numpy.mean

numpy.mean

numpy.mean(a, axis=None, dtype=None, out=None, keepdims=False)[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:
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.
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 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 doc.ufuncs for 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 original arr.
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.

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