5 Best Ways to Return the Cumulative Sum of Array Elements Treating NaNs as Zero in Python

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πŸ’‘ 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.