《人工智能基础及应用（Python 微课版）》程序代码汇 王军选 第2-7章 数据处理与分析-自然语言处理

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.asarray((2))#初始猜测值res=minimize(fun(args),x0,method='SLSQP')print(res.fun)print(res.success)print(res.x)【例2.45】最小二乘法举例importnumpyasnpfromscipy.optimizeimportleastsqimportmatplotlib.pyplotasplt#推荐用plt，而不是pylabfrommatplotlibimportrcParamsimportmatplotlibmatplotlib.use('TkAgg')#——中文显示&负号————————————————————————rcParams['font.sans-serif']=['SimHei']#黑体，Windows自带rcParams['axes.unicode_minus']=False#正常显示负号#——拟合函数————————————————————————————————deffunc(x,p):"""A*sin(2πkx+θ)"""A,k,theta=preturnA*np.sin(2*np.pi*k*x+theta)deferrf(p,y,x):"""残差"""returny-func(x,p)#——构造数据————————————————————————————————x=np.linspace(0,-2*np.pi,100)#注意：终点是-2πA,k,theta=10,0.34,np.pi/6#真实参数y0=func(x,[A,k,theta])#真值y1=y0+2*np.random.randn(len(x))#加噪声#——拟合————————————————————————————————————p0=[7,0.2,0]#初值plsq,cov,info,mesg,ier=leastsq(errf,p0,args=(y1,x),full_output=True)#——打印结果————————————————————————————————print("真实参数：",[A,k,theta])print("拟合参数：",plsq)#——画图————————————————————————————————————plt.figure()plt.plot(x,y0,'r-',label='真实数据')plt.plot(x,y1,'g.',label='带噪声的实验数据',markersize=3)plt.plot(x,func(x,plsq),'b--',label='拟合曲线')plt.legend()plt.tight_layout()plt.show()第3章数据可视化【例3.1】Matplotlib绘图举例importmatplotlibmatplotlib.use('TkAgg')importmatplotlib.pyplotaspltimportnumpyasnp#让中文正常显示matplotlib.rcParams['font.family']='KaiTi'matplotlib.rcParams['axes.unicode_minus']=False#解决负号显示成方块的问题#π×2的正确写法是2*np.pi或2*math.pix=np.arange(0,2*np.pi,0.05)y=np.sin(x)fig=plt.figure()ax=fig.add_axes([0.1,0.1,0.8,0.8])#2.坐标轴范围别贴着边界，留一点边距ax.plot(x,y)ax.set_title('正弦曲线示例')ax.set_xlabel('横轴值')ax.set_ylabel('纵轴值')#y轴范围应该是[-1,1]，而不是[1,1]ax.set_xlim(0,2*np.pi)ax.set_ylim(-1,1)plt.show()【例3.2】subplot举例importmatplotlibmatplotlib.use('TkAgg')importmatplotlib.pyplotaspltimportnumpyasnpfig=plt.figure(num=1,figsize=(4,4))plt.subplot(221)#画布分为2x2的区域plt.plot([1,2,3,4],[1,2,3,4])plt.subplot(222)plt.plot([1,2,3,4],[2,2,3,4])plt.subplot(223)plt.plot([1,2,3,4],[1,2,2,4])plt.subplot(224)plt.plot([1,2,3,4],[1,2,3,3])plt.show()【例3.3】add_subplot举例importmatplotlibmatplotlib.use('TkAgg')importmatplotlib.pyplotaspltimportnumpyasnp#让中文正常显示fig=plt.figure(num=1,figsize=(4,4))ax1=fig.add_subplot(221)#画布分为2x2的区域rect=plt.Rectangle((0.1,0.2),0.3,0.6,color='r')#创建一个矩形，参数：(x,y),width,heightax1.add_patch(rect)#将形状添加到子图上ax2=fig.add_subplot(222)circ=plt.Circle((0.5,0.3),0.2,color='r',alpha=0.3)#创建一个椭圆，参数：中心点，半径，默认圆形随窗口大小进行长宽压缩ax2.add_patch(circ)#将形状添加到子图上ax3=fig.add_subplot(223)pgon=plt.Polygon([[0.2,0.2],[0.65,0.6],[0.2,0.6]])#创建一个多边形，参数：每个顶点坐标ax3.add_patch(pgon)#将形状添加到子图上fig.canvas.draw()#子图绘制plt.show()【例3.4】折线图举例importnumpyasnpimportmatplotlibmatplotlib.use('TkAgg')#强制走Tk图形后端，保证弹出独立窗口frommatplotlibimportpyplotaspltx=np.arange(1,11)y=2*x*x+5plt.title("plotfigure")plt.xlabel("xaxis")plt.ylabel("yaxis")plt.plot(x,y)plt.show()【例3.5】散点图举例importnumpyasnpimportmatplotlib.pyplotaspltx=np.arange(1,10)#产生测试数据y=xfig=plt.figure()ax1=fig.add_subplot(111)ax1.set_title('ScatterPlot')#设置标题plt.xlabel('X')#设置X轴标签plt.ylabel('Y')#设置Y轴标签ax1.scatter(x,y,c='r',marker='o')#画散点图plt.legend('x1')#设置图标plt.show()#显示所画的图【例3.6】饼状图举例importmatplotlib.pyplotaspltimportnumpyasnplabels=['Mon','Tue','Wed','Thu','Fri','Sat','Sun']data=np.random.rand(7)*100plt.pie(data,labels=labels,autopct='%1.1f%%')plt.axis('equal')plt.legend()plt.show()【例3.7】条形图举例importmatplotlibmatplotlib.use('TkAgg')importmatplotlib.pyplotaspltimportnumpyasnpplt.rcParams['font.size']=10#全局字号plt.rcParams['axes.unicode_minus']=False#解决负号显示方框labels=['Apple','Banana','Orange','Pear','Watermelon']sales=[83,61,55,47,38]fig,ax=plt.subplots(figsize=(6,4))bars=ax.bar(labels,sales,color='tomato',edgecolor='k',linewidth=0.8)#数值标签置顶forbinbars:height=b.get_height()ax.text(b.get_x()+b.get_width()/2,height+1,f'{height}',ha='center',va='bottom')ax.set_ylabel('Sales/boxes')ax.set_title('Fruitsales')plt.tight_layout()plt.show()【例3.8】箱线图举例importmatplotlibmatplotlib.use('TkAgg')importmatplotlib.pyplotaspltimportnumpyasnp#1.造数据np.random.seed(42)data=np.concatenate([np.random.normal(100,10,90),np.random.normal(150,15,10)])#2.画图fig,ax=plt.subplots(figsize=(6,3))bp=ax.boxplot(data,vert=False,patch_artist=True,boxprops=dict(facecolor='lightblue'),showmeans=False,widths=0.3)#3.关键数值q1,q2,q3=np.percentile(data,[25,50,75])iqr=q3-q1whis_lo=q1-1.5*iqrwhis_hi=q3+1.5*iqr#4.标注（纯英文，位置用y=0.15保证在图内）style=dict(fontsize=9,va='center',ha='center')y_txt=0.15ax.text(q1,y_txt,f'Q1\n{q1:.1f}',**style)ax.text(q2,y_txt,f'Q2\n{q2:.1f}',**style)ax.text(q3,y_txt,f'Q3\n{q3:.1f}',**style)#IQR箭头ax.annotate('',xy=(q3,0.35),xytext=(q1,0.35),arrowprops=dict(arrowstyle='<->',color='red'))ax.text((q1+q3)/2,0.4,f'IQR={iqr:.1f}',color='red',**style)#须线ax.text(whis_lo,y_txt,f'Lower\n{whis_lo:.1f}',**style)ax.text(whis_hi,y_txt,f'Upper\n{whis_hi:.1f}',**style)ax.set_xlim(50,180)ax.set_yticks([])ax.set_title('BoxplotElements')plt.tight_layout()plt.show()【例3.9】面积图举例#0.