pyDataverseOps 0.1.2

A Python library to facilitate operations on Microsoft Dataverse

pyDataverseOps Library

Overview

The pyDataverseOps library facilitates operations on Microsoft Dataverse, handling authentication, data manipulation, and batch operations. It's designed for developers needing to interface with Dataverse for creating, reading, updating, and deleting records.

Features

• Authenticate with OAuth2.
• Read and write data using pandas DataFrames.
• Perform batch updates and deletes to optimize network use.

Installation

First, clone this repository to your local machine:

git clone  https://github.com/ray2199/pyDataverseOps.git
cd your-repository-folder

Then install the library using pip:

pip install pyDataverseOps

Requirements

• Python 3.6+
• pandas
• requests

Configuration

Before using the pyDataverseOps library, you must configure it with your Dataverse access credentials and endpoint. Here are the steps and parameters required:

Parameters

• domain: Your Dataverse domain.
• tenant_id: Tenant ID for OAuth authentication.
• client_id: Client ID for OAuth authentication.
• client_secret: Client Secret for OAuth authentication.

Example Initialization

from dataverse_ops import pyDataverseOps

dv_ops = pyDataverseOps(
    domain='your_dataverse_domain',
    tenant_id='your_tenant_id',
    client_id='your_client_id',
    client_secret='your_client_secret'
)

This initializes your connection to Dataverse with the necessary authentication headers.

Usage Examples

Reading Data

Retrieve data from a Dataverse table with optional filtering and pagination:

Parameters

• table_logical_name: Logical name of the Dataverse table.
• filters: Optional dictionary for OData filter expressions.
• pagination: Set to True to paginate results if your query exceeds one page.

Example

filters = {'statuscode': ('eq', 1)}  # Equality filter example
data = dv_ops.read_data('Contacts', filters=filters, pagination=True)
print(data)

Writing Data

Write data to a Dataverse table from a pandas DataFrame:

Parameters

• table_logical_name: Logical name of the Dataverse table.
• data_df: DataFrame where each row represents a new record.

Example

import pandas as pd

data_df = pd.DataFrame({
    'firstname': ['John', 'Jane'],
    'lastname': ['Doe', 'Doe']
})
dv_ops.write_data('Contacts', data_df)

Updating Records

Update existing records in a Dataverse table:

Parameters

• table_logical_name: Logical name of the table.
• unique_column: Column name that uniquely identifies each record.
• data_df: DataFrame containing the updated data.

Example

update_data = pd.DataFrame({
    'contactid': ['guid1', 'guid2'],
    'new_field_value': [123, 456]
})
dv_ops.update_records('Contacts', 'contactid', update_data)

Deleting Records

Batch delete records from a Dataverse table based on their IDs:

Parameters

• table_logical_name: Logical name of the table.
• unique_column: Unique identifier column for the records.

Example

dv_ops.batch_delete_records('Contacts', 'contactid')

Contributions

We welcome contributions to improve this library. Please fork the repository, make your changes, and submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.