Quick Introduction to PySpark DataFrame
DataFrame is a core data structure in PySpark. PySpark DataFrames are distributed across multiple nodes in a cluster. Each node stores and processes a portion of the data independently, allowing for parallel processing. This distribution enables PySpark to handle large-scale datasets by leveraging the combined computational power of multiple machines in the cluster.
Key Characteristics of PySpark DataFrame
- Distributed: Unlike a traditional DataFrame in Python (Pandas), a PySpark DataFrame is distributed across the cluster, making it suitable for parallel processing large datasets.
- Immutable: Once created, DataFrames cannot be changed. Transformations on a DataFrame return a new DataFrame without altering the original data.
- Schema-based: PySpark DataFrames have a schema that defines the structure of the data, including column names and data types. This schema is essential for data validation and manipulation.
- Lazy Evaluation: PySpark employs lazy evaluation, meaning that transformations on DataFrames are not executed immediately. Instead, they are recorded as a plan that is executed only when an action (like count or collect) is called.
Continue reading Create a PySpark DataFrame Using createDataFrame() and Import Files into PySpark DataFrames to learn how to create DataFrames from various data structures and sources.
