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Use pandas to Visualize SurveyMonkey Data in Python

The CData Python Connector for SurveyMonkey enables you use pandas and other modules to analyze and visualize live SurveyMonkey 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 SurveyMonkey, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build SurveyMonkey-connected Python applications and scripts for visualizing SurveyMonkey data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to SurveyMonkey data, execute queries, and visualize the results.

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

Connecting to SurveyMonkey Data

Connecting to SurveyMonkey 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.

SurveyMonkey uses the OAuth 2 authentication standard. See the Getting Started section in the help documentation for a guide.

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

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

engine = create_engine("surveymonkey:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to SurveyMonkey

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

df = pandas.read_sql("SELECT RespondentId, ChoiceId FROM MySurvey_Responses WHERE ChoiceText = 'blue'", engine)

Visualize SurveyMonkey Data

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

df.plot(kind="bar", x="RespondentId", y="ChoiceId")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the SurveyMonkey Python Connector to start building Python apps and scripts with connectivity to SurveyMonkey 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("surveymonkey:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT RespondentId, ChoiceId FROM MySurvey_Responses WHERE ChoiceText = 'blue'", engine)

df.plot(kind="bar", x="RespondentId", y="ChoiceId")
plt.show()