电子商务数据挖掘python案例_吴裕雄 数据挖掘与分析案例实战（3）——python数值计算工具：Numpy...

# 导入模块，并重命名为np

import numpy as np

# 单个列表创建一维数组

arr1 = np.array([3,10,8,7,34,11,28,72])

print('一维数组：n',arr1)

# 一维数组元素的获取

print(arr1[[2,3,5,7]])

# 嵌套元组创建二维数组

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)))

print('二维数组：n',arr2)

# 二维数组元素的获取

# 第2行第3列元素

print(arr2[1,2])

# 第3行所有元素

print(arr2[2,:])

# 第2列所有元素

print(arr2[:,1])

# 第2至4行，2至5行

print(arr2[1:4,1:5])

# 第一行、最后一行和第二列、第四列构成的数组

print(arr2[[0,-1],[1,3]])

# 第一行、最后一行和第一列、第三列、第四列构成的数组

print(arr2[[0,-1,0],[1,2,3]])

# 第一行、最后一行和第二列、第四列构成的数组

print(arr2[np.ix_([0,-1],[1,3])])

# 第一行、最后一行和第一列、第三列、第四列构成的数组

print(arr2[np.ix_([0,-1],[1,2,3])])

# 读入数据

stu_score = np.genfromtxt(fname = r'F:\python_Data_analysis_and_mining\04\stu_socre.txt',delimiter='t',skip_header=1)

# 查看数据结构

print(type(stu_score))

# 查看数据维数

print(stu_score.ndim)

# 查看数据行列数

print(stu_score.shape)

# 查看数组元素的数据类型

print(stu_score.dtype)

# 查看数组元素个数

print(stu_score.size)

arr3 = np.array([[1,5,7],[3,6,1],[2,4,8],[5,8,9],[1,5,9],[8,5,2]])

# 数组的行列数

print(arr3.shape)

# 使用reshape方法更改数组的形状

print(arr3.reshape(2,9))

# 打印数组arr3的行列数

print(arr3.shape)

arr4 = np.array([[1,10,100],[2,20,200],[3,30,300]])

print('原数组：n',arr4)

# 默认排序降维

print('数组降维：n',arr4.ravel())

print(arr4.flatten())

print(arr4.reshape(-1))

# 改变排序模式的降维

print(arr4.ravel(order = 'F'))

print(arr4.flatten(order = 'F'))

print(arr4.reshape(-1, order = 'F'))

# 更改预览值

arr4.flatten()[0] = 2000

print('flatten方法：n',arr4)

arr4.ravel()[1] = 1000

print('ravel方法：n',arr4)

arr4.reshape(-1)[2] = 3000

print('reshape方法：n',arr4)

arr4 = np.array([[1,10,100],[2,20,200],[3,30,300]])

arr5 = np.array([1,2,3])

print(arr4)

print(arr5)

print('vstack纵向合并数组：n',np.vstack([arr4,arr5]))

print('row_stack纵向合并数组：n',np.row_stack([arr4,arr5]))

arr6 = np.array([[5],[15],[25]])

print('hstack横向合并数组：n',np.hstack([arr4,arr6]))

print('column_stack横向合并数组：n',np.column_stack([arr4,arr6]))

print(arr4)

print('垂直方向计算数组的和：n',np.sum(arr4,axis = 0))

print('水平方向计算数组的和：n',np.sum(arr4, axis = 1))

# 加法运算

math = np.array([98,83,86,92,67,82])

english = np.array([68,74,66,82,75,89])

chinese = np.array([92,83,76,85,87,77])

tot_symbol = math+english+chinese

tot_fun = np.add(np.add(math,english),chinese)

print('符号加法：n',tot_symbol)

print('函数加法：n',tot_fun)

# 除法运算

height = np.array([165,177,158,169,173])

weight = np.array([62,73,59,72,80])

