Скачать или смотреть Understanding Python Object Representation: Why You See Memory Locations Instead of Names

Discover the reasons behind Python printing memory locations instead of class names and learn how to rectify the issue in your code.
---
This video is based on the question https://stackoverflow.com/q/71081293/ asked by the user 'Flann3l' ( https://stackoverflow.com/u/14902589/ ) and on the answer https://stackoverflow.com/a/71081406/ provided by the user 'Blomex' ( https://stackoverflow.com/u/9541724/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Why does python print memory location rather than class.name in some cases but not others in my code?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding Python Object Representation: Why You See Memory Locations Instead of Names

When working with objects in Python, you might notice that sometimes the output displays memory locations instead of the expected class names. This can be particularly perplexing when you're working with custom classes, such as in the case of a County class that you might be using in your code. Today, we’ll explore why this happens and how you can resolve the issue effectively.

The Problem at Hand

In your code, you have a function that iterates over a list of County class objects to determine which county has the highest voter turnout. When you tested your function, it behaved as expected, returning the correct county name alongside the turnout value. However, upon changing the data (specifically voters for Allegheny), you encountered an output that included a memory address instead of the county name:

Expected Output: ('chester', 0.7215045058280377)

Unexpected Output: (<submission.County object at 0x7f8e37d3cc18>, 0.9999900048976001)

This disparity raises the question: why does Python print memory locations in some scenarios?

The Underlying Issue

The crux of the problem lies in how variables are assigned within your function. Let's break it down:

Initial Assignment: In your code, you start by assigning the entire County object to max_turnout_county:

[[See Video to Reveal this Text or Code Snippet]]

Subsequent Assignment: Later, within your loop, you are changing this variable to just the name attribute of the County object:

[[See Video to Reveal this Text or Code Snippet]]

When a County object is assigned to max_turnout_county initially, it retains the full object reference. However, subsequently, you're changing the content of this variable to a string representing just the county name.

In scenarios where you end up reassigning max_turnout_county only to the memory address of the County object (because none of the conditions in your loop were met to switch it back to a name), the function ultimately returns that memory address instead of the county name.

The Solution

To rectify this behavior, a simple change is required in your code. In the first line of the highest_turnout function, you should assign only the name property of the first county object to max_turnout_county immediately:

[[See Video to Reveal this Text or Code Snippet]]

Here’s the corrected function:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By making this small adjustment, your function will now consistently return the county name as expected, even when the voter counts change. Learning to understand how Python handles object references and values is crucial for effective coding, especially when designing your own classes. With this insight, you can avoid similar issues in the future and write more reliable Python code.

So the next time you’re puzzled by Python’s output, remember to check how you’re handling your objects and make sure you’re referencing them appropriately. Happy coding!