Seaborn Tutorial – Statistical Data Visualisation in Python

Setting Up Seaborn

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

# 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())

Distribution Plots

Visualise the distribution of a single variable.

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.

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()

Heatmaps – Correlation Matrices

Visualise correlations between all numerical variables at once.

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()

Seaborn: Statistical Plots in One Line

Seaborn is built on matplotlib but aimed at statistical visualization. It works directly with pandas DataFrames and produces attractive, informative charts with far less code — matplotlib would need many lines for the same result.

import seaborn as sns
# pass the DataFrame + column names — seaborn handles the rest
sns.scatterplot(data=df, x="height", y="weight", hue="sex")
sns.boxplot(data=df, x="dept", y="salary")     # distribution per group
sns.heatmap(df.corr(), annot=True)             # correlation matrix

Why it's productive: seaborn understands "tidy" DataFrames — you pass column names and it maps data to aesthetics (position, color via hue, size) automatically, including legends and axis labels. The hue parameter alone — splitting any plot by a category with color — would be tedious in raw matplotlib. Remember: seaborn returns matplotlib axes, so you can still fine-tune with plt/ax calls afterward and save with plt.savefig(). Use seaborn for exploration and statistical insight; drop to matplotlib for pixel-level control.

Interview Questions on Seaborn

• What is Seaborn and how does it relate to Matplotlib?
• When would you choose Seaborn over plain Matplotlib?
• What is a distribution plot and what does it show?
• What does a correlation heatmap visualize?
• What is the hue parameter used for?
• What is the difference between a boxplot and a violin plot?
• How does Seaborn integrate with pandas DataFrames?

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