How to calculate a pandas column based on the previous value in the same column that is calculated?-CodePudding

I want to achieve the following table, where the values in "md" needs to be calculated :

   msotc   md
0      0    0
1      1    1
2      2    3
3      3    7
4      4   15
5      5   31
6      6   63
7      7  127

Given:

The total rows is based on a given value (msotc 1)
The first value in column "md" needs to be 0
The value for row[1] to row[-1] are calculate based in formula: (prev_md * soss) sopd

Solutions (I think):

Create a new column with a formula
Create an empty column "md" with the value 0 on index[0] and calculate the other rows

import pandas as pd
import numpy as np

msotc = 7
sopd = 1 # (= not fixed, could be eg. 0.5)
soss = 2 # (= not fixed, could be eg. 1.05)

arr = [np.NaN] * (msotc   1)
arr[0] = 0

data = {
    "msotc": range(0, msotc   1, 1),
    "md": arr
}

df = pd.DataFrame(
    data=data
)

# df["md"] = (df["md"].shift(1) * soss)   sopd <- This doesn't work

CodePudding user response：

You can use math to convert your formula into a geometric series.

md[n] = md[n-1]*soss   sopd

expressed in terms of md[0] and using the formula for the sum of powers:

md[n] = md[0]*soss**n   sopd * (soss**(n 1) -1)/(soss-1)

Thus no need to loop, you can vectorize:

msotc = 7
sopd = 1
soss = 2
md0 = 0

n = np.arange(msotc)
df = pd.DataFrame({'msotc': n, 'md': md0*soss**n   sopd*(soss**(n 1)-1)/(soss-1)})

With your values, this simplifies to:

n = np.arange(msotc)
df = pd.DataFrame({'msotc': n, 'md': 2**(n 1)-1})

output:

   msotc     md
0      0    1.0
1      1    3.0
2      2    7.0
3      3   15.0
4      4   31.0
5      5   63.0
6      6  127.0

CodePudding user response：

This should work fine. It is quite easy to understand, just a simple loop.

arr = [0] * (msotc   1)
for i in range(msotc   1):
    if i == 0:
        continue
    arr[i] = (arr[i - 1] * soss)   sopd

CodePudding user response：

Does this answer your question ?

import pandas as pd

msotc = 7
sopd = 1
soss = 2

msotc_vals = []
arr = [0]
for val in range(msotc   1):
    msotc_vals  = [val]
    arr  = [arr[-1] * soss   sopd]

data = {"msotc": msotc_vals, "md": arr[:-1]}

df = pd.DataFrame(data=data)