On Mon, Apr 24, 2017 at 4:06 PM, Robert Kern <robert.kern@gmail.com> wrote:
I am not unfamiliar with this problem. I still work with files that have fields that are supposed to be in EBCDIC but actually contain text in ASCII, UTF-8 (if I'm lucky) or any of a variety of East European 8-bit encodings. In that experience, I have found that just treating the data as latin-1 unconditionally is not a pragmatic solution. It's really easy to implement, and you do get a program that runs without raising an exception (at the I/O boundary at least), but you don't often get a program that really runs correctly or treats the data properly. 

Can you walk us through the problems that you are having with working with these columns as arrays of `bytes`?

This is very simple and obvious but I will state for the record.  Reading an HDF5 file with character data currently gives arrays of `bytes` [1].  In Py3 this cannot be compared to a string literal, and comparing to (or assigning from) explicit byte strings everywhere in the code quickly spins out of control.  This generally forces one to convert the data to `U` type and incur the 4x memory bloat.

In [22]: dat = np.array(['yes', 'no'], dtype='S3')

In [23]: dat == 'yes'  # FAIL (but works just fine in Py2)
Out[23]: False

In [24]: dat == b'yes'  # Right answer but not practical
Out[24]: array([ True, False], dtype=bool)

- Tom

[1]: Using h5py or pytables.  Same with FITS, although astropy.io.fits does some tricks under the hood to auto-convert to `U` type as needed.
 


> So I would beg to actually move forward with a pragmatic solution that addresses very real and consequential problems that we face instead of waiting/praying for a perfect solution.

Well, I outlined a solution: work with `bytes` arrays with utilities to convert to/from the Unicode-aware string dtypes (or `object`).

A UTF-8-specific dtype and maybe a string-specialized `object` dtype address the very real and consequential problems that I face (namely and respectively, working with HDF5 and in-memory manipulation of string datasets).

I'm happy to consider a latin-1-specific dtype as a second, workaround-for-specific-applications-only-you-have-been-warned-you're-gonna-get-mojibake option. It should not be *the* Unicode string dtype (i.e. named np.realstring or np.unicode as in the original proposal).

--
Robert Kern

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