diff options
| -rw-r--r-- | numpy/core/code_generators/ufunc_docstrings.py | 24 | ||||
| -rw-r--r-- | numpy/core/fromnumeric.py | 36 | ||||
| -rw-r--r-- | numpy/lib/function_base.py | 18 |
3 files changed, 40 insertions, 38 deletions
diff --git a/numpy/core/code_generators/ufunc_docstrings.py b/numpy/core/code_generators/ufunc_docstrings.py index 8c6884003..53ccfcfda 100644 --- a/numpy/core/code_generators/ufunc_docstrings.py +++ b/numpy/core/code_generators/ufunc_docstrings.py @@ -2170,14 +2170,12 @@ add_newdoc('numpy.core.umath', 'maximum', """ Element-wise maximum of array elements. - Compare two arrays and returns a new array containing - the element-wise maxima. If one of the elements being - compared is a nan, then that element is returned. If - both elements are nans then the first is returned. The - latter distinction is important for complex nans, - which are defined as at least one of the real or - imaginary parts being a nan. The net effect is that - nans are propagated. + Compare two arrays and returns a new array containing the element-wise + maxima. If one of the elements being compared is a nan, then that element + is returned. If both elements are nans then the first is returned. The + latter distinction is important for complex nans, which are defined as at + least one of the real or imaginary parts being a nan. The net effect is + that nans are propagated. Parameters ---------- @@ -2206,15 +2204,15 @@ add_newdoc('numpy.core.umath', 'maximum', Notes ----- - Equivalent to ``np.where(x1 > x2, x1, x2)`` but faster and does proper - broadcasting. + The maximum is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither + x1 nor x2 are nans, but it is faster and does proper broadcasting. Examples -------- >>> np.maximum([2, 3, 4], [1, 5, 2]) array([2, 5, 4]) - >>> np.maximum(np.eye(2), [0.5, 2]) + >>> np.maximum(np.eye(2), [0.5, 2]) # broadcasting array([[ 1. , 2. ], [ 0.5, 2. ]]) @@ -2277,6 +2275,8 @@ add_newdoc('numpy.core.umath', 'minimum', >>> np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan]) array([ NaN, NaN, NaN]) + >>> np.minimum(-np.Inf, 1) + -inf """) @@ -2339,8 +2339,6 @@ add_newdoc('numpy.core.umath', 'fmax', add_newdoc('numpy.core.umath', 'fmin', """ - fmin(x1, x2[, out]) - Element-wise minimum of array elements. Compare two arrays and returns a new array containing the element-wise 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) diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 9f3b2a0f8..d782f454a 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -1478,20 +1478,23 @@ def nansum(a, axis=None): def nanmin(a, axis=None): """ - Return the minimum of an array or minimum along an axis ignoring any NaNs. + Return the minimum of an array or minimum along an axis, ignoring any NaNs. Parameters ---------- a : array_like - Array containing numbers whose minimum is desired. + Array containing numbers whose minimum is desired. If `a` is not + an array, a conversion is attempted. axis : int, optional - Axis along which the minimum is computed.The default is to compute + Axis along which the minimum is computed. The default is to compute the minimum of the flattened array. Returns ------- nanmin : ndarray - A new array or a scalar array with the result. + An array with the same shape as `a`, with the specified axis removed. + If `a` is a 0-d array, or if axis is None, an ndarray scalar is + returned. The same dtype as `a` is returned. See Also -------- @@ -1519,7 +1522,6 @@ def nanmin(a, axis=None): If the input has a integer type the function is equivalent to np.min. - Examples -------- >>> a = np.array([[1, 2], [3, np.nan]]) @@ -1581,7 +1583,7 @@ def nanargmin(a, axis=None): def nanmax(a, axis=None): """ - Return the maximum of an array or maximum along an axis ignoring any NaNs. + Return the maximum of an array or maximum along an axis, ignoring any NaNs. Parameters ---------- @@ -1596,8 +1598,8 @@ def nanmax(a, axis=None): ------- nanmax : ndarray An array with the same shape as `a`, with the specified axis removed. - If `a` is a 0-d array, or if axis is None, a ndarray scalar is - returned. The the same dtype as `a` is returned. + If `a` is a 0-d array, or if axis is None, an ndarray scalar is + returned. The same dtype as `a` is returned. See Also -------- |