Seaborn
Table of Contents
Description
Seaborn is a powerful Python data visualization library built on top of Matplotlib. It simplifies the process of creating statistically rich visualizations and comes with built-in themes and color palettes to make plots more attractive. Seaborn is especially great for:
Statistical plots (like boxplots, violin plots)
Distribution plots (histograms, KDE)
Correlation heatmaps
Advanced categorical visualizations
Prerequisites
- Understanding of basic Python and Pandas
- Familiarity with Matplotlib
- Basic knowledge of statistics (mean, median, correlation)
Examples
Here's a simple example of a data science task using Python:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
# Sample data
data = sns.load_dataset("tips") # Built-in Seaborn dataset
# Box Plot - useful for understanding distribution
sns.boxplot(x="day", y="total_bill", data=data)
plt.title("Boxplot of Total Bill by Day")
plt.show()
# Correlation Heatmap - visualize feature relationships
corr = data.corr(numeric_only=True) # Get numeric correlation
sns.heatmap(corr, annot=True, cmap="coolwarm")
plt.title("Correlation Heatmap")
plt.show()
# Styling & Distribution Plot
sns.set_style("whitegrid") # Change plot theme
sns.histplot(data['total_bill'], kde=True, color="skyblue")
plt.title("Histogram with KDE of Total Bill")
plt.show()
Real-World Applications
Healthcare
Compare distributions of lab results across age groups
Correlation between symptoms and diagnosis
Finance
Analyzing transaction patterns
Detecting outliers in expenditures
Marketing
Understanding purchase amounts by weekday
Visualizing engagement score correlations
Where topic Is Applied
Healthcare
- Patient vitals distribution
- Diagnostic factor correlation via heatmaps
E-commerce
- Product price vs sales heatmap
- Distribution of customer purchase frequency
Manufacturing
- Quality control via defect distribution
- Correlation between temperature, pressure, and output
Resources
Data Science topic PDF
Harvard Data Science Course
Free online course from Harvard covering data science foundations
Interview Questions
➤ Seaborn is used for creating informative and attractive statistical graphics in Python.
➤ Seaborn provides high-level functions for complex plots, better default aesthetics, and built-in themes. It works on top of Matplotlib.
➤ Use sns.heatmap(data, annot=True) where data is a correlation matrix or 2D numeric data.
➤ It changes the visual style/theme of the plots (e.g., whitegrid, dark, ticks).
➤ Yes. For example, boxplots for numerical by category, and bar plots for categorical summaries.