Python学习:面向对象与图形编程,文本数据处理详解

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本课程专注于Python语言的学习,特别是针对初学者和进阶者,涵盖了重要的编程基础和实用技巧。课程内容包括程序设计思想与方法,从理论层面引导学生理解如何运用Python解决实际问题。章节4至5着重讲解了字符串处理,这是信息管理中不可或缺的一部分,因为计算机中的文本数据,如姓名、地址和简历等,通常以字符串形式存储。学习者将掌握字符串类型的概念,如何定义和处理字符串字面值,以及遇到包含引号时的解决方案。 字符串的机内表示是关键知识点,学员会了解到字符串在计算机内存中的存储方式,以及如何正确地处理包含特殊字符的字符串。此外,课程还涉及格式化输出,让学员学会如何按照特定格式打印字符串,提高数据呈现的清晰度。 文件处理部分介绍了如何在Python中处理文本数据,包括输入和输出操作。学员通过实践学习到如何使用`input()`函数获取用户输入,以及为什么在输入字符串时需要使用引号或者切换到`raw_input()`函数以避免解析错误。同时,课程还强调了`input()`与`raw_input()`之间的区别,帮助学生避免常见误区。 本课程不仅教授Python语法,而且通过实例和练习,让学生掌握如何有效地处理字符串和文件,提升在实际项目中的编程技能。同时,提供的FTP链接提供了额外的学习资源,便于学生深入学习和实践。无论是对编程入门者还是有一定基础的学习者来说,这都是一门非常实用且系统化的Python教程。
2018-01-06 上传
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