数据采集与融合作业2
作业①:
要求：在中国气象网（http://www.weather.com.cn）给定城市集的7日天气预报，并保存在数据库。
输出信息:
Gitee文件夹链接
1.1代码
#导入相应库
from bs4 import BeautifulSoup
from bs4 import UnicodeDammit
import urllib.request
import sqlite3
#定义数据库类，并定义打开，关闭，插入，展示数据等方法
class WeatherDB:
def openDB(self):
self.con=sqlite3.connect("weathers.db")
self.cursor=self.con.cursor()
try:
self.cursor.execute("create table weathers (wCity varchar(16),wDate varchar(16),wWeather varchar(64),wTemp varchar(32),constraint pk_weather primary key(wCity,wDate))")
except:
self.cursor.execute("drop table if exists weathers")
self.cursor.execute("create table weathers (wCity varchar(16), wDate varchar(16), wWeather varchar(64), wTemp varchar(32), constraint pk_weather primary key(wCity, wDate))")
def closeDB(self):
self.con.commit()
self.con.close()
def insert(self,city,date,weather,temp):
try:
self.cursor.execute("insert into weathers (wCity,wDate,wWeather,wTemp) values(?,?,?,?)",(city,date,weather,temp))
except Exception as err:
print(err)
def show(self):
self.cursor.execute("select * from weathers")
rows=self.cursor.fetchall()
print("%-18s%-20s%-32s%-16s" %("city","date","weather","temp"))
for row in rows:
print("%-16s%-16s%-32s%-16s" %(row[0],row[1],row[2],row[3]))
#定义天气预报类
class WeatherForecast:
def __init__(self):
self.headers={"User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36 Edg/141.0.0.0"}
self.cityCode={"北京":"101010100","上海":"101020100","广州":"101280101","深圳":"101280601"}
def forecastCity(self,city):
if city not in self.cityCode.keys():
print(city+"code cannot be found")
return
url="https://www.weather.com.cn/weather/"+self.cityCode[city]+".shtml"
try:
i=0
req=urllib.request.Request(url,headers=self.headers)
data=urllib.request.urlopen(req)
data=data.read()
dammit=UnicodeDammit(data,["utf-8","gbk"])
data=dammit.unicode_markup
soup=BeautifulSoup(data,"lxml")
lis=soup.select("ul[class='t clearfix'] li")#找到7天天气预报所在的li标签
for li in lis:#对每一天天气提取需要的特征并将其插入数据库中
try:
i+=1
date=li.select('h1')[0].text
weather=li.select('p[class="wea"]')[0].text
if i==1:#如果是第一天的话，那么其只有一个温度，没有最高温最低温
temp=li.select('p[class="tem"] i')[0].text
else:
temp=li.select('p[class="tem"] span')[0].text+'/'+li.select('p[class="tem"] i')[0].text
print(city,date,weather,temp)
self.db.insert(city,date,weather,temp)
except Exception as err:
print(err)
except Exception as err:
print(err)
def process(self,citys):#运行爬取天气预报信息函数
self.db=WeatherDB()
self.db.openDB()
for city in citys:
self.forecastCity(city)
self.db.show()
self.db.closeDB()
ws=WeatherForecast()
ws.process(["北京","上海","广州","深圳"])
print("completed")
1.2结果
数据库中的数据
1.3心得体会
此次实践加深了我对BeautifulSoup和sqlite3的相关方法的理解，也进一步提高了我爬虫的编程能力，为后续更难的任务打下基础
Gitee文件夹链接
https://gitee.com/linyuars/2025_crawl_project/blob/master/作业2/1.py
作业②
要求：用requests和json解析方法定向爬取股票相关信息，并存储在数据库中。
候选网站：东方财富网：https://www.eastmoney.com/
新浪股票：http://finance.sina.com.cn/stock/
技巧：在谷歌浏览器中进入F12调试模式进行抓包，查找股票列表加载使用的url，并分析api返回的值，并根据所要求的参数可适当更改api的请求参数。根据URL可观察请求的参数f1、f2可获取不同的数值，根据情况可删减请求的参数。
参考链接：https://zhuanlan.zhihu.com/p/50099084
输出信息：
Gitee文件夹链接
2.1代码
#导入相关库
import json
import urllib.request
import re
import sqlite3
import requests
def drop():#如果stock数据库中原本存在stocks表则删去
try:
con=sqlite3.connect("stock.db")
cursor=con.cursor()
try:
cursor.execute("drop table stocks")
except:
pass
con.commit()
con.close()
except Exception as err:
print(err)
def saveDB(row):#如果没有stocks表就创建表,然后将股票数据插入该表中
if len(row)==0:
return
try:
con=sqlite3.connect("stock.db")
cursor=con.cursor()
try:
sql="create table stocks (rank int,code varchar(128) primary key,name varchar(128),price float,change_percent varchar(128),change_amount float,volume varchar(128),turnover varchar(128),other varchar(128))"
cursor.execute(sql)
except:
pass
if len(row)==9:
sql="insert into stocks (rank,code,name,price,change_percent,change_amount,volume,turnover,other) values(?,?,?,?,?,?,?,?,?)"
