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 LinkedIn Marketing Solutions, you can use the 本製品's connect function to create a connection using a valid LinkedIn Marketing Solutions connection string. If you prefer not to use a direct connection, you can use a SQLAlchemy engine.import petl as etl import cdata.linkedinads as mod cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;")
Extract, Transform, and Load the LinkedIn Marketing Solutions Data
Create a SQL query string and store the query results in a DataFrame.sql = "SELECT VisibilityCode, Comment FROM Analytics " 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')