Calling Another Custom Python Function from Pyspark UDF

Python-coded PySpark UDFs provide the ability to call other Python functions, whether they are built-in or user-defined functions from outside libraries. By enabling users to make use of existing Python code, this feature improves the modularity and reusability of UDFs. Within the distributed PySpark environment, users may easily implement their domain-specific logic, carry out challenging calculations, or use cutting-edge algorithms. Users may take advantage of the full potential of Python’s vast ecosystem of libraries and features by invoking Python functions from PySpark UDFs.

PySpark, often known as Python API for Apache Spark, was created for distributed data processing. It gives users the ability to efficiently and scalable do complex computations and transformations on large datasets. User-Defined Functions (UDFs), which let users create their unique functions and apply them to Spark DataFrames or RDDs, which is one of the main features of PySpark. Using UDFs, PySpark’s capabilities may be expanded and customized to meet certain needs. In this article, we will learn how to call another custom Python function from Pyspark UDF.