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Use pandas to Visualize Authorize.Net Data in Python

The CData Python Connector for Authorize.Net enables you use pandas and other modules to analyze and visualize live Authorize.Net 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 Authorize.Net, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Authorize.Net-connected Python applications and scripts for visualizing Authorize.Net data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Authorize.Net data, execute queries, and visualize the results.

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

Connecting to Authorize.Net Data

Connecting to Authorize.Net 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.

You can obtain the necessary connection properties on the Security Settings -> General Settings page after logging into your Merchant Account.

  • UseSandbox: The Authorize.Net API to be used to process transactions. If you are using a production account, this property can be left blank. If you are using a developer test account, set this to 'TRUE'.
  • LoginID: The API login Id associated with your payment gateway account. This property is used to authenticate that you are authorized to submit website transactions. Note that this value is not the same as the login Id that you use to log in to the Merchant Interface.
  • TransactionKey: The transaction key associated with your payment gateway account. This property is used to authenticate that you are authorized to submit website transactions.

Follow the procedure below to install the required modules and start accessing Authorize.Net 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 Authorize.Net Data in Python

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

engine = create_engine("authorizenet:///?LoginId=MyLoginId&TransactionKey=MyTransactionKey")

Execute SQL to Authorize.Net

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

df = pandas.read_sql("SELECT MarketType, TotalCharge FROM SettledBatchList WHERE IncludeStatistics = 'True'", engine)

Visualize Authorize.Net Data

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

df.plot(kind="bar", x="MarketType", y="TotalCharge")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Authorize.Net Python Connector to start building Python apps and scripts with connectivity to Authorize.Net 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("authorizenet:///?LoginId=MyLoginId&TransactionKey=MyTransactionKey")
df = pandas.read_sql("SELECT MarketType, TotalCharge FROM SettledBatchList WHERE IncludeStatistics = 'True'", engine)

df.plot(kind="bar", x="MarketType", y="TotalCharge")
plt.show()