Скачать или смотреть How to Remove Columns from a New Matrix in Python Without Modifying the Original Matrix

Learn to create a separate copy of a matrix and modify it without affecting the original in Python. This guide explains the use of deep copying to prevent unintended changes.
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This video is based on the question https://stackoverflow.com/q/63085124/ asked by the user 'Mike S.' ( https://stackoverflow.com/u/13992247/ ) and on the answer https://stackoverflow.com/a/63085206/ provided by the user 'PApostol' ( https://stackoverflow.com/u/12881844/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Solving the Matrix Modification Dilemma in Python

When working with matrices in Python, you may come across a scenario where you want to create a modified version of an existing matrix without affecting the original. This is a common requirement among beginners, especially when you’re experimenting with data or performing operations on matrices. In this article, we'll address a typical problem: removing a row and a column from a new matrix derived from an existing one, while ensuring the original matrix remains intact.

Understanding the Problem

Suppose you have the following matrix:

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

You want to create a new matrix (new_matrix) that starts as a copy of matrix, and you aim to remove the first row and the first column from new_matrix. However, every attempt results in the original matrix also being altered, which is not your intention.

Here’s the problem in action with Python code:

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

The output reveals that changes were made to matrix, not just new_matrix:

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

Clearly, using matrix.copy() created a shallow copy, which doesn’t suit our needs since it references the nested lists directly.

The Solution: Deep Copying the Matrix

To solve this problem, we need to differentiate between shallow and deep copies in Python.

What is a Shallow Copy?

A shallow copy creates a new object but inserts references into it to the objects found in the original. If the original nested objects change, the shallow copy reflects those changes.

What is a Deep Copy?

A deep copy creates a new object and recursively adds copies of nested objects found in the original. Thus, modifications to the deep copy do not affect the original.

Modifying the Code

To create a deep copy of the matrix, we can use the copy module in Python. Here’s how to implement it:

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

The Result

When you run the code above, the output will be:

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

As you can see, the original matrix remains unchanged while the new_matrix is modified as intended.

Conclusion

In conclusion, when working with matrices in Python, knowing the difference between shallow and deep copies is crucial. By using copy.deepcopy(), you can operate on a new matrix without affecting the original data. This method ensures that your data integrity is maintained while providing the flexibility to manipulate a copy for any required modifications.

Now you can confidently create, modify and manage your matrices in Python!