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How to Update Python Lists Without Overwriting Values

Learn how to effectively manage a list in Python without losing previous values with simple techniques for copying lists. --- This video is based on the question https://stackoverflow.com/q/72673804/ asked by the user 'DaytaSigntist' ( https://stackoverflow.com/u/10374379/ ) and on the answer https://stackoverflow.com/a/72673813/ provided by the user 'Samathingamajig' ( https://stackoverflow.com/u/12101554/ ) 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: How do I update python list without overwriting value? 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. --- How to Update Python Lists Without Overwriting Values In the world of programming with Python, you may often find yourself needing to update values in a list, particularly when you want to keep track of changes across multiple iterations. One common issue is unintentionally overwriting previous values when you store references instead of copies of the list. This can lead to frustrating scenarios, especially when you want to maintain a history of changes. In this guide, we’ll discuss how to effectively update lists in Python without losing your precious data and how to fix the common pitfalls associated with list management. The Problem Explained Let's say you have a list called thetas initialized with initial values, and another list called theta_history where you want to store the value of thetas for each iteration. However, when you update the values in thetas, you find that theta_history only contains repeated final values instead of a history of values from each iteration. Here’s a simple example to illustrate the problem: [[See Video to Reveal this Text or Code Snippet]] The output of this code will be: [[See Video to Reveal this Text or Code Snippet]] As you can see, instead of capturing the values from each iteration, theta_history is filled with the same final list repeated multiple times. This happens because you are appending a reference to the same list (thetas), rather than the actual values contained within it. The Solution: Making a Copy of the List To maintain a history of values, you need to ensure that you are appending a copy of the list to theta_history during each iteration. By doing so, each entry in theta_history will contain the values from that specific iteration instead of just references to the same list. Steps to Implement the Solution Use List Copying: In Python, you can create a shallow copy of a list using the spread operator (*). This ensures that you store the actual values rather than just a reference. Modify the original loop to append a copy of the thetas list. Here’s how you can adjust the code accordingly: [[See Video to Reveal this Text or Code Snippet]] Expected Output The expected output of this modified code is: [[See Video to Reveal this Text or Code Snippet]] Now theta_history properly maintains a record of each state of thetas throughout the iterations! This method not only resolves the problem effectively but also keeps your code clean and efficient. Conclusion In summary, if you find yourself in a situation where your list contains repeated references instead of the desired history of values, remember to create copies when appending lists. The spread operator (*) is a simple yet powerful way to achieve this in Python. With this technique at your disposal, you can confidently track changes in your lists without any worries of overwriting your valuable data.