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Groups > comp.lang.python > #105583
| From | Nobody <nobody@nowhere.invalid> |
|---|---|
| Newsgroups | comp.lang.python |
| Subject | Re: numpy arrays |
| Date | 2016-03-23 13:45 +0000 |
| Organization | A noiseless patient Spider |
| Message-ID | <pan.2016.03.23.13.45.27.770000@nowhere.invalid> (permalink) |
| References | <3774dc9b-f9d3-462b-bbe4-41b8b2244db7@googlegroups.com> <mailman.43.1458729342.2244.python-list@python.org> |
> 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 MartÃnez <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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