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Onyejiaku Theophilus Chidalu

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The ** numpy.nancumsum() function** in NumPy is used to return the cumulative sum of elements in a given array over a given axis in such a way that

`NaN`

values are treated as zeroes.```
numpy.nancumsum(a, axis=None, dtype=None, out=None)
```

The `numpy.nancumsum()`

function takes the following parameter values:

: This is the input array containing numbers to be computed. This is a required parameter.`a`

: This is the axis along which the product is determined. This is an optional parameter.`axis`

: This is the data type of the output array. This is an optional parameter.`dtype`

: This is the alternate array where the result is placed. This is an optional parameter.`out`

The `numpy.nancumsum()`

function returns an output array holding the result.

import numpy as np# creating an arrayx = np.array([1, 2, np.nan, 2, np.nan])# Implementing the nancumsum() functionmyarray = np.nancumsum(x, axis=0)print(x)print(myarray)

Implementing the numpy.nancumsum() function

- Line 1: We import the
`numpy`

module. - Line 4: We create an array,
`x`

, using the`array()`

method. - Line 7: We implement the
`np.nancumsum()`

function on the array. The result is assigned to a variable`myarray`

. - Line 9: We print the input array
`x`

. - Line 10: We print the variable
`myarray`

.

Note:The`NaN`

values are treated as`0`

.

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Onyejiaku Theophilus Chidalu

Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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