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Extract, Transform, and Load UPS Data in Python

The CData Python Connector for UPS enables you to create ETL applications and pipelines for UPS data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for UPS and the petl framework, you can build UPS-connected applications and pipelines for extracting, transforming, and loading UPS data. This article shows how to connect to UPS with the CData Python Connector and use petl and pandas to extract, transform, and load UPS data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live UPS data in Python. When you issue complex SQL queries from UPS, the driver pushes supported SQL operations, like filters and aggregations, directly to UPS and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to UPS Data

Connecting to UPS data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The driver uses five pieces of information in order to authenticate its actions with the UPS service.

  • Server: This controls the URL where the requests should be sent. Common testing options for this are: https://wwwcie.ups.com/ups.app/xml and https://wwwcie.ups.com/webservices
  • AccessKey: This is an identifier that is required to connect to a UPS Server. This value will be provided to you by UPS after registration.
  • UserId: This value is used for logging into UPS. This value is the one you chose to login with when registering for service with UPS.
  • Password: This value is used for logging into UPS. This value is the one you chose to login with when registering for service with UPS.
  • AccountNumber: This is a valid 6-digit or 10-digit UPS account number.
  • PrintLabelLocation: This property is required if one intends to use the GenerateLabels or GenerateReturnLabels stored procedures. This should be set to the folder location where generated labels should be stored.

After installing the CData UPS Connector, follow the procedure below to install the other required modules and start accessing UPS through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for UPS Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.ups as mod

You can now connect with a connection string. Use the connect function for the CData UPS Connector to create a connection for working with UPS data.

cnxn = mod.connect("Server=https://wwwcie.ups.com/ups.app/xml;AccessKey=myAccessKey;Password=myPassword;AccountNumber=myAccountNumber;UserId=myUserIdInitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query UPS

Use SQL to create a statement for querying UPS. In this article, we read data from the Senders entity.

sql = "SELECT FirstName, Phone FROM Senders WHERE SenderID = '25'"

Extract, Transform, and Load the UPS Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the UPS data. In this example, we extract UPS data, sort the data by the Phone column, and load the data into a CSV file.

Loading UPS Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Phone')

etl.tocsv(table2,'senders_data.csv')

In the following example, we add new rows to the Senders table.

Adding New Rows to UPS

table1 = [ ['FirstName','Phone'], ['NewFirstName1','NewPhone1'], ['NewFirstName2','NewPhone2'], ['NewFirstName3','NewPhone3'] ]

etl.appenddb(table1, cnxn, 'Senders')

With the CData Python Connector for UPS, you can work with UPS data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the UPS Python Connector to start building Python apps and scripts with connectivity to UPS data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.ups as mod

cnxn = mod.connect("Server=https://wwwcie.ups.com/ups.app/xml;AccessKey=myAccessKey;Password=myPassword;AccountNumber=myAccountNumber;UserId=myUserIdInitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT FirstName, Phone FROM Senders WHERE SenderID = '25'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Phone')

etl.tocsv(table2,'senders_data.csv')

table3 = [ ['FirstName','Phone'], ['NewFirstName1','NewPhone1'], ['NewFirstName2','NewPhone2'], ['NewFirstName3','NewPhone3'] ]

etl.appenddb(table3, cnxn, 'Senders')