221 lines
8.3 KiB
Python
221 lines
8.3 KiB
Python
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# 导入模块，并重命名为np
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import numpy as np
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# 单个列表创建一维数组
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arr1 = np.array([3,10,8,7,34,11,28,72])
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# 嵌套元组创建二维数组
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arr2 = np.array(((8.5,6,4.1,2,0.7),(1.5,3,5.4,7.3,9),(3.2,3,3.8,3,3),(11.2,13.4,15.6,17.8,19)))
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print('一维数组：\n',arr1)
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print('二维数组：\n',arr2)
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# 一维数组元素的获取
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print(arr1[[2,3,5,7]])
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# 二维数组元素的获取
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# 第2行第3列元素
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print("# 第2行第3列元素",arr2[1,2])
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# 第3行所有元素
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print("# 第3行所有元素",arr2[2,:])
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# 第2列所有元素
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print("# 第2列所有元素",arr2[:,1])
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# 第2至4行，2至5行
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print("# 第2至4行，2至5列",arr2[1:4,1:5])
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# 第一行、最后一行和第二列、第四列构成的数组
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print("# 第一行、最后一行和第二列、第四列构成的数组",arr2[[0,-1],[1,3]])
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# 第一行、最后一行和第一列、第三列、第四列构成的数组
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print("# 第一行、最后一行和第一列、第三列、第四列构成的数组",arr2[np.ix_([0,-1],[1,2,3])])
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# 第一行、最后一行和第二列、第四列构成的数组
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print("# 第一行、最后一行和第二列、第四列构成的数组",arr2[np.ix_([0,-1],[1,3])])
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# 第一行、最后一行和第一列、第三列、第四列构成的数组
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print("# 第一行、最后一行和第二列、第三列、第四列构成的数组",arr2[np.ix_([0,-1],[1,2,3])])
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# 读入数据
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stu_score = np.genfromtxt(fname = r'stu_socre.txt',delimiter='\t',skip_header=1)
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print("# 查看数据结构",type(stu_score))
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print("# 查看数据维数",stu_score.ndim)
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print("# 查看数据行列数",stu_score.shape)
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print("# 查看数组元素的数据类型",stu_score.dtype)
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print("# 查看数组元素个数",stu_score.size)
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arr3 = np.array([[1,5,7],[3,6,1],[2,4,8],[5,8,9],[1,5,9],[8,5,2]])
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print("# 数组的行列数",arr3.shape)
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print("# 使用reshape方法更改数组的形状",arr3.reshape(2,9))
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print("# 打印数组arr3的行列数",arr3.shape)
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print("# 使用resize方法更改数组的形状",arr3.resize(2,9))
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print("# 打印数组arr3的行列数",arr3.shape)
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arr4 = np.array([[1,10,100],[2,20,200],[3,30,300]])
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print('原数组：\n',arr4)
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# 默认排序降维
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print('数组降维：\n',arr4.ravel())
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print(arr4.flatten())
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print(arr4.reshape(-1))
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print("# 改变排序模式的降维",arr4.ravel(order = 'F'))
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print(arr4.flatten(order = 'F'))
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print(arr4.reshape(-1, order = 'F'))
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# 更改预览值
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arr4.flatten()[0] = 2000
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print('flatten方法：\n',arr4)
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arr4.ravel()[1] = 1000
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print('ravel方法：\n',arr4)
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arr4.reshape(-1)[4] = 3000
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print('reshape方法：\n',arr4)
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arr5 = np.array([1,2,3])
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print('vstack纵向合并数组：\n',np.vstack([arr4,arr5]))
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print('row_stack纵向合并数组：\n',np.row_stack([arr4,arr5]))
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arr6 = np.array([[5],[15],[25]])
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print('hstack横向合并数组：\n',np.hstack([arr4,arr6]))
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print('column_stack横向合并数组：\n',np.column_stack([arr4,arr6]))
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print(arr4)
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print('垂直方向计算数组的和：\n',np.sum(arr4,axis = 0))
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print('水平方向计算数组的和：\n',np.sum(arr4, axis = 1))
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# 加法运算
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math = np.array([98,83,86,92,67,82])
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english = np.array([68,74,66,82,75,89])
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chinese = np.array([92,83,76,85,87,77])
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tot_symbol = math+english+chinese
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tot_fun = np.add(np.add(math,english),chinese)
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print('符号加法：\n',tot_symbol)
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print('函数加法：\n',tot_fun)
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# 除法运算
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height = np.array([165,177,158,169,173])
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weight = np.array([62,73,59,72,80])
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BMI_symbol = weight/(height/100)**2
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BMI_fun = np.divide(weight,np.divide(height,100)**2)
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print('符号除法：\n',BMI_symbol)
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print('函数除法：\n',BMI_fun)
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arr7 = np.array([[1,2,10],[10,8,3],[7,6,5]])
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arr8 = np.array([[2,2,2],[3,3,3],[4,4,4]])
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print('数组arr7：\n',arr7)
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print('数组arr8：\n',arr8)
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# 求余数
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print('计算余数：\n',arr7 % arr8)
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# 求整除
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print('计算整除：\n',arr7 // arr8)
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# 求指数
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print('计算指数：\n',arr7 ** arr8)
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# 整除部分
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np.modf(arr7/arr8)[1]
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# 取子集
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# 从arr7中取出arr7大于arr8的所有元素
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print(arr7)
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print('满足条件的二维数组元素获取：\n',arr7[arr7>arr8])
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# 从arr9中取出大于10的元素
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arr9 = np.array([3,10,23,7,16,9,17,22,4,8,15])
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print('满足条件的一维数组元素获取：\n',arr9[arr9>10])
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# 判断操作
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# 将arr7中大于7的元素改成5，其余的不变
