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

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

Connecting to MailChimp Data

Connecting to MailChimp 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 can set the APIKey to the key you generate in your account settings, or, instead of providing your APIKey, you can use the OAuth standard to authenticate the application. OAuth can be used to enable other users to access their own data. To authenticate using OAuth, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app with MailChimp.

See the "Getting Started" chapter in the help documentation for a guide to using OAuth.

After installing the CData MailChimp Connector, follow the procedure below to install the other required modules and start accessing MailChimp 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 MailChimp 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.mailchimp as mod

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

cnxn = mod.connect("APIKey=myAPIKey;")

Create a SQL Statement to Query MailChimp

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

sql = "SELECT Name, Stats_AvgSubRate FROM Lists WHERE Contact_Country = 'US'"

Extract, Transform, and Load the MailChimp Data

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

Loading MailChimp Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Lists table.

Adding New Rows to MailChimp

table1 = [ ['Name','Stats_AvgSubRate'], ['NewName1','NewStats_AvgSubRate1'], ['NewName2','NewStats_AvgSubRate2'], ['NewName3','NewStats_AvgSubRate3'] ]

etl.appenddb(table1, cnxn, 'Lists')

With the CData Python Connector for MailChimp, you can work with MailChimp 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 MailChimp to start building Python apps and scripts with connectivity to MailChimp data. Reach out to our Support Team if you have any questions.

Full Source Code

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

cnxn = mod.connect("APIKey=myAPIKey;")

sql = "SELECT Name, Stats_AvgSubRate FROM Lists WHERE Contact_Country = 'US'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Stats_AvgSubRate'], ['NewName1','NewStats_AvgSubRate1'], ['NewName2','NewStats_AvgSubRate2'], ['NewName3','NewStats_AvgSubRate3'] ]

etl.appenddb(table3, cnxn, 'Lists')