Are you in a hurry? Python stock data analysis, making dynamic histogram

Some bullshit written on the front:

Founded in 2010, Snowball is an investor community launched by Beijing Snowball Information Technology Co., Ltd. Snowball has been committed to providing Chinese investors with cross-market (Shanghai and Shenzhen, Hong Kong, the United States), cross-variety (stocks, funds, bonds, etc.) data query, information acquisition and interactive exchange and transaction services.

module usage

requests >>> pip install requests (data request third-party module)
re # Regular expression to match and extract data json
pandas pyecharts

development environment

Python 3.8 interpreter
Pycharm 2021.2 version

Code implementation steps

Send a request to visit a website
retrieve data
Parse data (extract data)
save data
Simple visualization of histogram

start code

1. Send a request to visit the website

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. Get data

json_data = response.json()

3. Data analysis (filtering data)

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 = {
        '股票代码': data1,
        '股票名称': data2,
        '当前价': data3,
        '涨跌额': data4,
        '涨跌幅': data5,
        '年初至今': data6,
        '成交量': data7,
        '成交额': data8,
        '换手率': data9,
        '市盈率(TTM)': data10,
        '股息率': data11,
        '市值': data12,
    }
    csv_write.writerow(data_dict)

4. Save the address
and run the code to see the effect
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5. Data visualization

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


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

print('数据可视化结果完成,请在当前目录下查找打开 data.html 文件!')

Please add image description

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