How to use Azure Table storage with Python


The content in this article applies to Azure Table storage. However, there is now a premium offering for table storage, the Azure Cosmos DB Table API that offers throughput-optimized tables, global distribution, and automatic secondary indexes. To learn more and try out the premium experience, please check out Azure Cosmos DB Table API. This article's programming language is not yet supported in the premium offering, but will be added in the future.

This guide shows you how to perform common Azure Table storage scenarios in Python using the Microsoft Azure Storage SDK for Python. The scenarios covered include creating and deleting a table, and inserting and querying entities.

While working through the scenarios in this tutorial, you may wish to refer to the Azure Storage SDK for Python API reference.

What is Table storage

Azure Table storage stores large amounts of structured data. The service is a NoSQL datastore which accepts authenticated calls from inside and outside the Azure cloud. Azure tables are ideal for storing structured, non-relational data. Common uses of Table storage include:

  • Storing TBs of structured data capable of serving web scale applications
  • Storing datasets that don't require complex joins, foreign keys, or stored procedures and can be denormalized for fast access
  • Quickly querying data using a clustered index
  • Accessing data using the OData protocol and LINQ queries with WCF Data Service .NET Libraries

You can use Table storage to store and query huge sets of structured, non-relational data, and your tables will scale as demand increases.

Table storage concepts

Table storage contains the following components:

Tables storage component diagram

  • URL format: Code addresses tables in an account using this address format:
    http://<storage account><table>

    You can address Azure tables directly using this address with the OData protocol. For more information, see

  • Storage Account: All access to Azure Storage is done through a storage account. See Azure Storage Scalability and Performance Targets for details about storage account capacity.
  • Table: A table is a collection of entities. Tables don't enforce a schema on entities, which means a single table can contain entities that have different sets of properties. The number of tables that a storage account can contain is limited only by the storage account capacity limit.
  • Entity: An entity is a set of properties, similar to a database row. An entity can be up to 1MB in size.
  • Properties: A property is a name-value pair. Each entity can include up to 252 properties to store data. Each entity also has three system properties that specify a partition key, a row key, and a timestamp. Entities with the same partition key can be queried more quickly, and inserted/updated in atomic operations. An entity's row key is its unique identifier within a partition.

For details about naming tables and properties, see Understanding the Table Service Data Model.

Create an Azure storage account

The easiest way to create your first Azure storage account is by using the Azure portal. To learn more, see Create a storage account.

You can also create an Azure storage account by using Azure PowerShell, Azure CLI, or the Storage Resource Provider Client Library for .NET.

If you prefer not to create a storage account at this time, you can also use the Azure storage emulator to run and test your code in a local environment. For more information, see Use the Azure Storage Emulator for Development and Testing.

Install the Azure Storage SDK for Python

Once you've created a storage account, your next step is to install the Microsoft Azure Storage SDK for Python. For details on installing the SDK, refer to the README.rst file in the Storage SDK for Python repository on GitHub.

Create a table

To work with the Azure Table service in Python, you must import the TableService module. Since you'll be working with Table entities, you also need the Entity class. Add this code near the top your Python file to import both:

from import TableService, Entity

Create a TableService object, passing in your storage account name and account key. Replace myaccount and mykey with your account name and key, and call create_table to create the table in Azure Storage.

table_service = TableService(account_name='myaccount', account_key='mykey')


Add an entity to a table

To add an entity, you first create an object that represents your entity, then pass the object to the TableService.insert_entity method. The entity object can be a dictionary or an object of type Entity, and defines your entity's property names and values. Every entity must include the required PartitionKey and RowKey properties, in addition to any other properties you define for the entity.

This example creates a dictionary object representing an entity, then passes it to the insert_entity method to add it to the table:

task = {'PartitionKey': 'tasksSeattle', 'RowKey': '001', 'description' : 'Take out the trash', 'priority' : 200}
table_service.insert_entity('tasktable', task)

This example creates an Entity object, then passes it to the insert_entity method to add it to the table:

task = Entity()
task.PartitionKey = 'tasksSeattle'
task.RowKey = '002'
task.description = 'Wash the car'
task.priority = 100
table_service.insert_entity('tasktable', task)

PartitionKey and RowKey

You must specify both a PartitionKey and a RowKey property for every entity. These are the unique identifiers of your entities, as together they form the primary key of an entity. You can query using these values much faster than you can query any other entity properties because only these properties are indexed.

