5 Best Ways to Program to Find Minimum Amount Needed to Be Paid to All Good Performers in Python

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πŸ’‘ Problem Formulation: In any organization, it is essential to recognize and reward good performers. Let’s say we have a list of employees with their respective performance scores. Our goal is to determine the minimum total payment to be distributed among all employees who are considered good performers based on a certain score threshold. For example, given a list of scores [80, 90, 78, 95, 70] and a performance threshold of 80, the desired output would be the sum of scores for all employees who scored at least 80.

Method 1: Using a For-Loop

This method utilizes a simple for-loop to iterate through a list of performance scores. We initialize a total amount variable to zero. For every score in the list, we check if it meets or exceeds the performance threshold. If so, we add this score to the total amount. This approach is straightforward and doesn’t require importing any additional libraries.

Here’s an example:

scores = [80, 90, 78, 95, 70]
threshold = 80
total_payment = 0

for score in scores:
    if score >= threshold:
        total_payment += score

print(total_payment)

Output: 265

This code snippet declares a list of scores and a threshold. It then initializes variable total_payment and iterates over the scores, adding each score that is above or equal to the threshold to the total payment. Finally, it prints out the total payment, which is the sum of all qualifying scores.

Method 2: Using List Comprehension and sum()

Python’s list comprehension, combined with the built-in sum() function, makes this task less verbose and more efficient by encapsulating the iteration and condition in one line. List comprehension is an elegant way to define and create lists based on existing lists.

Here’s an example:

scores = [80, 90, 78, 95, 70]
threshold = 80

total_payment = sum(score for score in scores if score >= threshold)

print(total_payment)

Output: 265

The list comprehension iterates through each score in scores, adding it to a temporary list if it meets or exceeds the threshold. The sum() function then calculates the total payment by summing up the elements in this temporary list.

Method 3: Using filter() and sum()

Python’s filter() function can be used to filter out all the values in the list that do not meet the performance threshold. Together with the sum() function, this can achieve a similar result to the list comprehension but in a way that may be more readable to some people. The filter function returns an iterator, which the sum function then exhausts to find the total payment.

Here’s an example:

scores = [80, 90, 78, 95, 70]
threshold = 80

total_payment = sum(filter(lambda score: score >= threshold, scores))

print(total_payment)

Output: 265

In this snippet, filter() creates an iterator with scores that are greater than or equal to the threshold using a lambda function. The sum() function then adds up these filtered scores to find the total payment.

Method 4: Using NumPy Library

For those who work with large datasets or need high performance, using the NumPy library to compute the total payment can be advantageous as it is optimized for numerical operations on arrays. This method assumes familiarity with the NumPy library, which is widely used in the Python scientific computing community.

Here’s an example:

import numpy as np

scores_array = np.array([80, 90, 78, 95, 70])
threshold = 80

total_payment = np.sum(scores_array[scores_array >= threshold])

print(total_payment)

Output: 265

The code first imports the numpy library and creates a numpy array from the list of scores. It then utilizes numpy’s indexing capabilities to filter scores greater than or equal to the threshold, and the np.sum() function computes the total payment.

Bonus One-Liner Method 5: Using a List Comprehension Inside sum()

This one-liner combines the elegance of a list comprehension directly inside the sum() function call. It’s ideal for quick calculations or when trying to reduce code to the smallest size possible, while still remaining readable.

Here’s an example:

total_payment = sum(score for score in [80, 90, 78, 95, 70] if score >= 80)
print(total_payment)

Output: 265

This snippet places the list comprehension directly into the sum() function call, streamlining the process into a one-liner. This results in concise, though potentially less readable code.

Summary/Discussion

Method 1: For-Loop. Easy to understand. Can be slow for larger datasets.
Method 2: List Comprehension and sum(). More concise and potentially faster than a for-loop. Elegant Pythonic way.
Method 3: filter() and sum(). Readable to those who prefer functional programming style. May be slightly less intuitive to beginners.
Method 4: Using NumPy Library. Highly efficient, especially for large datasets. Requires familiarity with an external library.
Method 5: List Comprehension in sum(). Extremely concise. Can be a one-liner but may be less readable to novice programmers.