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Yeah, I'm not sure that this is a fair comment: how would _you_ avoid the necessity of pandas and numpy? Besides which, in most projects where you're interested

Yeah, I'm not sure that this is a fair comment: how would _you_ avoid the necessity of pandas and numpy? Besides which, in most projects where you're interested in confidence intervals you'll probably already have both imported for other functionally anyway.

pbnjay 10y ago
TBH, I haven't used python for science in a few years, so maybe numpy is the norm now and I'm showing my age. But when I was doing more python, I wrote bootstrapping, monte carlo and CI code without anything but the standard lib. I probably used pypy to get it fast enough, but if everyone has numpy now then that's definitely the way to go and I retract my comment! I'm not trying to sound arrogant or anything, if numpy is the standard now then there's definitely no point in reinventing the wheel. (But pandas is still overkill...)
- lumpypua 10y ago
  pandas is the standard now for most python data munging I've come across. numpy is "low level".
  - pbnjay 10y ago
    bootstrapping does not require any munging.
- spencebeecher 10y ago
  I agree with you! Pandas is only used in the power analysis code (which also has matplotlib for plotting). The best thing would be to pair this down. We would gladly take contributions - i think the path forward on this feedback is clear but will take a little code =) Most of the important stuff is just numpy which i feel is pretty fair for most peeps.
nurettin 10y ago
Accumulating percentile data isn't a very difficult technique. I did the same thing on arduino using nesC so it is surely doable without pandas or numpy.
- spencebeecher 10y ago
  Completely agree it is easy. Doing it quickly (for Python) is what this is optimized for. We would love a contribution if you have a method for resampling + percentiles that beats numpy.
  - nurettin 10y ago
    I will surely give it a look if I have a need for this. Until then, thanks for the contribution and the invitation, and I have nothing against numpy.
    - spencebeecher 10y ago
      Thanks for the feedback - happy hacking =)

pbnjay 10y ago

TBH, I haven't used python for science in a few years, so maybe numpy is the norm now and I'm showing my age. But when I was doing more python, I wrote bootstrapping, monte carlo and CI code without anything but the standard lib. I probably used pypy to get it fast enough, but if everyone has numpy now then that's definitely the way to go and I retract my comment! I'm not trying to sound arrogant or anything, if numpy is the standard now then there's definitely no point in reinventing the wheel. (But pandas is still overkill...)

lumpypua 10y ago
pandas is the standard now for most python data munging I've come across. numpy is "low level".
- pbnjay 10y ago
  bootstrapping does not require any munging.
spencebeecher 10y ago
I agree with you! Pandas is only used in the power analysis code (which also has matplotlib for plotting). The best thing would be to pair this down. We would gladly take contributions - i think the path forward on this feedback is clear but will take a little code =) Most of the important stuff is just numpy which i feel is pretty fair for most peeps.

nurettin 10y ago

Accumulating percentile data isn't a very difficult technique. I did the same thing on arduino using nesC so it is surely doable without pandas or numpy.

spencebeecher 10y ago
Completely agree it is easy. Doing it quickly (for Python) is what this is optimized for. We would love a contribution if you have a method for resampling + percentiles that beats numpy.
- nurettin 10y ago
  I will surely give it a look if I have a need for this. Until then, thanks for the contribution and the invitation, and I have nothing against numpy.
  - spencebeecher 10y ago
    Thanks for the feedback - happy hacking =)