import numpy as np- Array 数组
print(np.zeros((2, 2))) # [[0. 0.] [0. 0.]]
print(np.ones((2, 2))) # [[1. 1.] [1. 1.]]
print(np.full((2, 2), 7)) # [[7 7] [7 7]]
print(np.eye(2)) # [[1. 0.] [0. 1.]]
print(np.random.random((2, 2))) # [[0.67151478 0.61234823] [0.85594251 0.0654221 ]]a = np.array(np.arange(9).reshape(3, 3))
print(a) # [[0 1 2] [3 4 5] [6 7 8]]
print(a[0, 0]) # 0
print(a[0][0]) # 0
print(type(a[0, 0])) # <class 'numpy.int64'>
print(type(a[0][0])) # <class 'numpy.int64'>
print(a[0, 0].shape) # ()
print(a[0][0].shape) # ()
print(a[1, :]) # [3 4 5]
print(a[1][:]) # [3 4 5]
print(type(a[1, :])) # <class 'numpy.ndarray'>
print(type(a[1][:])) # <class 'numpy.ndarray'>
print(a[1, :].shape) # (3,)
print(a[1][:].shape) # (3,)a = np.array(np.arange(9).reshape(3, 3))
print(a) # [[0 1 2] [3 4 5] [6 7 8]]
print(a[1, :]) # [3 4 5]
print(a[1:2, :]) # [[3 4 5]]
print(a[1, :].shape) # (3,)
print(a[1:2, :].shape) # (1, 3)
print(a[:, 1]) # [1 4 7]
print(a[:, 1:2]) # [[1] [4] [7]]
print(a[:, 1].shape) # (3,)
print(a[:, 1:2].shape) # (3, 1)a = np.array(np.arange(1, 7, 1).reshape(3, 2))
print(a) # [[1 2] [3 4] [5 6]]
print(a[[0, 1, 2], [0, 1, 0]]) # [1 4 5]
print(np.array([a[0, 0], a[1, 1], a[2, 0]])) # [1 4 5]
a = np.array(np.arange(1, 13, 1).reshape(3, 4))
print(a) # [[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]]
b = np.array([0, 2])
print(a[np.arange(1, 3, 1), b]) # [ 5 11]
a[np.arange(1, 3, 1), b] += 10
print(a) # [[ 1 2 3 4] [15 6 7 8] [ 9 10 21 12]]- bool 布尔
a = np.array(np.arange(1, 7, 1).reshape(3, 2))
print(a) # [[1 2] [3 4] [5 6]]
bool_idx = (a > 2)
print(bool_idx) # [[False False] [ True True] [ True True]]
print(a[bool_idx]) # [3 4 5 6]
print(a[a > 2]) # [3 4 5 6]- type 类型
x = np.array([1, 2])
print(x.dtype) # int64
x = np.array([1.0, 2.0])
print(x.dtype) # float64
x = np.array([1, 2], dtype=np.int8)
print(x.dtype) # int8- 基本运算
x = np.array(np.arange(1, 5, 1).reshape(2, 2), dtype=np.float64)
y = np.array(np.arange(5, 9, 1).reshape(2, 2), dtype=np.float64)
print(x) # [[1. 2.] [3. 4.]]
print(y) # [[5. 6.] [7. 8.]]
print(x + y)
print(np.add(x, y)) # [[ 6. 8.] [10. 12.]]
print(x - y)
print(np.subtract(x, y)) # [[-4. -4.] [-4. -4.]]
print(x * y)
print(np.multiply(x, y)) # [[ 5. 12.] [21. 32.]]
print(x / y)
print(np.divide(x, y)) # [[0.2 0.33333333] [0.42857143 0.5 ]]
print(np.sqrt(x)) # [[1. 1.41421356] [1.73205081 2. ]]- 矩阵乘法
x = np.array([[1, 2], [3, 4]])
y = np.array([[5, 6], [7, 8]])
v = np.array([9, 10])
w = np.array([11, 12])
print(v.dot(w))
print(np.dot(v, w)) # 219
print(x.dot(v))
print(np.dot(x, v)) # [29 67]
print(x.dot(y))
print(np.dot(x, y)) # [[19 22] [43 50]]- sum 求和
x = np.array([[1, 2], [3, 4]])
print(x) # [[1 2] [3 4]]
print(np.sum(x)) # 10
print(np.sum(x, axis=0)) # [4 6] Compute sum of each column
print(np.sum(x, axis=1)) # [3 7] Compute sum of each row- .T 转置
x = np.array([[1, 2], [3, 4]])
v = np.array([1, 2, 3])
print(x.T) # [[1 3] [2 4]] 2D array
print(v.T) # [1 2 3] 1D array- broadcasting 广播
x = np.array(np.arange(1, 13, 1).reshape(4, 3)) # [[ 1 2 3] [ 4 5 6] [ 7 8 9] [10 11 12]]
v = np.array([1, 0, 1])
y = np.empty_like(x)
print(y) # [[ 1 2 3] [ 4 5 6] [ 7 8 9] [10 11 12]]
for i in range(4):
y[i, :] = x[i, :] + v
print(y) # [[ 2 2 4] [ 5 5 7] [ 8 8 10] [11 11 13]]
x = np.array(np.arange(1, 13, 1).reshape(4, 3))
v = np.array([1, 0, 1])
vv = np.tile(v, (4, 1)) # [[1 0 1] [1 0 1] [1 0 1] [1 0 1]]
y = x + vv
print(y) # [[ 2 2 4] [ 5 5 7] [ 8 8 10] [11 11 13]]
y = x + v # broadcaste
print(y) # [[ 2 2 4] [ 5 5 7] [ 8 8 10] [11 11 13]]
v = np.array([1, 2, 3])
w = np.array([4, 5])
print(np.reshape(v, (3, 1)) * [0, 1, 2]) # [[0 1 2] [0 2 4] [0 3 6]] w has 3 numbers,so has 3 rows
x = np.array([[1, 2, 3], [4, 5, 6]])
print(x + v) # [[2 4 6] [5 7 9]]
print((x.T + w).T) # [[ 5 6 7] [ 9 10 11]]
print(x + np.reshape(w, (2, 1))) # [[ 5 6 7] [ 9 10 11]]
print(x * 2) # [[ 2 4 6] [ 8 10 12]]原文地址：https://www.cnblogs.com/yangzhaonan/p/10427462.html
时间： 2024-08-29 08:37:22