dpnp.nansum

dpnp.nansum(a, /, *, axis=None, dtype=None, keepdims=False, out=None, initial=0, where=True)[source]

Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.

For full documentation refer to numpy.nansum.

Parameters:

  • a ({dpnp.ndarray, usm_ndarray}) – Input array.

  • axis (int or tuple of ints, optional) – Axis or axes along which sums must be computed. If a tuple of unique integers, sums are computed over multiple axes. If None, the sum is computed over the entire array. Default: None.

  • dtype (dtype, optional) –

    Data type of the returned array. If None, the default data type is inferred from the “kind” of the input array data type.

    • If a has a real-valued floating-point data type,

      the returned array will have the default real-valued floating-point data type for the device where input array a is allocated.

    • If a has signed integral data type, the returned array

      will have the default signed integral type for the device where input array a is allocated.

    • If a has unsigned integral data type, the returned array

      will have the default unsigned integral type for the device where input array a is allocated.

    • If a has a complex-valued floating-point data type,

      the returned array will have the default complex-valued floating-pointer data type for the device where input array a is allocated.

    • If a has a boolean data type, the returned array will

      have the default signed integral type for the device where input array a is allocated.

    If the data type (either specified or resolved) differs from the data type of a, the input array elements are cast to the specified data type before computing the sum. Default: None.

  • out ({None, dpnp.ndarray, usm_ndarray}, optional) – Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary. Default: None.

  • keepdims (bool, optional) – If True, the reduced axes (dimensions) are included in the result as singleton dimensions, so that the returned array remains compatible with the input array according to Array Broadcasting rules. Otherwise, if False, the reduced axes are not included in the returned array. Default: False.

Returns:

out – An array containing the sums. If the sum is computed over the entire array, a zero-dimensional array is returned. The returned array has the data type as described in the dtype parameter description above. Zero is returned for slices that are all-NaN or empty.

Return type:

dpnp.ndarray

Limitations

Parameters initial and where are supported with their default values. Otherwise NotImplementedError exception will be raised.

See also

dpnp.sum

Sum across array propagating NaNs.

dpnp.isnan

Show which elements are NaN.

dpnp.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

>>> import dpnp as np
>>> np.nansum(np.array([1]))
array(1)
>>> np.nansum(np.array([1, np.nan]))
array(1.)
>>> a = np.array([[1, 1], [1, np.nan]])
>>> np.nansum(a)
array(3.)
>>> np.nansum(a, axis=0)
array([2.,  1.])
>>> np.nansum(np.array([1, np.nan, np.inf]))
array(inf)
>>> np.nansum(np.array([1, np.nan, np.NINF]))
array(-inf)
>>> # both +/- infinity present
>>> np.nansum(np.array([1, np.nan, np.inf, -np.inf]))
array(nan)