diff options
Diffstat (limited to 'numpy/core/fromnumeric.py')
| -rw-r--r-- | numpy/core/fromnumeric.py | 36 |
1 files changed, 19 insertions, 17 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index bf92d6539..5735b6124 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -1903,11 +1903,11 @@ def amax(a, axis=None, out=None, keepdims=False): a : array_like Input data. axis : int, optional - Axis along which to operate. By default flattened input is used. + Axis along which to operate. By default, flattened input is used. out : ndarray, optional - Alternate output array in which to place the result. Must be of - the same shape and buffer length as the expected output. See - `doc.ufuncs` (Section "Output arguments") for more details. + Alternative output array in which to place the result. Must + be of the same shape and buffer length as the expected output. + See `doc.ufuncs` (Section "Output arguments") for more details. 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, @@ -1938,7 +1938,7 @@ def amax(a, axis=None, out=None, keepdims=False): Notes ----- NaN values are propagated, that is if at least one item is NaN, the - corresponding max value will be NaN as well. To ignore NaN values + corresponding max value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmax. Don't use `amax` for element-wise comparison of 2 arrays; when @@ -1951,11 +1951,11 @@ def amax(a, axis=None, out=None, keepdims=False): >>> a array([[0, 1], [2, 3]]) - >>> np.amax(a) + >>> np.amax(a) # Maximum of the flattened array 3 - >>> np.amax(a, axis=0) + >>> np.amax(a, axis=0) # Maxima along the first axis array([2, 3]) - >>> np.amax(a, axis=1) + >>> np.amax(a, axis=1) # Maxima along the second axis array([1, 3]) >>> b = np.arange(5, dtype=np.float) @@ -1972,7 +1972,7 @@ def amax(a, axis=None, out=None, keepdims=False): except AttributeError: return _methods._amax(a, axis=axis, out=out, keepdims=keepdims) - # NOTE: Dropping and keepdims parameter + # NOTE: Dropping the keepdims parameter return amax(axis=axis, out=out) else: return _methods._amax(a, axis=axis, @@ -1987,7 +1987,7 @@ def amin(a, axis=None, out=None, keepdims=False): a : array_like Input data. axis : int, optional - Axis along which to operate. By default a flattened input is used. + Axis along which to operate. By default, flattened input is used. out : ndarray, optional Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. @@ -1999,8 +1999,10 @@ def amin(a, axis=None, out=None, keepdims=False): Returns ------- - amin : ndarray - A new array or a scalar array with the result. + amin : ndarray or scalar + Minimum of `a`. If `axis` is None, the result is a scalar value. + If `axis` is given, the result is an array of dimension + ``a.ndim - 1``. See Also -------- @@ -2019,9 +2021,9 @@ def amin(a, axis=None, out=None, keepdims=False): Notes ----- - NaN values are propagated, that is if at least one item is nan, the - corresponding min value will be nan as well. To ignore NaN values (matlab - behavior), please use nanmin. + NaN values are propagated, that is if at least one item is NaN, the + corresponding min value will be NaN as well. To ignore NaN values + (MATLAB behavior), please use nanmin. Don't use `amin` for element-wise comparison of 2 arrays; when ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than @@ -2035,9 +2037,9 @@ def amin(a, axis=None, out=None, keepdims=False): [2, 3]]) >>> np.amin(a) # Minimum of the flattened array 0 - >>> np.amin(a, axis=0) # Minima along the first axis + >>> np.amin(a, axis=0) # Minima along the first axis array([0, 1]) - >>> np.amin(a, axis=1) # Minima along the second axis + >>> np.amin(a, axis=1) # Minima along the second axis array([0, 2]) >>> b = np.arange(5, dtype=np.float) |