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Python Pandas ETL Tutorial: Cleaning, Transforming, and Aggregating CSV Data

In this hands-on tutorial, you will learn how to build an ETL (Extract, Transform, Load) pipeline using Python and the Pandas library. We’ll start by hardcoding a sample dataset, simulating real-world customer transaction data in CSV format. From there, you'll learn how to extract data into a Pandas DataFrame, perform essential transformations like filtering, data cleaning, and aggregation, and finally load the processed data into a new CSV file. This practical example is ideal for anyone looking to improve their data manipulation skills in Python and work through common ETL operations such as handling missing data, working with dates, and calculating summary statistics. By the end of the tutorial, you'll have a solid understanding of how to use Pandas for basic ETL tasks, all without needing any paid cloud services or external libraries beyond Pandas. Perfect for beginner and intermediate Python users looking to sharpen their data engineering skills! #PythonETL #DataTransformation #PandasTutorial #PythonDataScience #ETLPipeline #DataCleaning #DataEngineering #CSVProcessing #LearnPython #PandasDataFrame #PythonForBeginners #DataManipulation #PythonCodeTutorial