pyguides

Matplotlib Basics: Plots and Charts

· 3 min read ·Updated May 20, 2026 ·beginner
matplotlibvisualizationdataplottingbeginner

Matplotlib is the most widely used visualization library in Python. It pairs naturally with NumPy arrays and pandas DataFrames, making it the standard tool for exploratory data analysis. This tutorial covers the essential plot types you will use every day.

Installing and Importing

pip install matplotlib
import matplotlib.pyplot as plt
import numpy as np

The pyplot module provides a MATLAB-style interface. Most examples use plt.subplots() which returns a figure and axis object — the modern, more flexible approach.

Line Plots

The most common plot type:

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

fig, ax = plt.subplots()
ax.plot(x, y)
plt.show()

Customize the line with color, width, and style:

ax.plot(x, y, color='navy', linewidth=2, linestyle='--', label='sin(x)')
ax.set_title('Sine Wave')
ax.set_xlabel('x')
ax.set_ylabel('sin(x)')
ax.legend()
ax.grid(True, alpha=0.3)

Key parameters: color, linewidth, linestyle (solid, dashed, dotted), marker (o, s, ^ for points on the line).

Scatter Plots

Scatter plots show relationships between two variables:

np.random.seed(42)
x = np.random.randn(100)
y = 2 * x + np.random.randn(100) * 0.5

fig, ax = plt.subplots()
ax.scatter(x, y, alpha=0.6, edgecolors='white', linewidth=0.5, c='steelblue')
ax.set_title('Linear Relationship with Noise')
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()

alpha controls transparency — essential when plotting many overlapping points. c sets color.

Bar Charts

Compare categorical data:

languages = ['Python', 'Rust', 'Go', 'TypeScript', 'Java']
scores = [92, 78, 85, 88, 74]

fig, ax = plt.subplots()
bars = ax.bar(languages, scores, color=['#3572A5', '#dea584', '#00add8', '#3178c6', '#b07219'])
ax.set_ylabel('Popularity Score')
ax.set_title('Language Popularity')

# Add value labels on bars
for bar, score in zip(bars, scores):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
            str(score), ha='center', fontsize=10)

plt.tight_layout()
plt.show()

Horizontal bars use barh. Grouped bars require multiple bar calls with offset.

Histograms

Show the distribution of numerical data:

data = np.random.randn(1000)

fig, ax = plt.subplots()
ax.hist(data, bins=40, edgecolor='black', alpha=0.75, color='slategray')
ax.set_xlabel('Value')
ax.set_ylabel('Frequency')
ax.set_title('Normal Distribution Sample')
plt.show()

bins controls the number of bars. Too few hides structure; too many creates noise.

Saving Figures

Save to file before plt.show():

fig, ax = plt.subplots()
ax.plot(x, y)
fig.savefig('plot.png', dpi=300, bbox_inches='tight')
fig.savefig('plot.pdf', bbox_inches='tight')

Use PNG for raster images, PDF or SVG for vector graphics that scale without losing quality. dpi=300 is standard for print. bbox_inches='tight' trims excess whitespace.

Multiple Subplots

Show several plots in one figure:

fig, axes = plt.subplots(2, 2, figsize=(12, 8))

# Flatten the 2D array for easy indexing
axes = axes.flatten()

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

axes[0].plot(x, np.sin(x), color='blue')
axes[0].set_title('Sine')

axes[1].plot(x, np.cos(x), color='red')
axes[1].set_title('Cosine')

axes[2].plot(x, x**2, color='green')
axes[2].set_title('Quadratic')

np.random.seed(0)
axes[3].scatter(np.random.randn(50), np.random.randn(50), alpha=0.6)
axes[3].set_title('Scatter')

plt.tight_layout()
plt.show()

figsize sets width and height in inches. subplots(2, 2) creates a 2x2 grid.

Common Customizations

Every plot benefits from labels and a title:

ax.set_xlabel('Time (seconds)')
ax.set_ylabel('Amplitude')
ax.set_title('Signal Over Time')

Control axis limits:

ax.set_xlim(0, 100)
ax.set_ylim(-1.5, 1.5)

Log scale axes:

ax.set_xscale('log')
ax.set_yscale('log')

Add a horizontal or vertical reference line:

ax.axhline(y=0, color='black', linewidth=0.8, linestyle='--')
ax.axvline(x=50, color='gray', linewidth=0.8)

matplotlib vs Seaborn

Matplotlib gives you full control. Seaborn builds on it with prettier defaults:

import seaborn as sns
sns.set_style("whitegrid")

fig, ax = plt.subplots()
ax.plot(x, y)
plt.show()

Seaborn automatically improves colors, fonts, and spacing. Use matplotlib for custom work, seaborn for quick exploratory plots.

See Also

Saving Figures

Save plots as image files for reports and presentations:

fig, ax = plt.subplots()
ax.plot(x, y)
fig.savefig("plot.png", dpi=300, bbox_inches='tight')
fig.savefig("plot.pdf")  # vector format for publications

The dpi argument controls resolution — 300 for print, 150 for web. Use bbox_inches='tight' to trim excess whitespace. PNG for raster output, PDF for vector graphics that scale without quality loss.