


Resizing Figure Box to Accommodate Expanding Legend
When moving the legend outside of the axis in Matplotlib, there is a challenge encountered where the legend may be cut off by the figure box. This issue occurs when the legend expands beyond the boundary of the plot area. Traditionally, adjusting the axis to accommodate the larger legend has been the recommended solution, but it may result in reducing the size of the data, making it more difficult to interpret.
Dynamic Resizing of Figure Box
To address this issue, the proposed solution is to dynamically resize the figure box to accommodate the expanding legend without altering the data size. This behavior is observed in R and LaTeX but was not immediately apparent in Python.
Code to Dynamically Resize Figure Box
The suggested code to accomplish this dynamic resizing is as follows:
<code class="python">fig.savefig('samplefigure', bbox_extra_artists=(lgd,), bbox_inches='tight')</code>
In this code, lgd represents the legend object. By specifying bbox_extra_artists=(lgd,), we allow savefig to dynamically adjust the figure box to fit the legend.
Example with Complex Legend
Here is an example with a complex legend:
<code class="python">import matplotlib.pyplot as plt import numpy as np # Create figure and subplot fig = plt.figure(1) ax = fig.add_subplot(111) # Plot data and create legend ax.plot(x, np.sin(x), label='Sine') ax.plot(x, np.cos(x), label='Cosine') ax.plot(x, np.arctan(x), label='Inverse tan') lgd = ax.legend(loc='upper center', bbox_to_anchor=(0.5,-0.1)) # Add arbitrary text for testing text = ax.text(-0.2,1.05, "Aribitrary text", transform=ax.transAxes) # Set title and grid ax.set_title("Trigonometry") ax.grid('on') # Save figure with dynamic resizing fig.savefig('samplefigure', bbox_extra_artists=(lgd,text), bbox_inches='tight')</code>
This code generates a plot with a complex legend that extends beyond the axis area. The bbox_extra_artists parameter adjusts the figure box to accommodate the legend, resulting in the entire legend being displayed.
Note:
Since 2019, the code for dynamic resizing has become simplified. The following command is now sufficient:
<code class="python">plt.savefig('x.png', bbox_inches='tight')</code>
The above is the detailed content of How to Dynamically Resize Figure Box to Fit Expanding Legend in Python?. For more information, please follow other related articles on the PHP Chinese website!

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

How to teach computer novice programming basics within 10 hours? If you only have 10 hours to teach computer novice some programming knowledge, what would you choose to teach...


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Atom editor mac version download
The most popular open source editor

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function

Dreamweaver Mac version
Visual web development tools

Notepad++7.3.1
Easy-to-use and free code editor