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: 


Returns: 

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 the dtype
keyword can alleviate this issue.
By default, float16
results are computed using float32
intermediates for extra precision.
>>> 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
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.mean.html