How to create the histogram of an array with masked values, in Numpy?

Try hist(m_arr.compressed()).

This is a super old question, but these days I just use:

numpy.histogram(m_arr, bins=.., range=.., density=False, weights=m_arr_mask)

Where m_arr_mask is an array with the same shape as m_arr, consisting of 0 values for elements of m_arr to be excluded from the histogram and 1 values for elements that are to be included.

(Undeleting this as per discussion above...)

I'm not sure whether or not the numpy developers would consider this a bug or expected behavior. I asked on the mailing list, so I guess we'll see what they say.

Either way, it's an easy fix. Patching numpy/lib/function_base.py to use numpy.asanyarray rather than numpy.asarray on the inputs to the function will allow it to properly use masked arrays (or any other subclass of an ndarray) without creating a copy.

Edit: It seems like it is expected behavior. As discussed here:

If you want to ignore masked data it's just on extra function call

histogram(m_arr.compressed())

I don't think the fact that this makes an extra copy will be relevant, because I guess full masked array handling inside histogram will be a lot more expensive.

Using asanyarray would also allow matrices in and other subtypes that might not be handled correctly by the histogram calculations.

For anything else besides dropping masked observations, it would be necessary to figure out what the masked array definition of a histogram is, as Bruce pointed out.