Employing NumPy's NPY Format for Faster Than Parquet DataFrame Storage

Employing NumPy's NPY Format for Faster Than Parquet DataFrame Storage

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Memory Mapping an Array

20 of 24

20 of 24

Memory Mapping an Array

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Classroom Contents

Employing NumPy's NPY Format for Faster Than Parquet DataFrame Storage

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  1. 1 Intro
  2. 2 The Quest for Complete DataFrame Serialization
  3. 3 NumPy Enhancement Proposal (NEP) 1
  4. 4 Promising Performance of NPZ versus Parquet
  5. 5 Overview
  6. 6 Components of a DataFrame
  7. 7 Block-Consolidation Strategies Unconsolidated Blocks
  8. 8 Block Consolidation & Complexity
  9. 9 The NPY Format
  10. 10 Converting Contiguous Bytes to an Array
  11. 11 NPY & Object Arrays
  12. 12 NPY Versions
  13. 13 The NPZ Format
  14. 14 Encoding a DataFrame as an NPZ
  15. 15 JSON Metadata
  16. 16 NPY Performance in Numpy
  17. 17 Lies, Damned Lles, and Benchmarks
  18. 18 Nine DataFrame Fixtures
  19. 19 Memory Maps
  20. 20 Memory Mapping an Array
  21. 21 Memory Mapping a DataFrame
  22. 22 Current State
  23. 23 Future Work
  24. 24 Conclusions

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