1小时速成Python:为编程问题提供完美解决方案

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"1小时学会Python"是一本旨在引导读者快速掌握Python编程语言的教程。Python作为一款既简单又强大的编程工具,对初学者和专业人士都具有吸引力,因为它的易学性和高效性使得编程过程充满乐趣。该书的核心目标是通过实际操作,教会读者如何使用Python轻松解决各种问题,提供编程问题的解决方案。 对于有编程经验的读者,特别是熟悉Java和JavaScript的人来说,这本书将帮助他们更快地过渡到Python。例如,书中提到的一个实例是检查局域网内电脑的连通性,通常使用shell编程技术(如Linux的bash或Windows的批处理脚本)。作者举例了在Windows系统上,通过`ping`命令逐个测试IP范围内的电脑,并通过控制台输出的"Reply from"和"Time out"来判断网络状态。 Java代码展示了如何使用系统命令执行器`Runtime.getRuntime().exec()`来运行ping命令,并读取其输出。然而,为了使这段Java代码能够运行,读者需要进行额外的准备工作,如创建一个包含`main`方法的类文件,将其编译为字节码,然后可能需要编写一个启动脚本来执行生成的字节码。这个过程表明,在Python之外的其他语言(如C/C++)实现类似功能时,可能存在更高的复杂性和不跨平台的问题。 尽管C/C++在性能上可能更胜一筹,但Python凭借其简洁的语法和广泛的应用场景,在许多情况下(如数据处理、Web开发、人工智能等)更加便捷。通过本书,读者不仅能学习到如何利用Python进行网络检测这类基础任务,还能了解到如何利用Python的优势,提高编程效率和代码的可读性。 "1小时学会Python"提供了Python编程的快速入门指南,适合想要跨越语言障碍,或者寻找更高效工具的开发者。通过实践和理解Python的基础概念,读者将能够构建自己的解决方案,并享受编程带来的乐趣。无论是初学者还是经验丰富的程序员,都能在本书中找到适合自己的学习路径和实例。
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Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computat ional methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Specific topics that are covered include: How to work with vectors and matrices using NumPy How to work with symbolic computing using SymPy How to plot and visualize data with Matplotlib How to solve linear and nonlinear equations with SymPy and SciPy How to solve solve optimization, interpolation, and integration problems using SciPy How to solve ordinary and partial differential equations with SciPy and FEniCS How to perform data analysis tasks and solve statistical problems with Pandas and SciPy How to work with statistical modeling and machine learning with statsmodels and scikit-learn How to handle file I/O using HDF5 and other common file formats for numerical data How to optimize Python code using Numba and Cython