numpy.nansum(a, axis=None, dtype=None, out=None, keepdims=0)[source]
Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.
In Numpy versions <= 1.8 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 axis : int, optional Axis 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 New in version 1.8.0. out : ndarray, optional Alternate output array in which to place the result. The default is New in version 1.8.0. keepdims : bool, optional If 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 New in version 1.8.0. |
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Returns: |
y : ndarray or numpy scalar |
See also
If both positive and negative infinity are present, the sum will be Not A Number (NaN).
Numpy integer arithmetic is modular. If the size of a sum exceeds the size of an integer accumulator, its value will wrap around and the result will be incorrect. Specifying dtype=double
can alleviate that problem.
>>> 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 >>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present nan
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https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.nansum.html