我有两个pandas数据帧（df1和df2）：
df1有12列，其中a1，a2，...，a9是空列。以下是df1的示例：
Stock Start_Date End_Date a1 a2 a3 a4 .... a9
A 09-12-2015 20:04 10-12-2015 23:04
B 09-12-2015 10:04 09-12-2015 20:14
A 11-12-2015 00:22 11-12-2015 08:04
C 08-12-2015 06:56 10-12-2015 20:54
df2有4列。以下是一个示例：
Stock date_time Opening closing
A 09-12-2015 21:24 144.3 10
A 09-12-2015 21:27 225.51 24
B 09-12-2015 10:20 134.42 11
A 09-12-2015 20:04 231.22 17
B 09-12-2015 10:24 399.55 32
A 09-12-2015 20:04 246.77 21
B 09-12-2015 14:22 76.23 8
C 08-12-2015 09:44 232.22 15
C 09-12-2015 20:04 222.91 12
A 11-12-2015 02:06 93.21 7
B 09-12-2015 20:04 211.36 26
C 09-12-2015 20:04 111.21 8
现在，我希望输出像这样，df1：
Stock Start_Date End_Date a1 a2 a3 a4 ....a9
A 09-12-2015 20:04 10-12-2015 23:04 0 2 2 0 0
B 09-12-2015 10:04 09-12-2015 20:14 1 1 2 0 0
A 11-12-2015 00:22 11-12-2015 08:04 1 0 0 0 0
C 08-12-2015 06:56 10-12-2015 20:54 0 0 0 1 0
即每个股票，Start_Date＆amp; End_Date df1的组合，结果应该具有df2的日期时间范围内每个类别的计数。
这里在最终输出中，a1 =计数[打开（0-100）和关闭（0-10）]，a2 =计数[打开（101-200）和关闭（11-20）]，a3 =计数[打开（201-400）和关闭（21-50）]，a4 =计数[打开（0-100）和关闭（11-20）]等等，所有9种组合。
我有R代码，但对于更大的数据集效果不佳。任何人都可以帮我解决如何在python / pandas中执行此操作。任何帮助表示赞赏!!
答案 0 :(得分：1)
您可以尝试使用此解决方案，我会删除df1的空列，但它也适用于它们：
#merge dataframes by Stock, select datetimes between start and end
df = df1.merge(df2,on='Stock', how='left')
df = df[(df.date_time >= df.Start_Date) & (df.date_time <= df.End_Date)]
#remove column date_time
df = df.drop(['date_time'], axis=1)
print df
# Stock Start_Date End_Date Opening closing
#0 A 2015-09-12 20:04:00 2015-10-12 23:04:00 144.30 10
#1 A 2015-09-12 20:04:00 2015-10-12 23:04:00 225.51 24
#2 A 2015-09-12 20:04:00 2015-10-12 23:04:00 231.22 17
#3 A 2015-09-12 20:04:00 2015-10-12 23:04:00 246.77 21
#5 B 2015-09-12 10:04:00 2015-09-12 20:14:00 134.42 11
#6 B 2015-09-12 10:04:00 2015-09-12 20:14:00 399.55 32
#7 B 2015-09-12 10:04:00 2015-09-12 20:14:00 76.23 8
#8 B 2015-09-12 10:04:00 2015-09-12 20:14:00 211.36 26
#13 A 2015-11-12 00:22:00 2015-11-12 08:04:00 93.21 7
#14 C 2015-08-12 06:56:00 2015-10-12 20:54:00 232.22 15
#15 C 2015-08-12 06:56:00 2015-10-12 20:54:00 222.91 12
#16 C 2015-08-12 06:56:00 2015-10-12 20:54:00 111.21 8
#values to new columns by conditions - cast boolean to integers
df['a1'] = ((df.Opening.between(0,100)) & (df.closing.between(0,10))).astype(int)
df['a2'] = ((df.Opening.between(100,200)) & (df.closing.between(11,20))).astype(int)
#add other columns like a1 and a2
print df
# Stock Start_Date End_Date Opening closing a1 a2
#0 A 2015-09-12 20:04:00 2015-10-12 23:04:00 144.30 10 0 0
#1 A 2015-09-12 20:04:00 2015-10-12 23:04:00 225.51 24 0 0
#2 A 2015-09-12 20:04:00 2015-10-12 23:04:00 231.22 17 0 0
#3 A 2015-09-12 20:04:00 2015-10-12 23:04:00 246.77 21 0 0
#5 B 2015-09-12 10:04:00 2015-09-12 20:14:00 134.42 11 0 1
#6 B 2015-09-12 10:04:00 2015-09-12 20:14:00 399.55 32 0 0
#7 B 2015-09-12 10:04:00 2015-09-12 20:14:00 76.23 8 1 0
#8 B 2015-09-12 10:04:00 2015-09-12 20:14:00 211.36 26 0 0
#13 A 2015-11-12 00:22:00 2015-11-12 08:04:00 93.21 7 1 0
#14 C 2015-08-12 06:56:00 2015-10-12 20:54:00 232.22 15 0 0
#15 C 2015-08-12 06:56:00 2015-10-12 20:54:00 222.91 12 0 0
#16 C 2015-08-12 06:56:00 2015-10-12 20:54:00 111.21 8 0 0
#groupby and sum rows
df= df.groupby(['Stock', 'Start_Date', 'End_Date']).sum()
df = df.drop(['Opening', 'closing'], axis=1)
print df.reset_index()
# Stock Start_Date End_Date a1 a2
#0 A 2015-09-12 20:04:00 2015-10-12 23:04:00 0 0
#1 A 2015-11-12 00:22:00 2015-11-12 08:04:00 1 0
#2 B 2015-09-12 10:04:00 2015-09-12 20:14:00 1 1
#3 C 2015-08-12 06:56:00 2015-10-12 20:54:00 0 0