统一导入importmatplotlibmatplotlib.use('TkAgg')importmatplotlib.pyplotaspltimportnumpyasnpplt.rcParams['font.size']=10plt.rcParams['axes.unicode_minus']=Falsex=np.linspace(0,10,200)y=2+np.sin(x)*np.exp(-x*0.2)days=np.arange(1,8)#一周7天A=np.array([20,22,18,25,30,28,32])B=np.array([15,19,16,22,24,26,29])C=np.array([10,12,14,15,18,20,23])labels=['ProductA','ProductB','ProductC']colors=['#ff7f0e','#2ca02c','#1f77b4']plt.figure(figsize=(6,4))plt.stackplot(days,A,B,C,labels=labels,colors=colors,alpha=0.8)plt.legend(loc='upperleft')plt.title('StackedAreaChart')plt.xlabel('Day')plt.ylabel('Sales')plt.tight_layout()plt.show()【例3.10】环形图举例#0.统一导入importmatplotlibmatplotlib.use('TkAgg')importmatplotlib.pyplotaspltimportnumpyasnpplt.rcParams['font.size']=10plt.rcParams['axes.unicode_minus']=False#内环：大类outer_labels=['Mobile','Desktop']outer_sizes=[60,40]outer_colors=['#ff9f43','#0984e3']#外环：细分inner_labels=['iOS','Android','Win','macOS']inner_sizes=[35,25,22,18]inner_colors=['#feca57','#ff7675','#74b9ff','#a29bfe']fig,ax=plt.subplots(figsize=(6,6),subplot_kw=dict(aspect='equal'))#外环（细分数）ax.pie(inner_sizes,labels=inner_labels,colors=inner_colors,autopct='%1.1f%%',startangle=90,radius=1,#外半径wedgeprops=dict(width=0.3,edgecolor='w'))#内环（大类）ax.pie(outer_sizes,labels=outer_labels,colors=outer_colors,autopct='%1.1f%%',startangle=90,radius=0.7,#内半径wedgeprops=dict(width=0.3,edgecolor='w'))#中心白圆centre_circle=plt.Circle((0,0),0.4,fc='white')fig.gca().add_artist(centre_circle)ax.set_title('Device&OSSplitDonutChart')plt.tight_layout()plt.show()【例3.11】三维图两种创建方式方法一：利用关键字frommatplotlibimportpyplotaspltfrommpl_toolkits.mplot3dimportAxes3D#定义坐标轴fig=plt.figure()ax1=plt.axes(projection='3d')#ax=fig.add_subplot(111,projection='3d')#可以绘制多个子图方法二：利用三维轴方法frommatplotlibimportpyplotaspltfrommpl_toolkits.mplot3dimportAxes3D#定义图像和三维格式坐标轴fig=plt.figure()ax2=Axes3D(fig)【例3.12】绘制三角螺旋线importmatplotlibmatplotlib.use('TkAgg')importmatplotlib.pyplotaspltimportnumpyasnpplt.rcParams['font.size']=10plt.rcParams['axes.unicode_minus']=False#1.创建图+3D子图fig=plt.figure(figsize=(6,4))ax=fig.add_subplot(111,projection='3d')#2.数据zline=np.linspace(0,15,1000)xline=np.sin(zline)yline=np.cos(zline)#3.绘图ax.plot3D(xline,yline,zline,'gray')#4.显示plt.show()【例3.13】绘制三维散点的数据importmatplotlib.pyplotaspltimportnumpyasnpax=plt.axes(projection='3d')zdata=15*np.random.random(100)xdata=np.sin(zdata)+0.1*np.random.randn(100)ydata=np.cos(zdata)+0.1*np.random.randn(100)ax.scatter3D(xdata,ydata,zdata,c=zdata,cmap='Reds')【例3.14】绘制三维等高线图frommpl_toolkitsimportmplot3dimportmatplotlib.pyplotaspltimportnumpyasnpdeff(x,y):returnnp.sin(np.sqrt(x**2+y**2))x=np.linspace(-6,6,30)y=np.linspace(-6,6,30)X,Y=np.meshgrid(x,y)Z=f(X,Y)fig=plt.figure()ax=plt.axes(projection='3d')ax.contour3D(X,Y,Z,50,cmap='binary')ax.set_xlabel('x')ax.set_ylabel('y')ax.set_zlabel('z')#俯仰角设为60度，把方位角调整为35度ax.view_init(60,35)【例3.15】直方图举例importnumpyasnpimportmatplotlibmatplotlib.use('TkAgg')#强制Tk后端，独立窗口frommatplotlibimportpyplotaspltimportseabornassnsimportwarningswarnings.filterwarnings('ignore')#可选：样式sns.set_style('darkgrid')plt.rcParams['figure.figsize']=(8,4)plt.rcParams['figure.dpi']=100plt.rcParams['axes.unicode_minus']=False#生成100个成标准正态分布的随机数x=np.random.normal(size=100)#kde=True进行核密度估计sns.distplot(x,kde=True,hist=False)#hist为FALSE，直接绘制密度图而没有直方图plt.show()【例3.16】核密度图举例importnumpyasnpimportmatplotlibmatplotlib.use('TkAgg')#强制Tk后端，独立窗口frommatplotlibimportpyplotaspltimportseabornassnsfig,ax=plt.subplots()np.random.seed(4)#可复现Gaussian=np.random.normal(0,1,1000)#标准正态数据#1.直方图（密度尺度）ax.hist(Gaussian,bins=25,histtype="stepfilled",density=True,alpha=0.6,color='skyblue')#2.KDE曲线sns.kdeplot(Gaussian,shade=True,ax=ax,color='navy')ax.set_title('StandardNormalDistribution')ax.set_xlabel('Value')ax.set_ylabel('Density')plt.tight_layout()plt.show()【例3.17】小提琴图举例importnumpyasnpimportmatplotlibmatplotlib.use('TkAgg')#强制Tk后端，独立窗口frommatplotlibimportpyplotaspltimportseabornassnsimportwarningswarnings.filterwarnings('ignore')#可选：样式sns.set_style('darkgrid')plt.rcParams['figure.figsize']=(8,4)plt.rcParams['figure.dpi']=100plt.rcParams['axes.unicode_minus']=False#加载irisiris=sns.load_dataset('iris')#画小提琴图sns.violinplot(data=iris,palette='hls')plt.title('IrisFeaturesDistribution')plt.xlabel('Features')plt.ylabel('Value')#关键：弹出窗口plt.show()【例3.18】绘制热力图importnumpyasnpimportmatplotlibmatplotlib.use('TkAgg')#强制Tk后端，独立窗口frommatplotlibimportpyplotaspltimportseabornassnsimportwarningswarnings.filterwarnings('ignore')#可选：样式sns.set_style('darkgrid')plt.rcParams['figure.figsize']=(8,4)plt.rcParams['figure.dpi']=100plt.rcParams['axes.unicode_minus']=Falseimportnumpyasnp;np.random.seed(0)importseabornassns;sns.set()importmatplotlib.pyplotaspltuniform_data=np.random.rand(10,12)f,ax=plt.subplots(figsize=(9,6))ax=sns.heatmap(uniform_data)plt.show()【例3.19】等高线举例importseabornassnsimportnumpyasnpimportmatplotlibmatplotlib.use('TkAgg')#强制走Tk图形后端，保证弹出独立窗口frommatplotlibimportpyplotasplt#1.加载官方示例数据（新版Seaborn自带）penguins=sns.load_dataset("penguins")#2.去掉缺失值，防止KDE警告penguins=penguins.dropna(subset=["bill_length_mm","bill_depth_mm"])#3.