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In this tutorial, we walk through how to validate your CSV data using Great Expectations and pandas. You’ll learn how to load your data into a DataFrame, check if it’s empty or has the expected number of rows, and define expectations for columns such as value ranges, non-null checks, and string lengths. We also cover how to run a validation checkpoint and automatically catch errors in your dataset.

This guide is perfect for data engineers, analysts, or anyone who wants to ensure their data is accurate and reliable. By the end of the video, you’ll have a solid understanding of how to implement automated data validation in Python and improve the quality of your datasets.