**π‘ Problem Formulation:** You have a collection of height measurements but they include duplicate entries for individuals. Our goal is to compute the average height per individual, then, using only the distinct data points, to calculate the overall average height. Imagine input as a list of tuples (`('Name', Height_in_cm)`

), and the desired output is a single number representing the average height.

## Method 1: Using a Dictionary for Distinct Entries

This method involves iterating through each entry, adding it to a dictionary if not present, and updating the average if it is. This ensures that each person’s height is counted only once. The final average is the sum of all unique heights divided by the number of individuals.

Here’s an example:

heights = [('Alice', 170), ('Bob', 180), ('Alice', 170), ('Charlie', 190)] unique_heights = {} for name, height in heights: unique_heights[name] = height average_height = sum(unique_heights.values()) / len(unique_heights) print(average_height)

Output: `180.0`

This code snippet creates a dictionary, `unique_heights`

, ensuring that each individual contributes only one height entry. We then calculate the average height from the value collection of the dictionary. This method is straightforward and efficient for small datasets.

## Method 2: Using Set with List Comprehension

This method streamlines the process of removing duplicates by employing a set to automatically discard duplicates and list comprehension to build a list of distinct heights. It’s a more Pythonic solution compared to loops.

Here’s an example:

heights = [('Alice', 170), ('Bob', 180), ('Alice', 170), ('Charlie', 190)] unique_names = set() unique_heights = [unique_names.add(name) or height for name, height in heights if name not in unique_names] average_height = sum(unique_heights) / len(unique_heights) print(average_height)

Output: `180.0`

The code uses a set, `unique_names`

, to filter out duplicate entries while a list comprehension populates `unique_heights`

. It’s a compact and efficient method but may be less readable to beginners.

## Method 3: Using pandas Library

If you’re working with large datasets, the pandas library offers powerful and efficient methods for handling duplicates. We can create a DataFrame from the list and then use the `drop_duplicates`

along with `mean`

functions.

Here’s an example:

import pandas as pd heights = [('Alice', 170), ('Bob', 180), ('Alice', 170), ('Charlie', 190)] df = pd.DataFrame(heights, columns=['Name', 'Height']) average_height = df.drop_duplicates(subset='Name')['Height'].mean() print(average_height)

Output: `180.0`

This snippet first converts the list to a pandas DataFrame, then drops duplicate names before computing the mean of the height column. It’s elegant and highly efficient for processing large amounts of data.

## Method 4: Using Collections.defaultdict

A defaultdict automatically initializes any new key with a default value. For our problem, this feature can be utilized to ensure that each individual is accounted for without the need to check if the key is already in the dictionary.

Here’s an example:

from collections import defaultdict heights = [('Alice', 170), ('Bob', 180), ('Alice', 170), ('Charlie', 190)] heights_dict = defaultdict(list) for name, height in heights: heights_dict[name].append(height) average_height = sum(map(lambda x: sum(x)/len(x), heights_dict.values())) / len(heights_dict) print(average_height)

Output: `180.0`

In the given code, `heights_dict`

is a default dictionary that accumulates heights for each name. We then calculate the average height for each individual and finally, the overall average. This approach neatly separates heights by individual but can be overkill for our case where each individual has only one unique value.

## Bonus One-Liner Method 5: Using Functional Programming

This one-liner method leverages the power of Python’s functional programming capabilities, combining `map()`

, `filter()`

, and lambda functions to create a succinct solution.

Here’s an example:

heights = [('Alice', 170), ('Bob', 180), ('Alice', 170), ('Charlie', 190)] average_height = sum(set(map(lambda x: x[1], heights))) / len(set(map(lambda x: x[0], heights))) print(average_height)

Output: `180.0`

This code maps each tuple to its height value, converts the list to a set to eliminate duplicates, and then calculates the average. It’s a succinct and clever use of Python’s functional features, yet may be too cryptic for those unfamiliar with such concepts.

## Summary/Discussion

**Method 1:**Using a Dictionary. Strengths: Easy to understand, works well with small datasets. Weaknesses: Can become inefficient with larger sets of data.**Method 2:**Using Set with List Comprehension. Strengths: Compact, Pythonic. Weaknesses: Potentially confusing for new programmers.**Method 3:**Using pandas Library. Strengths: Ideal for large datasets, leverages efficient pandas operations. Weaknesses: Requires pandas installation, could be overkill for small data.**Method 4:**Using Collections.defaultdict. Strengths: Handles default values automatically, clearly separates data by individual. Weaknesses: Might be unnecessary for simple problems with unique values.**Method 5:**Using Functional Programming. Strengths: One-liner, elegant. Weaknesses: Harder to read and understand, not suitable for those unfamiliar with functional programming.