5 Best Ways to Find Achievable Points in a Contest with Python

💡 Problem Formulation: Python programmers often face challenges in calculating potential points in various contests, where they need to account for dynamic scoring rules, bonus points, and penalties. For instance, if a contest awards different points for various difficulty levels and subtracts points for incorrect submissions, a Python program can help calculate the maximum achievable points. The desired output would be the total points a participant could potentially earn.

Method 1: Brute Force Iteration

Brute force is the most straightforward approach where you iterate through all possible combinations of contest tasks and calculate the respective points. This method ensures all scenarios are considered, although it can be slow for contests with a large number of tasks.

Here’s an example:

def calculate_points(tasks):
    max_points = 0
    for task in tasks:
        if task['completed']:
            max_points += task['points']
    return max_points

contest_tasks = [
    {'completed': True, 'points': 10},
    {'completed': False, 'points': 20},
    {'completed': True, 'points': 30},
]

print(calculate_points(contest_tasks))

Output:

40

This code snippet defines a function calculate_points() that iterates over a list of tasks, each represented by a dictionary with the task’s completion status and point value. It accumulates points only for tasks that have been completed.

Method 2: Functional Programming with filter and map

This method utilizes functional programming concepts to filter completed tasks and map their point values, followed by summing them up. It provides a concise way to process task lists and can be more readable.

Here’s an example:

contest_tasks = [
    {'completed': True, 'points': 10},
    {'completed': False, 'points': 20},
    {'completed': True, 'points': 30},
]

max_points = sum(map(lambda x: x['points'], filter(lambda x: x['completed'], contest_tasks)))
print(max_points)

Output:

40

This snippet uses filter() to keep only completed tasks and map() to extract their points. Finally, sum() adds up all the points. It’s a clean and functional way of achieving the result with less code.

Method 3: List Comprehensions

List comprehensions provide a more Pythonic and expressive way to create lists derived from existing lists while applying conditions. This method is fast and often simpler to understand for Python developers.

Here’s an example:

contest_tasks = [
    {'completed': True, 'points': 10},
    {'completed': False, 'points': 20},
    {'completed': True, 'points': 30},
]

max_points = sum(task['points'] for task in contest_tasks if task['completed'])
print(max_points)

Output:

40

Here, a list comprehension is used within the sum() function to calculate the total points, excluding tasks that have not been completed. This approach is very direct and succinct.

Method 4: Using a Custom Scoring Function

Creating a custom scoring function provides flexibility in implementing complex scoring algorithms which may involve bonuses for early completion, penalties for wrong submissions, etc. This method can adapt to various contest rules.

Here’s an example:

def custom_scoring(task):
    score = 0
    if task['completed']:
        score += task['points']
        if task['early_submission']:
            score += 5  # bonus points
    else:
        score -= 2  # penalty for unattempted tasks
    return score

contest_tasks = [
    {'completed': True, 'points': 10, 'early_submission': True},
    {'completed': False, 'points': 20, 'early_submission': False},
    {'completed': True, 'points': 30, 'early_submission': False},
]

max_points = sum(custom_scoring(task) for task in contest_tasks)
print(max_points)

Output:

43

The custom scoring function, custom_scoring(), applies rules for bonus points and penalties based on the task properties. This flexible approach can be extended to handle any additional rules in the future.

Bonus One-Liner Method 5: Using Reduce with Lambda

The reduce() function from the functools module helps in applying a specific function cumulatively to the items of a list, effectively reducing it to a single value. This method is often used for custom accumulation logic in a single line of code.

Here’s an example:

from functools import reduce

contest_tasks = [
    {'completed': True, 'points': 10},
    {'completed': False, 'points': 20},
    {'completed': True, 'points': 30},
]

max_points = reduce(lambda acc, task: acc + task['points'] if task['completed'] else acc, contest_tasks, 0)
print(max_points)

Output:

40

This code snippet uses reduce() to accumulate points for only the completed tasks. The lambda function applied to each element of the list checks if the task is completed and adds the points to the accumulator.

Summary/Discussion

• Method 1: Brute Force Iteration. Straightforward and ensures all scenarios are considered. However, it may be slow for large datasets.
• Method 2: Functional Programming with filter and map. Concise and readable. Preferred for its functional approach but may be less intuitive for those unfamiliar with functional programming.
• Method 3: List Comprehensions. Pythonic and expressive. Recommended for most Python developers due to its clarity and speed.
• Method 4: Using a Custom Scoring Function. Highly adaptable to complex rules. Best where scoring rules are not straightforward, but might be overkill for simple scenarios.
• Method 5: Using Reduce with Lambda. Compact code for custom accumulation. Effective for single-pass computations but can be harder to debug and read compared to more explicit loops.