Linux/OSX/Windows Python安装与版本管理

需积分: 11 9 下载量 144 浏览量 更新于2024-09-09 收藏 7KB MD 举报
本资源是一份关于Python入门的指南,特别关注于在不同操作系统上的安装与配置。由于初始章节提供的简单安装方法可能不适用于所有情况,这份文档详细介绍了如何在Linux、OS X、以及Windows系统上安装Python,特别是针对Python 3版本的升级和管理。 1. **Python版本与选择**: Python有多个版本,每个操作系统可能预装了不同的默认版本。在Linux系统中,尽管Python通常作为基础软件包提供,但用户可能希望使用非默认版本。检查当前已安装的Python版本可以使用命令`$ python --version`,如示例中的Python 2.7.6。 2. **Linux安装Python 3**: 对于Linux用户,如果需要安装Python 3而不是默认版本,这部分提供了具体的步骤,比如查找已安装的Python 3版本并安装Homebrew(在某些情况下可能需要)。 3. **Homebrew安装**: Homebrew是一个Mac OS的包管理器,它简化了在Mac上安装Python 3和其他软件的过程。对于那些在OS X系统中寻求安装Python 3的用户,了解如何通过Homebrew进行安装至关重要。 4. **OS X安装Python 3**: 对于OS X用户,指南也包括了如何在该系统上安装Python 3的详细步骤,确保用户能够在苹果环境里运行Python 3。 5. **Windows安装Python 3**: 对于Windows用户,这里提供了针对Python 3的特定安装指南,因为Windows系统中的Python配置可能与Linux和OS X有所不同。 6. **找到Python解释器**: 安装完成后,了解如何定位和使用Python解释器是必不可少的,这对于开发和测试脚本来说是基础操作。 7. **添加Python到PATH变量**: 为了能够方便地在终端或命令提示符中直接运行Python,需要将Python的路径添加到系统的环境变量PATH中,这样可以在任何目录下执行Python命令。 这份资源旨在帮助初学者在各种操作系统环境下正确安装和配置Python,特别关注于如何处理不同版本之间的切换,以及确保Python环境的顺利集成。无论是为了学习编程,还是进行实际项目开发,理解这些安装步骤都是非常关键的。
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