Software Engineering for Data Scientists
Small Big Data: large data on a single computer
As described in Alex Voss, Ilia Lvov, and Jon Lewis’s Small Big Data manifesto, you don’t need a Big Data cluster to process large amounts of data; a single computer is often sufficient. In this planned series of articles you’ll learn the relevant principles and techniques, and how to apply them to tools like NumPy and Pandas.
- When your data doesn’t fit in memory: the basic techniques
You can still process data that doesn’t fit in memory by using four basic techniques: spending money, compression, chunking, and indexing.
Reducing Pandas memory usage #1: lossless compression
How do you load a large CSV into Pandas without using as much memory? Learn the basic techniques: dropping columns, lower-range numeric dtypes, categoricals, and sparse columns.
Reducing Pandas memory usage #2: lossy compression
In this article you’ll learn techniques that lose some details in return for reducing memory usage.
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