5 Best Ways to Flatten a Given List of Dictionaries in Python

πŸ’‘ Problem Formulation: When working with data in Python, you may encounter a list of dictionaries where you want to consolidate all the dictionaries into a single, flattened dictionary. This can be particularly useful for data manipulation or preprocessing before loading into a database or spreadsheet. For example, given [{"a": 1}, {"b": 2}, {"c": 3}], the goal is to flatten this into {"a": 1, "b": 2, "c": 3}.

Method 1: Using a Simple For Loop

A straightforward approach to flatten a list of dictionaries is by using a simple for loop to iterate over the list and update a new dictionary with the items from each dictionary. This method is easy to understand and implement for those new to Python.

Here’s an example:

result = {}
for d in [{"a": 1}, {"b": 2}, {"c": 3}]:
    result.update(d)

Output: {"a": 1, "b": 2, "c": 3}

This snippet creates an empty dictionary and then iterates through the list of dictionaries, updating the result dictionary with each item. It is simple and readable, but can be inefficient if the list is very large.

Method 2: Using Dictionary Comprehension

Dictionary comprehension is a concise and pythonic way to create a dictionary from an iterable. In this method, we iterate over each dictionary in the list and for each key-value pair, set them in a new, flattened dictionary.

Here’s an example:

list_of_dicts = [{"a": 1}, {"b": 2}, {"c": 3}]
flattened_dict = {k: v for d in list_of_dicts for k, v in d.items()}

Output: {"a": 1, "b": 2, "c": 3}

This code uses nested for-loops within a dictionary comprehension to iterate over the dictionaries and their items. Dictionary comprehension is efficient and the code is compact, but it may be less readable for those unfamiliar with this Pythonic construct.

Method 3: Using the itertools.chain() method

Python’s itertools library provides a method chain() that can be used to flatten a list of dictionaries. It takes several iterators and merges them into one. This will require converting dictionary items to iterators before chaining.

Here’s an example:

from itertools import chain

list_of_dicts = [{"a": 1}, {"b": 2}, {"c": 3}]
flattened_dict = dict(chain.from_iterable(d.items() for d in list_of_dicts))

Output: {"a": 1, "b": 2, "c": 3}

In this example, chain.from_iterable() is used to merge multiple dictionary item iterators into one, which is then converted into a single dictionary. This method is both concise and efficient for larger datasets.

Method 4: Using the collections.ChainMap()

The collections module provides a ChainMap class which groups multiple dictionaries into a single view. Later, you can convert this view back into a dictionary to achieve a flattened dictionary.

Here’s an example:

from collections import ChainMap

list_of_dicts = [{"a": 1}, {"b": 2}, {"c": 3}]
flattened_dict = dict(ChainMap(*list_of_dicts))

Output: {"a": 1, "b": 2, "c": 3}

The ChainMap constructor is used to create a single view of the list of dictionaries, which is then converted to a dictionary. This method is elegant and fast, but might not preserve the order of the original dictionaries in versions of Python before 3.7.

Bonus One-Liner Method 5: Reduce Function with a Lambda

For fans of functional programming, Python’s functools.reduce() can apply a function cumulatively to items of an iterable, which, when combined with a lambda function, can flatten a list of dictionaries into one.

Here’s an example:

from functools import reduce

list_of_dicts = [{"a": 1}, {"b": 2}, {"c": 3}]
flattened_dict = reduce(lambda a, b: {**a, **b}, list_of_dicts)

Output: {"a": 1, "b": 2, "c": 3}

This code uses reduce with a lambda function that merges two dictionaries. It applies this function cumulatively to the elements of the list to reduce it to a single dictionary. It is concise and powerful, but can be harder to read and debug for those not comfortable with functional programming concepts.

Summary/Discussion

  • Method 1: Simple For Loop. It’s beginner-friendly and easy to understand. It’s not the most performant for large datasets.
  • Method 2: Dictionary Comprehension. This method is clean and Pythonic. It might be less understandable for beginners.
  • Method 3: itertools.chain(). Provides a clean and efficient solution. Requires familiarity with itertools and generators.
  • Method 4: collections.ChainMap(). An elegant solution, with good performance. Earlier versions of Python may not maintain input order.
  • Method 5: Reduce with Lambda. A clever one-liner for those comfortable with functional programming. Readability may suffer.