Skip to main content

This is a library allows you to import csv data into neo4j database

Project description

Neo4j Big Bang the CSV importer for Neo4j

This is a python package to import csv data into neo4j database. You can import both Nodes and Relationships. You don't need to write any Cypher script at all.

What it can do

Importing Nodes

  • This shell automatically detects the types of properties.
  • You can choose skipping or updating if nodes already exist.
  • You can choose Node labels row by row. Multiple labels are supported.
  • You can set labels as your importing file name too.

Importing Relationships

  • This shell automatically detects the types of properties.
  • You can choose skipping or updating if relationship already exists.
  • You can choose if you want to create nodes at the same time.
  • You can choose Relationship type row by row.
  • You can set type as your importing file name too.

Getting Started

Install this package

$ pip install neo4j-bigbang

Prepare CSV Data

Put your importing CSV file under importing directory

Run command with environment variables

NEO4J_URI=bolt://localhost:7687 NEO4J_USER=neo4j NEO4J_PASSWORD={your-password} bigbang-node

Or set envilonment variable

export NEO4J_URI=bolt://localhost:7687 NEO4J_USER=neo4j NEO4J_PASSWORD=password

Getting Started from source

$ cd [path to this repo]
$ python -m venv .env
$ source .env/bin/activate
$ pip install -r requirements.txt

Setup your .env file under src/bigbang directory

/src/bigbang/.env

NEO4J_URI='bolt://localhost:7687'
NEO4J_USER='neo4j'
NEO4J_PASSWORD='password'

Import Nodes from CSV

Run

$ python src/bigbang/import_nodes.py

# Case 1: Use Labels from file name Person|Employee.csv
$ python src/bigbang/import_nodes.py -n Person|Employee.csv -a

# Case 2: Update if target node already exists
$ python src/bigbang/import_nodes.py -n Person|Employee.csv -a -l Person|Teacher -p name|employee_id -u

# Case 2': Update if target node already exists ( labels should be on CSV )
$ python src/bigbang/import_nodes.py -n Person|Employee.csv -a -p name|employee_id -u

# Case 3: Skip if target node already exists
$ python src/bigbang/import_nodes.py -n Person|Employee.csv -a -l Person|Teacher -p name|employee_id

# Case 3': Skip if target node already exists ( labels should be on CSV )
$ python src/bigbang/import_nodes.py -n Person|Employee.csv -a -l -p name|employee_id

Options

  • -p with specified properties: Unique Properties. If not set, target property will be 'neme'.
  • -l with specified labels : If set, specified labels will be targeted if already in Neo4j.
  • -n : Specify CSV file name. Its' required when you set -f
  • -a : Set if you want to use file name as labels. If not set, labels field is required in the CSV file
  • -f : Force inserting node. -p and -l -u are ignored.
  • -u : If set, update node when it exists. ( both -l and -p are required )

Import Relationships from CSV

Run

$ python src/bigbang/import_relationships.py

# Case 1: Use Type from file name YOUR_RELATION_TYPE.csv
$ python src/bigbang/import_relationships.py -n YOUR_RELATION_TYPE.csv -a

# Case 2: Update if same type relationship already exists
$ python src/bigbang/import_relationships.py -u

# Case 3: Create relationship and nodes at once, even though nodes does not exist
$ python src/bigbang/import_relationships.py -c

Options

  • -u : If set, update relationship when it exists
  • -n : Specify CSV file name. Its' required when you set -a
  • -a : Set if you want to use file name as relationship type. If not set, type field is required in the CSV file
  • -c : If you want to create nodes at the same time, use this option

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for neo4j-bigbang, version 0.0.25
Filename, size File type Python version Upload date Hashes
Filename, size neo4j_bigbang-0.0.25-py3-none-any.whl (22.5 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size neo4j-bigbang-0.0.25.tar.gz (14.4 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page