《人工智能基础及应用（Python 微课版）》程序代码 第2章 数据处理与分析

第2章数据处理与分析【例2.1】array举例importnumpyasnpa=np.array([1,2,3])b=np.array([[1,2],[3,4]])#多于一个维度c=np.array([1,2,3,4,5],ndmin=2)#最小维度d=np.array([1,2,3],dtype=complex)#dtype参数print(a,b,c,d)【例2.2】查看ndarray对象举例importnumpyasnp#引入numpy库a2=np.array(([1,5,3,4,5],[6,2,7,9,5]))#创建二维的narray对象print(type(a2))#a的类型是数组print(a2)print(a2.dtype)#查看a5数组中每个元素的类型print(a2.shape)#查看数组的行列，返回行列的元组，5行5列print(a2.shape[1])#查看a5的列数print(a2.ndim)print(a2.T)#简单转置矩阵ndarray【例2.3】索引和切片举例importnumpyasnpa=np.array([[1,2,3,4,5],[6,7,8,9,10]])print(a)print(a[:])#选取全部元素print(a[1])#选取行为1的全部元素print(a[0:1])#截取[0,1)的元素print(a[1,2:5])#截取第2行第[2,5）的元素[8910]print(a[1,:])#截取第2行,返回[678910]print(a[1,2])#截取行号为1，列号为2的元素8print(a[1][2])#截取行号为1，列号为2的元素8,与上面的等价print(a[a>3])#截取矩阵a中大于3的数，范围的是一维数组b=np.arange(10)#[0123456789]x=slice(2,7,2)print(b[x])【例2.4】numpy.zeros举例importnumpyasnp#1)默认浮点x=np.zeros(5)print(x)#2)整数：下面三种写法方法1：y=np.zeros(5,dtype=int)#Python原生int方法2：y=np.zeros(5,dtype=_)#NumPy的默认整数类型（64位）方法3：y=np.zeros(5,dtype=32)#明确32位整数print(y)#3)自定义结构化dtypez=np.zeros((2,2),dtype=[('x','i4'),('y','i4')])print(z)【例2.5】numpy.ones举例importnumpyasnp#默认为浮点数x=np.ones(5)print(x)#自定义类型x=np.ones([2,2],dtype=int)print(x)【例2.6】numpy.diag举例importnumpyasnp#默认为浮点数x=np.diag([1,2,3])print(x)【例2.7】arange举例importnumpyasnpa=np.arange(10)#利用arange函数创建数组print(a)a5=np.arange(1,2,0.1)print(a5)【例2.8】linspace举例importnumpyasnpa=np.linspace(0,1,10)#从0开始到1结束，共10个数的等差数列print(a)【例2.9】logspace举例importnumpyasnpa=np.logspace(0,1,5)#生成首位是10的0次方，末位是10的1次方，含5个数的等比数列print(a)【例2.10】数组维度变换举例importnumpyasnpa=np.array([1,2,3,4,5,6])b=a.reshape(2,3)c=a.reshape((2,3))d=np.reshape(a,(2,3))print(a)#输出[123456]print(b)#输出[[123][456]]print(c)#输出[[123][456]]print(d)#输出[[123][456]]a1=np.array([[1,2,3],[4,5,6]])b1=a.flatten()c1=a.ravel()#多维转一维d1=a.reshape(-1)#参数为-1，表示数组的维度通过数据本身判断print(a1)#输出[[123][456]]print(b1)#输出[123456]print(c1)#输出[123456]print(d1)#输出[123456]a2=np.array([[1,2,3],[4,5,6]])b2=a.transpose()c2=a.Td2=a.swapaxes(0,1)e2=np.transpose(a,(1,0))print(a2)#输出[[123][[456]]print(b2)#输出[[14][25][36]]print(c2)#输出[[14][25][36]]print(d2)#输出[[14][25][36]]print(e2)#输出[[14][25][36]]【例2.11】数组拼接举例importnumpyasnpa=np.array([[1,2,3],[4,5,6]])b=np.arange(2,8).reshape(2,3)c1=np.concatenate([a,b],axis=0)c2=np.vstack([a,b])d1=np.concatenate([a,b],axis=1)d2=np.hstack([a,b])print(a)#输出[[123][456]]print(b)#输出[[234][567]]print(c1)#输出[[123][456][234][567]]print(c2)#输出[[123][456][234][567]]print(d1)#输出[[123234][456567]]print(d2)#输出[[123234][456567]]【例2.12】数组分割举例importnumpyasnpa=np.arange(1,19).reshape(6,3)b,c=np.split(a,[4],axis=0)d,e=np.vsplit(a,[4])print(a)#输出[[123][456][789][101112][131415][161718]]print(b)#前4个样本为1个数组[[123][456][789][101112]]print(c)#余下的样本为1个数组[[131415][161718]]print(d)#前4个样本为1个数组[[123][456][789][101112]]print(e)#余下的样本为1个数组[[131415][161718]]【例2.13】数组复制举例importnumpyasnpa=np.array([1,2,3])b=ac=a[:]d=np.copy(a)print(bisa,cisa,disa)#输出TrueFalseFalsed[0]=10print(a,d)#输出[123][1023]c[0]=100print(a,c)#输出[10023][10023]【例2.14】通过list创建Series对象importpandasaspdseries=pd.Series([1,2,3])print(series)【例2.15】通过字典创建Series对象importpandasaspddict={'a':0,'b':1,'c':5}print(pd.Series(dict))【例2.16】通过ndarray创建Seriesimportpandasaspdimportnumpyasnpprint('通过ndarray创建的Series为：\n',pd.Series(np.arange(3),index=['a','b','c'],name='ndarray'))【例2.17】访问Series的属性importpandasaspdseries1=pd.Series([1,2,3,10])print("series1:\n{}\n".format(series1))print("series1.values:{}\n".format(series1.values))#Series中的数据print("series1.index:{}\n".format(series1.index))#Series中的索引print("series1.shape:{}\n".format(series1.shape))#Series中的形状print("series1.ndim:{}\n".format(series1.ndim))#Series中的维度【例2.18】访问Seriesimportpandasaspdseries2=pd.Series([1,2,3,4,5,6,7],index=["C","D","E","F","G","A","B"])#通过索引位置访问Series数据子集print("series2位于第1位置的数据为:",series2[0])#通过索引名称（标签）也可以访问Series数据print("Eis{}\n".format(series2["E"]))【例2.19】

更新Seriesimportpandasaspdlist1=[1,2,3,4,5]series1=pd.Series(list1,index=['a','b','c','d','e'],name='list')print("series1:\n{}\n".format(series1))series1['a']=3print('更新后的Series1为：\n',series1)【例2.20】

追加Seriesimportpandasaspdlist1=[2,3,10]series1=pd.Series(list1,index=['a','b','c'],name='list')print("series1:\n{}\n".format(series1))series2=pd.Series([5],index=['f'])result=pd.concat([series1,series2])print('合并后：\n',result)【例2.21】删除Series元素举例importpandasaspdlist1=[0,1,2,3,10]series1=pd.Series(list1,index=['a','b','c','d','e'],name='list')print("series1:\n{}\n".format(series1))#1)不修改原Seriess2=series1.drop('a')#或series1.drop('a',inplace=False)print('删除索引a后（返回新对象）：\n',s2)#2)原地删除series1.drop('e',inplace=True)print('再删除索引e后（原对象已改变）：\n',series1)【例2.22】通过字典创建DataFrameimportpandasaspddata={'name':['张三','李四','王五'],'sex':['female','male','female'],'age':[23,21,20]}df=pd.DataFrame(data,columns=['name','age','sex','address'])print('通过dict创建的DataFrame为：\n',df)【例2.23】通过list创建DataFrame举例importpandasaspdlist5=[[0,5],[1,6],[2,7],[3,8],[10,9]]print('通过list创建的DataFrame为：\n',pd.DataFrame(list5,index=['a','b','c','d','e'],columns=['col1','col5']))【例2.24】通过Series创建DataFrame举例importpandasaspdnoteSeries=pd.Series(["C","D","E","F","G","A","B"])weekdaySeries=pd.Series(["Mon","Tue","Wed","Thu","Fri","Sat","Sun"],index=[1,2,3,4,5,6,7])df10=pd.DataFrame([noteSeries,weekdaySeries])print("df10:\n{}\n".format(df10))【例2.25】访问DataFrame的属性importpandasaspddf=pd.DataFrame({'col1':[0,1,2,3,4],'col2':[5,6,7,8,9]},index=['a','b','c','d','e'])print('DataFrame的Index