Pyplot (Stateful) API and Object-Oriented (OO) API which is better?

Matplotlib provides two main ways to create plots:

1. Pyplot (Stateful) API – A MATLAB-like interface that relies on implicit states.
2. Object-Oriented (OO) API – A more explicit and flexible approach using Figure and Axes objects.

Pyplot (Stateful) API – The Quick and Easy Way

The pyplot interface in Matplotlib works like MATLAB. It keeps track of the current figure and axes and applies changes to them.

When to use?

• Quick plots
• Simple visualizations
• When you don’t need much customization

How does it work?

• You just call functions like plt.plot(), plt.title(), plt.xlabel(), etc.
• Matplotlib automatically creates the figure and axes for you.

Drawbacks of Pyplot API:

• Harder to manage multiple plots or subplots.
• Less flexibility when customizing figures.
• Code can become messy for complex visualizations

Object-Oriented API

The Object-Oriented (OO) API in Matplotlib is a structured way to create visualizations by explicitly managing Figure and Axes objects.

Instead of relying on global state management (as in the Pyplot API), the OO API provides better control, flexibility, and scalability by treating plots as objects that can be customized independently.

Key Components

1. Figure (fig) Represents the entire drawing area or canvas. Can contain multiple subplots (Axes). Created using plt.figure() or plt.subplots().
2. Axes (ax) Represents an individual plot inside a Figure. Contains elements like labels, title, legend, grid, and the actual plot. Created using fig.add_subplot() or plt.subplots().

Advantages of Using the Object-Oriented API

1.More structured – Each figure and axes are objects that can be modified ??????independently. 2. Easier subplot management – Works well for multiple plots in one figure. 3. Better customization – Allows fine control over plot elements. 4. Avoids confusion – No reliance on global state like Pyplot API.

?? When to use?

• When making multiple plots (subplots).
• When customizing figures in detail.
• When you want cleaner, scalable code.

?? How does it work?

• You first create a Figure (fig) and one or more Axes (ax).
• You call methods like ax.plot(), ax.set_title(), ax.set_xlabel(), etc.

Why is the Object-Oriented API better?

Scalability – Works well for multiple subplots. Readability – Code is more explicit and structured. Better Control – You can modify figures and axes independently.

Conclusion

• For simple plots → Pyplot (plt.plot()) is fine.
• For multiple subplots & professional work → Use Object-Oriented API (fig, ax = plt.subplots()).

Let's compare Pyplot API vs. OO API for multiple plots.

Problem: Hard to control figure layout and design.

Advantages:

• Each subplot (axes[0], axes[1]) is controlled independently.
• Code is cleaner and easier to manage.
• More flexibility in customizing subplots.