How to Visualize Harvest 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 Harvest-connected Python applications and scripts for visualizing Harvest data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Harvest data, execute queries, and visualize the results.

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

Connecting to Harvest Data

Connecting to Harvest 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 Harvest Profile on disk (e.g. C:\profiles\Harvest.apip). Next, set the ProfileSettings connection property to the connection string for Harvest (see below).

Harvest API Profile Settings

To authenticate to Harvest, you can use either Token authentication or the OAuth standard. Use Basic authentication to connect to your own data. Use OAuth to allow other users to connect to their data.

Using Token Authentication

To use Token Authentication, set the APIKey to your Harvest Personal Access Token in the ProfileSettings connection property. In addition to APIKey, set your AccountId in ProfileSettings to connect.

Using OAuth Authentication

First, register an OAuth2 application with Harvest. The application can be created from the "Developers" section of Harvest ID.

After setting the following connection properties, you are ready to connect:

• ProfileSettings: Set your AccountId in ProfileSettings.
• AuthScheme: Set this to OAuth.
• OAuthClientId: Set this to the client ID that you specified in your app settings.
• OAuthClientSecret: Set this to the client secret that you specified in your app settings.
• CallbackURL: Set this to the Redirect URI that you specified in your app settings.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage how the driver obtains and refreshes the OAuthAccessToken.

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

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

engine = create_engine("api:///?Profile=C:\profiles\Harvest.apip&ProfileSettings='APIKey=my_personal_key&AccountId=_your_account_id'")

Execute SQL to Harvest

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, ClientName FROM Invoices WHERE State = 'open'", engine)

Visualize Harvest Data

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

df.plot(kind="bar", x="Id", y="ClientName")
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 Harvest 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\Harvest.apip&ProfileSettings='APIKey=my_personal_key&AccountId=_your_account_id'")
df = pandas.read_sql("SELECT Id, ClientName FROM Invoices WHERE State = 'open'", engine)

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