Python enthusiasts, rejoice! Reading and writing Parquet files just got a whole lot easier with PyArrow and FastParquet. In this latest installment of our "All About Parquet" series, we dive into the practical side of working with Parquet files in Python. Whether you're dealing with large datasets or everyday data analysis, these two libraries have got you covered.

With PyArrow, you can read and write Parquet files seamlessly, even handling partitioned datasets with ease. FastParquet, on the other hand, offers a lightweight solution for common tasks, making it perfect for data science workflows.

Both libraries provide robust support for the Parquet format, offering flexibility and performance when working with large-scale data. The choice between PyArrow and FastParquet ultimately depends on your specific use case. So, what are you waiting for? Dive into the world of Parquet files in Python today!

Source: https://dev.to/alexmercedcoder/all-about-parquet-part-08-reading-and-writing-parquet-files-in-python-338d