绘制二维KDE联合图g=sns.jointplot(x="bill_length_mm",y="bill_depth_mm",kind="kde",data=penguins,fill=True,#填充颜色更直观thresh=0.05#去掉极低密度边缘)g.fig.suptitle("PenguinBillDimensions–KDEJointPlot",y=1.02)plt.show()第4章数据社交网【例4.1】无向图importmatplotlibmatplotlib.use('TkAgg')importmatplotlib.pyplotaspltimportnumpyasnpimportnetworkxasnxG=nx.Graph()#创建一个空的无向图G.add_edge(1,2,weight=4.2)#添加一个从节点1到节点2的边，权重为4.2G.add_nodes_from([3,4])#添加多个节点nx.draw(G,with_labels=True)plt.show()【例4.2】有向图importnetworkxasnximportmatplotlibimportmatplotlib.pyplotasplt#若PyCharm不弹图，强制使用TkAgg后端matplotlib.use('TkAgg')DG=nx.DiGraph()#创建空有向图DG.add_edge(1,2)#1->2DG.add_edge(3,4)#3->4#画出来plt.figure(figsize=(3,2))pos=nx.spring_layout(DG,seed=2025)#固定布局，方便复现nx.draw(DG,pos,with_labels=True,node_color='skyblue',node_size=800,arrowsize=15,font_size=10)plt.title('PyCharm下最简单的有向图示例')plt.tight_layout()plt.show()【例4.3】多重图importnetworkxasnximportmatplotlibimportmatplotlib.pyplotaspltmatplotlib.use('TkAgg')#1.创建空的多重图G=nx.MultiGraph()#2.添加节点G.add_nodes_from([1,2,3])#3.添加多条平行边G.add_edge(1,2,key='a',weight=7)G.add_edge(1,2,key='b',weight=10)G.add_edge(1,3)G.add_edge(2,3)#4.访问特定边print("边(1,2,key='a')的属性：",G[1][2]['a'])#{'weight':7}#5.获取1-2之间所有边的keyprint("1-2的所有key：",list(G.get_edge_data(1,2).keys()))#dict_keys(['a','b'])#6.删除key='a'的边G.remove_edge(1,2,key='a')#7.重新确认剩余keyprint("删除后1-2的key：",list(G.get_edge_data(1,2).keys()))#只剩['b']#8.绘图pos=nx.spring_layout(G,seed=2025)#固定布局，便于复现plt.figure(figsize=(4,3))nx.draw_networkx_nodes(G,pos,node_color='lightblue',node_size=800)nx.draw_networkx_labels(G,pos,font_size=10,font_family='SimHei')#为平行边手动做弯曲，避免重叠nx.draw_networkx_edges(G,pos,width=2,edge_color='gray',connectionstyle='arc3,rad=0.15',#弯曲，让平行边错开arrows=False)plt.axis('off')plt.tight_layout()plt.show()【例4.4】节点属性importnetworkxasnx#创建一个无向图G=nx.Graph()#添加节点G.add_node('A')G.add_node('B')G.add_node('C')#添加边G.add_edge('A','B')G.add_edge('A','C')#使用add_node方法添加属性G.add_node('A',color='red',size=10)G.add_node('B',color='blue',size=20)#使用nodes[node]['attr']方式添加属性G.nodes['C']['color']='green'G.nodes['C']['size']=15#获取节点'A'的属性node_A_attrs=G.nodes['A']print(f"Node'A'attributes:{node_A_attrs}")#获取节点'B'的颜色属性color_B=G.nodes['B']['color']print(f"Node'B'color:{color_B}")#获取节点'C'的大小属性size_C=G.nodes['C']['size']print(f"Node'C'size:{size_C}")【程序运行结果】Node'A'attributes:{'color':'red','size':10}Node'B'color:blueNode'C'size:15【例4.5】节点操作importnetworkxasnximportmatplotlib.pyplotaspltimportmatplotlibmatplotlib.use('TkAgg')G=nx.Graph()#建立一个空的无向图GG.add_node('a')#添加一个节点G.add_nodes_from(['b','c','d','e'])#加点集合nx.add_cycle(G,['f','g','h','j'])#加环H=nx.path_graph(10)#返回