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Python Numpy Scipy Cython 视频讲座 by ENTH0UGHT
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ENTH0UGHT 公司推出的 Python / Numpy / Scipy / Cython 系列培训视频。 百度网盘： http://pan.baidu.com/s/1dDChwp7 无密码 （迅雷附件只给出教程的第一部分 (IPython)，完整内容请见上面百度网盘链接。） 既然窃取了人家的收费内容，下面就给他做个广告吧。 http://www.enthought.com 是ETS的主要开发者。Mayavi、Traits等诸多开放源代码的 Python 库均由该公司出品。 它同时提供完整的科学计算Python distribution Canopy（类似于Python xy, Continuum Anaconda）。 所有用户可以免费下载使用Canopy Express，而学术用户注册后可免费使用其完整版（edu 邮箱）。 资源出处：https://training.enthought.com/courses。原资源多为webm格式，我已经将其转码为MP4。 以下是抄来的简介： Tools to Learn and Develop in Python 48 mins | 4 lectures This course provides an introduction to helpful tools commonly used to develop programs in Python. We begin the course by looking at the IPython prompt, an enhanced interactive and science-centric console. Next we review the IPython notebook, a cell-based environment that renders scripts in a web-like interface, making it ideal for sharing and publishing analysis with peers. We present and demo these tools, fundamental for both learning and programming in the Python language, and show how they integrate with the Enthought Canopy platform. These tools will be central to your ongoing Python code development and we will use them extensively in the lecture notes and exercises throughout Enthought Training on Demand. Python Essentials 3 hours 42 mins | 31 lectures | 33 exercises This course provides a foundational understanding for programming in Python. It begins with a twenty-minute whirlwind tour of Python\\\'s features and then settles into a more comprehensive discussion of the built-in data structures. The tour offers guidance on how and where each might be used, what trade-offs are present, and insight into Python’s design choices that will help you understand why Python works the way it does. The numeric types are covered first. Particular attention is spent on strings, lists, and dictionaries. Sets and tuples also make a showing. Generic patterns, such as indexing and slicing that work across multiple data structures, are also covered. Looping, control flow, and exception handling build upon the data structure discussion for more complex applications. We end with coverage of code organization using functions, classes, modules, and packages. NumPy 4 hours 57 mins | 46 lectures | 13 exercises NumPy is an elegant and efficient tool for numeric computation in Python. Whether you are a scientist writing short scripts to analyze and plot your analytical results or an analyst writing large-scale quantitative finance applications for Wall Street, NumPy should be part of your toolbox. This lecture series provides a comprehensive discussion of the array data structure, and how to model your computations using it. The discussion covers high-level design patterns, like broadcasting, that provide so much power, down to details such as memory layout for those interested in the performance and interfacing with other languages. SciPy 2 hours 43 mins | 20 lectures | 5 exercises This course provides an introduction to performing scientific computations in Python using high-level packages like SciPy, NumPy, and SymPy. The topics include optimization, statistics, interpolation, integration, ODE solving, and functional curve fitting. Advanced Python 16 lectures | 6 exercises | 2 hours 39 mins | Advanced This course covers a number of useful Python tools and concepts that aren\\\'t necessary for getting started with the language, but are really valuable as your skills and needs progress. Python concepts such as iterators, generators, decorators, and contexts are covered. Modules for regular expression handling, advanced file I/O, and accessing databases from outside of the standard library are also covered. Interfacing with other languages 16 lectures | 3 exercises | 2 hours 10 mins | Advanced In this course, you will learn how to interface Python with code written in other languages, allowing you to complement the strengths of Python with the speed and performance of C, C++, and Fortran. 迅雷附件只给出了关于IPython的第一部分教程，完整内容请见帖子开头的百度网盘链接。 |
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