# PowerPlatform-DataverseClient-Python

**Repository Path**: mirrors_microsoft/PowerPlatform-DataverseClient-Python

## Basic Information

- **Project Name**: PowerPlatform-DataverseClient-Python
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-19
- **Last Updated**: 2026-04-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PowerPlatform Dataverse Client for Python

[![PyPI version](https://img.shields.io/pypi/v/PowerPlatform-Dataverse-Client.svg)](https://pypi.org/project/PowerPlatform-Dataverse-Client/)
[![Python](https://img.shields.io/pypi/pyversions/PowerPlatform-Dataverse-Client.svg)](https://pypi.org/project/PowerPlatform-Dataverse-Client/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A Python client library for Microsoft Dataverse that provides a unified interface for CRUD operations, SQL queries, table metadata management, and file uploads through the Dataverse Web API.

**[Source code](https://github.com/microsoft/PowerPlatform-DataverseClient-Python)** | **[Package (PyPI)](https://pypi.org/project/PowerPlatform-Dataverse-Client/)** | **[API reference documentation](https://learn.microsoft.com/python/api/dataverse-sdk-docs-python/dataverse-overview?view=dataverse-sdk-python-latest)** | **[Product documentation](https://learn.microsoft.com/power-apps/developer/data-platform/sdk-python/)** | **[Samples](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/tree/main/examples)**

> [!IMPORTANT]
> This library is currently in **preview**. Preview versions are provided for early access to new features and may contain breaking changes.

## Table of contents

- [Key features](#key-features)
- [Getting started](#getting-started)
  - [Prerequisites](#prerequisites)
  - [Install the package](#install-the-package)  
  - [Authenticate the client](#authenticate-the-client)
- [Key concepts](#key-concepts)
- [Examples](#examples)
  - [Quick start](#quick-start)
  - [Basic CRUD operations](#basic-crud-operations)
  - [Bulk operations](#bulk-operations)
  - [Upsert operations](#upsert-operations)
  - [DataFrame operations](#dataframe-operations)
  - [Query data](#query-data) *(QueryBuilder, SQL, raw OData)*
  - [Table management](#table-management)
  - [Relationship management](#relationship-management)
  - [File operations](#file-operations)
- [Next steps](#next-steps)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)

## Key features

- **🔄 CRUD Operations**: Create, read, update, and delete records with support for bulk operations and automatic retry
- **⚡ True Bulk Operations**: Automatically uses Dataverse's native `CreateMultiple`, `UpdateMultiple`, `UpsertMultiple`, and `BulkDelete` Web API operations for maximum performance and transactional integrity
- **🔍 Fluent QueryBuilder**: Type-safe query construction with method chaining, composable filter expressions, and automatic OData generation
- **📊 SQL Queries**: Execute read-only SQL queries via the Dataverse Web API `?sql=` parameter
- **🏗️ Table Management**: Create, inspect, and delete custom tables and columns programmatically
- **🔗 Relationship Management**: Create one-to-many and many-to-many relationships between tables with full metadata control
- **🐼 DataFrame Support**: Pandas wrappers for all CRUD operations, returning DataFrames and Series
- **📎 File Operations**: Upload files to Dataverse file columns with automatic chunking for large files
- **🔐 Azure Identity**: Built-in authentication using Azure Identity credential providers with comprehensive support
- **🛡️ Error Handling**: Structured exception hierarchy with detailed error context and retry guidance

## Getting started

### Prerequisites

- **Python 3.10+** (3.10, 3.11, 3.12, 3.13 supported)  
- **Microsoft Dataverse environment** with appropriate permissions
- **OAuth authentication configured** for your application

### Install the package

Install the PowerPlatform Dataverse Client using [pip](https://pypi.org/project/pip/):

```bash
# Install the latest stable release
pip install PowerPlatform-Dataverse-Client
```

(Optional) Install Claude Skill globally with the Client:

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

This installs two Claude Skills that enable Claude Code to:
- **dataverse-sdk-use**: Apply SDK best practices for using the SDK in your applications
- **dataverse-sdk-dev**: Provide guidance for developing/contributing to the SDK itself

The skills work with both the Claude Code CLI and VSCode extension. Once installed, Claude will automatically use the appropriate skill when working with Dataverse operations. For more information on Claude Skill see https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview. See skill definitions here: [.claude/skills/dataverse-sdk-use/SKILL.md](.claude/skills/dataverse-sdk-use/SKILL.md) and [.claude/skills/dataverse-sdk-dev/SKILL.md](.claude/skills/dataverse-sdk-dev/SKILL.md).

