By Magnus Lie Hetland
ISBN-10: 1430232382
ISBN-13: 9781430232384
Python Algorithms explains the Python method of set of rules research and layout.
Written via Magnus Lie Hetland, writer of starting Python, this booklet is sharply inquisitive about classical algorithms, however it additionally supplies a pretty good realizing of primary algorithmic problem-solving innovations.
The ebook bargains with probably the most very important and demanding components of programming and desktop technological know-how, yet in a hugely pedagogic and readable manner.
The ebook covers either algorithmic concept and programming perform, demonstrating how idea is mirrored in genuine Python programs.
Well-known algorithms and information constructions which are outfitted into the Python language are defined, and the consumer is proven how one can enforce and assessment others himself.
Read or Download Python Algorithms: Mastering Basic Algorithms in the Python Language PDF
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Python Algorithms: Mastering Basic Algorithms in the Python Language
Python Algorithms explains the Python method of set of rules research and layout.
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Extra resources for Python Algorithms: Mastering Basic Algorithms in the Python Language
Example text
Jeremy P. Spinrad writes, in his book Efficient Graph Representations, that most introductory texts are “particularly irritating” to him as a researcher in computer representations of graphs. Their formal definitions of the most well-known representations (adjacency matrices and adjacency lists) are mostly adequate, but the more general explanations are often faulty. He presents, based on misstatements from several texts, the following strawman’s15 comments on graph representations: There are two methods for representing a graph in a computer; adjacency matrices, and adjacency lists.
This is a rather deep concept that I’ll revisit several times, when discussing different algorithmic techniques. In short, most problems can be decomposed into subproblems: smaller problems that often have quite similar 15 That is, the comments are inadequate and are presented to demonstrate the problem with most explanations. 35 CHAPTER 2 ■ THE BASICS structure. These form the nodes of the subproblem graph, and the dependencies (that is, which subproblems depend on which) form the edges. Although we rarely apply graph algorithms directly to such subproblem graphs (they are more of a conceptual or mental tool), they do offer significant insights into such techniques as divide and conquer (Chapter 6) and dynamic programming (Chapter 8).
Such guesses are quite often wrong. Instead of guessing wildly, let a profiler find out for you! Python comes with a few profiler variants, but the recommended one is cProfile. It’s as easy to use as timeit but gives more detailed information about where the execution time is spent. run('main()') This should print out timing results about the various functions in your program. If the cProfile module isn’t available on your system, use profile instead. Again, more information is available in the library reference.