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print('二维数组的条件操作：\n',np.where(arr7>7,5,arr7))
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# 将arr9中大于10 的元素改为1，否则改为0
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print('一维数组的条件操作：\n',np.where(arr9>10,1,0))
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# 各输入数组维度一致，对应维度值相等
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arr10 = np.arange(12).reshape(3,4)
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arr11 = np.arange(101,113).reshape(3,4)
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print('3×4的二维矩阵运算：\n',arr10 + arr11)
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# 各输入数组维度不一致，对应维度值相等
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arr12 = np.arange(60).reshape(5,4,3)
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arr10 = np.arange(12).reshape(4,3)
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print('维数不一致，但末尾的维度值一致：\n',arr12 + arr10)
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# 各输入数组维度不一致，对应维度值不相等，但其中有一个为1
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arr12 = np.arange(60).reshape(5,4,3)
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arr13 = np.arange(4).reshape(4,1)
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print('维数不一致，维度值也不一致，但维度值至少一个为1：\n',arr12 + arr13)
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# 加1补齐
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arr14 = np.array([5,15,25])
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print('arr14的维度自动补齐为(1,3)：\n',arr10 + arr14)
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# 一维数组的点积
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vector_dot = np.dot(np.array([1,2,3]), np.array([4,5,6]))
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print('一维数组的点积：\n',vector_dot)
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# 二维数组的乘法
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print('两个二维数组：')
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print(arr10)
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print(arr11)
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arr2d = np.dot(arr10,arr11)
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print('二维数组的乘法：\n',arr2d)
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# diag的使用
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arr15 = np.arange(16).reshape(4,-1)
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print('4×4的矩阵：\n',arr15)
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print('取出矩阵的主对角线元素：\n',np.diag(arr15))
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print('由一维数组构造的方阵：\n',np.diag(np.array([5,15,25])))
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# 计算方阵的特征向量和特征根
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arr16 = np.array([[1,2,5],[3,6,8],[4,7,9]])
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print('计算3×3方阵的特征根和特征向量：\n',arr16)
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print('求解结果为：\n',np.linalg.eig(arr16))
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# 计算偏回归系数
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X = np.array([[1,1,4,3],[1,2,7,6],[1,2,6,6],[1,3,8,7],[1,2,5,8],[1,3,7,5],[1,6,10,12],[1,5,7,7],[1,6,3,4],[1,5,7,8]])
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Y = np.array([3.2,3.8,3.7,4.3,4.4,5.2,6.7,4.8,4.2,5.1])
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X_trans_X_inverse = np.linalg.inv(np.dot(np.transpose(X),X))
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beta = np.dot(np.dot(X_trans_X_inverse,np.transpose(X)),Y)
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print('偏回归系数为：\n',beta)
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# 多元线性方程组
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A = np.array([[3,2,1],[2,3,1],[1,2,3]])
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b = np.array([39,34,26])
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X = np.linalg.solve(A,b)
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print('三元一次方程组的解：\n',X)
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# 范数的计算
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arr17 = np.array([1,3,5,7,9,10,-12])
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# 一范数
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res1 = np.linalg.norm(arr17, ord = 1)
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print('向量的一范数：\n',res1)
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# 二范数
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res2 = np.linalg.norm(arr17, ord = 2)
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print('向量的二范数：\n',res2)
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# 无穷范数
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res3 = np.linalg.norm(arr17, ord = np.inf)
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print('向量的无穷范数：\n',res3)
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import seaborn as sns
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import matplotlib.pyplot as plt
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from scipy import stats
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# 生成各种正态分布随机数
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np.random.seed(1234)
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rn1 = np.random.normal(loc = 0, scale = 1, size = 1000)
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rn2 = np.random.normal(loc = 0, scale = 2, size = 1000)
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rn3 = np.random.normal(loc = 2, scale = 3, size = 1000)
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rn4 = np.random.normal(loc = 5, scale = 3, size = 1000)
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# 绘图
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plt.style.use('ggplot')
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sns.distplot(rn1, hist = False, kde = False, fit = stats.norm,
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fit_kws = {'color':'black','label':'u=0,s=1','linestyle':'-'})
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sns.distplot(rn2, hist = False, kde = False, fit = stats.norm,
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fit_kws = {'color':'red','label':'u=0,s=2','linestyle':'--'})
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sns.distplot(rn3, hist = False, kde = False, fit = stats.norm,
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fit_kws = {'color':'blue','label':'u=2,s=3','linestyle':':'})
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sns.distplot(rn4, hist = False, kde = False, fit = stats.norm,
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fit_kws = {'color':'purple','label':'u=5,s=3','linestyle':'-.'})
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# 呈现图例
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plt.legend()
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# 呈现图形
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plt.show()
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# 生成各种指数分布随机数
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np.random.seed(1234)
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re1 = np.random.exponential(scale = 0.5, size = 1000)
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re2 = np.random.exponential(scale = 1, size = 1000)
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re3 = np.random.exponential(scale = 1.5, size = 1000)
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# 绘图
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sns.distplot(re1, hist = False, kde = False, fit = stats.expon,
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fit_kws = {'color':'black','label':'lambda=0.5','linestyle':'-'})
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sns.distplot(re2, hist = False, kde = False, fit = stats.expon,
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fit_kws = {'color':'red','label':'lambda=1','linestyle':'--'})
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sns.distplot(re3, hist = False, kde = False, fit = stats.expon,
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fit_kws = {'color':'blue','label':'lambda=1.5','linestyle':':'})
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# 呈现图例
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plt.legend()
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# 呈现图形
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plt.show()
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