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,
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 -1000>>> aarray([-1000, 1, -1000, 27, -1000, 125, 216, 343, 512, 729])>>> a[...
>>> a = arange(10)**3>>> aarray([ 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 ...
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 np.exp(arr) #Returns the exponentials of each element np.sin(arr) #Returns the sin of each element np.c...
>>> b =array( [ (1.5,2,3), (4,5,6) ] ) >>> barray([[1.5,2. ,3. ], [4. ,5. ,6. ]]) 数组类型可以在创建时显示指定 >>> c =array( [ [1,2], [3,4] ], dtype=complex ) >>> carray([[1.+0.j,2.+0.j], ...
array([1, 2, 3, 4, 5, 6, 7]) In [2]: # 创建数组:array()函数,括号内可以是列表、元组、数组、生成器等 ar1 = np.array(range(10)) # 整型 ar2 = np.array([1,2,3.14,4,5]) # 浮点型 ar3 = np.array([[1,2,3],('a','b','c')]) # 二维数组:嵌套序列(列表,元组...
I can then define a new array called z2, which is just z1 with one added to every single element of the array. 然后我可以定义一个名为z2的新数组,它只是z1,数组的每个元素都添加了一个。 We can now look at these two arrays to see what their contents are. 现在我们可以看看这两个数组,...
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 ...
注意numpy.array和标准Python库类array.array并不相同,后者只处理一维数组和提供少量功能。numpy 数组的属性ndarray.shape 数组的维度。这是一个指示数组在每个维度上大小的整数元组。例如一个n排m列的矩阵,它的shape属性将是(2,3),这个元组的长度显然是秩,即维度或者ndim属性 import numpy as np a = np.array(...
= -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, -1000, 27, -1000, 1, -1000]) >>> for i in a:...