分析Python7个爬虫小案例(附源码)
????????本次的7個(gè)python爬蟲小案例涉及到了re正則、xpath、beautiful soup、selenium等知識(shí)點(diǎn),非常適合剛?cè)腴Tpython爬蟲的小伙伴參考學(xué)習(xí)。注:若涉及到版權(quán)或隱私問題,請(qǐng)及時(shí)聯(lián)系我刪除即可。
1.使用正則表達(dá)式和文件操作爬取并保存“百度貼吧”某帖子全部?jī)?nèi)容(該帖不少于5頁。
?本次選取的是百度貼吧中的NBA吧中的一篇帖子,帖子標(biāo)題是“克萊和哈登,誰歷史地位更高”。爬取的目標(biāo)是帖子里面的回復(fù)內(nèi)容。
源程序和關(guān)鍵結(jié)果截圖:
import csv import requests import re import timedef main(page):url = f'https://tieba.baidu.com/p/7882177660?pn={page}'headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/106.0.0.0 Safari/537.36'}resp = requests.get(url,headers=headers)html = resp.text# 評(píng)論內(nèi)容comments = re.findall('style="display:;"> (.*?)</div>',html)# 評(píng)論用戶users = re.findall('class="p_author_name j_user_card" href=".*?" target="_blank">(.*?)</a>',html)# 評(píng)論時(shí)間comment_times = re.findall('樓</span><span class="tail-info">(.*?)</span><div',html)for u,c,t in zip(users,comments,comment_times):# 篩選數(shù)據(jù),過濾掉異常數(shù)據(jù)if 'img' in c or 'div' in c or len(u)>50:continuecsvwriter.writerow((u,t,c))print(u,t,c)print(f'第{page}頁爬取完畢')if __name__ == '__main__':with open('01.csv','a',encoding='utf-8')as f:csvwriter = csv.writer(f)csvwriter.writerow(('評(píng)論用戶','評(píng)論時(shí)間','評(píng)論內(nèi)容'))for page in range(1,8): # 爬取前7頁的內(nèi)容main(page)time.sleep(2)2.實(shí)現(xiàn)多線程爬蟲爬取某小說部分章節(jié)內(nèi)容并以數(shù)據(jù)庫存儲(chǔ)(不少于10個(gè)章節(jié)。?
?本次選取的小說網(wǎng)址是全本小說網(wǎng)https://www.qb5.tw/,這里我們選取第一篇小說進(jìn)行爬取
然后通過分析網(wǎng)頁源代碼分析每章小說的鏈接
找到鏈接的位置后,我們使用Xpath來進(jìn)行鏈接和每一章標(biāo)題的提取
在這里,因?yàn)樯婕暗蕉啻问褂胷equests發(fā)送請(qǐng)求,所以這里我們把它封裝成一個(gè)函數(shù),便于后面的使用
每一章的鏈接獲取后,我們開始進(jìn)入小說章節(jié)內(nèi)容頁面進(jìn)行分析
通過網(wǎng)頁分析,小說內(nèi)容都在網(wǎng)頁源代碼中,屬于靜態(tài)數(shù)據(jù)
這里我們選用re正則表達(dá)式進(jìn)行數(shù)據(jù)提取,并對(duì)最后的結(jié)果進(jìn)行清洗
然后我們需要將數(shù)據(jù)保存到數(shù)據(jù)庫中,這里我將爬取的數(shù)據(jù)存儲(chǔ)到mysql數(shù)據(jù)庫中,先封住一下數(shù)據(jù)庫的操作
接著將爬取到是數(shù)據(jù)進(jìn)行保存
最后一步就是使用多線程來提高爬蟲效率,這里我們創(chuàng)建了5個(gè)線程的線程池
?源代碼及結(jié)果截圖:
import requests from lxml import etree import re import pymysql from time import sleep from concurrent.futures import ThreadPoolExecutordef get_conn():# 創(chuàng)建連接conn = pymysql.connect(host="127.0.0.1",user="root",password="root",db="novels",charset="utf8")# 創(chuàng)建游標(biāo)cursor = conn.cursor()return conn, cursordef close_conn(conn, cursor):cursor.close()conn.close()def get_xpath_resp(url):headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/106.0.0.0 Safari/537.36'}resp = requests.get(url, headers=headers)tree = etree.HTML(resp.text) # 用etree解析htmlreturn tree,respdef get_chapters(url):tree,_ = get_xpath_resp(url)# 獲取小說名字novel_name = tree.xpath('//*[@id="info"]/h1/text()')[0]# 