我正在寻找巩固以下Dataframe如下。 DF
Expiry F K Type sigma
0 2017-10-27 125.109375 123.5 P 0.045410
1 2017-10-27 125.109375 127.5 P 0.047965
2 2017-10-27 125.109375 124.5 P 0.041822
3 2017-10-27 125.109375 125.5 P 0.041526
4 2017-10-27 125.109375 120.5 P 0.045410
5 2017-10-27 125.109375 121.5 P 0.045410
6 2017-10-27 125.109375 121.0 P 0.045410
7 2017-10-27 125.109375 122.0 P 0.045410
8 2017-10-27 125.109375 123.0 P 0.045410
9 2017-10-27 125.109375 124.0 P 0.043341
10 2017-10-27 125.109375 125.0 P 0.041143
11 2017-10-27 125.109375 126.0 P 0.043123
12 2017-10-27 125.109375 127.0 P 0.047965
13 2017-10-27 125.109375 128.0 P 0.047965
14 2017-10-27 125.109375 128.5 P 0.047965
15 2017-10-27 125.109375 129.0 P 0.047965
16 2017-10-27 125.109375 129.5 P 0.047965
17 2017-10-27 125.109375 130.0 P 0.047965
18 2017-10-27 125.109375 126.5 P 0.046020
19 2017-10-27 125.109375 122.5 P 0.045410
20 2017-10-27 125.109375 123.5 C 0.045410
21 2017-10-27 125.109375 127.5 C 0.047965
22 2017-10-27 125.109375 124.5 C 0.041822
23 2017-10-27 125.125000 125.5 C 0.041629
24 2017-10-27 125.125000 120.5 C 0.045487
25 2017-10-27 125.125000 121.5 C 0.045487
26 2017-10-27 125.125000 121.0 C 0.045487
27 2017-10-27 125.125000 122.0 C 0.045487
28 2017-10-27 125.125000 123.0 C 0.045487
29 2017-10-27 125.125000 124.0 C 0.043292
.. ... ... ... ... ...
70 2017-11-03 125.109375 125.0 C 0.040830
71 2017-11-03 125.109375 126.0 C 0.042517
72 2017-11-03 125.109375 127.0 C 0.046631
73 2017-11-03 125.109375 128.0 C 0.046631
74 2017-11-03 125.109375 128.5 C 0.046631
75 2017-11-03 125.109375 129.0 C 0.046631
76 2017-11-03 125.109375 129.5 C 0.046631
77 2017-11-03 125.109375 130.0 C 0.046631
78 2017-11-03 125.109375 126.5 C 0.044948
79 2017-11-03 125.109375 122.5 C 0.044366
80 2017-10-20 125.109375 123.5 P 0.046512
81 2017-10-20 125.109375 127.5 P 0.048400
82 2017-10-20 125.109375 124.5 P 0.041512
83 2017-10-20 125.109375 125.5 P 0.042744
84 2017-10-20 125.109375 120.5 P 0.046512
85 2017-10-20 125.109375 121.5 P 0.046512
86 2017-10-20 125.109375 121.0 P 0.046512
87 2017-10-20 125.109375 122.0 P 0.046512
88 2017-10-20 125.109375 123.0 P 0.046512
89 2017-10-20 125.109375 124.0 P 0.044166
90 2017-10-20 125.109375 125.0 P 0.041220
91 2017-10-20 125.109375 126.0 P 0.045406
92 2017-10-20 125.109375 127.0 P 0.048400
93 2017-10-20 125.109375 128.0 P 0.048400
94 2017-10-20 125.109375 128.5 P 0.048400
95 2017-10-20 125.109375 129.0 P 0.048400
96 2017-10-20 125.109375 129.5 P 0.048400
97 2017-10-20 125.109375 130.0 P 0.048400
98 2017-10-20 125.109375 126.5 P 0.048400
99 2017-10-20 125.109375 122.5 P 0.046512
根据以下条件选择Sigma： 如果F> K使用西格玛来输入Type = P. 否则使用sigma作为type = C. 我正在寻找的结果应该如下所示：
123 123.5 124 124.5 125 125.5 126 126.5
Expiry
2017-10-20 0.051 0.047 0.043 0.04 0.040 0.039 0.041 0.043
2017-10-27 0.045 0.041 0.041 0.04 0.039 0.039 0.040 0.042
.....
非常感谢您的帮助。
答案 0 :(得分：1)
# create auxiliary column to check condition option 1
df['to_use'] = df.apply(lambda x: 1 if (x['F'] > x['K'] and x['Type'] == 'P') or x['Type'] == 'C' else 0, axis = 1)
# create auxiliary column to check condition option 2 (fastest option)
df['to_use'] = (df['F'] > df['K'])*1*(df['Type'] == 'P' )*1 + (df['Type'] == 'C' )*1
# sort data to place condition match values on top and P-type higher than C-type
df.sort_values(by = ['Expiry', 'K', 'to_use', 'Type'], ascending = [True, True, False, False], inplace = True)
# leave only values matching to condition (if P-type than it'll be higher otherwise C-type
new_df = df.drop_duplicates(subset = ['Expiry', 'K'], keep = 'first')
# now we are done to present result as a pivot table
new_df.pivot_table(index='Expiry', columns='K', values = 'sigma').reset_index()