try:
rank=row[0]
code=row[1]
name=row[2]
price=row[3]
change_percent=row[4]
change_amount=row[5]
volume=row[6]
turnover=row[7]
other=row[8]
cursor.execute(sql,(rank,code,name,price,change_percent,change_amount,volume,turnover,other))
except Exception as err:
print(err)
con.commit()
con.close()
except Exception as err:
print(err)
def showDB():#展示数据库中的数据
try:
con=sqlite3.connect("stock.db")
cursor=con.cursor()
try:
cursor.execute("select * from stocks")
rows=cursor.fetchall()
print("%-16s%-16s%-15s%-16s%-16s%-16s%-16s%-16s%-16s" %("排名","代码","名称","最新价","涨跌幅","涨跌额","成交量","成交额","涨幅"))
for row in rows:
print("%-16s%-16s%-18s%-18s%-18s%-18s%-18s%-18s%-18s" %(row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8]))
except:
pass
con.commit()
con.close()
except Exception as err:
print(err)
url='https://push2.eastmoney.com/api/qt/clist/get?np=1&fltt=1&invt=2&cb=jQuery37103071409561710594_1762478212220&fs=m%3A0%2Bt%3A6%2Bf%3A!2%2Cm%3A0%2Bt%3A80%2Bf%3A!2%2Cm%3A1%2Bt%3A2%2Bf%3A!2%2Cm%3A1%2Bt%3A23%2Bf%3A!2%2Cm%3A0%2Bt%3A81%2Bs%3A262144%2Bf%3A!2&fields=f12%2Cf13%2Cf14%2Cf1%2Cf2%2Cf4%2Cf3%2Cf152%2Cf5%2Cf6%2Cf7%2Cf15%2Cf18%2Cf16%2Cf17%2Cf10%2Cf8%2Cf9%2Cf23&fid=f3&pn=1&pz=20&po=1&dect=1&ut=fa5fd1943c7b386f172d6893dbfba10b&wbp2u=%7C0%7C0%7C0%7Cweb&_=1762478212229'
headers={'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36 Edg/141.0.0.0'}
response = requests.get(url,headers = headers)
response.encoding = "utf-8"
data = response.text#爬取股票数据
print(f"{'排名':<2} {'代码':<2} {'名称':<6} {'最新价':<4} {'涨跌幅':<3} {'涨跌额':<4} {'成交量':>6} {'成交额':>4} {'涨幅':<4}")
drop()
for i in range(20):#将20个股票的信息依次提取打印出来并存到数据库中
stock=[]
rank=i+1
code=(data['data']['diff'][i]['f12'])
name=(data['data']['diff'][i]['f14'])
price=(data['data']['diff'][i]['f2']/100)
change_percent=(data['data']['diff'][i]['f3']/100)
change_amount=data['data']['diff'][i]['f4']/100
volume=round(data['data']['diff'][i]['f5']/10000,2)
turnover=round(data['data']['diff'][i]['f6']/100000000,2)
other=data['data']['diff'][i]['f7']/100
stock.append(rank)
stock.append(code)
stock.append(name)
stock.append(price)
stock.append(str(change_percent)+'%')
stock.append(change_amount)
stock.append(str(volume)+'万')
stock.append(str(turnover)+'亿')
stock.append(str(other)+'%')
saveDB(stock)
print(f"{rank:<2} {code:>6} {name:<6} {price:<6.2f} {change_percent:>3}% {change_amount:>6.2f} {volume:>8}万 {turnover:>6.1f}亿 {other:>4.1f}%")
showDB()
2.2结果
数据库中的数据
2.3心得体会
通过本次实践，我掌握了进入F12调试模式进行抓包的技巧，也学会了提取json格式数据的信息，丰富了我爬取数据的方法，也巩固了简单爬取网页数据并存储到数据库的能力，实践能力得到进一步提升。
