On Tue, Dec 6, 2011 at 4:11 PM, Ralf Gommers <ralf.gommers@googlemail.com> wrote:
On Mon, Dec 5, 2011 at 8:43 PM, Ralf Gommers <ralf.gommers@googlemail.com> wrote:
Hi all,
It's been a little over 6 months since the release of 1.6.0 and the NA debate has quieted down, so I'd like to ask your opinion on the timing of 1.7.0. It looks to me like we have a healthy amount of bug fixes and small improvements, plus three larger chucks of work:
- datetime - NA - Bento support
My impression is that both datetime and NA are releasable, but should be labeled "tech preview" or something similar, because they may still see significant changes. Please correct me if I'm wrong.
There's still some maintenance work to do and pull requests to merge, but a beta release by Christmas should be feasible.
To be a bit more detailed here, these are the most significant pull requests / patches that I think can be merged with a limited amount of work: meshgrid enhancements: http://projects.scipy.org/numpy/ticket/966 sample_from function: https://github.com/numpy/numpy/pull/151 loadtable function: https://github.com/numpy/numpy/pull/143
Other maintenance things: - un-deprecate putmask - clean up causes of "DType strings 'O4' and 'O8' are deprecated..." - fix failing einsum and polyfit tests - update release notes
Cheers, Ralf
What do you all think?
Cheers, Ralf
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This isn't the place for this discussion but we should start talking about building a *high performance* flat file loading solution with good column type inference and sensible defaults, etc. It's clear that loadtable is aiming for highest compatibility-- for example I can read a 2800x30 file in < 50 ms with the read_table / read_csv functions I wrote myself recent in Cython (compared with loadtable taking > 1s as quoted in the pull request), but I don't handle European decimal formats and lots of other sources of unruliness. I personally don't believe in sacrificing an order of magnitude of performance in the 90% case for the 10% case-- so maybe it makes sense to have two functions around: a superfast custom CSV reader for well-behaved data, and a slower, but highly flexible, function like loadtable to fall back on. I think R has two functions read.csv and read.csv2, where read.csv2 is capable of dealing with things like European decimal format. - Wes