mean(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None)

This docstring was copied from numpy.mean.

Some inconsistencies with the Dask version may exist.

Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. :None:None:`float64` intermediate and return values are used for integer inputs.

Notes

The arithmetic mean is the sum of the elements along the axis divided by the number of elements.

Note that for floating-point 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 :None:None:`float32` (see example below). Specifying a higher-precision accumulator using the :None:None:`dtype` keyword can alleviate this issue.

By default, :None:None:`float16` results are computed using :None:None:`float32` intermediates for extra precision.

Parameters

a : array_like

Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.

axis : None 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.

versionadded

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.

dtype : data-type, optional

Type to use in computing the mean. For integer inputs, the default is :None:None:`float64`; for floating point inputs, it is the same as the input dtype.

out : ndarray, 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 ufuncs-output-type for more details.

keepdims : bool, 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 sub-classes of :None:None:`ndarray`, however any non-default value will be. If the sub-class' method does not implement keepdims any exceptions will be raised.

where : array_like of bool, optional (Not supported in Dask)

Elements to include in the mean. See :None:None:`~numpy.ufunc.reduce` for details.

versionadded

Returns

m : ndarray, see dtype parameter above

If :None:None:`out=None`, returns a new array containing the mean values, otherwise a reference to the output array is returned.

Compute the arithmetic mean along the specified axis.

See Also

average

Weighted average

nanmean

nanstd

nanvar

std

var

Examples

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

>>> a = np.array([[1, 2], [3, 4]])  # doctest: +SKIP
... np.mean(a)  # doctest: +SKIP
2.5

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

>>> np.mean(a, axis=0)  # doctest: +SKIP
array([2., 3.])

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

>>> np.mean(a, axis=1)  # doctest: +SKIP
array([1.5, 3.5])

In single precision, mean can be inaccurate:

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

>>> a = np.zeros((2, 512*512), dtype=np.float32)  # doctest: +SKIP
... a[0, :] = 1.0  # doctest: +SKIP
... a[1, :] = 0.1  # doctest: +SKIP
... np.mean(a)  # doctest: +SKIP
0.54999924

Computing the mean in float64 is more accurate:

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

>>> np.mean(a, dtype=np.float64)  # doctest: +SKIP
0.55000000074505806 # may vary

Specifying a where argument: >>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]]) # doctest: +SKIP >>> np.mean(a) # doctest: +SKIP 12.0 >>> np.mean(a, where=[[True], [False], [False]]) # doctest: +SKIP 9.0

See :

Back References

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

dask.array.reductions.sum dask.array.reductions.var dask.array.reductions.nanmean dask.array.reductions.median dask.array.reductions.nanmedian dask.array.reductions.mean dask.array.reductions.nansum dask.array.reductions.nanvar dask.array.reductions.std dask.array.reductions.nanstd

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: /dask/array/reductions.py#632
type: <class 'function'>
Commit: