I am doing a Python project and trying to cut down on some computational time at the start using Pandas. The code currently is:
for c1 in centres1: for c2 in centres2: if ((c1[0]-c2[0])**2 + (c1[1]-c2[1])**2) < search_rad*search_rad: possible_commet.append([c1,c2]) I am trying to put centres1 and centres2 into data frames then compare each value to each other value. Would pandas help me cut some time off it? (currently 2 mins). If not how could I work around it? Thanks
Unfortunately this is never going to be fast as you are going to be performing n squared operations. For example if you are comparing n objects where n = 1000 then you only have 1 million comparisons. If however you have n = 10_000 then you 100 million comparisons. A problem 10x bigger becomes 100 times slower.
nevertheless, for loops in python are relatively expensive. Using a library like pandas may mean that you can only make one function call and will shave some time off. Without any input data it's hard to assist further but the below should provide some building blocks
import pandas df1 = pandas.Dataframe(centres1) df2 = pandas.Dataframe(centres2) df3 = df1.merge(df2, how = 'cross') df3['combined_centre'] = ((df3['0_x']-df2['0_y']**2 + (df1['1_x']-df['1_y'])**2) df3[df3['prod'] > search_rad**2 Yes, for sure pandas will help in cutting-off atleast some time which will be less that what you are getting right now, but you can try this out:
for i,j in zip(center1, center2): if ((c1[0]-c2[0])**2 + (c1[1]-c2[1])**2) < search_rad*search_rad: possible_commet.append([c1,c2]) 
zip would work here as from what I understand from the question they are looking all possible permutations not simply the first entry from each list
zip()