Calculating Student Average Scores from a Python Dictionary

💡 Problem Formulation: Educators and programmers often need to calculate the average score for each student when provided with a dataset of scores. Imagine having a dictionary where each key represents a student’s name, and the associated value is a list of their scores. The goal is to write a Python program that can iterate over this dictionary and calculate the average score for each student. The input might look like {"Alice": [88, 76, 92], "Bob": [70, 80, 68]}, and the desired output would be {"Alice": 85.33, "Bob": 72.67}.

Method 1: Using Basic Loops

In this traditional approach, we iterate through the dictionary’s items, sum up the scores, and then divide by the count of the scores to find the average. This is a straightforward method and is best suited for beginners who are more comfortable with for loops and basic arithmetic operations.

Here’s an example:

student_scores = {"Alice": [88, 76, 92], "Bob": [70, 80, 68]}
averages = {}

for student, scores in student_scores.items():
    averages[student] = sum(scores) / len(scores)

print(averages)

The output will be:

{"Alice": 85.33, "Bob": 72.67}

This code snippet creates an empty dictionary called averages. Then it loops over student_scores, calculating the average score for each student by dividing the sum of their scores by the number of scores. Finally, it prints out the new dictionary containing the average scores.

Method 2: Using Dictionary Comprehension

Dictionary comprehension is a concise and readable way to create a new dictionary from an existing one. It’s ideal for those who prefer a more Pythonic or idiomatic way of working with dictionaries.

Here’s an example:

student_scores = {"Alice": [88, 76, 92], "Bob": [70, 80, 68]}
averages = {student: sum(scores)/len(scores) for student, scores in student_scores.items()}

print(averages)

The output will be:

{"Alice": 85.33, "Bob": 72.67}

This code uses dictionary comprehension to iterate through the student_scores dictionary. It calculates the average for each student directly in the comprehension and creates a new dictionary averages with the results. This method is both efficient and succinct.

Method 3: Using the map() Function

The map() function applies a given function to each item of an iterable and returns a list of the results. This method is suitable for those who want to apply a function across an iterable in a functional programming style.

Here’s an example:

student_scores = {"Alice": [88, 76, 92], "Bob": [70, 80, 68]}

def calculate_average(scores):
    return sum(scores) / len(scores)

averages = dict(map(lambda item: (item[0], calculate_average(item[1])), student_scores.items()))

print(averages)

The output will be:

{"Alice": 85.33, "Bob": 72.67}

This snippet defines a function calculate_average to calculate the average of a list of scores. It then uses map() to apply this function to each set of scores in the student_scores.items(). The result is converted back into a dictionary and printed.

Method 4: Using the statistics Module

The statistics module provides functions for calculating mathematical statistics of numeric data. Using the mean() function from this module is great for those who prefer using built-in library functions for statistical operations.

Here’s an example:

from statistics import mean

student_scores = {"Alice": [88, 76, 92], "Bob": [70, 80, 68]}
averages = {student: mean(scores) for student, scores in student_scores.items()}

print(averages)

The output will be:

{"Alice": 85.33, "Bob": 72.67}

In this example, the mean() function is imported from the statistics module and used in dictionary comprehension to calculate the average of scores for each student.

Bonus One-Liner Method 5: Using a Function and Dictionary Comprehension

Combining a single function call for the average calculation with dictionary comprehension can lead to a very concise one-liner solution, which is preferred by experienced Python developers who like elegance and minimalism.

Here’s an example:

student_scores = {"Alice": [88, 76, 92], "Bob": [70, 80, 68]}
averages = {student: (lambda scores: sum(scores)/len(scores))(scores) for student, scores in student_scores.items()}

print(averages)

The output will be:

{"Alice": 85.33, "Bob": 72.67}

This clever one-liner uses an inline lambda function to calculate the average score within a dictionary comprehension. The lambda function is immediately called with the scores as its argument.

Summary/Discussion

• Method 1: Using Basic Loops: Clearly illustrates the average calculation process. Easy to understand. Not as concise as other methods.
• Method 2: Using Dictionary Comprehension: More Pythonic and concise. Ideal for single-line transformations. Requires familiarity with comprehensions.
• Method 3: Using the map() Function: Demonstrates a functional programming approach. Can be less clear to those not familiar with functional paradigms.
• Method 4: Using the statistics Module: Utilizes Python’s standard library for clarity in statistical operations. Less transparent about the mechanics of average calculation.
• Method 5: Using a Function and Dictionary Comprehension: Extremely concise. May sacrifice some readability for brevity. Illustrates the power of lambdas in Python.