5 Best Ways to Return the Maximum of an Array with Positive Infinity or Maximum Ignoring Any NaNs in Python

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💡 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 NaNs, 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.