Скачать или смотреть How to Create a Single Overlay Plot with Matplotlib in Python

Learn how to overlay multiple charts in a single plot using Python's Matplotlib library, ensuring clear and engaging visualizations for your data.
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This video is based on the question https://stackoverflow.com/q/65975232/ asked by the user 'Drew Heasman' ( https://stackoverflow.com/u/14821088/ ) and on the answer https://stackoverflow.com/a/65975290/ provided by the user 'Andrei' ( https://stackoverflow.com/u/6259479/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Overlaying Multiple Plots in Matplotlib: A Step-by-Step Guide

In the world of data visualization, creating effective and informative plots is crucial. A common challenge arises when you want to overlay multiple charts on a single figure using Matplotlib in Python. This task can be particularly tricky for new users who may not understand how to structure their functions correctly. Let’s dive into this problem and explore a solution that will allow you to display multiple lines on a single plot seamlessly.

The Problem

You may find yourself struggling to overlay multiple plots in a single chart when using a predefined function. Here's a common scenario that illustrates this issue:

Objective: Create a Tera-Wasserburg Concordia diagram with multiple lines that overlay on a single plot.

Challenge: When calling the plotting function multiple times, you end up with separate charts rather than a combined one. This is frustrating as it prevents you from comparing the data effectively in a single visual representation.

Understanding the Code

Before we jump into the solution, let's briefly review the provided code snippet. The function plot_tera_wasserburg_concordia is designed to plot a specific chart but creates a new figure every time it is called. The key lines of the function to note are:

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

Why It's Not Working

The problem lies within how the figure and axes are created inside the function. Every time you call plot_tera_wasserburg_concordia, a new figure is generated, which results in separate charts being displayed. To resolve this issue, we need to manage the plotting process better.

The Solution

To successfully overlay multiple plots in a single figure, follow these steps:

Step 1: Create the Figure Outside the Function

The first step is to move the figure creation and labeling outside the plotting function. This way, you will create only one figure for the multiple plots.

Step 2: Modify the Function to Accept an Axes Parameter

We'll also update plot_tera_wasserburg_concordia to accept an axes parameter. This allows the function to plot on the existing axes rather than creating a new one.

Step 3: Call the Function with the Same Axes

Call the modified function twice (or more) with the same axes. Here's how the implementation looks:

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

Key Changes Explained:

Figure Creation: The figure and axes are created using fig, ax = plt.subplots() outside the function.

Function Parameter: The plot_tera_wasserburg_concordia function now takes ax as an argument so it can plot onto the specified axes.

Single Call to plt.show(): After both plots have been made on the same axes, finally call plt.show() to display the combined results.

Conclusion

By restructuring your plotting function and managing your Matplotlib figures effectively, you can create engaging data visualizations that overlay multiple datasets on a single chart. This approach not only makes your plots easier to compare but also enhances their overall clarity and readability.

So next time you're looking to overlay charts in Matplotlib, remember this guide to keep your visuals tidy and informative!