将功能创建的列追加到现有数据框

Question

I currently have a dataframe as below:

and wish to add a column, E, that is calculated based on the following function.

def geometric_brownian_motion(T = 1, N = 100, mu = 0.1, sigma = 0.01, S0 = 20):
    dt = float(T)/N
    t = np.linspace(0, T, N)
    W = np.random.standard_normal(size = N)
    W = np.cumsum(W)*np.sqrt(dt) ### standard brownian motion ###
    X = (mu-0.5*sigma**2)*t + sigma*W
    S = S0*np.exp(X) ### geometric brownian motion ###
    return S

(originating from here )

How to i create a time-series for all of the dates contained within the data-frame and append it?

The function input parameters are as follows:

T = (#days between df row 1 and df last)/365

N = # rows in data frame

S0 = 100

Answer 1

As i understand the essense of question is how to apply some method to every column, taking into account, the fact that to calculate a new value you need an index from dataframe:

I suggest you to extract index as separate column and use apply as usually.

from functools import partial
df['index'] = df.index
T = # precalculate T here
N = df.shape[0]
applying_method = partial(geometric_brownian_motion,T=T,N=N, S0=100)
df['E'] = df.apply(lambda row: applying_method(*row),axis=1)

Or if you rename columns of dataframe accroding to you function arguments:

df['E'] = df.apply(lambda row: applying_method(**row),axis=1)

Hope that helps.