獲取小說數(shù)據(jù)節(jié)點(diǎn)dds = tree.xpath('/html/body/div[4]/dl/dd')title_list = []link_list = []for d in dds[:15]:title = d.xpath('./a/text()')[0] # 章節(jié)標(biāo)題title_list.append(title)link = d.xpath('./a/@href')[0] # 章節(jié)鏈接chapter_url = url +link # 構(gòu)造完整鏈接link_list.append(chapter_url)return title_list,link_list,novel_namedef get_content(novel_name,title,url):try:cursor = Noneconn = Noneconn, cursor = get_conn()# 插入數(shù)據(jù)的sqlsql = 'INSERT INTO novel(novel_name,chapter_name,content) VALUES(%s,%s,%s)'tree,resp = get_xpath_resp(url)# 獲取內(nèi)容content = re.findall('<div id="content">(.*?)</div>',resp.text)[0]# 對(duì)內(nèi)容進(jìn)行清洗content = content.replace('<br />','\n').replace(' ',' ').replace('全本小說網(wǎng) www.qb5.tw,最快更新<a href="https://www.qb5.tw/book_116659/">宇宙職業(yè)選手</a>最新章節(jié)!<br><br>','')print(title,content)cursor.execute(sql,[novel_name,title,content]) # 插入數(shù)據(jù)conn.commit() # 提交事務(wù)保存數(shù)據(jù)except:passfinally:sleep(2)close_conn(conn, cursor) # 關(guān)閉數(shù)據(jù)庫if __name__ == '__main__':# 獲取小說名字,標(biāo)題鏈接,章節(jié)名稱title_list, link_list, novel_name = get_chapters('https://www.qb5.tw/book_116659/')with ThreadPoolExecutor(5) as t: # 創(chuàng)建5個(gè)線程for title,link in zip(title_list,link_list):t.submit(get_content, novel_name,title,link) # 啟動(dòng)線程?3. 分別使用XPath和Beautiful Soup4兩種方式爬取并保存非異步加載的“豆瓣某排行榜”如https://movie.douban.com/top250的名稱、描述、評(píng)分和評(píng)價(jià)人數(shù)等數(shù)據(jù)。
?先分析:
首先,來到豆瓣Top250頁面,首先使用Xpath版本的來抓取數(shù)據(jù),先分析下電影列表頁的數(shù)據(jù)結(jié)構(gòu),發(fā)下都在網(wǎng)頁源代碼中,屬于靜態(tài)數(shù)據(jù)
接著我們找到數(shù)據(jù)的規(guī)律,使用xpath提取每一個(gè)電影的鏈接及電影名
然后根據(jù)鏈接進(jìn)入到其詳情頁
分析詳情頁的數(shù)據(jù),發(fā)現(xiàn)也是靜態(tài)數(shù)據(jù),繼續(xù)使用xpath提取數(shù)據(jù)
最后我們將爬取的數(shù)據(jù)進(jìn)行存儲(chǔ),這里用csv文件進(jìn)行存儲(chǔ)
接著是Beautiful Soup4版的,在這里,我們直接在電影列表頁使用bs4中的etree進(jìn)行數(shù)據(jù)提取
最后,同樣使用csv文件進(jìn)行數(shù)據(jù)存儲(chǔ)
源代碼即結(jié)果截圖:
XPath版:
import re from time import sleep import requests from lxml import etree import random import csvdef main(page,f):url = f'https://movie.douban.com/top250?start={page*25}&filter='headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.35 Safari/537.36',}resp = requests.get(url,headers=headers)tree = etree.HTML(resp.text)# 獲取詳情頁的鏈接列表href_list = tree.xpath('//*[@id="content"]/div/div[1]/ol/li/div/div[1]/a/@href')# 獲取電影名稱列表name_list = tree.xpath('//*[@id="content"]/div/div[1]/ol/li/div/div[2]/div[1]/a/span[1]/text()')for url,name in zip(href_list,name_list):f.flush() # 刷新文件try:get_info(url,name) # 獲取詳情頁的信息except:passsleep(1 + random.random()) # 休息print(f'第{i+1}頁爬取完畢')def get_info(url,name):headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.35 Safari/537.36','Host': 'movie.douban.com',}resp = requests.get(url,headers=headers)html = resp.texttree = etree.HTML(html)# 導(dǎo)演dir = tree.xpath('//*[@id="info"]/span[1]/span[2]/a/text()')[0]# 電影類型type_ = re.findall(r'property="v:genre">(.