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Python - Matplotlib Pyplot Part 5: Advanced Plots (Pie Charts, Subplots, and 3D Plots)

Advanced plotting capabilities include creating pie charts, subplots, and 3D visualizations.

Examples and Explanation

Pie Chart

labels = ['A', 'B', 'C']

sizes = [50, 30, 20]

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)

plt.title("Pie Chart")

plt.show()

Explanation: The pie() function creates a circular chart representing proportions.

Subplots

plt.subplot(1, 2, 1)

plt.plot([1, 2, 3], [4, 5, 6])

plt.title("Line Plot")

plt.subplot(1, 2, 2)

plt.bar(['A', 'B', 'C'], [3, 7, 5])

plt.title("Bar Chart")

plt.show()

Explanation: Use subplot() to display multiple plots in one figure.

3D Plot

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()

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

x = [1, 2, 3]

y = [4, 5, 6]

z = [7, 8, 9]

ax.plot(x, y, z)

plt.title("3D Plot")

plt.show()

Explanation: 3D plots add depth to data visualization, useful in scientific research.

Conclusion

Matplotlib Pyplot provides a versatile and comprehensive set of tools for creating visualizations. By mastering these five parts, you can create compelling visual representations of your data, catering to a variety of needs, from exploratory data analysis to presentation-ready plots.