numpy.mean¶

numpy.
mean
(a, axis=None, dtype=None, out=None, keepdims=<no value>)[source]¶ Compute the arithmetic mean along the specified axis.
Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis.
float64
intermediate and return values are used for integer inputs. Parameters
 aarray_like
Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.
 axisNone or int or tuple of ints, optional
Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.
New in version 1.7.0.
If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before.
 dtypedatatype, optional
Type to use in computing the mean. For integer inputs, the default is
float64
; for floating point inputs, it is the same as the input dtype. outndarray, optional
Alternate output array in which to place the result. The default is
None
; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncsoutputtype for more details. keepdimsbool, optional
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.
If the default value is passed, then keepdims will not be passed through to the
mean
method of subclasses ofndarray
, however any nondefault value will be. If the subclass’ method does not implement keepdims any exceptions will be raised.
 Returns
 mndarray, see dtype parameter above
If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned.
Notes
The arithmetic mean is the sum of the elements along the axis divided by the number of elements.
Note that for floatingpoint input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for
float32
(see example below). Specifying a higherprecision accumulator using thedtype
keyword can alleviate this issue.By default,
float16
results are computed usingfloat32
intermediates for extra precision.Examples
>>> a = np.array([[1, 2], [3, 4]]) >>> np.mean(a) 2.5 >>> np.mean(a, axis=0) array([2., 3.]) >>> np.mean(a, axis=1) array([1.5, 3.5])
In single precision,
mean
can be inaccurate:>>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.mean(a) 0.54999924
Computing the mean in float64 is more accurate:
>>> np.mean(a, dtype=np.float64) 0.55000000074505806 # may vary