Python：Numpy学习

 import numpy as np
 # 基础属性
 array = np.array([[[1,2,3], [0,0,1]], [[1,2,3], [0,0,1]]],
                  dtype = np.int64)

 print(array)
 print(array.ndim)  # number of dim
 print(array.shape) # shape
 print(array.size)  # number of elements
 print(array.dtype)

 # 创建array
 a = np.array([1,2,3,4]) # 1 dim

 b = np.array([[1,2,3,4]]) # row vector, 2 dim
 c = np.array([[1], [2], [3] ,[4]]) # column vector, 2 dim
 print(a.shape, b.shape, c.shape)

 a = np.zeros( (2,3), dtype = np.float)
 a = np.ones( (2,3), dtype = np.float)
 a = np.empty( (2,3), dtype = np.float)
 a = np.arange(10, 20) # alike function range
 a = np.linspace(1, 10, 5) # interval
 print(a)

 # 基础运算（向量式运算）
 '''向量'''
 a = np.array([10, 20, 30, 40])
 b = np.arange(4)
 print( a + b)
 print( a**2)
 print( a < 20) 

 '''矩阵'''
 a = np.array([[1,1],
               [0,1]])
 b = np.arange(4).reshape((2,2))
 print( a*b )
 print( np.dot(a, b) ) # equal a.dot(b)

 print(np.argmax(a))
 print(np.argmin(a)) 

 A = np.arange(14, 2, -1).reshape((3,4))
 print(np.clip(A, 5, 9))

 '''随机数''' # module: np.random
 a = np.random.random((2,4))
 print(a)
 print(np.sum(a, axis = 1))
 print(np.min(a, axis = 0))

 # 索引
 '''一维array'''
 A = np.arange(3, 15)
 print(A[2])
 print(A[0:5:2])

 '''二维array'''
 A = np.arange(3, 15).reshape(3, 4)
 print(A[2])
 print(A[2,:])

 print(A[2][1])
 print(A[2, 1])

 # array合并
 A = np.array([1,1,1])
 B = np.array([2,2,2])

 print(np.vstack((A,B))) # vertival stack
 print(np.hstack((A,B))) # horizontal stack

 A[np.newaxis, :] # 1 * 3
 A[:, np.newaxis] # 3 * 1

 a = np.array([[1, 2], [3, 4]])
 b = np.array([[5, 6]])

 # array分割
 A = np.arange(12).reshape((3,4))

 print(np.split(A, 2, axis = 1))
 print(np.array_split(A, 3, axis = 1))
 print(np.split(A, 3, axis = 0))

 print(np.vsplit(A, 3))
 print(np.hsplit(A, 2))

 # copy and deep copy
 a = np.array([1,2,3,10])
 b = a
 c = a
 d = b

 b = a.copy()
 a[3] = 44
 print(a)
 print(b)