# 每个元素加 1 print ("Adding 1 to every element:", a + 1) # 从每个元素中减去 2 print ("\nSubtracting 2 from each element:", b - 2) # 数组元素的总和执行一元运算 print ("\nSum of all array " "elements: ", a.sum()) # 添加两个数组执行二元运算 print ("\nArray sum:\n",...
When we provide a 1-dimensional array to Numpy square, np.square computes the square of every element. EXAMPLE 3: Compute the squares of values in a 2D array Finally, let’s use Numpy square on a 2-dimensional array. This really works the same as computing the values in a 1D array. ...
27, 64]) >>> # equivalent to a[0:6:2] = 1000; >>> # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512,
print('---') # numpy.zeros(shape, dtype=float, order='C'):返回给定形状和类型的新数组,用零填充。 # shape:数组纬度,二维以上需要用(),且输入参数为整数 # dtype:数据类型,默认numpy.float64 # order:是否在存储器中以C或Fortran连续(按行或列方式)存储多维数据。 ar3 = np.array([list(ran...
array([8,27,64])>>> a[:6:2] = -1000# equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -1000>>> a array([-1000,1, -1000,27, -1000,125,216,343,512,729])>>> a[ : :-1]# reversed a ...
arr / arr #Divides each element by itself 我们还可以对数组执行标量运算,NumPy 通过广播机制使其成为可能: arr + 50 #This adds 50 to every element in that array NumPy 还允许在数组上执行通用函数,如平方根函数、指数函数和三角函数等。 np.sqrt(arr) #Returns the square root of each element ...
>>> a = arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) >>> a[:6:2] = -1000 # equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -...
When we use a 2D NumPy array as the input, the np.sqrt function simply calculates the square root of every element of the array. The output of the function is simply an array of those calculated square roots, arranged in exactly the same shape as the input array. So if the input array...
print("Sqrt: ",np.sqrt(arr))#Returns the square root of each element print("Exp: ",np.exp(arr)) #Returns the exponentials of each element print("Sin: ",np.sin(arr)) #Returns the sin of each element print("Cos: ",np.cos(arr)) #Returns the cosine of each element ...
>>> a[:6:2] = -1000 # equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -1000 >>> a array([-1000, 1, -1000, 27, -1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125,...