diff options
Diffstat (limited to 'numpy/core/fromnumeric.py')
| -rw-r--r-- | numpy/core/fromnumeric.py | 34 |
1 files changed, 29 insertions, 5 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index a94be7b4d..ebeea6319 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -877,14 +877,22 @@ def argsort(a, axis=-1, kind='quicksort', order=None): array([[0, 3], [2, 2]]) - >>> np.argsort(x, axis=0) + >>> np.argsort(x, axis=0) # sorts along first axis (down) array([[0, 1], [1, 0]]) - >>> np.argsort(x, axis=1) + >>> np.argsort(x, axis=1) # sorts along last axis (across) array([[0, 1], [0, 1]]) + Indices of the sorted elements of a N-dimensional array: + + >>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape) + >>> ind + (array([0, 1, 1, 0]), array([0, 0, 1, 1])) + >>> x[ind] # same as np.sort(x, axis=None) + array([0, 2, 2, 3]) + Sorting with keys: >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')]) @@ -947,11 +955,19 @@ def argmax(a, axis=None, out=None): >>> np.argmax(a, axis=1) array([2, 2]) + Indexes of the maximal elements of a N-dimensional array: + + >>> ind = np.unravel_index(np.argmax(a, axis=None), a.shape) + >>> ind + (1, 2) + >>> a[ind] + 5 + >>> b = np.arange(6) >>> b[1] = 5 >>> b array([0, 5, 2, 3, 4, 5]) - >>> np.argmax(b) # Only the first occurrence is returned. + >>> np.argmax(b) # Only the first occurrence is returned. 1 """ @@ -1003,11 +1019,19 @@ def argmin(a, axis=None, out=None): >>> np.argmin(a, axis=1) array([0, 0]) + Indices of the minimum elements of a N-dimensional array: + + >>> ind = np.unravel_index(np.argmin(a, axis=None), a.shape) + >>> ind + (0, 0) + >>> a[ind] + 0 + >>> b = np.arange(6) >>> b[4] = 0 >>> b array([0, 1, 2, 3, 0, 5]) - >>> np.argmin(b) # Only the first occurrence is returned. + >>> np.argmin(b) # Only the first occurrence is returned. 0 """ @@ -2457,7 +2481,7 @@ def prod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): raised on overflow. That means that, on a 32-bit platform: >>> x = np.array([536870910, 536870910, 536870910, 536870910]) - >>> np.prod(x) #random + >>> np.prod(x) # random 16 The product of an empty array is the neutral element 1: |