NumPy用户指南：设置、快速入门和高级使用

需积分: 10 29 浏览量更新于2024-07-20 收藏 542KB PDF 举报

NumPy用户指南 NumPy是一个流行的Python科学计算库，提供了大量的数学函数和高性能的多维数组对象。下面是根据给定的文件信息生成的相关知识点： **Setting up** * 什么是 NumPy？ NumPy是一个 Python 库，提供了大量数学函数和高性能的多维数组对象，用于科学计算和数据分析。 * 安装 NumPy：NumPy 可以使用 pip 等工具安装，安装完成后可以使用 Python 导入 NumPy 库。 **Quickstart Tutorial** * 前提条件：在开始使用 NumPy 之前，需要了解 Python 的基本语法和数据类型。 * 基础知识：NumPy 提供了多维数组对象 ndarray，支持矢量化操作和广播机制。 * 形状操作：NumPy 提供了多种形状操作函数，例如 reshape、flatten 等。 * 拷贝和视图：NumPy 提供了拷贝和视图机制，允许用户创建数组的拷贝或视图。 * 高级索引：NumPy 提供了高级索引机制，允许用户使用整数、布尔值、列表等索引数组。 * 线性代数：NumPy 提供了线性代数函数，例如矩阵乘法、逆矩阵等。 * 技巧和提示：NumPy 提供了许多技巧和提示，例如使用 NumPy 的随机数生成函数、优化数组操作等。 **Numpy Basics** * 数据类型：NumPy 提供了多种数据类型，例如整数、浮点数、复数等。 * 数组创建：NumPy 提供了多种数组创建函数，例如 zeros、ones、arange 等。 * 输入输出：NumPy 提供了多种输入输出函数，例如 loadtxt、savetxt 等。 * 索引：NumPy 提供了多种索引机制，例如整数索引、布尔索引等。 * 广播：NumPy 提供了广播机制，允许用户在不同的数组之间进行操作。 **Miscellaneous** * IEEE754 浮点数特殊值：NumPy 支持 IEEE754 浮点数特殊值，例如 NaN、Inf 等。 * 数值异常处理：NumPy 提供了数值异常处理机制，例如处理溢出、下溢等。 * C、Fortran、C++ 接口：NumPy 提供了多种语言接口，例如 C、Fortran、C++ 等。 **Numpy for Matlab Users** * 介绍：NumPy 和 Matlab 都是科学计算库，但 NumPy 是开源的，提供了更多的自由度。 * 差异：NumPy 和 Matlab 有一些差异，例如数组索引、矩阵运算等。 * ‘array’ or ‘matrix’？：NumPy 提供了两种数组类型，array 和 matrix，用户可以根据需要选择合适的类型。 * Matlab 用户设施：NumPy 提供了多种设施，例如矩阵运算、图形绘制等，帮助 Matlab 用户快速上手 NumPy。

NumPy User Guide, Release 1.11.0

>>> b[ : ,1] # equivalent to the previous example

array([ 1, 11, 21, 31, 41])

>>> b[1:3, : ] # each column in the second and third row of b

array([[10, 11, 12, 13],

[20, 21, 22, 23]])

When fewer indices are provided than the number of axes, the missing indices are considered complete slices:

>>> b[-1] # the last row. Equivalent to b[-1,:]

array([40, 41, 42, 43])

The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent

the remaining axes. NumPy also allows you to write this using dots as b[i,...].

The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is a rank

5 array (i.e., it has 5 axes), then

• x[1,2,...] is equivalent to x[1,2,:,:,:],

• x[...,3] to x[:,:,:,:,3] and

• x[4,...,5,:] to x[4,:,:,5,:].

>>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays)

... [ 10, 12, 13]],

... [[100,101,102],

... [110,112,113]]])

>>> c.shape

(2, 2, 3)

>>> c[1,...] # same as c[1,:,:] or c[1]

array([[100, 101, 102],

[110, 112, 113]])

>>> c[...,2] # same as c[:,:,2]

array([[ 2, 13],

[102, 113]])

Iterating over multidimensional arrays is done with respect to the ﬁrst axis:

>>> for row in b:

... print(row)

...

[0 1 2 3]

[10 11 12 13]

[20 21 22 23]

[30 31 32 33]

[40 41 42 43]

However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is

an iterator over all the elements of the array:

>>> for element in b.flat:

... print(element)

...

12 Chapter 2. Quickstart tutorial

NumPy User Guide, Release 1.11.0

For arrays of with more than two dimensions, hstack stacks along their second axes, vstack stacks along their

ﬁrst axes, and concatenate allows for an optional arguments giving the number of the axis along which the con-

catenation should happen.

Note

In complex cases, r_ and c_ are useful for creating arrays by stacking numbers along one axis. They allow the use of

range literals (”:”) :

>>> np.r_[1:4,0,4]

array([1, 2, 3, 0, 4])

When used with arrays as arguments, r_ and c_ are similar to vstack and hstack in their default behavior, but

allow for an optional argument giving the number of the axis along which to concatenate.

See also:

hstack, vstack, column_stack, concatenate, c_, r_

2.3.3 Splitting one array into several smaller ones

Using hsplit, you can split an array along its horizontal axis, either by specifying the number of equally shaped

arrays to return, or by specifying the columns after which the division should occur:

>>> a = np.floor(10

np.random.random((2,12)))

>>> a

array([[ 9., 5., 6., 3., 6., 8., 0., 7., 9., 7., 2., 7.],

[ 1., 4., 9., 2., 2., 1., 0., 6., 2., 2., 4., 0.]])

>>> np.hsplit(a,3) # Split a into 3

[array([[ 9., 5., 6., 3.],

[ 1., 4., 9., 2.]]), array([[ 6., 8., 0., 7.],

[ 2., 1., 0., 6.]]), array([[ 9., 7., 2., 7.],

[ 2., 2., 4., 0.]])]

>>> np.hsplit(a,(3,4)) # Split a after the third and the fourth column

[array([[ 9., 5., 6.],

[ 1., 4., 9.]]), array([[ 3.],

[ 2.]]), array([[ 6., 8., 0., 7., 9., 7., 2., 7.],

[ 2., 1., 0., 6., 2., 2., 4., 0.]])]

vsplit splits along the vertical axis, and array_split allows one to specify along which axis to split.

2.4 Copies and Views

When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is

often a source of confusion for beginners. There are three cases:

2.4.1 No Copy at All

Simple assignments make no copy of array objects or of their data.

>>> a = np.arange(12)

>>> b = a # no new object is created

>>> b is a # a and b are two names for the same ndarray object

True

>>> b.shape = 3,4 # changes the shape of a

2.4. Copies and Views 15

剩余134页未读，继续阅读

k_shmily

粉丝: 79
资源: 102

NumPy用户指南：设置、快速入门和高级使用

无密码解压指南：numpy-1.26.3-cp311-cp311-win_amd64.whl.zip文件

深度学习利器：下载numpy-1.26.4版本

全新Python库文件numpy-1.10.4-cp35-none-win32.whl发布

numpy-ref-1.11.0.pdf

numpy-1.11.0.tar.gz

numpy-user-1.17.0.pdf

numpy-user-guide-1.17.0.pdf

tensorflow-auto-detect-1.11.0.tar.gz

Numerical Python numpy-1.11.0.zip

numpy-1.9.2-win64.zip

最新资源