Gitee文件夹链接
https://gitee.com/linyuars/2025_crawl_project/blob/master/作业2/2.py
作业③:
要求：爬取中国大学2021主榜（https://www.shanghairanking.cn/rankings/bcur/2021）所有院校信息，并存储在数据库中，同时将浏览器F12调试分析的过程录制Gif加入至博客中。
技巧：分析该网站的发包情况，分析获取数据的api
输出信息：
Gitee文件夹链接
| 排名 | 学校 | 省市 | 类型 | 得分 |
|---|---|---|---|---|
| 1 | 清华大学 | 北京 | 综合 | 969.2 |
GIF
3.1代码
#导入相应的库
import urllib.request
from bs4 import BeautifulSoup
headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36 Edg/141.0.0.0'}
url='https://www.shanghairanking.cn/_nuxt/static/1761118404/rankings/bcur/2021/payload.js'
req=urllib.request.Request(url,headers=headers)
info=urllib.request.urlopen(req).read().decode('utf-8')
def drop():#如果原本存在schools表就删去
try:
con=sqlite3.connect("school.db")
cursor=con.cursor()
try:
cursor.execute("drop table schools")
except:
pass
con.commit()
con.close()
except Exception as err:
print(err)
def saveDB(row):#如果没有表就创建一个schools表，并将学校的相应信息存储到表中
if len(row)==0:
return
try:
con=sqlite3.connect("school.db")
cursor=con.cursor()
try:
sql="create table schools (rank varchar(128),name varchar(128),province varchar(128),category varchar(128),score varchar(128))"
cursor.execute(sql)
except:
pass
if len(row)==5:
sql="insert into schools (rank,name,province,category,score) values(?,?,?,?,?)"
try:
rank=row[0]
name=row[1]
province=row[2]
category=row[3]
score=row[4]
cursor.execute(sql,(rank,name,province,category,score))
except Exception as err:
print(err)
con.commit()
con.close()
except Exception as err:
print(err)
def showDB():#将school数据库的schools表中学校的信息显示出来
try:
con=sqlite3.connect("school.db")
cursor=con.cursor()
try:
cursor.execute("select * from schools")
rows=cursor.fetchall()
print("排名 学校 省市 类型 总分")
for row in rows:
print(f' {row[0]:<2} {row[1]:<5} {row[2]} {category[3]} {row[4]}')
except:
pass
con.commit()
con.close()
except Exception as err:
print(err)
start=re.search(r'univData:',info).end()
end=re.search(r',indList',info).start()
data=info[start:end]#提取出有用的信息段
var1='a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q,R,S,T,U,V,W,X,Y,Z,_,$,aa,ab,ac,ad,ae,af,ag,ah,ai,aj,ak,al,am,an,ao,ap,aq,ar,as,at,au,av,aw,ax,ay,az,aA,aB,aC,aD,aE,aF,aG,aH,aI,aJ,aK,aL,aM,aN,aO,aP,aQ,aR,aS,aT,aU,aV,aW,aX,aY,aZ,a_,a$,ba,bb,bc,bd,be,bf,bg,bh,bi,bj,bk,bl,bm,bn,bo,bp,bq,br,bs,bt,bu,bv,bw,bx,by,bz,bA,bB,bC,bD,bE,bF,bG,bH,bI,bJ,bK,bL,bM,bN,bO,bP,bQ,bR,bS,bT,bU,bV,bW,bX,bY,bZ,b_,b$,ca,cb,cc,cd,ce,cf,cg,ch,ci,cj,ck,cl,cm,cn,co,cp,cq,cr,cs,ct,cu,cv,cw,cx,cy,cz,cA,cB,cC,cD,cE,cF,cG,cH,cI,cJ,cK,cL,cM,cN,cO