为：',df.index)#Index(['a','b','c','d','e'],dtype='object')print('DataFrame的列标签为：',df.columns)#Index(['col1','col2'],dtype='object')print('DataFrame的列数据类型为：',df.dtypes)#col1int64print('DataFrame的轴标签为：',df.axes)print('DataFrame的维度为：',df.ndim)#2print('DataFrame的形状为：',df.shape)#(5,2)print('DataFrame的数据为：',df.values)print('DataFrame的元素个数为：',df.size)#10【例2.26】选取行列数据importpandasaspddata={'name':['张三','李四','王五'],'sex':['female','male','female'],'age':[23,21,20],'address':['西安市','郑州市','北京市']}df=pd.DataFrame(data,columns=['name','age','sex','address'],index=['a','b','c'])print('默认返回前5行数据为：\n',df.head())print('返回后2行数据为：\n',df.tail(2))print(df[1:2])df2=df.iloc[[0,2],[1,3]]#提取不连续行和列的数据，提取第0,2行，第1,3列的数据print(df2)df3=df.iat[1,1]#提取某一个数据，提取第2行，第2列数据（默认从0开始）print(df3)w1=df['name']print(w1)w2=df[df['age']>21]print(w2)w3=df.query('age==21')print(w3)【例2.27】更新DataFrame举例importpandasaspddf=pd.DataFrame({'col1':[0,1,2,3,4],'col5':[5,6,7,8,9]},index=['a','b','c','d','e'])print('DataFrame为：\n',df)更新列df['col1']=[10,11,12,13,14]print('更新列后的DataFrame为：\n',df)【例2.28】插入DataFrame举例importpandasaspddf3=pd.DataFrame({"note":["C","D","E","F","G","A","B"],"weekday":["Mon","Tue","Wed","Thu","Fri","Sat","Sun"]})print("df3:\n{}\n".format(df3))df3["No."]=pd.Series([1,2,3,4,5,6,7])#采用赋值的方法插入列print("df3:\n{}\n".format(df3))【例2.29】删除DataFrame举例importpandasaspddf=pd.DataFrame({'col1':[0,1,2,3,10],'col5':[5,6,7,8,9]},index=['a','b','c','d','e'])df['col3']=[10,19,2,19,20]print('插入列后的DataFrame为：\n',df)df.drop(['col3'],axis=1,inplace=True)print('删除col3列DataFrame为：\n',df)df.drop('a',axis=0,inplace=True)print('删除a行DataFrame为：\n',df)【例2.30】df.replace()举例importpandasaspd#1)先把数据改成“混合型”：字符串列保留字符串，数值列直接给数字df=pd.DataFrame({'名称':['产品1','产品2','产品3','产品10','产品5','产品6','产品7','产品110'],'数量':[0.1,0.7,0.110,0.10,0.7,None,0.76,0.2110],'金额':[0,0.10110,0.33,None,0.710,0,0,0.22],'合计':[None,0.37,0.2110,None,0.57,None,0,0.06],})print('原始df:\n',df,'\n')#2)整表替换：把“缺失值”先统一换成0df1=df.fillna(0)print('df1(缺失值->0):\n',df1,'\n')#3)只替换“名称”这一列里的部分字符串df['名称']=df['名称'].str.replace('产品','product',regex=False)print('仅替换“名称”列后的df:\n',df,'\n')#4)对“合计”列做字典替换，并直接改原表df['合计'].replace({None:0.11111},inplace=True)#这里None就是原来的NaNprint('替换“合计”列后的df:\n',df)【例2.31】df[].map举例importpandasaspdimportnumpyasnpdata={'姓名':['周元哲','潘婧','詹涛','王颖','李震'],'性别':['1','0','0','0','1']}df=pd.DataFrame(data)df['成绩']=[91,56,82,67,77]print(df)defgrade(x):ifx>=90:return'优秀'elifx>=80:return'良好'elifx>=70:return'中等'elifx>=60:return'及格'else:return'不及格'df['等级']=df['成绩'].map(grade)print(df)【例2.32】pd.merge(df1,df2)举例importpandasaspdleft=pd.DataFrame({'key':['K0','K1','K2','K3'],'A':['A0','A1','A2','A3'],'B':['B0','B1','B2','B3']})right=pd.DataFrame({'key':['K0','K1','K2','K3'],'C':