diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/core/fromnumeric.py | 2 | ||||
| -rw-r--r-- | numpy/lib/function_base.py | 7 | ||||
| -rw-r--r-- | numpy/lib/nanfunctions.py | 2 | ||||
| -rw-r--r-- | numpy/ma/extras.py | 7 |
4 files changed, 12 insertions, 6 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index bde37fca3..3314e516e 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -3419,7 +3419,7 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): instead of a single axis or all the axes as before. dtype : data-type, optional Type to use in computing the variance. For arrays of integer type - the default is `float32`; for arrays of float types it is the same as + the default is `float64`; for arrays of float types it is the same as the array type. out : ndarray, optional Alternate output array in which to place the result. It must have diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 9d380e67d..21532838b 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -316,14 +316,17 @@ def average(a, axis=None, weights=None, returned=False): The weights array can either be 1-D (in which case its length must be the size of `a` along the given axis) or of the same shape as `a`. If `weights=None`, then all data in `a` are assumed to have a - weight equal to one. + weight equal to one. The 1-D calculation is:: + + avg = sum(a * weights) / sum(weights) + + The only constraint on `weights` is that `sum(weights)` must not be 0. returned : bool, optional Default is `False`. If `True`, the tuple (`average`, `sum_of_weights`) is returned, otherwise only the average is returned. If `weights=None`, `sum_of_weights` is equivalent to the number of elements over which the average is taken. - Returns ------- retval, [sum_of_weights] : array_type or double diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 9a03d0b39..6cffab6ac 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -1443,7 +1443,7 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): the variance of the flattened array. dtype : data-type, optional Type to use in computing the variance. For arrays of integer type - the default is `float32`; for arrays of float types it is the same as + the default is `float64`; for arrays of float types it is the same as the array type. out : ndarray, optional Alternate output array in which to place the result. It must have diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py index 639b3dd1f..de1aa3af8 100644 --- a/numpy/ma/extras.py +++ b/numpy/ma/extras.py @@ -549,8 +549,11 @@ def average(a, axis=None, weights=None, returned=False): The weights array can either be 1-D (in which case its length must be the size of `a` along the given axis) or of the same shape as `a`. If ``weights=None``, then all data in `a` are assumed to have a - weight equal to one. If `weights` is complex, the imaginary parts - are ignored. + weight equal to one. The 1-D calculation is:: + + avg = sum(a * weights) / sum(weights) + + The only constraint on `weights` is that `sum(weights)` must not be 0. returned : bool, optional Flag indicating whether a tuple ``(result, sum of weights)`` should be returned as output (True), or just the result (False). |