7 High-performance pandas and Apache Arrow
This chapter covers
- Optimizing memory usage with pandas’ data frame creation
- Decreasing computational cost of pandas operations
- Using Cython, NumExpr, and Numpy to accelerate pandas operations
- Optimizing pandas with Apache Arrow
Data analytics is essentially synonymous with using pandas. pandas is a data frame library, or a library to process tabular data. pandas is the de facto standard in the Python world to process in-memory tabular data. In this chapter, we will discuss approaches to optimize pandas usage. This will be a two-pronged approach: we will optimize pandas usage directly, and we will also optimize it using Apache Arrow.
Apache Arrow provides language-agnostic functionality to efficiently ...
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