乘以列的所有组合

Question

I am trying to find an efficient way of multiplying each column combination within a pandas dataframe. I have managed to achieve this with itertools, however when the size of the dataframe increases it dramatically slows down. I am going to need to perform this on a dataframe with a size of about (100,1000)

Example of working code with smaller dataframe below,

import numpy as np
import pandas as pd
from itertools import combinations_with_replacement

df = pd.DataFrame(np.random.randn(3, 10))
new_df = pd.DataFrame()

for p in combinations_with_replacement(df.columns,2):
        title = p
        new_df[title] = df[p[0]]*df[p[1]]  

Does anybody have any suggestions on how this could be achieved?

Answer 1

Combining index view and array.prod(axis) , this runs ~100 times faster:

def f1():
    #with loop
    new_df = pd.DataFrame()
    for p in combinations_with_replacement(df.columns,2):
            title = p
            new_df[title] = df[p[0]]*df[p[1]]
    return new_df

def f2():
    n = len(df.columns)
    ix = np.indices((n,n))[:, ~np.tri(n, k=-1, dtype=bool)]
    return pd.DataFrame(df.values.T[ix.T].prod(1).T, columns=list(map(tuple, ix.T)))