- numpy.mean(a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)#
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.
float64intermediate and return values are used for integer inputs.
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.
- dtypedata-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.
- 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 Output type determination 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
meanmethod of sub-classes of
ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.
- wherearray_like of bool, optional
Elements to include in the mean. See
New in version 1.20.0.
- 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.
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-precision accumulator using the
dtypekeyword can alleviate this issue.
>>> 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,
meancan 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
Specifying a where argument:
>>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]]) >>> np.mean(a) 12.0 >>> np.mean(a, where=[[True], [False], [False]]) 9.0