average(a, axis=None, weights=None, returned=False)

Parameters

a : array_like

Array containing data to be averaged. If a is not an array, a conversion is attempted.

axis : None or int or tuple of ints, optional

Axis or axes along which to average a. The default, axis=None, will average over all of the elements of the input array. If axis is negative it counts from the last to the first axis.

versionadded

If axis is a tuple of ints, averaging is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before.

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 :None:None:`weights=None`, then all data in a are assumed to have a weight equal to one. The 1-D calculation is:

avg = sum(a * weights) / sum(weights)

The only constraint on weights is that :None:None:`sum(weights)` must not be 0.

returned : bool, optional

Default is :None:None:`False`. If :None:None:`True`, the tuple (average , :None:None:`sum_of_weights`) is returned, otherwise only the average is returned. If :None:None:`weights=None`, :None:None:`sum_of_weights` is equivalent to the number of elements over which the average is taken.

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.

Returns

retval, [sum_of_weights] : array_type or double

Return the average along the specified axis. When :None:None:`returned` is :None:None:`True`, return a tuple with the average as the first element and the sum of the weights as the second element. :None:None:`sum_of_weights` is of the same type as retval . The result dtype follows a genereal pattern. If weights is None, the result dtype will be that of a , or float64 if a is integral. Otherwise, if weights is not None and a is non- integral, the result type will be the type of lowest precision capable of representing values of both a and weights . If a happens to be integral, the previous rules still applies but the result dtype will at least be float64 .

Compute the weighted average along the specified axis.

See Also

ma.average

average for masked arrays -- useful if your data contains "missing" values

mean

numpy.result_type

Returns the type that results from applying the numpy type promotion rules to the arguments.

Examples

This example is valid syntax, but we were not able to check execution

>>> data = np.arange(1, 5)
... data
array([1, 2, 3, 4])

This example is valid syntax, but we were not able to check execution

>>> np.average(data)
2.5

This example is valid syntax, but we were not able to check execution

>>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))
4.0

This example is valid syntax, but we were not able to check execution

>>> data = np.arange(6).reshape((3,2))
... data
array([[0, 1],
       [2, 3],
       [4, 5]])

This example is valid syntax, but we were not able to check execution

>>> np.average(data, axis=1, weights=[1./4, 3./4])
array([0.75, 2.75, 4.75])

This example is valid syntax, but we were not able to check execution

>>> 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.

This example is valid syntax, but we were not able to check execution

>>> a = np.ones(5, dtype=np.float128)
... w = np.ones(5, dtype=np.complex64)
... avg = np.average(a, weights=w)
... print(avg.dtype)
complex256

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.average dask.array.routines.average numpy.mean numpy.sum numpy.nanmean

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GitHub : /numpy/lib/function_base.py#395
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