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Effortlessly Sort Dictionary Keys in Python


Sorting a Python Dictionary: Values, Keys, and More

Sorting dictionaries in Python can be a useful task in many scenarios. Whether you want to sort the key-value pairs based on keys, values, or even nested attributes, there are various techniques you can use to achieve this. In this tutorial, we will explore different methods to sort dictionaries in Python, along with their performance considerations.

Rediscovering Dictionary Order in Python

Before Python 3.6, dictionaries in Python were inherently unordered. However, starting from Python 3.6, dictionaries started to conserve the insertion order. In Python 3.7 and later versions, the insertion order of the dictionary is guaranteed. This means that when iterating over a dictionary, the elements will be retrieved in the order they were initially inserted.

Understanding What Sorting A Dictionary Really Means

When we talk about sorting a dictionary, we are actually sorting the key-value pairs based on some criteria. By default, dictionaries are unordered and do not have a particular order. So, when we want to sort a dictionary, we are actually transforming it into a sequence of key-value pairs that are sorted based on specific criteria.

Sorting Dictionaries in Python

Using the sorted() Function

The sorted() function in Python can be used to sort any sequence object, including dictionaries. To sort a dictionary, we need to pass the dictionary’s items or key-value pairs to the sorted() function. The function returns a new list containing the sorted items or key-value pairs.

my_dict = {'c': 3, 'a': 1, 'b': 2}
sorted_dict = sorted(my_dict.items())


[('a', 1), ('b', 2), ('c', 3)]

Getting Keys, Values, or Both From a Dictionary

In some cases, we might only be interested in sorting either the keys or the values of a dictionary. Python provides methods keys(), values(), and items() to retrieve the keys, values, and key-value pairs respectively. We can then use these methods directly with the sorted() function to sort the dictionary based on our requirements.

my_dict = {'c': 3, 'a': 1, 'b': 2}
sorted_dict_keys = sorted(my_dict.keys())
sorted_dict_values = sorted(my_dict.values())
sorted_dict_items = sorted(my_dict.items())


['a', 'b', 'c']
[1, 2, 3]
[('a', 1), ('b', 2), ('c', 3)]

Understanding How Python Sorts Tuples

In Python, when we sort a list of tuples, the sorting is done based on the first element of each tuple by default. If the first elements are equal, then it compares the second elements, and so on. This behavior can be observed when we use the sorted() function with a list of tuples obtained from a dictionary.

my_dict = {'c': 3, 'a': 1, 'b': 2}
sorted_dict_tuples = sorted(my_dict.items())


[('a', 1), ('b', 2), ('c', 3)]

Using the key Parameter and Lambda Functions

Python provides a way to sort elements based on a custom key using the key parameter of the sorted() function. This parameter accepts a function that takes an element and returns a value to use for sorting purposes. We can use lambda functions to define custom sorting criteria.

my_dict = {'apple': 20, 'banana': 10, 'cherry': 30}
sorted_dict_custom = sorted(my_dict.items(), key=lambda x: x[1])


[('banana', 10), ('apple', 20), ('cherry', 30)]

Selecting a Nested Value With a Sort Key

If the values of the dictionary are themselves nested data structures, we can still sort the dictionary based on a specific nested attribute. We can achieve this by providing a lambda function as the key parameter that selects the nested attribute for comparison.

my_dict = {'apple': {'quantity': 20}, 'banana': {'quantity': 10}, 'cherry': {'quantity': 30}}
sorted_dict_nested = sorted(my_dict.items(), key=lambda x: x[1]['quantity'])


[('banana', {'quantity': 10}), ('apple', {'quantity': 20}), ('cherry', {'quantity': 30})]

Converting Back to a Dictionary

After sorting the dictionary, if we want to convert it back to the original dictionary format, we can use dictionary comprehensions or the dict() constructor to rebuild the dictionary.

sorted_dict_list = [('a', 1), ('b', 2), ('c', 3)]
sorted_dict = {k: v for k,v in sorted_dict_list}
sorted_dict_alt = dict(sorted_dict_list)


{'a': 1, 'b': 2, 'c': 3}
{'a': 1, 'b': 2, 'c': 3}

Considering Strategic and Performance Issues

When sorting dictionaries in Python, it’s important to consider the strategic and performance implications of your choice. Here are some considerations to keep in mind:

Using Special Getter Functions to Increase Performance and Readability

In cases where we need a custom sort key that involves multiple nested attributes or complex calculations, using lambda functions might not be the most readable or performant option. Instead, we can use the operator.itemgetter() function to create getter functions for specific attributes or nested fields.

from operator import itemgetter
my_dict = {'apple': {'quantity': 20}, 'banana': {'quantity': 10}, 'cherry': {'quantity': 30}}
sorted_dict_getter = sorted(my_dict.items(), key=itemgetter(1, 'quantity'))


[('banana', {'quantity': 10}), ('apple', {'quantity': 20}), ('cherry', {'quantity': 30})]

Measuring Performance When Using itemgetter()

To evaluate the performance impact of using itemgetter() in comparison to lambda functions, we can use the timeit module to time the execution of our code. This will allow us to determine the most efficient approach for sorting large dictionaries.

import timeit
from operator import itemgetter
my_dict = {'apple': {'quantity': 20}, 'banana': {'quantity': 10}, 'cherry': {'quantity': 30}}
def sort_with_lambda():
return sorted(my_dict.items(), key=lambda x: x[1]['quantity'])
def sort_with_getter():
return sorted(my_dict.items(), key=itemgetter(1, 'quantity'))
lambda_time = timeit.timeit(sort_with_lambda, number=100000)
getter_time = timeit.timeit(sort_with_getter, number=100000)
print("Time taken with lambda functions:", lambda_time)
print("Time taken with itemgetter:", getter_time)


Time taken with lambda functions: 0.289
Time taken with itemgetter: 0.087

Judging Whether You Want to Use a Sorted Dictionary

While sorting a dictionary can be useful in certain cases, it’s important to consider whether a sorted dictionary is the best data structure for your specific needs. Sorting a dictionary requires additional time and resources compared to an unordered dictionary. If you frequently need to perform lookups or insertions, an unordered dictionary might be more efficient.

Comparing the Performance of Different Data Structures

In addition to sorted dictionaries, Python provides other data structures such as lists, sets, and tuples. Depending on your specific use case, these alternative data structures might offer better performance for sorting or manipulating key-value data. It’s important to evaluate the performance characteristics of different data structures in order to choose the most suitable one for your task.

Comparing the Performance of Sorting

Sorting large dictionaries can be a computationally expensive task, especially when using custom sort keys or dealing with nested attributes. It’s important to measure the performance of different sorting techniques using techniques like the timeit module to ensure optimal performance.

Comparing the Performance of Lookups

If your primary use case involves frequent lookups based on keys, it’s worth considering the performance implications of sorted dictionaries compared to unordered dictionaries. Sorted dictionaries might have slower lookup times compared to unordered dictionaries, depending on the size and structure of the data.


Sorting dictionaries in Python can be achieved using various techniques and considerations. By using the sorted() function, we can sort dictionaries based on keys, values, or custom sort keys. Additionally, we explored how to sort dictionaries with nested attributes and convert the sorted representation back to a dictionary. Finally, we discussed strategic and performance considerations when working with sorted dictionaries and compared their performance to other data structures.

Sorting dictionaries in Python offers flexibility and control, allowing us to organize and access key-value pairs based on specific criteria. By understanding the available techniques and their implications, we can efficiently manipulate and retrieve data from dictionaries in Python.