For development from source (Claude Skill auto loaded):

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

### Authenticate the client

The client requires any Azure Identity `TokenCredential` implementation for OAuth authentication with Dataverse:

```python
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://yourorg.crm.dynamics.com", credential)
```

> **Complete authentication setup**: See **[Use OAuth with Dataverse](https://learn.microsoft.com/power-apps/developer/data-platform/authenticate-oauth)** for app registration, all credential types, and security configuration.

## Key concepts

The SDK provides a simple, pythonic interface for Dataverse operations:

| Concept | Description |
|---------|-------------|
| **DataverseClient** | Main entry point; provides `records`, `query`, `tables`, and `files` namespaces |
| **Context Manager** | Use `with DataverseClient(...) as client:` for automatic cleanup and HTTP connection pooling |
| **Namespaces** | Operations are organized into `client.records` (CRUD & OData queries), `client.query` (QueryBuilder & SQL), `client.tables` (metadata), and `client.files` (file uploads) |
| **Records** | Dataverse records represented as Python dictionaries with column schema names |
| **Schema names** | Use table schema names (`"account"`, `"new_MyTestTable"`) and column schema names (`"name"`, `"new_MyTestColumn"`). See: [Table definitions in Microsoft Dataverse](https://learn.microsoft.com/en-us/power-apps/developer/data-platform/entity-metadata) |
| **Bulk Operations** | Efficient bulk processing for multiple records with automatic optimization |
| **Paging** | Automatic handling of large result sets with iterators |
| **Structured Errors** | Detailed exception hierarchy with retry guidance and diagnostic information |
| **Customization prefix values** | Custom tables and columns require a customization prefix value to be included for all operations (e.g., `"new_MyTestTable"`, not `"MyTestTable"`). See: [Table definitions in Microsoft Dataverse](https://learn.microsoft.com/en-us/power-apps/developer/data-platform/entity-metadata) |

## Examples

### Quick start

```python
from azure.identity import InteractiveBrowserCredential
from PowerPlatform.Dataverse.client import DataverseClient

# Connect to Dataverse
credential = InteractiveBrowserCredential()

with DataverseClient("https://yourorg.crm.dynamics.com", credential) as client:
    # Create a contact
    contact_id = client.records.create("contact", {"firstname": "John", "lastname": "Doe"})

    # Read the contact back
    contact = client.records.get("contact", contact_id, select=["firstname", "lastname"])
    print(f"Created: {contact['firstname']} {contact['lastname']}")

    # Clean up
    client.records.delete("contact", contact_id)
# Session closed, caches cleared automatically
```

### Basic CRUD operations

```python
# Create a record
account_id = client.records.create("account", {"name": "Contoso Ltd"})

# Read a record
account = client.records.get("account", account_id)
print(account["name"])

# Update a record
client.records.update("account", account_id, {"telephone1": "555-0199"})

# Delete a record
client.records.delete("account", account_id)
```

### Bulk operations

```python
# Bulk create
payloads = [
    {"name": "Company A"},
    {"name": "Company B"},
    {"name": "Company C"}
]
ids = client.records.create("account", payloads)

# Bulk update (broadcast same change to all)
client.records.update("account", ids, {"industry": "Technology"})

# Bulk delete
client.records.delete("account", ids, use_bulk_delete=True)
```

### Upsert operations

Use `client.records.upsert()` to create or update records identified by alternate keys. When the
key matches an existing record it is updated; otherwise the record is created. A single item uses
a PATCH request; multiple items use the `UpsertMultiple` bulk action.

> **Prerequisite**: The table must have an **alternate key** configured in Dataverse for the
> columns used in `alternate_key`. Alternate keys are defined in the table's metadata (Power Apps
> maker portal → Table → Keys, or via the Dataverse API). Without a configured alternate key,
> upsert requests will be rejected by Dataverse with a 400 error.

```python
from PowerPlatform.Dataverse.models.upsert import UpsertItem

# Upsert a single record
client.records.upsert("account", [
    UpsertItem(
        alternate_key={"accountnumber": "ACC-001"},
        record={"name": "Contoso Ltd", "telephone1": "555-0100"},
    )
])

# Upsert multiple records (uses UpsertMultiple bulk action)
client.records.upsert("account", [
    UpsertItem(
        alternate_key={"accountnumber": "ACC-001"},
        record={"name": "Contoso Ltd"},
    ),
    UpsertItem(
        alternate_key={"accountnumber": "ACC-002"},
        record={"name": "Fabrikam Inc"},
    ),
])

# Composite alternate key (multiple columns identify the record)
client.records.upsert("account", [
    UpsertItem(
        alternate_key={"accountnumber": "ACC-001", "address1_postalcode": "98052"},
        record={"name": "Contoso Ltd"},
    )
])

# Plain dict syntax (no import needed)
client.records.upsert("account", [
    {
        "alternate_key": {"accountnumber": "ACC-001"},
        "record": {"name": "Contoso Ltd"},
    }
])
```

