Use Dash to Build to Web Apps on Strava Data

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 module, and the Dash framework, you can build Strava-connected web applications for Strava data. This article shows how to connect to Strava with the CData Connector and use pandas and Dash to build a simple web app for visualizing Strava data.

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;

After installing the CData Strava Connector, follow the procedure below to install the other required modules and start accessing Strava through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize Strava Data in Python

Once the required modules and frameworks are installed, we are ready to build our web 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 os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

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

cnxn = mod.connect("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 result set in a DataFrame.

df = pd.read_sql("SELECT , FROM Athlete WHERE = ''", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-apiedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our Strava data and configure the app layout.

trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Strava Athlete Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
 app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the Strava data.

python api-dash.py

👁 Strava data in a Dash web app (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Strava data. Reach out to our Support Team if you have any questions.

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Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\Strava.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackURL=http://localhost:33333;")

df = pd.read_sql("SELECT , FROM Athlete WHERE = ''", cnxn)
app_name = 'dash-apidataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Strava Athlete Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
 app.run_server(debug=True)