Скачать или смотреть How to Fix the AttributeError: 'Line3D' object has no attribute '_verts3d' in Matplotlib

Discover how to resolve the AttributeError when plotting 3D regression lines in Matplotlib. Learn the correct approach to define your variables for a smooth plotting experience.
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This video is based on the question https://stackoverflow.com/q/65363781/ asked by the user 'Lord Cinders' ( https://stackoverflow.com/u/12913718/ ) and on the answer https://stackoverflow.com/a/66991563/ provided by the user 'Ali A. Noroozi' ( https://stackoverflow.com/u/8111558/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Troubleshooting Matplotlib: Fixing the AttributeError for 3D Line Plots

When working with data visualization, especially in Python using libraries like Matplotlib, encountering errors is quite common. One error that can be particularly frustrating is the AttributeError: 'Line3D' object has no attribute '_verts3d'. This error often arises when trying to plot regression lines in a 3D space using incompatible data structures. In this post, we'll break down how to resolve this issue, focusing specifically on a multivariable regression model involving CO2 emissions.

The Problem at Hand

The error arises in scenarios where you are trying to plot a 3D regression line using Matplotlib, particularly when your x and y coordinates are not in the correct format. In the example provided, the user tried to plot a regression line while having defined x and y as two-dimensional arrays:

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

While this approach may seem intuitive, it leads to complications when you attempt to use the ax.plot3D function that requires one-dimensional arrays.

Understanding the Error Messages

The following error messages were encountered:

ValueError: input operand has more dimensions than allowed by the axis remapping

AttributeError: Line3D object has no attribute '_verts3d'

Both errors stem from trying to pass multi-dimensional data (two-dimensional in this case) to a plotting function that expects one-dimensional inputs for its x and y axes.

The Solution: Converting to One-Dimensional Variables

To fix this issue, you need to convert your x and y variables from two-dimensional to one-dimensional by accessing the underlying data directly from the DataFrame. Here's how you do it:

Step 1: Modify Your x and y Variables

Instead of wrapping your variable selections in double square brackets (which creates a DataFrame), use single brackets to obtain a Series type. Here’s the updated code:

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

Step 2: Validate the Dimensions

Before proceeding, it’s also good practice to check the dimensions of your variables using the ndim property to confirm they are one-dimensional:

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

Step 3: Re-run Your Plotting Code

Once you’ve made these adjustments, you can then re-run your plotting code:

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

Conclusion

By ensuring that your x and y variables are one-dimensional, you eliminate the potential for the errors you were encountering. In summary, remember:

Use single brackets to define x and y to ensure they are Series rather than DataFrames.

Always check the dimensions of your data before plotting to catch issues early.

This approach not only resolves the AttributeError but also sets you up for a smoother experience in plotting with Matplotlib.

Mastering data visualization in Python requires practice and attention to detail, but with these tips, you’ll be on your way to troubleshooting and creating amazing graphs in no time. Happy coding!