numpy.ma.mean¶

numpy.ma.mean(self, axis=None, dtype=None, out=None) = <numpy.ma.core._frommethod instance at 0x424506ac>¶

Returns the average of the array elements.

Masked entries are ignored. The average is taken over the flattened array by default, otherwise over the specified axis. Refer to numpy.mean for the full documentation.

Parameters:

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.
Returns:	mean : 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 : 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.

Returns:

mean : 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

numpy.ma.mean: Equivalent function.
numpy.mean: Equivalent function on non-masked arrays.
numpy.ma.average: Weighted average.

Examples

>>> a = np.ma.array([1,2,3], mask=[False, False, True])
>>> a
masked_array(data = [1 2 --],
             mask = [False False  True],
       fill_value = 999999)
>>> a.mean()
1.5