On Mon, Apr 24, 2017 at 4:06 PM, Aldcroft, Thomas < aldcr...@head.cfa.harvard.edu> wrote: > > On Mon, Apr 24, 2017 at 4:06 PM, Robert Kern <robert.k...@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.
I appreciate it. What is obvious to you is not obvious to me. > 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) I'm curious why you think this is not practical. It seems like a very practical solution to me. -- Robert Kern
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