I am seeking to populate a pandas dataframe row-by-row, whereby each new row is calculated on the basis of the contents of the previous row. I am using this for simple financial projections.
Let us take a dataframe ‘df_basic_financials’:
df_basic_financials = pd.DataFrame({'current_account': [18357.], 'savings_account': [14809.]})
Now I want to forecast what my current and saving accounts will look like in five years, assuming that I earn 24000 a year and that my saving accounts yields 2% yearly, assuming I spend zero money and do not transfer any money to my savings account.
How do I write the code so that I get this:
current_account savings_account 0 18357 14809 1 42357 15105.18 2 66357 15407.2836
etc… for any number of years I want, each time using the calculation ‘value of the previous row in the same column + 24000’ for current_account and ‘value of the previous row in the same column*1.02’ for savings_account.
Answers:
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Method 1
You can get the input from user on number of years and then run the code this way
import pandas as pd
df = pd.DataFrame({'current_account': [18357], 'savings_account':[14809]})
years = int(input("Enter years: "))
for n in range(years):
lastrow = df.iloc[len(df)-1]
print(lastrow[0], lastrow[1])
df.loc[len(df.index)] = [int(lastrow[0]) +24000, int(lastrow[1])*1.02]
df
Out will be….
Method 2
Just use math
df_basic_financials = pd.DataFrame({'current_account': [18357.], 'savings_account': [14809.]})
current_account_projection = [df_basic_financials['current_account'].iloc[-1] + (24000 * i) for i in range(10)]
savings_account_projection = [df_basic_financials['savings_account'].iloc[-1] * (1.02 ** i) for i in range(10)]
df_basic_financials = pd.DataFrame({'current_account': current_account_projection, 'savings_account': savings_account_projection})
if you really want an interative solution, apply the function on savings_account.iloc[-1]
current_account_next = df_basic_financials.iloc[-1]['current_account'] + 24000
savings_account_next = df_basic_financials.iloc[-1]['savings_account'] * 1.02
df_basic_financials = df_basic_financials.append(pd.Series({'current_account': current_account_next, 'savings_account': savings_account_next}))
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