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So i'm not using python on my day to day basis so this is kind of new to me, but I have large amount of csv files to edit and I imagine a simple script can save me a lot of time.

suppose I have a table input data

I want to create a new table that perform the following operation on each set y[i] (a column in the table) z[i] = (y[i]-y[0])/(y[5]-y[0]) - i

So far I have some issue including the index i (the row index) in the arithmetic operation

What I've managed so far: `

import pandas as pd
#import data file
csv_in = pd.read_csv('data.csv')
#creating the denominator
lsb = csv_in.iloc[5] - csv_in.iloc[0]
#here i'm missing -i in the end
inl = (csv_in.iloc[0] + csv_in)/lsb - csv_in.index.to_series()
print(inl)

So i'm wondering if there is a way to do it with a one liner like this? the csv_temp.index.to_series() didn't work, I assume i'm messing with the dimensions of the arrays i'm trying to operate on. do I have to do some kind of a loop?

the result should be output data

Thanks!

ActualTurtle
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1 Answers1

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so for now i'm doing it with a loop i don't think it is the most efficient way though. but calling each column in a loop and performing the operation on a series of the indexes does the trick

for i in range(csv_in.shape[1]):
    csv_in[csv_in.columns[i]] = csv_in[csv_in.columns[i]] - csv_in.index.to_series()

still would like to know if there is a more clever way to do it

ActualTurtle
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