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author | endolith <endolith@gmail.com> | 2013-04-19 22:37:01 -0300 |
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committer | endolith <endolith@gmail.com> | 2013-04-19 22:37:01 -0300 |
commit | e3cd6a48108236cdee681e2de453a8aca1799125 (patch) | |
tree | 7706283d2ec4d76e197a794627a9dab710e3cb3e /numpy/lib/shape_base.py | |
parent | 1975606394d577421c4b4e21abb8fdadbdc572c0 (diff) | |
download | numpy-e3cd6a48108236cdee681e2de453a8aca1799125.tar.gz |
DOC: Change example to demonstrate function
"a * 0.5" example might as well be new_func(a) directly, it doesn't demonstrate the purpose of apply_along_axis().
Diffstat (limited to 'numpy/lib/shape_base.py')
-rw-r--r-- | numpy/lib/shape_base.py | 13 |
1 files changed, 5 insertions, 8 deletions
diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py index de8606167..aa9661cae 100644 --- a/numpy/lib/shape_base.py +++ b/numpy/lib/shape_base.py @@ -55,14 +55,11 @@ def apply_along_axis(func1d,axis,arr,*args): For a function that doesn't return a scalar, the number of dimensions in `outarr` is the same as `arr`. - >>> def new_func(a): - ... \"\"\"Divide elements of a by 2.\"\"\" - ... return a * 0.5 - >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]]) - >>> np.apply_along_axis(new_func, 0, b) - array([[ 0.5, 1. , 1.5], - [ 2. , 2.5, 3. ], - [ 3.5, 4. , 4.5]]) + >>> b = np.array([[8,1,7], [4,3,9], [5,2,6]]) + >>> np.apply_along_axis(sorted, 1, b) + array([[1, 7, 8], + [3, 4, 9], + [2, 5, 6]]) """ arr = asarray(arr) |