numpy.average的用法

最新推荐文章于 2025-05-25 19:47:48 发布

qingqingpiaoguo

最新推荐文章于 2025-05-25 19:47:48 发布

阅读量2.3w

点赞数 1

分类专栏： Python

Python 专栏收录该内容

31 篇文章

订阅专栏

本文详细介绍了NumPy库中的average函数，该函数用于计算指定轴上的加权平均值。文章解释了average函数的参数，包括输入数据数组、权重数组等，并通过实例展示了如何使用这些参数来计算不同情况下的平均值。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

numpy.average

numpy. average ( a, axis=None, weights=None, returned=False ) [source]

Compute the weighted average along the specified axis.

Parameters:

	a : array_like Array containing data to be averaged. If a is not an array, aconversion is attempted. axis : int, optional Axis along which to average a. If None, averaging is done overthe flattened array. weights : array_like, optional An array of weights associated with the values in a. Each value ina contributes to the average according to its associated weight.The weights array can either be 1-D (in which case its length must bethe 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 aweight 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 ofelements over which the average is taken.
Returns:	average, [sum_of_weights] : array_type or double Return the average along the specified axis. When returned is True,return a tuple with the average as the first element and the sumof the weights as the second element. The return type is Floatif 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 aversion robust to this type of error. TypeError When the length of 1D weights is not the same as the shape of aalong axis.

a : array_like

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

axis : int, optional

Axis along which to average a. If None, averaging is done overthe flattened array.

weights : array_like, optional

An array of weights associated with the values in a. Each value ina contributes to the average according to its associated weight.The weights array can either be 1-D (in which case its length must bethe 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 aweight 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 ofelements over which the average is taken.

Returns:

average, [sum_of_weights] : array_type or double

Return the average along the specified axis. When returned is True,return a tuple with the average as the first element and the sumof the weights as the second element. The return type is Floatif 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 aversion robust to this type of error.

TypeError

When the length of 1D weights is not the same as the shape of aalong axis.

See also

mean

ma.average

average for masked arrays – useful if your data contains “missing” values

Examples

  >>> 
  >>> data = range(1,5)
>>> data
[1, 2, 3, 4]
>>> np.average(data)
2.5
>>> np.average(range(1,11), weights=range(10,0,-1))
4.0
 
 

  >>> 
  >>> data = np.arange(6).reshape((3,2))
>>> data
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.

From：https://docs.scipy.org/doc/numpy/reference/generated/numpy.average.html 
 
 