Скачать или смотреть How to Plot a 30x3 Grid in Matplotlib with Multiple Datasets

Learn how to effectively visualize multiple datasets side by side in Python using Matplotlib. This guide will help you create a `30x3` grid for a clear analysis of your time series data.
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This video is based on the question https://stackoverflow.com/q/70385653/ asked by the user 'Fabio' ( https://stackoverflow.com/u/11870626/ ) and on the answer https://stackoverflow.com/a/70385779/ provided by the user 'Z Li' ( https://stackoverflow.com/u/14751619/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Creating a 30x3 Grid Plot with Matplotlib

Visualizing data effectively is crucial for gaining insights, especially when dealing with multiple datasets. If you find yourself juggling 30 different time series across 3 datasets, plotting them side by side in a grid can help you compare their behaviors simultaneously. In this guide, we'll guide you through the process of creating a 30x3 grid in Matplotlib. Let's dive in!

Understanding the Problem

You have three datasets, each containing thirty time series. You want to visualize them in a grid format where each of the rows corresponds to a different time series, and each column displays data from a different dataset (training, validation, and test). Unfortunately, the initial code you were using only results in one column of plots. Here's the breakdown of the original issue encountered:

The goal is to arrange the plots in a grid of 30x3, i.e., 30 rows and 3 columns.

Your initial plotting code only produced a single column of plots, causing the results to visually clutter.

The Solution

To solve this issue, you'll need to use the subplots function in Matplotlib, which is designed to create grids of plots. Here's how to do it step-by-step:

Step 1: Set Up the Figure and Axes

Reduce complexity by creating an array of axes where each subplot can be addressed. Instead of setting the subplot within the loop, you will create the entire grid first:

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

Step 2: Plot Each Dataset

Next, you'll loop over the dataset, plotting the training, validation, and testing data into the appropriate axes. Here’s the revised code that achieves this:

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

Step 3: Display the Results

After plotting all of the time series, use plt.show() at the end to render the entire grid of plots:

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

Complete Code Example

Putting it all together, here is the full example for your use case:

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

Conclusion

By utilizing Matplotlib's built-in functionality, you can efficiently display multiple datasets in a neatly organized grid format. Not only will this approach help clarify the comparisons among your datasets, but it will also enable more straightforward data-driven decisions. If you're dealing with time series analysis, mastering grid plots will enhance your data visualization skills tremendously. Happy plotting!