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📊 Data Science

Seaborn Tutorial – Statistical Data Visualisation in Python

Seaborn is a statistical data visualisation library built on top of Matplotlib. It produces beautiful charts with minimal code, integrates seamlessly with Pandas DataFrames, and provides specialised statistical plots — distribution plots, categorical plots, regression plots, and heatmaps — that Matplotlib lacks.

⏱️ 22 min read🎯 Advanced📅 Updated 2026

Setting Up Seaborn

Install with pip and set a theme. Seaborn's defaults are already more beautiful than Matplotlib's.

Python
# pip install seaborn
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="whitegrid", palette="husl")

# Seaborn ships with example datasets
tips = sns.load_dataset("tips")
print(tips.head())
▶ Output
total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3

Distribution Plots

Visualise the distribution of a single variable.

Python
tips = sns.load_dataset("tips")

# Histogram + KDE
sns.histplot(data=tips, x="total_bill", hue="sex",
             kde=True, bins=20)
plt.title("Bill Distribution by Gender")
plt.show()

# KDE only
sns.kdeplot(data=tips, x="tip", fill=True)
plt.show()

Categorical Plots – Boxplot and Violin

Compare distributions across categories.

Python
tips = sns.load_dataset("tips")

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Boxplot
sns.boxplot(data=tips, x="day", y="total_bill",
            hue="sex", ax=axes[0])
axes[0].set_title("Bill by Day and Gender")

# Violin plot (shows full distribution)
sns.violinplot(data=tips, x="day", y="tip", ax=axes[1])
axes[1].set_title("Tip Distribution by Day")

plt.tight_layout()
plt.show()
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Heatmaps – Correlation Matrices

Visualise correlations between all numerical variables at once.

Python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

# Correlation matrix
corr = tips.select_dtypes("number").corr()

sns.heatmap(corr, annot=True, fmt=".2f",
            cmap="coolwarm", vmin=-1, vmax=1,
            square=True, linewidths=0.5)
plt.title("Correlation Heatmap")
plt.show()
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