DQ Check for DataFrame - Complete Guide to Data Quality Validation
Introduction to DQ Check- DQ Check (Data Quality Check) is the process of validating data to ensure it is accurate, complete, consistent, and reliable before analysis or machine learning tasks. type of data quality checks with example In data engineering and data science projects, DataFrames (Pandas or Spark) are widely used. Performing DQ checks on DataFrames helps: Detect missing or invalid values Ensure correct data types Identify duplicates Improve ML model accuracy Prevent pipeline failures Why DQ Check is Important? Poor data quality leads to: Wrong business insights Poor ML model performance Data pipeline failures Incorrect reporting A proper DQ check ensures clean, trustworthy, and usable data for analytics and AI models. Common Data Quality Checks for DataFrame 1.Null / Missing Value Check Pandas Example df .isnull () .sum () Spark Example from pyspark .sql .functions import col df .select ([col(c) .isNull () .sum () .alias (c) for c in df .columns ]) .show () P...