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Getting started

This article describes how to set up your Python environment to access Dataverse through the Dataverse SDK for Python. It then shows some simple coding examples to get you started.

Prerequisites

  • Read/write access to a Dataverse environment. You can use a trial environment.
  • OAuth authentication configured for your application.
  • An activated Python 3.10+ environment.
  • Pandas version 2.0.0+.
  • Network access to pypi.org to obtain the SDK packages.

Install the SDK and dependencies

From a terminal window, run the following command. This command installs the latest stable release of the SDK from a pypi.org package.

pip install PowerPlatform-Dataverse-Client

Run the following command to install the SDK from the project's GitHub source instead of from the package. Install the client from the PyPi package or from the source, but not both.

git clone <https://github.com/microsoft/PowerPlatform-DataverseClient-Python.git>
cd PowerPlatform-DataverseClient-Python
pip install -e .

Install Claude Skill globally

To improve the Python development experience, you can optionally install two Claude Skills when installing the SDK from a package.

pip install PowerPlatform-Dataverse-Client && dataverse-install-claude-skill

Important

The skills are automatically loaded when installing from source.

The two skills are described in the following list:

  • dataverse-sdk-use: Apply best practices for using the SDK in your applications.
  • dataverse-sdk-dev: Provide guidance for developing and contributing to the SDK itself.

The skills work with both the Claude Code CLI and the Visual Studio Code extension. Once installed, Claude automatically uses the appropriate skill when working with Dataverse operations. For more information on Claude Skill, see Agent Skills. Once installed, you can find skill definitions in the .claude/skills/dataverse-sdk-use/SKILL.md and .claude/skills/dataverse-sdk-dev/SKILL.mdfiles on your development computer.

Connect to Dataverse

The SDK client requires any Azure Identity TokenCredential implementation for OAuth authentication with Dataverse.

This code example imports the Dataverse client and Azure Identity types and establishes a connection to a Dataverse environment. Be sure to change myorg in the URL to the correct name of your environment.

from azure.identity import (
    InteractiveBrowserCredential, 
    ClientSecretCredential,
    CertificateCredential,
    AzureCliCredential
)
from PowerPlatform.Dataverse.client import DataverseClient

# Development options
credential = InteractiveBrowserCredential()  # Browser authentication
# credential = AzureCliCredential()          # If logged in via 'az login'

# Production options  
# credential = ClientSecretCredential(tenant_id, client_id, client_secret)
# credential = CertificateCredential(tenant_id, client_id, cert_path)

client = DataverseClient("https://myorg.crm.dynamics.com", credential)

You can customize the connection by using optional HTTP tunable settings to handle connection timeouts, retries, and more. Access these settings via the DataverseConfig class.

Now that you have an established client connection to a Dataverse environment, you can start using the SDK to work with business data, table metadata, and more. The next article covers some examples of these operations.

For more information, see Use OAuth with Dataverse.

Namespaces

The PowerPlatform.Dataverse.operations package contains modules that organize SDK operations into logical groups as described in the following table.

Name Description
client.records Create, update, delete, and get records (single or paginated queries)
client.query Query and search operations
client.tables Table and column metadata management
client.files File upload operations
client.dataframe Pandas DataFrame support
client.batch Batch operations

For examples of using these operations, see the Querying data and Working with data articles.

Next steps

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