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Graph Data Modeling in Python

You're reading from   Graph Data Modeling in Python A practical guide to curating, analyzing, and modeling data with graphs

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781804618035
Length 236 pages
Edition 1st Edition
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Authors (2):
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Gary Hutson Gary Hutson
Author Profile Icon Gary Hutson
Gary Hutson
Matt Jackson Matt Jackson
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Matt Jackson
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Table of Contents (16) Chapters Close

Preface 1. Part 1: Getting Started with Graph Data Modeling
2. Chapter 1: Introducing Graphs in the Real World FREE CHAPTER 3. Chapter 2: Working with Graph Data Models 4. Part 2: Making the Graph Transition
5. Chapter 3: Data Model Transformation – Relational to Graph Databases 6. Chapter 4: Building a Knowledge Graph 7. Part 3: Storing and Productionizing Graphs
8. Chapter 5: Working with Graph Databases 9. Chapter 6: Pipeline Development 10. Chapter 7: Refactoring and Evolving Schemas 11. Part 4: Graphing Like a Pro
12. Chapter 8: Perfect Projections 13. Chapter 9: Common Errors and Debugging 14. Index 15. Other Books You May Enjoy

Technical requirements

We will be using the Jupyter Notebook to run our coding exercises, which requires python>=3.8.0. In addition, the following packages will need to be installed, with the pip install command, in your environment:

  • igraph==0.9.8
  • spacy==3.4.4
  • scispacy==0.5.1
  • matplotlib

Alternatively, you could run pip install –r requirements.txt, in the supporting requirements file, to install all the supporting dependencies for this chapter.

For this chapter, you will also need to install a text corpus for some Natural Language Processing (NLP). Go to https://allenai.github.io/scispacy/ and download the en_core_sci_sm model. In a command prompt or terminal window, navigate to where this is downloaded and run pip install en_core_sci_sm-0.5.1.tar.gz.

All notebooks, with the coding exercises, are available at the following GitHub link: https://github.com/PacktPublishing/Graph-Data-Modeling-in-Python/tree/main/CH04.

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