**π‘ Problem Formulation:** When handling arrays with numeric values in Python, it’s commonplace to encounter `NaN`

(Not a Number) elements, especially when working with datasets in scientific computing or machine learning. The challenge is to calculate the maximum value of an array that may include negative infinity and `NaN`

values, disregarding the `NaN`

s and treating them as if they don’t exist. For example, if we have an input array like `[NaN, -inf, 3, 5]`

, the desired output would be `5`

.

## Method 1: Using NumPy’s nanmax Function

The `nanmax`

function from the NumPy library is designed to handle arrays with `NaN`

values efficiently. It ignores all `NaN`

values and computes the maximum value in the array. If the array contains negative infinity, it is also considered, but `NaN`

s are excluded from the computation.

Here’s an example:

import numpy as np array = [np.nan, -np.inf, 3, 5] max_value = np.nanmax(array) print(max_value)

The output of this code snippet:

`5.0`

This code snippet first imports the NumPy library and creates an array that includes a `NaN`

and negative infinity. The `nanmax`

function is then called on this array, which calculates the maximum value while ignoring `NaN`

values, resulting in `5.0`

as the maximum.

## Method 2: Filtering NaNs with a List Comprehension

For those who prefer not to rely on external libraries like NumPy, a list comprehension can be used to remove `NaN`

values from the array before using the built-in `max`

function to find the largest value. This approach requires a bit more code but uses only Python’s standard library.

Here’s an example:

import math array = [float('nan'), float('-inf'), 3, 5] filtered_array = [x for x in array if not math.isnan(x)] max_value = max(filtered_array) print(max_value)

The output of this code snippet:

`5`

This snippet employs a list comprehension to filter out `NaN`

values, using the `math.isnan()`

function to check for the presence of `NaN`

s. Once the list is clean, the built-in `max`

function finds the maximum value, which is then printed out.

## Method 3: Using pandas’ Series.max

The pandas library, commonly used for data manipulation, provides a `Series.max`

method that can ignore `NaN`

values when calculating the maximum. This is highly effective when working with data in pandas Series format.

Here’s an example:

import pandas as pd array = [pd.NA, float('-inf'), 3, 5] series = pd.Series(array) max_value = series.max() print(max_value)

The output of this code snippet:

`5`

By creating a pandas Series from the array and using the `max`

method, this code effectively ignores any `NaN`

or `NA`

(pandas’ own missing value marker) and computes the maximum of the remaining values.

## Method 4: Using filter and reduce

Python’s `filter`

function combined with `functools.reduce`

can achieve the same result. `filter`

can exclude `NaN`

values from the array and `reduce`

can apply a cumulative operation to find the maximum value.

Here’s an example:

from functools import reduce import math array = [math.nan, float('-inf'), 3, 5] filtered_array = filter(lambda x: not math.isnan(x), array) max_value = reduce(lambda a, b: a if a > b else b, filtered_array) print(max_value)

The output of this code snippet:

`5`

The lambda function inside `filter`

removes any `NaN`

values from the array. Then the `reduce`

function with a lambda expression iterates through the filtered array and returns the maximum value.

## Bonus One-Liner Method 5: Using List Comprehension with max and isnan

If brevity is key, a one-liner using list comprehension, `max`

, and `math.isnan`

combines filtering and finding the maximum elegantly.

Here’s an example:

import math array = [math.nan, float('-inf'), 3, 5] max_value = max(x for x in array if not math.isnan(x)) print(max_value)

The output of this code snippet:

`5`

In this concise one-liner, the list comprehension syntax is used directly within the `max`

function call to filter out `NaN`

values and compute the maximum in a single step.

## Summary/Discussion

**Method 1:**NumPy’s nanmax.*Strengths:*Very straightforward and efficient, especially for those who already use NumPy in their workflow.*Weaknesses:*Requires the NumPy library, which might be considered heavy for simple tasks.**Method 2:**List Comprehension with max.*Strengths:*Doesn’t rely on external libraries and is quite readable.*Weaknesses:*Might be less efficient with very large arrays compared to NumPy.**Method 3:**pandas’ Series.max.*Strengths:*Ideal for data stored in pandas Series and integrates well with data analysis workflows.*Weaknesses:*Overkill if pandas is not already being used.**Method 4:**filter and reduce.*Strengths:*Functional programming-inspired method that is very Pythonic.*Weaknesses:*Can be less intuitive for those not familiar with these concepts.**Bonus Method 5:**One-Liner.*Strengths:*Extremely concise.*Weaknesses:*Readability may suffer for those not familiar with list comprehensions.