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Big Data processing with Pandas, a powerful Python library for data manipulation and analysis, involves implementing strategies to handle large datasets efficiently. Scaling to sizable datasets requires adopting techniques such as processing data in smaller chunks using the 'chunksize' parameter in Pandas read_csv function. This approach facilitates reading and processing large datasets in more manageable portions, preventing memory overload. To further optimize memory usage, it's essential to leverage Pandas' features like data types optimization, using more memory-efficient data types when possible. Additionally, utilizing advanced functionalities like the 'skiprows' parameter and filtering columns during data import can significantly enhance performance. By mastering these strategies, one can effectively manage and analyze vast datasets in Python with Pandas, ensuring both computational efficiency and memory optimization in the face of Big Data challenges
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Pandas
- Lab 1: 1,000,000 Sales Records
- Lab 2: NYC Yellow Taxi Trip Data
- Lab 3: NYC Taxi Trip Duration EDA notebook
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- Lab 5: eCommerce behavior data from multi category store (285 million users)
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