NUMPY告一段落，接下來，進入pandas.

import numpy as np

# Numpy 線性代數運算
# Numpy 統計和數學函數

print(‘==========計算矩陣與其轉置矩陣的內積。===========‘)
X = np.arange(15).reshape((3, 5))
print(X)
print(X.T)
print(np.dot(X.T, X))
print(‘==========計算兩個一維數組的外積。===========‘)
arr1 = np.array([12, 43, 10], float)
arr2 = np.array([21, 42, 14], float)
 
print(np.outer(arr1, arr2))
print(‘==========計算兩個一維數組的內積。===========‘)
print(np.inner(arr1, arr2))
print(‘==========計算兩個一維數組的向量積。===========‘)
print(np.cross(arr1, arr2))
matrix = np.array([[74, 22, 10], [92, 31, 17], [21, 22, 12]], float)
print(matrix)
print(‘==========使用linalg子模塊det計算矩陣的行列式值。===========
 
‘)
print(np.linalg.det(matrix))
print(‘==========使用linalg子模塊inv生成逆矩陣。===========‘)
inv_matrix = np.linalg.inv(matrix)
print(inv_matrix)
print(‘==========計算矩陣和逆矩陣的內積。===========‘)
print(np.dot(inv_matrix, matrix))
print(‘==========使用linalg的eig計算矩陣的特征值和特征向量。===========‘)
vals, vecs = np.linalg.eig(matrix)
 
print(vals)
print(vecs)
arr = np.random.rand(8, 4)
print(‘==========計算均值。===========‘)
print(arr.mean())
print(np.mean(arr))
print(‘==========求和。===========‘)
print(arr.sum())

PS C:\test> & C:/Python37/python.exe c:/test/ml.py
==========計算矩陣與其轉置矩陣的內積。===========
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]]
[[ 0  5 10]
 [ 1  6 11]
 [ 2  7 12]
 [ 3  8 13]
 [ 4  9 14]]
[[125 140 155 170 185]
 [140 158 176 194 212]
 [155 176 197 218 239]
 [170 194 218 242 266]
 [185 212 239 266 293]]
==========計算兩個一維數組的外積。===========
[[ 252.  504.  168.]
 [ 903. 1806.  602.]
 [ 210.  420.  140.]]
==========計算兩個一維數組的內積。===========
2198.0
==========計算兩個一維數組的向量積。===========
[ 182.   42. -399.]
[[74. 22. 10.]
 [92. 31. 17.]
 [21. 22. 12.]]
==========使用linalg子模塊det計算矩陣的行列式值。===========
-2852.000000000003
==========使用linalg子模塊inv生成逆矩陣。===========
[[ 0.00070126  0.01542777 -0.02244039]
 [ 0.26192146 -0.23772791  0.11851332]
 [-0.48141655  0.4088359  -0.09467041]]
==========計算矩陣和逆矩陣的內積。===========
[[ 1.00000000e+00  1.66533454e-16  5.55111512e-17]
 [-2.66453526e-15  1.00000000e+00  2.22044605e-16]
 [-2.44249065e-15  4.44089210e-16  1.00000000e+00]]
==========使用linalg的eig計算矩陣的特征值和特征向量。===========
[107.99587441  11.33411853  -2.32999294]
[[-0.57891525 -0.21517959  0.06319955]
 [-0.75804695  0.17632618 -0.58635713]
 [-0.30036971  0.96052424  0.80758352]]
==========計算均值。===========
0.4850533513332038
0.4850533513332038
==========求和。===========
15.521707242662522

python---Numpy模塊中線性代數運算，統計和數學函數