*?)</span>',html)type_ = '/'.join(type_)# 國家country = re.findall(r'地區(qū):</span> (.*?)<br',html)[0]# 上映時(shí)間time = tree.xpath('//*[@id="content"]/h1/span[2]/text()')[0]time = time[1:5]# 評(píng)分rate = tree.xpath('//*[@id="interest_sectl"]/div[1]/div[2]/strong/text()')[0]# 評(píng)論人數(shù)people = tree.xpath('//*[@id="interest_sectl"]/div[1]/div[2]/div/div[2]/a/span/text()')[0]print(name,dir,type_,country,time,rate,people) # 打印結(jié)果csvwriter.writerow((name,dir,type_,country,time,rate,people)) # 保存到文件中if __name__ == '__main__':# 創(chuàng)建文件用于保存數(shù)據(jù)with open('03-movie-xpath.csv','a',encoding='utf-8',newline='')as f:csvwriter = csv.writer(f)# 寫入表頭標(biāo)題csvwriter.writerow(('電影名稱','導(dǎo)演','電影類型','國家','上映年份','評(píng)分','評(píng)論人數(shù)'))for i in range(10): # 爬取10頁main(i,f) # 調(diào)用主函數(shù)sleep(3 + random.random())Beautiful Soup4版:?
import random import urllib.request from bs4 import BeautifulSoup import codecs from time import sleepdef main(url, headers):# 發(fā)送請(qǐng)求page = urllib.request.Request(url, headers=headers)page = urllib.request.urlopen(page)contents = page.read()# 用BeautifulSoup解析網(wǎng)頁soup = BeautifulSoup(contents, "html.parser")infofile.write("")print('爬取豆瓣電影250: \n')for tag in soup.find_all(attrs={"class": "item"}):# 爬取序號(hào)num = tag.find('em').get_text()print(num)infofile.write(num + "\r\n")# 電影名稱name = tag.find_all(attrs={"class": "title"})zwname = name[0].get_text()print('[中文名稱]', zwname)infofile.write("[中文名稱]" + zwname + "\r\n")# 網(wǎng)頁鏈接url_movie = tag.find(attrs={"class": "hd"}).aurls = url_movie.attrs['href']print('[網(wǎng)頁鏈接]', urls)infofile.write("[網(wǎng)頁鏈接]" + urls + "\r\n")# 爬取評(píng)分和評(píng)論數(shù)info = tag.find(attrs={"class": "star"}).get_text()info = info.replace('\n', ' ')info = info.lstrip()print('[評(píng)分評(píng)論]', info)# 獲取評(píng)語info = tag.find(attrs={"class": "inq"})if (info): # 避免沒有影評(píng)調(diào)用get_text()報(bào)錯(cuò)content = info.get_text()print('[影評(píng)]', content)infofile.write(u"[影評(píng)]" + content + "\r\n")print('')if __name__ == '__main__':# 存儲(chǔ)文件infofile = codecs.open("03-movie-bs4.txt", 'a', 'utf-8')# 消息頭headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36'}# 翻頁i = 0while i < 10:print('頁碼', (i + 1))num = i * 25 # 每次顯示25部 URL序號(hào)按25增加url = 'https://movie.douban.com/top250?start=' + str(num) + '&filter='main(url, headers)sleep(5 + random.random())infofile.write("\r\n\r\n")i = i + 1infofile.close()?
