"The greatest value of a picture is when it forces us to notice what we never expected to see." — John W. Tukey

10 chart types. 3 libraries. 1 notebook. Zero boring visuals. 🎨

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📖 About

This notebook is a hands-on, colorful guide to data visualization in Python — built for anyone who wants to turn raw data into charts that actually tell a story. No prior visualization experience needed.

Every section covers a different chart type, explains when to use it (not just how), and comes with live # TRY: experiments so you can explore and customize immediately.

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🗺️ Table of Contents

• What's Inside
• Chart Gallery
• Quick Start
• The Chart Chooser
• Requirements
• Run on Kaggle
• Run Locally
• Libraries Compared
• Experiment Ideas
• What's Next

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📦 What's Inside

#	Section	Library	Key Concepts
0	Setup	—	Imports, global theme, custom palette
1	Line Chart	Matplotlib	Multi-series, filled areas, value annotations
2	Bar Chart	Matplotlib	Grouped bars, horizontal bars, sorted ordering
3	Scatter / Bubble	Matplotlib	4-variable encoding, trend lines, alpha blending
4	Histogram	Matplotlib	4 distribution shapes explained with examples
5	Pie & Donut	Matplotlib	Exploded slices, center text, legend placement
6	Heatmap	Seaborn	Correlation matrix, masking, color divergence
7	Box + Violin	Seaborn	IQR, outliers, density comparison side by side
8	Interactive Chart	Plotly	Hover tooltips, unified mode, zoom & pan
9	Dashboard Layout	Matplotlib	subplot_mosaic, KPI cards, multi-panel design
10	Chart Chooser	—	Quick-reference table for picking the right chart

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🎨 Chart Gallery

📈  Line Chart        ╱‾‾╲_╱‾‾╲___╱‾        Trends over time
📊  Bar Chart         ▌ ▌▌ ▌▌ ▌▌             Compare categories
🔵  Scatter / Bubble  · ·  · ·  ·  ·         Two-variable relationships
📉  Histogram         ▁▃▇█▇▅▃▁               Data distribution shapes
🥧  Pie / Donut       ◔ ◑ ◕ ●                Part-to-whole breakdown
🟥  Heatmap           🟩🟨🟥🟥🟩🟨          Correlation between variables
📦  Box / Violin      |—[  |  ]—|            Statistical summaries
🌐  Plotly            ≋≋≋ (hover me!) ≋≋≋   Interactive exploration
🖥️  Dashboard         [line ][KPI]           Multi-chart layouts
              [donut][bar   ]

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⚡ Open Notebook in Kaggle

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🧭 The Chart Chooser

Not sure which chart to use? Use this table:

I want to show...	Best Chart	One-liner
Change over time	Line Chart	ax.plot(x, y)
Compare categories	Bar Chart	ax.bar(x, y)
Relationship between 2 variables	Scatter Plot	ax.scatter(x, y)
How data is distributed	Histogram	ax.hist(data, bins=30)
Part-to-whole breakdown	Pie / Donut	ax.pie(sizes)
Correlation between many variables	Heatmap	sns.heatmap(df.corr())
Statistical summary	Box / Violin	sns.boxplot()
Interactive web chart	Plotly	px.line(df, x=..., y=...)
Multiple charts together	Dashboard	plt.subplot_mosaic(...)

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📋 Requirements

matplotlib>=3.5.0
seaborn>=0.12.0
plotly>=5.0.0
numpy>=1.21.0
pandas>=1.3.0

Install everything at once:

pip install -r requirements.txt

✅ All dependencies are pre-installed on Kaggle and Google Colab.

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🏁 Run on Kaggle

1. Upload the notebook via File → Import Notebook
2. No additional installs needed — all libraries are available
3. Plotly charts render inline in the notebook output
4. Any saved files (PNG exports) appear in /kaggle/working/

To save a chart as an image:

plt.savefig('my_chart.png', dpi=150, bbox_inches='tight')

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💻 Run Locally

# 1. Clone the repo
git clone https://github.com/nachiket-1/data-visualization-python.git
cd data-visualization-python

# 2. (Recommended) Create a virtual environment
python -m venv venv
source venv/bin/activate        # macOS / Linux
venv\Scripts\activate           # Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Launch Jupyter
jupyter notebook
# or JupyterLab:
jupyter lab

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⚖️ Libraries Compared

Feature	Matplotlib	Seaborn	Plotly
Output type	Static image	Static image	Interactive HTML
Learning curve	Medium	Low	Low–Medium
Best for	Full control, custom layouts	Statistical charts	Web & dashboards
Works with DataFrames	Manually	Natively	Natively
Default aesthetics	Basic	Beautiful	Beautiful
Animations	Yes (FuncAnimation)	No	Yes
3D charts	Yes	No	Yes

Rule of thumb:

• Start with Seaborn for quick EDA
• Use Matplotlib when you need full control or custom layouts
• Use Plotly when your audience will interact with the chart

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🧪 Experiment Ideas

After going through the notebook, try these challenges:

• 📅 Real data — load sns.load_dataset('titanic') and recreate every chart with real data
• 🌍 Choropleth map — use px.choropleth() to make an interactive world map
• 🎨 Custom theme — create your own plt.rcParams style and apply it globally
• 📐 Annotation master — add arrows, text boxes, and highlights to explain chart insights
• 📦 Pair plot — use sns.pairplot(df) to explore all variable relationships at once
• 🌡️ Diverging bar chart — show positive vs negative values with color coding
• 🕸️ Radar chart — compare multi-attribute profiles in a spider web layout

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📁 Repository Structure

data-visualization-python/
│
├── 📓 data_visualization_python.ipynb   ← Main notebook
├── 📋 README.md                          ← This file
└── 📦 requirements.txt                   ← Dependencies

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🚀 What's Next?

Topic	Resource
More chart types	Matplotlib Gallery
Statistical deep dive	Seaborn Tutorial
Interactive dashboards	Plotly Dash
No-code dashboards	Streamlit
Geographic maps	Folium
Practice datasets	Kaggle Datasets

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🤝 Contributing

Have a chart type that should be added? Found a bug?

1. Fork the repository
2. Create a branch: git checkout -b feature/add-radar-chart
3. Make your changes and submit a Pull Request

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📄 License

This project is open source under the MIT License — free to use, share, and build on.

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Made with ❤️, Python, and way too many color gradients

If this helped you learn something new, a ⭐ on the repo means the world!