# azure-data-tables-py

This skill guides Python work with Azure Tables and the Cosmos DB Table API, from entity CRUD and filtered queries to batch transactions.

- skill · `type=skill` · Claude Code, GitHub Copilot · 2 installs · ★ 3149
- Author: Microsoft
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-data-tables-py/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/azure-data-tables-py

## What it does

The skill covers Python examples for creating, listing, and deleting tables, as well as creating, reading, updating, and removing entities. It also explains filtered queries, selecting properties, and using synchronous or asynchronous clients. Each entity needs both `PartitionKey` and `RowKey`.

Batch transactions accept entities from one partition only, so changes across different `PartitionKey` values cannot go into the same batch. The examples use `DefaultAzureCredential` and context managers for client cleanup. Install the SDK packages with `pip install azure-data-tables azure-identity`; the endpoint is set for the Azure Tables or Cosmos DB Table API service you use.

## Requirements
- Works with: Azure

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add microsoft/skills --skill azure-data-tables-py -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/azure-data-tables-py/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/azure-data-tables-py/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/azure-data-tables-py/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/azure-data-tables-py/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install microsoft/skills azure-data-tables-py
```

2. **Manually: put the files in place**
- File: `.github/skills/azure-data-tables-py/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-sdk-python/skills/azure-data-tables-py
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/azure-data-tables-py/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-data-tables-py/SKILL.md

## When to use it
- create a table and list existing tables
- add, fetch, update, or remove an entity
- filter entities by partition and property values
- group changes into a transaction for one partition

## Example prompts
- Write Python code that creates an `orders` table and adds an entity with `PartitionKey` and `RowKey`.
- Show a query for entities in the `sales` partition, filtered by the `quantity` property.
- Build an async example that adds and reads entities with the Azure Tables SDK.

## Source text · SKILL.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: 562dfbf6ef81ddb8.

BEGIN THIRD-PARTY FILE 562dfbf6ef81ddb8
````text
---
name: azure-data-tables-py
description: |
  Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.
  Triggers: "table storage", "TableServiceClient", "TableClient", "entities", "PartitionKey", "RowKey".
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"
  package: azure-data-tables
---

# Azure Tables SDK for Python

NoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API).

## Installation

```bash
pip install azure-data-tables azure-identity
```

## Environment Variables

```bash
# Azure Storage Tables
AZURE_STORAGE_ACCOUNT_URL=https://<account>.table.core.windows.net  # Required for Azure Storage Tables

# Cosmos DB Table API
COSMOS_TABLE_ENDPOINT=https://<account>.table.cosmos.azure.com  # Required for Cosmos DB Table API
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```

## Authentication & Lifecycle

> **🔑 Two rules apply to every code sample below:**
>
> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
>    - Local dev: `DefaultAzureCredential` works as-is.
>    - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) to constrain the credential chain to production-safe credentials.
> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:
>    - Sync: `with <Client>(...) as client:`
>    - Async: `async with <Client>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)
>
> Snippets may abbreviate this setup, but production code should always follow both rules.

```python
import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.data.tables import TableServiceClient, TableClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

endpoint = "https://<account>.table.core.windows.net"

# Service client (manage tables)
with TableServiceClient(endpoint=endpoint, credential=credential) as service_client:
    # Use service_client here (see following sections for operations)
    ...

# Table client (work with entities)
with TableClient(endpoint=endpoint, table_name="mytable", credential=credential) as table_client:
    # Use table_client here (see following sections for operations)
    ...
```

## Client Types

| Client | Purpose |
|--------|---------|
| `TableServiceClient` | Create/delete tables, list tables |
| `TableClient` | Entity CRUD, queries |

## Table Operations

```python
# Create table
service_client.create_table("mytable")

# Create if not exists
service_client.create_table_if_not_exists("mytable")

# Delete table
service_client.delete_table("mytable")

# List tables
for table in service_client.list_tables():
    print(table.name)
````
END THIRD-PARTY FILE 562dfbf6ef81ddb8

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