Cleaning Date Columns in Python with Pandas

Data is rarely clean, and dates are often the messiest part of any dataset. This text-based course teaches you how to transform inconsistent date columns, extract year values, and format time data so it is ready for accurate analysis. You will start with the fundamental concepts of date representation in data structures before moving into practical cleaning workflows. By reading through our clear explanations and practicing with structured code examples, you will gain the confidence to handle real-world data issues. What you'll learn: Understand how dates are represented as strings, integers, and datetime objects; Clean inconsistent string formats and extract numeric years using Pandas; Handle missing, malformed, or corrupt date values safely; Apply modern Python type hints to your data cleaning functions for better code reliability; Convert object columns into standardized datetime types. This course starts with foundational definitions and basic string manipulation, then guides you through building robust, reusable cleaning functions. This course is designed for beginners who want to work with data in Python, with no prior data cleaning experience required. Start reading today to master the art of preparing clean, analysis-ready datasets.