update

language_learning/2022.03.16_frequently_used_python_package/cmath_example.py

@@ -0,0 +1,2 @@
+import cmath
+print(cmath.exp(1j*cmath.pi))

language_learning/2022.03.16_frequently_used_python_package/copy_example.py

@@ -0,0 +1,26 @@
+import copy
+import numpy as np
+
+array_1 = [1, 2, 3]
+array_2 = array_1
+array_1[0] = 100
+print('array_1=', array_1)
+print('array_2=', array_2, '\n')
+
+array_1 = np.array([1, 2, 3])
+array_2 = array_1
+array_1[0] = 100
+print('array_1=', array_1)
+print('array_2=', array_2, '\n')
+
+array_1 = [1, 2, 3]
+array_2 = copy.deepcopy(array_1)
+array_1[0] = 100
+print('array_1=', array_1)
+print('array_2=', array_2, '\n')
+
+array_1 = np.array([1, 2, 3])
+array_2 = copy.deepcopy(array_1)
+array_1[0] = 100
+print('array_1=', array_1)
+print('array_2=', array_2)

language_learning/2022.03.16_frequently_used_python_package/functools_example.py

@@ -0,0 +1,7 @@
+import functools
+
+def func(x, y, z):
+    return x-y+z
+
+partial_func = functools.partial(func, x=5, z=0)
+print(partial_func(y=2))

language_learning/2022.03.16_frequently_used_python_package/math_example.py

@@ -0,0 +1,13 @@
+import math
+print(math.pi)
+print(math.e)
+print(math.exp(1))
+print(math.cos(math.pi))
+print(math.sqrt(2), '\n')
+
+import numpy as np
+print(np.pi)
+print(np.e)
+print(np.exp(1))
+print(np.cos(np.pi))
+print(np.sqrt(2))

language_learning/2022.03.16_frequently_used_python_package/matplotlib_example.py

@@ -0,0 +1,8 @@
+import matplotlib.pyplot as plt
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(range(10), range(10))
+ax.set_title('Example', fontsize=20, fontfamily='Times New Roman')
+ax.set_xlabel('x', fontsize=20, fontfamily='Times New Roman') 
+ax.set_ylabel('y', fontsize=20, fontfamily='Times New Roman')
+plt.show()

language_learning/2022.03.16_frequently_used_python_package/multiprocessing_example.py

@@ -0,0 +1,23 @@
+from multiprocessing import Process
+import time
+
+def f(name):
+    time.sleep(5)
+    print('Hello', name)
+
+if __name__ == '__main__':
+    start_time = time.time()
+    p1 = Process(target=f, args=('Bob',))
+    p2 = Process(target=f, args=('Alice',))
+    p1.start()
+    p2.start()
+    p1.join()
+    p2.join()
+    end_time = time.time()
+    print(end_time - start_time, '\n')
+
+    start_time = time.time()
+    f('Bob')
+    f('Alice')
+    end_time = time.time()
+    print(end_time - start_time)

language_learning/2022.03.16_frequently_used_python_package/numpy_example.py

@@ -0,0 +1,21 @@
+import numpy as np
+np.zeros((2, 3)) # 零矩阵
+np.identity(3) # 单位矩阵
+np.diag([1, 3, 5])  # 对角矩阵
+matrix1 = np.array([[3, 5+1j], [2, 7]]) # numpy矩阵
+matrix1.shape # 矩阵的维度
+matrix1.transpose() # 矩阵转置
+matrix1.conj() # 矩阵所有元素共轭
+np.conj(matrix1) # 矩阵所有元素共轭（同上）
+np.arange(1, 5, 1) # 数列（左闭右开）
+np.linspace(-2, 2, 5) # 数列（左闭右闭）
+np.random.uniform(-2, 2) # 随机数
+np.random.randint(0, 2)  # 随机整数（左闭右开）
+np.sort([1, 7, 0, 3])  # 排列
+np.argsort([1, 7, 0, 3]) # 排列索引
+np.linalg.det(matrix1)  # 行列式
+matrix2 = np.linalg.inv(matrix1)  # 求逆
+np.matmul(matrix1, matrix2) # 矩阵乘积
+np.dot(matrix1, matrix2) # 矩阵乘积（同上）
+eigenvalue, eigenvector = np.linalg.eig(matrix1)  # 求本征值，本征向量
+matrix3 = np.append(matrix1, matrix2, axis=0) # 增加数组元素或者矩阵的行

language_learning/2022.03.16_frequently_used_python_package/os_example.py

@@ -0,0 +1,6 @@
+import os
+os.getcwd()  # 获取路径
+if os.path.exists('new_dir') == False: # 判断路径是否存在
+    os.makedirs('new_dir') # 新建文件夹
+os.chdir('new_dir')  # 切换到该文件夹
+print(os.walk('/'))  # 游走目录

language_learning/2022.03.16_frequently_used_python_package/time_example.py

@@ -0,0 +1,5 @@
+import time
+start_time = time.time()
+time.sleep(5)
+end_time = time.time()
+print(end_time-start_time)