Azure Table Storage Tutorial - Python Azure Tables SDK with NBA Stats API

Scenario:
The NBA playoffs start on 15 April following the conclusion of the Play-In Tournament on 14 April. In this tutorial, we use the nba_api Python API Client for NBA.com to fetch NBA teams stats and then run the Python Azure Tables SDK to create Azure table and entries. Finally, with Azure Storage Explorer we can easily build search query and visualize the results.

 

Objective:
To illustrate Azure Tables Python SDK usage using NBA API, followed by querying Azure Table Storage with Azure Storage Explorer's Query Builder.

Pre-requisites:
For this example, you would need:

• An Azure Table Storage

Steps:

1. Install nba_api
2. Install Azure Tables client library for Python
3. Python code to retrieve data, create table, and insert entries
4. Run nba.py
5. Visualize the NBA table (with Azure Storage Explorer)
6. Sample query (with Azure Storage Explorer)

 

[STEP 1]: Install nba_api

 

 

pip install nba_api

 

 

[STEP 2]: Install Azure Tables client library for Python

 

 

pip install azure-data-tables

 

 

[STEP 3]: Python code to retrieve data, create table, and insert entries

 

Note:

• replace STORAGE_CONNECTION_STRING with your storage connection string.
• I use "state" as PartitionKey and "abbreviation" as RowKey.
• This article describes best practice when choosing partition & row key for entities.
• The response from calling teams.get_teams() is like the following:

 

 

[ { "id":1610612737, "full_name":"Atlanta Hawks", "abbreviation":"ATL", "nickname":"Hawks", "city":"Atlanta", "state":"Georgia", "year_founded":1949 }, { "id":1610612738, "full_name":"Boston Celtics", "abbreviation":"BOS", "nickname":"Celtics", "city":"Boston", "state":"Massachusetts", "year_founded":1946 }, { "id":1610612739, "full_name":"Cleveland Cavaliers", "abbreviation":"CLE", "nickname":"Cavaliers", "city":"Cleveland", "state":"Ohio", "year_founded":1970 } ]

 

 

nba.py

 

 

from nba_api.stats.static import teams # Retrieve Data result = teams.get_teams() print("- NBA Stats Retrieved") # Create Table from azure.data.tables import TableServiceClient table_service_client = TableServiceClient.from_connection_string(conn_str=STORAGE_CONNECTION_STRING) table_name = "NBA" table_client = table_service_client.create_table(table_name=table_name) print("- Table created") # Create Entity from azure.data.tables import TableServiceClient for team in result: my_entity = { u'PartitionKey': team['state'], u'RowKey': team['abbreviation'], u'id': team['id'], u'city': team['city'], u'state': team['state'], u'nickname': team['nickname'], u'full_name': team['full_name'], u'year_founded': team['year_founded'], u'abbreviation': team['abbreviation'] } entity = table_client.create_entity(entity=my_entity) print("- Entity created")

 

 

[STEP 4]: Run nba.py

 

 

PS C:\Users\xxxxxx> python .\nba.py - NBA Stats Retrieved - Table created - Entity created

 

 

 

[STEP 5]: Visualize the NBA table (with Azure Storage Explorer)

 

The NBA table contains data of the 30 NBA teams with the following info:

• ID
• City Name
• State Name
• Nickname
• Full Name
• Year Founded
• Abbreviation

 

[STEP 6]: Sample query (with Azure Storage Explorer)

 

Query Example: To find out all NBA teams that are based in California and were founded in 1948.

 

1. Click the "Query" button on the upper-left corner of the table.

 

2. Use Query Builder to build the query. Then click ▶ to execute.

 

3. Result is Lakers (LAL) & Kings (SAC).

 

Conclusion:

This example shows how to use the nba_api to retrieve NBA teams stats and the Azure Tables Python SDK to work with Azure Table storage. The Azure Tables client library for Python provides a simple and intuitive API for working with Azure Table Storage, with methods for creating and managing table clients, table operations, and entities.

 

References:

• https://pypi.org/project/nba-api/
• https://learn.microsoft.com/en-us/azure/storage/tables/table-storage-overview 
• https://learn.microsoft.com/en-us/python/api/overview/azure/data-tables-readme?view=azure-python
• https://learn.microsoft.com/en-us/python/api/azure-data-tables/azure.data.tables?view=azure-python