🎨📈 Matplotlib Library in Python

Welcome to the Matplotlib Library repository! This project provides a comprehensive collection of examples and tutorials to help you harness the power of Matplotlib, a versatile library for creating stunning visualizations in Python.

📁 Contents

📚 Notebooks

• Advance_Plot.ipynb: Explore advanced plotting techniques and create intricate visualizations.
• Ploting.ipynb: Get started with basic plotting examples to familiarize yourself with Matplotlib.

📊 Datasets

• IPL_Ball_by_Ball_2008_2022.csv: Ball-by-ball data of IPL matches from 2008 to 2022.
• batsman_season_record.csv: Season records of various batsmen.
• batsman_season_record (3).csv: Additional batsman season records.
• batter.csv: Statistics for various batters.
• batter (1).csv: Additional statistics for batters.
• big-array.npy: A large NumPy array dataset for analysis.
• big-array (1).npy: An additional large NumPy array dataset.
• fours-sixes.csv: Statistics dataset for fours and sixes hit in matches.
• gayle-175.csv: Dataset of Chris Gayle's innings where he scored 175 runs.
• iris.csv: The classic Iris dataset, ideal for classification examples.
• sample.png: A sample image for visualization purposes.
• sharma-kohli.csv: Dataset containing statistics for players Sharma and Kohli.
• vk.csv: Dataset featuring statistics for Virat Kohli.

🚀 Getting Started

To begin exploring the examples and datasets, clone this repository to your local machine:

git clone https://github.com/Latif1218/Matplotlib-Library-in-python/tree/master

Ensure you have Matplotlib and its dependencies installed:

• pip install matplotlib

🛠️ Usage

Open the Jupyter notebooks to discover how to create and customize plots using the datasets provided. Each notebook includes code snippets and explanations to guide you through the various features of Matplotlib.

🤝 Contributing

Contributions are welcome! If you have suggestions for more examples or datasets, feel free to open an issue or submit a pull request.