DataFrames in PySpark

<h1>1. Introduction to PySpark</h1> <p>PySpark is the Python API for Apache Spark, a distributed data processing framework that is designed for speed and ease of use. It allows you to work with large datasets in parallel and perform data processing tasks efficiently. PySpark provides a high-level API for distributed data manipulation, which includes the DataFrame API, a key component of Spark&rsquo;s structured data processing capabilities.</p> <h1>2. What is a DataFrame?</h1> <p>A DataFrame in PySpark is a distributed collection of data organized into named columns, similar to a table in a relational database or a spreadsheet in Excel. DataFrames are designed to handle structured data, making them ideal for a wide range of data manipulation and analysis tasks. They are also fault-tolerant and can handle large datasets that don&rsquo;t fit in memory.</p> <p><a href="https://medium.com/@anirudhkanukanti/dataframes-in-pyspark-89cebe138b09"><strong>Read More</strong></a></p>