Use pandas to Visualize Cloudant Data in Python

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

The CData Python Connector for Cloudant enables you use pandas and other modules to analyze and visualize live Cloudant 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 Cloudant, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Cloudant-connected Python applications and scripts for visualizing Cloudant data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Cloudant data, execute queries, and visualize the results.

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

Connecting to Cloudant Data

Connecting to Cloudant 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.

Set the following connection properties to connect to Cloudant:

  • User: Set this to your username.
  • Password: Set this to your password.

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

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

engine = create_engine("cloudant:///?User=abc123& Password=abcdef")

Execute SQL to Cloudant

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

df = pandas.read_sql("SELECT MovieRuntime, MovieRating FROM Movies WHERE MovieRating = 'R'", engine)

Visualize Cloudant Data

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

df.plot(kind="bar", x="MovieRuntime", y="MovieRating")

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Download a free, 30-day trial of the Cloudant Python Connector to start building Python apps and scripts with connectivity to Cloudant 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("cloudant:///?User=abc123& Password=abcdef")
df = pandas.read_sql("SELECT MovieRuntime, MovieRating FROM Movies WHERE MovieRating = 'R'", engine)

df.plot(kind="bar", x="MovieRuntime", y="MovieRating")