Troubleshooting Matplotlib’s Hanging Issue

Resolving a Common Problem in Matplotlib

At times, when working with Matplotlib, you may encounter a situation where the plotting process hangs and doesn’t complete as expected, requiring manual intervention to terminate it. This can be frustrating, especially during data visualization tasks. Let’s delve into solving this issue together.

What You Will Learn

In this guide, you will discover how to effectively address problems related to Matplotlib processes not finishing on their own. We will explore a practical solution ensuring your plots are displayed correctly without the need for manual interruptions.

Introduction to the Problem and Solution

When using Matplotlib, it is common to face scenarios where plot windows do not close automatically or scripts appear to hang indefinitely post-plotting. This typically occurs due to interactive mode settings or backend configurations used by Matplotlib.

To tackle this issue, our method involves configuring Matplotlib appropriately for your specific environment and usage context. We will focus on setting the correct backend and adjusting interactive mode based on whether scripts should pause at plot displays or run continuously without interruptions.


import matplotlib.pyplot as plt

# Ensure using a non-interactive backend (e.g., 'Agg' for batch processing)

# Your plotting code here
plt.plot([1, 2, 3], [4, 5, 6])
plt.savefig('plot.png') # Save the figure instead of displaying it

# If you need to display the plot in an interactive session:
plt.switch_backend('TkAgg') # Switch back for interactivity

# Copyright PHD


The solution revolves around two main adjustments: changing the backend and controlling interactive mode. Here’s a breakdown:

  • Backends: These handle drawing/rendering tasks in Matplotlib (directly onto screens or through file formats like PNG). Selecting an appropriate backend manually can resolve hanging issues.

  • Interactive Mode: By switching between backends like ‘Agg’ for saving plots directly and ‘TkAgg’ for interactive displays, we gain flexibility based on script execution modes.

    1. How do I check which backend I’m currently using?

    2. import matplotlib
    3. # Copyright PHD
    4. Can I set a default backend globally? Yes! Specify your preferred default in matplotlibrc config file under backend : <YourBackend> line.

    5. Why would I switch backends mid-script? Switching helps leverage different strengths of each backend; e.g., faster batch processing vs interactive explorations.

    6. Is it necessary always switch backends? Not always � only if experiencing hangs or needing specific features from another backend.

    7. Can changing backends affect my plot’s appearance? Minor differences might occur due to how each renders elements; testing is advised if appearance is crucial.


Resolving hanging processes in Matplotlib might seem challenging initially. Understanding its architecture�especially regarding different rendering engines (backends)�can help efficiently address such issues. Experimentation is key as optimal solutions can vary based on factors like operating systems and application design considerations.

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