diff options
author | Pieter <pmvz_github@outlook.com> | 2023-02-10 18:19:39 +0100 |
---|---|---|
committer | GitHub <noreply@github.com> | 2023-02-10 18:19:39 +0100 |
commit | 96e07907e3bb44afd46187d4e4b2c16745b24299 (patch) | |
tree | 0d69cd27e9fc8ff2aecd5ebdbb7cf6a8a2d4577a /doc/source/user | |
parent | baa84cfa9c0b68d9d540be749da77a7c9928980e (diff) | |
download | numpy-96e07907e3bb44afd46187d4e4b2c16745b24299.tar.gz |
DOC: Limit line lengths
Diffstat (limited to 'doc/source/user')
-rw-r--r-- | doc/source/user/how-to-index.rst | 10 |
1 files changed, 8 insertions, 2 deletions
diff --git a/doc/source/user/how-to-index.rst b/doc/source/user/how-to-index.rst index 02db91670..97c451260 100644 --- a/doc/source/user/how-to-index.rst +++ b/doc/source/user/how-to-index.rst @@ -310,7 +310,11 @@ result as dimensions with size one:: <BLANKLINE> [[2, 2, 2, 2, 2]]]) -To get the indices of each maximum or minimum value for each (N-1)-dimensional array in an N-dimensional array, use :meth:`reshape` to reshape the array to a 2D array, apply :meth:`argmax` or :meth:`argmin` along ``axis=1`` and use :meth:`unravel_index` to recover the index of the values per slice:: +To get the indices of each maximum or minimum value for each +(N-1)-dimensional array in an N-dimensional array, use :meth:`reshape` +to reshape the array to a 2D array, apply :meth:`argmax` or :meth:`argmin` +along ``axis=1`` and use :meth:`unravel_index` to recover the index of the +values per slice:: >>> x = np.arange(2*2*3).reshape(2, 2, 3) % 7 # 3D example array >>> x @@ -326,7 +330,9 @@ To get the indices of each maximum or minimum value for each (N-1)-dimensional a >>> np.unravel_index(indices_2d, x.shape[1:]) (array([1, 0]), array([2, 0])) -The first array returned contains the indices along axis 1 in the original array, the second array contains the indices along axis 2. The highest value in ``x[0]`` is therefore ``x[0, 1, 2]``. +The first array returned contains the indices along axis 1 in the original +array, the second array contains the indices along axis 2. The highest +value in ``x[0]`` is therefore ``x[0, 1, 2]``. Index the same ndarray multiple times efficiently ================================================= |