Use Dash to Build to Web Apps on Neo4J Data

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 module, and the Dash framework, you can build Neo4J-connected web applications for Neo4J data. This article shows how to connect to Neo4J with the CData Connector and use pandas and Dash to build a simple web app for visualizing Neo4J data.

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.

After installing the CData Neo4J Connector, follow the procedure below to install the other required modules and start accessing Neo4J through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize Neo4J Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.neo4j as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Neo4J Connector to create a connection for working with Neo4J data.

cnxn = mod.connect("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 result set in a DataFrame.

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

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-neo4jedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our Neo4J data and configure the app layout.

trace = go.Bar(x=df.CategoryId, y=df.CategoryName, name='CategoryId')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Neo4J ProductCategory Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
 app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the Neo4J data.

python neo4j-dash.py

👁 Neo4J data in a Dash web app (Salesforce is shown).

Free Trial & More Information

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

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Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.neo4j as mod
import plotly.graph_objs as go

cnxn = mod.connect("Server=localhost;Port=7474;User=my_user;Password=my_password;")

df = pd.read_sql("SELECT CategoryId, CategoryName FROM ProductCategory WHERE CategoryOwner = 'CData Software'", cnxn)
app_name = 'dash-neo4jdataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.CategoryId, y=df.CategoryName, name='CategoryId')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Neo4J ProductCategory Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
 app.run_server(debug=True)