numpy.mean

numpy.mean(a, axis=None, dtype=None, out=None)

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:

a : array_like

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

axis : int, optional

Axis along which the means are computed. The default is to compute the mean of the flattened array.

dtype : dtype, 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.

out : ndarray, optional

Alternative output array in which to place the result. It must have the same shape as the expected output but the type will be cast if necessary. See doc.ufuncs for details.

Returns:

m : ndarray, see dtype parameter above

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

See also

average

Weighted average

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 float32 (see example below). Specifying a higher-accuracy accumulator using the dtype keyword can alleviate this issue.

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)
0.546875

Computing the mean in float64 is more accurate:

>>> np.mean(a, dtype=np.float64)
0.55000000074505806