**π‘ Problem Formulation:** Dealing with arrays in Python can be tricky when they contain `NaN`

(Not a Number) values or infinite values. The goal is to find the maximum value of an array that also might contain `numpy.inf`

(positive infinity) and `NaN`

elements. Proper handling of these special cases is essential. For instance, given an input array `[3, NaN, 5, inf, 7]`

, the desired output is `inf`

or `7`

, depending on whether we include infinity or not.

## Method 1: Use NumPy’s nanmax Function

Numpy’s `nanmax`

function returns the maximum value within an array while ignoring all `NaN`

values. This is the perfect tool for cases where the inclusion of infinite values is acceptable. It’s efficient and widely used in numerical computations with Python.

Here’s an example:

import numpy as np arr = np.array([3, np.nan, 5, np.inf, 7]) max_value = np.nanmax(arr)

Output:

inf

This code snippet first imports the `numpy`

library under the alias `np`

and creates an array containing positive infinity and `NaN`

values. It then applies `numpy.nanmax`

to find the maximum value while ignoring `NaN`

s, returning positive infinity in this case.

## Method 2: Combine max with a Filtered Comprehension

For those avoiding NumPy, one can use a list comprehension to filter out `NaN`

values combined with Python’s built-in `max`

function to find the maximum finite number. This method will not return infinity as part of the maximum.

Here’s an example:

import math arr = [3, math.nan, 5, math.inf, 7] finite_max = max(x for x in arr if not math.isnan(x) and x != math.inf)

Output:

7

We use the `math`

module to identify `NaN`

and infinite values. The line of code with `max`

goes through a filtered array comprehension that excludes `NaN`

and `inf`

values, finding the maximal finite number.

## Method 3: Use pandas Series max

Pandas Series objects provide a high-level interface that can handle `NaN`

values and infinity when calculating the maximum. This can be particularly useful when working within the pandas framework for data analysis.

Here’s an example:

import pandas as pd s = pd.Series([3, np.nan, 5, np.inf, 7]) max_value = s.max()

Output:

inf

After importing pandas and defining a Series with our array, the `max`

method on the Series object is called. It correctly returns `inf`

, as pandas, by default, treats infinity as a valid value for max computations.

## Method 4: Using max with pandas and Argument skipna

Adjusting behavior with pandas Series, one can set `skipna=True`

to ignore `NaN`

values while using `max`

. For added control, one could first replace infinity with `NaN`

and then calculate the max, which would only focus on finite numbers.

Here’s an example:

import pandas as pd s = pd.Series([3, np.nan, 5, np.inf, 7]) finite_max = s.replace(np.inf, np.nan).max()

Output:

7

This approach builds upon the pandas library’s features for handling missing or infinite data. By replacing `inf`

values with `NaN`

temporarily, applying `max`

will only return the maximum finite value thanks to `skipna=True`

by default.

## Bonus One-Liner Method 5: Using Filter and max Function

When working with pure Python and you’re looking for a simple one-liner, combine `filter`

with `max`

to exclude `NaN`

and infinity. Caution is advised though since this approach only works well with finite numbers.

Here’s an example:

import math arr = [3, math.nan, 5, math.inf, 7] finite_max = max(filter(lambda x: x != math.inf and not math.isnan(x), arr))

Output:

7

This concise snippet applies a lambda function to filter out `NaN`

and infinity before determining the maximum with Python’s built-in `max`

function.

## Summary/Discussion

**Method 1:**NumPy’s nanmax. Efficient with arrays; includes infinity. It’s the best fit for numerical computations within the NumPy environment.**Method 2:**Max with Filtered Comprehension. Pure Python; excludes infinity. It’s simple and does not require additional libraries, though potentially less efficient than NumPy on large data sets.**Method 3:**Pandas Series max. Handles NaNs and infinity well; integrates smoothly into pandas workflows. It is particularly good for data tables and pre-existing pandas data structures.**Method 4:**Pandas max with skipna. Offers control over how NaNs and infinity are treated prior to finding max. It’s versatile within pandas, allowing for precise handling of special cases.**Method 5:**Filter and max Function. Pure Python one-liner; excludes infinity. Best for quick computations where a library like NumPy or pandas is not desired or needed.