Comprehensive Guide to Data Visualization with Matplotlib in Python

Matplotlib is a powerful Python library for creating 2D and 3D visualizations, essential for data analysis and presentation. This guide walks through its installation, core architecture, and a variety of plotting techniques.

Installation

Install via conda or pip:

conda install matplotlib

pip install matplotlib

Library Architecture

Matplotlib consists of three layers: the Scripting layer (high‑level API), the Artist layer (objects representing visual elements), and the Backend layer (rendering engines). This separation enables flexible figure creation and output formats.

Basic Usage

Import the required modules and generate sample data:

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-np.pi, np.pi, 30)
y = np.sin(x)
print('x =', x)
print('y =', y)

Plot a simple curve:

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

Multiple Curves, Axes Labels, and Customization

Plot a sine curve together with a linear function, set figure size, titles, axis labels, limits, and custom tick labels:

plt.figure(figsize=(8, 6))
plt.plot(x, y)
plt.plot(x, 0.2*x + 0.1, color='red', linestyle='--')
plt.title('y = sin(x) and y = 0.2x + 0.1')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(-np.pi, np.pi)
plt.ylim(-1, 1)
xticks = np.linspace(-np.pi, np.pi, 5)
xtick_labels = [r'$\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$']
plt.xticks(xticks, xtick_labels)
plt.show()

Centered Axes

Move the x‑ and y‑axes to the origin:

ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_position(('data', 0))
ax.spines['left'].set_position(('data', 0))
plt.show()

Legends

Add labels to curves and display a legend with a chosen location:

plt.plot(x, y, label='y = sin(x)')
plt.plot(x, 0.2*x + 0.1, color='red', linestyle='--', label='y = 0.2x + 0.1')
plt.legend(loc='lower right', fontsize=12)
plt.show()

The loc parameter accepts strings such as 'best', 'upper right', 'lower left', etc., as shown in the accompanying table.

Bar Charts

Create simple and side‑by‑side bar charts:

plt.figure(figsize=(16, 12))
x = np.arange(1, 9)
y = np.array([3, 5, 7, 6, 2, 6, 10, 15])
plt.plot(x, y, 'r', lw=5)
plt.bar(x, np.array([13, 25, 17, 36, 21, 16, 10, 15]), 0.2, color='b')
plt.show()

For comparative bars above and below the x‑axis:

plt.bar(x, +y1, facecolor='#9999ff', edgecolor='white')
plt.bar(x, -y2, facecolor='#ff9999', edgecolor='white')
plt.xlim(-0.5, n)
plt.ylim(-1.25, 1.25)
plt.show()

Scatter Plot

N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2
plt.scatter(x, y, s=area, c=colors, alpha=0.8)
plt.show()

Contour Plot

Generate contour lines and filled contours for a 2‑D function:

def f(x, y):
    return (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)

n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
X, Y = np.meshgrid(x, y)
C = plt.contour(X, Y, f(X, Y), 10, colors='black', linewidths=0.5)
plt.clabel(C, inline=True, fontsize=12)
plt.contourf(X, Y, f(X, Y), 10, alpha=0.75, cmap=plt.cm.hot)
plt.show()

Image Processing

Load and display an image with a colormap:

import matplotlib.image as mpimg
import matplotlib.cm as cm
img = mpimg.imread('image/fuli.jpg')
plt.imshow(img, cmap='hot')
plt.colorbar()
plt.show()

Inspect the NumPy array shape and values to understand pixel data.

Matrix Image

size = 8
a = np.linspace(0, 1, size**2).reshape(size, size)
plt.figure(figsize=(16, 12))
plt.imshow(a)
plt.show()

3D Plot

Create a 3‑D surface plot using Axes3D :

from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111, projection='3d')
x = np.arange(-4, 4, 0.25)
y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(x, y)
Z = np.sqrt(X**2 + Y**2)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))
plt.show()

By adapting these examples to real project data, developers can produce clear, informative, and visually appealing charts and figures.