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In this article, we'll look at how to fix ValueError: could not broadcast input array from shape (224,224,3) into shape (224,224) in Python. Here, we'll go through all of the possible solutions as well as how this mistake occurs. So, let's get this article started.

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Table of the Content:

                                1 - Valueerror: could not broadcast input array from shape
                                2 - The Reasons
                                3 - The Solutions
                                                1 - The Solution One
                                                2 - The Solution Two
                                4 - The Conclusion

Valueerror: could not broadcast input array from shape

The error message "Valueerror: could not broadcast input array from shape" basically means that pandas won't be able to remove two data frames if they don't have any common indexes or column names. The NumPy array was used to generate the data frame, and the index is essentially a set of generic numbers (ie 1,2, 3, etc). Each asset will have its column, as well as a general integer label. The data frame with an index that was built from the pipeline specification.

You must equalize the indexes of the two data frames. Resetting the index could be a good idea. This will provide data frames with compatible indexes.

The Reasons

At least one item in your list isn't three-dimensional, or its second or third dimension doesn't match the others. The arrays are still matched if just the first dimension does not match, but no attempt is made to reconcile them into a new array as distinct objects. Below are some examples:

That is, the form of the problematic

element!= (?, 224, 3)
ndim!= 3 (with the? denoting a non-negative integer)

That's what's causing the problem.

To convert your list into a four (or three) dimensional array, you'll need to change that. It's hard to identify whether you want to remove a dimension from 3D things or add one to 2D items (in the first scenario) or modify the second or third dimension without knowing the context (in the second case).

The Solutions

The solution to this error can be explained in two ways, I will explain both ways of the solution one by one.

The Solution One

Simply use astype(object) to convert numpy.ndarray to object, as seen in the code below. It will assist you in resolving your issue.

a = [np.zeros((224,224,3)).astype(object), np.zeros((224,224,3)).astype(object), np.zeros((224,224,13)).astype(object)]

The Solution Two

You can use this addition to entirely fix your problem.


The Conclusion

So those were all the options for fixing the problem. I hope this information has helped you address your problem. Tell us which solution worked in the comments. If you like our story, please share it on social media and leave a comment with your thoughts.

All of these articles are designed to assist you in resolving any difficulties with error messages that programmers working on various platforms may encounter throughout their job. Your input will assist us in achieving our goals.

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