Скачать или смотреть How To Load Large CSV Files In Pandas Without Memory Errors? - Python Code School

How To Load Large CSV Files In Pandas Without Memory Errors? Are you working with large CSV files in Python and worried about memory errors? In this helpful video, we’ll guide you through effective methods to load big data files into Pandas without overwhelming your system. We’ll start by explaining why loading entire large files at once can cause issues and what strategies you can use to manage this. You’ll learn how to read CSV files in smaller, manageable pieces called chunks using the chunksize parameter, which helps process data efficiently without running into memory errors. We’ll also cover how to select only the columns you need, reducing the overall data load and speeding up your analysis. Additionally, we’ll show you how to specify data types explicitly to optimize memory usage, such as using smaller integer types or converting text columns to categories. If your data is compressed, we’ll explain how Pandas can handle compressed files directly, saving disk space and potentially improving load times. You’ll also discover how to process each chunk immediately—filtering, aggregating, or transforming data on the fly—so you don’t need to load everything into memory at once. Finally, we’ll introduce alternative libraries like Dask and Vaex that are designed to handle larger datasets more easily. Join us to learn how to work smarter with big data in Python and keep your projects running smoothly.

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About Us: Welcome to Python Code School! Our channel is dedicated to teaching you the essentials of Python programming. Whether you're just starting out or looking to refine your skills, we cover a range of topics including Python basics for beginners, data types, functions, loops, conditionals, and object-oriented programming. You'll also find tutorials on using Python for data analysis with libraries like Pandas and NumPy, scripting, web development, and automation projects.