def stock_zh_a_spot() -> pd.DataFrame:
    '\n    从新浪财经-A股获取所有A股的实时行情数据, 重复运行本函数会被新浪暂时封 IP\n    http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk\n    :return: pandas.DataFrame\n                symbol    code  name   trade pricechange changepercent     buy      0     sh600000  600000  浦发银行  12.920      -0.030        -0.232  12.920\n    1     sh600004  600004  白云机场  18.110      -0.370        -2.002  18.110\n    2     sh600006  600006  东风汽车   4.410      -0.030        -0.676   4.410\n    3     sh600007  600007  中国国贸  17.240      -0.360        -2.045  17.240\n    4     sh600008  600008  首创股份   3.320      -0.030        -0.896   3.310\n            ...     ...   ...     ...         ...           ...     ...\n    3755  sh600096  600096   云天化   5.270      -0.220        -4.007   5.270\n    3756  sh600097  600097  开创国际  10.180      -0.120        -1.165  10.180\n    3757  sh600098  600098  广州发展   6.550      -0.040        -0.607   6.540\n    3758  sh600099  600099  林海股份   6.540      -0.150        -2.242   6.540\n    3759  sh600100  600100  同方股份   8.200      -0.100        -1.205   8.200\n            sell settlement    open    high     low    volume     amount      0     12.930     12.950  12.950  13.100  12.860  46023920  597016896\n    1     18.120     18.480  18.510  18.510  17.880  24175071  437419344\n    2      4.420      4.440   4.490   4.490   4.410   4304900   19130233\n    3     17.280     17.600  17.670  17.670  17.220    684801   11879731\n    4      3.320      3.350   3.360   3.360   3.300   8284294   27579688\n          ...        ...     ...     ...     ...       ...        ...\n    3755   5.280      5.490   5.490   5.500   5.220  16964636   90595172\n    3756  10.190     10.300  10.220  10.340  10.090   1001676   10231669\n    3757   6.550      6.590   6.560   6.620   6.500   1996449   13098901\n    3758   6.580      6.690   6.650   6.680   6.530   1866180   12314997\n    3759   8.210      8.300   8.300   8.310   8.120  12087236   99281447\n          ticktime      per     pb        mktcap           nmc  turnoverratio\n    0     15:00:00    6.984  0.790  3.792289e+07  3.631006e+07        0.16376\n    1     15:00:07   32.927  2.365  3.747539e+06  3.747539e+06        1.16826\n    2     15:00:02   15.926  1.207  8.820000e+05  8.820000e+05        0.21525\n    3     15:00:02   22.390  2.367  1.736555e+06  1.736555e+06        0.06798\n    4     15:00:07   22.912  1.730  1.887569e+06  1.600444e+06        0.17185\n            ...      ...    ...           ...           ...            ...\n    3755  15:00:00   56.728  1.566  7.523847e+05  6.963668e+05        1.28386\n    3756  15:00:00   17.552  1.434  2.452734e+05  2.303459e+05        0.44268\n    3757  15:00:00   25.476  1.059  1.785659e+06  1.785659e+06        0.07323\n    3758  15:00:00  540.496  3.023  1.433045e+05  1.433045e+05        0.85167\n    3759  15:00:07   -6.264  1.465  2.430397e+06  2.430397e+06        0.40782\n    '
    big_df = pd.DataFrame()
    page_count = _get_zh_a_page_count()
    zh_sina_stock_payload_copy = zh_sina_a_stock_payload.copy()
    for page in tqdm(range(1, (page_count + 1)), desc='Please wait for a moment'):
        zh_sina_stock_payload_copy.update({'page': page})
        r = requests.get(zh_sina_a_stock_url, params=zh_sina_stock_payload_copy)
        data_json = demjson.decode(r.text)
        big_df = big_df.append(pd.DataFrame(data_json), ignore_index=True)
    return big_df
从新浪财经-A股获取所有A股的实时行情数据, 重复运行本函数会被新浪暂时封 IP
http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk
:return: pandas.DataFrame
            symbol    code  name   trade pricechange changepercent     buy      0     sh600000  600000  浦发银行  12.920      -0.030        -0.232  12.920
1     sh600004  600004  白云机场  18.110      -0.370        -2.002  18.110
2     sh600006  600006  东风汽车   4.410      -0.030        -0.676   4.410
3     sh600007  600007  中国国贸  17.240      -0.360        -2.045  17.240
4     sh600008  600008  首创股份   3.320      -0.030        -0.896   3.310
        ...     ...   ...     ...         ...           ...     ...
3755  sh600096  600096   云天化   5.270      -0.220        -4.007   5.270
3756  sh600097  600097  开创国际  10.180      -0.120        -1.165  10.180
3757  sh600098  600098  广州发展   6.550      -0.040        -0.607   6.540
3758  sh600099  600099  林海股份   6.540      -0.150        -2.242   6.540
3759  sh600100  600100  同方股份   8.200      -0.100        -1.205   8.200
        sell settlement    open    high     low    volume     amount      0     12.930     12.950  12.950  13.100  12.860  46023920  597016896
1     18.120     18.480  18.510  18.510  17.880  24175071  437419344
2      4.420      4.440   4.490   4.490   4.410   4304900   19130233
3     17.280     17.600  17.670  17.670  17.220    684801   11879731
4      3.320      3.350   3.360   3.360   3.300   8284294   27579688
      ...        ...     ...     ...     ...       ...        ...
