Transforming a Python List into a Dictionary of Lists

💡 Problem Formulation: The task at hand involves taking a list of elements in Python and converting it into a dictionary, where each unique element in the list becomes a key in the dictionary and the associated value is a list of indices at which each key appears. For example, given the input list ['apple', 'banana', 'apple', 'pear', 'banana'], the desired output would be a dictionary {'apple': [0, 2], 'banana': [1, 4], 'pear': [3]}.

Method 1: Using a for loop and setdefault()

This method involves iterating over the list and using the setdefault() method of dictionaries to append the index of each element to a list corresponding to that element in the dictionary.

Here’s an example:

fruits = ['apple', 'banana', 'apple', 'pear', 'banana']
index_dict = {}

for index, fruit in enumerate(fruits):
    index_dict.setdefault(fruit, []).append(index)

print(index_dict)

{'apple': [0, 2], 'banana': [1, 4], 'pear': [3]}

In the given snippet, we use the enumerate() function to get both the index and the value of each item in the list. The setdefault() method ensures that a new list is created if the key doesn’t exist, and then appends the index to the list corresponding to that key.

Method 2: Using defaultdict from the collections module

By utilizing the defaultdict from the collections module, we can simplify the process of accumulating indices. The defaultdict automatically initializes each new key with an empty list.

Here’s an example:

from collections import defaultdict

fruits = ['apple', 'banana', 'apple', 'pear', 'banana']
index_dict = defaultdict(list)

for index, fruit in enumerate(fruits):
    index_dict[fruit].append(index)

print(dict(index_dict))

{'apple': [0, 2], 'banana': [1, 4], 'pear': [3]}

This method makes the code more readable and eliminates the need to check if the key exists in the dictionary before appending to the list. Once initialized as a defaultdict, appending directly to the list associated with each key is all that’s needed.

Method 3: Using dictionary comprehension

Dictionary comprehension is a concise and readable way to create dictionaries. This method involves using a single expression that combines a for loop and conditional expressions.

Here’s an example:

fruits = ['apple', 'banana', 'apple', 'pear', 'banana']
index_dict = {fruit: [index for index, f in enumerate(fruits) if f == fruit] for fruit in set(fruits)}

print(index_dict)

{'banana': [1, 4], 'pear': [3], 'apple': [0, 2]}

In this example, the outer comprehension iterates over the unique fruit names, and the inner comprehension builds the list of indices for each fruit. It is a concise way to construct the dictionary but may not be as efficient for large lists due to repeated scanning of the list.

Method 4: Using groupby from itertools

The groupby() function from the itertools module can group list items which are adjacent and identical. When the list is sorted by elements, we can easily gather the indices for each group.

Here’s an example:

from itertools import groupby

fruits = ['apple', 'banana', 'apple', 'pear', 'banana']
sorted_fruits = sorted((fruit, index) for index, fruit in enumerate(fruits))
index_dict = {key: [i for _, i in group] for key, group in groupby(sorted_fruits, lambda x: x[0])}

print(index_dict)

{'apple': [0, 2], 'banana': [1, 4], 'pear': [3]}

We sort the pairs of fruit names and indices, and then group by the fruit names. The dictionary comprehension iterates over these groups, extracting the indices. This method guarantees the results are sorted by key, but it requires sorting, which can be inefficient for large data sets.

Bonus One-Liner Method 5: Using a lambda function and reduce

A single line of Python code can often achieve what a longer script might with the help of reduce from the functools module, combined with a lambda function.

Here’s an example:

from functools import reduce

fruits = ['apple', 'banana', 'apple', 'pear', 'banana']
index_dict = reduce(lambda d, p: d.setdefault(p[1], []).append(p[0]) or d, enumerate(fruits), {})

print(index_dict)

{'apple': [0, 2], 'banana': [1, 4], 'pear': [3]}

Using reduce(), we cumulatively process the pairs of indices and elements. For each pair, we append the index to the list in the dictionary at the key of the fruit. This one-liner is compact but may be harder to read and debug for those unfamiliar with the reduce function.

Summary/Discussion

• Method 1: Using a for loop and setdefault(). Strengths: Intuitive for beginners, no external libraries required. Weaknesses: Slightly verbose.
• Method 2: Using defaultdict from the collections module. Strengths: Clean and efficient for assigning default values. Weaknesses: Requires understanding of collections module.
• Method 3: Using dictionary comprehension. Strengths: Compact and Pythonic. Weaknesses: Can be less efficient for large datasets, because the list is scanned multiple times.
• Method 4: Using groupby() from the itertools module. Strengths: Groups are sorted, useful when order matters. Weaknesses: Not as intuitive and requires a sorted list beforehand.
• Method 5: Using lambda function and reduce(). Strengths: One-liner, compact. Weaknesses: Less readable, harder to maintain, and can be tricky for those not familiar with functional programming concepts.