numpoly.mean

numpoly.mean(a: numpoly.typing.PolyLike, axis: Union[None, int, Sequence[int]] = None, dtype: Union[numpy.dtype[Any], None, Type[Any], numpy._typing._dtype_like._SupportsDType[numpy.dtype[Any]], str, Tuple[Any, int], Tuple[Any, Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], numpy._typing._dtype_like._DTypeDict, Tuple[Any, Any]] = None, out: Optional[numpoly.baseclass.ndpoly] = None, **kwargs: Any) → numpoly.baseclass.ndpoly

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.

Args:

a:

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

axis:

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:

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:

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.

keepdims:

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.

kwargs:

Keyword args passed to numpy.ufunc.

Return:

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

Example:

>>> q0, q1 = numpoly.variable(2)
>>> poly = numpoly.polynomial([[1, 2*q0], [3*q1+q0, 4]])
>>> numpoly.mean(poly)
polynomial(0.75*q1+0.75*q0+1.25)
>>> numpoly.mean(poly, axis=0)
polynomial([1.5*q1+0.5*q0+0.5, q0+2.0])
>>> numpoly.mean(poly, axis=1)
polynomial([q0+0.5, 1.5*q1+0.5*q0+2.0])