Introduction to Seaborn

What is Seaborn?

Seaborn is built on top of matplotlib but:

Easier to use
Better looking by default
Great for statistics

Install Seaborn

terminal

pip install seaborn

Import Convention

code.py

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

Line Plot

code.py

import seaborn as sns
import matplotlib.pyplot as plt

data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr'],
        'Sales': [100, 120, 115, 140]}
df = pd.DataFrame(data)

sns.lineplot(data=df, x='Month', y='Sales')
plt.show()

Bar Plot

code.py

sns.barplot(data=df, x='Month', y='Sales')
plt.show()

Scatter Plot

code.py

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

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

Histogram

code.py

sns.histplot(data=df, x='Salary')
plt.show()

Box Plot

code.py

df = pd.DataFrame({
    'Department': ['Sales', 'Sales', 'IT', 'IT', 'HR', 'HR'],
    'Salary': [50000, 55000, 70000, 75000, 45000, 48000]
})

sns.boxplot(data=df, x='Department', y='Salary')
plt.show()

Why Seaborn is Easier

Matplotlib:

code.py

fig, ax = plt.subplots()
for dept in df['Department'].unique():
    dept_data = df[df['Department'] == dept]['Salary']
    ax.boxplot(dept_data)
# ... lots more code

Seaborn:

code.py

sns.boxplot(data=df, x='Department', y='Salary')

One line!

Color by Category

code.py

df = pd.DataFrame({
    'Age': [25, 30, 35, 40, 45, 50],
    'Salary': [40000, 50000, 60000, 70000, 80000, 90000],
    'Gender': ['M', 'F', 'M', 'F', 'M', 'F']
})

sns.scatterplot(data=df, x='Age', y='Salary', hue='Gender')
plt.show()

hue automatically colors by category!

Quick Summary

Matplotlib	Seaborn
plt.plot()	sns.lineplot()
plt.bar()	sns.barplot()
plt.scatter()	sns.scatterplot()
plt.hist()	sns.histplot()
plt.boxplot()	sns.boxplot()

Complete Example

code.py

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

# Sample data
df = pd.DataFrame({
    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'Sales': [100, 120, 115, 140, 160, 180],
    'Region': ['East', 'West', 'East', 'West', 'East', 'West']
})

# Create nice chart
plt.figure(figsize=(10, 6))
sns.barplot(data=df, x='Month', y='Sales', hue='Region')
plt.title('Sales by Month and Region')
plt.show()

Key Points

import seaborn as sns is standard
Works directly with pandas DataFrames
Pass column names as strings
Use hue to color by category
Use plt.show() to display
Much less code than matplotlib!

What's Next?

Learn Seaborn's statistical plots for deeper analysis.