Skip to main content

Using Neo4j with Python : the Open-Source Library "NeoAccess"

So, you want to build a python app or Jupyter notebook to utilize Neo4j, but aren't too keen on coding a lot of string manipulation to programmatic create ad-hoc Cypher queries?   You're in the right place: the NeoAccess library can do take care of all that, sparing you from lengthy, error-prone development that requires substantial graph-database and software-development expertise!

This article is part 4 of a growing, ongoing series on Graph Databases and Neo4j


"NeoAccess" is the bottom layer of the technology stack provided by the BrainAnnex open-source project.  All layers are very modular, and the NeoAccess library may also be used by itself, entirely separately from the rest of the technology stack.  (A diagram of the full stack is shown later in this article.)

NeoAccess interacts with the Neo4j Python driver, which is provided by the Neo4j company, to access the database from Python; the API to access that driver is very powerful, but complex - and does not provide higher-level services.

The Handover from Graph Database Specialists to Data Scientists & General Python Programmers

One could do a lot directly with the Cypher query language...  And there are times when working directly with Cypher is necessary or convenient - just like when one may want to work interactively with SQL in a relational database. 

But what if you need to do something programmatically?

Or what if you don't want to deal with writing Cypher queries, or if someone working on the project may not be an expert in Cypher?

Well, that's where the NeoAccess library comes in to save the day!

In some ways, the NeoAccess library can be thought of as a handover from data engineering that requires expertise in Cypher, passing the baton to data scientists and software engineers who may have only limited understanding of what graph databases are, and limited or no knowledge of Cypher.

The goal of NeoAccess is to make it easy and convenient for a Python programmer or Data Scientist to manage a lot of the typical database operations - without having to deal with Cypher.

If you're accessing Neo4j programmatically, and you wanted to create functions to provide typical database operations (such as create a new node, or link 2 existing nodes), you'd be faced with a lot of tedious - and very error prone - string manipulations to create the Cypher queries in your Python scripts.  Not to mention having to deal with pesky special cases (such as, can one have blank spaces in a node label?  What if I later need to have more than 1 label per node?)

Those aggravations are something best avoided - and left to the NeoAccess library, which has grappled with these issues for a long time!  And it has been unit-tested at great length...

Example: Create a New Node

By using the NeoAccess library, all you have to do is: create a connection database, and then run the method "create_node" and pass arguments to it.

That's all!  (Compare to the Cypher query-language equivalent, below...)

And if you want to have a second "label" for the node, just change the label string into a list - and you don't have to worry about it!  Similarly, if you have blank spaces in any of the labels, you can just simply do it and not worry about it.

The NeoAccess library will worry about the Cypher syntax needed to deal with multiple labels, or needed to deal with blank spaces and labels.

No Deep Graph-Database Expertise Needed for Many Typical Operations

Some of the beauty of using a library like NeoAccess is that all you need is a relatively modest knowledge of the essentials of graph databases; something that you can really explain very quickly, with a diagram in the style of the familiar "friends-of-friends" (as done, for example, in Part 1 of this series of articles.)

As long as someone knows the basics... knows about nodes representing records with fields in it... and knows that nodes can be connected by relationships (aka links) that have a name and can also have attributes...  and, finally, that each node can have one or more labels (which are reminiscent of table names in relation databases)...

Well, that's the fundamental knowledge - and just about all that one needs to know - to do quite a bit using the NeoAccess library!

Conversely, people who are experts in Cypher, can easily submit any arbitrary query to NeoAccess - and get the results (including errors and status of the operations) in a variety of convenient Python data structures.

Block Diagram of NeoAccess

Repeated from the top of this article, here's a block diagram of what the the NeoAccess library consists of:

Shown at the center, there's a Foundational Layer, and a set of Cypher utilities.

The foundational layer takes care of operations like connecting to the database, running generic Cypher queries, and receiving complex data structures (and status/error info) from the database.

The Cypher utilities take care of a lot of the very specific syntactic operations to create correct Cypher queries.  

Armed with this foundation layer, one can easily run any Cypher query.

