mindspore.numpy.nancumsum

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mindspore.numpy.nancumsum(a, axis=None, dtype=None)[source]

Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros.

Zeros are returned for slices that are all-NaN or empty.

Note

If a.dtype is int8, int16 or bool, the result dtype will be elevated to int32.

Parameters

  • a (Tensor) – Input tensor.

  • axis (int, optional) – Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array.

  • dtype (mindspore.dtype, optional) – If not specified, stay the same as a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used. Default: None .

Returns

Tensor.

Raises

  • TypeError – If input arguments have types not specified above.

  • ValueError – If axis is out of range.

Supported Platforms:

GPU CPU

Examples

>>> import mindspore.numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> output = np.nancumsum(a)
>>> print(output)
[1. 3. 6. 6.]
>>> output = np.nancumsum(a, axis=0)
>>> print(output)
[[1. 2.]
[4. 2.]]
>>> output = np.nancumsum(a, axis=1)
>>> print(output)
[[1. 3.]
[3. 3.]]