BMI_symbol = weight/(height/100)**2

BMI_fun = np.divide(weight,np.divide(height,100)**2)

print('符号除法：n',BMI_symbol)

print('函数除法：n',BMI_fun)

arr7 = np.array([[1,2,10],[10,8,3],[7,6,5]])

arr8 = np.array([[2,2,2],[3,3,3],[4,4,4]])

print('数组arr7：n',arr7)

print('数组arr8：n',arr8)

# 求余数

print('计算余数：n',arr7 % arr8)

# 求整除

print('计算整除：n',arr7 // arr8)

# 求指数

print('计算指数：n',arr7 ** arr8)

print(np.modf(arr7/arr8))

# 整除部分

print(np.modf(arr7/arr8)[1])

arr7 = np.array([[1,2,10],[10,8,3],[7,6,5]])

arr8 = np.array([[2,2,2],[3,3,3],[4,4,4]])

print('数组arr7：n',arr7)

print('数组arr8：n',arr8)

# 取子集

# 从arr7中取出arr7大于arr8的所有元素

print('满足条件的二维数组元素获取：n',arr7[arr7>arr8])

# 从arr9中取出大于10的元素

arr9 = np.array([3,10,23,7,16,9,17,22,4,8,15])

print('满足条件的一维数组元素获取：n',arr9[arr9>10])

# 判断操作

# 将arr7中大于7的元素改成5，其余的不变

print('二维数组的条件操作：n',np.where(arr7>7,5,arr7))

# 将arr9中大于10 的元素改为1，否则改为0

print('一维数组的条件操作：n',np.where(arr9>10,1,0))

# 各输入数组维度一致，对应维度值相等

arr10 = np.arange(12).reshape(3,4)

print(arr10)

arr11 = np.arange(101,113).reshape(3,4)

print(arr11)

print('3×4的二维矩阵运算：n',arr10 + arr11)

# 各输入数组维度不一致，对应维度值相等

arr12 = np.arange(60).reshape(5,4,3)

print(arr12)

arr10 = np.arange(12).reshape(4,3)

print(arr10)

print('维数不一致，但末尾的维度值一致：n',arr12 + arr10)

# 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

arr12 = np.arange(60).reshape(5,4,3)

print(arr12)

arr13 = np.arange(4).reshape(4,1)

print(arr13)

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)

# 加1补齐

arr14 = np.array([5,15,25])

print('arr14的维度自动补齐为(1,3)：n',arr10 + arr14)

# 一维数组的点积

vector_dot = np.dot(np.array([1,2,3]), np.array([4,5,6]))

print('一维数组的点积：n',vector_dot)

# 二维数组的乘法

print('两个二维数组：')

print(arr10)

print(arr11)

arr2d = np.dot(arr10,arr11)

print('二维数组的乘法：n',arr2d)

import numpy as np

# diag的使用

arr15 = np.arange(16).reshape(4,-1)

print('4×4的矩阵：n',arr15)

print('取出矩阵的主对角线元素：n',np.diag(arr15))

print('由一维数组构造的方阵：n',np.diag(np.array([5,15,25])))

print('由一维数组构造的方阵：n',np.diag(np.array(np.diag(arr15))))

# 计算方阵的特征向量和特征根

arr16 = np.array([[1,2,5],[3,6,8],[4,7,9]])

print(arr16)

a,b = np.linalg.eig(arr16)

print('求解结果为：n',a,b)

# 计算偏回归系数

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]])

print(X)

print(X.shape)

Y = np.array([3.2,3.8,3.7,4.3,4.4,5.2,6.7,4.8,4.2,5.1])

print(Y)

print(Y.shape)

X_trans_X_inverse = np.linalg.inv(np.dot(np.transpose(X),X))

print(X_trans_X_inverse)

beta = np.dot(np.dot(X_trans_X_inverse,np.transpose(X)),Y)

print('偏回归系数为：n',beta)

# 多元线性方程组

A = np.array([[3,2,1],[2,3,1],[1,2,3]])

print(A)

b = np.array([39,34,26])

print(b)

X = np.linalg.solve(A,b)

print('三元一次方程组的解：n',X)

# 范数的计算

arr17 = np.array([1,3,5,7,9,10,-12])

print(arr17)

