Python Programming Language LiveLessons

Python Programming Language LiveLessons
Python Programming Language LiveLessons

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 6h 27m | 2.05 GB
eLearning | Skill level: All Levels

Python Programming Language LiveLessons provides developers with a guided tour of the Python programming language, including an introduction to many of the advanced techniques used in libraries and frameworks.

In this video training, David Beazley covers the essential features of Python programming through a series of practical programming examples. David assumes you are a programmer, familiar with central programming concepts such as control flow, functions, and data structures. The course provides programmers with an accelerated introduction to the essential parts of Python. Those with some previous Python experience will benefit from being exposed to modern Python style and programming idioms used by experienced programmers. Each lesson covers a “big idea” rather than taking an exhaustive, reference-style approach. In each lesson David addresses a specific, practical problem and demonstrates the solution through code. He explains the steps taken and why he is taking a particular approach.

What You Will Learn

  • Python program structure and execution model
  • How to read and write data from files
  • How to effectively manipulate data using tuples, lists, sets, and dictionaries
  • How to define new functions
  • How to handle exceptions and errors
  • How to create new objects with classes
  • Essential object-oriented programming techniques
  • Customization of objects with special methods, properties, and descriptors
  • Metaprogramming features including decorators, class decorators, and metaclasses
  • Iterative data processing with generator functions
  • Placing code into modules and packages
  • How to use higher-order functions and closures
  • An introduction to coroutines and asynchronous I/O handling

Lesson 1: Working Environment
Lesson 1 introduces the basics of working in the Python programming environment. This includes starting and stopping the interpreter, using the interactive console, editing and running programs, and turning simple programs into useful scripts. Getting help and some basic debugging tips are also described.

Lesson 2: Program Structure and Execution Model
Lesson 2 covers Python’s execution model and describes the statements related to variables, expressions, and simple control flow. It also covers using Python to perform simple mathematical calculations, making formatted output, and writing to a file.

Lesson 3: Text Processing and Files
To do almost anything useful, you need to be able to read data into your program. Lesson 3 addresses the problem of reading data from a file, basic techniques for manipulating text strings, converting input data, and performing calculations. It also introduces the csv module for reading data from CSV files.

Lesson 4: Functions and Error Handling
As you write larger programs, you will want to get organized. The primary way of doing this in Python is to write functions. And if you write functions, you will want them to play nicely with others. Lesson 4 addresses the problem of writing function definitions, handling exceptions, and coding practices that will help you when you start to write larger programs.

Lesson 5: Data Structures and Data Manipulation
One of the most critical Python skills to develop is being able to effectively use lists, tuples, sets, and dictionaries. Lesson 5 includes how to read a file into a useful data structure. It then explores different ways of performing calculations on the data, including data reductions, filtering, joining, and sorting. Particular attention is given to list, set, and dict comprehensions a feature that greatly simplifies a wide variety of data processing tasks.

Lesson 6: Library Functions and Import
As you write larger Python programs, you will want to write library functions and split your code across multiple files. To do this, you need to start working with modules and packages. Lesson 6 delves into creating and using modules. It starts with how to use the import statement and some of its gotchas. Next it covers how to create a general purpose CSV parsing function and apply it to some of our earlier code. The lesson concludes with a discussion of organizing larger code bases into packages.

Lesson 7: Classes and Objects
Object-oriented programming is a fundamental part of the Python language. You see this whenever you use its built-in types and execute methods on the resulting instances. If you want to make your own objects, you can do so using the class statement. A class is often a convenient way to define a data structure and to attach methods that carry out operations on the data. Lesson 7 covers the basics of defining a new object, creating instances, and manipulating objects. It then helps you see how the dynamic nature of Python enables you to write highly generic functions. The Lesson concludes with a special topic on how to write classes that need to have more than one initializer method.

Lesson 8: Inheritance
One of the most challenging problems in writing larger programs is that of code reuse and extensibility. A common tool used to address this problem is inheritance. Lesson 8 addresses using inheritance to make a program extensible as well as some of the tricky practical concerns that might arise as a result. Some advanced inheritance concepts, such as multiple inheritance with mixin classes and abstract base classes, are also introduced. The lesson concludes by discussing a few important design considerations to take into account if you’re going to use inheritance.

Lesson 9: Python Magic Methods (a.k.a., Speaking Python)
When defining new objects, it is usually beneficial to make your objects play nicely with other parts of Python. This is typically done by adding special or “magic” methods to your class. Lesson 9 demonstrates some of the common customizations that are made to objects to simplify debugging, create containers, and manage resources. Although this section doesn’t cover every possible customization that can be carried out, it gives you a taste of what’s possible and a foundation for more exploration.

Lesson 10: Encapsulation (Owning the Dot)
Major issues that arise in larger programs are how to encapsulate internal implementation details and how to have more control over the ways in which developers interact with objects. Lesson 10 dives into some of the low-level details that make the Python object system tick. It then turns to techniques for taking control over attribute access and custom-tailoring the environment to address issues such as data validation, type checking, and more. Topics include defining private attributes, properties, descriptors, and redefining magic methods for attribute access.

