Return the non-negative square-root of an array, element-wise. 8)numpy.square numpy.square(x, *args, **kwargs) Return the element-wise square of the input. 3.三角函数 1 )numpy.sin numpy.sin(x, *args, **kwargs) Trigonometric sine, element-wise. 2 )numpy.cos numpy.cos(x, *args,...
square() Return Value The square() function returns the array containing the element-wise squares of the input array. Example 1: Use of dtype Argument in square() import numpy as np # create an array array1 = np.array([1, 2, 3, 4]) # compute the square of array1 with different...
>>> A*B# elementwise productarray([[2,0], [0,4]]) >>> dot(A,B)# matrix productarray([[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...
hypot(x1, x2[, out]) 求直角三角形斜边 arctan2(x1, x2[, out]) Element-wise arc tangent of x1/x2 choosing the quadrant correctly. degrees(x[, out]) 弧度求角度 radians(x[, out]) 角度求弧度 unwrap(p[, discont, axis]) Unwrap by changing deltas between values to 2*pi complement....
numpy.sqrt(x, args, kwargs) Return the non-negative square-root of an array, element-wise. numpy.square numpy.square(x, args, kwargs) Return the element-wise square of the input. 2.2.2三角函数 numpy.sin numpy.sin(x, args, kwargs) Trigonometric sine, element-wise. ...
importnumpyasnp# 创建一个二维数组arr=np.array([[1,2],[3,4]])# 矩阵乘法result=np.dot(arr,arr)print("矩阵乘法:")print(result)# 转置transpose_arr=arr.Tprint("转置:")print(transpose_arr)# 元素级操作elementwise_square=np.square(arr)print("元素级操作(平方):")print(elementwise_square) ...
但是matrix的优势就是相对简单的运算符号,比如两个矩阵相乘,就是用符号*,但是array相乘不能这么用,得用方法.dot() array的优势就是不仅仅表示二维,还能表示3、4、5...维,而且在大部分Python程序里,array也是更常用的。 现在我们讨论numpy的多维数组
Return the non-negative square-root of an array, element-wise. numpy.square(x, *args, **kwargs) Return the element-wise square of the input. x = np.arange(1, 5) print(x) # [1 2 3 4] y = np.sqrt(x) print(y) # [1. 1.41421356 1.73205081 2. ] print(np.power(x, 0.5...
importnumpyasnpa=np.array([1,2,3])# Create a rank 1 arrayprint(type(a))# Prints "<class 'numpy.ndarray'>"print(a.shape)# Prints "(3,)"print(a[0],a[1],a[2])# Prints "1 2 3"a[0]=5# Change an element of the arrayprint(a)# Prints "[5, 2, 3]"b=np.array([[1,...
它也被别名array所知。注意,numpy.array并不等同于标准 Python 库的array.array类,后者只处理一维数组并提供较少的功能。ndarray对象的更重要的属性有: ndarray.ndim 数组的轴(维度)数量。 ndarray.shape 数组的维度。这是一个整数元组,指示每个维度上数组的大小。对于一个有n行和m列的矩阵,shape将是(n,m)。