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Writing Clean, Pythonic Code With namedtuple (Summary)

Christopher Trudeau
Writing Clean, Pythonic Code With namedtuple Christopher Trudeau 02:10
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Writing Pythonic code is an in-demand skill in the Python development space. Pythonic code is readable, explicit, clean, maintainable, and takes advantage of Python idioms and best practices. In this video course, you learned about creating namedtuple classes and instances and how they can help you improve the quality of your Python code.

In this video course, you learned:

  • How to create and use namedtuple classes and instances
  • How to take advantage of cool namedtuple features
  • When to use namedtuple instances to write Pythonic code
  • When to use a namedtuple instead of a similar data structure
  • How to subclass a namedtuple to add new features

With this knowledge, you can deeply improve the quality of your existing and future code. If you frequently use tuples, then consider turning them into named tuples whenever it makes sense. Doing so will make your code much more readable and Pythonic.

To learn more about the concepts in this course, check out the following resources:

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00:00 In the previous lesson, I showed you how to create a subclass based on a named tuple. In this lesson, I’ll summarize the course. Named tuples make your code more readable than their non-named associates, and memory-wise have identical footprints.

00:15 You create a named tuple using the namedtuple() factory found in the collections module. The factory takes a string containing the name of the class you want to create and the names of the attributes in the tuple.

00:27 You can specify the attribute names in a space- or comma-delimited string or by using an iterable. The factory has optional arguments for turning on automatic field renaming, specifying default values, or altering the module name associated with the created class. Named tuples can make your code more readable by being specific about the parts of a tuple through dot notation by making functions that return multiple values more explicit, reducing the number of arguments to a function, or grouping associated attributes of a chunk of data together.

01:03 Generally, named tuples take up less memory than most other data structures and have faster construction times than everything else except regular tuples.

01:12 They are immutable, though. If the program you’re writing needs the data to change, they might not be the best choice.

01:20 For more information on named tuples, visit the most excellent Python docs. If you’re thinking about understanding your code’s memory footprint, the pympler library is a useful profiling tool.

01:33 Or if you’re interested in the speed part of performance, this article shows you different ways of measuring that.

01:40 For more information on tuples and other data structures, you can dig further into some Real Python content. This first article compares lists and tuples and is available as both a written tutorial and a video course, or if you want to go real deep on Python data structures, this guide is exhaustive, so much so the corresponding video course comes in three parts.

02:05 Thanks for your attention. I hope you enjoyed the course.

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