Lesson 11: Higher Order Functions and Closures
Functions are the basic organizational unit of all Python programs regardless of whether or not they serve as a stand-alone function or the method of a class. Lesson 11 introduces some important functional programming concepts, including the idea of functions as first class objects, passing functions as data, and creating functions as results. Particular emphasis is placed on closures as a tool for generating and simplifying code.

Lesson 12: Metaprogramming and Decorators
One of the most difficult problems in developing larger programs is dealing with highly repetitive code. For example, as a general rule you want to avoid situations where you’re cutting and pasting common code fragments between functions or across files. Restructuring the design of your program might help solve such problems. However, another common technique is to encapsulate common code-oriented tasks into a decorator. Lesson 12 covers how to write decorators for processing both function and class definitions.

Lesson 13: Metaclasses
One of the problems faced by the creators of large applications and frameworks is exercising control over the greater programming environment. Code is often organized using classes, but sometimes there is much more to it than simply making class definitions. For example, classes might need to register their existence with some other part of a framework. Or perhaps the definition of a class is too verbose and needs to be simplified in some way. Or perhaps some other aspect of classes needs to be managed. An advanced technique for addressing these problems is to use a metaclass. Lesson 13 introduces the metaclass concept and some practical applications are shown.

Lesson 14: Iterators and Generators
One of Python’s most useful features is the for-statement, which is used to iterate over data. As you may have noticed, the for-statement works with a wide variety of objects such as lists, dicts, sets, and files. It’s also used in a variety of related features, such as list comprehensions. One of the most powerful features of iteration is that it can be easily customized. Lesson 14 starts with the iteration protocol and how to customize it using generator functions. It also covers how to apply iteration to data processing pipelines a particularly powerful technique for working with data and problems involving workflows.

Lesson 15: Coroutines
Starting in Python 3.5, a special kind of function known as a “coroutine” could be defined using special async/await syntax. On the surface, coroutines look a lot like normal Python functions. However, under the covers they run under the supervision of a manager that coordinates their execution. Lesson 15 introduces coroutines and shows how they can be used to implement a simple network service that can handle thousands of concurrent client connections. It concludes by peeling back some of the covers to see how coroutines actually work and to explore their relationship to generators.

+ Table of Contents

01 Python Programming Language – Introduction
02 Topics
03 1.1 Starting, Stopping, and Typing Commands into the Interactive Interpreter
04 1.2 Project – Catching the Bus
05 1.3 Project – Reading Command Line Arguments and Making a Script
06 1.4 Debugging
07 Topics
08 2.1 Project – Mortgage Calculator
09 2.2 Project – Formatted Output and File I_O
10 Topics
11 3.1 File and String Basics
12 3.2 Project – Reading from a File and Performing a Calculation
13 3.3 Project – Using the CSV Module to Read Data
14 Topics
15 4.1 Defining and Using Simple Functions
16 4.2 Project – Moving a Script into a Function
17 4.3 Project – Handling Bad Data and Exception Handling
18 4.4 Project – Function Design Considerations
19 Topics
20 5.1 Basic Material – Tuples, Lists, Sets, and Dicts
21 5.2 Project – Building a Data Structure from a File
22 5.3 Data Manipulation
23 5.4 Example – Sorting and Grouping
24 Topics
25 6.1 Module Basics
26 6.2 Project – Writing a General Purpose CSV Parsing Module
27 6.3 Making a Package
28 Topics
29 7.1 Introduction – From Simple Data Structures to Classes
30 7.2 Understanding Attribute Access
31 7.3 Advanced – Class Methods and Alternate Constructors
32 Topics
33 8.1 Inheritance Concepts
34 8.2 Inheritance in Practice – Building an Extensible Library
35 8.3 Advanced Inheritance
36 8.4 Designing for Inheritance
37 8.5 Defensive Programming with Abstract Base Classes
38 8.6 Advanced – How Inheritance Actually Works
39 Topics
40 9.1 Background – Use of Magic Methods to Implement Operators
41 9.2 Making Objects Printable and Debuggable
42 9.3 Making a Custom Container Object
43 9.4 Making a Custom Context Manager
44 Topics
45 10.1 Instance Representation, Attribute Access and Naming Conventions
46 10.2 Managed Attributes with Properties
47 10.3 Managed Attributes with Descriptors
48 10.4 Object Wrappers and Proxies
49 Topics
50 11.1 Functions as Objects
51 11.2 Generating Code with Closures
52 Topics
53 12.1 Background – Function Argument Passing and Calling Conventions
54 12.2 Don’t Repeat Yourself—Introducing Decorators
55 12.3 Decorators with Arguments
56 12.4 Class Decorators
57 Topics
58 13.1 Background – Types and Metaclasses Introduced
59 13.2 Project – Tracking Subclasses in a Framework
60 13.3 Project – Filling in Missing Details and Code Generation
61 Topics
62 14.1 Iteration Protocol and Customization via Generators
63 14.2 Project – Watching a Real-Time Data Source with a Generator
64 14.3 Processing Pipelines and Workflows
65 Topics
66 15.1 Defining and Calling Coroutines with async_await
67 15.2 Project – Asynchronous Echo Server with Coroutines and asyncio
68 15.3 Under the Covers – Enhanced Generators
69 Python Programming Language – Summary