Seaborn Assignments — Simplify. Style. Storytell.

Seaborn is a powerful Python visualization library built on top of Matplotlib. It is designed for statistical data visualization, providing beautiful defaults and simplified APIs to create complex plots with minimal code.

These assignments will help you master Seaborn step by step, from the very basics to advanced customization, multi-plot grids, and publishing-ready figures. Each set is divided into 20 Basic, 20 Intermediate, and 10 Advanced questions, ensuring a gradual and structured learning path.

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Why Practice with These Assignments?

  • Quickly move from raw data to polished visualizations.
  • Learn Seaborn’s high-level plotting functions for distributions, categorical data, regression, and relationships.
  • Practice themes, palettes, and styling for professional reports.
  • Build interactive and multi-panel dashboards with Seaborn’s grid system.
  • Combine Seaborn with Matplotlib customization for maximum flexibility.
  • Apply visualization skills directly to Data Science, Machine Learning, and Analytics projects.

How It Works

  • Attempt assignments in order: Basic → Intermediate → Advanced.
  • Use Jupyter Notebook, PyCharm, or VS Code for coding and visualization.
  • Compare Seaborn figure-level vs axes-level APIs (catplot vs boxplot).
  • Save outputs in multiple formats (PNG, PDF, SVG) for practice.
  • Document key learnings: themes, palettes, annotations, and grid customizations.

What You’ll Achieve

  • Create publication-ready Seaborn visualizations.
  • Understand the statistical underpinnings of Seaborn plots.
  • Build interactive, multi-faceted dashboards for data exploration.
  • Customize Seaborn with Matplotlib-level control.
  • Apply Seaborn to real-world datasets like tips, flights, penguins, and iris.
  • Gain strong visualization skills to support analytics, ML, and reporting workflows.

Browse the Assignments

  • Assignment 1 — Seaborn Basics
    Introduction to Seaborn datasets, distribution plots, scatterplots, boxplots, violin plots, pairplots, jointplots, and heatmaps. Covers figure-level vs axes-level plots.
  • Assignment 2 — Styling & Themes
    Master Seaborn’s styles, themes, contexts, and palettes. Learn to apply built-in themes, customize rcParams, and build brand/publishing-ready visualization kits.
  • Assignment 3 — Distribution Plots
    Work with histograms, KDEs, ECDFs, rug plots, and 2D density plots. Explore bandwidth tuning, probability scaling, mixture modeling, and dashboards for distribution analysis.
  • Assignment 4 — Categorical Plots
    Visualize categorical data with bar, box, violin, strip, swarm, and boxen plots. Learn advanced techniques: grouping, splitting, ordering, combining multiple plot types, and dashboards.
  • Assignment 5 — Relational Plots
    Explore scatter and line plots with hue, size, and style encodings. Create faceted relational plots, trend visualizations, multi-dimensional encodings, and time-series dashboards.
  • Assignment 6 — Statistical Plots
    Dive into regression and model-based plots. Practice with linear, polynomial, robust, and logistic regression fits, residual diagnostics, heteroscedasticity checks, and publishing-quality regression dashboards.
  • Assignment 7 — Multi-Plot Grids & Faceting
    Use FacetGrid, PairGrid, catplot, and relplot to create dashboards and multi-panel reports. Learn faceting, shared legends, annotations, correlation grids, and high-dimensional faceting.
  • Assignment 8 — Advanced Customization
    Master scales, ticks, heatmaps, clustermaps, and colorbar customizations. Work with log/linear/symlog axes, advanced palettes, metadata colorbars, high-res publishing pipelines, and expert-level dashboards.

Tips for Success

  • Always start with Seaborn defaults and then customize.
  • Compare Seaborn axes-level (sns.boxplot) vs figure-level (sns.catplot) APIs.
  • Use themes and palettes consistently across all plots.
  • For large datasets, adjust alpha, sizes, and sampling.
  • Save multiple versions of plots to compare readability and style.

Ready to build real confidence in Seaborn? Pick a set below and start solving!

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Assignment-2

Assignment-3

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Assignment-8

FAQs

Q1. What datasets are used in these assignments?
Seaborn comes with built-in datasets like tips, penguins, flights, and iris which are used extensively.

Q2. Do I need Matplotlib for Seaborn?
Yes. Seaborn is built on Matplotlib. Many tasks involve customizing plots with Matplotlib commands.

Q3. Are these assignments beginner-friendly?
Yes. Assignment 1 starts with basics and gradually progresses to advanced dashboards.

Q4. How much time should I allocate per assignment?
Plan 1–2 hours per assignment depending on your level.

Q5. Will this help in Data Science projects?
Definitely. Seaborn is widely used in data analysis, ML pipelines, and exploratory data analysis (EDA).

Q6. Can I use Seaborn with Pandas and NumPy?
Yes. Seaborn works seamlessly with DataFrames and arrays.

Q7. Do these assignments cover themes and customization?
Yes. Assignments 2 and 8 focus heavily on styling, palettes, scales, and publishing pipelines.

Q8. What’s the difference between catplot, relplot, and lmplot?

  • catplot → Categorical plots (bar, box, violin)

  • relplot → Relational plots (scatter, line)

  • lmplot → Regression/linear model plots

Q9. Can I export plots for publications?
Yes. Assignments include PNG, PDF, and SVG exports at high DPI for research and reports.

Q10. What’s the final outcome?
By the end, you’ll be able to analyze any dataset and present insights visually with clear, professional-quality Seaborn plots.