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

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

Connecting to FreshBooks Data

Connecting to FreshBooks 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 connect to FreshBooks, you can set the CompanyName and Token connection properties. Alternatively, you can use the OAuth authentication standard.

OAuth can be used to enable other users to access their own company data. To authenticate using OAuth, you will need to obtain the OAuthClientId and OAuthClientSecret by registering an app. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

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

engine = create_engine("freshbooks:///?CompanyName=CData&Token=token")

Execute SQL to FreshBooks

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

df = pandas.read_sql("SELECT Username, Credit FROM Clients WHERE Email = 'Captain Hook'", engine)

Visualize FreshBooks Data

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

df.plot(kind="bar", x="Username", y="Credit")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for FreshBooks to start building Python apps and scripts with connectivity to FreshBooks 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("freshbooks:///?CompanyName=CData&Token=token")
df = pandas.read_sql("SELECT Username, Credit FROM Clients WHERE Email = 'Captain Hook'", engine)

df.plot(kind="bar", x="Username", y="Credit")
plt.show()