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Re: numpy arrays

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Subject Re: numpy arrays
Date Wed, 23 Mar 2016 13:45:29 +0000
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> What you want is called *transposing* the array:
> 
> http://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html
> 
> That should be a sufficiently fast operation.

Transposing itself is fast, as it just swaps the strides and dimensions
without touching the data (i.e. it returns a new view of the original
array), but subsequent operations may be slower as the data is no longer
contiguous (i.e. iterating over the flattened array in order won't result
in sequential memory access).

If that's an issue, you can use numpy.ascontiguousarray() to make a
contiguous copy of the data.

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numpy arrays Heli <hemla21@gmail.com> - 2016-03-23 03:06 -0700
  Re: numpy arrays Steven D'Aprano <steve@pearwood.info> - 2016-03-23 21:32 +1100
  Re: numpy arrays Chris Angelico <rosuav@gmail.com> - 2016-03-23 21:35 +1100
    Re: numpy arrays Nobody <nobody@nowhere.invalid> - 2016-03-23 13:45 +0000
      Re: numpy arrays Heli <hemla21@gmail.com> - 2016-04-06 09:26 -0700
        Re: numpy arrays Heli <hemla21@gmail.com> - 2016-04-07 07:31 -0700
          Re: numpy arrays Heli <hemla21@gmail.com> - 2016-04-11 02:17 -0700
          Re: numpy arrays Heli <hemla21@gmail.com> - 2016-04-11 02:32 -0700
  Re: numpy arrays Manolo Martínez <manolo@austrohungaro.com> - 2016-03-23 11:26 +0100
  Re: numpy arrays Heli <hemla21@gmail.com> - 2016-03-23 03:47 -0700
  Re: numpy arrays Simon Ward <simon+python@bleah.co.uk> - 2016-03-23 10:47 +0000

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