But in practice, most of the time, all that one needs to do are a small set of very typical operations.  For example: create notes, delete notes, modify fields, add/delete relationships, restructure relationships, follow links.  As well as operations involving labels, indexes and constraints.

NeoAccess allows the execution of those typical scenarios with simple, streamlined function calls.

It also provides a group operations about imports and exports.  For example, dealing with files or Pandas.   Oftentimes, the import/export operations are done at a higher layer, such as the schema layer (see next section), not at the NeoAccess layer.  So this is something of a separate group - and might end up getting split off in the future.

Who uses NeoAccess, and how does one use it?

The biggest user of the NeoAccess library is the NeoSchema library, the layer above it in the technology stack of the BrainAnnex open-source project:

And the layers above the NeoSchema library could in principle also use NeoAccess directly - although it's probably not a good idea; that's why I'm using dashed arrows there.

But anyone can use the NeoAccess library directly, if they so wish: you can use it with a Python script, or you can use it with a Jupyter notebook.  The remainder of the technology stack shown above is NOT required; if it doesn't suit your needs, you can simply opt to use the NeoAccess library directly.  

In situations where you don't really care about the schema, maybe because you're doing exploratory analysis or a demo, that's a use case of using the NeoAccess library directly, and by itself.  (In fact, it is independently released, and all you need to do to use it, is to install that one library from the standard python library repository.)

But in most situations, it may be better to go through the NeoSchema layer (the 2nd layer from the bottom, in the above diagram.)  However, be aware that the NeoSchema layer is currently (as of June 2023) in late Beta, whereas the NeoAccess library has been stable - and out of beta - for quite some time.

Brief History and Current Status

Back in 2020 I created, and released to open source, a library called Neo4jLiaison, now obsolete, which was a forerunner of what has become the NeoAccess library.

In the following year, 2021, I was working at pharmaceutical giant GSK.  At that time, I combined my library with another library that someone else in the company had created.  

Our two libraries conveniently happened to be fairly complimentary.  So, we merged them, and then expanded the combined library quite substantially - and added a lot of unit testing.

After a good part of a year of development, GSK pharmaceuticals very graciously released it as open source at the end of 2021, under the name of the NeoInterface library - after requiring a VERY EXTENSIVE amount of unit testing.

At that point I forked the open-source library, renamed it NeoAccess - and expanded it a fair bit throughout 2022 and 2023, using it as the new foundation of the overhauled BrainAnnex open-source project (which dates back to 2015, and was originally built on top of a relational database), as well as at 2 other use cases at jobs I held during that time. 

In 2023, the NeoAccess library started being distributed  independently from the rest of the BrainAnnex technology stack.

How to Use NeoAccess

IMPORTANT: starting Apr. 2023, the NeoAccess library is distributed independently , as a python package named "neoaccess" in the standard PyPI distribution.  One reason for the separate distribution, besides modularity, is that the NeoAccess library is stable and well-tested, while the upper layers of the BrainAnnex technology stack are in a late Beta stage.

You do NOT need to install the full BrainAnnex app if you only need the NeoAccess library!

To use the NeoAccess library:  pip install it from the standard PyPI distribution  (e.g., place neoaccess in your "requirements.txt" file, or use your IDE to install the latest "neoaccess" library).  Then, in your code, include:

from neoaccess import NeoAccess

At that point, you can issue commands such as:

host="you_host_info"    # EXAMPLES:  bolt://  OR  neo4j://localhost

db = NeoAccess(host=host,  credentials=("neo4j", your_password_info))

neo_car = db.create_node("Car", {'color': 'white', 'make': 'Toyota'})

neo_person = db.create_node("Person", {'name': 'Julian'})

number_links_added = db.add_links(neo_car, neo_person, rel_name="OWNED_BY")

Of course, you will also need an installed Neo4j database - or simply register for the free "sandbox" short-term hosted solution described in a previous article.   (Be aware that the "sandbox" is for the very new version 5 of the Neo4j database, while the NeoAccess library is based on the well-established 4.x versions of the database; however, most basic functionality seems to hold up.)


