>>> import numpy as np #导入numpy模块，np是为了使用方便的简写

 >>> array = np.array([[1,2,3],[2,3,4]])  #列表转化为矩阵

 >>> print(array)

  [[1 2 3]

   [2 3 4]]

 >>>

 >>> print('number of dim:',array.ndim)  # 维度

 number of dim: 2

 >>> print('shape :',array.shape)    # 行数和列数

 shape : (2, 3)

 >>> print('size:',array.size)   # 元素个数

 size: 6

 #创建数组

 >>> a = np.array([2,23,4])  # list 1d

 >>> print(a)

 [ 2 23  4]

 #指定类型

 >>> a = np.array([2,23,4],dtype=np.int)

 >>> print(a.dtype)

 int32

 >>> a = np.array([2,23,4],dtype=np.int32)

 >>> print(a.dtype)

 int32

 >>> a = np.array([2,23,4],dtype=np.float)

 >>> print(a.dtype)

 float64

 >>> a = np.array([2,23,4],dtype=np.float32)

 >>> print(a.dtype)

 float32

 #创建特定数据

 >>> a = np.array([[2,23,4],[2,32,4]])  # 2d 矩阵 2行3列

 >>> print(a)

 [[ 2 23  4]

  [ 2 32  4]]

 #创建全零数组

 >>> a = np.zeros((3,4)) # 数据全为0，3行4列

 >>> print(a)

 [[0. 0. 0. 0.]

  [0. 0. 0. 0.]

  [0. 0. 0. 0.]]

 #创建全1数组

 >>> a = np.ones((3,4),dtype = np.int)   # 数据为1，3行4列

 >>> print(a)

 [[1 1 1 1]

  [1 1 1 1]

  [1 1 1 1]]

 #创建全空数组, 其实每个值都是接近于零的数:

 >>> a = np.empty((3,4)) # 数据为empty，3行4列

 >>> print(a)

 [[0. 0. 0. 0.]

  [0. 0. 0. 0.]

  [0. 0. 0. 0.]]

 #用 arange 创建连续数组:

 >>> a = np.arange(10,20,2) # 10-19 的数据，2步长

 >>> print(a)

 [10 12 14 16 18]

 #使用 reshape 改变数据的形状

 >>> a = np.arange(12).reshape((3,4))    # 3行4列，0到11

 >>> print(a)

 [[ 0  1  2  3]

  [ 4  5  6  7]

  [ 8  9 10 11]]

 #用 linspace 创建线段型数据:

 >>> 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

 >>> print(a)

 [ 1.          1.47368421  1.94736842  2.42105263  2.89473684  3.36842105

   3.84210526  4.31578947  4.78947368  5.26315789  5.73684211  6.21052632

   6.68421053  7.15789474  7.63157895  8.10526316  8.57894737  9.05263158

   9.52631579 10.        ]

 #同样也能进行 reshape 工作:

 >>> a = np.linspace(1,10,20).reshape((5,4)) # 更改shape

 >>> print(a)

 [[ 1.          1.47368421  1.94736842  2.42105263]

  [ 2.89473684  3.36842105  3.84210526  4.31578947]

  [ 4.78947368  5.26315789  5.73684211  6.21052632]

  [ 6.68421053  7.15789474  7.63157895  8.10526316]

  [ 8.57894737  9.05263158  9.52631579 10.        ]]