Explore Seaborn through a single hands-on Jupyter Notebook that surveys the library’s major plotting families. Using the Iris data set and generated matrix data, you will compare Seaborn with Matplotlib, apply built-in styling, and create relational, categorical, distribution, regression, and matrix visualizations.
This is a broad API tour rather than a slow, multi-lab sequence. The notebook shows how high-level Seaborn functions quickly map data columns to visual properties such as position, color, and marker style, while also explaining where figure-level and axes-level interfaces fit alongside Matplotlib.
What You Will Learn
- Apply Seaborn themes, contexts, styles, and palettes to Matplotlib plots
- Load the Iris data set and map categories with
hueandstyle - Create relational views with
relplot,scatterplot, andlineplot - Compare categorical distributions with strip, swarm, box, violin, boxen, point, bar, and count plots
- Examine univariate and bivariate distributions with KDE, joint, and pair plots
- Add fitted relationships with
regplotandlmplot - Visualize matrices with heatmaps and clustered heatmaps
- Distinguish figure-level convenience functions from axes-level functions that integrate closely with Matplotlib
Who This Course Is For
This course is for Python users who want a compact survey of Seaborn’s plotting vocabulary for exploratory data analysis. It is best for learners comfortable reading notebook code and basic tabular data. Because many APIs are presented in one notebook, it works better as guided exploration than as a first introduction to Python or statistics.
Prerequisites: Basic Python, Jupyter Notebook, and familiarity with variables, function calls, and table-like data are recommended. Some exposure to Matplotlib is helpful but not required.
Learning environment: The course provides an Ubuntu 22.04 Jupyter environment with a prepared notebook. You run and modify cells that install/import the libraries, load data, and render plots inline.
Frequently Asked Questions
Is this a full course with multiple labs?
No. It is one beginner lab built around a substantial notebook. The notebook surveys many Seaborn chart families, but it does not provide separate challenges or a final project.
Which data will I use?
Most examples use Seaborn’s Iris data set to compare flower measurements and species. The heatmap examples also use a NumPy-generated matrix.
Does the course teach statistical theory?
No. It demonstrates visual tools such as distributions, regression fits, and clustered heatmaps, but it does not teach the statistical or clustering theory needed for deeper interpretation.
Are all examples based on the newest Seaborn API?
Not entirely. The notebook includes the older distplot API alongside kdeplot, jointplot, and pairplot. Treat it as a conceptual survey and consult current Seaborn documentation when adapting that example to new code.
