How to Visualize Neo4J Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Neo4J, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Neo4J-connected Python applications and scripts for visualizing Neo4J data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Neo4J data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Neo4J data in Python. When you issue complex SQL queries from Neo4J, the driver pushes supported SQL operations, like filters and aggregations, directly to Neo4J and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Neo4J Data

Connecting to Neo4J data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

To connect to Neo4j, set the following connection properties:

• Server: The server hosting the Neo4j instance.
• Port: The port on which the Neo4j service is running. The provider connects to port 7474 by default.
• User: The username of the user using the Neo4j instance.
• Password: The password of the user using the Neo4j instance.

Follow the procedure below to install the required modules and start accessing Neo4J through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Neo4J Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Neo4J data.

engine = create_engine("neo4j:///?Server=localhost&Port=7474&User=my_user&Password=my_password")

Execute SQL to Neo4J

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT CategoryId, CategoryName FROM ProductCategory WHERE CategoryOwner = 'CData Software'", engine)

Visualize Neo4J Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Neo4J data. The show method displays the chart in a new window.

df.plot(kind="bar", x="CategoryId", y="CategoryName")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Neo4J to start building Python apps and scripts with connectivity to Neo4J data. Reach out to our Support Team if you have any questions.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("neo4j:///?Server=localhost&Port=7474&User=my_user&Password=my_password")
df = pandas.read_sql("SELECT CategoryId, CategoryName FROM ProductCategory WHERE CategoryOwner = 'CData Software'", engine)

df.plot(kind="bar", x="CategoryId", y="CategoryName")
plt.show()