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Python

Matplotlib Figure Size: A Quick Guide to Resizing Plots

By Filip on 10/05/2024

Learn various methods, including DPI settings and figure size arguments, to effectively adjust the size of figures generated using Matplotlib in Python.

Matplotlib Figure Size: A Quick Guide to Resizing Plots

Table of Contents

Introduction

When using Matplotlib in Python, you might want to adjust the size of your figures to fit your needs, whether it's for presentations, reports, or any other purpose. This article will guide you on how to easily control the size of your Matplotlib figures using the figsize parameter.

Step-by-Step Guide

To control the size of your Matplotlib figures in Python, use the figsize parameter. Here's how:

  1. Import the necessary library:

    import matplotlib.pyplot as plt

  2. Create your plot:

    plt.plot([1, 2, 3, 4], [10, 20, 25, 30])

  3. Specify the figure size using figsize:

    plt.figure(figsize=(8, 6))  # Creates a figure 8 inches wide and 6 inches tall
    • Place this line before your plotting code (plt.plot() in this example).
    • The figsize argument takes a tuple: (width, height).
    • Units are in inches.

  4. Display your plot:

    plt.show()

Now, your plot will appear with the specified dimensions.

Code Example

The Python code uses the matplotlib library to create a simple line plot. It defines data points for x and y axes, sets the figure size to 8 inches wide and 6 inches tall, plots the data, and then displays the plot.

import matplotlib.pyplot as plt

# Create data for the plot
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]

# Specify the figure size using figsize
plt.figure(figsize=(8, 6))  # Creates a figure 8 inches wide and 6 inches tall

# Create the plot
plt.plot(x, y)

# Display the plot
plt.show()

Additional Notes

  • Figure size units: While inches are the default, you can use other units like centimeters or pixels. Refer to the Matplotlib documentation for details on specifying different units.
  • Figure size persistence: figsize only affects the figure it's called upon. Subsequent figures will revert to default sizes unless you set figsize again.
  • Alternative to plt.figure(): You can create a figure and axes object directly using fig, ax = plt.subplots(figsize=(8, 6)). This combines figure creation and size setting in one step.
  • Aspect ratio: Changing figsize affects the aspect ratio of your plot. Be mindful of this, especially if your visualization relies on accurate proportions.
  • Resolution: Figure size and resolution are distinct. figsize controls the physical dimensions of the figure, while resolution (DPI) determines the number of pixels per inch. Adjust resolution separately if needed.
  • Saving figures: When saving figures using plt.savefig(), the figsize you set will determine the dimensions of the saved image file.
  • Contextual figure sizing: Libraries like Seaborn, built on top of Matplotlib, might have their own ways to control figure size. Consult their documentation if needed.

Summary

Feature Description
Library matplotlib.pyplot (imported as plt)
Figure Size Control plt.figure(figsize=(width, height))
Units Inches
Placement Before plotting code (e.g., plt.plot())
Example plt.figure(figsize=(8, 6)) creates a figure 8 inches wide and 6 inches tall.

Conclusion

Mastering figure sizing in Matplotlib gives you greater control over the visual presentation of your data. Whether you're creating plots for publications, presentations, or simply want to fine-tune their appearance, the figsize parameter is an essential tool in your Matplotlib toolkit. Remember to adjust figure size contextually, considering the specific requirements of your output and the audience for your visualizations.

References

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