Data Visualization with Python — Complete Guide
Welcome to the complete data visualization tutorial for Python. This is not a one-library guide — it is the comparative playbook that teaches you which library to reach for, when, and why. Matplotlib, Seaborn, Plotly, Altair, Bokeh, Dash, Streamlit, Panel, Datashader — all in one place, with side-by-side examples.
| 10 Chapters | ~70 Lessons | Intermediate Level | ~14 hours |
Tutorial Details
| Level | Intermediate |
| Chapters | 10 |
| Lessons | ~70 |
| Time to complete | ~14 hours |
| Prerequisites | Python + Pandas + basic Matplotlib (you’ve made a few charts before). |
| Python version | 3.10+ |
| Matplotlib / Seaborn / Plotly | 3.7+ / 0.13+ / 5.20+ |
What You’ll Learn
By the end of this tutorial, you will be able to:
- Apply visualization principles — perceptual accuracy, data-ink ratio, Gestalt grouping
- Use a decision flowchart to pick the right library for any project
- Pick the right chart type for comparison, distribution, relationship, composition, time, and geo
- Build publication-quality Matplotlib figures with full typographic control
- Use Seaborn for fast statistical EDA
- Build interactive Plotly + Altair charts with animation, hover, and faceting
- Tame millions of points with Datashader and Holoviews
- Ship full dashboards with Dash, Streamlit, and Panel
- Apply WCAG color accessibility and avoid common chart anti-patterns
- Complete 5 capstone projects end-to-end
Chapters
| # | Chapter | Topics |
|---|---|---|
| 1 | Introduction to Data Visualization | Why Visualize Data, Chart Types Taxonomy, Great Visualizations Through History, Visualization Qualities, Your First Multi-Library Comparison |
| 2 | The Python Visualization Landscape | Matplotlib and Seaborn, Plotly and Bokeh, Altair and Plotnine, Dash, Streamlit & Panel, Library Decision Guide |
| 3 | Chart Selection Guide | Comparison Charts, Distribution Charts, Relationship Charts, Composition Charts, Time Series & Geographic Charts |
| 4 | Matplotlib Deep Dive | Figure Anatomy, Chart Types in Matplotlib, Styling and Themes, Annotations and Reference Lines, Publication-Quality Figures |
| 5 | Seaborn Deep Dive | Figure-Level vs Axes-Level, Distribution Plots, Relationship Plots, Categorical Plots, Matrix & Pair Plots |
| 6 | Plotly Deep Dive | Plotly Express — Complete Reference, Graph Objects — Advanced, Subplots & Mixed Charts, Plotly Animations, Altair — A Quick Tour |
| 7 | Big Data Visualization | The Overplotting Problem, Datashader — Rasterized Visualization, HoloViews + Datashader, Bokeh — Streaming Data, Alternative Approaches |
| 8 | Dashboard Design | Plotly Dash — Architecture & Layout, Dash Callbacks, Streamlit — The Fastest Path, Panel (HoloViz), Dash vs Streamlit vs Panel |
| 9 | Design Principles & Accessibility | Data-Ink Ratio, Color Theory for Data Viz, WCAG & Color-Blind Accessibility, Typography & Layout, Gestalt Principles |
| 10 | Practice Projects & Next Steps | Project 1 — COVID-19 Dashboard, Project 2 — Stock Price Explorer, Project 3 — E-Commerce Sales Report, Project 4 — Model Explainability, Next Steps & Career Paths |
Learning Path
Python Basics → NumPy → Pandas → Matplotlib → Seaborn → Plotly → **Data Visualization Mastery (you are here)** → Dash / Streamlit production apps
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