,cP,cQ,cR,cS,cT,cU,cV,cW,cX,cY,cZ,c_,c$,da,db,dc,dd,de,df,dg,dh,di,dj,dk,dl,dm,dn,do0,dp,dq,dr,ds,dt,du,dv,dw,dx,dy,dz,dA,dB,dC,dD,dE,dF,dG,dH,dI,dJ,dK,dL,dM,dN,dO,dP,dQ,dR,dS,dT,dU,dV,dW,dX,dY,dZ,d_,d$,ea,eb,ec,ed,ee,ef,eg,eh,ei,ej,ek,el,em,en,eo,ep,eq,er,es,et,eu,ev,ew,ex,ey,ez,eA,eB,eC,eD,eE,eF,eG,eH,eI,eJ,eK,eL,eM,eN,eO,eP,eQ,eR,eS,eT,eU,eV,eW,eX,eY,eZ,e_,e$,fa,fb,fc,fd,fe,ff,fg,fh,fi,fj,fk,fl,fm,fn,fo,fp,fq,fr,fs,ft,fu,fv,fw,fx,fy,fz,fA,fB,fC,fD,fE,fF,fG,fH,fI,fJ,fK,fL,fM,fN,fO,fP,fQ,fR,fS,fT,fU,fV,fW,fX,fY,fZ,f_,f$,ga,gb,gc,gd,ge,gf,gg,gh,gi,gj,gk,gl,gm,gn,go,gp,gq,gr,gs,gt,gu,gv,gw,gx,gy,gz,gA,gB,gC,gD,gE,gF,gG,gH,gI,gJ,gK,gL,gM,gN,gO,gP,gQ,gR,gS,gT,gU,gV,gW,gX,gY,gZ,g_,g$,ha,hb,hc,hd,he,hf,hg,hh,hi,hj,hk,hl,hm,hn,ho,hp,hq,hr,hs,ht,hu,hv,hw,hx,hy,hz,hA,hB,hC,hD,hE,hF,hG,hH,hI,hJ,hK,hL,hM,hN,hO,hP,hQ,hR,hS,hT,hU,hV,hW,hX,hY,hZ,h_,h$,ia,ib,ic,id,ie,if0,ig,ih,ii,ij,ik,il,im,in0,io,ip,iq,ir,is,it,iu,iv,iw,ix,iy,iz,iA,iB,iC,iD,iE,iF,iG,iH,iI,iJ,iK,iL,iM,iN,iO,iP,iQ,iR,iS,iT,iU,iV,iW,iX,iY,iZ,i_,i$,ja,jb,jc,jd,je,jf,jg,jh,ji,jj,jk,jl,jm,jn,jo,jp,jq,jr,js,jt,ju,jv,jw,jx,jy,jz,jA,jB,jC,jD,jE,jF,jG,jH,jI,jJ,jK,jL,jM,jN,jO,jP,jQ,jR,jS,jT,jU,jV,jW,jX,jY,jZ,j_,j$,ka,kb,kc,kd,ke,kf,kg,kh,ki,kj,kk,kl,km,kn,ko,kp,kq,kr,ks,kt,ku,kv,kw,kx,ky,kz,kA,kB,kC,kD,kE,kF,kG,kH,kI,kJ,kK,kL,kM,kN,kO,kP,kQ,kR,kS,kT,kU,kV,kW,kX,kY,kZ,k_,k$,la,lb,lc,ld,le,lf,lg,lh,li,lj,lk,ll,lm,ln,lo,lp,lq,lr,ls,lt,lu,lv,lw,lx,ly,lz,lA,lB,lC,lD,lE,lF,lG,lH,lI,lJ,lK,lL,lM,lN,lO,lP,lQ,lR,lS,lT,lU,lV,lW,lX,lY,lZ,l_,l$,ma,mb,mc,md,me,mf,mg,mh,mi,mj,mk,ml,mm,mn,mo,mp,mq,mr,ms,mt,mu,mv,mw,mx,my,mz,mA,mB,mC,mD,mE,mF,mG,mH,mI,mJ,mK,mL,mM,mN,mO,mP,mQ,mR,mS,mT,mU,mV,mW,mX,mY,mZ,m_,m$,na,nb,nc,nd,ne,nf,ng,nh,ni,nj,nk,nl,nm,nn,no,np,nq,nr,ns,nt,nu,nv,nw,nx,ny,nz,nA,nB,nC,nD,nE,nF,nG,nH,nI,nJ,nK,nL,nM,nN,nO,nP,nQ,nR,nS,nT,nU,nV,nW,nX,nY,nZ,n_,n$,oa,ob,oc,od,oe,of,og,oh,oi,oj,ok,ol,om,on,oo,op,oq,or,os,ot,ou,ov,ow,ox,oy,oz,oA,oB,oC,oD,oE,oF,oG,oH,oI,oJ,oK,oL,oM,oN,oO,oP,oQ,oR,oS,oT,oU,oV,oW,oX,oY,oZ,o_,o$,pa,pb,pc,pd,pe,pf,pg,ph,pi,pj,pk,pl,pm,pn,po,pp,pq,pr,ps,pt,pu,pv,pw,px,py,pz,pA,pB,pC,pD,pE'.split(',')
var2=["",'false','null',0,"理工","综合",'true',"师范","双一流","211","江苏","985","农业","山东","河南","河北","北京","辽宁","陕西","四川","广东","湖北","湖南","浙江","安徽","江西",1,"黑龙江","吉林","上海",2,"福建","山西","云南","广西","贵州","甘肃","内蒙古","重庆","天津","新疆","467","496","2025,2024,2023,2022,2021,2020","林业","5.8","533","2023-01-05T00:00:00+08:00","23.1","7.3","海南","37.9","28.0","4.3","12.1","16.8","11.7","3.7","4.6","297","397","21.8","32.2","16.6