Series/DataFrame sample method with/without replacement

[wesm]

Should use a more intelligent algorithm than using np.random.permutation

[changhiskhan]

Something like Series/DataFrame.sample(ntrials, shape=None, axis=0, replace=True, iterator=False)?

[wesm]

Or even just .sample(size, replace=True/False) would be fine. @rkern had a reservoir sampling impl floating around (for efficient sampling w/o replacement), maybe only on the mailing list

[wesm]

This doesn't need to get done for 0.10

[shoyer]

I would like to propose that we should copy the API from dplyr for this method: namely, we should have two methods, sample_n and sample_frac. These methods are especially nice when coupled with groupby.

CC @hayd

[TomAugspurger]

Steal all the dplyr!

To keep the number of new methods low, would you favor a single method df.sample(sample_size) where the behavior is like sample_frac if sample_size is between (0, 1), and like sample_n if it's a positive integer? There's precendce for this in scikit-learn's train_test_split:

test_size: If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples...

And we can have a with_replacement keyword argument as well. np.random.choice has a default of replace=True.

[shoyer]

@TomAugspurger Hmm. I've used train_test_split, but don't like the degeneracy of size = 1. I think Hadley Wickham has the right idea in dplyr with "each function does only one thing, but does it well." So I would prefer for two methods with the same prefix. In my opinion, similarly named methods do not cause much more cognitive load than a single method.