import numpy as np
arr1 = np.array([1, 2, 3, 4, 5, 6])
print(arr1)
arr2 = np.array([[1, 2, 3], [4, 5, 6]])
print(arr2)
arr3= np.array([1, 2, 3.5], dtype=np.float32) # 指定元素类型
print(arr3)
print(arr3.dtype)
# [1 2 3 4 5 6]
# [[1 2 3]
# [4 5 6]]
# float32
arr4 = np.zeros((3, 4)) # 3行4列全0的数组
print(arr4)
# [[0. 0. 0. 0.]
# [0. 0. 0. 0.]
# [0. 0. 0. 0.]]
arrr5 = np.ones((3, 4),dtype=int) # 3行4列全1的数组
print(arrr5)
# [[1 1 1 1]
# [1 1 1 1]
# [1 1 1 1]]
arr6 = np.empty((3, 4)) # 3行4列空数组，未初始化
# print(arr6)
# [[6.23042070e-307 3.56043053e-307 1.60219306e-306 2.44763557e-307]
# [1.69119330e-306 1.78020169e-306 8.90103560e-307 8.45599366e-307]
# [1.37962388e-306 8.90076398e-307 9.34562257e-307 3.22643519e-307]]
arr7 = np.full((3, 4), 100) # 3行4列全100的数组
print(arr7)
# [[100 100 100 100]
# [100 100 100 100]
# [100 100 100 100]]
print("-----------------------------------------")
arr8 = np.ones_like(arr1) # 与arr1形状相同的数组
print(arr8)
# [1 1 1 1 1 1]
print("-----------------------------------------")
arr9 = np.empty_like(arr6) # 与arr3形状相同的空数组
print(arr9)
# [[6.23042070e-307 3.56043053e-307 1.60219306e-306 2.44763557e-307]
# [1.69119330e-306 1.78020169e-306 8.90103560e-307 8.45599366e-307]
# [1.37962388e-306 8.90076398e-307 9.34562257e-307 4.89531867e-307]]
print("-----------------------------------------")
arr8 = np.full_like(arr8,100)
print(arr8)
# [100 100 100 100 100 100]
