How to Build an ETL App for SurveyMonkey Data in Python with CData

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 and the petl framework, you can build SurveyMonkey-connected applications and pipelines for extracting, transforming, and loading SurveyMonkey data. This article shows how to connect to SurveyMonkey with the CData Python Connector and use petl and pandas to extract, transform, and load SurveyMonkey data.

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.

After installing the CData SurveyMonkey Connector, follow the procedure below to install the other required modules and start accessing SurveyMonkey through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for SurveyMonkey Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.surveymonkey as mod

You can now connect with a connection string. Use the connect function for the CData SurveyMonkey Connector to create a connection for working with SurveyMonkey data.

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query SurveyMonkey

Use SQL to create a statement for querying SurveyMonkey. In this article, we read data from the MySurvey_Responses entity.

sql = "SELECT RespondentId, ChoiceId FROM MySurvey_Responses WHERE ChoiceText = 'blue'"

Extract, Transform, and Load the SurveyMonkey Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the SurveyMonkey data. In this example, we extract SurveyMonkey data, sort the data by the ChoiceId column, and load the data into a CSV file.

Loading SurveyMonkey Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ChoiceId')

etl.tocsv(table2,'mysurvey_responses_data.csv')

With the CData Python Connector for SurveyMonkey, you can work with SurveyMonkey data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for SurveyMonkey to start building Python apps and scripts with connectivity to SurveyMonkey data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.surveymonkey as mod

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT RespondentId, ChoiceId FROM MySurvey_Responses WHERE ChoiceText = 'blue'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ChoiceId')

etl.tocsv(table2,'mysurvey_responses_data.csv')