Python matplotlib Module: Detailed Overview and Examples
matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is highly customizable and can be used to generate plots, histograms, power spectra, bar charts, error charts, scatter plots, and more.
Importing matplotlib
To use matplotlib, you typically import the pyplot submodule:
Basic Plotting
Line Plot
Example
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
# Plot
plt.plot(x, y)
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Line Plot')
plt.show()
Scatter Plot
Example
import matplotlib.pyplot as plt
# Data
x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11]
y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78]
# Plot
plt.scatter(x, y)
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Scatter Plot')
plt.show()
Bar Chart
Example
import matplotlib.pyplot as plt
# Data
x = ['A', 'B', 'C', 'D']
y = [3, 12, 5, 18]
# Plot
plt.bar(x, y)
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Chart')
plt.show()
Histogram
Example
import matplotlib.pyplot as plt
# Data
data = [1.5, 2.5, 2.1, 3.5, 3.7, 2.8, 3.2, 4.1, 3.9, 3.7]
# Plot
plt.hist(data, bins=5)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()
Pie Chart
Example
import matplotlib.pyplot as plt
# Data
labels = 'A', 'B', 'C', 'D'
sizes = [15, 30, 45, 10]
# Plot
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title('Pie Chart')
plt.show()
Customizing Plots
Adding Legends
Example
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4]
y1 = [10, 20, 25, 30]
y2 = [15, 25, 20, 35]
# Plot
plt.plot(x, y1, label='Series 1')
plt.plot(x, y2, label='Series 2')
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Line Plot with Legend')
plt.legend()
plt.show()
Changing Line Styles and Colors
Example
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
# Plot
plt.plot(x, y, linestyle='--', color='r', marker='o')
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Custom Line Plot')
plt.show()
Adding Grid
Example
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
# Plot
plt.plot(x, y)
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Plot with Grid')
plt.grid(True)
plt.show()
Subplots
Example
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4]
y1 = [10, 20, 25, 30]
y2 = [15, 25, 20, 35]
# Plot
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(x, y1)
ax1.set_title('Subplot 1')
ax2.plot(x, y2)
ax2.set_title('Subplot 2')
plt.show()
Advanced Plotting
3D Plotting
Example
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Data
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
z = [5, 15, 20, 10]
# Plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(x, y, z)
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_zlabel('Z-axis')
ax.set_title('3D Line Plot')
plt.show()
Contour Plots
Example
import matplotlib.pyplot as plt
import numpy as np
# Data
x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))
# Plot
plt.contour(X, Y, Z)
plt.title('Contour Plot')
plt.show()
Saving Plots
Save Plot to File
Example
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
# Plot
plt.plot(x, y)
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Plot to Save')
# Save
plt.savefig('plot.png')
plt.show()
Practical Examples
Example 1: Visualizing a Sine Wave
import matplotlib.pyplot as plt
import numpy as np
# Data
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
# Plot
plt.plot(x, y)
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Sine Wave')
plt.show()
Example 2: Comparing Multiple Series
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4]
y1 = [10, 20, 25, 30]
y2 = [15, 25, 20, 35]
# Plot
plt.plot(x, y1, label='Series 1')
plt.plot(x, y2, label='Series 2')
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Comparison of Two Series')
plt.legend()
plt.show()
Example 3: Creating a Heatmap
import matplotlib.pyplot as plt
import numpy as np
# Data
data = np.random.rand(10, 10)
# Plot
plt.imshow(data, cmap='hot', interpolation='nearest')
plt.title('Heatmap')
plt.colorbar()
plt.show()
Conclusion
The matplotlib module is a versatile and powerful library for creating a wide range of visualizations in Python. From simple line plots to complex 3D plots, matplotlib provides the tools needed to create informative and visually appealing graphics. By leveraging the customization options and advanced plotting capabilities, you can tailor your visualizations to effectively communicate your data and insights.