**π‘ Problem Formulation:** In Python, when working with NumPy arrays comprised of floating-point numbers, it can sometimes be necessary to filter out the integers to focus on purely non-integer values. For instance, given a NumPy array `np.array([1.0, 2.5, 3.0, 4.8])`

, our goal is to obtain an array where integers disguised as floats, such as 1.0 and 3.0, have been removed, resulting in `np.array([2.5, 4.8])`

.

## Method 1: Using Modulo and Boolean Indexing

This method employs modulo operation and boolean indexing to identify and filter out integer values within a float NumPy array. Specifically, we will use the modulo operator to find array elements that are effectively zero when divided by one, indicating an integer.

Here’s an example:

import numpy as np # Define the float array float_array = np.array([1.0, 2.5, 3.0, 4.8]) # Filter out the integers non_integers = float_array[float_array % 1 != 0] print(non_integers)

The output of this code snippet will be:

[2.5 4.8]

This code snippet creates an array neglecting the integer values, using a boolean mask that checks if the remainder of the division by 1 is non-zero, a condition only satisfied by non-integer numbers.

## Method 2: Using NumPy’s isclose Function

By leveraging the NumPy’s `isclose`

function, we can compare each element in the array to its nearest integer counterpart, filtering out elements that are “close enough” to be considered integers.

Here’s an example:

import numpy as np # Define the float array float_array = np.array([1.0, 2.5, 3.0, 4.8]) # Identify integers masked as floats is_integer = np.isclose(float_array % 1, 0, atol=1e-08) # Filter out the integers non_integers = float_array[~is_integer] print(non_integers)

The output will be:

[2.5 4.8]

This snippet uses the `isclose`

method with a threshold to identify integer-valued floats. The tilde (~) operator is then used to invert the boolean array marking those integers, thereby filtering them out.

## Method 3: Using NumPy’s floor Function

We can use the NumPy `floor`

function which rounds down to the nearest whole number and then identify which elements were not affected by this operationβindicating they were already integers.

Here’s an example:

import numpy as np # Define the float array float_array = np.array([1.0, 2.5, 3.0, 4.8]) # Compare element-wise with floored version non_integers = float_array[float_array != np.floor(float_array)] print(non_integers)

The output will be:

[2.5 4.8]

This code snippet filters out integer values by comparing the original array with its ‘floored’ version and retaining the values that do not match, indicating they are non-integers.

## Method 4: Using NumPy’s subtract and abs Functions

We can utilize the `subtract`

function to find the difference between the original numbers and their nearest integer values. Coupled with the `abs`

function, we can locate the exact integers within the array and filter them out.

Here’s an example:

import numpy as np # Define the float array float_array = np.array([1.0, 2.5, 3.0, 4.8]) # Subtract nearest integers and use absolute to ensure comparison consistency non_integers = float_array[np.abs(float_array - np.rint(float_array)) > 1e-10] print(non_integers)

The output will be:

[2.5 4.8]

This block of code works by subtracting the rounded integer from each element, then comparing the absolute result against a small threshold to discount numerical precision errors, thus distinguishing true non-integers.

## Bonus One-Liner Method 5: Using List Comprehension and the int Typecast

A snappy one-liner using list comprehension combines typecasting to `int`

and a comparison to exclude integers. It’s Pythonic and concise, ideal for quick operations.

Here’s an example:

import numpy as np # Define the float array float_array = np.array([1.0, 2.5, 3.0, 4.8]) # One-liner to filter out integers non_integers = np.array([num for num in float_array if num != int(num)]) print(non_integers)

And the output will show:

[2.5 4.8]

This code iterates through the array and filters out the numbers that are equal to their integer cast, effectively removing integers from the array.

## Summary/Discussion

**Method 1:**Modulo and Boolean Indexing. Strengths: Intuitive and straightforward. Weaknesses: Requires exact float representation, which may be problematic for very large numbers or numbers with precision errors.**Method 2:**NumPy’s isclose Function. Strengths: Takes into account floating-point precision issues. Weaknesses: Requires setting an appropriate tolerance value.**Method 3:**NumPy’s floor Function. Strengths: Simple and clear logic. Weaknesses: Similar to modulo, it can fail with very large numbers.**Method 4:**NumPy’s subtract and abs Functions. Strengths: Accounts for precision issues by using a threshold. Weaknesses: Setting the right threshold can be tricky.**Method 5:**List Comprehension and int Typecast. Strengths: Quick and Pythonic, good for scripting. Weaknesses: Not as fast as vectorized NumPy operations for large arrays.