**π‘ Problem Formulation:** In Python, working with numerical data often involves managing NaN (Not a Number) values, especially when the data comes from real-world sources. In this article, we’ll explore methods to compute the cumulative sum of an array while treating NaN values as zero. For instance, given an input array `[1, NaN, 3, NaN, 5]`

, the desired output would be `[1, 1, 4, 4, 9]`

.

## Method 1: Using `numpy`

Array with `numpy.nan_to_num`

The `numpy`

library offers tools for handling numerical arrays efficiently. The `numpy.nan_to_num`

function can be used to convert NaN values to zero, allowing the use of `numpy.cumsum`

to obtain the cumulative sum without any NaN disruption.

Here’s an example:

import numpy as np arr = np.array([1, np.nan, 3, np.nan, 5]) cum_sum = np.cumsum(np.nan_to_num(arr)) print(cum_sum)

Output:

[1. 1. 4. 4. 9.]

This code snippet replaces NaN values in the array with zero using `np.nan_to_num`

and then proceeds to calculate the cumulative sum with `np.cumsum`

. The result is an array that effectively ignores NaNs in the calculation.

## Method 2: Using `pandas`

Series with `fillna`

The `pandas`

library, well-suited for data manipulation, allows handling NaN values seamlessly. The Series object provides a `fillna`

method, which can be followed by the `cumsum`

method to compute the cumulative sum while NaNs are treated as zero.

Here’s an example:

import pandas as pd series = pd.Series([1, np.nan, 3, np.nan, 5]) cum_sum = series.fillna(0).cumsum() print(cum_sum)

Output:

0 1.0 1 1.0 2 4.0 3 4.0 4 9.0 dtype: float64

In this snippet, `fillna(0)`

converts all NaNs in the pandas Series to zero. The cumulative sum is then calculated using `cumsum`

.

## Method 3: Using List Comprehension and `math.isnan`

For those who prefer standard Python without additional libraries, list comprehension combined with the `math.isnan`

function allows us to filter out NaN values and accumulate the sums manually. This is less efficient but works without external dependencies.

Here’s an example:

import math arr = [1, float('nan'), 3, float('nan'), 5] cum_sum = [] total = 0 for val in arr: total += 0 if math.isnan(val) else val cum_sum.append(total) print(cum_sum)

Output:

[1, 1, 4, 4, 9]

The code iterates through each element in the array, checks if it is NaN using `math.isnan`

, and increments the total by the element or zero. The cumulative total is appended to the result list `cum_sum`

at each iteration.

## Method 4: Using `itertools.accumulate`

with Custom Function

The `itertools.accumulate`

function is a powerful tool in the itertools module for making cumulative calculations. Pairing it with a custom function to handle NaNs as zeroes provides a Pythonic approach to this problem.

Here’s an example:

from itertools import accumulate import math arr = [1, float('nan'), 3, float('nan'), 5] cum_sum = list(accumulate(arr, lambda x, y: x + (0 if math.isnan(y) else y))) print(cum_sum)

Output:

[1, 1, 4, 4, 9]

This code uses the `accumulate`

function, applying a custom lambda function that adds elements together while treating NaNs as zero. It provides a concise and readable solution.

## Bonus One-Liner Method 5: Using List Comprehension with Conditional Expression

For a quick, one-liner solution, list comprehension with a conditional expression can achieve the same result in a very Pythonic and compact format.

Here’s an example:

arr = [1, float('nan'), 3, float('nan'), 5] cum_sum = [sum(0 if math.isnan(x) else x for x in arr[:i+1]) for i in range(len(arr))] print(cum_sum)

Output:

[1, 1, 4, 4, 9]

This one-liner uses list comprehension with slicing to sum up the elements up to the current index `i`

, replacing NaNs with zero during the sum computation for each slice of the array.

## Summary/Discussion

**Method 1:**Using`numpy`

with`nan_to_num`

. Strengths: Highly efficient and part of a widely-used numerical computing library. Weaknesses: Requires the installation of numpy.**Method 2:**Using`pandas`

Series with`fillna`

. Strengths: Part of pandas, great for datasets. Weaknesses: Overkill for small tasks, requires the installation of pandas.**Method 3:**Using standard Python with list comprehension and`math.isnan`

. Strengths: No external dependencies. Easy to understand. Weaknesses: Less efficient for large datasets.**Method 4:**Using`itertools.accumulate`

with a custom function. Strengths: Pythonic and concise. Weaknesses: Can be hard to read for those unfamiliar with itertools or lambda functions.**Bonus Method 5:**One-liner list comprehension. Strengths: Compact and Pythonic. Weaknesses: Potentially slow for large arrays, and the code is less readable.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.