On Mon, Apr 24, 2017 at 11:13 AM, Chris Barker <chris.bar...@noaa.gov> wrote:
> On the other hand, if this is the use-case, perhaps we really want an >> encoding closer to "Python 2" string, i.e, "unknown", to let this be >> signaled more explicitly. I would suggest that "text[unknown]" should >> support operations like a string if it can be decoded as ASCII, and >> otherwise error. But unlike "text[ascii]", it will let you store arbitrary >> bytes. >> > > I _think_ that is what using latin-1 (Or latin-9) gets you -- if it really > is ascii, then it's perfect. If it really is latin-*, then you get some > extra useful stuff, and if it's corrupted somehow, you still get the ascii > text correct, and the rest won't barf and can be passed on through. > I am totally in agreement with Thomas that "We are living in a messy world right now with messy legacy datasets that have character type data that are *mostly* ASCII, but not infrequently contain non-ASCII characters." My question: What are those non-ASCII characters? How often are they truly latin-1/9 vs. some other text encoding vs. non-string binary data? I don't think that silently (mis)interpreting non-ASCII characters as latin-1/9 is a good idea, which is why I think it would be a mistake to use 'latin-1' for text data with unknown encoding. I could get behind a data type that compares equal to strings for ASCII only and allows for *storing* other characters, but making blind assumptions about characters 128-255 seems like a recipe for disaster. Imagine text[unknown] as a one character string type, but it supports .decode() like bytes and every character in the range 128-255 compares for equality with other characters like NaN -- not even equal to itself.
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