weights
: array_like, optional
An array of weights associated with the values in
a
. Each value in
a
contributes to the average according to its associated weight.
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.
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:
average, [sum_of_weights]
: {array_type, double}
Return the average along the specified axis. When returned is
True
,
return a tuple with the average as the first element and the sum
of the weights as the second element. The return type is
Float
if
a
is of integer type, otherwise it is of the same type as
a
.
sum_of_weights
is of the same type as
average
.
Raises:
ZeroDivisionError
:
When all weights along axis are zero. See
numpy.ma.average
for a
version robust to this type of error.
TypeError
:
When the length of 1D
weights
is not the same as the shape of
a
along axis.
>>>
np
.
average
(
data
)
>>>
np
.
average
(
range
(
1
,
11
),
weights
=
range
(
10
,
0
,
-
1
))
>>> data = np.arange(6).reshape((3,2))
array([[0, 1],
[2, 3],
[4, 5]])
>>> np.average(data, axis=1, weights=[1./4, 3./4])
array([ 0.75, 2.75, 4.75])
>>> np.average(data, weights=[1./4, 3./4])
Traceback (most recent call last):
TypeError: Axis must be specified when shapes of a and weights differ.
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Last updated on May 03, 2016.
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