具体描述
Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide. Whether you're an intermediate-level Python programmer, or a student of computational modeling, you'll examine data structures, complexity science, and other fascinating topics through a series of exercises, easy-to-understand explanations, and case studies. Think Complexity presents features that make Python such a simple and powerful language. Author Allen Downey provides code to help you get started, along with a solution for each exercise. With this book, you will: Work with graphs and graph algorithms, NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables. Discover complexity science, the field that studies abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines. Explore the philosophy of science through the models and results in this book about the nature of scientific laws, theory choice, and realism and instrumentalism, and more.
用户评价
##version2 py代码很详实，especially for usage of matplotlib and numpy.
评分##《Think Python》引流过来的，是很浅显的入门书，胜在广而不胜在深，不过对于现阶段的我很有用，一是入门了networkx，如果不是为了测试代码我估计都不会去配置这个模块，后续做了不少图论的实验，多个工具；二是启发我结合pagerank算法研究一个新的课题，现在开了个头。练习没有完全跟做，另外代码部分有些错误，比较明显。
评分 评分##泛读
评分##《Think Python》引流过来的，是很浅显的入门书，胜在广而不胜在深，不过对于现阶段的我很有用，一是入门了networkx，如果不是为了测试代码我估计都不会去配置这个模块，后续做了不少图论的实验，多个工具；二是启发我结合pagerank算法研究一个新的课题，现在开了个头。练习没有完全跟做，另外代码部分有些错误，比较明显。
评分##version2 py代码很详实，especially for usage of matplotlib and numpy.
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