# 1. 發(fā)送請求 訪問網(wǎng)站
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36'
}

url = 'https://xueqiu.com/service/v5/stock/screener/quote/list?page=1&size=30&order=desc&order_by=amount&exchange=CN&market=CN&type=sha&_=1641730868838'

response = requests.get(url=url, headers=headers)

# 2. 獲取數(shù)據(jù)
json_data = response.json() 

# 3. 數(shù)據(jù)解析(篩選數(shù)據(jù))
data_list = json_data['data']['list']
for data in data_list:
    data1 = data['symbol']
    data2 = data['name']
    data3 = data['current']
    data4 = data['chg']
    data5 = data['percent']
    data6 = data['current_year_percent']
    data7 = data['volume']
    data8 = data['amount']
    data9 = data['turnover_rate']
    data10 = data['pe_ttm']
    data11 = data['dividend_yield']
    data12 = data['market_capital']
    print(data1, data2, data3, data4, data5, data6, data7, data8, data9, data10, data11, data12)
    data_dict = {
        '股票代碼': data1,
        '股票名稱': data2,
        '當(dāng)前價': data3,
        '漲跌額': data4,
        '漲跌幅': data5,
        '年初至今': data6,
        '成交量': data7,
        '成交額': data8,
        '換手率': data9,
        '市盈率(TTM)': data10,
        '股息率': data11,
        '市值': data12,
    }
    csv_write.writerow(data_dict)

4. 保存地址
file = open('data2.csv', mode='a', encoding='utf-8', newline='')
csv_write = csv.DictWriter(file, fieldnames=['股票代碼','股票名稱','當(dāng)前價','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_write.writeheader()

運行效果

數(shù)據(jù)可視化

data_df = pd.read_csv('data2.csv')
df = data_df.dropna()
df1 = df[['股票名稱', '成交量']]
df2 = df1.iloc[:20]
print(df2['股票名稱'].values)
print(df2['成交量'].values)


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

print('數(shù)據(jù)可視化結(jié)果完成,請在當(dāng)前目錄下查找打開 data.html 文件!')