Use pandas to Visualize MarkLogic Data in Python

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

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

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

Connecting to MarkLogic Data

Connecting to MarkLogic 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 User, Password, and Server to the credentials for the MarkLogic account and the address of the server you want to connect to. You should also specify the REST API Port if you want to use a specific instance of a REST Server.

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

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

engine = create_engine("marklogic:///?User='myusername'&Password='mypassword'&Server='http://marklogic'")

Execute SQL to MarkLogic

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

df = pandas.read_sql("SELECT Name, TotalDue FROM Customer WHERE Id = '1'", engine)

Visualize MarkLogic Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the MarkLogic Python Connector to start building Python apps and scripts with connectivity to MarkLogic 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("marklogic:///?User='myusername'&Password='mypassword'&Server='http://marklogic'")
df = pandas.read_sql("SELECT Name, TotalDue FROM Customer WHERE Id = '1'", engine)

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