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Small Big Data: using NumPy and Pandas when your data doesn't fit in memory

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Your data is too big to fit in memory—loading it crashes your program—but it's also too small for a complex Big Data cluster. How to process your data simply and quickly? In this talk you'll learn the basic techniques for dealing with Small Big Data: money, compression, batching and parallelization, and indexing. In particular, you'll learn how to apply these techniques to NumPy and Pandas.

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