putting it all Together - Coding
17:26
73
Application: A Simple Function Timer
19:09
74
Parameter Defaults - Beware!!
18:45
75
Parameter Defaults - Beware Again!!
19:23
76
Introduction
04:06
77
Docstrings and Annotations - Lecture
15:59
78
Docstrings and Annotations - Coding
15:03
79
Lambda Expressions - Lecture
12:11
80
Lambda Expressions - Coding
15:00
81
Lambdas and Sorting
15:57
82
Challenge - Randomize an Iterable using Sorted!!
02:56
83
Function Introspection - Lecture
19:31
84
Function Introspection - Coding
28:37
85
Callables
14:47
86
Map, Filter, Zip and List Comprehensions - Lecture
21:44
87
Map, Filter, Zip and List Comprehensions - Coding
21:15
88
Reducing Functions - Lecture
25:53
89
Reducing Functions - Coding
21:11
90
Partial Functions - Lecture
11:13
91
Partial Functions - Coding
25:33
92
The operator Module - Lecture
15:36
93
The operator Module - Coding
32:44
94
Introduction
01:32
95
Global and Local Scopes - Lecture
34:55
96
Global and Local Scopes - Coding
15:41
97
Nonlocal Scopes - Lecture
22:18
98
Nonlocal Scopes - Coding
14:38
99
Closures - Lecture
38:36
100
Closures - Coding
32:06
101
Closure Applications - Part 1
15:39
102
Closure Applications - Part 2
18:41
103
Decorators (Part 1) - Lecture
21:07
104
Decorators (Part 1) - Coding
21:00
105
Decorator Application (Timer)
35:17
106
Decorator Application (Logger, Stacked Decorators)
23:48
107
Decorator Application (Memoization)
29:15
108
Decorators (Part 2) - Lecture
11:45
109
Decorators (Part 2) - Coding
25:58
110
Decorator Application (Decorator Class)
09:41
111
Decorator Application (Decorating Classes)
48:24
112
Decorator Application (Dispatching) - Part 1
31:46
113
Decorator Application (Dispatching) - Part 2
35:46
114
Decorator Application (Dispatching) - Part 3
26:51
115
Introduction
03:19
116
Tuples as Data Structures - Lecture
19:02
117
Tuples as Data Structures - Coding
25:25
118
Named Tuples - Lecture
27:50
119
Named Tuples - Coding
35:15
120
Named Tuples - Modifying and Extending - Lecture
14:26
121
Named Tuples - Modifying and Extending - Coding
21:47
122
Named Tuples - DocStrings and Default Values - Lecture
13:31
123
Named Tuples - DocStrings and Default Values - Coding
15:47
124
Named Tuples - Application - Returning Multiple Values
06:23
125
Named Tuples - Application - Alternative to Dictionaries
28:46
126
Introduction
03:02
127
What is a Module?
24:31
128
How does Python Import Modules?
49:33
129
Imports and importlib
27:40
130
Import Variants and Misconceptions - Lecture
14:01
131
Import Variants and Misconceptions - Coding
27:04
132
Reloading Modules
18:30
133
Using __main__
27:02
134
Modules Recap
13:03
135
What are Packages? - Lecture
20:25
136
What are Packages ? - Coding
27:12
137
Why Packages?
13:08
138
Structuring Packages - Part 1
36:42
139
Structuring Packages - Part 2
27:28
140
Namespace Packages
10:39
141
Importing from Zip Archives
03:29
142
Introduction
03:41
143
Additional Resources
12:54
144
Python 3.6 Highlights
07:50
145
Python 3.6 - Dictionary Ordering
19:46
146
Python 3.6 - Preserved Order of kwargs and Named Tuple Application
05:33
147
Python 3.6 - Underscores in Numeric Literals
03:39
148
Python 3.6 - f-Strings
09:20
149
Random: Seeds
17:27
150
Random Choices
26:09
151
Random Samples
07:03
152
Timing code using *timeit*
16:18
153
Don't Use *args and **kwargs Names Blindly
07:36
154
Command Line Arguments
01:00:08
155
Sentinel Values for Parameter Defaults
11:03
156
Simulating a simple switch in Python
19:01

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