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Lists are one of the most versatile and commonly used data structures in Python. Sorting lists into a meaningful order is a fundamental skill for any Python programmer. Whether you need to display a list of names in alphabetical order, rank your top 10 movies, or find the least expensive item in a price list, sorting is the way to go.

In this guide, we‘ll take an in-depth look at how to sort lists in Python. We‘ll cover the built-in methods for sorting, how to sort in ascending vs descending order, sorting more complex lists, and more. Let‘s dive in!

The Basics of Lists and Sorting

First, let‘s quickly review what lists are in Python. A list is an ordered, mutable collection of elements. The elements in a list can be of any data type, and lists can even contain other lists. Here are a few examples:

numbers = [4, 2, 9, 7, 1, 8, 3] languages = [‘Python‘, ‘Java‘, ‘C++‘, ‘JavaScript‘] info = [‘Alice‘, 24, True, [‘reading‘, ‘hiking‘]]

So why would you need to sort a list? Here are a few common scenarios:

• Displaying data in a user interface, like a list of search results or a leaderboard in a game
• Processing data in a particular order, like finding the most frequent or least frequent elements
• Improving search efficiency by using algorithms like binary search that rely on sorted lists

Python provides two main ways to sort lists: the sort() method, which modifies a list in-place, and the sorted() function, which returns a new sorted list. Let‘s look at each one.

Sorting Lists with the sort() Method

The sort() method sorts a list in-place, meaning it modifies the original list directly. It uses the efficient Timsort algorithm under the hood, which has O(n log n) complexity. Here‘s the basic syntax:

my_list.sort()

By default, sort() will arrange the elements in ascending order. For example:

numbers = [4, 2, 9, 7, 1, 8, 3] numbers.sort()
print(numbers) # [1, 2, 3, 4, 7, 8, 9]

languages = [‘Python‘, ‘Java‘, ‘C++‘, ‘JavaScript‘] languages.sort()
print(languages) # [‘C++‘, ‘Java‘, ‘JavaScript‘, ‘Python‘]

To sort a list in descending order, you can pass the reverse=True argument to sort():

numbers = [4, 2, 9, 7, 1, 8, 3] numbers.sort(reverse=True)
print(numbers) # [9, 8, 7, 4, 3, 2, 1]

Sorting Lists with the sorted() Function

The sorted() function returns a new sorted list, leaving the original list unmodified. It takes the original list as an argument:

my_sorted_list = sorted(original_list)

Here‘s an example:

numbers = [4, 2, 9, 7, 1, 8, 3] sorted_numbers = sorted(numbers)

print(sorted_numbers) # [1, 2, 3, 4, 7, 8, 9] print(numbers) # [4, 2, 9, 7, 1, 8, 3] (unchanged)

You can also pass reverse=True to sorted() to get a new list in descending order:

sorted_numbers = sorted(numbers, reverse=True)
print(sorted_numbers) # [9, 8, 7, 4, 3, 2, 1]

Custom Sorting with the key Parameter

Both sort() and sorted() have an optional key parameter that lets you specify a function to be called on each element before making comparisons. This is useful for sorting more complex lists or sorting based on a particular attribute.

For example, let‘s say we have a list of strings and we want to sort them by length:

words = [‘banana‘, ‘pie‘, ‘Washington‘, ‘book‘]

We can pass a key function that returns the length of each string:

words.sort(key=len)
print(words) # [‘pie‘, ‘book‘, ‘banana‘, ‘Washington‘]

Here‘s an example with a list of dictionaries representing people:

people = [
{‘name‘: ‘Alice‘, ‘age‘: 24},
{‘name‘: ‘Bob‘, ‘age‘: 42},
{‘name‘: ‘Charlie‘, ‘age‘: 18},
{‘name‘: ‘David‘, ‘age‘: 35}
]

To sort this list by age, we can pass a key function that returns the ‘age‘ value of each dictionary:

people.sort(key=lambda person: person[‘age‘])
print(people)

We used a lambda function here for conciseness, but we could also define a regular function and pass that as the key.

Sorting by Multiple Criteria

You can even sort by multiple criteria by returning a tuple from the key function. The elements of the tuple will be compared in order until a difference is found.

For example, let‘s sort a list of names by last name, then by first name as a tiebreaker:

names = [‘Alice Smith‘, ‘Bob Jones‘, ‘Charlie Smith‘, ‘Alice Jones‘]

def get_name_key(name):
last, first = name.split()
return (last, first)

names.sort(key=get_name_key)
print(names)

Here the get_name_key function splits each name into a tuple of (last name, first name). The sort compares last names first, then uses the first names to break any ties.

Sorting Efficiency

As mentioned earlier, Python‘s built-in sorting uses the Timsort algorithm, which is a hybrid of merge sort and insertion sort. This algorithm has a guaranteed worst-case performance of O(n log n) and performs especially well on lists that are partially sorted.

This means if you have a list of n elements, a sorting operation will take roughly n * log(n) comparisons. This scales well even for large lists – sorting a list of 1 million elements would take about 20 million comparisons, which is quite efficient.

Of course, the exact runtime will depend on factors like your computer‘s hardware and what kinds of elements are in your list. But in general, the built-in sort() and sorted() are the most efficient way to sort lists in Python.

Real-World Examples

Sorting comes up all the time in real-world programming. Here are a few examples:

• Sorting a list of users by join date on a social media platform
• Sorting search results by relevance or price on an e-commerce site
• Sorting a list of files by size or last modified date in a file explorer
• Sorting player scores from highest to lowest in a game leaderboard
• Sorting transactions by timestamp in a financial application

Any time you‘re dealing with a collection of data that needs to be ordered in a meaningful way, sorting is likely to play a role.

Conclusion

In this guide, we‘ve covered the essentials of sorting lists in Python. We‘ve seen how to use the sort() method and sorted() function, how to sort in ascending or descending order, how to define custom sorting criteria with the key parameter, and how to sort by multiple criteria. We also discussed the efficiency of Python‘s sorting algorithm.

Sorting is a core skill for working with data in Python. With the built-in sorting capabilities, it‘s easy to order your lists however you need. The key is to think about what order you want your data in, and what criteria you should compare to get that order.

I hope this guide has been helpful in understanding and applying list sorting in Python. Happy sorting!