# dataverse-python-real-world-usecases

A Copilot rule with Python templates for Dataverse migration and cleanup, so suggestions follow real integration patterns instead of generic advice.

- rule · `type=rule` · GitHub Copilot · ★ 40418
- Author: github · official GitHub repository
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/github/awesome-copilot/tree/HEAD/instructions/dataverse-python-real-world-usecases.instructions.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/rules/dataverse-python-real-world-usecases

## What it does

The file gives Copilot context for working with the Dataverse SDK for Python. Two scenarios ship inside: moving data out of a legacy system through a staging database, and a data quality agent that spots duplicates and merges them. The rule applies to every file in the project — `applyTo: '**'`.

The migration template runs three steps: extract with pandas, map source fields onto the Dataverse schema, then load in batches while tracking failures. It also reconciles what landed and reports the success rate. The second scenario keys records on a set of fields, flags matches, and copies values from the duplicate into the primary record before deleting it.

The code imports `DataverseClient` from `PowerPlatform.Dataverse.client` and `DefaultAzureCredential` from `azure.identity`. Without Azure authentication configured and access to a Dataverse environment, none of the examples will run — check that first before asking Copilot to adapt anything. The source text is truncated, so the quality report section is only partially shown.

## Requirements
- Works with: Azure

## Install

### GitHub Copilot (native platform)

**Manually: put the files in place**
- File: `.github/instructions/dataverse-python-real-world-usecases.instructions.md` — The applyTo field sets which files it applies to

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/dataverse-python-real-world-usecases.md` — The paths frontmatter field limits the files: move applyTo from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### Cursor

**Manually: put the files in place**
- File: `.cursor/rules/dataverse-python-real-world-usecases.mdc` — Frontmatter: description, globs, alwaysApply. Move applyTo from the source into globs, or the rule will not attach on its own

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/dataverse-python-real-world-usecases.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/dataverse-python-real-world-usecases.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/github/awesome-copilot/tree/HEAD/instructions/dataverse-python-real-world-usecases.instructions.md

## When to use it
- move a company list from a legacy CRM into Dataverse
- flag duplicate records by name and phone number
- merge a repeated record into the primary one without losing fields
- check how many rows survived the migration
- build a completeness report for a table's fields

## Example prompts
- show the template for migrating accounts from a legacy database into Dataverse
- write an agent that finds duplicates in the account table by name and telephone1
- add reconciliation and a success-rate count to the pipeline

## Source text · dataverse-python-real-world-usecases.instructions.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 74491a182d91203d.

BEGIN THIRD-PARTY FILE 74491a182d91203d
````text
---
applyTo: '**'
---

# Dataverse SDK for Python — Real-World Use Cases & Templates

Based on official Dataverse data migration and integration patterns.

## 1. Data Migration from Legacy Systems

### Migration Architecture

```
Legacy System → Staging Database → Dataverse
    (Extract)    (Transform)        (Load)
```

### Complete Migration Example

```python
import pandas as pd
import time
from PowerPlatform.Dataverse.client import DataverseClient
from PowerPlatform.Dataverse.core.errors import DataverseError
from azure.identity import DefaultAzureCredential

class DataMigrationPipeline:
    """Migrate data from legacy system to Dataverse."""
    
    def __init__(self, org_url: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
        self.success_records = []
        self.failed_records = []
    
    def extract_from_legacy(self, legacy_db_connection, query: str):
        """Extract data from source system."""
        return pd.read_sql(query, legacy_db_connection)
    
    def transform_accounts(self, df: pd.DataFrame) -> list:
        """Transform source data to Dataverse schema."""
        payloads = []
        
        for _, row in df.iterrows():
            # Map source fields to Dataverse
            payload = {
                "name": row["company_name"][:100],  # Limit to 100 chars
                "telephone1": row["phone"],
                "websiteurl": row["website"],
                "revenue": float(row["annual_revenue"]) if row["annual_revenue"] else None,
                "numberofemployees": int(row["employees"]) if row["employees"] else None,
                # Track source ID for reconciliation
                "new_sourcecompanyid": str(row["legacy_id"]),
                "new_importsequencenumber": row["legacy_id"]
            }
            payloads.append(payload)
        
        return payloads
    
    def load_to_dataverse(self, payloads: list, batch_size: int = 200):
        """Load data to Dataverse with error tracking."""
        total = len(payloads)
        
        for i in range(0, total, batch_size):
            batch = payloads[i:i + batch_size]
            
            try:
                ids = self.client.create("account", batch)
                self.success_records.extend(ids)
                print(f"✓ Created {len(ids)} records ({len(self.success_records)}/{total})")
                
                # Prevent rate limiting
                time.sleep(0.5)
                
            except DataverseError as e:
                self.failed_records.extend(batch)
                print(f"✗ Batch failed: {e.message}")
    
    def reconcile_migration(self, df: pd.DataFrame):
        """Verify migration and track results."""
        
        # Query created records
        created_accounts = self.client.get(
            "account",
            filter="new_importsequencenumber ne null",
            select=["accountid", "new_sourcecompanyid", "new_importsequencenumber"],
            top=10000
        )
        
        created_df = pd.DataFrame(list(created_accounts))
        
        # Update source table with Dataverse IDs
        merged = df.merge(
            created_df,
````
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