Building Web Applications with Python and Neo4j by Sumit Gupta
By Sumit Gupta
Strengthen intriguing real-world Python-based net purposes with Neo4j utilizing frameworks similar to Flask, Py2neo, and Django
About This Book
boost a suite of universal purposes and options with Neo4j and Python
safe and set up the Neo4j database in production
A step by step consultant on imposing and deploying interactive Python-based net functions on graph info model
Who This publication Is For
If you're a Python developer and need to extend your knowing of Python-based internet purposes over Neo4j graph info types, this can be the ebook for you.
What you'll Learn
comprehend the licensing and deploy of the Neo4j database and paintings with its numerous instruments and utilities
examine the intricacies of Cypher as a graph question language
paintings with Cypher to create and regulate graph facts models
combine Python and Neo4j utilizing Py2neo
strengthen REST-based providers over social community facts utilizing Flask and item graph types over Neo4j
combine Django-based internet purposes over graph facts types utilizing Neomodel
discover diversified deployment types and their applicability with current applications
Py2neo is an easy and pragmatic Python library that offers entry to the preferred graph database Neo4j through its RESTful net carrier interface. This brings with it a seriously refactored middle, a cleanser API, larger functionality, and a few new idioms.
You will commence with licensing and fitting Neo4j, studying the basics of Cypher as a graph question language, and exploring Cypher optimizations. you'll find find out how to combine with a number of Python frameworks reminiscent of Flask and its extensions: Py2neo, Neomodel, and Django. eventually, the deployment points of your Python-based Neo4j functions in a construction atmosphere can be coated. by means of sequentially operating during the steps in each one bankruptcy, you are going to fast study and grasp a few of the implementation information and integrations of Python and Neo4j, aiding you to increase your use circumstances extra quick.
Read or Download Building Web Applications with Python and Neo4j PDF
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Additional resources for Building Web Applications with Python and Neo4j
Use this to calculate similarity between tags and see if you can find any that are almost identical. Find some items that could have been tagged “programming” but were not. 3. User-based efficiency. The user-based filtering algorithm is inefficient because it compares a user to all other users every time a recommendation is needed. Write a function to precompute user similarities, and alter the recommendation code to use only the top five other users to get recommendations. 4. Item-based bookmark filtering.
This is a great dataset to experiment with to see how different scoring methods affect the outcomes, and to understand how item-based and user-based filtering perform differently. The GroupLens web site has a few other datasets to play with, including books, jokes, and more movies. User-Based or Item-Based Filtering? Item-based filtering is significantly faster than user-based when getting a list of recommendations for a large dataset, but it does have the additional overhead of maintaining the item similarity table.
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