Extract, Transform, and Load USPS Data in Python

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USPS Python Connector

Python Connector Libraries for USPS Data Connectivity. Integrate USPS with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for USPS enables you to create ETL applications and pipelines for USPS 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 USPS and the petl framework, you can build USPS-connected applications and pipelines for extracting, transforming, and loading USPS data. This article shows how to connect to USPS with the CData Python Connector and use petl and pandas to extract, transform, and load USPS data.

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

Connecting to USPS Data

Connecting to USPS 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.

To authenticate with USPS, set the following connection properties.

  • PostageProvider: The postage provider to use to process requests. Available options are ENDICIA and STAMPS. If unspecified, this property will default to ENDICIA.
  • UseSandbox: This controls whether live or test requests are sent to the production or sandbox servers. If set to true, the Password, AccountNumber, and StampsUserId properties are ignored.
  • StampsUserId: This value is used for logging into authentication to the Stamps servers. This value is not applicable for Endicia and is optional if UseSandbox is true.
  • Password: This value is used for logging into Endicia and Stamps servers. If the postage provider is Endicia, this will be the pass phrase associated with your postage account. It is optional if UseSandbox is true.
  • AccountNumber: The shipper's account number. It is optional if UseSandbox is true.
  • PrintLabelLocation: This property is required to use the GenerateLabels or GenerateReturnLabels stored procedures. This should be set to the folder location where generated labels should be stored.

The Cache Database

Many of the useful task available from USPS require a lot of data. To ensure this data is easy to input and recall later, utilize a cache database to make requests. Set the cache connection properties in order to use the cache:

  • CacheLocation: The path to the cache location, for which a connection will be configured with the default cache provider. For example, C:\users\username\documents\uspscache

As an alternative to CacheLocation, set the combination of CacheConnection and CacheProvider to configure a cache connection using a provider separate from the default.

After installing the CData USPS Connector, follow the procedure below to install the other required modules and start accessing USPS 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 USPS 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.usps as mod

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

cnxn = mod.connect("PostageProvider=ENDICIA; RequestId=12345; Password='abcdefghijklmnopqr'; AccountNumber='12A3B4C'")

Create a SQL Statement to Query USPS

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

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

Extract, Transform, and Load the USPS Data

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

Loading USPS 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 USPS

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

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

With the CData Python Connector for USPS, you can work with USPS 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 USPS Python Connector to start building Python apps and scripts with connectivity to USPS 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.usps as mod

cnxn = mod.connect("PostageProvider=ENDICIA; RequestId=12345; Password='abcdefghijklmnopqr'; AccountNumber='12A3B4C'")

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')