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

Import the modules, including the CData Python Connector for Jira. You can then use the 本製品's connect function to create a connection using a valid Jira connection string. A SQLAlchemy engine may also be used instead of a direct connection.

import petl as etl
import cdata.jira as mod
cnxn = mod.connect("User=admin;APIToken=myApiToken;Url=https://yoursitename.atlassian.net")

Extract, Transform, and Load the Jira Data

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

sql = "SELECT	Key, Name FROM Projects "
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 Jira tables using Petl's appenddb function.

table1 = [['Key','Name'],['Test Project.','New project!']]
etl.appenddb(table1,cnxn,'Projects')