15 Matplotlib Charting Techniques for Data Visualization

Master 15 essential Matplotlib charting techniques for effective data visualization, from simple line plots to advanced 3D surface plots, with detailed code examples.

Matplotlib is a very popular Python plotting library that helps us create a wide variety of charts, from simple line plots to complex 3D graphics.

This article will introduce 15 common Matplotlib charting techniques in detail, helping you better understand and use this powerful tool.

1. Line Plot

A line plot is one of the most basic chart types used to display the trend of data over time or with respect to a variable.

import matplotlib.pyplot as plt
import numpy as np

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

# Plot the line plot
plt.plot(x, y, label='sin(x)')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Line Plot Example')
plt.legend()
plt.show()

Code explanation:
`np.linspace(0, 10, 100)`: Generates 100 equally spaced points between 0 and 10.
`np.sin(x)`: Calculates the sine of these points.
`plt.plot(x, y, label=’sin(x)’)`: Plots the line chart and adds a label.
`plt.xlabel(‘X axis’)` and `plt.ylabel(‘Y axis’)`: Sets the labels for the X and Y axes.
`plt.title(‘Line Plot Example’)`: Sets the title of the chart.
`plt.legend()`: Displays the legend.
`plt.show()`: Displays the chart.