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authorrgommers <ralf.gommers@googlemail.com>2010-06-02 13:07:10 +0000
committerrgommers <ralf.gommers@googlemail.com>2010-06-02 13:07:10 +0000
commit76e25d22ca216d8f30c74b8df79edd1d8ffdd242 (patch)
tree11e01eaa2f3a0962532d6b7728b7cbedb7fbc9da /numpy/core/fromnumeric.py
parente7c87d78cb3a6a3cb24276a5cf3fdb3f4d0bc43c (diff)
downloadnumpy-76e25d22ca216d8f30c74b8df79edd1d8ffdd242.tar.gz
DOC: merge wiki edits for module core.
Diffstat (limited to 'numpy/core/fromnumeric.py')
-rw-r--r--numpy/core/fromnumeric.py2
1 files changed, 1 insertions, 1 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py
index b255e89af..c142cd1ed 100644
--- a/numpy/core/fromnumeric.py
+++ b/numpy/core/fromnumeric.py
@@ -2371,7 +2371,7 @@ def std(a, axis=None, dtype=None, out=None, ddof=0):
The standard deviation is the square root of the average of the squared
deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``.
- The mean is normally calculated as ``x.sum() / N``, where
+ The average squared deviation 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``