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It would probably be even better to iterate the file content and get
the non-quoted non-alphanumeric characters as separator
candidates (instead of a hard-coded list) however Python does not seem
to have a decoding iterator (taking bytes and yielding an iterator of
codepoints or even grapheme clusters) — incidentally uniseg seems to
require up-front decoding as well — so that's not really convenient as
we may be dealing with large-ish files and not want to load it
entirely in memory.
An alternative would be to use TextIOWrapper and iterate the file by
buffers of a few ks, and classify that based on either codepoints or
grapheme clusters.
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* Handle a leading + in import data, some contexts (e.g. bank
statements) will mark positive sums explicitly for clarity
* Add basic grouping/decimal separator inference for people importing
data from many localisations or to avoid them *having* to configure
their separators if we can handle it for them, currently very basic
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Task 40692
Various changes to import/export (mainly) UIs:
* default to excel & "full" (non-import-compatible) export
* auto-detect encoding of CSV using chardet
* remember column -> field mapping after having imported a file (useful
for repeated imports where auto-matching failed)
* better handle localised booleans & column names
* automatically select source list view's fields when exporting
* better integrate import templates feature and add a number of templates
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bzr revid: tch@tinyerp.com-20121010111037-y4ft0bvr0lysvbuo
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