numpy简单入门学习

港控/mmm° 2022-07-21 12:23 199阅读 0赞

为了快速的学习numpy，只要参阅了官网的快速入门教程进行学习，官网的网址：[https://docs.scipy.org/doc/numpy-dev/user/quickstart.html][https_docs.scipy.org_doc_numpy-dev_user_quickstart.html]。虽然和matlab的操作大同小异，但是还是需要很多明确的python的概念，比如序列，列表以及元组的概念，当然这也是python里面需要注意最多的基本的数据类型。现将学习的基本过程叙述如下：

**numpy**是通过python语言实现的用于科学研究中的计算。可以方便的进行代数计算、傅里叶计算等。这是Numpy学习的官方网站：[http://www.numpy.org/][http_www.numpy.org]。所以在进行numpy的学习之前，确保你有一定的python基础，能够理解序列、列表以及元组的概念。

## numpy的基本概念 === array ##

在numpy的主要对象是**array**,这里需要与python中的序列、列表以及元组区分一下，在python中，列表等元素的数据类型是可以不一样的，但是在array中的数据类型是一致的。array具有多维性。array的元素可以通过索引的方式进行访问。在Numpy中，array的维度称为**axes**。axes的大小称为rank。列如array\[1,2,1\]，它的维度为1，rank的值为1，因为只有一维。例如以下array,rank的大小为2，第一维度或者axes的的长度为2，第二维度或者axes的长度为3，他们的索引分别为0和1.

![20160715145749175][]

array的重要属性如下：（注释：在numpy,array类声明为ndarray,但是为我们所熟知的这是ndarray的别名，array）

*  **ndarray.ndims**

axes的大小，在python中，被定义为rank

*  **ndarray.shape**

array的维度表示，它是一个元组，通过整数类型表征的，列如对于一个矩阵而言，有n行m列，那么它的shape则是(n,m)。

*  **ndarray.size**

array元素的个数，它等于shape中元素的乘积

*  **ndarray.dtype**

array元素的类型，在numpy中，基本的类型包括int32，int16以及float64等，当然也可以自定义数据类型

*  **ndarray.itemsize**

array中数据类型的大小，以字节为单位表征。列如int32类型的元素，itemsize的大小这位4

*  **ndarray.data**

包含array的数据的缓冲区，但是这个属性并不常用，更加常用的是通过索引访问元素

![20160715151504691][]

## array的创建 ##

array可以通过python的列表或者元组创建，通过array函数进行创建，但是不能以多个单独的元素进行创建。

![20160715152050348][]

二维array的创建可以通过序列的序列进行创建，三维的array则可以通过序列的序列的序列进行创建

![20160715152437617][]

当然，在最开始就可以指明序列元素的类型：

![20160715152637372][]

此外，还可以通过特定的函数进行创建，比如zeros函数是创建元素均为0的元素，ones创建元素均为1的函数，empty函数创建一个空的array，所有函数的默认类型为float64。

![20160715153617343][]

如果需要创建一序列的数字，可以通过arrange和linspace创建，arange和linspace的不同在于，arrage是指明了数字序列的上界（不包含上界）以及步长，而linspace则指定了在上界和下界之间的元素的个数，包含上界：

![20160715154307925][]

## array的基本操作 ##

array的算术操作是面向元素的。包含加法，减法以及乘法以及在python中称为Universal Function的函数，例如sin,cos以及sqrt等

![20160715154916059][]

array的乘法需要主要的是：在面临矩阵的运算时，**\***一般意义上是矩阵乘积，而**.\***则是面向元素的乘积（在Matlab中）。但是在numpy中**\***是面向元素乘积的，而矩阵乘积则是需要通过函数**dot**实现。

![20160715155348533][]

此外还有自更新运算\*=记忆+=等，注意没有++运算

获取array的最大值，最小值，max以及min，可以指定列或者行得到每行或者每列的最大最小值。

![20160715155944508][]

## array的迭代，索引以及切片访问 ##

熟悉列表等的应该会比较熟悉索引或者切片操作，这里就不详细介绍了，这里主要介绍迭代操作访问。

迭代访问仅仅适用于行迭代。

![20160715160906184][]

## array的堆积方式 ##

在行或者列的值大小相同时，可以采取行堆积或者列堆积，从而组成一个新的array。注意行或者列的大小一定要匹配，或者堆积不能执行。

![20160715161830937][]

大致内容介绍到这里。此外，以下是应用比较多的函数列表：

Array Creation

[arange][], [array][], [copy][], [empty][], [empty\_like][empty_like], [eye][], [fromfile][], [fromfunction][], [identity][], [linspace][], [logspace][], [mgrid][], [ogrid][], [ones][], [ones\_like][ones_like], *r*, [zeros][], [zeros\_like][zeros_like]

Conversions

[ndarray.astype][], [atleast\_1d][atleast_1d], [atleast\_2d][atleast_2d], [atleast\_3d][atleast_3d], [mat][]

Manipulations

[array\_split][array_split], [column\_stack][column_stack], [concatenate][], [diagonal][], [dsplit][], [dstack][], [hsplit][], [hstack][], [ndarray.item][], [newaxis][], [ravel][], [repeat][], [reshape][], [resize][], [squeeze][], [swapaxes][], [take][], [transpose][], [vsplit][], [vstack][]

Questions

[all][], [any][], [nonzero][], [where][]

Ordering

[argmax][], [argmin][], [argsort][], [max][], [min][], [ptp][], [searchsorted][], [sort][]

Operations

[choose][], [compress][], [cumprod][], [cumsum][], [inner][], [ndarray.fill][], [imag][], [prod][], [put][], [putmask][], [real][], [sum][]

Basic Statistics

[cov][], [mean][], [std][], [var][]

Basic Linear Algebra

[cross][], [dot][], [outer][], [linalg.svd][], [vdot][]

[https_docs.scipy.org_doc_numpy-dev_user_quickstart.html]: https://docs.scipy.org/doc/numpy-dev/user/quickstart.html
[http_www.numpy.org]: http://www.numpy.org/
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