Social and technical reasons I guess. Empirically it's just not used much. You can see my comments about numpy.ma in my 2010 paper about pandas
https://conference.scipy.org/proceedings/scipy2010/pdfs/mckinney.pdf At least in 2010, there were notable performance problems when using MaskedArray for computations "We chose to use NaN as opposed to using NumPy MaskedArrays for performance reasons (which are beyond the scope of this paper), as NaN propagates in floating-point operations in a natural way and can be easily detected in algorithms." On Mon, Mar 30, 2020 at 11:20 AM Daniel Nugent <[email protected]> wrote: > > Thanks! Since I'm just using it to jump to Arrow, I think I'll stick with it. > > Do you have any feelings about why Numpy's masked arrays didn't gain favor > when many data representation formats explicitly support nullity (including > Arrow)? Is it just that not carrying nulls in computations forward is > preferable (that is, early filtering/value filling was easier)? > > -Dan Nugent > > > On Mon, Mar 30, 2020 at 11:40 AM Wes McKinney <[email protected]> wrote: >> >> On Mon, Mar 30, 2020 at 8:31 AM Daniel Nugent <[email protected]> wrote: >> > >> > Didn’t want to follow up on this on the Jira issue earlier since it's sort >> > of tangential to that bug and more of a usage question. You said: >> > >> > > I wouldn't recommend building applications based on them nowadays since >> > > the level of support / compatibility in other projects is low. >> > >> > In my case, I am using them since it seemed like a straightforward >> > representation of my data that has nulls, the format I’m converting from >> > has zero cost numpy representations, and converting from an internal >> > format into Arrow in memory structures appears zero cost (or close to it) >> > as well. I guess I can just provide the mask as an explicit argument, but >> > my original desire to use it came from being able to exploit >> > numpy.ma.concatenate in a way that saved some complexity in implementation. >> > >> > Since Arrow itself supports masking values with a bitfield, is there >> > something intrinsic to the notion of array masks that is not well >> > supported? Or do you just mean the specific numpy MaskedArray class? >> > >> >> I mean just the numpy.ma module. Not many Python computing projects >> nowadays treat MaskedArray objects as first class citizens. Depending >> on what you need it may or may not be a problem. pyarrow supports >> ingesting from MaskedArray as a convenience, but it would not be >> common in my experience for a library's APIs to return MaskedArrays. >> >> > If this is too much of a numpy question rather than an arrow question, >> > could you point me to where I can read up on masked array support or maybe >> > what the right place to ask the numpy community about whether what I'm >> > doing is appropriate or not. >> > >> > Thanks, >> > >> > >> > -Dan Nugent
