Creating Attractive Charts with Python Seaborn: Step-by-Step Examples

Seaborn is a powerful Python library for data visualization built on top of Matplotlib, providing simple functions to produce attractive statistical graphics.

1. Import Seaborn and Matplotlib import seaborn as sns import matplotlib.pyplot as plt

2. Set Seaborn style sns.set_style("whitegrid")

3. Bar plot # Sample data x = ["A", "B", "C", "D"] y = [10, 8, 6, 4] sns.barplot(x=x, y=y) plt.show()

4. Scatter plot # Sample data x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] sns.scatterplot(x=x, y=y) plt.show()

5. Line plot # Sample data x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] sns.lineplot(x=x, y=y) plt.show()

6. Box plot # Sample data data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] sns.boxplot(data=data) plt.show()

7. Histogram # Sample data data = [1, 2, 2, 3, 3, 3, 4, 4, 5] sns.histplot(data=data) plt.show()

8. Heatmap # Sample data data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] sns.heatmap(data) plt.show()

9. Violin plot # Sample data x = ["A", "A", "B", "B", "C", "C"] y = [1, 2, 3, 4, 5, 6] sns.violinplot(x=x, y=y) plt.show()

10. KDE plot # Sample data data = [1, 2, 2, 3, 3, 3, 4, 4, 5] sns.kdeplot(data=data) plt.show()

These examples showcase common Seaborn chart types; you can choose the appropriate plot based on your data and customize appearance using many available parameters. For more details and advanced examples, refer to the official Seaborn documentation at https://seaborn.pydata.org/.