Python科学计算入门：NumPy、Matplotlib与SciPy

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"《ScipyLectures》是一份针对Python科学计算的教程，由Gaël Varoquaux、Emmanuelle Gouillart、Olav Vahtras、Pierre de Buyl等人编著，发布于2019年。这份资料深入浅出地介绍了Python在科学计算领域的应用，特别是与NumPy、Matplotlib、SciPy等核心库的结合。课程内容涵盖了Python科学计算生态系统的重要性，以及如何安装和配置一个适合科学计算的工作环境。章节一"Getting started with Python for science"引导读者了解Python作为科学计算工具的原因，包括其易用性、丰富的库支持和广泛的社区。首先，通过比较Python与其他语言的优势，强调了Python在科学计算中的地位。接着，讲解了如何安装必要的软件包如Anaconda或Miniconda来创建一个完整的Python科学计算环境，并介绍了交互式环境（如IPython）和文本编辑器的使用。第二章详细介绍了Python语言本身，从基础语法如变量类型、控制流结构（如if-else、for和while循环）、函数定义到代码复用（通过脚本和模块）。此外，还涵盖了输入输出操作、标准库的使用以及异常处理，这些都是科学编程中的关键要素。第三章专讲NumPy，它是Python科学计算的核心库，用于处理大型多维数组和矩阵运算。NumPy提供了高效的数值计算功能，如数组创建、数学运算、索引和切片，以及与内存管理相关的高级特性。学习者将在此部分深入了解如何利用NumPy进行数据处理和分析。后续章节可能会继续介绍Matplotlib，这是用于数据可视化的强大工具，以及SciPy库，它扩展了Python的数学、优化、统计和信号处理等功能。此外，可能还会涉及Cython，一种能够将Python代码编译为C代码以提高性能的工具。《ScipyLectures》是一份全面的指南，不仅适合初学者入门Python科学计算，也适合有一定经验的开发者深化理解并提升技能。无论是数据预处理、模型构建还是结果展示，都能在这里找到相应的教学内容。"

Scipy lecture notes, Edition 2019.1

• %debug allows you to enter post-mortem debugging. That is to say, if the code you try to execute, raises

an exception, using %debug will enter the debugger at the point where the exception was thrown.

In [4]: x === 10

File "<ipython-input-6-12fd421b5f28>", line 1

x === 10

SyntaxError: invalid syntax

In [5]: %debug

> /.../IPython/core/compilerop.py (87)ast_parse()

86 and are passed to the built-in compile function."""

---> 87 return compile(source, filename, symbol, self.flags | PyCF_ONLY_AST, 1)

ipdb>locals()

{'source': u'x === 10\n', 'symbol': 'exec', 'self':

<IPython.core.compilerop.CachingCompiler instance at 0x2ad8ef0>,

'filename': '<ipython-input-6-12fd421b5f28>'}

See also:

Chapter on debugging

Aliases Furthermore IPython ships with various aliases which emulate common UNIX command line tools

such as ls to list ﬁles, cp to copy ﬁles and rm to remove ﬁles (a full list of aliases is shown when typing alias).

Getting help

• The built-in cheat-sheet is accessible via the %quickref magic function.

• A list of all available magic functions is shown when typing %magic.

1.4. The workﬂow: interactive environments and text editors 11

Scipy lecture notes, Edition 2019.1

• a language for which a large variety of high-quality packages are available for various applications, from

web frameworks to scientiﬁc computing.

• a language very easy to interface with other languages, in particular C and C++.

• Some other features of the language are illustrated just below. For example, Python is an object-oriented

language, with dynamic typing (the same variable can contain objects of different types during the

course of a program).

See https://www.python.org/about/ for more information about distinguishing features of Python.

2.1 First steps

Start the Ipython shell (an enhanced interactive Python shell):

• by typing “ipython” from a Linux/Mac terminal, or from the Windows cmd shell,

• or by starting the program from a menu, e.g. in the ‘Python(x,y)‘_ or EPD menu if you have installed one

of these scientiﬁc-Python suites.

• or by starting the program from a menu, e.g. the Anaconda Navigator, the ‘Python(x,y)‘_ menu or the

EPD menu if you have installed one of these scientiﬁc-Python suites.

Tip: If you don’t have Ipython installed on your computer, other Python shells are available, such as the plain

Python shell started by typing “python” in a terminal, or the Idle interpreter. However, we advise to use the

Ipython shell because of its enhanced features, especially for interactive scientiﬁc computing.

Once you have started the interpreter, type

>>> print("Hello, world!")

Hello, world!

Tip: The message “Hello, world!” is then displayed. You just executed your ﬁrst Python instruction, congratu-

lations!

To get yourself started, type the following stack of instructions

>>> a = 3

>>> b = 2*a

>>> type(b)

>>> print(b)

>>> a*b

>>> b = 'hello'

>>> type(b)

>>> b + b

'hellohello'

>>> 2*b

'hellohello'

Tip: Two variables a and b have been deﬁned above. Note that one does not declare the type of a variable

before assigning its value. In C, conversely, one should write:

2.1. First steps 13

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Python科学计算入门：NumPy、Matplotlib与SciPy

scipylectures

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