以某股票为例，调用自定义的一个类，读取数据。

class BigAData:# 获取资金流向数据def get_money_flow(self, stock_code, page=1, num=20, sort='opendate', asc=0):'''该函数通过股票代码从新浪财经API获取资金流向数据。参数包括股票代码、页数、每页数量、排序字段及升序标志。以JSON格式返回数据或在请求失败时返回None。stock_code: 股票代码page: 页码num: 每页数据量sort: 排序字段asc: 排序方式'''url = f'https://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_qsfx_zjlrqs?page={page}&num={num}&sort={sort}&asc={asc}&daima={stock_code}'# 获取数据resp = requests.get(url)# 返回数据if resp.status_code == 200:return resp.json()else:return None

import pandas as pd
from bad import BigAData# 获取数据
bad = BigAData()
json = bad.get_money_flow('sz002231')
# 转换为DataFrame
df = pd.DataFrame(json)
print(df)

      opendate   trade changeratio turnover       netamount ratioamount  \
0   2024-08-23  4.5100   0.0296804  2382.92   37363281.0000    0.118193   
1   2024-08-22  4.3800    0.100503  362.524   42948385.0000    0.930168   
2   2024-08-21  3.9800  0.00251889  179.015   -3115710.0000   -0.147867   
3   2024-08-20  3.9700  -0.0197531  240.578   -3530293.0100   -0.124667   
4   2024-08-19  4.0500  -0.0169903   352.25   -6396122.7800   -0.150614   
5   2024-08-16  4.1200  0.00487805   427.51    7383940.1800    0.141605   
6   2024-08-15  4.1000   0.0173697  439.131    7624788.0800    0.144833   
7   2024-08-14  4.0300   0.0151134  300.415    5807306.0000    0.162652   
8   2024-08-13  3.9700   0.0127551  208.087     374879.0000   0.0155296   
9   2024-08-12  3.9200  -0.0224439  260.522   -3826773.5000   -0.126509   
10  2024-08-09  4.0100           0  323.677    1130124.0000    0.029524   
11  2024-08-08  4.0100  -0.0337349  452.193   -7898189.0000   -0.146874   
12  2024-08-07  4.1500   0.0453401  654.428   16418773.1200    0.207017   
13  2024-08-06  3.9700   0.0284974  328.024    1869527.8200   0.0491337   
14  2024-08-05  3.8600  -0.0445545   427.89  -10943624.0000    -0.21893   
15  2024-08-02  4.0400  -0.0334928  472.996  -14696772.0000   -0.257066   
16  2024-08-01  4.1800  0.00966184  497.435    7710109.8000    0.126305   
17  2024-07-31  4.1400   0.0298507  690.852    9603634.0000    0.115494   
18  2024-07-30  4.0200   0.0151515  816.721    2341341.9200   0.0239997   
19  2024-07-29  3.9600   0.0179949  373.239    5214741.0000     0.12152   r0_net     r0_ratio r0x_ratio cnt_r0x_ratio      cate_ra  \
0   -12144784.0000  -0.03841832  -52.3118            -1    0.0214639   
1     4221340.0000   0.09142500   42.2921             1   -0.0486805   
...
16   -1288309434.8920  
17   13484819579.9720  
18    4050788991.6900  
19     796606779.8250  

重新命名列名，让数据可以看懂：

# 重新命名列名，并返回一个新的DataFrame（inplace=False）
new_columns={'opendate': '交易日', 'trade': '收盘价', 'changeratio': '涨跌幅', 'turnover':'换手率', 'netamount': '净流入（元）', 'ratioamount': '净流入率', 'r0_net': '主力净流入（元）', 'r0_ratio':'主力净流入率', 'r0x_ratio': '主力罗盘（°）', 'cate_ra': '行业净流入率'}
# 返回新的DataFrame
result = df.rename(columns=new_columns, inplace=False)
print(result)
result.to_excel('bad_demo.xlsx', index=False)