基本运算 NumPy

今天药忘吃喽~ 2022-07-11 04:26 221阅读 0赞

Python 2.7.11 (v2.7.11:6d1b6a68f775, Dec  5 2015, 20:32:19) \[MSC v.1500 32 bit (  
Intel)\] on win32  
Type "help", "copyright", "credits" or "license" for more information.  
>>> from numpy import \*  
>>> a = array( \[20,30,40,50\] )  
>>> b=array(\[50,40,30,20\])  
>>> a+b  
array(\[70, 70, 70, 70\])  
>>> a-b  
array(\[-30, -10,  10,  30\])  
>>> x=a+b  
>>> y=a-b  
>>> c=(x+y)/2  
>>> d=x-c  
>>> c  
array(\[20, 30, 40, 50\])  
>>> d  
array(\[50, 40, 30, 20\])  
>>> a  
array(\[20, 30, 40, 50\])  
>>> b  
array(\[50, 40, 30, 20\])  
>>> a+=b  
>>> a  
array(\[70, 70, 70, 70\])  
>>> a-=b  
>>> a  
array(\[20, 30, 40, 50\])  
>>> a = ones(3, dtype=int32)  
>>> a  
array(\[1, 1, 1\])  
>>> b = linspace(0,pi,3)  
>>> b  
array(\[ 0.        ,  1.57079633,  3.14159265\])  
>>> a.dtype  
dtype('int32')  
>>> a.dtype.name  
'int32'  
>>> b.dtype.name  
'float64'  
>>> c=a+b  
>>> c.dtype.name  
'float64'  
>>> c  
array(\[ 1.        ,  2.57079633,  4.14159265\])  
>>> d = exp(c\*1j)  
>>> d  
array(\[ 0.54030231+0.84147098j, -0.84147098+0.54030231j,  
       -0.54030231-0.84147098j\])  
>>> d.dtype.name  
'complex128'  
>>> a = random.random((2,3))  
>>> a  
array(\[\[ 0.36639821,  0.44035901,  0.74424943\],  
       \[ 0.74506957,  0.9945401 ,  0.59842882\]\])  
>>> a.sum()  
3.8890451481386368  
>>> a.min()  
0.36639821076181855  
>>> a.max()  
0.99454010014688865  
>>> b = arange(12).reshape(3,4)  
>>> b  
array(\[\[ 0,  1,  2,  3\],  
       \[ 4,  5,  6,  7\],  
       \[ 8,  9, 10, 11\]\])  
>>> b.sum()  
66  
>>> b.sum(0)  
array(\[12, 15, 18, 21\])  
>>> b.sum(1)  
array(\[ 6, 22, 38\])  
>>> b.min(0)  
array(\[0, 1, 2, 3\])  
>>> b.min(1)  
array(\[0, 4, 8\])  
>>>

### 基本运算 ###

数组的算术运算是按元素的。新的数组被创建并且被结果填充。

>>> a = array( [20,30,40,50] ) >>> b = arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> b**2 array([0, 1, 4, 9]) >>> 10*sin(a) array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854]) >>> a<35 array([True, True, False, False], dtype=bool)

不像许多矩阵语言，NumPy中的乘法运算符 `*` 指示按元素计算，矩阵乘法可以使用 `dot` 函数或创建矩阵对象实现(参见教程中的矩阵章节)

>>> A = array( [[1,1], ... [0,1]] )
    >>> B = array( [[2,0], ... [3,4]] )
    >>> A*B                         # elementwise product
    array([[2, 0], [0, 4]])
    >>> dot(A,B)                    # matrix product
    array([[5, 4], [3, 4]])

有些操作符像 `+=` 和 `*=` 被用来更改已存在数组而不创建一个新的数组。

>>> a = ones((2,3), dtype=int) >>> b = random.random((2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[ 3.69092703, 3.8324276 , 3.0114541 ], [ 3.18679111, 3.3039349 , 3.37600289]]) >>> a += b # b is converted to integer type >>> a array([[6, 6, 6], [6, 6, 6]])

当运算的是不同类型的数组时，结果数组和更普遍和精确的已知(这种行为叫做upcast)。

>>> a = ones(3, dtype=int32) >>> b = linspace(0,pi,3) >>> b.dtype.name 'float64' >>> c = a+b >>> c array([ 1. , 2.57079633, 4.14159265]) >>> c.dtype.name 'float64' >>> d = exp(c*1j) >>> d array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j, -0.54030231-0.84147098j]) >>> d.dtype.name 'complex128' 许多非数组运算，如计算数组所有元素之和，被作为ndarray类的方法实现 >>> a = random.random((2,3)) >>> a array([[ 0.6903007 , 0.39168346, 0.16524769], [ 0.48819875, 0.77188505, 0.94792155]]) >>> a.sum() 3.4552372100521485 >>> a.min() 0.16524768654743593 >>> a.max() 0.9479215542670073

这些运算默认应用到数组好像它就是一个数字组成的列表，无关数组的形状。然而，指定 `axis` 参数你可以吧运算应用到数组指定的轴上：

>>> b = arange(12).reshape(3,4) >>> b array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> b.sum(axis=0) # sum of each column array([12, 15, 18, 21]) >>> >>> b.min(axis=1) # min of each row array([0, 4, 8]) >>> >>> b.cumsum(axis=1) # cumulative sum along each row array([[ 0, 1, 3, 6], [ 4, 9, 15, 22], [ 8, 17, 27, 38]])