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

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

Connecting to Foursquare Data

Connecting to Foursquare 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.

Using API Key Authentication

Foursquare Places API uses Service Key (Bearer token) authentication. To obtain a Service Key:

1. Go to the Foursquare Developer Console at https://foursquare.com/developers/
2. Create a new project or select an existing one
3. Navigate to the API Keys section
4. Generate a new Service Key for the Places API

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• ServiceKey: Set this to your Foursquare Service Key obtained from the Developer Console.
• XPlacesApiVersion: (Optional) Set this to the API version date. Defaults to 2025-06-17.

Example APIKey connection string

Profile=C:\profiles\Foursquare.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_personal_access_token';

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

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

cnxn = mod.connect("Profile=C:\profiles\Foursquare.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_personal_access_token';")

Create a SQL Statement to Query Foursquare

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

sql = "SELECT , FROM Autocomplete WHERE Query = 'abc'"

Extract, Transform, and Load the Foursquare Data

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

Loading Foursquare Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Foursquare 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 API Driver for Python to start building Python apps and scripts with connectivity to Foursquare 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Foursquare.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_personal_access_token';")

sql = "SELECT , FROM Autocomplete WHERE Query = 'abc'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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