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Get the Report →Use pandas to Visualize FreshBooks Data in Python
The CData Python Connector for FreshBooks enables you use pandas and other modules to analyze and visualize live FreshBooks 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 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()