1 files changed, 12 insertions, 11 deletions
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py
index 285b75314..fa8941828 100644
--- a/numpy/core/fromnumeric.py
+++ b/numpy/core/fromnumeric.py
@@ -3436,17 +3436,18 @@ def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
     Notes
     -----
     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 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`` provides a maximum likelihood
-    estimate of the variance for normally distributed variables. The
-    standard deviation computed in this function is the square root of
-    the estimated variance, so even with ``ddof=1``, it will not be an
-    unbiased estimate of the standard deviation per se.
+    deviations from the mean, i.e., ``std = sqrt(mean(x))``, where 
+    ``x = abs(a - a.mean())**2``.
+
+    The average squared deviation is typically 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. The standard deviation computed in this
+    function is the square root of the estimated variance, so even with
+    ``ddof=1``, it will not be an unbiased estimate of the standard deviation
+    per se.
 
     Note that, for complex numbers, `std` takes the absolute
     value before squaring, so that the result is always real and nonnegative.