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COMPAT: Consider dropping numpy 1.6 support #7711
NumPy 1.6 introduced numerous bugs around datetimes, forcing a plethora of hacks, and causing numerous stress-induced ulcers for an uncountably infinite number of people.
The time for pandas to lose this vestigial organ has come. The main reason for this is that datetime support is just broken, in nearly every sense of the word. Take a look at some of the conversion code in
I think we've probably reached a point where supporting numpy 1.6 is just going to cause more pain than it's worth. To me it makes no sense to want to keep upgrading pandas while keeping
here is the current state of numpy:
so jessie will have the "modern" numpy, but current stable is at 1.6.2 and current pandas still happy on it. It would be great if dropping it could may be postponed after the next pandas release? according to reasoning above current release has broken handling of datetime on wheezy with numpy 1.6.2 -- then "dropping" support of numpy 1.6 in the next release would simply leave people on stable debian with a broken pandas, or have I misread it?
@yarikoptic you have misread.
pandas 'fixes' the timedelta support on < 1.7 numpy (using pandas operations). even though numpy is hopelessly broken (its much better in 1.8/1.9). In reality we now DO raise on < 1.7 numpy for unsupported ops.
the issue is that we have lots of test that are skipped on numpy < 1.7 and a fair number of ugly work-arounds for these.
I actually don't see dropping support for < 1.7 as much of a problem, because this is ONLY an issue if:
why ANYONE would do this is beyond me. If you are going to install pandas latest, what's the big deal with install numpy current?
@jreback regarding three bullet points. "you install/upgrade to current pandas using PIP" -- I agree that if someone does it, he is on his own ;) I was primarily concerned to have a reasonably good version within NeuroDebian for wheezy for at least another year (so people have time to upgrade from wheezy to jessie). If you think that 0.14.0+git17-g3849d5d-1~nd70+1 should be fine -- then go ahead with dropping the support. If you feel that it might be worth making a quick point or snapshot release before you drop 1.6.2 numpy support -- might even be better ;-)
referenced this issue
Jul 14, 2014
Mailing list seems to be a bit silent on this one. FWIW conda ships with numpy 1.8.1 and It seemed like a lot of people at SciPy 2014 were using conda. That said, I'm sure there are stragglers out there. I still don't understand why anyone would keep their numpy at <= 1.6.x and continue to follow pandas releases.