Python's namedtuples

Hey there, and welcome to this short post on namedtuples! namedtuples are part of the wonderful collections module, and they let us use names to access tuple elements, rather than indices, making our code far more readable. I personally think they're absolutely amazing, so let's see them in action!

Defining a namedtuple

In order to use a namedtuple, we need to define a blueprint for it. For example, if we want a namedtuple called Student, where the elements are the student's name, their age, and their primary faculty, it might look something like this:

from collections import namedtuple

Student = namedtuple("Student", ["name", "age", "faculty"])

Let's break this down.

First it's important to note that we're doing an assignment here, and the naming convention is to use an uppercase letter for the variable name, just like if we were defining a class.

On the right hand side of the assignment, we have namedtuple along with a set of parentheses, and inside, we have all of our configuration for the Student namedtuple.

The first item inside the brackets is the name of the variable we're assigning to, and the second is a list of keys for the elements in Student.

Creating instances of a namedtuple

Now that we have our blueprint defined, we can make use of Student in our code. For example, we might do something like this:

from collections import namedtuple

Student = namedtuple("Student", ["name", "age", "faculty"])

names = ["John", "Steve", "Mary"]
ages = [19, 20, 18]
faculties = ["Politics", "Economics", "Engineering"]

students = [
    Student(*student_details)
    for student_details in zip(names, ages, faculties)
]

Here we've taken some connected data from three different lists, and we've use a list comprehension in conjunction with zip to create a single list of Student objects.

In the example above, we use the * syntax to destructure the each tuple in the zip object, but we have some other options. For example, we can use keyword arguments to define values for a namedtuple:

example_student = Student(name="James", age=18, faculty="Music")

Accessing elements within a namedtuple

Returning to our earlier example with the list of students, we might want to process this list in some way to determine something about our data. For example, we might want to use max to find the oldest student in the list.

We could do that like so:

oldest_student = max(students, key=lambda student: student.age)

Here we can see our first example of accessing values inside a named tuple. We need to use this . syntax to access values by key, putting the name of the key after the dot.

In the code above, we've set a custom key for max so that it compares our tuples based on the age element in each tuple. You can learn more about this in the documentation.

Using a regular tuple, or a list, this might have looked more like this:

oldest_student = max(students, key=lambda student: student[1])

Now that we've had to use an index, the meaning of the lambda function is a lot less clear. We have to refer back to the original data to see what exactly is at index 1.

If for some reason you want to use an index with a namedtuple, that still works, so you're free to do so.

Just to finish off our example above, let's print the oldest_student:

Student(name='Steve', age=20, faculty='Economics')

The output is actually very readable, but to make it look a little less like code for our users, we can use an f-string:

print(f"{oldest_student.name} ({oldest_student.age}) - {oldest_student.faculty}")

# Steve (20) - Economics

As you can see, namedtuples make it really clear which values we're accessing, making our code very readable. Everyone should be using them more often!

Wrapping up

That's it for this short post on namedtuples. I think namedtuples are awesome, and hopefully now you do too. I'd definitely recommend adding them to the types you use regularly, as they have great potential for improving code readability.

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