Data Structures and Algorithms This book is about the fundamentals of data structures and algorithms--the basic elements from which large and complex software artifacts are built. To develop a solid understanding of a data structure requires three thingsData Structures and Algorithms This book is about the fundamentals of data structures and algorithms--the basic elements from which large and complex software artifacts are built. To develop a solid understanding of a data structure requires three things
Structures and Algorithms
This book is about the fundamentals of data structures and algorithms--the basic elements from which large and complex software artifacts are built. To develop a solid understanding of a data structure requires three things: First, you must learn how the information is arranged in the memory of the computer. Second, you must become familiar with the algorithms for manipulating the information contained in the data structure. And third, you must understand the performance characteristics of the data structure so that when called upon to select a suitable data structure for a particular application, you are able to make an appropriate decision. This book also illustrates object-oriented design and it promotes the use of common, object-oriented design patterns. The algorithms and data structures in the book are presented in the Python programming language.
Processing in Python
I put up the above PayPal button a bunch of months back. Some of you make donations each month (out of about a thousand of you reading the text each week). Tragedy of the commons and all that... but if some more of you would donate a few bucks, that would be great support of the author. If you want to buy the printed version this Amazon link lets me get a small commission on each sale (royalties do not usually earn past the advance; mine have not yet). Feel free to email me also. In a community spirit (and with permission of my publisher), I am making my book available to the Python community. Minor corrections can be made to later printings, and at the least errata noted on this website. Email me at <[email protected]> .A few caveats: (1) This stuff is copyrighted by AW (except the code samples which are released to the public domain). Feel free to use this material personally; but no permission is given for further distribution beyond your personal use.
to Thinks Like a Computer Scientist
This book owes its existence to the collaboration made possible by the Internet and the free software movement. Its three authors a college professor, a high school teacher, and a professional programmer have yet to meet face to face, but we have been able to work closely together and have been aided by many wonderful folks who have donated their time and energy to helping make this book better.
We think this book is a testament to the benefits and future possibilities of this kind of collaboration, the framework for which has been put in place by Richard Stallman and the Free Software Foundation.
This book lives at http://diveintopython.org/. If you're reading it somewhere else, you may not have the latest version. Permission is granted to copy, distribute, and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in Appendix G, GNU Free Documentation License. The example programs in this book are free software; you can redistribute and/or modify them under the terms of the Python license as published by the Python Software Foundation. A copy of the license is included in Appendix H, Python license.
You can download the current version of Thinking in Python here. This includes the BackTalk comment collection system that I built in Zope.The page describing this project is here.The current version of the book is 0.1. This is a preliminary release; please note that not all the chapters in the book have been translated. The source code is in the download package. When you unzip everything (remember to use the -a flag if you're on Unix), the code will be put into subdirectories for each chapter.This is not an introductory Python book. This book assumes you've learned the basics of Python elsewhere. I personally like Learning Python by Lutz & Ascher, from O'Reilly. Although it does not cover all the Python 2.0, 2.1 and 2.2 features that I use in this book, most books don't at this point.
Practical Python offers a highly useful guide to the Python programming language. In disarmingly readable prose, author Magnus Lie Hetland guides readers through those concepts of most importance to the budding Python developer. Following a succinct overview of the installation process, Hetland brings the reader up to speed regarding the general programming precepts common to every high-level language, including datatypes, conditionals and looping, abstraction, and modularity. Notably, Hetland takes great care to provide a thorough introduction to Python's potentially confusing datatype offering, including lists, tuples, and dictionaries, providing numerous examples aimed towards familiarizing the reader with their subtle, yet important differences.
Python Imaging Library
The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool. The Python Imaging Library is ideal for for image archival and batch processing applications. You can use the library to create thumbnails, convert between file formats, print images, etc.The current version identifies and reads a large number of formats. Write support is intentionally restricted to the most commonly used interchange and presentation formats.
Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. Python is considered easy to learn, but there's no quicker way to mastery of the language than learning from an expert teacher. This edition of Learning Python puts you in the hands of two expert teachers, Mark Lutz and David Ascher, whose friendly, well-structured prose has guided many a programmer to proficiency with the language. Learning Python, Second Edition offers programmers a comprehensive learning tool for Python and object-oriented programming. Thoroughly updated for the numerous language and class presentation changes that have taken place since the release of the first edition in 1999, this guide introduces the basic elements of the latest release of Python 2.3 and covers new features, such as list comprehensions, nested scopes, and iterators/generators.
This chapter concludes our look at Python Internet programming by exploring a handful of Internet-related topics and packages. We've covered many Internet topics in the previous five chapters--socket basics, client and server-side scripting tools, and programming full-blown web sites with Python. Yet we still haven't seen many of Python's standard built-in Internet modules in action. Moreover, there is a rich collection of third-party extensions for scripting the Web with Python that we have not touched on at all. we explore a grab-bag of additional Internet-related tools and third-party extensions of interest to Python Internet developers. Along the way, we meet larger Internet packages, such as HTMLgen, JPython, Zope, PSP, Active Scripting, and Grail.