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Learn how to display multiple plots in PyCharm using Matplotlib without closing each figure individually. Follow this guide for seamless data visualization!
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How to Display Multiple Plots with plt.show() in PyCharm?

Working with plots in Python, especially using libraries like Matplotlib and Seaborn, is a common task for data analysts and scientists. However, if you're using an IDE like PyCharm, you may find that calling plt.show() in your code requires you to close each figure before moving on to the next one. This can be frustrating when you want to view multiple plots at once.

In this guide, we will explore how to effectively display multiple plots in PyCharm without the hassle of closing each figure sequentially.

The Problem: Multiple Figures and plt.show()

When we use plt.show() in different parts of our code, the execution halts until the current figure is closed. If you run the following code:

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After the first plt.show(), execution stops, and you'll need to close the figure to view the next one. This is not ideal if you want to compare figures side-by-side.

The Solution: Create Separate Figures

To display multiple plots at once, you need to create separate figures. Here’s how to do it:

Step-by-Step Guide

Create Multiple Figures: Use plt.subplots() to create multiple figures and axes.

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Plot Your Data: Use the specified axes to plot your data.

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Show All Figures Together: Finally, use plt.show() once at the end to render all the figures.

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Complete Example Code

Here’s the complete code for reference:

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Alternative: Using Subplots

If you prefer to have your plots in a single figure but with separate axes, you can accomplish this by creating subplots in one figure:

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This method allows you to have the heatmaps side-by-side within a single graphic window.

Conclusion

By following these steps, you can overcome the challenge of displaying multiple plots in PyCharm, allowing for better comparisons and visual aids in your data analysis workflow. Remember to utilize plt.subplots() to manage your figures and axes effectively. Happy plotting!