?4.實(shí)現(xiàn)某東商城某商品評(píng)論數(shù)據(jù)的爬取(評(píng)論數(shù)據(jù)不少于100條,包括評(píng)論內(nèi)容、時(shí)間和評(píng)分)。
?先分析:
?本次選取的某東官網(wǎng)的一款聯(lián)想筆記本電腦,數(shù)據(jù)為動(dòng)態(tài)加載的,通過開發(fā)者工具抓包分析即可。
源代碼及結(jié)果截圖:
import requests import csv from time import sleep import randomdef main(page,f):url = 'https://club.jd.com/comment/productPageComments.action'params = {'productId': 100011483893,'score': 0,'sortType': 5,'page': page,'pageSize': 10,'isShadowSku': 0,'fold': 1}headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.35 Safari/537.36','referer': 'https://item.jd.com/'}resp = requests.get(url,params=params,headers=headers).json()comments = resp['comments']for comment in comments:content = comment['content']content = content.replace('\n','')comment_time = comment['creationTime']score = comment['score']print(score,comment_time,content)csvwriter.writerow((score,comment_time,content))print(f'第{page+1}頁爬取完畢')if __name__ == '__main__':with open('04.csv','a',encoding='utf-8',newline='')as f:csvwriter = csv.writer(f)csvwriter.writerow(('評(píng)分','評(píng)論時(shí)間','評(píng)論內(nèi)容'))for page in range(15):main(page,f)sleep(5+random.random())5. 實(shí)現(xiàn)多種方法模擬登錄知乎,并爬取與一個(gè)與江漢大學(xué)有關(guān)問題和答案。
首先使用selenium打開知乎登錄頁面,接著使用手機(jī)進(jìn)行二維碼掃描登錄
進(jìn)入頁面后,打開開發(fā)者工具,找到元素,,定位輸入框,輸入漢江大學(xué),然后點(diǎn)擊搜索按鈕
?
以第二條帖子為例,進(jìn)行元素分析?。
源代碼及結(jié)果截圖:
from time import sleep from selenium.webdriver.chrome.service import Service from selenium.webdriver import Chrome,ChromeOptions from selenium.webdriver.common.by import By import warningsdef main():#忽略警告warnings.filterwarnings("ignore")# 創(chuàng)建一個(gè)驅(qū)動(dòng)service = Service('chromedriver.exe')options = ChromeOptions()# 偽造瀏覽器options.add_experimental_option('excludeSwitches', ['enable-automation','enable-logging'])options.add_experimental_option('useAutomationExtension', False)# 創(chuàng)建一個(gè)瀏覽器driver = Chrome(service=service,options=options)# 繞過檢測(cè)driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {"source": """Object.defineProperty(navigator, 'webdriver', {get: () => false})"""})# 打開知乎登錄頁面driver.get('https://www.zhihu.com/')sleep(30)# 點(diǎn)擊搜索框driver.find_element(By.ID,'Popover1-toggle').click()# 輸入內(nèi)容driver.find_element(By.ID,'Popover1-toggle').send_keys('漢江大學(xué)')sleep(2)# 點(diǎn)擊搜索圖標(biāo)driver.find_element(By.XPATH,'//*[@id="root"]/div/div[2]/header/div[2]/div[1]/div/form/div/div/label/button').click()# 等待頁面加載完driver.implicitly_wait(20)# 獲取標(biāo)題title = driver.find_element(By.XPATH,'//*[@id="SearchMain"]/div/div/div/div/div[2]/div/div/div/h2/div/a/span').text# 點(diǎn)擊閱讀全文driver.find_element(By.XPATH,'//*[@id="SearchMain"]/div/div/div/div/div[2]/div/div/div/div/span/div/button').click()sleep(2)# 獲取帖子內(nèi)容content = driver.find_element(By.XPATH,'//*[@id="SearchMain"]/div/div/div/div/div[2]/div/div/div/div/span[1]/div/span/p').text# 點(diǎn)擊評(píng)論driver.find_element(By.XPATH,'//*[@id="SearchMain"]/div/div/div/div/div[2]/div/div/div/div/div[3]/div/div/button[1]').click()sleep(2)# 