**π‘ Problem Formulation:** When working with numerical data in Python, it is common to encounter the need to find the maximum value of an array along a specified axis. This task becomes slightly complex when the array contains ‘Not a Number’ (NaN) values, which must be ignored to prevent incorrect results. For example, given an input 2D array `[[1, 2, NaN], [NaN, 3, 4]]`

, we want to obtain `[2, 4]`

as the output when searching along axis 1, excluding any NaNs.

## Method 1: Using NumPy’s `nanmax`

Function

NumPy is a powerful numerical computing library in Python that provides the `nanmax`

function to find the maximum value in an array, ignoring any NaN values. The function takes the array and the axis along which to operate as arguments. It is particularly efficient for large datasets and maintains compatibility with NumPy’s multi-dimensional arrays.

Here’s an example:

import numpy as np array_with_nans = np.array([[1, 2, np.nan], [np.nan, 3, 4]]) max_values = np.nanmax(array_with_nans, axis=1) print(max_values)

Output:

[2. 4.]

In this snippet, the `nanmax`

function of NumPy is called on a 2D array with NaNs present. By specifying `axis=1`

, it calculates the maximum along each row while ignoring NaNs. The result is a new array consisting of the maximum values for each row.

## Method 2: Using a List Comprehension with NumPy’s `nanmax`

For situations where NumPy is already in use and a more Pythonic approach is preferred, a list comprehension can be combined with NumPy’s `nanmax`

function. This method involves iterating over elements along the desired axis (typically axis 0 or axis 1) and is straightforward to read and understand.

Here’s an example:

import numpy as np array_with_nans = np.array([[1, 2, np.nan], [np.nan, 3, 4]]) max_values = [np.nanmax(row) for row in array_with_nans] print(max_values)

Output:

[2. 4.]

Here, a list comprehension iterates through each row of the array, and `np.nanmax`

finds the maximum value in each row, ignoring NaNs. The list `max_values`

contains the row-wise maximum values of the original array.

## Method 3: Using NumPy’s `amax`

Function with a Mask

For users who prefer working with masks and NumPy’s generic functions, the `amax`

function can be used alongside a Boolean mask to ignore NaN values. The mask is created to identify NaNs, which are then replaced temporarily for calculation purposes.

Here’s an example:

import numpy as np array_with_nans = np.array([[1, 2, np.nan], [np.nan, 3, 4]]) nan_mask = ~np.isnan(array_with_nans) max_values = np.array([np.amax(row[nan_mask[idx]]) for idx, row in enumerate(array_with_nans)]) print(max_values)

Output:

[2. 4.]

In the provided code, `np.isnan`

is used to create a mask that identifies NaN values within the array. The `amax`

function is then employed in a list comprehension while applying the mask to ignore NaNs. The result is the same row-wise maximum without considering NaNs.

## Method 4: Using pandas’ `max`

Method

Pandas is a data manipulation and analysis library with robust handling of NaNs by default. Its `max`

method can be used on a DataFrame to compute the row-wise maxima ignoring any NaNs, which is ideal for mixed-type datasets or when integrating with other Pandas operations.

Here’s an example:

import pandas as pd df_with_nans = pd.DataFrame([[1, 2, np.nan], [np.nan, 3, 4]]) max_values = df_with_nans.max(axis=1) print(max_values)

Output:

0 2.0 1 4.0 dtype: float64

This snippet converts the NumPy array into a Pandas DataFrame and then utilizes the DataFrame’s `max`

method to calculate the maximum value for each row, excluding NaNs. The output is a Pandas Series with the maximum values for each original row.

## Bonus One-Liner Method 5: Using Python’s Built-in `max`

Function with a Conditional Expression

For those who prefer vanilla Python without additional libraries, Python’s built-in `max`

function can be combined with a generator expression to create a one-liner that filters NaNs while calculating the row-wise maximum.

Here’s an example:

array_with_nans = [[1, 2, float('nan')], [float('nan'), 3, 4]] max_values = [max(x for x in row if not math.isnan(x)) for row in array_with_nans] print(max_values)

Output:

[2, 4]

The snippet uses a list comprehension with `max`

to compute the maximum non-NaN value for each row. The `math.isnan`

function serves as a filter within the generator expression. It’s a straightforward and library-independent solution, but may not be as efficient for large datasets.

## Summary/Discussion

**Method 1:**NumPy’s`nanmax`

. Efficient for large datasets. Requires NumPy.**Method 2:**List Comprehension with NumPy’s`nanmax`

. Pythonic and readable. Requires NumPy.**Method 3:**NumPy’s`amax`

with a Mask. Flexible and mask-oriented. Requires NumPy and slightly more complex code.**Method 4:**Pandas’`max`

Method. Integrates well with Pandas workflows. Requires Pandas and might be overkill for simple tasks.**Method 5:**Python’s Built-in`max`

Function. No external dependencies. Might not be as efficient for large arrays and lacks some functionalities of library-specific functions.