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Diffstat (limited to 'doc/source/user/basics.creation.rst')
| -rw-r--r-- | doc/source/user/basics.creation.rst | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/doc/source/user/basics.creation.rst b/doc/source/user/basics.creation.rst index 523a05379..c0a4fd7cf 100644 --- a/doc/source/user/basics.creation.rst +++ b/doc/source/user/basics.creation.rst @@ -109,9 +109,9 @@ examples are shown:: >>> np.arange(10) array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> np.arange(2, 10, dtype=float) - array([ 2., 3., 4., 5., 6., 7., 8., 9.]) + array([2., 3., 4., 5., 6., 7., 8., 9.]) >>> np.arange(2, 3, 0.1) - array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9]) + array([2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9]) Note: best practice for :func:`numpy.arange` is to use integer start, end, and step values. There are some subtleties regarding ``dtype``. In the second @@ -124,7 +124,7 @@ spaced equally between the specified beginning and end values. For example: :: >>> np.linspace(1., 4., 6) - array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ]) + array([1. , 1.6, 2.2, 2.8, 3.4, 4. ]) The advantage of this creation function is that you guarantee the number of elements and the starting and end point. The previous @@ -217,8 +217,8 @@ specified shape. The default dtype is ``float64``:: ``zeros`` in all other respects as such:: >>> np.ones((2, 3)) - array([[ 1., 1., 1.], - [ 1., 1., 1.]]) + array([[1., 1., 1.], + [1., 1., 1.]]) >>> np.ones((2, 3, 2)) array([[[1., 1.], [1., 1.], @@ -300,10 +300,10 @@ arrays into a 4-by-4 array using ``block``:: >>> C = np.zeros((2, 2)) >>> D = np.diag((-3, -4)) >>> np.block([[A, B], [C, D]]) - array([[ 1., 1., 1., 0. ], - [ 1., 1., 0., 1. ], - [ 0., 0., -3., 0. ], - [ 0., 0., 0., -4. ]]) + array([[ 1., 1., 1., 0.], + [ 1., 1., 0., 1.], + [ 0., 0., -3., 0.], + [ 0., 0., 0., -4.]]) Other routines use similar syntax to join ndarrays. Check the routine's documentation for further examples and syntax. |