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Module « numpy »

Fonction nanmean - module numpy

Signature de la fonction nanmean

def nanmean(a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>) 

Description

help(numpy.nanmean)

Compute 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
----------
a : array_like
    Array containing numbers whose mean is desired. If `a` is not an
    array, a conversion is attempted.
axis : {int, tuple of int, None}, optional
    Axis or axes along which the means are computed. The default is to compute
    the mean of the flattened array.
dtype : data-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.
out : ndarray, 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 :ref:`ufuncs-output-type` for more details.
keepdims : bool, optional
    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
    `keepdims` will be passed through to the `mean` or `sum` methods
    of sub-classes of `ndarray`.  If the sub-classes methods
    does not implement `keepdims` any exceptions will be raised.
where : array_like of bool, optional
    Elements to include in the mean. See `~numpy.ufunc.reduce` for details.

    .. versionadded:: 1.22.0

Returns
-------
m : ndarray, see dtype parameter above
    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, nanvar

Notes
-----
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.

Examples
--------
>>> import numpy as np
>>> 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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