如何有效地将数据框中特定列中的行值与其自己的滞后值相乘

Question

I have a dataframe with two columns 'actp' and 'modr': 'actp' contains an actual price series, 'modr' contains forecasted returns for the series. I want to create a third column 'modp' which takes the price series values when they exist (non NaN) or which multiplies the previous price series value 'actp' by 1 + log return ('modr') to generate a forecasted price value ('modp') and then multiply the lagged values of 'modp' by the return. It's a simple problem and easy enough to solve using a for loop. I would like to know what an efficient and elegant solution might look like as I need it to be optimised for speed and scalability.

I can accomplish this with a for loop, although it cureently doesn't add the actual value from 'actp' to 'modp' when 'actp' has a value in it.

for i in range(2,5):
    df['modp'].iloc[i] = df['actp'].iloc[i-1] * np.exp(df['modr'].iloc[i])
    df['actp'].iloc[i] = df['modp'].iloc[i]

original data

The expected outcome

Answer 1

As Dan mentioned, you can use shift

If I understood your calculations correctly, this should be it:

import pandas as pd
import numpy as np

df = pd.DataFrame({
                   'actp':[10,20,30,40],
                   'modr':[2,2,0,2]})

df['actp'] = df.actp.shift(1) * np.exp(df.modr)

print(df)
         actp  modr
0         NaN     2
1   73.890561     2
2   20.000000     0
3  221.671683     2