USE the above row to calculate the value for below row iteratively using pandas dataframe

Question

I want create dataframe by reusing the above row to calculate the value of below row.
Currently I am using variables to stores values and creating list and pushing list to cf dataframe to calculate Discount Cash Flows.

Current Reproducible code-

import math
import pandas as pd

#User input
cashflow = 3.6667
fcf_growth_for_first_5_years = 14/100
fcf_growth_for_last_5_years = 7/100
no_of_years = 10
t_g_r = 3.50/100 ##Terminal Growth Rate
discount_rate = 10/100

##fcf calculaton for 10 Years
future_cash_1_year = cashflow*(1+fcf_growth_for_first_5_years)
future_cash_2_year = future_cash_1_year*(1+fcf_growth_for_first_5_years)
future_cash_3_year = future_cash_2_year*(1+fcf_growth_for_first_5_years)
future_cash_4_year = future_cash_3_year*(1+fcf_growth_for_first_5_years)
future_cash_5_year = future_cash_4_year*(1+fcf_growth_for_first_5_years)
future_cash_6_year = future_cash_5_year*(1+fcf_growth_for_last_5_years)
future_cash_7_year = future_cash_6_year*(1+fcf_growth_for_last_5_years)
future_cash_8_year = future_cash_7_year*(1+fcf_growth_for_last_5_years)
future_cash_9_year = future_cash_8_year*(1+fcf_growth_for_last_5_years)
future_cash_10_year = future_cash_9_year*(1+fcf_growth_for_last_5_years)

fcf = []
fcf.extend(value for name, value in locals().items() if name.startswith('future_cash_'))

cf = pd.DataFrame()
cf.insert(0, 'Sr_No', range(1,11))
cf.insert(1, 'Year', range(23,33))
cf['fcf'] = fcf
cf

Desired Output-

I am getting desired output by using lst method code as given above, but I am looking for more efficient way to calculate values using pandas df instead of using lst & variables.


  Sr_No Year    fcf
0   1   23  4.180038
1   2   24  4.765243
2   3   25  5.432377
3   4   26  6.192910
4   5   27  7.059918
5   6   28  7.554112
6   7   29  8.082900
7   8   30  8.648703
8   9   31  9.254112
9   10  32  9.901900

Asked By: Divyank

||

Source

Answer 1

Using a for loop makes this much more easier to handle

import math
import pandas as pd

#User input
cashflow = 3.6667
fcf_growth_for_first_5_years = 14/100
fcf_growth_for_last_5_years = 7/100
no_of_years = 10
t_g_r = 3.50/100 ##Terminal Growth Rate
discount_rate = 10/100

cf = pd.DataFrame()
cf.insert(0, 'Sr_No', range(1,11))
cf.insert(1, 'Year', range(23,33))

##fcf calculaton for 10 Years
fcf=[]
for row in range(len(cf)):
    if cf.Sr_No[row]==1:
        fcf.append(cashflow*(1+fcf_growth_for_first_5_years))
    elif cf.Sr_No[row]<6:
        fcf.append(fcf[row-1]*(1+fcf_growth_for_first_5_years))
    else:
        fcf.append(fcf[row-1]*(1+fcf_growth_for_last_5_years))
cf['fcf'] = fcf
cf

Answered By: Irsyaduddin

Answer 2

… I am looking for more efficient way to calculate values using pandas df …

You could use .cumprod():

cashflow = 3.6667
fcf_growth_for_first_5_years = 14/100
fcf_growth_for_last_5_years = 7/100

df = pd.DataFrame({
    "Sr_No": range(1, 1 + no_of_years), "Year": range(23, 23 + no_of_years)
})
df.loc[df.index[:no_of_years // 2], "fcf"] = 1 + fcf_growth_for_first_5_years
df["fcf"] = df["fcf"].fillna(1 + fcf_growth_for_last_5_years).cumprod() * cashflow

Result:

   Sr_No  Year       fcf
0      1    23  4.180038
1      2    24  4.765243
2      3    25  5.432377
3      4    26  6.192910
4      5    27  7.059918
5      6    28  7.554112
6      7    29  8.082900
7      8    30  8.648703
8      9    31  9.254112
9     10    32  9.901900

Answered By: Timus

USE the above row to calculate the value for below row iteratively using pandas dataframe

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