Why Is A Figure Created Before Plot Python

Python Programming

When delving into the world of Python, one of the first things that caught my attention was the concept of creating a figure before plotting in Python. As I explored this concept further, I realized the importance and benefits of this approach. In this article, I will dive deep into the reasons why creating a figure before plotting in Python is essential and how it has enhanced my coding experience.

Understanding the Concept

Before we dive into the reasons behind creating a figure before plotting in Python, it’s essential to understand the concept itself. In Python’s matplotlib library, a figure is the top-level container for all the plot elements. It serves as a canvas where all the plotting elements such as axes, titles, and labels reside. By creating a figure first, we establish the groundwork for our plot and define the space in which our visualizations will come to life.

Clarity and Organization

One of the primary reasons I’ve come to appreciate the practice of creating a figure before plotting is the clarity and organization it brings to my code. By explicitly defining the figure, I can easily keep track of various aspects of my plot, such as subplot arrangements and figure dimensions. It enhances the readability of my code and makes it easier to understand and maintain, especially when working on complex visualizations.

Customization and Flexibility

Creating a figure before plotting allows for extensive customization and flexibility in Python. Once the figure is created, I have the freedom to modify and customize various attributes such as the figure size, background color, and layout. This level of flexibility empowers me to tailor my visualizations to specific requirements and ensures that the end result aligns with my vision.

Efficient Plotting Workflow

Another aspect that has significantly impacted my coding workflow is the efficiency that comes with creating a figure before plotting. It streamlines the process of adding multiple plots to a single figure, especially when working with subplots. This approach enables me to efficiently organize and manage a multitude of plots within a single overarching figure, thereby enhancing my productivity and reducing redundancies in my code.

Managing Plotting Resources

By creating a figure before plotting, I gained a newfound understanding of the resource management aspect of plotting in Python. It allows me to allocate plotting resources such as memory and processing power more effectively. This becomes particularly crucial when dealing with large datasets and intricate visualizations, as it helps in optimizing the performance of my plotting operations.

Embracing the Practice

Embracing the practice of creating a figure before plotting has undeniably transformed the way I approach visualizations in Python. It has instilled a sense of discipline in my coding process and has empowered me to elevate the quality of my visualizations while maintaining a structured and organized workflow.


In conclusion, the practice of creating a figure before plotting in Python offers a myriad of benefits that have significantly enriched my coding experience. From enhancing clarity and organization to facilitating efficient plotting workflows, this approach has undoubtedly become an integral part of my coding repertoire. As I continue to explore the ever-evolving landscape of Python visualizations, creating a figure before plotting remains a fundamental practice that continues to shape and elevate my coding journey.