They are in the form of annotated and illustrated Jupyterlab notebooks, which come as part of the Brain Annex repository:

Tutorial 1

Tutorial 2

The above link are just VIEWS of notebooks.  If you want to actually run them from your machine, follow the instructions in the previous section.

Further Directions

Programmer's Guide

List of functions in the NeoAccess library

The NeoAccess library repository

Versions & changelog (combined with the whole BrainAnnex technology stack)

This article is part 4 of a growing, ongoing series on Graph Databases and Neo4j



Popular posts from this blog

Discussing Neuroscience with ChatGPT

UPDATED Apr. 2023 - I'm excited by ChatGPT 's possibilities in terms of facilitating advanced learning .  For example, I got enlightening answers to questions that I had confronted when I first studied neuroscience.  The examples below are taken from a very recent session I had with ChatGPT (mid Jan. 2023.) Source: In case you're not familiar with ChatGPT, it's a very sophisticated "chatbot" - though, if you call it that way, it'll correct you!  'I am not a "chatbot", I am a language model, a sophisticated type of AI algorithm trained on vast amounts of text data to generate human-like text'. For a high-level explanation of how ChatGPT actually works - which also gives immense insight into its weaknesses, there's an excellent late Jan. 2023 talk by Stephen Wolfram, the brilliant author of the Mathematica software and of Wolfram Alpha , a product that could be combined with ChatGPT to imp

Graph Databases (Neo4j) - a revolution in modeling the real world!

UPDATED Oct. 2023 - I was "married" to Relational Databases for many years... and it was a good "relationship" full of love and productivity - but SOMETHING WAS MISSING! Let me backtrack.   In college, I got a hint of the "pre-relational database" days...  Mercifully, that was largely before my time, but  - primarily through a class - I got a taste of what the world was like before relational databases.  It's an understatement to say: YUCK! Gratitude for the power and convenience of Relational Databases and SQL - and relief at having narrowly averted life before it! - made me an instant mega-fan of that technology.  And for many years I held various jobs that, directly or indirectly, made use of MySQL and other relational databases - whether as a Database Administrator, Full-Stack Developer, Data Scientist, CTO or various other roles. UPDATE: This article is now part 1 of a growing, ongoing series on Graph Databases and Neo4j But ther

What are Graph Databases - and Why Should I Care?? : "Graph Databases for Poets"

  This is a very gentle introduction to the subject.  The subtitle is inspired by university courses such as "Physics for Poets"!  (if you're technically inclined, there's an alternate article for you.) It has been said that "The language of physics (or of God) is math".  On a similar note, it could be said that: The language of the biological world - or of any subject or endeavor involving complexity - is networks ('meshes') What is a network?  Think of  it as the familiar 'friends of friends' diagram from social media. Everywhere one turns in biology, there's a network – at the cellular level, tissue level, organ level, ecosystem level.  The weather and other earth systems are networks.  Human societal organization is a network.  Electrical circuits, the Internet, our own brains...  Networks are everywhere! What can we do with networks, to better understand the world around us, or to create something that we need? Broadly s

Full-Text Search with the Neo4j Graph Database

(UPDATED Oct. 2023)   Now that we have discussed a full technology stack based on Neo4j (or other graph databases), and that we a design and implementation available from the open-source project  , what next?  What shall we build on top? Well, how about  Full-Text Search ?  This article is part of a growing, ongoing series on Graph Databases and Neo4j Full-Text Searching/Indexing Starting with the  Version 5, Beta 26.1  release, the Brain Annex open-source project includes a straightforward but working implementation of a design that uses the convenient services of its Schema Layer , to provide indexing of word-based documents using Neo4j. The python class FullTextIndexing ( source code ) provides the necessary methods, and it can parse both plain-text and HTML documents (for example, used in "formatted notes"); parsing of PDF files and other formats will be added at a later date. No grammatical analysis ( stemming or lemmatizing ) is done on

