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Extract, Transform, and Load FedEx Data in Python

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

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

Connecting to FedEx Data

Connecting to FedEx 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.

There are five pieces of information needed in order to authenticate its actions with the FedEx service. This information is below.

  • Server: This controls the URL where the requests should be sent. Common testing options for this are: "https://gatewaybeta.fedex.com:443/xml", "https://wsbeta.fedex.com:443/xml", "https://gatewaybeta.fedex.com:443/web-service", and "https://wsbeta.fedex.com:443/web-service"
  • DeveloperKey: This is the identifier part of the authentication key for the sender's identity. This value will be provided to you by FedEx after registration.
  • Password: This is the secret part of the authentication key for the sender's identity. This value will be provided to you by FedEx after registration.
  • AccountNumber: This valid 9-digit FedEx account number is used for logging into the FedEx server.
  • MeterNumber: This value is used for submitting requests to FedEx. This value will be provided to you by FedEx after registration.
  • 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.

The Cache Database

Many of the useful tasks available from FedEx require a lot of data. To ensure this data is easy to input and recall later, utilizes a cache database to make these requests. You must set the cache connection properties:

  • CacheProvider: The specific database you are using to cache with. For example, org.sqlite.JDBC.
  • CacheConnection: The connection string to be passed to the cache provider. For example, jdbc:sqlite:C:\users\username\documents\fedexcache.db

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

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

cnxn = mod.connect("Server='https://gatewaybeta.fedex.com:443/xml';DeveloperKey='alsdkfjpqoewiru';Password='zxczxqqtyiuowkdlkn';AccountNumber='110371337';MeterNumber='240134349';
PrintLabelLocation='C:\users\username\documents\mylabels';CacheProvider='org.sqlite.JDBC';CacheConnection='jdbc:sqlite:C:\users\username\documents\fedexcache.db';")

Create a SQL Statement to Query FedEx

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

sql = "SELECT FirstName, Phone FROM Senders WHERE SenderID = 'ab26f704-5edf-4a9f-9e4c-25'"

Extract, Transform, and Load the FedEx Data

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

Loading FedEx 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 FedEx

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

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

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

cnxn = mod.connect("Server='https://gatewaybeta.fedex.com:443/xml';DeveloperKey='alsdkfjpqoewiru';Password='zxczxqqtyiuowkdlkn';AccountNumber='110371337';MeterNumber='240134349';
PrintLabelLocation='C:\users\username\documents\mylabels';CacheProvider='org.sqlite.JDBC';CacheConnection='jdbc:sqlite:C:\users\username\documents\fedexcache.db';")

sql = "SELECT FirstName, Phone FROM Senders WHERE SenderID = 'ab26f704-5edf-4a9f-9e4c-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')