Beyond “Simple” Plotting

Many beginners start with plt.plot() and quickly get frustrated when they can’t control the details of their figure. To master Matplotlib, you must understand its Object-Oriented (OO) Architecture.

The Hierarchy of a Plot

A Matplotlib plot is not just a bunch of lines; it’s a hierarchy of objects called Artists.

skinparam componentStyle rectangle

package "Figure" as Fig {
component "Axes" as AX1
}

component "Axis" as Axis
component "Title" as Title
component "Legend" as Legend
component "Ticks" as Ticks
component "Grid" as Grid
component "Lines" as Lines

AX1 *-- Axis
AX1 *-- Title
AX1 *-- Legend
AX1 *-- Ticks
AX1 *-- Grid
AX1 *-- Lines

note bottom of AX1: "A Figure can contain\nmultiple Axes (subplots)."

The Figure vs. The Axes

1. Figure: The top-level container. Think of it as the physical page or empty window.
2. Axes: A “subplot.” It is the area where the data is actually plotted. Most operations (setting titles, labels, grids) happen on an Axes object.

Creating Subplots the Pro Way

The most common way to start a plot is with plt.subplots().

import matplotlib.pyplot as plt
import numpy as np

# Create figure and axes
fig, ax = plt.subplots(figsize=(10, 5))

# Generate data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Plotting on the Axes object
ax.plot(x, y, label='Sine Wave', color='#2563eb', linewidth=2.5)

# Setting attributes on the Axes object
ax.set_title('Hierarchical Plot Construction', fontsize=16, fontweight='bold')
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.grid(True, linestyle='--', alpha=0.6)
ax.legend()

plt.show()

Anatomy of a Figure

Each element you see—the title, the tick labels, the lines—is a separate Artist object that can be customized.

Multiple Subplots

The power of the OO API becomes clear when you have multiple plots in a single figure.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)

# Create a 2x1 grid of plots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8), sharex=True)

ax1.plot(x, np.sin(x), 'r')
ax1.set_title('Sine')

ax2.plot(x, np.cos(x), 'b')
ax2.set_title('Cosine')

# Automatically adjust spacing
plt.tight_layout()
plt.show()

In the next lesson, we will explore how to style these components to create publication-quality figures.