nlcpy.nanmean

nlcpy.nanmean(a, axis=None, dtype=None, out=None, keepdims=nlcpy._NoValue)

Computes the arithmetic mean along the specified axis, ignoring NaNs.

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. For all-NaN slices, NaN is returned and a RuntimeWarning is raised.

Parameters

aarray_like: Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.
axisint, None, optional: Axis along which the means are computed. The default is to compute the mean of the flattened array.
dtypedata-type, optional: Type to use in computing the mean. For integer inputs, the default is float64; for inexact 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 ufuncs for details.
keepdimsbool, optional: If this is set to True, the axis which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a.

Returns

mndarray: If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned. Nan is returned for slices that contain only NaNs.

See also

average: Weighted average
mean: Arithmetic mean taken while not ignoring NaNs
var: Computes the variance along the specified axis.
nanvar: Computes the variance along the specified axis, while ignoring NaNs.

Note

The arithmetic mean is the sum of the non-NaN elements along the axis divided by the number of non-NaN 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. Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.

Restriction

If axis is neither a scalar nor None : NotImplementedError occurs.
For complex numbers, NotImplementedError occurs.

Examples

>>> import nlcpy as vp
>>> a = vp.array([[1, vp.nan], [3, 4]])
>>> vp.nanmean(a)   
array(2.66666667)
>>> vp.nanmean(a, axis=0)  
array([2., 4.])
>>> vp.nanmean(a, axis=1)  
array([1. , 3.5])