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COVID-19 肺炎疫情数据实时监控（python 爬虫 + pyecharts 数据可视化 + wordcloud 词云图）

發(fā)布時間：2023/12/10 python 28 豆豆

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文章目錄

- 【1x00】前言
- 【2x00】思維導(dǎo)圖
- 【3x00】數(shù)據(jù)結(jié)構(gòu)分析
- 【4x00】主函數(shù) main()
- 【5x00】數(shù)據(jù)獲取模塊 data_get
- - 【5x01】初始化函數(shù) init()
  - 【5x02】中國總數(shù)據(jù) china_total_data()
  - 【5x03】全球總數(shù)據(jù) global_total_data()
  - 【5x04】中國每日數(shù)據(jù) china_daily_data()
  - 【5x05】境外每日數(shù)據(jù) foreign_daily_data()
- 【6x00】詞云圖繪制模塊 data_wordcloud
- - 【6x01】中國累計確診詞云圖 foreign_daily_data()
  - 【6x02】全球累計確診詞云圖 foreign_daily_data()
- 【7x00】地圖繪制模塊 data_map
- - 【7x01】中國累計確診地圖 china_total_map()
  - 【7x02】全球累計確診地圖 global_total_map()
  - 【7x03】中國每日數(shù)據(jù)折線圖 china_daily_map()
  - 【7x04】境外每日數(shù)據(jù)折線圖 foreign_daily_map()
- 【8x00】結(jié)果截圖
- - 【8x01】數(shù)據(jù)儲存 Excel
  - 【8x02】詞云圖
  - 【8x03】地圖 + 折線圖
- 【9x00】完整代碼

這里是一段防爬蟲文本，請讀者忽略。本文原創(chuàng)首發(fā)于 CSDN，作者 TRHX。博客首頁：https://itrhx.blog.csdn.net/ 本文鏈接：https://itrhx.blog.csdn.net/article/details/107140534 未經(jīng)授權(quán)，禁止轉(zhuǎn)載！惡意轉(zhuǎn)載，后果自負(fù)！尊重原創(chuàng)，遠(yuǎn)離剽竊！

【1x00】前言

本來兩三個月之前就想搞個疫情數(shù)據(jù)實(shí)時數(shù)據(jù)展示的，由于各種不可抗拒因素一而再再而三的鴿了，最近終于抽空寫了一個，數(shù)據(jù)是用 Python 爬取的百度疫情實(shí)時大數(shù)據(jù)報告，請求庫用的 requests，解析用的 Xpath 語法，詞云用的 wordcloud 庫，數(shù)據(jù)可視化用 pyecharts 繪制的地圖和折線圖，數(shù)據(jù)儲存在 Excel 表格里面，使用 openpyxl 對表格進(jìn)行處理。

本程序?qū)崿F(xiàn)了累計確診地圖展示和每日數(shù)據(jù)變化折線圖展示，其他更多數(shù)據(jù)的獲取和展示均可在程序中進(jìn)行拓展，可以將程序部署在服務(wù)器上，設(shè)置定時運(yùn)行，即可實(shí)時展示數(shù)據(jù)，pyecharts 繪圖模塊也可以整合到 Web 框架（Django、Flask等）中使用。

在獲取數(shù)據(jù)時有全球和境外兩個概念，全球包含中國，境外不包含中國，后期繪制的四個圖：中國累計確診地圖、全球累計確診地圖（包含中國）、中國每日數(shù)據(jù)折線圖、境外每日數(shù)據(jù)折線圖（不包含中國）。

注意項：直接向該網(wǎng)頁發(fā)送請求獲取的響應(yīng)中，沒有每個國家的每日數(shù)據(jù)，該數(shù)據(jù)獲取的地址是：https://voice.baidu.com/newpneumonia/get?target=trend&isCaseIn=1&stage=publish

預(yù)覽地址：http://cov.itrhx.com/
數(shù)據(jù)來源：https://voice.baidu.com/act/newpneumonia/newpneumonia/
pyecharts 文檔：https://pyecharts.org/
openpyxl 文檔：https://openpyxl.readthedocs.io/
wordcloud 文檔：http://amueller.github.io/word_cloud/

