Mastering Python's Named Tuples in 2021

Tiffany Hwang, PythonData Science

As a data scientist, it's essential to have a deep understanding of Python's data structures. One such structure is the named tuple. Named tuples provide an easy and efficient way to organize your data and improve the readability of your code.

So, what is a named tuple, and why should you care? Let's dive into the fundamentals.

What are Named Tuples in Python?

A named tuple is a subclass of the tuple class. Like tuples, named tuples are immutable, meaning that they cannot be changed after creation. However, named tuples also have named fields, which allows us to refer to them using dot notation, making them easier to use.

Let's see an example:

from collections import namedtuple

Person = namedtuple('Person', ['name', 'age', 'gender'])
John = Person(name='John', age=30, gender='Male')

print(    # Output: John
print(John.age)     # Output: 30
print(John.gender)  # Output: Male

As you can see, we created a named tuple Person with three fields: name, age, and gender. We then created an instance of the Person named tuple and assigned values to each field. Finally, we accessed the values using dot notation.

Benefits of Using Named Tuples

Now that we know what named tuples are let's see why we should use them.


One of the biggest advantages of named tuples is that they improve the readability of the code. The named fields let us know what each value represents. We don't need to remember the position of each element, making the code less error-prone.

Memory Efficiency

Named tuples are memory-efficient since Python only stores a reference to the field name once. In contrast, a regular tuple stores the field names every time it creates an instance.

Protection against key errors

Named tuples provide some protection against key errors since they allow you to access the fields with dot notation, which is less prone to typos or misspellings.

Applications of Named Tuples

So far, we have discussed the fundamentals of named tuples and their benefits. But when should we use them? Here are some use cases:

Database Queries

When we query a database, we often get a tuple back from the database cursor. Named tuples can make it easier to work with this data by allowing you to access fields by their names.

Configuration Files

Configuration files usually contain settings and values. By using named tuples, it's easier to represent configuration data and access the parameters.

Data Analysis

In data analysis, we often work with large datasets containing a mix of various types of data. Named tuples can help to keep different fields organized and linked.

Named Tuples vs. Dictionaries

You might be wondering how named tuples compare with dictionaries. After all, dictionaries also provide a way to access fields using keys.

Named tuples are more memory-efficient than dictionaries since Python only stores a reference to the field name once, while dictionaries store the key value for every entry.

While both named tuples and dictionaries allow for easy data indexing, named tuples provide added protection against misspelling and key errors.


Named tuples provide a simple, efficient, and easy-to-use way to organize your data. Their named fields make your code more readable, and their immutability ensures that accidental changes to the data don't happen.

In summary, named tuples offer a range of benefits for data scientists and developers. Try using them for your next project to increase code readability and efficiency.

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