diff options
Diffstat (limited to 'numpy/core/fromnumeric.py')
| -rw-r--r-- | numpy/core/fromnumeric.py | 21 |
1 files changed, 14 insertions, 7 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index ed66a1ccf..cd9762d3c 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -819,7 +819,6 @@ def diagonal(a, offset=0, axis1=0, axis2=1): >>> a array([[[0, 1], [2, 3]], - <BLANKLINE> [[4, 5], [6, 7]]]) >>> a.diagonal(0,-2,-1) @@ -2132,9 +2131,10 @@ def var(a, axis=None, dtype=None, out=None, ddof=0): Alternative output array in which to place the result. It must have the same shape as the expected output but the type is cast if necessary. - ddof : positive int,optional + ddof : int, optional "Delta Degrees of Freedom": the divisor used in calculation is - N - ddof. + ``N - ddof``, where ``N`` represents the number of elements. By + default `ddof` is zero. Returns ------- @@ -2150,10 +2150,17 @@ def var(a, axis=None, dtype=None, out=None, ddof=0): Notes ----- The variance is the average of the squared deviations from the mean, - i.e., var = mean(abs(x - x.mean())**2). The computed variance is biased, - i.e., the mean is computed by dividing by the number of elements, N, - rather than by N-1. Note that for complex numbers the absolute value is - taken before squaring, so that the result is always real and nonnegative. + i.e., ``var = mean(abs(x - x.mean())**2)``. + + The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``. + If, however, `ddof` is specified, the divisor ``N - ddof`` is used + instead. In standard statistical practice, ``ddof=1`` provides an + unbiased estimator of the variance of the infinite population. ``ddof=0`` + provides a maximum likelihood estimate of the variance for normally + distributed variables. + + Note that for complex numbers, the absolute value is taken before + squaring, so that the result is always real and nonnegative. Examples -------- |