","37.6","24.6","13.6","13.9","3.3","5.2","8.1","3.9","5.1","5.6","5.4","2.6","162",93.5,89.4,"宁夏","青海","西藏",7,"11.3","35.2","9.5","35.0","32.7","23.7","33.2","9.2","30.6","8.5","22.7","26.3","8.0","10.9","26.0","3.2","6.8","5.7","13.8","6.5","5.5","5.0","13.2","13.3","15.6","18.3","3.0","21.3","12.0","22.8","3.6","3.4","3.5","95","109","117","129","138","147","159","185","191","193","196","213","232","237","240","267","275","301","309","314","318","332","334","339","341","354","365","371","378","384","388","403","416","418","420","423","430","438","444","449","452","457","461","465","474","477","485","487","491","501","508","513","518","522","528",83.4,"538","555",2021,11,14,10,"12.8","42.9","18.8","36.6","4.8","40.0","37.7","11.9","45.2","31.8","10.4","40.3","11.2","30.9","37.8","16.1","19.7","11.1","23.8","29.1","0.2","24.0","27.3","24.9","39.5","20.5","23.4","9.0","4.1","25.6","12.9","6.4","18.0","24.2","7.4","29.7","26.5","22.6","29.9","28.6","10.1","16.2","19.4","19.5","18.6","27.4","17.1","16.0","27.6","7.9","28.7","19.3","29.5","38.2","8.9","3.8","15.7","13.5","1.7","16.9","33.4","132.7","15.2","8.7","20.3","5.3","0.3","4.0","17.4","2.7","160","161","164","165","166","167","168",130.6,105.5,2025,"学生、家长、高校管理人员、高教研究人员等","中国大学排名（主榜）",25,13,12,"全部","1","88.0",5,"2","36.1","25.9","3","34.3","4","35.5","21.6","39.2","5","10.8","4.9","30.4","6","46.2","7","0.8","42.1","8","32.1","22.9","31.3","9","43.0","25.7","10","34.5","10.0","26.2","46.5","11","47.0","33.5","35.8","25.8","12","46.7","13.7","31.4","33.3","13","34.8","42.3","13.4","29.4","14","30.7","15","42.6","26.7","16","12.5","17","12.4","44.5","44.8","18","10.3","15.8","19","32.3","19.2","20","21","28.8","9.6","22","45.0","23","30.8","16.7","16.3","24","25","32.4","26","9.4","27","33.7","18.5","21.9","28","30.2","31.0","16.4","29","34.4","41.2","2.9","30","38.4","6.6","31","4.4","17.0","32","26.4","33","6.1","34","38.8","17.7","35","36","38.1","11.5","14.9","37","14.3","18.9","38","13.0","39","27.8","33.8","3.1","40","41","28.9","42","28.5","38.0","34.0","1.5","43","15.1","44","31.2","120.0","14.4","45","149.8","7.5","46","47","38.6","48","49","25.2","50","19.8","51","5.9","6.7","52","4.2","53","1.6","54","55","20.0","56","39.8","18.1","57","35.6","58","10.5","14.1","59","8.2","60","140.8","12.6","61","62","17.6","63","64","1.1","65","20.9","66","67","68","2.1","69","123.9","27.1","70","25.5","37.4","71","72","73","74","75","76","27.9","7.0","77","78","79","80","81","82","83","84","1.4","85","