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102302145 黄加鸿数据采集与融合技术作业2

news 2026/5/11 14:58:02

作业2

目录
作业2
作业①
1）代码与结果
2）心得体会
3）Gitee链接
作业②
1）代码与结果
2）心得体会
3）Gitee链接
作业③
1）代码与结果
F12调试分析Gif
2）心得体会
3）Gitee链接

作业①

1）代码与结果

中国气象网在之前任务中已经进行了网页分析，这里不再展示分析过程，最后是选用BeautifulSoup来解析网页。

核心代码

class WeatherForecast:def __init__(self):self.headers = {"User-Agent": "Mozilla/5.0 (Windows; U; Windows NT 6.0 x64; en-US; rv:1.9pre) Gecko/2008072421 Minefield/3.0.2pre"}self.cityCode = {"福州": "101230101", "厦门": "101230201", "泉州": "101230501"}  # 城市集def forecastCity(self, city):if city not in self.cityCode.keys():print(city + " code cannot be found")return# 根据城市代码不同设计url进行多网页爬取url = "http://www.weather.com.cn/weather/" + self.cityCode[city] + ".shtml"try: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_markupsoup = BeautifulSoup(data, "lxml")#定位到表格信息lis = soup.select("ul[class='t clearfix'] li")for li in lis:try:date = li.select('h1')[0].textweather = li.select('p[class="wea"]')[0].textif li.select('p[class="tem"] span'):temp = li.select('p[class="tem"] span')[0].text + "/" + li.select('p[class="tem"] i')[0].textelse:temp = li.select('p[class="tem"] i')[0].textprint(city, date, weather, temp)self.db.insert(city, date, weather, temp)except Exception as err:print(err)except Exception as err:print(err)

运行结果

查看数据库保存情况：

2）心得体会

解决这类任务基本上都需要定义一个爬虫类和一个数据库类，思路清晰。学习了数据库类的定义和编写方法，实现打开、关闭、插入、查询等数据库操作。

3）Gitee链接

· https://gitee.com/jh2680513769/2025_crawler_project/blob/master/%E4%BD%9C%E4%B8%9A2/1.py

作业②

1）代码与结果

先上网站进行F12分析调试，刷新页面后在“网络日志”中找到存放网站股票信息的JSON文件（以“get？”为开头），如图：

网页分析

通过分析加载不同股票页面的url之间的参数差异，比如参数‘pn’表示页面数，可以设计分页或多页爬取。

另外，json文件的内容类似‘{Key}:{Value}’字典类型要先引入json库进行结构化解析，然后再根据‘f2’‘f12’‘f14’等参数提取出股票代码、名称等信息。

