Data Validation Using Python

Data validation is the process of ensuring that data is accurate, complete, and consistent. It involves verifying that data meets certain criteria, such as data type, value range, and formatting rules. Data quality control, on the other hand, is a broader process that includes all activities related to maintaining and improving the quality of data. It involves identifying and addressing data quality issues, implementing processes to improve data quality, and ensuring that data is fit for purpose. Data validation and quality control are important in data wrangling, which is the process of preparing data for analysis. During data wrangling, data may be sourced from various systems, and it may need to be cleaned, transformed, or combined with other data. This process can introduce errors or inconsistencies, which can negatively impact the accuracy and reliability of subsequent analyses.