有沒有辦法在 PANDAS df 上以矢量化方式（即不使用 for 回圈）有條件地復制值？
import numpy as np
import pandas as pd
Open = {'Open': np.array([86.34, 84.04, 79.06, 78.46, 75.85, 80.78, 79.66, 80.67, 82.32,80.1 , 77.63, 77. , 79.15, 76.32, 77. , 77.11, 77.04, 79.74,79.92, 79.09])}
High = {'High': np.array([86.45, 84.24, 80.29, 79.11, 79.98, 80.98, 80.57, 82.18, 83.25,81.25, 78.28, 79.2 , 79.19, 77.55, 79. , 77.5 , 81.93, 81.04,82.48, 86.74])}
Low = {'Low': np.array([83.15, 79.07, 75.59, 76.99, 74.78, 77.45, 78.48, 80.11, 80.35, 77. , 71.96, 76.15, 76.73, 75.83, 76.11, 73.46, 76.55, 78.7 ,77.65, 78.47])
Close = {'Close': np.array([84.02, 79.17, 77.28, 77.56, 79.24, 79.86, 79.91, 82.03, 81.83,77.63, 76.19, 79.13, 76.85, 76.98, 78.31, 77.49, 81.65, 80.57,77.92, 85.51])}
pos = {'pos': np.array([0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0])}
stop = {'stop': np.array([ 0. , 84.24, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 79.19, 0. , 0. , 0. , 0. , 0. ,0. , 0. ])}
index = pd.date_range('2021-1-1',periods=20)
df = pd.DataFrame(dict(Open, **High, **Low, **Close, **pos, **stop), index = index)
df
Open High Low Close pos stop
2021-01-01 86.34 86.45 83.15 84.02 0 0.00
2021-01-02 84.04 84.24 79.07 79.17 -1 84.24
2021-01-03 79.06 80.29 75.59 77.28 0 0.00
2021-01-04 78.46 79.11 76.99 77.56 0 0.00
2021-01-05 75.85 79.98 74.78 79.24 0 0.00
2021-01-06 80.78 80.98 77.45 79.86 0 0.00
2021-01-07 79.66 80.57 78.48 79.91 0 0.00
2021-01-08 80.67 82.18 80.11 82.03 0 0.00
2021-01-09 82.32 83.25 80.35 81.83 0 0.00
2021-01-10 80.10 81.25 77.00 77.63 0 0.00
2021-01-11 77.63 78.28 71.96 76.19 0 0.00
2021-01-12 77.00 79.20 76.15 79.13 0 0.00
2021-01-13 79.15 79.19 76.73 76.85 -1 79.19
2021-01-14 76.32 77.55 75.83 76.98 0 0.00
2021-01-15 77.00 79.00 76.11 78.31 0 0.00
2021-01-16 77.11 77.50 73.46 77.49 0 0.00
2021-01-17 77.04 81.93 76.55 81.65 0 0.00
2021-01-18 79.74 81.04 78.70 80.57 0 0.00
2021-01-19 79.92 82.48 77.65 77.92 0 0.00
2021-01-20 79.09 86.74 78.47 85.51 0 0.00
在stop上述df的列中，我試圖stop根據以下條件將值復制到列中：
stop在當前行 == 0stop在上一行！= 0High在當前行 <stop在上一行
我可以使用以下命令一次執行一行感興趣的操作：
df.loc[:,'stop'] = np.where((df['stop'] == 0) & (df['stop'].shift(1) != 0) & (df['High'] < df['stop'].shift(1)), df['stop'].shift(1),df['stop'])
但是，我追求的結果是：
Open High Low Close pos stop
2021-01-01 86.34 86.45 83.15 84.02 0 0.00
2021-01-02 84.04 84.24 79.07 79.17 -1 84.24
2021-01-03 79.06 80.29 75.59 77.28 0 84.24
2021-01-04 78.46 79.11 76.99 77.56 0 84.24
2021-01-05 75.85 79.98 74.78 79.24 0 84.24
2021-01-06 80.78 80.98 77.45 79.86 0 84.24
2021-01-07 79.66 80.57 78.48 79.91 0 84.24
2021-01-08 80.67 82.18 80.11 82.03 0 84.24
2021-01-09 82.32 83.25 80.35 81.83 0 84.24
2021-01-10 80.10 81.25 77.00 77.63 0 84.24
2021-01-11 77.63 78.28 71.96 76.19 0 84.24
2021-01-12 77.00 79.20 76.15 79.13 0 84.24
2021-01-13 79.15 79.19 76.73 76.85 -1 79.19
2021-01-14 76.32 77.55 75.83 76.98 0 79.19
2021-01-15 77.00 79.00 76.11 78.31 0 79.19
2021-01-16 77.11 77.50 73.46 77.49 0 79.19
2021-01-17 77.04 81.93 76.55 81.65 0 0.00
2021-01-18 79.74 81.04 78.70 80.57 0 0.00
2021-01-19 79.92 82.48 77.65 77.92 0 0.00
2021-01-20 79.09 86.74 78.47 85.51 0 0.00
uj5u.com熱心網友回復：
您可以mask將stop列NaN全部設為 0 ，然后我們只需ffill要按條件屏蔽那些不需要的單元格
s = df[['stop']].mask(df['stop'].eq(0)).ffill()
df.update(s.mask(s['stop']<df['High']))
df
Open High Low Close pos stop
2021-01-01 86.34 86.45 83.15 84.02 0 0.00
2021-01-02 84.04 84.24 79.07 79.17 -1 84.24
2021-01-03 79.06 80.29 75.59 77.28 0 84.24
2021-01-04 78.46 79.11 76.99 77.56 0 84.24
2021-01-05 75.85 79.98 74.78 79.24 0 84.24
2021-01-06 80.78 80.98 77.45 79.86 0 84.24
2021-01-07 79.66 80.57 78.48 79.91 0 84.24
2021-01-08 80.67 82.18 80.11 82.03 0 84.24
2021-01-09 82.32 83.25 80.35 81.83 0 84.24
2021-01-10 80.10 81.25 77.00 77.63 0 84.24
2021-01-11 77.63 78.28 71.96 76.19 0 84.24
2021-01-12 77.00 79.20 76.15 79.13 0 84.24
2021-01-13 79.15 79.19 76.73 76.85 -1 79.19
2021-01-14 76.32 77.55 75.83 76.98 0 79.19
2021-01-15 77.00 79.00 76.11 78.31 0 79.19
2021-01-16 77.11 77.50 73.46 77.49 0 79.19
2021-01-17 77.04 81.93 76.55 81.65 0 0.00
2021-01-18 79.74 81.04 78.70 80.57 0 0.00
2021-01-19 79.92 82.48 77.65 77.92 0 0.00
2021-01-20 79.09 86.74 78.47 85.51 0 0.00
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