Trying to figure out how to use pandas.DataFrame.sample or any other function to balance this data: Given this DataFrame: d = {'class':['c1','c2','c1','c1','c2','c1','c1','c2','c3','c3'], 'val': [1,2,1,1,2,1,1,2,3,3] } df = pd.DataFrame(d) I would like to extract a sample that is balanced across the group class, that is, we should fix the class, and extract the same number of observations for each class. In particular, I would like the per-class number of observations to equal the sample size of the smallest class. UPDATE piRSquared's answer works but: How g.apply(lambda x: x.sample(g.size().min())) works? I know what 'lambda` is, but: What is passed to lambda in x in this case? What is g in g.size()? Why output contains 6,5,4, 1,8,9 numbers? What do they mean? Continue reading...
