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Learn how to create effective legends in Matplotlib subplot loops without overwriting previous entries. Read on for a step-by-step guide.
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Matplotlib Subplot Legends in a Loop: A Quick Fix!

When working with data visualization in Python using Matplotlib, you may run into common issues like legends not displaying correctly, especially when generating multiple plots in a loop. This guide addresses a specific scenario where a user wants to create a line plot for various stock pairs over different years but finds that only the last legend entry is saved. Let's explore the problem and its solution systematically.

The Problem

In a given scenario, the code attempts to plot multiple lines for different stock pairs across years and expects to show each corresponding legend. However, the user is facing an issue where the legend shows only the last stock pair instead of all pairs plotted. Here's a simplified view of the code leading to the problem:

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

The primary issue here is that every time yearaxis.legend() is called, it overwrites the existing legend with a new one. Therefore, when saving the plot, only the last legend entry remains.

The Solution: Using the label Keyword

To resolve this issue, we need to modify how the legend entries are handled. Instead of calling the legend with just a single pair in each loop iteration, we can use the label keyword directly inside the plot function. Below is the revised version of the code.

Revised Code

Here’s how to implement the fix:

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

Breakdown of the Changes

Adding Labels in Plotting:

The label parameter is now included directly in the plot method with label=stock1+'-'+stock2. This makes sure each line added to the plot has its corresponding label.

Legend Location:

By calling yearaxis.legend(loc='best') after the loop over pairs, Matplotlib will automatically decide the best location for the legend based on the current plot layout, which optimizes the viewing experience.

No Overwriting:

By moving away from calling legend inside the inner loop, we ensure that all defined labels are registered correctly before a single call to display the legend.

Conclusion

This adjustment allows for clear and descriptive legends for each line in your plots, making them not only informative but also visually appealing. Make sure to test this updated code with your datasets, and you should see all your stock pairs displayed in the legend, fixing the earlier issue.

Feel free to share your experiences or any other questions regarding Matplotlib plots in the comments below! Happy Coding!