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Create bar plots, box plots, violin plots, and histograms with built-in statistical aggregation.
Use `relplot`, `scatterplot`, and `lineplot` to explore relationships between variables.
Visualize linear and logistic regression fits with confidence intervals using `regplot` and `lmplot`.
Apply built-in themes, color palettes, and context settings for publication-ready visuals.
Use `pip install seaborn` and `import seaborn as sns` to get started.
Use Pandas or built-in Seaborn datasets like `tips`, `iris`, or `penguins`.
Use functions like `sns.barplot()`, `sns.heatmap()`, or `sns.scatterplot()`.
Set themes with `sns.set_theme()` and adjust axes, legends, and colors.
Use `plt.show()` or `plt.savefig()` to display or export your chart.
# Seaborn Bar Plot Example
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("sales.csv")
sns.barplot(x="Region", y="Revenue", data=df, palette="viridis")
plt.title("Revenue by Region")
plt.show()Quickly visualize distributions, relationships, and outliers in datasets.
Plot residuals, regression fits, and feature relationships for ML models.
Generate publication-quality charts for research and presentations.
Render charts in notebooks or web apps for interactive analysis.
Explore Seaborn’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Seaborn’s capabilities, usage, and ecosystem.