### DataFrame operations

The SDK provides pandas wrappers for all CRUD operations via the `client.dataframe` namespace, using DataFrames and Series for input and output.

```python
import pandas as pd

# Query records as a single DataFrame
df = client.dataframe.get("account", filter="statecode eq 0", select=["name", "telephone1"])
print(f"Found {len(df)} accounts")

# Limit results with top for large tables
df = client.dataframe.get("account", select=["name"], top=100)

# Fetch a single record as a one-row DataFrame
df = client.dataframe.get("account", record_id=account_id, select=["name"])

# Create records from a DataFrame (returns a Series of GUIDs)
new_accounts = pd.DataFrame([
    {"name": "Contoso", "telephone1": "555-0100"},
    {"name": "Fabrikam", "telephone1": "555-0200"},
])
new_accounts["accountid"] = client.dataframe.create("account", new_accounts)

# Update records from a DataFrame (id_column identifies the GUID column)
new_accounts["telephone1"] = ["555-0199", "555-0299"]
client.dataframe.update("account", new_accounts, id_column="accountid")

# Clear a field by setting clear_nulls=True (by default, NaN/None fields are skipped)
df = pd.DataFrame([{"accountid": new_accounts["accountid"].iloc[0], "websiteurl": None}])
client.dataframe.update("account", df, id_column="accountid", clear_nulls=True)

# Delete records by passing a Series of GUIDs
client.dataframe.delete("account", new_accounts["accountid"])
```

### Query data

The **QueryBuilder** is the recommended way to query records. It provides a fluent, type-safe interface that generates correct OData queries automatically — no need to remember OData filter syntax.

```python
# Fluent query builder (recommended)
for record in (client.query.builder("account")
               .select("name", "revenue")
               .filter_eq("statecode", 0)
               .filter_gt("revenue", 1000000)
               .order_by("revenue", descending=True)
               .top(100)
               .page_size(50)
               .execute()):
    print(f"{record['name']}: {record['revenue']}")
```

The QueryBuilder handles value formatting, column name casing, and OData syntax automatically. All filter methods are discoverable via IDE autocomplete:

```python
# Get results as a pandas DataFrame (consolidates all pages)
df = (client.query.builder("account")
      .select("name", "telephone1")
      .filter_eq("statecode", 0)
      .top(100)
      .to_dataframe())
print(f"Got {len(df)} accounts")
```

```python
# Comparison filters
query = (client.query.builder("contact")
         .filter_eq("statecode", 0)          # statecode eq 0
         .filter_gt("revenue", 1000000)      # revenue gt 1000000
         .filter_contains("name", "Corp")    # contains(name, 'Corp')
         .filter_in("statecode", [0, 1])     # Microsoft.Dynamics.CRM.In(...)
         .filter_between("revenue", 100000, 500000)  # (revenue ge 100000 and revenue le 500000)
         .filter_null("telephone1")          # telephone1 eq null
         )
```

For complex logic (OR, NOT, grouping), use the composable expression tree with `where()`:

```python
from PowerPlatform.Dataverse.models.filters import eq, gt, filter_in, between

# OR conditions: (statecode = 0 OR statecode = 1) AND revenue > 100k
for record in (client.query.builder("account")
               .select("name", "revenue")
               .where((eq("statecode", 0) | eq("statecode", 1))
                      & gt("revenue", 100000))
               .execute()):
    print(record["name"])

# NOT, between, and in operators
for record in (client.query.builder("account")
               .where(~eq("statecode", 2))                  # NOT inactive
               .where(between("revenue", 100000, 500000))    # revenue in range
               .execute()):
    print(record["name"])
```

**Formatted values and annotations** -- request localized labels, currency symbols, and display names:

```python
# Get formatted values (choice labels, currency, lookup names)
for record in (client.query.builder("account")
               .select("name", "statecode", "revenue")
               .include_formatted_values()
               .execute()):
    status = record["statecode@OData.Community.Display.V1.FormattedValue"]
    print(f"{record['name']}: {status}")
```

