Data quality is the foundation of any reliable analytics pipeline, and knowing how to pyspark find duplicate values in column is an essential skill for every data engineer and analyst working with Apache Spark. Duplicate entries can distort aggregations, bias machine learning models, and lead to incorrect business decisions. In PySpark, a DataFrame is a distributed collection of data organized into named columns, which means traditional pandas-style methods do not always apply directly. Instead, you must make use of Spark’s distributed operations such as groupBy, window functions, and Spark SQL to identify repeated values efficiently across a cluster.
Understanding Duplicates in PySpark DataFrames
Before diving into code, it is important to distinguish between two related but different concepts: duplicate rows and duplicate values in a single column. A duplicate row means every column in that row matches another row exactly. Duplicate values in a column, on the other hand, means that a specific column—such as email, employee_id, or transaction_id—contains the same value in multiple rows, even if the rest