# 一范数

res1 = np.linalg.norm(arr17, ord = 1)

print('向量的一范数：n',res1)

# 二范数

res2 = np.linalg.norm(arr17, ord = 2)

print('向量的二范数：n',res2)

# 无穷范数

res3 = np.linalg.norm(arr17, ord = np.inf)

print('向量的无穷范数：n',res3)

import seaborn as sns

import matplotlib.pyplot as plt

from scipy import stats

# 生成各种正态分布随机数

np.random.seed(1234)

rn1 = np.random.normal(loc = 0, scale = 1, size = 1000)

print(rn1.shape)

rn2 = np.random.normal(loc = 0, scale = 2, size = 1000)

print(rn2.shape)

rn3 = np.random.normal(loc = 2, scale = 3, size = 1000)

print(rn3.shape)

rn4 = np.random.normal(loc = 5, scale = 3, size = 1000)

print(rn4.shape)

# 绘图

plt.style.use('ggplot')

sns.distplot(rn1, hist = False, kde = False, fit = stats.norm, fit_kws = {'color':'black','label':'u=0,s=1','linestyle':'-'})

# 呈现图例

plt.legend()

plt.show()

sns.distplot(rn2, hist = False, kde = False, fit = stats.norm, fit_kws = {'color':'red','label':'u=0,s=2','linestyle':'--'})

# 呈现图例

plt.legend()

plt.show()

sns.distplot(rn3, hist = False, kde = False, fit = stats.norm, fit_kws = {'color':'blue','label':'u=2,s=3','linestyle':':'})

# 呈现图例

plt.legend()

plt.show()

sns.distplot(rn4, hist = False, kde = False, fit = stats.norm, fit_kws = {'color':'purple','label':'u=5,s=3','linestyle':'-.'})

# 呈现图例

plt.legend()

plt.show()

import seaborn as sns

import matplotlib.pyplot as plt

from scipy import stats

# 生成各种正态分布随机数

np.random.seed(1234)

rn1 = np.random.normal(loc = 0, scale = 1, size = 1000)

print(rn1.shape)

rn2 = np.random.normal(loc = 0, scale = 2, size = 1000)

print(rn2.shape)

rn3 = np.random.normal(loc = 2, scale = 3, size = 1000)

print(rn3.shape)

rn4 = np.random.normal(loc = 5, scale = 3, size = 1000)

print(rn4.shape)

# 绘图

plt.style.use('ggplot')

sns.distplot(rn1, hist = False, kde = False, fit = stats.norm, fit_kws = {'color':'black','label':'u=0,s=1','linestyle':'-'})

sns.distplot(rn2, hist = False, kde = False, fit = stats.norm, fit_kws = {'color':'red','label':'u=0,s=2','linestyle':'--'})

sns.distplot(rn3, hist = False, kde = False, fit = stats.norm, fit_kws = {'color':'blue','label':'u=2,s=3','linestyle':':'})

sns.distplot(rn4, hist = False, kde = False, fit = stats.norm, fit_kws = {'color':'purple','label':'u=5,s=3','linestyle':'-.'})

# 呈现图例

plt.legend()

plt.show()

# 生成各种指数分布随机数

np.random.seed(1234)

re1 = np.random.exponential(scale = 0.5, size = 1000)

re2 = np.random.exponential(scale = 1, size = 1000)

re3 = np.random.exponential(scale = 1.5, size = 1000)

print(re1.shape)

print(re2.shape)

print(re3.shape)

# 绘图

sns.distplot(re1, hist = False, kde = False, fit = stats.expon,

fit_kws = {'color':'black','label':'lambda=0.5','linestyle':'-'})

sns.distplot(re2, hist = False, kde = False, fit = stats.expon,

fit_kws = {'color':'red','label':'lambda=1','linestyle':'--'})

sns.distplot(re3, hist = False, kde = False, fit = stats.expon,

fit_kws = {'color':'blue','label':'lambda=1.5','linestyle':':'})

# 呈现图例

plt.legend()

# 呈现图形

plt.show()