Use for-loop instead of list comprehension

[rgaiacs]

entrofy/core.py has

    # Run the specified number of randomized trials
    results = [__entrofy(df_binary.values, n, rng,
                         w=target_weight,
                         q=target_prob,
                         pre_selects=pre_selects_i,
                         quantile=quantile,
                         alpha=alpha)
               for _ in range(n_trials)]

    # Select the trial with the best score
    max_score, best = results[0]
    for score, solution in results[1:]:
        if score > max_score:
            max_score = score
            best = solution

This could be replaced with

    # Run the specified number of randomized trials
    max_score = 0
    for _ in range(n_trials):
        score, solution = __entrofy(df_binary.values, n, rng,
                         w=target_weight,
                         q=target_prob,
                         pre_selects=pre_selects_i,
                         quantile=quantile,
                         alpha=alpha)
        if score > max_score:
            max_score = score
            best = solution

[bmcfee]

Yes, it could be replaced. Is there a reason to do that though?

[rgaiacs]

Is there a reason to do that though?

I think this is mostly my preference when coding and thinking about the logic behind it. I could try to write a technical reason related with memory (not allocate all the n_trials solution in memory) or speed (one n_trial for-loop instead of two) but I don't have data to support my idea.