86","87","88","89","90","91","92","93","109.0","94",235.7,"97","98","99","100","101","102","103","104","105","106","107","108",223.8,"111","112","113","114","115","116",215.5,"119","120","121","122","123","124","125","126","127","128",206.7,"131","132","133","134","135","136","137",201,"140","141","142","143","144","145","146",194.6,"149","150","151","152","153","154","155","156","157","158",183.3,"169","170","171","172","173","174","175","176","177","178","179","180","181","182","183","184",169.6,"187","188","189","190",168.1,167,"195",165.5,"198","199","200","201","202","203","204","205","206","207","208","209","210","212",160.5,"215","216","217","218","219","220","221","222","223","224","225","226","227","228","229","230","231",153.3,"234","235","236",150.8,"239",149.9,"242","243","244","245","246","247","248","249","250","251","252","253","254","255","256","257","258","259","260","261","262","263","264","265","266",139.7,"269","270","271","272","273","274",137,"277","278","279","280","281","282","283","284","285","286","287","288","289","290","291","292","293","294","295","296","300",130.2,"303","304","305","306","307","308",128.4,"311","312","313",125.9,"316","317",124.9,"320","321","Wuyi 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for i in range(len(var2)):
var2[i]=str(var2[i])
zipped=dict(zip(var1,var2))#根据该文本其中的映射构造相应的键值对
#找出文本中变量的值，如果其在构造的键值对的键中，将其用键值对中的值代替，还原其真实值
rank=re.findall(r'(?:ranking:)(.*?)(?:,)',data)
name=re.findall(r'(?:univNameCn:")(.*?)(?:",)',data)
province=re.findall(r'(?:province:)(.*?)(?:,)',data)
category=re.findall(r'(?:univCategory:)(.*?)(?:,)',data)
score=re.findall(r'(?:score:)(.*?)(?:,)',data)
for i in range(len(rank)):
if rank[i] in zipped.keys():
rank[i]=zipped.get(rank[i])
for i in range(len(province)):
if province[i] in zipped.keys():
province[i]=zipped.get(province[i])
for i in range(len(category)):
if category[i] in zipped.keys():
category[i]=zipped.get(category[i])
for i in range(len(score)):
if score[i] in zipped.keys():
score[i]=zipped.get(score[i])
print("排名 学校 省市 类型 总分")
drop()
for i in range(len(rank)):#打印出学校的信息并将其保存到数据库中
print(f' {rank[i]:<2} {name[i]:<5} {province[i]} {category[i]} {score[i]}')
saveDB([rank[i],name[i],province[i],category[i],score[i]])
showDB()#展示数据库中学校的信息
3.2结果
数据库中的数据
3.3心得体会
本次实践进一步丰富了我爬取数据的方法和技巧，加深了我对爬虫步骤的理解，提高了个人的编程实践能力和解决问题的思维逻辑，为后面更难的实践打下基础。
Gitee文件夹链接
https://gitee.com/linyuars/2025_crawl_project/blob/master/作业2/3.py
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