【2x00】思維導(dǎo)圖

【3x00】數(shù)據(jù)結(jié)構(gòu)分析

通過查看百度的疫情數(shù)據(jù)頁面，可以看到很多整齊的數(shù)據(jù)，猜測就是疫情相關(guān)的數(shù)據(jù)，保存該頁面，對其進(jìn)行格式化，很容易可以分析出所有的數(shù)據(jù)都在 <script type="application/json" id="captain-config"></script> 里面，其中 title 里面是一些 Unicode 編碼，將其轉(zhuǎn)為中文后更容易得到不同的分類數(shù)據(jù)。

由于數(shù)據(jù)繁多，可以將數(shù)據(jù)主體部分提取出來，刪除一些重復(fù)項和其他雜項，留下數(shù)據(jù)大體位置并分析數(shù)據(jù)結(jié)構(gòu)，便于后期的數(shù)據(jù)提取，經(jīng)過處理后的數(shù)據(jù)大致結(jié)構(gòu)如下：

【4x00】主函數(shù) main()

分別將數(shù)據(jù)獲取、詞云圖繪制、地圖繪制寫入三個文件：data_get()、data_wordcloud()、data_map()，然后使用一個主函數(shù)文件 main.py 來調(diào)用這三個文件里面的函數(shù)。

import data_get import data_wordcloud import data_mapdata_dict = data_get.init() data_get.china_total_data(data_dict) data_get.global_total_data(data_dict) data_get.china_daily_data(data_dict) data_get.foreign_daily_data(data_dict)data_wordcloud.china_wordcloud() data_wordcloud.global_wordcloud()data_map.all_map()

【5x00】數(shù)據(jù)獲取模塊 data_get

【5x01】初始化函數(shù) init()

使用 xpath 語法 //script[@id="captain-config"]/text() 提取里面的值，利用 json.loads 方法將其轉(zhuǎn)換為字典對象，以便后續(xù)的其他函數(shù)調(diào)用。

def init():headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.13 Safari/537.36'}url = 'https://voice.baidu.com/act/newpneumonia/newpneumonia/'response = requests.get(url=url, headers=headers)tree = etree.HTML(response.text)dict1 = tree.xpath('//script[@id="captain-config"]/text()')print(type(dict1[0]))dict2 = json.loads(dict1[0])return dict2

【5x02】中國總數(shù)據(jù) china_total_data()

def china_total_data(data):"""1、中國省/直轄市/自治區(qū)/行政區(qū)疫情數(shù)據(jù)省/直轄市/自治區(qū)/行政區(qū)：area現(xiàn)有確診： curConfirm累計確診： confirmed累計治愈： crued累計死亡： died現(xiàn)有確診增量： curConfirmRelative累計確診增量： confirmedRelative累計治愈增量： curedRelative累計死亡增量： diedRelative"""wb = openpyxl.Workbook() # 創(chuàng)建工作簿ws_china = wb.active # 獲取工作表ws_china.title = "中國省份疫情數(shù)據(jù)" # 命名工作表ws_china.append(['省/直轄市/自治區(qū)/行政區(qū)', '現(xiàn)有確診', '累計確診', '累計治愈','累計死亡', '現(xiàn)有確診增量', '累計確診增量','累計治愈增量', '累計死亡增量'])china = data['component'][0]['caseList']for province in china:ws_china.append([province['area'],province['curConfirm'],province['confirmed'],province['crued'],province['died'],province['curConfirmRelative'],province['confirmedRelative'],province['curedRelative'],province['diedRelative']])"""2、中國城市疫情數(shù)據(jù)城市：city現(xiàn)有確診：curConfirm累計確診：confirmed累計治愈：crued累計死亡：died累計確診增量：confirmedRelative"""ws_city = wb.create_sheet('中國城市疫情數(shù)據(jù)')ws_city.append(['城市', '現(xiàn)有確診', '累計確診','累計治愈', '累計死亡', '累計確診增量'])for province in china:for city in province['subList']:# 某些城市沒有 curConfirm 數(shù)據(jù)，則將其設(shè)置為 0，crued 和 died 為空時，替換成 0if 'curConfirm' not in city:city['curConfirm'] = '0'if city['crued'] == '':city['crued'] = '0'if city['died'] == '':city['died'] = '0'ws_city.append([city['city'], '0', city['confirmed'],city['crued'], city['died'], city['confirmedRelative']])"""3、中國疫情數(shù)據(jù)更新時間：mapLastUpdatedTime"""time_domestic = data['component'][0]['mapLastUpdatedTime']ws_time = wb.create_sheet('中國疫情數(shù)據(jù)更新時間')ws_time.column_dimensions['A'].width = 22 # 調(diào)整列寬ws_time.append(['中國疫情數(shù)據(jù)更新時間'])ws_time.append([time_domestic])wb.save('COVID-19-China.xlsx')print('中國疫情數(shù)據(jù)已保存至 COVID-19-China.xlsx！')

