Python - Matplotlib

Matplotlib in Python

Matplotlib is a comprehensive data visualization library in Python. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits. It is the most widely used 2D plotting library for Python and forms the foundation of other libraries such as Seaborn, Pandas visualization, and many more.

In this guide, we will explore the fundamentals, techniques, and advanced features of Matplotlib through various examples and use cases.

Installing Matplotlib

Using pip

pip install matplotlib

Using conda

conda install matplotlib

Getting Started

Importing Matplotlib

import matplotlib.pyplot as plt

Creating a Basic Plot

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

plt.plot(x, y)
plt.title('Simple Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Plot Types in Matplotlib

Line Plot

plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.title('Line Plot')
plt.show()

Bar Plot

labels = ['A', 'B', 'C', 'D']
values = [10, 15, 7, 12]

plt.bar(labels, values)
plt.title('Bar Plot')
plt.show()

Horizontal Bar Plot

plt.barh(labels, values)
plt.title('Horizontal Bar Plot')
plt.show()

Scatter Plot

x = [5, 7, 8, 7, 2, 17, 2, 9]
y = [99, 86, 87, 88, 100, 86, 103, 87]

plt.scatter(x, y)
plt.title('Scatter Plot')
plt.xlabel('X Values')
plt.ylabel('Y Values')
plt.show()

Histogram

import numpy as np

data = np.random.randn(1000)
plt.hist(data, bins=30)
plt.title('Histogram')
plt.show()

Pie Chart

sizes = [20, 30, 25, 25]
labels = ['Python', 'C++', 'Ruby', 'Java']

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
plt.title('Programming Language Usage')
plt.axis('equal')
plt.show()

Customizing Plots

Adding Titles, Labels, and Legends

plt.plot([1, 2, 3], [4, 5, 6], label='Line 1')
plt.plot([1, 2, 3], [6, 5, 4], label='Line 2')
plt.title('Two Lines')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

Line Style, Color, and Marker

plt.plot(x, y, color='red', linestyle='--', marker='o')
plt.title('Styled Line Plot')
plt.show()

Grid Lines

plt.plot(x, y)
plt.grid(True)
plt.title('Grid Enabled')
plt.show()

Figure Size and DPI

plt.figure(figsize=(10, 6), dpi=100)
plt.plot(x, y)
plt.title('Custom Figure Size')
plt.show()

Subplots

Using subplot()

plt.subplot(2, 1, 1)
plt.plot([1, 2, 3], [1, 2, 3])
plt.title('Top Subplot')

plt.subplot(2, 1, 2)
plt.plot([1, 2, 3], [3, 2, 1])
plt.title('Bottom Subplot')

plt.tight_layout()
plt.show()

Using subplots() Function

fig, axes = plt.subplots(2, 2)

axes[0, 0].plot([1, 2], [1, 4])
axes[0, 1].bar(['A', 'B'], [5, 7])
axes[1, 0].scatter([1, 2], [5, 6])
axes[1, 1].hist(np.random.randn(100))

plt.tight_layout()
plt.show()

Advanced Plotting

Plotting with Pandas

import pandas as pd

data = {
    'Month': ['Jan', 'Feb', 'Mar', 'Apr'],
    'Sales': [250, 300, 400, 350]
}
df = pd.DataFrame(data)

df.plot(x='Month', y='Sales', kind='bar')
plt.title('Monthly Sales')
plt.show()

Box Plot

data = np.random.normal(size=100)
plt.boxplot(data)
plt.title('Box Plot')
plt.show()

Area Plot

data = {
    'A': [3, 4, 5],
    'B': [1, 6, 4]
}
df = pd.DataFrame(data)
df.plot(kind='area', alpha=0.5)
plt.title('Area Plot')
plt.show()

Saving Figures

Save to PNG

plt.plot(x, y)
plt.savefig('plot.png')

Save to PDF

plt.plot(x, y)
plt.savefig('plot.pdf')

Annotations and Text

Adding Text

plt.plot(x, y)
plt.text(2, 5, 'Important Point')
plt.show()

Using annotate()

plt.plot(x, y)
plt.annotate('Peak', xy=(4, 11), xytext=(3, 12),
             arrowprops=dict(facecolor='black', shrink=0.05))
plt.show()

Log Scale and Axes Control

Logarithmic Scale

x = np.linspace(0.1, 100, 100)
y = np.log(x)

plt.plot(x, y)
plt.xscale('log')
plt.yscale('log')
plt.title('Log Scale')
plt.show()

Setting Axis Limits

plt.plot(x, y)
plt.xlim(0, 10)
plt.ylim(0, 3)
plt.title('Axis Limit Control')
plt.show()

Working with Styles

List Available Styles

print(plt.style.available)

Apply Style

plt.style.use('ggplot')
plt.plot(x, y)
plt.title('ggplot Style')
plt.show()

Interactive Plots with mplcursors

import mplcursors

plt.plot([10, 20, 30], [1, 4, 9])
mplcursors.cursor()
plt.show()

3D Plotting with mpl_toolkits

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

x = [1, 2, 3, 4, 5]
y = [10, 20, 25, 30, 35]
z = [5, 6, 7, 8, 9]

ax.plot(x, y, z)
plt.title('3D Plot')
plt.show()

Embedding Matplotlib in GUIs

You can embed matplotlib plots in Tkinter, PyQt, Kivy, and other GUI applications. This is useful for creating interactive dashboards and scientific applications.

Exporting for Web or Reports

Exporting for LaTeX

plt.rcParams.update({'text.usetex': True})
plt.plot(x, y)
plt.title('LaTeX Title')
plt.savefig('latex_plot.pdf')

Exporting High-Resolution Image

plt.plot(x, y)
plt.savefig('high_res.png', dpi=300)

Matplotlib is a fundamental tool in the data scientist’s toolbox. From basic line graphs to 3D charts and animations, Matplotlib allows users to create publication-quality visualizations. Mastering its object-oriented interface and built-in styles helps produce professional and aesthetically pleasing plots. When used in combination with pandas, NumPy, or Jupyter notebooks, Matplotlib is indispensable for exploratory data analysis, presentation, and reporting.

Understanding how to customize axes, labels, legends, styles, and export formats makes Matplotlib a highly versatile and professional plotting library. Whether you are a beginner or a professional data scientist, proficiency in Matplotlib is essential for communicating insights effectively through visuals.