How to Visualize Invoiced Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Invoiced-connected Python applications and scripts for visualizing Invoiced data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Invoiced data, execute queries, and visualize the results.

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

Connecting to Invoiced Data

Connecting to Invoiced 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.

Start by setting the Profile connection property to the location of the Invoiced Profile on disk (e.g. C:\profiles\Invoiced.apip). Next, set the ProfileSettings connection property to the connection string for Invoiced (see below).

Invoiced API Profile Settings

In order to authenticate to Invoiced, you'll need to provide your API Key. An API key can be obtained by signing in to your account, and then going to Settings > Developers > API Keys. Set the API Key in the ProfileSettings property to connect.

Follow the procedure below to install the required modules and start accessing Invoiced 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 Invoiced Data in Python

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

engine = create_engine("api:///?Profile=C:\profiles\Invoiced.apip&ProfileSettings='APIKey=your_api_key'")

Execute SQL to Invoiced

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

df = pandas.read_sql("SELECT Id, Name FROM Invoices WHERE Paid = 'false'", engine)

Visualize Invoiced Data

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

df.plot(kind="bar", x="Id", y="Name")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Invoiced 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("api:///?Profile=C:\profiles\Invoiced.apip&ProfileSettings='APIKey=your_api_key'")
df = pandas.read_sql("SELECT Id, Name FROM Invoices WHERE Paid = 'false'", engine)

df.plot(kind="bar", x="Id", y="Name")
plt.show()