The 本製品 can be used to create ETL applications and pipelines for CSV data in Python using Petl.

Install Required Modules

Install the Petl modules using the pip utility.

pip install petl

Connecting

After you import the modules, including the CData Python Connector for Azure DevOps, you can use the 本製品's connect function to create a connection using a valid Azure DevOps connection string. If you prefer not to use a direct connection, you can use a SQLAlchemy engine.

import petl as etl
import cdata.azuredevops as mod
cnxn = mod.connect("AuthScheme=Basic;Organization=MyAzureDevOpsOrganization;Catalog=Project_dev;Schema=Project;PersonalAccessToken=MyPAT;")

Extract, Transform, and Load the Azure DevOps Data

Create a SQL query string and store the query results in a DataFrame.

sql = "SELECT	Id, BuildNumber FROM WorkItems "
table1 = etl.fromdb(cnxn,sql)

Loading Data

With the query results stored in a DataFrame, you can load your data into any supported Petl destination. The following example loads the data into a CSV file.

etl.tocsv(table1,'output.csv')

Modifying Data

Insert new rows into Azure DevOps tables using Petl's appenddb function.

table1 = [['Id','BuildNumber'],['Jon Doe','John']]
etl.appenddb(table1,cnxn,'WorkItems')