numpy.nanmean#

numpy.nanmean(a,axis=None,dtype=None,out=None,keepdims=<novalue>,*,where=<novalue>)[source]#

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

Returns the average of the array elements. The average is taken overthe 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 aRuntimeWarning is raised.

Parameters:
aarray_like

Array containing numbers whose mean is desired. Ifa is not anarray, a conversion is attempted.

axis{int, tuple of int, None}, optional

Axis or axes along which the means are computed. The default is to computethe mean of the flattened array.

dtypedata-type, optional

Type to use in computing the mean. For integer inputs, the defaultisfloat64; for inexact inputs, it is the same as the inputdtype.

outndarray, optional

Alternate output array in which to place the result. The defaultisNone; if provided, it must have the same shape as theexpected output, but the type will be cast if necessary.SeeOutput type determination for more details.

keepdimsbool, optional

If this is set to True, the axes which are reduced are leftin the result as dimensions with size one. With this option,the result will broadcast correctly against the originala.

If the value is anything but the default, thenkeepdims will be passed through to themean orsum methodsof sub-classes ofndarray. If the sub-classes methodsdoes not implementkeepdims any exceptions will be raised.

wherearray_like of bool, optional

Elements to include in the mean. Seereduce for details.

New in version 1.22.0.

Returns:
mndarray, see dtype parameter above

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

See also

average

Weighted average

mean

Arithmetic mean taken while not ignoring NaNs

var,nanvar

Notes

The arithmetic mean is the sum of the non-NaN elements along the axisdivided by the number of non-NaN elements.

Note that for floating-point input, the mean is computed using the sameprecision the input has. Depending on the input data, this can causethe results to be inaccurate, especially forfloat32. Specifying ahigher-precision accumulator using thedtype keyword can alleviatethis issue.

Examples

>>>importnumpyasnp>>>a=np.array([[1,np.nan],[3,4]])>>>np.nanmean(a)2.6666666666666665>>>np.nanmean(a,axis=0)array([2.,  4.])>>>np.nanmean(a,axis=1)array([1.,  3.5]) # may vary
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