Resolving ValueError with Matplotlib's fill_between Function in Python

Описание: Learn how to fix the `ValueError` related to the `fill_between` function in Matplotlib when dealing with non-1-dimensional data arrays in Python.
---
This video is based on the question https://stackoverflow.com/q/64638425/ asked by the user 'Daddy Kropotkin' ( https://stackoverflow.com/u/10241491/ ) and on the answer https://stackoverflow.com/a/64698278/ provided by the user 'Daddy Kropotkin' ( https://stackoverflow.com/u/10241491/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Unexpected value error using fill_between function of matplotlib

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the Problem: ValueError with Matplotlib's fill_between

Are you facing a frustrating ValueError while trying to use the fill_between function in Matplotlib? If you have written a straightforward code to fill the area between two lines in a plot but are hit with an error saying, "'Input passed into argument u'x' is not 1-dimensional," you're not alone!

This problem usually arises due to the dimensionality of the data arrays you are using. In this guide, we'll explore what causes this error and how to resolve it by ensuring that your data is in the correct format for the fill_between function to work properly.

The Concept of fill_between in Matplotlib

The fill_between function is a convenient way in Matplotlib to create shaded areas between two curves on a plot. Here’s a quick overview of how it works:

Inputs Needed: The function requires three main inputs: the x-coordinates, the lower y-coordinates, and the upper y-coordinates.

Output Looks Like: When used correctly, it displays a message that fills the space between the lines, creating a visually appealing representation of a range.

However, if any of these inputs are not 1-dimensional arrays, you might run into unexpected errors. That's what happened in our case.

Identifying the Error Source

The error message indicates that one of your inputs (q, DelTheta5, or DelTheta95) may not be in the proper format. Let's break this down further:

1-Dimensional Requirement: The fill_between function expects that the x and y data are in 1-dimensional arrays.

Column Vectors Issue: Often, the data could inadvertently be in a column vector format with an extra dimension, resulting in a 2-dimensional array instead of the expected 1-dimensional array.

To better understand this, consider the example code you might have written:

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

In this code snippet, if any of the variables (q, DelTheta5, or DelTheta95) are not purely 1-dimensional, it triggers the ValueError.

Solution: Using np.squeeze to Reshape Data

The good news is that solving this issue is fairly straightforward. As indicated by a helpful comment from a user named Leonard, the solution lies in removing the extra dimension from your arrays using np.squeeze. Here’s how you can implement this fix:

Step-by-Step Fix

Import NumPy:
Make sure to import the NumPy library (if you haven’t already):

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

Use np.squeeze:
Wrap your data arrays with np.squeeze to flatten any extra dimensions:

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

Run Your Code Again:
After this modification, rerun your plotting code. The filled region should now render correctly without any errors:

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

Conclusion

Data dimensionality issues can often lead to errors when working with plotting libraries like Matplotlib. By ensuring your data is in the correct format and using tools like np.squeeze, you can avoid those pesky ValueErrors and create stunning visualizations. Remember, clarity in data shapes is key to successful plotting!

Now, go ahead and plot confidently, knowing that you've conquered the ValueError with fill_between!