【5x03】全球總數(shù)據(jù) global_total_data()

全球總數(shù)據(jù)在提取完成后，進(jìn)行地圖繪制時發(fā)現(xiàn)并沒有中國的數(shù)據(jù)，因此在寫入全球數(shù)據(jù)時注意要單獨(dú)將中國的數(shù)據(jù)插入 Excel 中。

def global_total_data(data):"""1、全球各國疫情數(shù)據(jù)國家：country現(xiàn)有確診：curConfirm累計確診：confirmed累計治愈：crued累計死亡：died累計確診增量：confirmedRelative"""wb = openpyxl.Workbook()ws_global = wb.activews_global.title = "全球各國疫情數(shù)據(jù)"# 按照國家保存數(shù)據(jù)countries = data['component'][0]['caseOutsideList']ws_global.append(['國家', '現(xiàn)有確診', '累計確診', '累計治愈', '累計死亡', '累計確診增量'])for country in countries:ws_global.append([country['area'],country['curConfirm'],country['confirmed'],country['crued'],country['died'],country['confirmedRelative']])# 按照洲保存數(shù)據(jù)continent = data['component'][0]['globalList']for area in continent:ws_foreign = wb.create_sheet(area['area'] + '疫情數(shù)據(jù)')ws_foreign.append(['國家', '現(xiàn)有確診', '累計確診', '累計治愈', '累計死亡', '累計確診增量'])for country in area['subList']:ws_foreign.append([country['country'],country['curConfirm'],country['confirmed'],country['crued'],country['died'],country['confirmedRelative']])# 在“全球各國疫情數(shù)據(jù)”和“亞洲疫情數(shù)據(jù)”兩張表中寫入中國疫情數(shù)據(jù)ws1, ws2 = wb['全球各國疫情數(shù)據(jù)'], wb['亞洲疫情數(shù)據(jù)']original_data = data['component'][0]['summaryDataIn']add_china_data = ['中國',original_data['curConfirm'],original_data['confirmed'],original_data['cured'],original_data['died'],original_data['confirmedRelative']]ws1.append(add_china_data)ws2.append(add_china_data)"""2、全球疫情數(shù)據(jù)更新時間：foreignLastUpdatedTime"""time_foreign = data['component'][0]['foreignLastUpdatedTime']ws_time = wb.create_sheet('全球疫情數(shù)據(jù)更新時間')ws_time.column_dimensions['A'].width = 22 # 調(diào)整列寬ws_time.append(['全球疫情數(shù)據(jù)更新時間'])ws_time.append([time_foreign])wb.save('COVID-19-Global.xlsx')print('全球疫情數(shù)據(jù)已保存至 COVID-19-Global.xlsx！')

【5x04】中國每日數(shù)據(jù) china_daily_data()

