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In-Memory Analytics with Apache Arrow

You're reading from   In-Memory Analytics with Apache Arrow Accelerate data analytics for efficient processing of flat and hierarchical data structures

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835461228
Length 406 pages
Edition 2nd Edition
Languages
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Author (1):
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Matthew Topol Matthew Topol
Author Profile Icon Matthew Topol
Matthew Topol
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1: Overview of What Arrow is, Its Capabilities, Benefits, and Goals FREE CHAPTER
2. Chapter 1: Getting Started with Apache Arrow 3. Chapter 2: Working with Key Arrow Specifications 4. Chapter 3: Format and Memory Handling 5. Part 2: Interoperability with Arrow: The Power of Open Standards
6. Chapter 4: Crossing the Language Barrier with the Arrow C Data API 7. Chapter 5: Acero: A Streaming Arrow Execution Engine 8. Chapter 6: Using the Arrow Datasets API 9. Chapter 7: Exploring Apache Arrow Flight RPC 10. Chapter 8: Understanding Arrow Database Connectivity (ADBC) 11. Chapter 9: Using Arrow with Machine Learning Workflows 12. Part 3: Real-World Examples, Use Cases, and Future Development
13. Chapter 10: Powered by Apache Arrow 14. Chapter 11: How to Leave Your Mark on Arrow 15. Chapter 12: Future Development and Plans 16. Index 17. Other Books You May Enjoy

Using the Datasets API in Python

Before you ask: yes, the Datasets API is available in Python too! Let’s do a quick rundown of all the same features we just covered but using the PyArrow Python module instead of C++. Since the majority of data scientists utilize Python for their work, it makes sense to show off how to use these APIs in Python for easy integration with existing workflows and utilities. Since Python’s syntax is simpler than C++, the code is much more concise, so we can run through everything really quickly in the following sections.

Creating our sample dataset

We can start by creating a similar sample dataset to what we were using for the C++ examples with three columns, but using Python:

>>> import pyarrow as pa
>>> import pyarrow.parquet as pq
>>> import pathlib
>>> import numpy as np
>>> import os
>>> base = pathlib.Path(os.getcwd())
>>> (base / "parquet_dataset").mkdir...
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