Python Essentials

Book Image

Python Essentials

By : Steven F. Lott

Book Image

Python Essentials

By: Steven F. Lott

Overview of this book

Python Essentials

Python Essentials

Credits

About the Author

About the Author

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Free Chapter

Getting Started

Getting Started

Installation or upgrade

Using the Read-Evaluate-Print Loop (REPL)

Interacting with the help subsystem

Creating simple script files

The Python ecosystem

Looking at other Python interpreters

Simple Data Types

Simple Data Types

Introducing the built-in operators

The numeric tower

The math libraries

Using bits and Boolean values

Working with sequences

Using string and bytes values

Using the tuple collection

The None object

The consequences of immutability

Using the built-in conversion functions

Expressions and Output

Expressions and Output

Expressions, operators, and data types

The print() function

Examining syntax rules

Splitting, partitioning, and joining strings

Summary of the standard string libraries

Variables, Assignment and Scoping Rules

Variables, Assignment and Scoping Rules

Simple assignment and variables

Multiple assignment

Using the head, *tail assignment

Augmented assignment

The input() function

Python language concepts

The Python namespace concept

Logic, Comparisons, and Conditions

Logic, Comparisons, and Conditions

Boolean data and the bool() function

Comparison operators

Logic operators – and, or, not, if-else

The if-elif-else statement

The pass statement as a placeholder

The assert statement

The logic of the None object

More Complex Data Types

More Complex Data Types

The mutability and immutability distinction

Using the list collection

Using collection functions

Using the set collection

Processing collections with the for statement

Iterating with the while statement

The continue and break statements

Using the else clause on a loop

Basic Function Definitions

Basic Function Definitions

Looking at the five kinds of callables

Defining functions with positional parameters

Mutable and immutable argument values

Defining optional parameters via default values

Using the "everything else" notations of * and **

Nested function definitions

Working with namespaces

Defining lambdas

Writing additional function annotations

More Advanced Functions

More Advanced Functions

Using the for statement with iterable collections

Consequences and next steps

Using generator expressions and comprehensions

Defining generator functions with the yield statement

Using the higher-order functions

Using the built-in reductions – max, min, and reduce

Three ways to sort a sequence

Functional programming design patterns

Exceptions

The core exception concept

Using the try and except statements

Matching exception classes in an except clause

Creating our own exceptions

Using a finally clause

Use cases for exceptions

Issuing warnings instead of exceptions

Permission versus forgiveness – a Pythonic approach

Files, Databases, Networks, and Contexts

Files, Databases, Networks, and Contexts

The essential file concept

Using a context manager via the with statement

Using the shelve module as a database

Web services and Internet protocols

Physical format considerations

Class Definitions

Class Definitions

Creating a class

Writing the suite of statements in a class

Using instance variables and methods

Pythonic object-oriented programming

Using properties

Using inheritance to simplify class definitions

Using class methods and attributes

Using __slots__ to save storage

The ABCs of abstract base classes

Scripts, Modules, Packages, Libraries, and Applications

Scripts, Modules, Packages, Libraries, and Applications

Script file rules

Running a script by the filename

Creating a reusable module

Creating a hybrid library/application module

Creating a package

Designing alternative implementations

Seeing the package search path

Metaprogramming and Decorators

Metaprogramming and Decorators

Simple metaprogramming with decorators

Defining our own decorator

More complex metaprogramming with metaclasses

Fit and Finish – Unit Testing, Packaging, and Documentation

Fit and Finish – Unit Testing, Packaging, and Documentation

Writing docstrings

Writing unit tests with doctest

Using the unittest library for testing

Using other add-on test libraries

Logging events and conditions

Writing documentation with RST markup

Organizing Python code

Next Steps

Leveraging the standard library

Leveraging PyPI – the Python Package Index

Types of applications

Building CLI applications

Building GUI applications

Building web applications

Plugging into a MapReduce framework

Index

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Functional programming design patterns

The presence of higher-order functions in Python allows us to leverage a great many functional programming design patterns. To learn more about these design patterns, a good place to start is the itertools module. The functions in this module provide many examples of how we can write simple functions that do sophisticated processing.

Additionally, we can use some of the features in the functools module. This contains the general-purpose reduce() function. It also contains some functions that can help us write decorators. A decorator, as we'll see in Chapter 13, Metaprogramming and Decorators, is another kind of higher-order function: it's a function that modifies the definition of an original function. This is another aspect of functional programming.

Most importantly, we have two ways to approach algorithms:

We can process items in large collections of data, creating additional collections that are copies, subsets, or transformations.
We can process items...