Скачать или смотреть Creating Multiple Plots in One Figure with Matplotlib

Learn how to use Python's `Matplotlib` to create multiple plots in one figure with `subplots` for clearer visualization.
---
This video is based on the question https://stackoverflow.com/q/65133509/ asked by the user 'VanN' ( https://stackoverflow.com/u/7107664/ ) and on the answer https://stackoverflow.com/a/65133681/ provided by the user 'Ruthger Righart' ( https://stackoverflow.com/u/4008657/ ) 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: Create multiple plots using loop and show separate plot in one

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.
---
How to Create Multiple Plots in One Figure Using Matplotlib

Creating visual representations of data is essential in data science and analytics, and Python offers robust libraries like Matplotlib to help with this task. However, a common issue many users face is effectively displaying multiple plots within a single figure. If you've ever struggled with managing multiple plots, you're not alone. In this post, we'll explore how to tackle this challenge by demonstrating the use of subplots in Python's Matplotlib library.

The Problem: Displaying Multiple Plots

Picture this: You've got multiple datasets to visualize, and you want to compare them side by side. The goal is to find a way to show separate plots in one plot. Initially, many users attempt to create a figure for each dataset but quickly realize that it clutters the screen and makes comparisons difficult.

For instance, you might start with a code snippet like the following:

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

This code creates separate figures for each dataset. Instead, we need to find a method to display them all together in a single figure. Let's see how you can achieve that!

The Solution: Using plt.subplots

To show multiple plots in one figure, we utilize the plt.subplots method, which allows us to define how many plots we want to show in a grid-like format. Here’s how we can do it step by step:

Step 1: Import Libraries

Begin by importing matplotlib.pyplot:

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

Step 2: Prepare Your Data

Set up your x and y data, which will be used for the plots. In this example, we are preparing four different datasets:

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

Step 3: Create a Figure and Subplots

Define a single figure and then add subplots to it. Here’s the revised code to visualize everything together:

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

Step 4: Display Your Plots

Finally, add the following line to display all your subplots in one figure:

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

Full Code Example

Putting it all together, here’s the complete code:

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

Conclusion

By following the steps outlined above, you can easily create multiple plots in a single figure using Python’s Matplotlib. This is an invaluable technique for data comparison and visualization, allowing you to glean insights more effectively. So next time you need to visualize your data, remember this method and leverage the capabilities of Matplotlib!

With this knowledge in your toolkit, you should feel confident tackling your data visualization projects. Happy plotting!