如何在 pandas 中的组上滚动应用自定义函数？

Question

import pandas as pd
import numpy as np

df = pd.DataFrame.from_dict({'date':[1,2,3,4,5,6,7,8,9,10] ,'open':[4,5,3,4,5,6,7,8,9,10],'close':[4,5,6,7,8,1,2,9,10,11],'stock':['A']*5 + ['B']*5})

df['flag'] = np.select([df['close']>df['open'],df['close']<df['open']],['up','down'],default='flat')
df

	date	open	close	stock	flag
0	1	4	4	A	flat
1	2	5	5	A	flat
2	3	3	6	A	up
3	4	4	7	A	up
4	5	5	8	A	up
5	6	6	1	B	down
6	7	7	2	B	down
7	8	8	9	B	up
8	9	9	10	B	up
9	10	10	11	B	up

我尝试了以下方法。 None 其中有效。他们都给我“没有要聚合的数字类型”错误

# flag if previous 3 days (t-2,t-1, and t) are all increase for each stock

df['3days_up'] = df.groupby('stock')['flag'].rolling(3).apply(lambda x: 'Yes' if all(x['flag']=='up') else 'No')
df['3days_up'] = df.groupby('stock')[['flag']].rolling(3).apply(lambda x: 'Yes' if all(x['flag']=='up') else 'No')
df['3days_up'] = df.groupby('stock').rolling(3).apply(lambda x: 'Yes' if all(x['flag']=='up') else 'No')

预期输出：

	date	open	close	stock	flag	3days_up
0	1	4	4	A	flat	No
1	2	5	5	A	flat	No
2	3	3	6	A	up	No
3	4	4	7	A	up	No
4	5	5	8	A	up	Yes
5	6	6	1	B	down	No
6	7	7	2	B	down	No
7	8	8	9	B	up	No
8	9	9	10	B	up	No
9	10	10	11	B	up	Yes

Answer 1

将 up 值转换为 True 并将其他值转换为 False 作为起点：

df['3days_up'] = np.where(df.assign(is_up=df['flag'] == 'up')
                            .groupby('stock').rolling(3)['is_up']
                            .sum() >= 3, 'Yes', 'No')
print(df)

# Output
   date  open  close stock  flag 3days_up
0     1     4      4     A  flat       No
1     2     5      5     A  flat       No
2     3     3      6     A    up       No
3     4     4      7     A    up       No
4     5     5      8     A    up      Yes
5     6     6      1     B  down       No
6     7     7      2     B  down       No
7     8     8      9     B    up       No
8     9     9     10     B    up       No
9    10    10     11     B    up      Yes

如何在 pandas 中的组上滚动应用自定义函数？

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