Looping Through Large Data Sets

[buran]

I timeit and there is some performance gain, but not as much as I expected

import pandas
import timeit

raw_data = pandas.DataFrame(columns=["Column_0", "Column_1", "Column_2", "Column_3", "Column_4", "Column_5", "Column_6", "Column_7", "Column_8"])
raw_data.loc[0,:] = (315589, "CHZ3", 1100, 218, 694.63, None, -1.589, 0, 1.3694)
raw_data.loc[1,:] = (364048, "CHZ3", 1100, 320, 12.09, None, -7.216, 0, 59.89)

print(timeit.timeit("raw_data['Column_5'] = raw_data['Column_3'] * 8950 + raw_data['Column_4']", setup='from __main__ import raw_data', number=20000))
print(timeit.timeit("raw_data.iloc[:,5] = raw_data.iloc[:,3] * 8950 + raw_data.iloc[:,4]", setup='from __main__ import raw_data', number=20000))

Output:
9.739402721000033
11.304438857000036