Pandas DataFrame - calculated column based on subset of columns
I have the following DataFrame
Qtr Premium Claim Rate
Type Code
A 3 14 3552.77 100991.7 0.004017
3 15 5610.67 105763.6 0.004017
3 16 6463.22 107740.6 0.004017
4 17 6129.91 106967.7 0.005638
4 18 4688.65 103625.6 0.005638
4 19 2158.94 97759.66 0.005638
4 20 8540.77 89369.72 0.005638
I have a constant "c"
I want to do a row-by-row calculation that uses the corresponding values from Qtr and Rate, but updates the Premium and Claim values.
Example:
Premium = Premium / (1+Rate)^(c-Qtr)
Claim = Claim / (1+Rate)^(c-Qtr)
I actually have many columns in which I want this calculation to be done.
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1 answer
When df is the name of your dataframe and c is your constant, try:
df['Premium'] = df.Premium / ( 1 + df.Rate ) ** (c - df.Qtr)
df['Claim'] = df.Claim / ( 1 + df.Rate ) ** ( c - df.Qtr )
Update to comment, I'm sure there is a more pythonic way to do this, but this works:
columns = df.columns
subset_cols = columns.drop(['Rate','Qtr'])
for col in subset_cols:
df[col] = df[col] / ( 1 + df.Rate ) ** (c- df.Qtr)
Second update, you can extract the computation into a function and do the process in a list comprehension
def calc(df, col, c):
df[col] = df[col] / ( 1 + df.Rate ) ** ( c - df.Qtr )
[calc(df, col, c) for col in df.columns.drop(['Rate','Qtr'])]
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