"我的问题"
我有三个 Dataframe ，我使用itertuple循环。
Itertuple曾经运行得很好，但是现在我运行了太多的迭代，Itertuple不够高效。
我想使用矢量化或者Numba，因为我听说它们都非常快。我试过让它们工作，但我不能弄清楚
所有三个数据框都是开盘、高、低、收盘蜡烛线数据，还有一些其他列，如“FG_Top”
Dataframe 为

1. dflong- 15分钟阴阳烛数据
2. dfshort- 5分钟阴阳烛数据
3. dfshorter- 1分钟阴阳烛数据
   注解中要求的 Dataframe 创建代码

import numpy as np
import pandas as pd

idx15m = ['2022-10-29 06:59:59.999', '2022-10-29 07:14:59.999', '2022-10-29 07:29:59.999', '2022-10-29 07:44:59.999',
         '2022-10-29 07:59:59.999', '2022-10-29 08:14:59.999', '2022-10-29 08:29:59.999']

opn15m = [19010, 19204, 19283, 19839, 19892, 20000, 20192]
hgh15m = [19230, 19520, 19921, 19909, 20001, 20203, 21065]
low15m = [18782, 19090, 19245, 19809, 19256, 19998, 20016]
cls15m = [19204, 19283, 19839, 19892, 20000, 20192, 20157]

FG_Bottom = [np.nan, np.nan, np.nan, np.nan, np.nan, 19909, np.nan]
FG_Top = [np.nan, np.nan, np.nan, np.nan, np.nan, 19998, np.nan]

dflong = pd.DataFrame({'Open': opn15m, 'High': hgh15m, 'Low': low15m, 'Close': cls15m, 'FG_Bottom': FG_Bottom, 'FG_Top': FG_Top},
                  index=idx15m)

idx5m = ['2022-10-29 06:59:59.999', '2022-10-29 07:05:59.999', '2022-10-29 07:10:59.999', '2022-10-29 07:15:59.999',
         '2022-10-29 07:20:59.999', '2022-10-29 07:25:59.999', '2022-10-29 07:30:59.999']

opn5m = [19012, 19102, 19165, 19747, 19781, 20009, 20082]
hgh5m = [19132, 19423, 19817, 19875, 20014, 20433, 21068]
low5m = [18683, 19093, 19157, 19758, 19362, 19893, 20018]
cls5m = [19102, 19165, 19747, 19781, 20009, 20082, 20154]

price_end5m = [np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]

dfshort = pd.DataFrame({'Open': opn5m, 'High': hgh5m, 'Low': low5m, 'Close': cls5m, 'price_end': price_end5m},
                  index=idx5m)

idx1m = ['2022-10-29 06:59:59.999', '2022-10-29 07:01:59.999', '2022-10-29 07:02:59.999', '2022-10-29 07:03:59.999',
         '2022-10-29 07:04:59.999', '2022-10-29 07:05:59.999', '2022-10-29 07:06:59.999']

opn1m = [19010, 19104, 19163, 19748, 19783, 20000, 20087]
hgh1m = [19130, 19420, 19811, 19878, 20011, 20434, 21065]
low1m = [18682, 19090, 19154, 19754, 19365, 19899, 20016]
cls1m = [19104, 19163, 19748, 19783, 20000, 20087, 20157]

price_end1m = [np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]

dfshorter = pd.DataFrame({'Open': opn1m, 'High': hgh1m, 'Low': low1m, 'Close': cls1m, 'price_end': price_end1m},
                  index=idx1m)

给予3个类似于以下DataFrame的DataFrame

** Dataframe 示例**

Open      High  ...          FG_Top          FG_Bottom
2022-10-29 06:59:59.999  20687.83  20700.46  ...             NaN                NaN
2022-10-29 07:14:59.999  20686.82  20695.74  ...             NaN                NaN
2022-10-29 07:29:59.999  20733.62  20745.30  ...        20733.62           20700.46
2022-10-29 07:44:59.999  20741.42  20762.75  ...             NaN                NaN
2022-10-29 07:59:59.999  20723.86  20777.00  ...             NaN                NaN
...                           ...       ...  ...             ...                ...
2022-11-10 02:14:59.999  16140.29  16167.09  ...             NaN                NaN
2022-11-10 02:29:59.999  16119.99  16195.19  ...             NaN                NaN
2022-11-10 02:44:59.999  16136.63  16263.15  ...             NaN                NaN
2022-11-10 02:59:59.999  16238.91  16238.91  ...             NaN                NaN
2022-11-10 03:14:59.999  16210.23  16499.00  ...             NaN                NaN

代码说明：

我有我的第一个 Dataframe ，我用第一个循环循环，然后用第二个嵌套循环再次循环。我有if语句，检查每次迭代的某些条件，如果这些条件得到满足，然后我在第一个 Dataframe np.nan上设置一些值。
在第二个循环中检查的条件之一调用包含第三个循环的函数，并检查其他2个 Dataframe 中的某些条件。

# First loop

        for fg_candle_idx, row in enumerate(dflong.itertuples()):
            top = row.FG_Top
            bottom = row.FG_Bottom
            fg_candle_time = row.Index
            if (pd.notnull(top)):

# Second loop

                for future_candle_idx, r in enumerate(dflong.itertuples()):
                    future_candle_time = r.Index
                    next_future_candle = future_candle_time + timedelta(minutes=minutes)
                    future_candle_high = r.High
                    future_candle_low = r.Low
                    future_candle_close = r.Close
                    future_candle_open = r.Open
                    if future_candle_idx > fg_candle_idx:
                        div = r.price_end

# Check conditions, call function check_no_divs

                        if (pd.isnull(check_no_divs(dfshort, future_candle_time, next_future_candle))) & (
                        pd.isnull(check_no_divs(dfshorter, future_candle_time, next_future_candle))) & (
                        pd.isnull(div)):

                            if future_candle_high < bottom:
                                continue

                            elif future_candle_low > top:
                                continue

                            elif (future_candle_close < bottom) & \
                                    (future_candle_open > top):
                                dflong.loc[fg_candle_time, 'FG_Bottom'] = np.nan
                                dflong.loc[fg_candle_time, 'FG_Top'] = np.nan
                                continue

# Many additional conditions checked...

下面的代码是函数check_no_divs

def check_no_divs(df, candle_time, next_candle):

        no_divs = []

# Third Loop

        for idx, row in enumerate(df.itertuples()):
            compare_candle_time = row.Index
            div = row.price_end
            if (compare_candle_time >= candle_time) & (compare_candle_time <= next_candle):
                if pd.notnull(div):
                    no_divs.append(True)
                else:
                    no_divs.append(False)

            elif compare_candle_time < candle_time:
                continue

            elif compare_candle_time > next_candle:
                break

        if all(no_divs) == False:
            return np.nan

        elif any(no_divs) == True:
            return 1

理想解决方案

显然，使用itertuples对于这个问题来说效率太低了。我认为使用高效的矢量化或Numba会有一个更快的解决方案。
有人知道怎么做吗？
附言：我对编码还是一个新手，我认为我目前的代码仍然可以使用迭代元组来提高效率，但可能还不够高效。如果有人知道如何大大提高代码的速度，我将不胜感激