numpy常见属性、创建数组

 1 >>> import numpy as np #导入numpy模块，np是为了使用方便的简写
 2 >>> array = np.array([[1,2,3],[2,3,4]])  #列表转化为矩阵
 3 >>> print(array)
 4  [[1 2 3]
 5   [2 3 4]]
 6 >>>
 7 >>> print(‘number of dim:‘,array.ndim)  # 维度
 8 number of dim: 2
 9 >>> print(‘shape :‘,array.shape)    # 行数和列数
10 shape : (2, 3)
11 >>> print(‘size:‘,array.size)   # 元素个数
12 size: 6

 1 #创建数组
 2 >>> a = np.array([2,23,4])  # list 1d
 3 >>> print(a)
 4 [ 2 23  4]
 5 
 6 #指定类型
 7 >>> a = np.array([2,23,4],dtype=np.int)
 8 >>> print(a.dtype)
 9 int32
10 
11 >>> a = np.array([2,23,4],dtype=np.int32)
12 >>> print(a.dtype)
13 int32
14 
15 >>> a = np.array([2,23,4],dtype=np.float)
16 >>> print(a.dtype)
17 float64
18 
19 >>> a = np.array([2,23,4],dtype=np.float32)
20 >>> print(a.dtype)
21 float32
22 
23 #创建特定数据
24 >>> a = np.array([[2,23,4],[2,32,4]])  # 2d 矩阵 2行3列
25 >>> print(a)
26 [[ 2 23  4]
27  [ 2 32  4]]
28 
29 #创建全零数组
30 >>> a = np.zeros((3,4)) # 数据全为0，3行4列
31 >>> print(a)
32 [[0. 0. 0. 0.]
33  [0. 0. 0. 0.]
34  [0. 0. 0. 0.]]
35 
36 #创建全1数组
37 >>> a = np.ones((3,4),dtype = np.int)   # 数据为1，3行4列
38 >>> print(a)
39 [[1 1 1 1]
40  [1 1 1 1]
41  [1 1 1 1]]
42 
43 #创建全空数组, 其实每个值都是接近于零的数:
44 >>> a = np.empty((3,4)) # 数据为empty，3行4列
45 >>> print(a)
46 [[0. 0. 0. 0.]
47  [0. 0. 0. 0.]
48  [0. 0. 0. 0.]]
49 
50 #用 arange 创建连续数组:
51 >>> a = np.arange(10,20,2) # 10-19 的数据，2步长
52 >>> print(a)
53 [10 12 14 16 18]
54 
55 #使用 reshape 改变数据的形状
56 >>> a = np.arange(12).reshape((3,4))    # 3行4列，0到11
57 >>> print(a)
58 [[ 0  1  2  3]
59  [ 4  5  6  7]
60  [ 8  9 10 11]]
61 
62 #用 linspace 创建线段型数据:
63 >>> a = np.linspace(1,10,20)    # 开始端1，结束端10，且分割成20个数据，生成线段
64 >>> print(a)
65 [ 1.          1.47368421  1.94736842  2.42105263  2.89473684  3.36842105
66   3.84210526  4.31578947  4.78947368  5.26315789  5.73684211  6.21052632
67   6.68421053  7.15789474  7.63157895  8.10526316  8.57894737  9.05263158
68   9.52631579 10.        ]
69 
70 
71 #同样也能进行 reshape 工作:
72 >>> a = np.linspace(1,10,20).reshape((5,4)) # 更改shape
73 >>> print(a)
74 [[ 1.          1.47368421  1.94736842  2.42105263]
75  [ 2.89473684  3.36842105  3.84210526  4.31578947]
76  [ 4.78947368  5.26315789  5.73684211  6.21052632]
77  [ 6.68421053  7.15789474  7.63157895  8.10526316]
78  [ 8.57894737  9.05263158  9.52631579 10.        ]]