['C0','C1','C2','C3'],'D':['D0','D1','D2','D3']})result=pd.merge(left,right,on='key')#on参数传递的key作为连接键print("left:\n{}\n".format(left))print("right:\n{}\n".format(right))print("merge:\n{}\n".format(result))【例2.33】bine_first(df2)举例importnumpyasnpimportpandasaspda=pd.Series([np.nan,2.5,np.nan,3.5,10.5],index=['f','e','d','c','b'])b=pd.Series([1,np.nan,3,10,5],index=['f','e','d','c','b'])print(a)print(b)c=bine_first(a)print(c)【例2.34】数据离散化举例importpandasaspdages=[20,7,37,31,68,105,52]bins=[0,18,35,50,60]cuts=pd.cut(ages,bins)print(cuts)【例2.35】df.fillna(num)举例fromnumpyimportnanasNaNimportpandasaspddf1=pd.DataFrame([[1,2,3],[NaN,NaN,2],[NaN,NaN,NaN],[8,8,NaN]])print("df1:\n{}\n".format(df1))print(df1.notnull())#notnull函数判断是否有空值df2=df1.fillna(50)print("df2:\n{}\n".format(df2))【例2.36】df.dropna()举例fromnumpyimportnanasNaNimportpandasaspddf1=pd.DataFrame([[1,2,3],[NaN,NaN,2],[NaN,NaN,NaN],[8,8,NaN]])print("df1:\n{}\n".format(df1))df2=df1.dropna()print("df2:\n{}\n".format(df2))【例2.37】重复值清洗举例importpandasaspd#导入Pandas库data=pd.DataFrame({'a':[2,2,2,2],'b':[2,2,2,2],'c':[2,2,1,3],'d':[1,1,3,3]})print(data)isDuplicated=data.duplicated()#判断重复数据记录print("重复值为：\n",isDuplicated)#打印输出print("删除重复值后：\n",data.drop_duplicates())data.drop_duplicates(subset=['a','b'],keep='first',inplace=True)print("有条件的删除重复值后：\n",data)【例2.38】稀疏矩阵举例fromscipy.sparseimportcoo_matriximportnumpyasnp#创建一个稀疏矩阵A=coo_matrix([[1,2,0],[0,0,3],[4,0,11]])print("原始稀疏矩阵A:")print(A)#转化为普通矩阵C=A.todense()#转化为普通矩阵print("转换为普通矩阵C:")print(C)#传入一个(data,(row,col))的元组来构建稀疏矩阵#注意：I和J的索引值不能超过矩阵的维度I=np.array([0,2,1,0])#行索引，确保索引值小于矩阵的行数J=np.array([0,3,1,2])#列索引，确保索引值小于矩阵的列数data=np.array([4,11,7,9])#对应的数据A_new=coo_matrix((data,(I,J)),shape=(4,4))#指定矩阵维度为4x4print("通过(data,(row,col))构建的稀疏矩阵A_new:")print(A_new)【例2.39】创建CSR矩阵。importnumpyasnpfromscipy.sparseimportcsr_matrixarr=np.array([0,0,0,0,0,1,1,0,2])print(csr_matrix(arr))【例2.40】创建CSR矩阵。importnumpyasnpfromscipy.sparseimportcsr_matrixarr=np.array([[0,0,0],[0,2,3],[1,0,2]])print(csr_matrix(arr).data)print(csr_matrix(arr).count_nonzero())【例2.41】矩阵运算举例fromscipyimportlinalgimportnumpyasnpA=np.matrix('[1,2;3,4]')print(A)print(A.T)#转置矩阵print(A.I)#逆矩阵print(linalg.inv(A))#逆矩阵【例2.42】线性方程组求解fromscipyimportlinalgimportnumpyasnpa=np.array([[1,3,5],[2,5,-1],[2,4,7]])b=np.array([10,6,4])x=linalg.solve(a,b)print(x)【例2.43】非线性方程组求解举例fromscipy.optimizeimportfsolvefrommathimportsindeff(x):

x0,x1,x2=x.tolist()

return[5*x1+3,4*x0*x0-2*sin(x1*x2),x1*x2-1.5]#f计算方程组的误差，[1,1,1]是初始值result=fsolve(f,[1,1,1])

#输出方程组的解print(result)#输出误差print(f(result))【例2.44】求的最小值fromscipy.optimizeimportminimizeimportnumpyasnp#计算1/x+x的最小值deffun(args):a=argsv=lambdax:a/x[0]+x[0]returnvif__name__=="__main__":args=(1)#ax0=np.asarr