Resolving ValueError: Understanding the 2D Plotting Limitation in Python

This guide explains how to tackle the `ValueError` regarding the dimensions of data arrays when plotting in Python. We offer a simple solution to reshape your data for successful visualization.
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Resolving ValueError: Understanding the 2D Plotting Limitation in Python

While working on data visualization in Python, especially using libraries like Matplotlib, it's common to encounter a variety of errors. A frequent issue is the ValueError that states: "x and y can be no greater than 2D". This error can be confusing, especially for beginners. In this post, we’ll walk through an example to illustrate this problem and demonstrate how to resolve it effectively.

Understanding the Problem

The error occurs when you try to plot an array that has more dimensions than Matplotlib can handle in the context of a standard 2D plot. In the provided code, the issue arises when plotting predicted values against the actual values.

Here's a brief summary of the provided code and the resulting error message:

Code Review:

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

Error Message:

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

The error arises because y_pred_FF, shaped (14152, 1, 1), is a 3D array, which cannot be plotted against y_test, which is a 1D array of shape (14152,). So how do we fix this?

The Solution: Squeezing Dimensions

The main idea is to reduce the dimensions of y_pred_FF to ensure that it matches the dimensionality of y_test. The solution can be broken down into a few easy steps:

Step 1: Import Necessary Libraries

First, ensure you have imported NumPy since we will be using it to manipulate the shape of our array:

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

Step 2: Squeeze the Dimensions of the Predicted Values

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

Before: The shape is (14152, 1, 1).

After: The shape will change to (14152,).

Step 3: Update the Plotting Function

Now that we have squeezed the dimensions to a suitable shape, we can plot the data as intended:

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

This will successfully visualize both the ground truth values and the predictions on a single graph without any ValueError.

Conclusion

Now you're ready to confidently plot your predictions alongside your actual data! Happy coding!