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

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

Connecting to Basecamp Data

Connecting to Basecamp 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.

Basecamp uses basic or OAuth 2.0 authentication. To use basic authentication you will need the user and password that you use for logging in to Basecamp. To authenticate to Basecamp via OAuth 2.0, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties by registering an app with Basecamp.

See the Getting Started section in the help documentation for a connection guide.

Additionally, you will need to specify the AccountId connection property. This can be copied from the URL after you log in.

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

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

cnxn = mod.connect("[email protected];Password=test123;")

Create a SQL Statement to Query Basecamp

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

sql = "SELECT Name, DocumentsCount FROM Projects WHERE Drafts = 'True'"

Extract, Transform, and Load the Basecamp Data

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

Loading Basecamp Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Basecamp

table1 = [ ['Name','DocumentsCount'], ['NewName1','NewDocumentsCount1'], ['NewName2','NewDocumentsCount2'], ['NewName3','NewDocumentsCount3'] ]

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

With the CData Python Connector for Basecamp, you can work with Basecamp 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 Basecamp to start building Python apps and scripts with connectivity to Basecamp 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.basecamp as mod

cnxn = mod.connect("[email protected];Password=test123;")

sql = "SELECT Name, DocumentsCount FROM Projects WHERE Drafts = 'True'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','DocumentsCount'], ['NewName1','NewDocumentsCount1'], ['NewName2','NewDocumentsCount2'], ['NewName3','NewDocumentsCount3'] ]

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