Think Different

  PySpark and pandas are both popular tools in the data science and analytics world, but they serve different purposes and are suited for different scenarios. Here's when and why you might choose PySpark over pandas: 1. Big Data Handling :    - PySpark: PySpark is designed for distributed data processing and is particularly well-suited for handling large-scale datasets. It can efficiently process data stored in distributed storage systems like Hadoop HDFS or cloud-based storage. PySpark's capabilities shine when dealing with terabytes or petabytes of data that would be impractical to handle with pandas.    - pandas: pandas is ideal for working with smaller datasets that can fit into memory on a single machine. While pandas can handle reasonably large datasets, their performance might degrade when dealing with very large data due to memory constraints. 2. Parallel and Distributed Processing:    - PySpark: PySpark performs distributed processing by le...