How to Visualize Strava 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 Strava-connected Python applications and scripts for visualizing Strava data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Strava data, execute queries, and visualize the results.

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

Connecting to Strava Data

Connecting to Strava 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.

To authenticate to Strava, and connect to your own data or to allow other users to connect to their data, you can use the OAuth standard.

Using OAuth Authentication

You must create a custom OAuth application to connect to Strava. To create a custom OAuth application:

1. Log into the Strava API Settings page
2. Create a new application or select an existing application
3. Set the "Authorization Callback Domain" to your callback URL domain (e.g. localhost)
4. Note down the Client ID and Client Secret

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

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage the process to obtain the OAuthAccessToken.
• OAuthClientId: Set this to the Client ID from your Strava API application.
• OAuthClientSecret: Set this to the Client Secret from your Strava API application.
• CallbackURL: Set this to the redirect URI matching your application's callback domain.

Example connection string:

Profile=C:\profiles\Strava.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackURL=http://localhost:33333;

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

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

engine = create_engine("api:///?Profile=C:\profiles\Strava.apip&AuthScheme=OAuth&InitiateOAuth=GETANDREFRESH&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackURL=http://localhost:33333")

Execute SQL to Strava

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 Athlete WHERE  = ''", engine)

Visualize Strava Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Strava 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 Strava 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\Strava.apip&AuthScheme=OAuth&InitiateOAuth=GETANDREFRESH&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackURL=http://localhost:33333")
df = pandas.read_sql("SELECT ,  FROM Athlete WHERE  = ''", engine)

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