**Nested expand with options** -- expand navigation properties with `$select`, `$filter`, `$orderby`, and `$top`:

```python
from PowerPlatform.Dataverse.models.query_builder import ExpandOption

# Expand related tasks with filtering and sorting
for record in (client.query.builder("account")
               .select("name")
               .expand(ExpandOption("Account_Tasks")
                       .select("subject", "createdon")
                       .filter("contains(subject,'Task')")
                       .order_by("createdon", descending=True)
                       .top(5))
               .execute()):
    print(record["name"], record.get("Account_Tasks"))
```

**Record count** -- include `$count=true` in the request:

```python
# Request count alongside results
results = (client.query.builder("account")
           .filter_eq("statecode", 0)
           .count()
           .execute())
```

**SQL queries** provide an alternative read-only query syntax:

```python
results = client.query.sql(
    "SELECT TOP 10 accountid, name FROM account WHERE statecode = 0"
)
for record in results:
    print(record["name"])
```

**Raw OData queries** are available via `records.get()` for cases where you need direct control over the OData filter string:

```python
for page in client.records.get(
    "account",
    select=["name"],
    filter="statecode eq 0",  # Raw OData: column names must be lowercase
    expand=["primarycontactid"],  # Navigation properties are case-sensitive
    top=100,
):
    for record in page:
        print(record["name"])
```

### Table management

```python
# Create a custom table, including the customization prefix value in the schema names for the table and columns.
table_info = client.tables.create("new_Product", {
    "new_Code": "string",
    "new_Price": "decimal",
    "new_Active": "bool"
})

# Create with custom primary column name and solution assignment
table_info = client.tables.create(
    "new_Product",
    columns={
        "new_Code": "string",
        "new_Price": "decimal"
    },
    solution="MyPublisher",  # Optional: add to specific solution
    primary_column="new_ProductName",  # Optional: custom primary column (default is "{customization prefix value}_Name")
)

# Get table information
info = client.tables.get("new_Product")
print(f"Logical name: {info['table_logical_name']}")
print(f"Entity set: {info['entity_set_name']}")

# List all tables
tables = client.tables.list()
for table in tables:
    print(table)

# Add columns to existing table (columns must include customization prefix value)
client.tables.add_columns("new_Product", {"new_Category": "string"})

# Remove columns
client.tables.remove_columns("new_Product", ["new_Category"])

# Clean up
client.tables.delete("new_Product")
```

> **Important**: All custom column names must include the customization prefix value (e.g., `"new_"`).
> This ensures explicit, predictable naming and aligns with Dataverse metadata requirements.

### Relationship management

Create relationships between tables using the relationship API. For a complete working example, see [examples/advanced/relationships.py](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/blob/main/examples/advanced/relationships.py).

```python
from PowerPlatform.Dataverse.models.relationship import (
    LookupAttributeMetadata,
    OneToManyRelationshipMetadata,
    ManyToManyRelationshipMetadata,
)
from PowerPlatform.Dataverse.models.labels import Label, LocalizedLabel

# Create a one-to-many relationship: Department (1) -> Employee (N)
# This adds a "Department" lookup field to the Employee table
lookup = LookupAttributeMetadata(
    schema_name="new_DepartmentId",
    display_name=Label(localized_labels=[LocalizedLabel(label="Department", language_code=1033)]),
)

relationship = OneToManyRelationshipMetadata(
    schema_name="new_Department_Employee",
    referenced_entity="new_department",   # Parent table (the "one" side)
    referencing_entity="new_employee",    # Child table (the "many" side)
    referenced_attribute="new_departmentid",
)

result = client.tables.create_one_to_many_relationship(lookup, relationship)
print(f"Created lookup field: {result['lookup_schema_name']}")

# Create a many-to-many relationship: Employee (N) <-> Project (N)
# Employees work on multiple projects; projects have multiple team members
m2m_relationship = ManyToManyRelationshipMetadata(
    schema_name="new_employee_project",
    entity1_logical_name="new_employee",
    entity2_logical_name="new_project",
)

result = client.tables.create_many_to_many_relationship(m2m_relationship)
print(f"Created M:N relationship: {result['relationship_schema_name']}")

# Query relationship metadata
rel = client.tables.get_relationship("new_Department_Employee")
if rel:
    print(f"Found: {rel['SchemaName']}")

# Delete a relationship
client.tables.delete_relationship(result['relationship_id'])
```

For simpler scenarios, use the convenience method:

```python
# Quick way to create a lookup field with sensible defaults
result = client.tables.create_lookup_field(
    referencing_table="contact",       # Child table gets the lookup field
    lookup_field_name="new_AccountId",
    referenced_table="account",        # Parent table being referenced
    display_name="Account",
)
```

### File operations

```python
# Upload a file to a record
client.files.upload(
    "account",
    account_id,
    "new_Document",  # If the file column doesn't exist, it will be created automatically
    "/path/to/document.pdf",
)
```

