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

Computes 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.


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

axisNone or int , 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 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 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 input array.

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.



Weighted average


Computes the standard deviation along the specified axis.


Computes the variance along the specified axis.


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


Computes the standard deviation along the specified axis, while ignoring NaNs.


Computes the variance along the specified axis, while ignoring NaNs.


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 dtype keyword can alleviate this issue.


  • If axis is neither a scalar nor None : NotImplementedError occurs.

  • For complex numbers, NotImplementedError occurs.


>>> import nlcpy as vp
>>> a = vp.array([[1, 2], [3, 4]])
>>> vp.mean(a)
>>> vp.mean(a, axis=0)
array([2., 3.])
>>> vp.mean(a, axis=1)
array([1.5, 3.5])

In single precision, mean can be inaccurate:

>>> a = vp.zeros((2, 512*512), dtype=vp.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> vp.mean(a)
array(0.5500002, dtype=float32)

Computing the mean in float64 is more accurate:

>>> vp.mean(a, dtype=vp.float64)