How to Extend Numpy Arrays with a New Column of Arrays

Learn how to effectively extend numpy arrays by appending a new column that contains entire arrays within it.
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How to Extend Numpy Arrays with a New Column of Arrays

Understanding the Problem

Imagine you have two 2D numpy arrays:

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

You would like to append array2D_2 to array2D_1, but not as separate columns. Instead, you want the entries of array2D_2 to be enclosed in brackets as a single column for each row. The desired output structure would look something like this:

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Common Approaches and Their Limitations

You might have tried several methods, such as:

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Both of these functions will yield a result that flattens the second array into new columns, rather than encapsulating its values in a single column. Additionally, trying to use these methods in conjunction with lists will often produce a ValueError, as numpy expects all concatenated structures to have matching dimensions.

The Solution: Using an Object Array

To achieve the output you're looking for, you can create an empty numpy array of type object that will allow you to store different dimensions in a single column. Here's how you can do it step by step:

Step-by-Step Implementation

Import numpy as usual and define your two arrays.

Create an empty object array that has the desired shape, which in this case would be (3, 4) since we want 3 rows and 4 columns.

Assign values of array2D_1 to the first three columns of the new array.

Assign the array2D_2 as a list to the fourth column.

Here's the complete code that implements this solution:

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Expected Output

Running the code above will give you the desired output:

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Conclusion

By utilizing an object array and manually managing the dimensions, you can effectively extend a numpy array by appending new columns of arrays without compromising the structural integrity of your data. This technique opens up new possibilities for organizing and manipulating data within numpy. Experiment with this method for your projects and see how easily it can enhance your data handling capabilities in Python!