Seaborn for Statistical Plots

Seaborn is a Python data visualisation library built on top of Matplotlib. It provides a high-level interface for drawing attractive, informative statistical graphics with less code. Seaborn integrates closely with Pandas DataFrames and includes built-in themes, colour palettes, and functions for common statistical visualisations.

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Installation and Setup

pip install seaborn

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Set the default theme
sns.set_theme(style="whitegrid")

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Seaborn vs Matplotlib

Feature	Matplotlib	Seaborn
Level of abstraction	Low-level — you control every element	High-level — sensible defaults
Code verbosity	More code for styled plots	Less code for attractive plots
Statistical features	Manual	Built-in (confidence intervals, regression lines)
DataFrame integration	Requires manual column access	Native support for column names
Customisation	Unlimited	Customise via Matplotlib underneath

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Built-in Datasets

Seaborn comes with several built-in datasets for practice:

# List available datasets
print(sns.get_dataset_names())

# Load a dataset
tips = sns.load_dataset("tips")
print(tips.head())

Dataset	Description
tips	Restaurant tipping data
flights	Monthly airline passenger numbers
iris	Classic iris flower measurements
penguins	Palmer penguins body measurements
titanic	Titanic passenger survival data
diamonds	Diamond pricing and attributes

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Distribution Plots

Histogram and KDE

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Histogram
sns.histplot(data=tips, x="total_bill", bins=20, ax=axes[0])
axes[0].set_title("Histogram")

# KDE (Kernel Density Estimate)
sns.kdeplot(data=tips, x="total_bill", fill=True, ax=axes[1])
axes[1].set_title("KDE Plot")

# Combined
sns.histplot(data=tips, x="total_bill", kde=True, ax=axes[2])
axes[2].set_title("Histogram + KDE")

plt.tight_layout()
plt.show()

Box Plot and Violin Plot

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))

sns.boxplot(data=tips, x="day", y="total_bill",
            palette="Set2", ax=ax1)
ax1.set_title("Box Plot: Total Bill by Day")

sns.violinplot(data=tips, x="day", y="total_bill",
               palette="Set2", ax=ax2)
ax2.set_title("Violin Plot: Total Bill by Day")

plt.tight_layout()
plt.show()

Strip Plot and Swarm Plot

These show individual data points — useful for small datasets.

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))

sns.stripplot(data=tips, x="day", y="total_bill",
              jitter=True, alpha=0.6, ax=ax1)
ax1.set_title("Strip Plot")

sns.swarmplot(data=tips, x="day", y="total_bill",
              palette="Set2", ax=ax2)
ax2.set_title("Swarm Plot")

plt.tight_layout()
plt.show()

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Relational Plots

Scatter Plot with Hue and Size

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

fig, ax = plt.subplots(figsize=(8, 6))
sns.scatterplot(data=tips, x="total_bill", y="tip",
                hue="time", size="size", sizes=(20, 200),
                palette="Set1", ax=ax)
ax.set_title("Tips vs Total Bill")
plt.tight_layout()
plt.show()

Line Plot with Confidence Interval

import seaborn as sns
import matplotlib.pyplot as plt