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Nivesh Bansal
Nivesh Bansal

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Top 20 Matplotlib Mastery Questions (Beginner to Pro)

From Beginner to Pro – a curated list of hands-on visualization challenges that will sharpen your Matplotlib skills 🚀


🔗 Resources


🏆 The Challenges

S.No Category Challenge
1 Basic & Essential Dynamic Scaling: Plot y = sin(x) from 0–2π, auto-rescale with new data.
2 Basic & Essential Custom Style Sheet: Create .mplstyle file to set global defaults.
3 Basic & Essential Data Transformation: Plot log(y) vs x but ticks in original values.
4 Customization & Polish Annotated Events: Plot stock prices, annotate highest/lowest points.
5 Customization & Polish Dual Axes: Temperature (°C) + Humidity (%) with shared time axis.
6 Customization & Polish Custom Ticks: Show only quarterly months (Jan, Apr, Jul, Oct).
7 Customization & Polish Gradient Color Line: Sine wave with color changing from blue → red.
8 Customization & Polish Custom Legends: Legend with unique marker size, color, and style.
9 Subplots & Layouts Complex Layout: 5 plots (2 top row, 3 bottom row) with unique titles.
10 Subplots & Layouts Shared Axes Zoom: Zooming in one subplot updates another subplot.
11 Real-Time & Animation Live Data Simulation: Random walk updating every 200ms.
12 Real-Time & Animation Animated Transition: Animate smoothly from sin(x)cos(x).
13 Real-Time & Animation Performance Challenge: Plot 1M points efficiently with zooming.
14 Advanced Visualization Heatmap: 10×10 matrix with annotated cell values.
15 Advanced Visualization 3D Plot: Parametric spiral (cos(t), sin(t), t) with gradient colors.
16 Advanced Visualization Custom Colormap: Red → Yellow → Green for a contour plot.
17 Interactivity & Widgets Interactive Slider: Control sine wave frequency in real-time.
18 Interactivity & Widgets Clickable Points: Scatter plot shows coordinates when clicked.
19 Integration & Export High-Quality Export: Multi-plot figure → 300 DPI PDF with metadata.
20 Integration & Export Embedding in GUI: Matplotlib plot inside Tkinter with refresh button.

✅ How to Use This

  1. Start with basic questions (1–3) to strengthen fundamentals.
  2. Move to customization & layouts (4–10) for polished plots.
  3. Experiment with real-time & advanced visualization (11–16).
  4. Explore interactivity & integration (17–20) for practical applications.

🚀 Save this list, fork the repos, and try solving each question step by step.

By the end, you’ll have a portfolio-ready set of Matplotlib projects!

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