resolved conflicts

1 files changed, 45 insertions, 19 deletions

diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py
index 820d6831f..764377bc9 100644
--- a/numpy/core/fromnumeric.py
+++ b/numpy/core/fromnumeric.py
@@ -1114,12 +1114,12 @@ def argsort(a, axis=-1, kind=None, order=None):
     return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)
 
 
-def _argmax_dispatcher(a, axis=None, out=None):
+def _argmax_dispatcher(a, axis=None, out=None, *, keepdims=np._NoValue):
     return (a, out)
 
 
 @array_function_dispatch(_argmax_dispatcher)
-def argmax(a, axis=None, out=None):
+def argmax(a, axis=None, out=None, *, keepdims=np._NoValue):
     """
     Returns the indices of the maximum values along an axis.
 
@@ -1133,12 +1133,18 @@ def argmax(a, axis=None, out=None):
     out : array, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the array.
 
     Returns
     -------
     index_array : ndarray of ints
         Array of indices into the array. It has the same shape as `a.shape`
-        with the dimension along `axis` removed.
+        with the dimension along `axis` removed. If `keepdims` is set to True,
+        then the size of `axis` will be 1 with the resulting array having same
+        shape as `a.shape`.
 
     See Also
     --------
@@ -1183,24 +1189,31 @@ def argmax(a, axis=None, out=None):
 
     >>> x = np.array([[4,2,3], [1,0,3]])
     >>> index_array = np.argmax(x, axis=-1)
-    >>> # Same as np.max(x, axis=-1, keepdims=True)
+    >>> # Same as np.amax(x, axis=-1, keepdims=True)
     >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)
     array([[4],
            [3]])
-    >>> # Same as np.max(x, axis=-1)
+    >>> # Same as np.amax(x, axis=-1)
     >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)
     array([4, 3])
 
+    Setting `keepdims` to `True`,
+
+    >>> x = np.arange(24).reshape((2, 3, 4))
+    >>> res = np.argmax(x, axis=1, keepdims=True)
+    >>> res.shape
+    (2, 1, 4)
     """
-    return _wrapfunc(a, 'argmax', axis=axis, out=out)
+    kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {}
+    return _wrapfunc(a, 'argmax', axis=axis, out=out, **kwds)
 
 
-def _argmin_dispatcher(a, axis=None, out=None):
+def _argmin_dispatcher(a, axis=None, out=None, *, keepdims=np._NoValue):
     return (a, out)
 
 
 @array_function_dispatch(_argmin_dispatcher)
-def argmin(a, axis=None, out=None):
+def argmin(a, axis=None, out=None, *, keepdims=np._NoValue):
     """
     Returns the indices of the minimum values along an axis.
 
@@ -1214,12 +1227,18 @@ def argmin(a, axis=None, out=None):
     out : array, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the array.
 
     Returns
     -------
     index_array : ndarray of ints
         Array of indices into the array. It has the same shape as `a.shape`
-        with the dimension along `axis` removed.
+        with the dimension along `axis` removed. If `keepdims` is set to True,
+        then the size of `axis` will be 1 with the resulting array having same
+        shape as `a.shape`.
 
     See Also
     --------
@@ -1264,16 +1283,23 @@ def argmin(a, axis=None, out=None):
 
     >>> x = np.array([[4,2,3], [1,0,3]])
     >>> index_array = np.argmin(x, axis=-1)
-    >>> # Same as np.min(x, axis=-1, keepdims=True)
+    >>> # Same as np.amin(x, axis=-1, keepdims=True)
     >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)
     array([[2],
            [0]])
-    >>> # Same as np.max(x, axis=-1)
+    >>> # Same as np.amax(x, axis=-1)
     >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)
     array([2, 0])
 
+    Setting `keepdims` to `True`,
+
+    >>> x = np.arange(24).reshape((2, 3, 4))
+    >>> res = np.argmin(x, axis=1, keepdims=True)
+    >>> res.shape
+    (2, 1, 4)
     """
-    return _wrapfunc(a, 'argmin', axis=axis, out=out)
+    kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {}
+    return _wrapfunc(a, 'argmin', axis=axis, out=out, **kwds)
 
 
 def _searchsorted_dispatcher(a, v, side=None, sorter=None):
@@ -1382,9 +1408,9 @@ def resize(a, new_shape):
 
     See Also
     --------
-    np.reshape : Reshape an array without changing the total size.
-    np.pad : Enlarge and pad an array.
-    np.repeat : Repeat elements of an array.
+    numpy.reshape : Reshape an array without changing the total size.
+    numpy.pad : Enlarge and pad an array.
+    numpy.repeat : Repeat elements of an array.
     ndarray.resize : resize an array in-place.
 
     Notes
@@ -2740,14 +2766,14 @@ def amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
     You can use an initial value to compute the maximum of an empty slice, or
     to initialize it to a different value:
 
-    >>> np.max([[-50], [10]], axis=-1, initial=0)
+    >>> np.amax([[-50], [10]], axis=-1, initial=0)
     array([ 0, 10])
 
     Notice that the initial value is used as one of the elements for which the
     maximum is determined, unlike for the default argument Python's max
     function, which is only used for empty iterables.
 
-    >>> np.max([5], initial=6)
+    >>> np.amax([5], initial=6)
     6
     >>> max([5], default=6)
     5
@@ -2863,7 +2889,7 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
     >>> np.nanmin(b)
     0.0
 
-    >>> np.min([[-50], [10]], axis=-1, initial=0)
+    >>> np.amin([[-50], [10]], axis=-1, initial=0)
     array([-50,   0])
 
     Notice that the initial value is used as one of the elements for which the
@@ -2872,7 +2898,7 @@ def amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
 
     Notice that this isn't the same as Python's ``default`` argument.
 
-    >>> np.min([6], initial=5)
+    >>> np.amin([6], initial=5)
     5
     >>> min([6], default=5)
     6