基于如上分析，设计一个多页爬取并保存数据，代码核心部分如下：

核心代码

import requests
import json
import sqlite3class StockDB:def openDB(self):self.con = sqlite3.connect("./stocks.db")self.cursor = self.con.cursor()try:self.cursor.execute("create table stocks(sNum varchar(16), sCode varchar(16), sName varchar(32), sNewest varchar(16), sUpdown varchar(16), sUpdown_num varchar(16), sTurnover varchar(32), sAmplitude varchar(16), constraint pk_stocks primary key (sCode))")except:self.cursor.execute("delete from stocks")def closeDB(self):self.con.commit()self.con.close()def insert(self, num, Code, name, newest, updown, updown_num, turnover, amplitude):try:self.cursor.execute("""insert into stocks (sNum, sCode, sName, sNewest, sUpdown, sUpdown_num, sTurnover, sAmplitude) values (?,?,?,?,?,?,?,?)""", (num, code, name, newest, updown, updown_num, turnover, amplitude))except Exception as err:print(err)#更改api参数
urls = [(f"https://push2.eastmoney.com/api/qt/clist/get?np=1&fltt=1&invt=2&cb=jQuery371037824690299744046_1762785231380&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&"f"pn={page}"    #利用api的pn参数设计分页爬取f"&pz=20&po=1&dect=1&ut=fa5fd1943c7b386f172d6893dbfba10b&wbp2u=%7C0%7C0%7C0%7Cweb&_=1762785231382")for page in range(1, 6)]
headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36 SLBrowser/9.0.6.8151 SLBChan/111 SLBVPV/64-bit'}def parse_jsonp(jsonp_str):"""解析JSONP格式数据"""try:#找到第一个左括号和最后一个右括号start = jsonp_str.find('(') + 1end = jsonp_str.rfind(')')json_str_clean = jsonp_str[start:end]return json.loads(json_str_clean)except Exception as e:print(f"解析JSONP失败: {e}")return None#创建数据库实例并打开
db = StockDB()
db.openDB()print(f"{'序号':<6}{'代码':<12}{'名称':<12}{'最新价':<8}{'涨跌幅':<8}{'涨跌额':<8}{'成交量(手)':<8}{'振幅':>6}")
num = 0
for url in urls:resp = requests.get(url, headers=headers)resp.encoding = 'utf-8'json_str = resp.textstructure_data = parse_jsonp(json_str)#提取信息if 'data' in structure_data and 'diff' in structure_data['data']:stocks = structure_data['data']['diff']# 打印前几个股票信息for i, stock in enumerate(stocks):num = num + 1code = stock.get('f12')name = stock.get('f14')newest = f"{stock.get('f2')/100:.2f}"updown = f"{stock.get('f3')/100:.2f}%"updown_num = f"{stock.get('f4')/100:.2f}"turnover = f"{stock.get('f5')/10000:.2f}万"amplitude = f"{stock.get('f7')/100:.2f}%"#把数据存入数据库db.insert(str(num), code, name, newest, updown, updown_num, turnover, amplitude)#打印前后各10行if num <= 10 or num >=90:print("%-6d%-12s%-12s%-10s%-10s%-10s%-15s%-10s" % (num, code, name, newest, updown, updown_num, turnover, amplitude))print("... ...")
#关闭数据库
db.closeDB()
print("completed")

运行结果

2）心得体会

用json库解析和提取信息也很方便，但是直接提取的信息和网页上显示的有所不同，还需要耐心进行格式化输出或者添加单位等小操作。学会了利用F12分析网络日志和分析api的参数，并利用api参数来设计爬虫程序。

3）Gitee链接

· https://gitee.com/jh2680513769/2025_crawler_project/blob/master/%E4%BD%9C%E4%B8%9A2/2.py

