diff options
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 30 |
1 files changed, 20 insertions, 10 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index b1fb50900..be2b86f63 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -1024,7 +1024,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('mean', ---------- axis : integer Axis along which the means are computed. The default is - to compute the standard deviation of the flattened array. + to compute the mean of the flattened array. dtype : type Type to use in computing the means. For arrays of integer type the default is float32, for arrays of float types it @@ -1277,7 +1277,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('squeeze', add_newdoc('numpy.core.multiarray', 'ndarray', ('std', - """a.std(axis=None, dtype=None, out=None) -> standard deviation. + """a.std(axis=None, dtype=None, out=None, ddof=0) -> standard deviation. Returns the standard deviation of the array elements, a measure of the spread of a distribution. The standard deviation is computed for the @@ -1296,6 +1296,9 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('std', Alternative output array in which to place the result. It must have the same shape as the expected output but the type will be cast if necessary. + ddof : {0, integer} + Means Delta Degrees of Freedom. The divisor used in calculations + is N-ddof. Returns ------- @@ -1311,9 +1314,11 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('std', Notes ----- The standard deviation is the square root of the average of the squared - deviations from the mean, i.e. var = sqrt(mean((x - x.mean())**2)). The - computed standard deviation is biased, i.e., the mean is computed by - dividing by the number of elements, N, rather than by N-1. + deviations from the mean, i.e. var = sqrt(mean(abs(x - x.mean())**2)). + The computed standard deviation is computed by dividing by the number of + elements, N-ddof. The option ddof defaults to zero, that is, a + biased estimate. Note that for complex numbers std takes the absolute + value before squaring, so that the result is always real and nonnegative. """)) @@ -1461,7 +1466,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('transpose', add_newdoc('numpy.core.multiarray', 'ndarray', ('var', - """a.var(axis=None, dtype=None, out=None) -> variance + """a.var(axis=None, dtype=None, out=None, ddof=0) -> variance Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, @@ -1480,6 +1485,9 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('var', Alternative output array in which to place the result. It must have the same shape as the expected output but the type will be cast if necessary. + ddof : {0, integer}, + Means Delta Degrees of Freedom. The divisor used in calculation is + N - ddof. Returns ------- @@ -1494,10 +1502,12 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('var', Notes ----- - The variance is the average of the squared deviations from the mean, i.e. - var = mean((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. + The variance is the average of the squared deviations from the mean, + i.e. var = mean(abs(x - x.mean())**2). The mean is computed by + dividing by N-ddof, where N is the number of elements. The argument + ddof defaults to zero; for an unbiased estimate supply ddof=1. Note + that for complex numbers the absolute value is taken before squaring, + so that the result is always real and nonnegative. """)) |