點(diǎn)擊獲取更多評(píng)論driver.find_element(By.XPATH,'//*[@id="SearchMain"]/div/div/div/div/div[2]/div/div/div/div[2]/div/div/div[2]/div[2]/div/div[3]/button').click()sleep(2)# 獲取評(píng)論數(shù)據(jù)的節(jié)點(diǎn)divs = driver.find_elements(By.XPATH,'/html/body/div[6]/div/div/div[2]/div/div/div/div[2]/div[3]/div')try:for div in divs:# 評(píng)論內(nèi)容comment = div.find_element(By.XPATH,'./div/div/div[2]').textf.write(comment) # 寫入文件f.write('\n')print(comment)except:driver.close()if __name__ == '__main__':# 創(chuàng)建文件存儲(chǔ)數(shù)據(jù)with open('05.txt','a',encoding='utf-8')as f:main()?6. 綜合利用所學(xué)知識(shí),爬取某個(gè)某博用戶前5頁的微博內(nèi)容。
這里我們選取了人民日?qǐng)?bào)的微博內(nèi)容進(jìn)行爬取,具體頁面我就不放這了,怕違規(guī)。
源代碼及結(jié)果截圖:
import requests import csv from time import sleep import randomdef main(page):url = f'https://weibo.com/ajax/statuses/mymblog?uid=2803301701&page={page}&feature=0&since_id=4824543023860882kp{page}'headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/106.0.0.0 Safari/537.36','cookie':'SINAGLOBAL=6330339198688.262.1661412257300; ULV=1661412257303:1:1:1:6330339198688.262.1661412257300:; PC_TOKEN=8b935a3a6e; SUBP=0033WrSXqPxfM725Ws9jqgMF55529P9D9WWoQDW1G.Vsux_WIbm9NsCq5JpX5KMhUgL.FoMNShMN1K5ESKq2dJLoIpjLxKnL1h.LB.-LxKqLBoBLB.-LxKqLBKeLB--t; ALF=1697345086; SSOLoginState=1665809086; SCF=Auy-TaGDNaCT06C4RU3M3kQ0-QgmTXuo9D79pM7HVAjce1K3W92R1-fHAP3gXR6orrHK_FSwDsodoGTj7nX_1Hw.; SUB=_2A25OTkruDeRhGeFJ71UW-S7OzjqIHXVtOjsmrDV8PUNbmtANLVKmkW9Nf9yGtaKedmyOsDKGh84ivtfHMGwvRNtZ; XSRF-TOKEN=LK4bhZJ7sEohF6dtSwhZnTS4; WBPSESS=PfYjpkhjwcpEXrS7xtxJwmpyQoHWuGAMhQkKHvr_seQNjwPPx0HJgSgqWTZiNRgDxypgeqzSMsbVyaDvo7ng6uTdC9Brt07zYoh6wXXhQjMtzAXot-tZzLRlW_69Am82CXWOFfcvM4AzsWlAI-6ZNA=='}resp = requests.get(url,headers=headers)data_list = resp.json()['data']['list']for item in data_list:created_time = item['created_at'] # 發(fā)布時(shí)間author = item['user']['screen_name'] # 作者title = item['text_raw'] # 帖子標(biāo)題reposts_count = item['reposts_count'] # 轉(zhuǎn)發(fā)數(shù)comments_count = item['comments_count'] # 評(píng)論數(shù)attitudes_count = item['attitudes_count'] # 點(diǎn)贊數(shù)csvwriter.writerow((created_time,author,title,reposts_count,comments_count,attitudes_count))print(created_time,author,title,reposts_count,comments_count,attitudes_count)print(f'第{page}頁爬取完畢')if __name__ == '__main__':# 創(chuàng)建保存數(shù)據(jù)的csv文件with open('06-2.csv','a',encoding='utf-8',newline='')as f:csvwriter = csv.writer(f)# 添加文件表頭csvwriter.writerow(('發(fā)布時(shí)間','發(fā)布作者','帖子標(biāo)題','轉(zhuǎn)發(fā)數(shù)','評(píng)論數(shù)','點(diǎn)贊數(shù)'))for page in range(1,6): # 爬取前5頁數(shù)據(jù)main(page)sleep(5+random.random())?7.自選一個(gè)熱點(diǎn)或者你感興趣的主題,爬取數(shù)據(jù)并進(jìn)行簡(jiǎn)要數(shù)據(jù)分析(例如,通過爬取電影的名稱、類型、總票房等數(shù)據(jù)統(tǒng)計(jì)分析不同類型電影的平均票房,十年間每年票房冠軍的票房走勢(shì)等;通過爬取中國各省份地區(qū)人口數(shù)量,統(tǒng)計(jì)分析我國人口分布等)。
本次選取的網(wǎng)址是藝恩娛數(shù),目標(biāo)是爬取里面的票房榜數(shù)據(jù),通過開發(fā)者工具抓包分析找到數(shù)據(jù)接口,然后開始編寫代碼進(jìn)行數(shù)據(jù)抓取。?