作业③

1）代码与结果

还是一样先F12分析一下网络日志，找到一个payload.js的文件存放的是大学排行榜的信息：

网页信息

核心代码

import requests
import re
import sqlite3class UniversityDB:def openDB(self):self.con = sqlite3.connect("./universities.db")self.cursor = self.con.cursor()try:self.cursor.execute("create table universities(uRank varchar(16), uName varchar(64), uProvince varchar(16), uCategory varchar(16), uScore varchar(16), constraint pk_stocks primary key (uName))")except:self.cursor.execute("delete from universities")def closeDB(self):self.con.commit()self.con.close()def insertDB(self, rank, name, province, category, score):try:self.cursor.execute("""insert into universities (uRank, uName, uProvince, uCategory, uScore) values (?,?,?,?,?)""", (rank, name, province, category, score))except Exception as err:print(err)def showDB(self):self.cursor.execute("select * from universities")rows = self.cursor.fetchall()print("\n数据库中的大学排名数据:")print(f"{'排名':<6}{'学校':<20}{'省市':<8}{'类型':<8}{'总分':<8}")for row in rows:print(f"{row[0]:<6}{row[1]:<20}{row[2]:<8}{row[3]:<8}{row[4]:<8}")url = "https://www.shanghairanking.cn/_nuxt/static/1762223212/rankings/bcur/2021/payload.js"
headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36 SLBrowser/9.0.6.8151 SLBChan/111 SLBVPV/64-bit','Referer':'https://www.shanghairanking.cn/rankings/bcur/2021'  #后面被反爬了因为没加Referer}
resp = requests.get(url, headers=headers)
resp.encoding = 'utf-8'
content = resp.text
#正则表达式析取信息
ranks = re.findall(r'ranking:([^,]+)', content)
names = re.findall(r'univNameCn:"([^"]+)"', content)
scores = re.findall(r'score:([^,]+)', content)
provinces = re.findall(r'province:([^,]+)', content)
categorys = re.findall(r'univCategory:([a-zA-Z])', content)
#检查提取数据长度是否一致
if len(names) != len(scores) or len(scores) != len(provinces) or len(provinces) != len(categorys):print("错误，提取信息数量不匹配！")
else:#创建数据库实例并打开db = UniversityDB()db.openDB()#映射词典创建arr1 = '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'arr2 = ["", '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 University", "322", "323", "324", "325", "326", "327", "328", "329", "330", "331", 120.9, 120.8, "Taizhou University", "336", "337", "338", 119.9, 119.7, "343", "344", "345", "346", "347", "348", "349", "350", "351", "352", "353", 115.4, "356", "357", "358", "359", "360", "361", "362", "363", "364", 112.6, "367", "368", "369", "370", 111, "373", "374", "375", "376", "377", 109.4, "380", "381", "382", "383", 107.6, "386", "387", 107.1, "390", "391", "392", "393", "394", "395", "396", "400", "401", "402", 104.7, "405", "406", "407", "408", "409", "410", "411", "412", "413", "414", "415", 101.2, 101.1, 100.9, "422", 100.3, "425", "426", "427", "428", "429", 99, "432", "433", "434", "435", "436", "437", 97.6, "440", "441", "442", "443", 96.5, "446", "447", "448", 95.8, "451", 95.2, "454", "455", "456", 94.8, "459", "460", 94.3, "463", "464", 93.6, "472", "473", 92.3, "476", 91.7, "479", "480", "481", "482", "483", "484", 90.7, 90.6, "489", "490", 90.2, "493", "494", "495", 89.3, "503", "504", "505", "506", "507", 87.4, "510", "511", "512", 86.8, "515", "516", "517", 86.2, "520", "521", 85.8, "524", "525", "526", "527", 84.6, "530", "531", "532", "537", 82.8, "540", "541", "542", "543", "544", "545", "546", "547", "548", "549", "550", "551", "552", "553", "554", 78.1, "557", "558", "559", "560", "561", "562", "563", "564", "565", "566", "567", "568", "569", "570", "571", "572", "573", "574", "575", "576", "577", "578", "579", "580", "581", "582", 4, "2025-04-15T00:00:00+08:00", "logo\u002Fannual\u002Fbcur\u002F2025.png", "软科中国大学排名于2015年首次发布，多年来以专业、客观、透明的优势赢得了高等教育领域内外的广泛关注和认可，已经成为具有重要社会影响力和权威参考价值的中国大学排名领先品牌。软科中国大学排名以服务中国高等教育发展和进步为导向，采用数百项指标变量对中国大学进行全方位、分类别、监测式评价，向学生、家长和全社会提供及时、可靠、丰富的中国高校可比信息。", 2024, 2023, 2022, 15, 2020, 2019, 2018, 2017, 2016, 2015]arr2_str = [str(i) for i in arr2]dict_map = dict(zip(arr1.split(', '), arr2_str))print(f"爬取中国大学排名（主榜）共{len(names)}所学校")print(f"{'排名':<6}{'学校':^20}{'省市':>8}{'类型':>8}{'总分':>8}")for i in range(len(names)):#使用get方法，如果找不到对应的映射就使用原值rank = dict_map.get(ranks[i], ranks[i])name = names[i]province = dict_map.get(provinces[i], provinces[i])category = dict_map.get(categorys[i], categorys[i])score = dict_map.get(scores[i], scores[i])#把数据存入数据库db.insertDB(rank, name, province, category, score)if int(rank) <=10 or int(rank) >= 570:print(f"{rank:<6}{name:^20}{province:>8}{category:>8}{score:>8}")elif int(rank) == 11:print("... ...")db.closeDB()print("所有数据已保存至数据库niversities.db，任务完成！")

中途，可能因为调试代码的过程反复请求网页，被反爬了，添加上Referer之后还能继续访问。下回还是试试先把文件保存本地的方法吧。

运行结果

F12调试分析Gif

2）心得体会

原本的数据文件不是标准的json文件，直接用json解析行不通。前面尝试了几种爬取方法，查看数据库保存数据时发现，总是有一些表格内容是奇怪的字符比如‘aB’‘jJ’等等，后面重新观察数据文件结构之后才知道字符串是代表了某个值，应该创建一个字典来映射，就不会有奇怪的字符。总结就是，不能太急于求成，应该先留心观察原始数据文件，把数据文件的格式结构等摸清之后再爬虫。