def china_daily_data(data):"""i_dict = data['component'][0]['trend']i_dict['updateDate']：日期i_dict['list'][0]：確診i_dict['list'][1]：疑似i_dict['list'][2]：治愈i_dict['list'][3]：死亡i_dict['list'][4]：新增確診i_dict['list'][5]：新增疑似i_dict['list'][6]：新增治愈i_dict['list'][7]：新增死亡i_dict['list'][8]：累計境外輸入i_dict['list'][9]：新增境外輸入"""ccd_dict = data['component'][0]['trend']update_date = ccd_dict['updateDate'] # 日期china_confirmed = ccd_dict['list'][0]['data'] # 每日累計確診數(shù)據(jù)china_crued = ccd_dict['list'][2]['data'] # 每日累計治愈數(shù)據(jù)china_died = ccd_dict['list'][3]['data'] # 每日累計死亡數(shù)據(jù)wb = openpyxl.load_workbook('COVID-19-China.xlsx')# 寫入每日累計確診數(shù)據(jù)ws_china_confirmed = wb.create_sheet('中國每日累計確診數(shù)據(jù)')ws_china_confirmed.append(['日期', '數(shù)據(jù)'])for data in zip(update_date, china_confirmed):ws_china_confirmed.append(data)# 寫入每日累計治愈數(shù)據(jù)ws_china_crued = wb.create_sheet('中國每日累計治愈數(shù)據(jù)')ws_china_crued.append(['日期', '數(shù)據(jù)'])for data in zip(update_date, china_crued):ws_china_crued.append(data)# 寫入每日累計死亡數(shù)據(jù)ws_china_died = wb.create_sheet('中國每日累計死亡數(shù)據(jù)')ws_china_died.append(['日期', '數(shù)據(jù)'])for data in zip(update_date, china_died):ws_china_died.append(data)wb.save('COVID-19-China.xlsx')print('中國每日累計確診/治愈/死亡數(shù)據(jù)已保存至 COVID-19-China.xlsx！')

【5x05】境外每日數(shù)據(jù) foreign_daily_data()

def foreign_daily_data(data):"""te_dict = data['component'][0]['allForeignTrend']te_dict['updateDate']：日期te_dict['list'][0]：累計確診te_dict['list'][1]：治愈te_dict['list'][2]：死亡te_dict['list'][3]：現(xiàn)有確診te_dict['list'][4]：新增確診"""te_dict = data['component'][0]['allForeignTrend']update_date = te_dict['updateDate'] # 日期foreign_confirmed = te_dict['list'][0]['data'] # 每日累計確診數(shù)據(jù)foreign_crued = te_dict['list'][1]['data'] # 每日累計治愈數(shù)據(jù)foreign_died = te_dict['list'][2]['data'] # 每日累計死亡數(shù)據(jù)wb = openpyxl.load_workbook('COVID-19-Global.xlsx')# 寫入每日累計確診數(shù)據(jù)ws_foreign_confirmed = wb.create_sheet('境外每日累計確診數(shù)據(jù)')ws_foreign_confirmed.append(['日期', '數(shù)據(jù)'])for data in zip(update_date, foreign_confirmed):ws_foreign_confirmed.append(data)# 寫入累計治愈數(shù)據(jù)ws_foreign_crued = wb.create_sheet('境外每日累計治愈數(shù)據(jù)')ws_foreign_crued.append(['日期', '數(shù)據(jù)'])for data in zip(update_date, foreign_crued):ws_foreign_crued.append(data)# 寫入累計死亡數(shù)據(jù)ws_foreign_died = wb.create_sheet('境外每日累計死亡數(shù)據(jù)')ws_foreign_died.append(['日期', '數(shù)據(jù)'])for data in zip(update_date, foreign_died):ws_foreign_died.append(data)wb.save('COVID-19-Global.xlsx')print('境外每日累計確診/治愈/死亡數(shù)據(jù)已保存至 COVID-19-Global.xlsx！')

【6x00】詞云圖繪制模塊 data_wordcloud

【6x01】中國累計確診詞云圖 foreign_daily_data()

def china_wordcloud():wb = openpyxl.load_workbook('COVID-19-China.xlsx') # 獲取已有的xlsx文件ws_china = wb['中國省份疫情數(shù)據(jù)'] # 獲取中國省份疫情數(shù)據(jù)表ws_china.delete_rows(1) # 刪除第一行china_dict = {} # 將省份及其累計確診按照鍵值對形式儲存在字典中for data in ws_china.values:china_dict[data[0]] = int(data[2])word_cloud = wordcloud.WordCloud(font_path='C:/Windows/Fonts/simsun.ttc',background_color='#CDC9C9',min_font_size=15,width=900, height=500)word_cloud.generate_from_frequencies(china_dict)word_cloud.to_file('WordCloud-China.png')print('中國省份疫情詞云圖繪制完畢！')

