By Magnus Lie Hetland
ISBN-10: 1484200551
ISBN-13: 9781484200551
Python Algorithms, moment version explains the Python method of set of rules research and layout. Written via Magnus Lie Hetland, writer of starting Python, this publication is sharply excited about classical algorithms, however it additionally supplies an effective realizing of primary algorithmic problem-solving techniques.
The e-book offers with the most vital and hard parts of programming and desktop technology in a hugely readable demeanour. It covers either algorithmic conception and programming perform, demonstrating how idea is mirrored in actual Python courses. recognized algorithms and knowledge buildings which are outfitted into the Python language are defined, and the person is proven how you can enforce and review others.
Read or Download Python Algorithms: Mastering Basic Algorithms in the Python Language (2nd Edition) PDF
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Python Algorithms explains the Python method of set of rules research and layout.
Written by means of Magnus Lie Hetland, writer of starting Python, this publication is sharply concerned with classical algorithms, however it additionally provides an exceptional figuring out of primary algorithmic problem-solving recommendations.
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Additional resources for Python Algorithms: Mastering Basic Algorithms in the Python Language (2nd Edition)
Sample text
I think of one of the particles in the visible universe, and you try to guess which one, using only yes/no questions. OK? Shoot! This game might seem like sheer insanity, but I assure you, that has more to do with the practicalities (such that keeping track of which particles have been ruled out) than with the number of alternatives. To simplify these practicalities a bit, let’s do “think of a number” instead. There are many estimates for the number of particles we’re talking about, but 1090 (that is, a one followed by 90 zeros) would probably be quite generous.
The second is about a topic that is not often discussed in algorithm books, but it is important to be aware of, that is, the many traps of computing with floating-point numbers. 34 CHAPTER 2 N THE BASICS Hidden Squares Consider the following two ways of looking for an element in a list: >>> from random import randrange >>> L = [randrange(10000) for i in range(1000)] >>> 42 in L False >>> S = set(L) >>> 42 in S False They’re both pretty fast, and it might seem pointless to create a set from the list—unnecessary work, right?
In some cases, this happens when the representation is external to our program. For example, when parsing XML documents or traversing directories in the file system, the tree structures are just there, with existing APIs. In other cases, we are constructing the graph ourselves, but it is implicit. For example, if you want to find the most efficient solution to a given configuration of Rubik’s Cube, you could define a cube state, as well as operators for modifying that state. Even though you don’t explicitly instantiate and store all possible configurations, the possible states form an implicit graph (or node set), with the change operators as edges.