Matplotlib Fundamentals

Matplotlib is the foundational plotting library in Python. Nearly every other Python visualisation library (Seaborn, Pandas plotting, even some Plotly features) is built on top of or integrates with Matplotlib. Understanding its architecture gives you complete control over every element of a chart.

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Installation

pip install matplotlib

The standard import convention:

import matplotlib.pyplot as plt
import numpy as np

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The Two Interfaces

Matplotlib offers two ways to create plots:

1. Pyplot Interface (Quick and Simple)

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.title("Simple Line Plot")
plt.show()

2. Object-Oriented Interface (Recommended)

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [10, 20, 25, 30])
ax.set_xlabel("X Axis")
ax.set_ylabel("Y Axis")
ax.set_title("Simple Line Plot")
plt.show()

Tip: Always use the object-oriented interface (fig, ax = plt.subplots()) for anything beyond the simplest plot. It gives you explicit control over figures and axes.

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Figure and Axes — The Core Concepts

Object	Description
Figure	The overall window or canvas — can contain multiple Axes
Axes	A single plot area with its own x-axis, y-axis, title, and data
Axis	The x-axis or y-axis of an Axes object — controls ticks, labels, limits
Artist	Every visual element on a figure — lines, text, patches, etc.

import matplotlib.pyplot as plt

# Create a figure with 1 row and 2 columns of Axes
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

ax1.bar(["A", "B", "C"], [10, 20, 15])
ax1.set_title("Bar Chart")

ax2.plot([1, 2, 3], [1, 4, 9], marker="o")
ax2.set_title("Line Plot")

fig.suptitle("Two Subplots", fontsize=14)
plt.tight_layout()
plt.show()

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Common Plot Types

Line Plot

import matplotlib.pyplot as plt
import numpy as np

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

fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(x, np.sin(x), label="sin(x)", linewidth=2)
ax.plot(x, np.cos(x), label="cos(x)", linewidth=2, linestyle="--")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title("Trigonometric Functions")
ax.legend()
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Bar Chart

import matplotlib.pyplot as plt

langs = ["Python", "R", "SQL", "Julia"]
users = [68, 18, 42, 5]

fig, ax = plt.subplots(figsize=(8, 5))
bars = ax.bar(langs, users, color=["#4C72B0", "#DD8452", "#55A868", "#C44E52"])
ax.set_ylabel("Usage (%)")
ax.set_title("Data Science Language Popularity")
ax.spines[["top", "right"]].set_visible(False)

# Add value labels on bars
for bar in bars:
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width() / 2, height + 1,
            str(height), ha="center", va="bottom")

plt.tight_layout()
plt.show()

Scatter Plot

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
x = np.random.randn(200)
y = x + np.random.randn(200) * 0.5
colours = np.random.rand(200)
sizes = np.abs(np.random.randn(200)) * 100

fig, ax = plt.subplots(figsize=(8, 6))
scatter = ax.scatter(x, y, c=colours, s=sizes, alpha=0.6,
                     cmap="viridis", edgecolors="white")
fig.colorbar(scatter, ax=ax, label="Colour Value")
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_title("Scatter Plot with Colour and Size")
plt.tight_layout()
plt.show()

Histogram

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
data1 = np.random.normal(50, 10, 1000)
data2 = np.random.normal(60, 12, 1000)