Seaborn Statistical Plots

What Makes Seaborn Special?

Seaborn has plots designed for statistics:

Show distributions
Show relationships
Show comparisons

Distribution Plots

Histogram with Density

code.py

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

data = np.random.normal(50, 10, 500)

sns.histplot(data, kde=True)  # kde = smooth line
plt.show()

KDE Plot (Smooth Distribution)

code.py

sns.kdeplot(data)
plt.show()

Compare Distributions

code.py

import pandas as pd

df = pd.DataFrame({
    'Score': [60, 65, 70, 75, 80, 50, 55, 60, 65, 70],
    'Group': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B']
})

sns.kdeplot(data=df, x='Score', hue='Group')
plt.show()

Violin Plot

Like box plot, but shows distribution shape:

code.py

sns.violinplot(data=df, x='Group', y='Score')
plt.show()

Relationship Plots

Regression Plot

Shows scatter with trend line:

code.py

df = pd.DataFrame({
    'Hours': [1, 2, 3, 4, 5, 6, 7],
    'Score': [50, 55, 60, 65, 75, 80, 85]
})

sns.regplot(data=df, x='Hours', y='Score')
plt.show()

Automatically adds best-fit line!

Pair Plot

See all relationships at once:

code.py

df = pd.DataFrame({
    'Age': [25, 30, 35, 40, 45],
    'Salary': [40000, 50000, 60000, 70000, 80000],
    'Experience': [2, 5, 8, 12, 15]
})

sns.pairplot(df)
plt.show()

Creates scatter plots for every pair of columns!

Count Plot

Count categories:

code.py

df = pd.DataFrame({
    'City': ['NYC', 'LA', 'NYC', 'Chicago', 'NYC', 'LA']
})

sns.countplot(data=df, x='City')
plt.show()

Heatmap

Show correlation matrix:

code.py

df = pd.DataFrame({
    'Age': [25, 30, 35, 40, 45],
    'Salary': [40000, 50000, 60000, 70000, 80000],
    'Experience': [2, 5, 8, 12, 15]
})

corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.show()

annot=True shows numbers in cells.

Strip Plot and Swarm Plot

Show individual points:

code.py

# Strip plot (points may overlap)
sns.stripplot(data=df, x='Group', y='Score')

# Swarm plot (points don't overlap)
sns.swarmplot(data=df, x='Group', y='Score')

Joint Plot

Scatter + histograms together:

code.py

sns.jointplot(data=df, x='Age', y='Salary')
plt.show()

Complete Example

code.py

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

# Sample data
df = pd.DataFrame({
    'Department': ['Sales']*30 + ['IT']*30 + ['HR']*30,
    'Salary': [50000, 52000, 55000, 48000, 53000]*6 +
              [70000, 72000, 75000, 68000, 73000]*6 +
              [45000, 47000, 48000, 44000, 46000]*6
})

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

# Box plot
sns.boxplot(data=df, x='Department', y='Salary', ax=axes[0])
axes[0].set_title('Box Plot')

# Violin plot
sns.violinplot(data=df, x='Department', y='Salary', ax=axes[1])
axes[1].set_title('Violin Plot')

# Strip plot
sns.stripplot(data=df, x='Department', y='Salary', ax=axes[2])
axes[2].set_title('Strip Plot')

plt.tight_layout()
plt.show()

Key Points

kde=True adds smooth distribution line
regplot shows trend line automatically
pairplot shows all variable relationships
heatmap visualizes correlations
violinplot shows distribution shape
All work directly with DataFrames

What's Next?

Learn to customize Seaborn with themes and styles.