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Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 11 |
1 files changed, 8 insertions, 3 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 9aec98cc8..d22e8c047 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -906,9 +906,9 @@ def gradient(f, *varargs, **kwargs): Returns ------- - gradient : ndarray - N arrays of the same shape as `f` giving the derivative of `f` with - respect to each dimension. + gradient : list of ndarray + Each element of `list` has the same shape as `f` giving the derivative + of `f` with respect to each dimension. Examples -------- @@ -918,6 +918,10 @@ def gradient(f, *varargs, **kwargs): >>> np.gradient(x, 2) array([ 0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ]) + For two dimensional arrays, the return will be two arrays ordered by + axis. In this example the first array stands for the gradient in + rows and the second one in columns direction: + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float)) [array([[ 2., 2., -1.], [ 2., 2., -1.]]), array([[ 1. , 2.5, 4. ], @@ -3735,6 +3739,7 @@ def insert(arr, obj, values, axis=None): [3, 5, 3]]) Difference between sequence and scalars: + >>> np.insert(a, [1], [[1],[2],[3]], axis=1) array([[1, 1, 1], [2, 2, 2], |