The Table service uses PartitionKey to intelligently distribute table entities across storage nodes. Entities that have the same PartitionKey are stored on the same node. RowKey is the unique ID of the entity within the partition it belongs to.

Update an entity

To update all of an entity's property values, call the update_entity method. This example shows how to replace an existing entity with an updated version:

task = {'PartitionKey': 'tasksSeattle', 'RowKey': '001', 'description' : 'Take out the garbage', 'priority' : 250}
table_service.update_entity('tasktable', task)

If the entity that is being updated doesn't already exist, then the update operation will fail. If you want to store an entity whether it exists or not, use insert_or_replace_entity. In the following example, the first call will replace the existing entity. The second call will insert a new entity, since no entity with the specified PartitionKey and RowKey exists in the table.

# Replace the entity created earlier
task = {'PartitionKey': 'tasksSeattle', 'RowKey': '001', 'description' : 'Take out the garbage again', 'priority' : 250}
table_service.insert_or_replace_entity('tasktable', task)

# Insert a new entity
task = {'PartitionKey': 'tasksSeattle', 'RowKey': '003', 'description' : 'Buy detergent', 'priority' : 300}
table_service.insert_or_replace_entity('tasktable', task)


The update_entity method replaces all properties and values of an existing entity, which you can also use to remove properties from an existing entity. You can use the merge_entity method to update an existing entity with new or modified property values without completely replacing the entity.

Modify multiple entities

To ensure the atomic processing of a request by the Table service, you can submit multiple operations together in a batch. First, use the TableBatch class to add multiple operations to a single batch. Next, call TableService.commit_batch to submit the operations in an atomic operation. All entities to be modified in batch must be in the same partition.

This example adds two entities together in a batch:

from import TableBatch
batch = TableBatch()
task004 = {'PartitionKey': 'tasksSeattle', 'RowKey': '004', 'description' : 'Go grocery shopping', 'priority' : 400}
task005 = {'PartitionKey': 'tasksSeattle', 'RowKey': '005', 'description' : 'Clean the bathroom', 'priority' : 100}
table_service.commit_batch('tasktable', batch)

Batches can also be used with the context manager syntax:

task006 = {'PartitionKey': 'tasksSeattle', 'RowKey': '006', 'description' : 'Go grocery shopping', 'priority' : 400}
task007 = {'PartitionKey': 'tasksSeattle', 'RowKey': '007', 'description' : 'Clean the bathroom', 'priority' : 100}

with table_service.batch('tasktable') as batch:

Query for an entity

To query for an entity in a table, pass its PartitionKey and RowKey to the TableService.get_entity method.

task = table_service.get_entity('tasktable', 'tasksSeattle', '001')

Query a set of entities

You can query for a set of entities by supplying a filter string with the filter parameter. This example finds all tasks in Seattle by applying a filter on PartitionKey:

tasks = table_service.query_entities('tasktable', filter="PartitionKey eq 'tasksSeattle'")
for task in tasks:

Query a subset of entity properties

You can also restrict which properties are returned for each entity in a query. This technique, called projection, reduces bandwidth and can improve query performance, especially for large entities or result sets. Use the select parameter and pass the names of the properties you want returned to the client.

The query in the following code returns only the descriptions of entities in the table.


The following snippet works only against the Azure Storage. It is not supported by the storage emulator.

tasks = table_service.query_entities('tasktable', filter="PartitionKey eq 'tasksSeattle'", select='description')
for task in tasks:

Delete an entity

Delete an entity by passing its PartitionKey and RowKey to the delete_entity method.

table_service.delete_entity('tasktable', 'tasksSeattle', '001')

Delete a table

If you no longer need a table or any of the entities within it, call the delete_table method to permanently delete the table from Azure Storage.


Next steps