Use pandas to Visualize Couchbase Data in Python

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Couchbase Python Connector

Python Connector Libraries for Couchbase Data Connectivity. Integrate Couchbase with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Couchbase enables you use pandas and other modules to analyze and visualize live Couchbase data in Python.

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

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

Connecting to Couchbase Data

Connecting to Couchbase 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 using the Login method, set User, Password, and Server to the credentials for the account and the address of the server you want to connect to.

Follow the procedure below to install the required modules and start accessing Couchbase 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 Couchbase Data in Python

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

engine = create_engine("couchbase:///?User=myuseraccount&Password=mypassword&Server=http://mycouchbaseserver")

Execute SQL to Couchbase

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

df = pandas.read_sql("SELECT FirstName, TotalDue FROM Customer WHERE FirstName = 'Bob'", engine)

Visualize Couchbase Data

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

df.plot(kind="bar", x="FirstName", y="TotalDue")

Free Trial & More Information

Download a free, 30-day trial of the Couchbase Python Connector to start building Python apps and scripts with connectivity to Couchbase 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("couchbase:///?User=myuseraccount&Password=mypassword&Server=http://mycouchbaseserver")
df = pandas.read_sql("SELECT FirstName, TotalDue FROM Customer WHERE FirstName = 'Bob'", engine)

df.plot(kind="bar", x="FirstName", y="TotalDue")