How to Visualize Foursquare Data in Python with pandas

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Foursquare-connected Python applications and scripts for visualizing Foursquare data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Foursquare data, execute queries, and visualize the results.

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';

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

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

engine = create_engine("api:///?Profile=C:\profiles\Foursquare.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_personal_access_token'")

Execute SQL to Foursquare

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

df = pandas.read_sql("SELECT ,  FROM Autocomplete WHERE Query = 'abc'", engine)

Visualize Foursquare Data

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

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

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.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("api:///?Profile=C:\profiles\Foursquare.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_personal_access_token'")
df = pandas.read_sql("SELECT ,  FROM Autocomplete WHERE Query = 'abc'", engine)

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