【6x02】全球累計確診詞云圖 foreign_daily_data()

def global_wordcloud():wb = openpyxl.load_workbook('COVID-19-Global.xlsx')ws_global = wb['全球各國疫情數(shù)據(jù)']ws_global.delete_rows(1)global_dict = {}for data in ws_global.values:global_dict[data[0]] = int(data[2])word_cloud = wordcloud.WordCloud(font_path='C:/Windows/Fonts/simsun.ttc',background_color='#CDC9C9',width=900, height=500)word_cloud.generate_from_frequencies(global_dict)word_cloud.to_file('WordCloud-Global.png')print('全球各國疫情詞云圖繪制完畢！')

【7x00】地圖繪制模塊 data_map

【7x01】中國累計確診地圖 china_total_map()

def china_total_map():wb = openpyxl.load_workbook('COVID-19-China.xlsx') # 獲取已有的xlsx文件ws_time = wb['中國疫情數(shù)據(jù)更新時間'] # 獲取文件中中國疫情數(shù)據(jù)更新時間表ws_data = wb['中國省份疫情數(shù)據(jù)'] # 獲取文件中中國省份疫情數(shù)據(jù)表ws_data.delete_rows(1) # 刪除第一行province = [] # 省份curconfirm = [] # 累計確診for data in ws_data.values:province.append(data[0])curconfirm.append(data[2])time_china = ws_time['A2'].value # 更新時間# 設(shè)置分級顏色pieces = [{'max': 0, 'min': 0, 'label': '0', 'color': '#FFFFFF'},{'max': 9, 'min': 1, 'label': '1-9', 'color': '#FFE5DB'},{'max': 99, 'min': 10, 'label': '10-99', 'color': '#FF9985'},{'max': 999, 'min': 100, 'label': '100-999', 'color': '#F57567'},{'max': 9999, 'min': 1000, 'label': '1000-9999', 'color': '#E64546'},{'max': 99999, 'min': 10000, 'label': '≧10000', 'color': '#B80909'}]# 繪制地圖ct_map = (Map().add(series_name='累計確診人數(shù)', data_pair=[list(z) for z in zip(province, curconfirm)], maptype="china").set_global_opts(title_opts=opts.TitleOpts(title="中國疫情數(shù)據(jù)（累計確診）",subtitle='數(shù)據(jù)更新至：' + time_china + '\n\n來源：百度疫情實(shí)時大數(shù)據(jù)報告'),visualmap_opts=opts.VisualMapOpts(max_=300, is_piecewise=True, pieces=pieces)))return ct_map

【7x02】全球累計確診地圖 global_total_map()