## Next steps

### More sample code

Explore our comprehensive examples in the [`examples/`](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/tree/main/examples) directory:

**🌱 Getting Started:**
- **[Installation & Setup](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/blob/main/examples/basic/installation_example.py)** - Validate installation and basic usage patterns
- **[Functional Testing](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/blob/main/examples/basic/functional_testing.py)** - Test core functionality in your environment

**🚀 Advanced Usage:**
- **[Complete Walkthrough](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/blob/main/examples/advanced/walkthrough.py)** - Full feature demonstration with production patterns
- **[Relationship Management](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/blob/main/examples/advanced/relationships.py)** - Create and manage table relationships
- **[File Upload](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/blob/main/examples/advanced/file_upload.py)** - Upload files to Dataverse file columns

📖 See the [examples README](https://github.com/microsoft/PowerPlatform-DataverseClient-Python/blob/main/examples/README.md) for detailed guidance and learning progression.

### Additional documentation

For comprehensive information on Microsoft Dataverse and related technologies:

| Resource | Description |
|----------|-------------|
| **[Dataverse Developer Guide](https://learn.microsoft.com/power-apps/developer/data-platform/)** | Complete developer documentation for Microsoft Dataverse |
| **[Dataverse Web API Reference](https://learn.microsoft.com/power-apps/developer/data-platform/webapi/)** | Detailed Web API reference and examples |  
| **[Azure Identity for Python](https://learn.microsoft.com/python/api/overview/azure/identity-readme)** | Authentication library documentation and credential types |
| **[Power Platform Developer Center](https://learn.microsoft.com/power-platform/developer/)** | Broader Power Platform development resources |
| **[Dataverse SDK for .NET](https://learn.microsoft.com/power-apps/developer/data-platform/org-service/overview)** | Official .NET SDK for Microsoft Dataverse |

## Troubleshooting

### General

The client raises structured exceptions for different error scenarios:

```python
from PowerPlatform.Dataverse.client import DataverseClient
from PowerPlatform.Dataverse.core.errors import HttpError, ValidationError

try:
    client.records.get("account", "invalid-id")
except HttpError as e:
    print(f"HTTP {e.status_code}: {e.message}")
    print(f"Error code: {e.code}")
    print(f"Subcode: {e.subcode}")
    if e.is_transient:
        print("This error may be retryable")
except ValidationError as e:
    print(f"Validation error: {e.message}")
```

### Authentication issues

**Common fixes:**
- Verify environment URL format: `https://yourorg.crm.dynamics.com` (no trailing slash)
- Ensure Azure Identity credentials have proper Dataverse permissions
- Check app registration permissions are granted and admin-consented

### Performance considerations

For optimal performance in production environments:

| Best Practice | Description |
|---------------|-------------|
| **Bulk Operations** | Pass lists to `records.create()`, `records.update()` for automatic bulk processing, for `records.delete()`, set `use_bulk_delete` when passing lists to use bulk operation |
| **Select Fields** | Specify `select` parameter to limit returned columns and reduce payload size |
| **Page Size Control** | Use `top` and `page_size` parameters to control memory usage |
| **Connection Reuse** | Reuse `DataverseClient` instances across operations |
| **Production Credentials** | Use `ClientSecretCredential` or `CertificateCredential` for unattended operations |
| **Error Handling** | Implement retry logic for transient errors (`e.is_transient`) |

### Limitations

- SQL queries are **read-only** and support a limited subset of SQL syntax
- Create Table supports a limited number of column types (string, int, decimal, bool, datetime, picklist)
- File uploads are limited by Dataverse file size restrictions (default 128MB per file)

## Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit [Contributor License Agreements](https://cla.opensource.microsoft.com).

When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

### API Design Guidelines

When contributing new features to this SDK, please follow these guidelines:

1. **Public methods in operation namespaces** - New public methods go in the appropriate namespace module under [operations/](src/PowerPlatform/Dataverse/operations/). Public types and constants live in their own modules (e.g., `models/metadata.py`, `common/constants.py`)
2. **Add README example for public methods** - Add usage examples to this README for public API methods
3. **Document public APIs** - Include Sphinx-style docstrings with parameter descriptions and examples for all public methods
4. **Update documentation** when adding features - Keep README and SKILL files (note that each skill has 2 copies) in sync
5. **Internal vs public naming** - Modules, files, and functions not meant to be part of the public API must use a `_` prefix (e.g., `_odata.py`, `_relationships.py`). Files without the prefix (e.g., `constants.py`, `metadata.py`) are public and importable by SDK consumers

## Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.