Module « numpy.matlib »
Signature de la fonction nansum
def nansum(a, axis=None, dtype=None, out=None, keepdims=<no value>)
Description
nansum.__doc__
Return the sum of array elements over a given axis treating Not a
Numbers (NaNs) as zero.
In NumPy versions <= 1.9.0 Nan is returned for slices that are all-NaN or
empty. In later versions zero is returned.
Parameters
----------
a : array_like
Array containing numbers whose sum 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 sum is computed. The default is to compute the
sum of the flattened array.
dtype : data-type, optional
The type of the returned array and of the accumulator in which the
elements are summed. By default, the dtype of `a` is used. An
exception is when `a` has an integer type with less precision than
the platform (u)intp. In that case, the default will be either
(u)int32 or (u)int64 depending on whether the platform is 32 or 64
bits. For inexact inputs, dtype must be inexact.
.. versionadded:: 1.8.0
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. The casting of NaN to integer
can yield unexpected results.
.. versionadded:: 1.8.0
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.
.. versionadded:: 1.8.0
Returns
-------
nansum : ndarray.
A new array holding the result is returned unless `out` is
specified, in which it is returned. The result has the same
size as `a`, and the same shape as `a` if `axis` is not None
or `a` is a 1-d array.
See Also
--------
numpy.sum : Sum across array propagating NaNs.
isnan : Show which elements are NaN.
isfinite: Show which elements are not NaN or +/-inf.
Notes
-----
If both positive and negative infinity are present, the sum will be Not
A Number (NaN).
Examples
--------
>>> np.nansum(1)
1
>>> np.nansum([1])
1
>>> np.nansum([1, np.nan])
1.0
>>> a = np.array([[1, 1], [1, np.nan]])
>>> np.nansum(a)
3.0
>>> np.nansum(a, axis=0)
array([2., 1.])
>>> np.nansum([1, np.nan, np.inf])
inf
>>> np.nansum([1, np.nan, np.NINF])
-inf
>>> from numpy.testing import suppress_warnings
>>> with suppress_warnings() as sup:
... sup.filter(RuntimeWarning)
... np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
nan
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