📊 Matplotlib Complete Notes & Practice

A comprehensive beginner-friendly repository covering Matplotlib from basics to advanced concepts through theory, syntax, parameters, examples, and visual outputs.

This repository documents my hands-on learning journey while studying Matplotlib for Data Analysis using Python.

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🚀 Topics Covered

📌 Introduction

• What is Matplotlib?
• Why Matplotlib?
• Pyplot vs Object-Oriented Style

📈 Line Plot

• Syntax
• Parameters
• Markers
• Colors
• Line Styles
• Grid
• Legend
• Axis Labels
• Title
• Multiple Line Plots

🔵 Scatter Plot

• Syntax
• Parameters
• Marker Styles
• Colors
• Alpha
• Edge Colors
• Colormap
• Point Size

📊 Bar Chart

• Vertical Bar Chart
• Horizontal Bar Chart
• Multiple Bar Charts
• Grouped Bar Chart
• Bar Width
• Colors

📉 Histogram

• Bins
• Density
• Edge Colors
• Alpha
• Range

🥧 Pie Chart

• Labels
• Colors
• Explode
• Autopct
• Shadow
• Start Angle

📦 Box Plot

• Quartiles
• Median
• Whiskers
• Outliers
• Customization

📑 Subplots

• Multiple Axes
• Figure Size
• Layouts
• Grid Arrangement

🎯 Object-Oriented Style

• Figure
• Axes
• plt.subplots()
• fig
• ax
• set_title()
• set_xlabel()
• set_ylabel()
• set_xlim()
• set_ylim()

🖼 Image Handling

• Reading Images
• Displaying Images
• matplotlib.image

💾 Saving Figures

• savefig()
• DPI
• Image Formats

⚙ Plot Customization

• Grid
• Tick Labels
• Axis Limits
• Legends
• Figure Size
• Colors
• Fonts
• Styles

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🛠 Technologies Used

• Python
• NumPy
• Matplotlib
• Jupyter Notebook

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🎯 Learning Objectives

• Understand Matplotlib fundamentals.
• Learn both Pyplot and Object-Oriented plotting styles.
• Explore commonly used visualization techniques.
• Practice plot customization using real examples.
• Build a strong foundation for Data Analysis and Machine Learning.

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📚 Who Can Use This Repository?

• Beginners learning Python Data Visualization
• Data Analysis Students
• Machine Learning Beginners
• College Students
• Anyone looking for quick Matplotlib revision notes

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⭐ Features

• ✔ Beginner-friendly explanations
• ✔ Theory + Practical examples
• ✔ Parameter descriptions
• ✔ Syntax references
• ✔ Output visualizations
• ✔ Clean and organized notebook
• ✔ Easy to revise before interviews

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📄 License

This project is licensed under the MIT License.

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👩‍💻 Author

Dilpreet Kaur

B.Tech (Computer Science Engineering)

Passionate about Python, Data Analysis, Machine Learning, and Web Development.

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