Using Schema in Graph Databases such as Neo4j

UPDATED Feb. 2024 - Graph databases have an easygoing laissez-faire attitude: "express yourself (almost) however you want"... By contrast, relational databases come across with an attitude like a micro-manager:  "my way or the highway"... Is there a way to take the best of both worlds and distance oneself from their respective excesses, as best suited for one's needs?  A way to marry the flexibility of Graph Databases and the discipline of Relational Databases? This article is part 5 of a growing,  ongoing  series  on Graph Databases and Neo4j Let's Get Concrete Consider a simple scenario with scientific data such as the Sample, Experiment, Study, Run Result , where Samples are used in Experiments, and where Experiments are part of Studies and produce Run Results.  That’s all very easy and intuitive to represent and store in a Labeled Graph Database such as Neo4j .   For example, a rough draft might go like this:   The “labels” (black tags) represent

Anti-Aging Research: Science, not Hype

Last updated May 2023 Q: "How is aging a disease?" A: It's a dynamic system that veers away from its homeostasis (normal equilibrium point): hence a form of slow-progressing illness. Labeling it as 'natural' is a surrender to our traditional state of ignorance and powerlessness, which fortunately is beginning to be changed! Aging is "normal" only from the point of view of the "selfish gene", for whom the body is a disposable carrier. Individuals organisms - for whom self-preservation has a different meaning than for genes - have received scant help from evolution... with rare exceptions such as the T. dohrnii jellyfish (which I discuss here )... but now the time has finally arrived for our rational design to remedy some of the cellular flaws that evolution never bothered to correct!   The above is my standard answer to an oft-asked question. The science of aging is by all evidence very misunderstood by the general public.  Hype,

Neo4j & Cypher Tutorial : Getting Started with a Graph Database and its Query Language

You have a general idea of what Graph Databases - and Neo4j in particular - are...  But how to get started?  Read on! This article is part 3 of a growing,  ongoing  series  on Graph Databases and Neo4j   If you're new to graph databases, please check out part 1 for an intro and motivation about them.  There, we discussed an example about an extremely simple database involving actors, movies and directors...  and saw how easy the Cypher query language makes it to answer questions such as "which directors have worked with Tom Hanks in 2016" - questions that, when done with relational databases and SQL, turn into a monster of a query and an overly-complicated data model involving a whopping 5 tables! In this tutorial, we will actually carry out that query - and get acquainted with Cypher and the Neo4j browser interface in the process.  This is the dataset we'll be constructing: Get the database in place If you don't already have a database installed locally

Neo4j Sandbox Tutorial : try Neo4j and learn Cypher - free and easy!

So, you have an itch to test-drive Neo4j and its Cypher query language.  Maybe you want to learn it, or evaluate it, or introduce colleagues/clients to it.  And you wish for: fast, simple and free! Well, good news: the Neo4j company kindly provides a free, short-term hosted solution called "the Neo4j sandbox" .  Extremely easy to set up and use! This article is part 2 of a growing, ongoing series on Graph Databases and Neo4j Register (free) for the Neo4j "Sandbox" Go to , and register with a working email and a password.  That's it! Note that this same email/password will also let you into the Neo4j Community Forums and Support ; the same login for all: very convenient! Launch your instance - blank or pre-populated After registering, go to  , and follow the steps in the diagram below (the choices might differ, but the "Blank Sandbox" should always be there): Too good to be true?  Is there

Visualization of Graph Databases Using Cytoscape.js

(UPDATED APR. 2024)   I have ample evidence from multiple sources that there are strong unmet needs in the area of visualization of graph databases. And whenever there's a vacuum, vendors circle like vultures - with incomplete, non-customizable, and at times ridiculously expensive, closed-box proprietary solutions.   Fortunately, coming to the rescue is the awesome open-source cytoscape.js library ,  an offshoot of the "Cytoscape" project of the  Institute for Systems Biology , a project with a long history that goes back to 2002. One can do amazing custom solutions, relatively easily, when one combines this Cytoscape library with:   1) a front-end framework such as Vue.js   2) backend libraries (for example in python) to prepare and serve the data   For example, a while back I created a visualizer for networks of chemical reactions, for another open-source project I lead ( )   This visualizer will look and feel generally familiar to anyone who has eve