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I frequently work with extremely unbalanced data where I care most about the edge cases. For example, right now, I'm looking at some data with almost 30,000 cases where Score is between 0 and .3 and 5,000 where Score ranges from .05 - over 8.
If my data was normally distributed (or even close), I'd run a histogram to get a feel for what the distribution is like, or maybe a summary, so I can see the quantiles, median and mean. But as it is, the histogram is unreadable since the y scale goes up to 30,000 and I'm mostly interested in the bins that are less than 100 and the minimum through the 3rd quantile are all zero.
Is there a standard technique for high-level exploration of this sort of highly skewed data in the same way that a histogram works? To be clear, I'm most interested in working around the skewness (which sabotages my standard techniques), rather than identifying/quantifying it.