def global_total_map():wb = openpyxl.load_workbook('COVID-19-Global.xlsx')ws_time = wb['全球疫情數(shù)據(jù)更新時間']ws_data = wb['全球各國疫情數(shù)據(jù)']ws_data.delete_rows(1)country = [] # 國家curconfirm = [] # 累計確診for data in ws_data.values:country.append(data[0])curconfirm.append(data[2])time_global = ws_time['A2'].value # 更新時間# 國家名稱中英文映射表name_map = {"Somalia": "索馬里","Liechtenstein": "列支敦士登","Morocco": "摩洛哥","W. Sahara": "西撒哈拉","Serbia": "塞爾維亞","Afghanistan": "阿富汗","Angola": "安哥拉","Albania": "阿爾巴尼亞","Andorra": "安道爾共和國","United Arab Emirates": "阿拉伯聯(lián)合酋長國","Argentina": "阿根廷","Armenia": "亞美尼亞","Australia": "澳大利亞","Austria": "奧地利","Azerbaijan": "阿塞拜疆","Burundi": "布隆迪","Belgium": "比利時","Benin": "貝寧","Burkina Faso": "布基納法索","Bangladesh": "孟加拉國","Bulgaria": "保加利亞","Bahrain": "巴林","Bahamas": "巴哈馬","Bosnia and Herz.": "波斯尼亞和黑塞哥維那","Belarus": "白俄羅斯","Belize": "伯利茲","Bermuda": "百慕大","Bolivia": "玻利維亞","Brazil": "巴西","Barbados": "巴巴多斯","Brunei": "文萊","Bhutan": "不丹","Botswana": "博茨瓦納","Central African Rep.": "中非共和國","Canada": "加拿大","Switzerland": "瑞士","Chile": "智利","China": "中國","C?te d'Ivoire": "科特迪瓦","Cameroon": "喀麥隆","Dem. Rep. Congo": "剛果（布）","Congo": "剛果（金）","Colombia": "哥倫比亞","Cape Verde": "佛得角","Costa Rica": "哥斯達(dá)黎加","Cuba": "古巴","N. Cyprus": "北塞浦路斯","Cyprus": "塞浦路斯","Czech Rep.": "捷克","Germany": "德國","Djibouti": "吉布提","Denmark": "丹麥","Dominican Rep.": "多米尼加","Algeria": "阿爾及利亞","Ecuador": "厄瓜多爾","Egypt": "埃及","Eritrea": "厄立特里亞","Spain": "西班牙","Estonia": "愛沙尼亞","Ethiopia": "埃塞俄比亞","Finland": "芬蘭","Fiji": "斐濟(jì)","France": "法國","Gabon": "加蓬","United Kingdom": "英國","Georgia": "格魯吉亞","Ghana": "加納","Guinea": "幾內(nèi)亞","Gambia": "岡比亞","Guinea-Bissau": "幾內(nèi)亞比紹","Eq. Guinea": "赤道幾內(nèi)亞","Greece": "希臘","Grenada": "格林納達(dá)","Greenland": "格陵蘭島","Guatemala": "危地馬拉","Guam": "關(guān)島","Guyana": "圭亞那合作共和國","Honduras": "洪都拉斯","Croatia": "克羅地亞","Haiti": "海地","Hungary": "匈牙利","Indonesia": "印度尼西亞","India": "印度","Br. Indian Ocean Ter.": "英屬印度洋領(lǐng)土","Ireland": "愛爾蘭","Iran": "伊朗","Iraq": "伊拉克","Iceland": "冰島","Israel": "以色列","Italy": "意大利","Jamaica": "牙買加","Jordan": "約旦","Japan": "日本","Siachen Glacier": "錫亞琴冰川","Kazakhstan": "哈薩克斯坦","Kenya": "肯尼亞","Kyrgyzstan": "吉爾吉斯斯坦","Cambodia": "柬埔寨","Korea": "韓國","Kuwait": "科威特","Lao PDR": "老撾","Lebanon": "黎巴嫩","Liberia": "利比里亞","Libya": "利比亞","Sri Lanka": "斯里蘭卡","Lesotho": "萊索托","Lithuania": "立陶宛","Luxembourg": "盧森堡","Latvia": "拉脫維亞","Moldova": "摩爾多瓦","Madagascar": "馬達(dá)加斯加","Mexico": "墨西哥","Macedonia": "馬其頓","Mali": "馬里","Malta": "馬耳他","Myanmar": "緬甸","Montenegro": "黑山","Mongolia": "蒙古國","Mozambique": "莫桑比克","Mauritania": "毛里塔尼亞","Mauritius": "毛里求斯","Malawi": "馬拉維","Malaysia": "馬來西亞","Namibia": "納米比亞","New Caledonia": "新喀里多尼亞","Niger": "尼日爾","Nigeria": "尼日利亞","Nicaragua": "尼加拉瓜","Netherlands": "荷蘭","Norway": "挪威","Nepal": "尼泊爾","New