Petl から
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 Ponparemall, you can use the 本製品's connect function to create a connection using a valid Ponparemall connection string. If you prefer not to use a direct connection, you can use a SQLAlchemy engine.import petl as etl import cdata.ponparemall as mod cnxn = mod.connect("InventoryUserId=Inventory UserId;InventoryPassword=Invetory Password")
Extract, Transform, and Load the Ponparemall Data
Create a SQL query string and store the query results in a DataFrame.sql = "SELECT OrderNumber, OrderStatus FROM Orders " 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 Ponparemall tables using Petl's appenddb function.table1 = [['OrderNumber','OrderStatus'],['Jon Doe','John']] etl.appenddb(table1,cnxn,'Orders')