Welcome to the PySpark Repository! This repository contains a collection of notes and notebooks covering various topics related to PySpark, which is the Python API for Apache Spark.
Introduction Getting Started Notebooks Topics Covered (read and write (save), Show df types, Data Types | Create and Drop Columns, Filtering Queries, Missing values handling, groupBy | orderBy, MLlib | Regression, MLlib | Classification , Categorical ---> Numeric Contributing License
PySpark is a powerful framework that allows you to perform distributed data processing and analysis using Apache Spark. This repository serves as a comprehensive resource for learning and working with PySpark. Whether you're new to PySpark or an experienced user, you'll find valuable information and examples here to enhance your PySpark skills.
To get started with PySpark, follow these steps: Clone or download the repository to your local machine. Set up a Python environment with the necessary dependencies (e.g., PySpark, Jupyter Notebook, etc.). Launch Jupyter Notebook or any other preferred environment. Open the notebooks provided in the repository to explore different topics and examples.
The repository includes a single Jupyter Notebook that cover various PySpark concepts and techniques. Here's an overview of the notebook available:
- Introduction to PySpark and DataFrame operations.
- Working with Spark SQL and SQL queries in PySpark.
- Machine learning with PySpark using MLlib.
- Real-time streaming data processing with PySpark Streaming.
- PySpark tips and best practices. Feel free to explore and run these notebooks to gain hands-on experience with PySpark.
The repository covers a wide range of topics related to PySpark. Some of the main topics covered include:
Data manipulation and transformations with DataFrames and Datasets. Spark SQL and working with structured data using SQL queries. Machine learning and advanced analytics with MLlib. Real-time data processing with PySpark Streaming. Best practices, tips, and tricks for efficient PySpark development. Each topic includes detailed explanations, code examples, and practical exercises to help you grasp the concepts effectively.
Contributions to this PySpark repository are welcome! If you would like to contribute, please follow these steps:
Create a new branch for your contribution. Make your changes and commit them with descriptive messages. Push your changes to your forked repository. Submit a pull request, explaining the changes you have made. Please ensure that your contributions align with the existing topics and provide value to the PySpark community.
The content in this repository is licensed under the MIT License. You are free to use, modify, and distribute the code and notebooks as per the terms and conditions of the license.
We hope this PySpark repository proves to be a valuable resource for learning and working with PySpark. Feel free to explore the notebooks, provide feedback, and contribute to enhance the repository further. Happy PySpark coding!