nanmean(a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)
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 :None:None:`RuntimeWarning`
is raised.
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.
Array containing numbers whose mean is desired. If a
is not an array, a conversion is attempted.
Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.
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.
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-output-type
for more details.
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 original a
.
If the value is anything but the default, then :None:None:`keepdims`
will be passed through to the mean
or sum
methods of sub-classes of ndarray
. If the sub-classes methods does not implement :None:None:`keepdims`
any exceptions will be raised.
Elements to include in the mean. See :None:None:`~numpy.ufunc.reduce`
for details.
If :None:None:`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.
Compute the arithmetic mean along the specified axis, ignoring NaNs.
average
Weighted average
mean
Arithmetic mean taken while not ignoring NaNs
>>> 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 varySee :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.ma.core.var
numpy.var
numpy.nanstd
numpy.nanquantile
dask.array.reductions.nanmean
numpy.nanpercentile
numpy.nanvar
numpy.ma.core.MaskedArray.var
numpy.std
numpy.mean
numpy.lib.nanfunctions
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