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Use pandas to Visualize FedEx Data in Python

The CData Python Connector for FedEx enables you use pandas and other modules to analyze and visualize live FedEx data in Python.

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build FedEx-connected Python applications and scripts for visualizing FedEx data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to FedEx data, execute queries, and visualize the results.

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

Follow the procedure below to install the required modules and start accessing FedEx through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize FedEx Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with FedEx data.

engine = create_engine("fedex:///?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'")

Execute SQL to FedEx

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT FirstName, Phone FROM Senders WHERE SenderID = 'ab26f704-5edf-4a9f-9e4c-25'", engine)

Visualize FedEx Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the FedEx data. The show method displays the chart in a new window.

df.plot(kind="bar", x="FirstName", y="Phone")
plt.show()

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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("fedex:///?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'")
df = pandas.read_sql("SELECT FirstName, Phone FROM Senders WHERE SenderID = 'ab26f704-5edf-4a9f-9e4c-25'", engine)

df.plot(kind="bar", x="FirstName", y="Phone")
plt.show()