Zealand": "新西蘭","Oman": "阿曼","Pakistan": "巴基斯坦","Panama": "巴拿馬","Peru": "秘魯","Philippines": "菲律賓","Papua New Guinea": "巴布亞新幾內(nèi)亞","Poland": "波蘭","Puerto Rico": "波多黎各","Dem. Rep. Korea": "朝鮮","Portugal": "葡萄牙","Paraguay": "巴拉圭","Palestine": "巴勒斯坦","Qatar": "卡塔爾","Romania": "羅馬尼亞","Russia": "俄羅斯","Rwanda": "盧旺達(dá)","Saudi Arabia": "沙特阿拉伯","Sudan": "蘇丹","S. Sudan": "南蘇丹","Senegal": "塞內(nèi)加爾","Singapore": "新加坡","Solomon Is.": "所羅門群島","Sierra Leone": "塞拉利昂","El Salvador": "薩爾瓦多","Suriname": "蘇里南","Slovakia": "斯洛伐克","Slovenia": "斯洛文尼亞","Sweden": "瑞典","Swaziland": "斯威士蘭","Seychelles": "塞舌爾","Syria": "敘利亞","Chad": "乍得","Togo": "多哥","Thailand": "泰國","Tajikistan": "塔吉克斯坦","Turkmenistan": "土庫曼斯坦","Timor-Leste": "東帝汶","Tonga": "湯加","Trinidad and Tobago": "特立尼達(dá)和多巴哥","Tunisia": "突尼斯","Turkey": "土耳其","Tanzania": "坦桑尼亞","Uganda": "烏干達(dá)","Ukraine": "烏克蘭","Uruguay": "烏拉圭","United States": "美國","Uzbekistan": "烏茲別克斯坦","Venezuela": "委內(nèi)瑞拉","Vietnam": "越南","Vanuatu": "瓦努阿圖","Yemen": "也門","South Africa": "南非","Zambia": "贊比亞","Zimbabwe": "津巴布韋","Aland": "奧蘭群島","American Samoa": "美屬薩摩亞","Fr. S. Antarctic Lands": "南極洲","Antigua and Barb.": "安提瓜和巴布達(dá)","Comoros": "科摩羅","Cura?ao": "庫拉索島","Cayman Is.": "開曼群島","Dominica": "多米尼加","Falkland Is.": "福克蘭群島馬爾維納斯","Faeroe Is.": "法羅群島","Micronesia": "密克羅尼西亞","Heard I. and McDonald Is.": "赫德島和麥克唐納群島","Isle of Man": "曼島","Jersey": "澤西島","Kiribati": "基里巴斯","Saint Lucia": "圣盧西亞","N. Mariana Is.": "北馬里亞納群島","Montserrat": "蒙特塞拉特","Niue": "紐埃","Palau": "帕勞","Fr. Polynesia": "法屬波利尼西亞","S. Geo. and S. Sandw. Is.": "南喬治亞島和南桑威奇群島","Saint Helena": "圣赫勒拿","St. Pierre and Miquelon": "圣皮埃爾和密克隆群島","S?o Tomé and Principe": "圣多美和普林西比","Turks and Caicos Is.": "特克斯和凱科斯群島","St. Vin. and Gren.": "圣文森特和格林納丁斯","U.S. Virgin Is.": "美屬維爾京群島","Samoa": "薩摩亞"}pieces = [{'max': 0, 'min': 0, 'label': '0', 'color': '#FFFFFF'},{'max': 49, 'min': 1, 'label': '1-49', 'color': '#FFE5DB'},{'max': 99, 'min': 50, 'label': '50-99', 'color': '#FFC4B3'},{'max': 999, 'min': 100, 'label': '100-999', 'color': '#FF9985'},{'max': 9999, 'min': 1000, 'label': '1000-9999', 'color': '#F57567'},{'max': 99999, 'min': 10000, 'label': '10000-99999', 'color': '#E64546'},{'max': 999999, 'min': 100000, 'label': '100000-999999', 'color': '#B80909'},{'max': 9999999, 'min': 1000000, 'label': '≧1000000', 'color': '#8A0808'}]gt_map = (Map().add(series_name='累計確診人數(shù)', data_pair=[list(z) for z in zip(country, curconfirm)], maptype="world", name_map=name_map, is_map_symbol_show=False).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="全球疫情數(shù)據(jù)（累計確診）",subtitle='數(shù)據(jù)更新至：' + time_global + '\n\n來源：百度疫情實(shí)時大數(shù)據(jù)報告'),visualmap_opts=opts.VisualMapOpts(max_=300, is_piecewise=True, pieces=pieces),))return gt_map

