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

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

Connecting to Outreach.io Data

Connecting to Outreach.io 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 must use OAuth to authenticate with Outreach. Set the InitiateOAuth connection property to "GETANDREFRESH". For more information, refer to the OAuth section in the Help documentation.

Follow the procedure below to install the required modules and start accessing Outreach.io 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 Outreach.io Data in Python

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

engine = create_engine("outreach:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Outreach.io

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

df = pandas.read_sql("SELECT Name, NumberOfEmployees FROM Accounts WHERE Industry = 'Textiles'", engine)

Visualize Outreach.io Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Outreach.io to start building Python apps and scripts with connectivity to Outreach.io 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("outreach:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Name, NumberOfEmployees FROM Accounts WHERE Industry = 'Textiles'", engine)

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