PlotlyVizPro is a modular, multi-notebook visualization project crafted to help you master interactive plotting using Plotly, Streamlit, and Python utilities. From chart fundamentals to advanced dashboards, this project equips you with:
✅ A reusable, utility-first visualization toolkit
✅ A thematic, notebook-driven learning framework
✅ A polished, production-grade portfolio project
- 📈 Express + Graph Objects: Built using both Plotly APIs
- 📊 Charts: Line, bar, pie, box, histogram, heatmap, KDE
- 🧭 Maps: Choropleth, Mapbox, GeoJSON overlays
- 🧮 Statistical Add-ons: Trendlines, Z-bands, moving averages
- 🔄 Animations: Sliders, dropdown filters,
animation_frame - 🧼 Clean Output: HTML for interactivity, PNG for documentation
- 🧰 Modular Design: Plotting utilities, reusable layout functions
- 🐳 Docker Support: Containerized Jupyter environment for reproducibility
| Feature | Matplotlib | Seaborn | Plotly |
|---|---|---|---|
| Interactivity | ❌ Static | ❌ Static | ✅ Fully interactive |
| Dashboards/Animations | ❌ Minimal | ❌ Minimal | ✅ Native support |
| Hover/Zoom Features | ❌ | ❌ | ✅ Built-in |
| Publication Quality | ✅ | ✅ | ✅ |
PlotlyVizPro builds on this by organizing all core concepts in modular, theme-consistent notebooks.
PlotlyVizPro was built to:
- 🧩 Encourage modular design using utilities and thematic structuring
- 📦 Package core visualization skills into reusable functions
- 💡 Help learners think like developers by designing reusable pipelines
- 🧱 Promote reproducibility with Docker + synthetic datasets
- 🚀 Act as a launchpad for portfolio enhancement and tech interviews
| Area | Concepts |
|---|---|
| Basic Charts | Line, scatter, bar, pie, histogram, box plots |
| Chart Styling | Theming, axis config, layout tuning, custom tooltips |
| Interactivity | Hover templates, sliders, dropdowns, callbacks |
| Statistical Layers | Trendlines, rolling averages, ±z-score confidence bands |
| Dashboard Design | Subplots, grids, shared axes, spacings, annotations |
| Geo Visuals | Choropleths, GeoJSON overlays, Mapbox tokens |
| Plot Architecture | .pipe() overlays, centralized plot_utils.py, layout automation |
| Exports | Dynamic HTML and static PNG renderings for reporting |
PlotlyVizPro/
├── exports/ # HTML and PNG exports by notebook
├── notebooks/ # 10 structured Jupyter notebooks
├── pages/ # Streamlit pages for app mode
├── utils/ # Reusable plotting utilities
├── cheatsheets/ # Markdown-based syntax guides
├── .gitignore # Ignores env folders, checkpoints, etc.
├── Dockerfile # Docker environment for reproducibility
├── app.py # Main Streamlit app entry point
├── generate_datasets.py # Generates synthetic datasets using Faker
├── requirements.txt # Minimal dependencies to run the project
├── requirements_dev.txt # Full dev environment
├── README.md # You’re here!| # | Notebook Title | Concepts & Charts |
|---|---|---|
| 01 | Line & Scatter Charts | Line, scatter, bubble, tooltips, color maps |
| 02 | Bar, Pie & Box Charts | Categorical visualizations |
| 03 | Histogram, KDE & Heatmaps | Distribution plots, hexbin overlays |
| 04 | Choropleth & GeoJSON Maps | Choropleth, projections, custom shape tuning |
| 05 | Animation & Interactivity | Sliders, dropdowns, animation_frame |
| 06 | Dashboards & Subplots | Grid layouts, spacing, multi-panel views |
| 07 | Graph Objects Deep Dive | Manual axis, layout, annotation control |
| 08 | Mapbox & Geo Layers | Mapbox tokens, styles, satellite maps |
| 09 | Real-World Visualizations | COVID & Superstore use cases |
| 10 | Statistical Overlays + .pipe() | Modular overlays, Z-bands, moving average |
# Clone the repo
git clone https://github.com/SatvikPraveen/PlotlyVizPro.git
cd PlotlyVizPro
# Create a virtual environment
python3 -m venv plotly_env
source plotly_env/bin/activate # On Windows: plotly_env\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Launch JupyterLab
jupyter lab# Build image
docker build -t plotlyvizpro .
# Run container (auto-launches JupyterLab at port 8888)
docker run -p 8888:8888 plotlyvizpro🛡️ No token/password required. Uses --allow-root for compatibility.
Launch the app with streamlit run app.py and explore:
- 🔄 Real-time filtering and visualization controls
- 📊 Embedded charts from exported HTMLs
- 🧮 Summary statistics + quick insights
- 🎨 Toggle between chart types with ease
All datasets are synthetically generated via generate_datasets.py using the faker library.
- 🔁 Fully reproducible
- 🔓 License-free
- 🔬 Customizable complexity (sales, dates, geography, etc.)
| Type | Utilities |
|---|---|
| Core Charts | line_plot(), bar_plot(), scatter_plot(), box_plot() |
| Interactivity | Sliders, hover templates, dropdowns |
| Stats Add-ons | add_trendline(), add_moving_average(), add_zscore_band() |
| Layout Tools | make_subplots_custom(), add_annotations(), apply_theme() |
| Export Tools | save_fig_as_html(), save_fig_as_png() |
Each notebook saves plots in:
exports/
├── html/ # Interactive outputs
│ ├── 01_line_scatter/
│ ├── ...
├── images/ # PNG renders
│ ├── 01_line_scatter/
│ ├── ...- 📊 Portfolio Project: Showcase your visualization skills to recruiters with modular, professional-quality notebooks and dashboards.
- 🧑🏫 Learning Companion: Study Plotly’s API differences (
plotly.expressvsgraph_objects) across notebooks. - 🧪 Interactive Reporting: Generate rich HTML reports or launch dashboards with Streamlit using synthetic, reproducible datasets.
- 🧰 Toolkit for Projects: Plug in your own data and reuse
plot_utils.pyutilities for fast prototyping and visualization pipelines.
A compact markdown cheatsheet available at:
cheatsheets/plotly_cheatsheet.mdIncludes:
- Plotly syntax patterns
- Utility usage demos
- Dashboard tips
- Export best practices
This project is licensed under the GNU General Public License v3.0. See the LICENSE file for more details.
- 🛠 Got a bug fix or improvement? Open a PR
- 🧠 Found a bug or want to request a feature? File an issue
- ⭐ If this helped you, consider starring the repository!
Got an idea, stuck on something, or want to share your use-case?
👉 Visit the Discussions tab and say hello!
Explore the full suite of Python data mastery repositories:
-
📊 PandasPlayground
Modular pipelines for mastering data wrangling, merging, and analysis using pandas. -
🔢 NumPyMasterPro
A concept-to-implementation NumPy project covering arrays, broadcasting, and indexing. -
📈 MatplotlibMasterPro
Comprehensive Matplotlib practice with style guides, animations, and thematic plots. -
🖼️ SeabornMasterPro
Complete mastery of Seaborn's statistical plots, themes, dashboards, and time series.
Each project is standalone but follows a consistent pedagogical and modular structure, forming a progressive learning track in data visualization and numerical computing with Python.