源代碼及結(jié)果截圖:
import requests import csv import pandas as pd import matplotlib.pyplot as plt import warnings warnings.filterwarnings('ignore') plt.rcParams['font.sans-serif'] = ['SimHei'] #解決中文顯示 plt.rcParams['axes.unicode_minus'] = False #解決符號(hào)無法顯示def main():headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36',}data = {'r': '0.9936776079863086','top': '50','type': '0',}resp = requests.post('https://ys.endata.cn/enlib-api/api/home/getrank_mainland.do', headers=headers, data=data)data_list = resp.json()['data']['table0']for item in data_list:rank = item['Irank'] # 排名MovieName = item['MovieName'] # 電影名稱ReleaseTime = item['ReleaseTime'] # 上映時(shí)間TotalPrice = item['BoxOffice'] # 總票房(萬)AvgPrice = item['AvgBoxOffice'] # 平均票價(jià)AvgAudienceCount = item['AvgAudienceCount'] # 平均場(chǎng)次# 寫入csv文件csvwriter.writerow((rank,MovieName,ReleaseTime,TotalPrice,AvgPrice,AvgAudienceCount))print(rank,MovieName,ReleaseTime,TotalPrice,AvgPrice,AvgAudienceCount)def data_analyze():# 讀取數(shù)據(jù)data = pd.read_csv('07.csv')# 從上映時(shí)間中提取出年份data['年份'] = data['上映時(shí)間'].apply(lambda x: x.split('-')[0])# 各年度上榜電影總票房占比df1 = data.groupby('年份')['總票房(萬)'].sum()plt.figure(figsize=(6, 6))plt.pie(df1, labels=df1.index.to_list(), autopct='%1.2f%%')plt.title('各年度上榜電影總票房占比')plt.show()# 各個(gè)年份總票房趨勢(shì)df1 = data.groupby('年份')['總票房(萬)'].sum()plt.figure(figsize=(6, 6))plt.plot(df1.index.to_list(), df1.values.tolist())plt.title('各年度上榜電影總票房趨勢(shì)')plt.show()# 平均票價(jià)最貴的前十名電影print(data.sort_values(by='平均票價(jià)', ascending=False)[['年份', '電影名稱', '平均票價(jià)']].head(10))# 平均場(chǎng)次最高的前十名電影print(data.sort_values(by='平均場(chǎng)次', ascending=False)[['年份', '電影名稱', '平均場(chǎng)次']].head(10))if __name__ == '__main__':# 創(chuàng)建保存數(shù)據(jù)的csv文件with open('07.csv', 'w', encoding='utf-8',newline='') as f:csvwriter = csv.writer(f)# 添加文件表頭csvwriter.writerow(('排名', '電影名稱', '上映時(shí)間', '總票房(萬)', '平均票價(jià)', '平均場(chǎng)次'))main()# 數(shù)據(jù)分析data_analyze()?
?從年度上榜電影票房占比來看,2019年占比最高,說明2019年這一年的電影質(zhì)量都很不錯(cuò),上榜電影多而且票房高。
從趨勢(shì)來看,從2016年到2019年,上榜電影總票房一直在增長(zhǎng),到2019年達(dá)到頂峰,說明這一年電影是非常的火爆,但是從2020年急劇下滑,最大的原因應(yīng)該是這一年年初開始爆發(fā)疫情,導(dǎo)致賀歲檔未初期上映,而且由于疫情影響,電影院一直處于關(guān)閉狀態(tài),所以這一年票房慘淡。
????????好了,本次案例分享到此結(jié)束,希望對(duì)剛?cè)胧峙老x的小伙伴有所幫助。?
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