3755   5.280      5.490   5.490   5.500   5.220  16964636   90595172
3756  10.190     10.300  10.220  10.340  10.090   1001676   10231669
3757   6.550      6.590   6.560   6.620   6.500   1996449   13098901
3758   6.580      6.690   6.650   6.680   6.530   1866180   12314997
3759   8.210      8.300   8.300   8.310   8.120  12087236   99281447
      ticktime      per     pb        mktcap           nmc  turnoverratio
0     15:00:00    6.984  0.790  3.792289e+07  3.631006e+07        0.16376
1     15:00:07   32.927  2.365  3.747539e+06  3.747539e+06        1.16826
2     15:00:02   15.926  1.207  8.820000e+05  8.820000e+05        0.21525
3     15:00:02   22.390  2.367  1.736555e+06  1.736555e+06        0.06798
4     15:00:07   22.912  1.730  1.887569e+06  1.600444e+06        0.17185
        ...      ...    ...           ...           ...            ...
3755  15:00:00   56.728  1.566  7.523847e+05  6.963668e+05        1.28386
3756  15:00:00   17.552  1.434  2.452734e+05  2.303459e+05        0.44268
3757  15:00:00   25.476  1.059  1.785659e+06  1.785659e+06        0.07323
3758  15:00:00  540.496  3.023  1.433045e+05  1.433045e+05        0.85167
3759  15:00:07   -6.264  1.465  2.430397e+06  2.430397e+06        0.40782
def stock_zh_a_spot() -> pd.DataFrame:
    '\n    从新浪财经-A股获取所有A股的实时行情数据, 重复运行本函数会被新浪暂时封 IP\n    http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk\n    :return: pandas.DataFrame\n                symbol    code  name   trade pricechange changepercent     buy      0     sh600000  600000  浦发银行  12.920      -0.030        -0.232  12.920\n    1     sh600004  600004  白云机场  18.110      -0.370        -2.002  18.110\n    2     sh600006  600006  东风汽车   4.410      -0.030        -0.676   4.410\n    3     sh600007  600007  中国国贸  17.240      -0.360        -2.045  17.240\n    4     sh600008  600008  首创股份   3.320      -0.030        -0.896   3.310\n            ...     ...   ...     ...         ...           ...     ...\n    3755  sh600096  600096   云天化   5.270      -0.220        -4.007   5.270\n    3756  sh600097  600097  开创国际  10.180      -0.120        -1.165  10.180\n    3757  sh600098  600098  广州发展   6.550      -0.040        -0.607   6.540\n    3758  sh600099  600099  林海股份   6.540      -0.150        -2.242   6.540\n    3759  sh600100  600100  同方股份   8.200      -0.100        -1.205   8.200\n            sell settlement    open    high     low    volume     amount      0     12.930     12.950  12.950  13.100  12.860  46023920  597016896\n    1     18.120     18.480  18.510  18.510  17.880  24175071  437419344\n    2      4.420      4.440   4.490   4.490   4.410   4304900   19130233\n    3     17.280     17.600  17.670  17.670  17.220    684801   11879731\n    4      3.320      3.350   3.360   3.360   3.300   8284294   27579688\n          ...        ...     ...     ...     ...       ...        ...\n    3755   5.280      5.490   5.490   5.500   5.220  16964636   90595172\n    3756  10.190     10.300  10.220  10.340  10.090   1001676   10231669\n    3757   6.550      6.590   6.560   6.620   6.500   1996449   13098901\n    3758   6.580      6.690   6.650   6.680   6.530   1866180   12314997\n    3759   8.210      8.300   8.300   8.310   8.120  12087236   99281447\n          ticktime      per     pb        mktcap           nmc  turnoverratio\n    0     15:00:00    6.984  0.790  3.792289e+07  3.631006e+07        0.16376\n    1     15:00:07   32.927  2.365  3.747539e+06  3.747539e+06        1.16826\n    2     15:00:02   15.926  1.207  8.820000e+05  8.820000e+05        0.21525\n    3     15:00:02   22.390  2.367  1.736555e+06  1.736555e+06        0.06798\n    4     15:00:07   22.912  1.730  1.887569e+06  1.600444e+06        0.17185\n            ...      ...    ...           ...           ...            ...\n    3755  15:00:00   56.728  1.566  7.523847e+05  6.963668e+05        1.28386\n    3756  15:00:00   17.552  1.434  2.452734e+05  2.303459e+05        0.44268\n    3757  15:00:00   25.476  1.059  1.785659e+06  1.785659e+06        0.07323\n    3758  15:00:00  540.496  3.023  1.433045e+05  1.433045e+05        0.85167\n    3759  15:00:07   -6.264  1.465  2.430397e+06  2.430397e+06        0.40782\n    '
    big_df = pd.DataFrame()
    page_count = _get_zh_a_page_count()
    zh_sina_stock_payload_copy = zh_sina_a_stock_payload.copy()
    for page in tqdm(range(1, (page_count + 1)), desc='Please wait for a moment'):
        zh_sina_stock_payload_copy.update({'page': page})
        r = requests.get(zh_sina_a_stock_url, params=zh_sina_stock_payload_copy)
        data_json = demjson.decode(r.text)
        big_df = big_df.append(pd.DataFrame(data_json), ignore_index=True)
    return big_df