# 5 Best Ways to Combine Values from Dictionary Lists in Python

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π‘ Problem Formulation: In Python programming, a common task is to combine values that are stored within a dictionary where each key points to a list of items. The goal is to merge these lists in a meaningful way that maintains the association of values to their original keys. For example, given a dictionary {'a': [1, 2], 'b': [3, 4]}, one might want to combine these values into a single list like [1, 2, 3, 4].

## Method 1: Using Loops

The loop method is a straightforward approach to iterate through the dictionary and extend a result list with the items found in each key-associated list. This is easy to understand and doesnβt require importing any additional libraries.

Here’s an example:

combined_list = []
my_dict = {'a': [1, 2], 'b': [3, 4]}

for key in my_dict:
combined_list.extend(my_dict[key])

print(combined_list)

Output:

[1, 2, 3, 4]

This method manually iterates over each key in the dictionary and then extends a new list combined_list with the elements of each associated list. Itβs a clear and straightforward solution but might not be the most efficient for very large dictionaries.

## Method 2: Using List Comprehension

List comprehension in Python is an elegant way to flatten a structure. You can achieve the same result as the loop method, but with a concise one-liner that Python developers favor for its readability and efficiency.

Here’s an example:

my_dict = {'a': [1, 2], 'b': [3, 4]}
combined_list = [item for sublist in my_dict.values() for item in sublist]

print(combined_list)

Output:

[1, 2, 3, 4]

This snippet uses list comprehension to iterate through the values of the dictionary and then iterate again through the items of each list. The result is a new list that combines all the elements in a single list, maintaining their order.

## Method 3: Using itertools.chain

The itertools.chain function is a powerful tool from Python’s itertools module that is used to chain multiple iterables together. In the context of dictionaries of lists, it can be employed to combine all the lists into a single iterable without creating intermediate data structures.

Here’s an example:

from itertools import chain

my_dict = {'a': [1, 2], 'b': [3, 4]}
combined_list = list(chain(*my_dict.values()))

print(combined_list)

Output:

[1, 2, 3, 4]

The chain function is unpacking each list found in the dictionary values and then combining them into a single iterable, which is then converted into a list. This approach is very efficient, especially for larger datasets, and is good at preserving the input order.

## Method 4: Using functools.reduce

The functools.reduce function is another higher-order function that can be used for combining elements in an iterable. When working with a dictionary of lists, reduce can be applied to successively merge lists into one.

Here’s an example:

from functools import reduce

my_dict = {'a': [1, 2], 'b': [3, 4]}
combined_list = reduce(lambda acc, x: acc + x, my_dict.values())

print(combined_list)

Output:

[1, 2, 3, 4]

This code uses reduce along with a lambda function to successively add the lists from the dictionary values, resulting in a single combined list. This is less commonly used for list flattening because it’s generally less readable and can be more difficult to understand for beginners.

## Bonus One-Liner Method 5: Using sum()

The built-in sum() function can be used to concatenate lists by providing an empty list as the start value. This approach is not only succinct but also intuitive to read and understand.

Here’s an example:

my_dict = {'a': [1, 2], 'b': [3, 4]}
combined_list = sum(my_dict.values(), [])

print(combined_list)

Output:

[1, 2, 3, 4]

Here, sum() iteratively adds the values (which are lists) of the dictionary to an initial empty list. Despite its elegance, this method is not recommended for very large lists as it can have a high time complexity due to the way lists are concatenated.

## Summary/Discussion

• Method 1: Using Loops. Easy to understand. Not as concise or efficient for large data sets.
• Method 2: Using List Comprehension. Very Pythonic and readable. Efficient and maintains order.
• Method 3: Using itertools.chain. Highly efficient for chaining iterables. Preserves order and suitable for large data sets.
• Method 4: Using functools.reduce. Functional programming approach, can be less readable. Efficient for small to medium-sized data.
• Bonus Method 5: Using sum(). Intuitively combines lists with a one-liner. Not recommended for very large lists due to poor performance.