Re: [Numpy-discussion] Fastest way to compute summary statistics for a specific axis

2015-03-17 Thread Dave Hirschfeld
Sebastian Berg sipsolutions.net> writes: > > On Mo, 2015-03-16 at 15:53 +, Dave Hirschfeld wrote: > > I have a number of large arrays for which I want to compute the mean and > > standard deviation over a particular axis - e.g. I want to compute the > > statistics for axis=1 as if the ot

Re: [Numpy-discussion] Fastest way to compute summary statistics for a specific axis

2015-03-16 Thread Eric Moore
On Mon, Mar 16, 2015 at 11:53 AM, Dave Hirschfeld wrote: > I have a number of large arrays for which I want to compute the mean and > standard deviation over a particular axis - e.g. I want to compute the > statistics for axis=1 as if the other axes were combined so that in the > example below I

Re: [Numpy-discussion] Fastest way to compute summary statistics for a specific axis

2015-03-16 Thread Sebastian Berg
On Mo, 2015-03-16 at 15:53 +, Dave Hirschfeld wrote: > I have a number of large arrays for which I want to compute the mean and > standard deviation over a particular axis - e.g. I want to compute the > statistics for axis=1 as if the other axes were combined so that in the > example below I

Re: [Numpy-discussion] Fastest way to compute summary statistics for a specific axis

2015-03-16 Thread Oscar Benjamin
On 16 March 2015 at 15:53, Dave Hirschfeld wrote: > I have a number of large arrays for which I want to compute the mean and > standard deviation over a particular axis - e.g. I want to compute the > statistics for axis=1 as if the other axes were combined so that in the > example below I get two

[Numpy-discussion] Fastest way to compute summary statistics for a specific axis

2015-03-16 Thread Dave Hirschfeld
I have a number of large arrays for which I want to compute the mean and standard deviation over a particular axis - e.g. I want to compute the statistics for axis=1 as if the other axes were combined so that in the example below I get two values back In [1]: a = randn(30, 2, 1) For the me