Matplotlib: Building Charts and Visualizations in Python

Create line plots, histograms, scatter plots, bar charts, box plots, and multi-panel subplot grids with Matplotlib’s pyplot interface.

Matplotlib is the foundational plotting library for Python. It gives you fine-grained control over every element of a chart — axes, tick marks, labels, legends, grids, colors, and line styles — making it possible to produce publication-quality figures from a few lines of code. Before you use higher-level libraries like Seaborn, understanding Matplotlib builds the conceptual foundation you’ll need to customize any chart to your exact requirements.

Installation and Import

pip install matplotlib

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

Two Interfaces

Matplotlib offers two ways to create charts:

Interface	Style	Best For
State-based (plt.xxx)	Call functions directly on pyplot	Single charts — simple and concise
Object-oriented (fig, ax)	Work with explicit figure and axes objects	Multiple subplots, advanced layouts

For single charts, use the state-based approach. Switch to the object-oriented approach when building subplot grids.

Your First Plot

x = [1, 2, 3]
y = [4, 5, 6]

plt.plot(x, y)
plt.show()

Customizing a Plot

You can control titles, labels, limits, grids, legends, and tick marks with plt. commands:

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

# Plot the data
plt.plot(x, y, label="Sine Wave", color="teal", linestyle="--", linewidth=2)

# Labels and title
plt.title("Customized Sine Wave Plot", fontsize=14, fontweight="bold", pad=15)
plt.xlabel("X-Axis Values (Time)", fontsize=11)
plt.ylabel("Y-Axis Values (Amplitude)", fontsize=11)

# Limits and grid
plt.xlim(0, 10)
plt.ylim(-1.5, 1.5)
plt.grid(True, linestyle=":", alpha=0.6)

# Legend and ticks
plt.legend(loc="upper right")
plt.xticks([0, np.pi, 2*np.pi, 3*np.pi], ["0", "π", "2π", "3π"])

plt.show()

Visualizing Numerical Data

Numerical (continuous) data is best explored using plots that reveal trends, distributions, or correlations.

Line Plots — Trends Over Time

years = [2021, 2022, 2023, 2024, 2025]
sales = [150, 220, 290, 410, 500]

plt.plot(years, sales, marker="o", color="blue", linewidth=2)
plt.title("Sales Growth Over Time")
plt.xlabel("Year")
plt.ylabel("Sales")
plt.grid(True, alpha=0.4)
plt.show()

Histograms — Distributions

Histograms group continuous values into “bins” to show frequency distribution.

# 1000 exam scores following a normal distribution
scores = np.random.normal(loc=75, scale=10, size=1000)

plt.hist(scores, bins=20, color="skyblue", edgecolor="black", alpha=0.7)
plt.title("Distribution of Student Scores")
plt.xlabel("Exam Scores")
plt.ylabel("Number of Students")
plt.show()

Adjust the bins parameter to control granularity. Too few bins hides the shape of the distribution; too many makes it jagged. A starting point of 20–30 bins works well for most datasets.

Scatter Plots — Correlations

Scatter plots reveal relationships between two continuous variables.

study_hours  = [2, 4, 5, 7, 8, 10, 11, 12]
exam_scores  = [55, 62, 70, 78, 85, 92, 95, 100]

plt.scatter(study_hours, exam_scores, color="orange", s=100, edgecolor="red")
plt.title("Study Hours vs. Exam Scores")
plt.xlabel("Hours Studied")
plt.ylabel("Exam Score")
plt.show()

Visualizing Categorical Data

Categorical data (discrete groups like departments or courses) is best represented with bar charts.

Vertical Bar Chart

courses  = ["Python", "SQL", "Machine Learning", "Git"]
students = [450, 320, 540, 180]

plt.bar(courses, students, color=["#10B981", "#3B82F6", "#8B5CF6", "#F59E0B"])
plt.title("Student Enrollment by Course")
plt.ylabel("Number of Students")
plt.show()

Horizontal Bar Chart (barh)

Use barh when category names are long — it prevents the labels from overlapping on the x-axis.

categories = ["Software Engineer", "Data Scientist", "Product Manager", "UI/UX Designer"]
salaries   = [110, 120, 105, 90]   # in thousands

plt.barh(categories, salaries, color="purple")
plt.title("Median Salaries by Role ($k)")
plt.xlabel("Salary ($k)")
plt.show()

Numerical vs. Categorical — Box Plots

A box plot (whisker plot) compares the distribution of a numerical variable across categories. It displays five statistics: minimum, Q1, median, Q3, and maximum, and highlights outliers.

class_A = [55, 62, 70, 78, 85, 90, 92, 100]
class_B = [40, 50, 55, 60, 72, 80, 85, 90]
class_C = [65, 75, 80, 85, 90, 95, 98, 100]

plt.boxplot(
    [class_A, class_B, class_C],
    labels=["Class A", "Class B", "Class C"],
    patch_artist=True
)
plt.title("Exam Score Distribution by Class")
plt.ylabel("Scores")
plt.show()

Multi-Panel Layouts with Subplots

When you need multiple charts side by side, switch to the object-oriented interface with plt.subplots().

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))

x = np.linspace(0, 5, 50)

# Top-left — Linear
axes[0, 0].plot(x, x, color="blue")
axes[0, 0].set_title("Linear")

# Top-right — Quadratic
axes[0, 1].plot(x, x**2, color="green")
axes[0, 1].set_title("Quadratic")

# Bottom-left — Cubic
axes[1, 0].plot(x, x**3, color="red")
axes[1, 0].set_title("Cubic")

# Bottom-right — Exponential
axes[1, 1].plot(x, np.exp(x), color="purple")
axes[1, 1].set_title("Exponential")

# Prevent overlapping titles and labels
plt.tight_layout()
plt.show()

plt.tight_layout() automatically adjusts spacing between subplots so titles and axis labels don’t overlap. Always call it before plt.show() when working with subplot grids.

Chart Type Quick Reference

Data Type	Chart	Matplotlib Function
Trend over time	Line plot	plt.plot()
Distribution of numbers	Histogram	plt.hist()
Correlation between two numbers	Scatter plot	plt.scatter()
Compare categories (vertical)	Bar chart	plt.bar()
Compare categories (horizontal)	Horizontal bar	plt.barh()
Number vs. category distributions	Box plot	plt.boxplot()
Multiple charts	Subplot grid	plt.subplots()