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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Looking at other Python interpreters

This book will focus on a particular implementation of Python called the CPython. What this means is that Python—the abstract language—can be processed by a variety of concrete Python runtimes or implementations. The CPython implementation is written in portable C and can be recompiled for many operating systems.

Python can be embedded into an application. This means that a complex application can include the entire Python language as a way to write scripts that customize the given application. One example of this is the Ganglia monitoring system (http://ganglia.sourceforge.net). Python is part of the system; we can customize the behavior using Python scripts that will interact with Ganglia components. We won't be looking more deeply into these kinds of applications in this book; we'll focus on standalone implementations of Python.

There are several alternative Python implementations. In the Installing Python on Windows section in this chapter, we noted that Iron Python (http://ironpython.net) and PTVS (http://pytools.codeplex.com) are available. These provide tighter integration with the .NET framework.

There are still more implementations that we might encounter:

Jython: This is a version of the Python interpreter that is written in Java and runs on the Java Virtual Machine (JVM). See http://www.jython.org. This project focuses on Python 2.7.
PyPy: This is a version of the Python interpreter written in Python. See http://pypy.org. The circularity of "Python written in Python" is broken by the RPython translation toolchain, which creates a very sophisticated implementation of Python programs. This can provide significant performance improvements for a variety of long-running applications, such as web servers.
Stackless: This version of Python has a different threading model from CPython. See http://www.stackless.com. This version can provide dramatic performance improvements for multithreaded servers.

Since the Python source is readily available, it's quite easy to look for optimization opportunities. The language is relatively simple, allowing experimentation to see what effect changes in implementation may have.