Скачать или смотреть How to Sort Multi-Dimensional Arrays in Python

Learn how to easily sort multi-dimensional arrays in Python by descending values derived from specific elements.
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Sorting Multi-Dimensional Arrays in Python

When working with multi-dimensional arrays in Python, especially with numerical data embedded within strings, sorting can sometimes become a challenge. This guide will show you a solution to sort an array based on derived values from its elements. Let’s delve into a practical example to illustrate how to achieve this using the NumPy library.

The Problem Statement

Imagine you have the following multi-dimensional array in Python:

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Each row in this array contains three elements: a string identifier (like "A", "B", etc.), and two numerical values. Now, if you want to sort this array based on the descending value of the second element divided by the third element, you're looking for a way to rearrange it into this order:

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In this post, we will break down how to achieve this in a clear manner.

Steps to Sort the Array

1. Import the NumPy Library

First, ensure you have NumPy installed and import it into your Python script:

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2. Create the Array

Next, define the multi-dimensional array. Note that we'll use a as the variable name to avoid overriding the built-in list keyword.

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3. Extract and Calculate Key Values

To sort based on a specific calculation (in our case, the second value divided by the third), you need to convert the data types of those columns from strings to integers. This step is essential because NumPy arrays must contain elements of the same type:

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4. Generate Sorting Indices

Now that you have the key values, use np.argsort to get indices that would sort the array. By adding [::-1], you reverse the order to achieve descending sorting:

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5. Order the Original Array

Finally, use the sorted indices to rearrange the original array:

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Output

When you run your script, the output will be:

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And there you have it! The multi-dimensional array sorted by the desired criteria in descending order.

Conclusion

Sorting multi-dimensional arrays in Python can be straightforward when using the NumPy library. By converting values, calculating derived metrics, and properly leveraging sorting functions, you can effectively manage and manipulate your data. Experiment with different arrays and sorting criteria to enhance your data handling skills!

Feel free to leave your questions or comments about more complex sorting scenarios in the comments below.