[FIXED] storing result from function directly into DataFrame with return

Issue

I'm new to programming and python,

I'm trying to create a function to iterate over a dataframe and directly store results from the function to dataframe, so far here is what I've done:

def principal_loop2(df, col1, col2, col3):
for i, row in df_principal.iterrows():
    balance = row[col1]
    Suku_bunga = row[col2]
    terms = int(row[col3])
    periode = range(1, terms+1)
    if balance > 0:
        p = npf.ppmt(
        rate=Suku_bunga/12, per=periode, nper=terms, pv=-balance
        )
        return (p)

after running it I'm able to get the NumPy array from p and store it to a variable then transform it into dataframe, but that's only work in the first datapoint since the return exits the function after it satisfies the first condition. What alternative I can do so Im able to get all the results from the function as a NumPy array or directly save it to dataframe

thank you

Solution

Here's what you need -

My data is made up, so the values might not be representative of the problem you are trying to solve, but it will work with the dataframe you have given the columns are located at the same index as assumed in solution below.

# payment against loan principal
# numpy_financial.ppmt(rate, per, nper, pv, fv=0, when='end')

data = {
    "Suku_bunga": [0.018, 0.018, 0.018, 0.018, 0.018, 0.018],
    "periode": [10, 10, 10, 10, 10, 10],
    "terms": [10, 10, 10, 10, 10, 10],
    "balance": [10000, 9000, 8000, 7000, 6000, 0]
}

data = pd.DataFrame(data)

import numpy_financial as npf

get_principal = lambda x: npf.ppmt(rate=x[0]/12, per=x[1], nper=x[2], pv=-x[3]) if x[3] > 0 else None
# where x[0] = Suku_bunga, x[1] = periode, x[2] = terms, x[3] = balance

data["principal"] = data.apply(get_principal, axis=1)

data

# Output

# Suku_bunga    periode terms   balance principal
# 0.018           10    10      10000   1006.758411
# 0.018           10    10      9000    906.082570
# 0.018           10    10      8000    805.406729
# 0.018           10    10      7000    704.730888
# 0.018           10    10      6000    604.055047
# 0.018           10    10      0       NaN

Answered By - Prashant