前言

截止2019年年底我國(guó)股票投資者數(shù)量為15975.24萬(wàn)戶(hù), 如此多的股民熱衷于炒股,首先拋開(kāi)炒股技術(shù)不說(shuō), 那么多股票數(shù)據(jù)是不是非常難找, 找到之后是不是看著密密麻麻的數(shù)據(jù)是不是頭都大了?

今天帶大家爬取雪球平臺(tái)的股票數(shù)據(jù), 并且實(shí)現(xiàn)數(shù)據(jù)可視化

先看下效果圖

基本環(huán)境配置

目標(biāo)地址

https://xueqiu.com/hq

爬蟲(chóng)代碼

請(qǐng)求網(wǎng)頁(yè)

import requestsurl = ’https://xueqiu.com/service/v5/stock/screener/quote/list’response = requests.get(url=url, params=params, headers=headers, cookies=cookies)html_data = response.json()

解析數(shù)據(jù)

data_list = html_data[’data’][’list’]for i in data_list: dit = {} dit[’股票代碼’] = i[’symbol’] dit[’股票名字’] = i[’name’] dit[’當(dāng)前價(jià)’] = i[’current’] dit[’漲跌額’] = i[’chg’] dit[’漲跌幅/%’] = i[’percent’] dit[’年初至今/%’] = i[’current_year_percent’] dit[’成交量’] = i[’volume’] dit[’成交額’] = i[’amount’] dit[’換手率/%’] = i[’turnover_rate’] dit[’市盈率TTM’] = i[’pe_ttm’] dit[’股息率/%’] = i[’dividend_yield’] dit[’市值’] = i[’market_capital’] print(dit)

保存數(shù)據(jù)

import csvf = open(’股票數(shù)據(jù).csv’, mode=’a’, encoding=’utf-8-sig’, newline=’’)csv_writer = csv.DictWriter(f, fieldnames=[’股票代碼’, ’股票名字’, ’當(dāng)前價(jià)’, ’漲跌額’, ’漲跌幅/%’, ’年初至今/%’, ’成交量’, ’成交額’, ’換手率/%’, ’市盈率TTM’, ’股息率/%’, ’市值’])csv_writer.writeheader()csv_writer.writerow(dit)f.close()

完整代碼

import pprintimport requestsimport timeimport csvf = open(’股票數(shù)據(jù).csv’, mode=’a’, encoding=’utf-8-sig’, newline=’’)csv_writer = csv.DictWriter(f, fieldnames=[’股票代碼’, ’股票名稱(chēng)’, ’當(dāng)前價(jià)’, ’漲跌額’, ’漲跌幅/%’, ’年初至今/%’, ’成交量’, ’成交額’, ’換手率/%’, ’市盈率TTM’, ’股息率/%’, ’市值’])csv_writer.writeheader()for page in range(1, 53): time.sleep(1) url = ’https://xueqiu.com/service/v5/stock/screener/quote/list’ date = round(time.time()*1000) params = { ’page’: ’{}’.format(page), ’size’: ’30’, ’order’: ’desc’, ’order_by’: ’amount’, ’exchange’: ’CN’, ’market’: ’CN’, ’type’: ’sha’, ’_’: ’{}’.format(date), } cookies = { ’Cookie’: ’acw_tc=2760824216007592794858354eb971860e97492387fac450a734dbb6e89afb; xq_a_token=636e3a77b735ce64db9da253b75cbf49b2518316; xqat=636e3a77b735ce64db9da253b75cbf49b2518316; xq_r_token=91c25a6a9038fa2532dd45b2dd9b573a35e28cfd; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOi0xLCJpc3MiOiJ1YyIsImV4cCI6MTYwMjY0MzAyMCwiY3RtIjoxNjAwNzU5MjY3OTEwLCJjaWQiOiJkOWQwbjRBWnVwIn0.bengzIpmr0io9f44NJdHuc_6g9EIjtrSlMgnqwKSWVzI4syI_yIH1F-GJfK4bTelWzDirufjWMW9DfDMyMkI75TpJqiwIq8PRsa1bQ7IuCXLbN71ebsiTOGfA5OsWSPQOdVXQA0goqC4yvXLOk5KgC5FQIzZut0N4uaRDLsq7vhmcb8CBw504tCZnbIJTfGGIFIfw7TkwuUCXGY6Q-0mlOG8U4EUTcOCuxN87Ej_OIKnXN8cTSVh7XW6SFxOgU6p3yUXDgvS04rt-nFewpNNqfbGAKk965N-HJ9Mq8E52BRJ3rt_ndYP8yCaeQ6xSsz5P2mNlKwNFe9EQeltim_mDg; u=501600759279498; device_id=24700f9f1986800ab4fcc880530dd0ed; Hm_lvt_1db88642e346389874251b5a1eded6e3=1600759286; _ga=GA1.2.2049292015.1600759388; _gid=GA1.2.391362708.1600759388; s=du11eogy79; __utma=1.2049292015.1600759388.1600759397.1600759397.1; __utmc=1; __utmz=1.1600759397.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); __utmt=1; __utmb=1.3.10.1600759397; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1600759448’ } headers = { ’Host’: ’xueqiu.com’, ’Pragma’: ’no-cache’, ’Referer’: ’https://xueqiu.com/hq’, ’User-Agent’: ’Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36’ } response = requests.get(url=url, params=params, headers=headers, cookies=cookies) html_data = response.json() data_list = html_data[’data’][’list’] for i in data_list: dit = {} dit[’股票代碼’] = i[’symbol’] dit[’股票名稱(chēng)’] = i[’name’] dit[’當(dāng)前價(jià)’] = i[’current’] dit[’漲跌額’] = i[’chg’] dit[’漲跌幅/%’] = i[’percent’] dit[’年初至今/%’] = i[’current_year_percent’] dit[’成交量’] = i[’volume’] dit[’成交額’] = i[’amount’] dit[’換手率/%’] = i[’turnover_rate’] dit[’市盈率TTM’] = i[’pe_ttm’] dit[’股息率/%’] = i[’dividend_yield’] dit[’市值’] = i[’market_capital’] csv_writer.writerow(dit) print(dit)f.close()

數(shù)據(jù)分析代碼

c = ( Bar() .add_xaxis(list(df2[’股票名稱(chēng)’].values)) .add_yaxis('股票成交量情況', list(df2[’成交量’].values)) .set_global_opts( title_opts=opts.TitleOpts(title='成交量圖表 - Volume chart'), datazoom_opts=opts.DataZoomOpts(), ) .render('data.html'))

以上就是Python爬取股票信息，并可視化數(shù)據(jù)的示例的詳細(xì)內(nèi)容，更多關(guān)于Python爬取股票信息的資料請(qǐng)關(guān)注好吧啦網(wǎng)其它相關(guān)文章！