numpy.nancumsum() is used when we want to compute the cumulative sum of array elements along a given axis, treating Not a Numbers (NaNs) as zero.
The cumulative amount does not change when NaNs are encountered and leading NaNs are replaced with zeros. Zeros are returned for slices that are completely NaN or empty.
Syntax: numpy.nancumsum (arr, axis = None, dtype = None, out = None) p>
arr: [array_like] Array containing numbers whose cumulative sum is desired. If arr is not an array, a conversion is attempted.
axis: Axis along which the cumulative sum is computed. The default is to compute the sum of the flattened array.
dtype: Type of the returned array, as well as of the accumulator in which the elements are multiplied. If dtype is not specified, it defaults to the dtype of arr, unless arr has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead.
out: [ndarray, optional] A location into which the result is stored.
- & gt; If provided, it must have a shape that the inputs broadcast to.
- & gt; If not provided or None, a freshly-allocated array is returned.
Return: A new array holding the result is returned unless out is specified, in which case it is returned.
Code # 1: Work
Input number: 10 cumulative sum of input number: 
Code # 2:
Input array: [[2 . 4. 6.] [1. 3. nan]] cumulative sum of array elements: [2. 6. 12. 13. 16. 16.]
Code # 3:
Input array : [[2. 4. 6.] [1. 3. nan]] cumulative sum of array elements taking axis 0: [[2. 4. 6.] [3. 7. 6.]]
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