【7x03】中國每日數(shù)據(jù)折線圖 china_daily_map()

def china_daily_map():wb = openpyxl.load_workbook('COVID-19-China.xlsx')ws_china_confirmed = wb['中國每日累計確診數(shù)據(jù)']ws_china_crued = wb['中國每日累計治愈數(shù)據(jù)']ws_china_died = wb['中國每日累計死亡數(shù)據(jù)']ws_china_confirmed.delete_rows(1)ws_china_crued.delete_rows(1)ws_china_died.delete_rows(1)x_date = [] # 日期y_china_confirmed = [] # 每日累計確診y_china_crued = [] # 每日累計治愈y_china_died = [] # 每日累計死亡for china_confirmed in ws_china_confirmed.values:y_china_confirmed.append(china_confirmed[1])for china_crued in ws_china_crued.values:x_date.append(china_crued[0])y_china_crued.append(china_crued[1])for china_died in ws_china_died.values:y_china_died.append(china_died[1])fi_map = (Line(init_opts=opts.InitOpts(height='420px')).add_xaxis(xaxis_data=x_date).add_yaxis(series_name="中國累計確診數(shù)據(jù)",y_axis=y_china_confirmed,label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="中國累計治愈趨勢",y_axis=y_china_crued,label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="中國累計死亡趨勢",y_axis=y_china_died,label_opts=opts.LabelOpts(is_show=False),).set_global_opts(title_opts=opts.TitleOpts(title="中國每日累計確診/治愈/死亡趨勢"),legend_opts=opts.LegendOpts(pos_bottom="bottom", orient='horizontal'),tooltip_opts=opts.TooltipOpts(trigger="axis"),yaxis_opts=opts.AxisOpts(type_="value",axistick_opts=opts.AxisTickOpts(is_show=True),splitline_opts=opts.SplitLineOpts(is_show=True),),xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),))return fi_map

【7x04】境外每日數(shù)據(jù)折線圖 foreign_daily_map()

def foreign_daily_map():wb = openpyxl.load_workbook('COVID-19-Global.xlsx')ws_foreign_confirmed = wb['境外每日累計確診數(shù)據(jù)']ws_foreign_crued = wb['境外每日累計治愈數(shù)據(jù)']ws_foreign_died = wb['境外每日累計死亡數(shù)據(jù)']ws_foreign_confirmed.delete_rows(1)ws_foreign_crued.delete_rows(1)ws_foreign_died.delete_rows(1)x_date = [] # 日期y_foreign_confirmed = [] # 累計確診y_foreign_crued = [] # 累計治愈y_foreign_died = [] # 累計死亡for foreign_confirmed in ws_foreign_confirmed.values:y_foreign_confirmed.append(foreign_confirmed[1])for foreign_crued in ws_foreign_crued.values:x_date.append(foreign_crued[0])y_foreign_crued.append(foreign_crued[1])for foreign_died in ws_foreign_died.values:y_foreign_died.append(foreign_died[1])fte_map = (Line(init_opts=opts.InitOpts(height='420px')).add_xaxis(xaxis_data=x_date).add_yaxis(series_name="境外累計確診趨勢",y_axis=y_foreign_confirmed,label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="境外累計治愈趨勢",y_axis=y_foreign_crued,label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="境外累計死亡趨勢",y_axis=y_foreign_died,label_opts=opts.LabelOpts(is_show=False),).set_global_opts(title_opts=opts.TitleOpts(title="境外每日累計確診/治愈/死亡趨勢"),legend_opts=opts.LegendOpts(pos_bottom="bottom", orient='horizontal'),tooltip_opts=opts.TooltipOpts(trigger="axis"),yaxis_opts=opts.AxisOpts(type_="value",axistick_opts=opts.AxisTickOpts(is_show=True),splitline_opts=opts.SplitLineOpts(is_show=True),),xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),))return fte_map

【8x00】結(jié)果截圖

【8x01】數(shù)據(jù)儲存 Excel

【8x02】詞云圖

【8x03】地圖 + 折線圖

【9x00】完整代碼

預(yù)覽地址：http://cov.itrhx.com/

完整代碼地址（點(diǎn)亮 star 有 buff 加成）：https://github.com/TRHX/Python3-Spider-Practice/tree/master/SpiderDataVisualization/COVID-19

其他爬蟲實(shí)戰(zhàn)代碼合集（持續(xù)更新）：https://github.com/TRHX/Python3-Spider-Practice

爬蟲實(shí)戰(zhàn)專欄（持續(xù)更新）：https://itrhx.blog.csdn.net/article/category/9351278

總結(jié)

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