如何在python中动态创建列本身的总和
我的原始数据是：如何在python中动态创建列本身的总和,python,dataframe,dynamic,sum,average,Python,Dataframe,Dynamic,Sum,Average,我的原始数据是： def f_ST(ST,F,T): a=ST/F-1-np.log(ST/F) return 2*a/T df=pd.DataFrame(range(50,140,5),columns=['K']) df['f(K0)']=df.apply(lambda x: f_ST(x.K,100,0.25),axis=1) df['f(K1)']=df['f(K0)'].shift(-1) df['dK']=df['K'].diff(1) 我想做的事情是：我有一个函数
def f_ST(ST,F,T):
a=ST/F-1-np.log(ST/F)
return 2*a/T
df=pd.DataFrame(range(50,140,5),columns=['K'])
df['f(K0)']=df.apply(lambda x: f_ST(x.K,100,0.25),axis=1)
df['f(K1)']=df['f(K0)'].shift(-1)
df['dK']=df['K'].diff(1)
nbr date k f(k) f1_f(k) d_k tmpw w
10 2019-02-19 100 0.000000 0.009679 5.0 0.001936 0.001936
11 2019-02-19 105 0.009679 0.037519 5.0 0.005568 0.003632
12 2019-02-19 110 0.037519 0.081904 5.0 0.008877 0.003309
13 2019-02-19 115 0.081904 0.141428 5.0 ...
14 2019-02-19 120 0.141428 0.214852 5.0 ...
15 2019-02-19 125 0.214852 0.301086 5.0
16 2019-02-19 130 0.301086 0.399163 5.0
非常感谢 我不完全理解你的问题，对我来说，所有这些符号都有点混乱 如果我得到了您想要的正确结果，那么对于每一行，您都希望有前面所有行的累积值。然后根据该累计值计算此行另一列的值 在这种情况下，我更喜欢一些东西，首先计算一个累计列，然后使用它 例如： 注意，您需要调用list（range（））而不是list，因此您的示例抛出了一个错误
import pandas as pd
import numpy as np
def f_ST(ST,F,T):
a=ST/F-1-np.log(ST/F)
return 2*a/T
df=pd.DataFrame(list(range(50,140,5)),columns=['K'])
df['f(K0)']=df.apply(lambda x: f_ST(x.K,100,0.25),axis=1)
df['f(K1)']=df['f(K0)'].shift(-1)
df['dK']=df['K'].diff(1)
df['accumulate'] = df['K'].shift(1).cumsum()
df['currentVal-accumulated'] = df['K'] - df['accumulate']
print(df)
K f(K0) ... accumulate currentVal-accumulated
0 50 1.545177 ... NaN NaN
1 55 1.182696 ... 50.0 5.0
2 60 0.886605 ... 105.0 -45.0
3 65 0.646263 ... 165.0 -100.0
4 70 0.453400 ... 230.0 -160.0
5 75 0.301457 ... 300.0 -225.0
6 80 0.185148 ... 375.0 -295.0
7 85 0.100151 ... 455.0 -370.0
8 90 0.042884 ... 540.0 -450.0
9 95 0.010346 ... 630.0 -535.0
10 100 0.000000 ... 725.0 -625.0
11 105 0.009679 ... 825.0 -720.0
12 110 0.037519 ... 930.0 -820.0
13 115 0.081904 ... 1040.0 -925.0
14 120 0.141428 ... 1155.0 -1035.0
15 125 0.214852 ... 1275.0 -1150.0
16 130 0.301086 ... 1400.0 -1270.0
17 135 0.399163 ... 1530.0 -1395.0
[18 rows x 6 columns]
nbr date k f(k) f1_f(k) d_k tmpw w
10 2019-02-19 100 0.000000 0.009679 5.0 0.001936 0.001936
11 2019-02-19 105 0.009679 0.037519 5.0 0.005568 0.003632
12 2019-02-19 110 0.037519 0.081904 5.0 0.008877 0.003309
13 2019-02-19 115 0.081904 0.141428 5.0 ...
14 2019-02-19 120 0.141428 0.214852 5.0 ...
15 2019-02-19 125 0.214852 0.301086 5.0
16 2019-02-19 130 0.301086 0.399163 5.0
import pandas as pd
import numpy as np
def f_ST(ST,F,T):
a=ST/F-1-np.log(ST/F)
return 2*a/T
df=pd.DataFrame(list(range(50,140,5)),columns=['K'])
df['f(K0)']=df.apply(lambda x: f_ST(x.K,100,0.25),axis=1)
df['f(K1)']=df['f(K0)'].shift(-1)
df['dK']=df['K'].diff(1)
df['accumulate'] = df['K'].shift(1).cumsum()
df['currentVal-accumulated'] = df['K'] - df['accumulate']
print(df)
K f(K0) ... accumulate currentVal-accumulated
0 50 1.545177 ... NaN NaN
1 55 1.182696 ... 50.0 5.0
2 60 0.886605 ... 105.0 -45.0
3 65 0.646263 ... 165.0 -100.0
4 70 0.453400 ... 230.0 -160.0
5 75 0.301457 ... 300.0 -225.0
6 80 0.185148 ... 375.0 -295.0
7 85 0.100151 ... 455.0 -370.0
8 90 0.042884 ... 540.0 -450.0
9 95 0.010346 ... 630.0 -535.0
10 100 0.000000 ... 725.0 -625.0
11 105 0.009679 ... 825.0 -720.0
12 110 0.037519 ... 930.0 -820.0
13 115 0.081904 ... 1040.0 -925.0
14 120 0.141428 ... 1155.0 -1035.0
15 125 0.214852 ... 1275.0 -1150.0
16 130 0.301086 ... 1400.0 -1270.0
17 135 0.399163 ... 1530.0 -1395.0
[18 rows x 6 columns]