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

Use pandas to Visualize Active Directory Data in Python

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

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

Connecting to Active Directory Data

Connecting to Active Directory 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 establish a connection, set the following properties:

  • Valid User and Password credentials (e.g., Domain\BobF or cn=Bob F,ou=Employees,dc=Domain).
  • Server information, including the IP or host name of the Server, as well as the Port.
  • BaseDN: This will limit the scope of LDAP searches to the height of the distinguished name provided.

    Note: Specifying a narrow BaseDN may greatly increase performance; for example, cn=users,dc=domain will only return results contained within cn=users and its children.

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

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

engine = create_engine("activedirectory:///?User=cn=Bob F,ou=Employees,dc=Domain&Password=bob123&Server=")

Execute SQL to Active Directory

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

df = pandas.read_sql("SELECT Id, LogonCount FROM User WHERE CN = 'Administrator'", engine)

Visualize Active Directory Data

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

df.plot(kind="bar", x="Id", y="LogonCount")

Free Trial & More Information

Download a free, 30-day trial of the Active Directory Python Connector to start building Python apps and scripts with connectivity to Active Directory 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("activedirectory:///?User=cn=Bob F,ou=Employees,dc=Domain&Password=bob123&Server=")
df = pandas.read_sql("SELECT Id, LogonCount FROM User WHERE CN = 'Administrator'", engine)

df.plot(kind="bar", x="Id", y="LogonCount")