Python Library for getting your Peloton workout data.
As someone who wants to see my progress over time, I've been wanting a way to pull and play with my ride data. However, I'm also cautious about linking myself to too many external parties. As I've been playing with other libraries out there, I wanted something that was a bit more intuitive and would play nicer with the rest of my python code. So, PylotonCycle is born.
import pylotoncycle
username = 'your username or email address'
password = 'your password'
conn = pylotoncycle.PylotonCycle(username, password)
workouts = conn.GetRecentWorkouts(5)
workouts is a list of workouts.
An example of a list element
{'achievement_templates': [{'description': 'Awarded for working out with a '
'friend.',
'id': '<some id hash>',
'image_url': 'https://s3.amazonaws.com/peloton-achievement-images-prod/702495cd985d4791bfd3d25f36e0df72',
'name': 'Dynamic Duo',
'slug': 'two_to_tango'},
{'description': 'Awarded for achieving Silver in '
'the May Cycling Challenge.',
'id': '<some id hash>',
'image_url': 'https://s3.amazonaws.com/challenges-and-tiers-image-prod/6b772477ccd04f189fba16f2f877faad',
'name': 'May Cycling Challenge',
'slug': 'may_cycling_challenge_silver'}],
'created': 1589642476,
'created_at': 1589642476,
'device_time_created_at': 1589617276,
'device_type': 'home_bike_v1',
'device_type_display_name': 'Bike',
'end_time': 1589644336,
'fitbit_id': None,
'fitness_discipline': 'cycling',
'ftp_info': {'ftp': 111,
'ftp_source': 'ftp_workout_source',
'ftp_workout_id': '<some id hash>'},
'has_leaderboard_metrics': True,
'has_pedaling_metrics': True,
'id': '<some id hash>',
'instructor_name': 'Matt Wilpers',
'is_total_work_personal_record': False,
'leaderboard_rank': 5015,
'metrics_type': 'cycling',
'name': 'Cycling Workout',
'overall_summary': {'avg_cadence': 85.48,
'avg_heart_rate': 0.0,
'avg_power': 179.24,
'avg_resistance': 47.61,
'avg_speed': 20.39,
'cadence': 0.0,
'calories': 496.71,
'distance': 10.19,
'heart_rate': 0.0,
'id': '<some id hash>',
'instant': 1589644336,
'max_cadence': 122.0,
'max_heart_rate': 0.0,
'max_power': 255.8,
'max_resistance': 60.95,
'max_speed': 23.48,
'power': 0.0,
'resistance': 0.0,
'seconds_since_pedaling_start': 0,
'speed': 0.0,
'total_work': 322417.21,
'workout_id': '<some id hash>'},
'peloton_id': '<some id hash>',
'platform': 'home_bike',
'ride': {'captions': ['en-US'],
'class_type_ids': ['<some id hash>'],
'content_format': 'video',
'content_provider': 'peloton',
'description': 'Max out the effectiveness of your training with this '
'ride. Instructors will expertly guide you through '
'specific output ranges 1 through 7 to help you build '
'endurance, strength and speed.',
'difficulty_estimate': 6.3779,
'difficulty_level': None,
'difficulty_rating_avg': 6.3779,
'difficulty_rating_count': 17157,
'duration': 1800,
'equipment_ids': [],
'equipment_tags': [],
'excluded_platforms': [],
'extra_images': [],
'fitness_discipline': 'cycling',
'fitness_discipline_display_name': 'Cycling',
'has_closed_captions': True,
'has_free_mode': False,
'has_pedaling_metrics': True,
'home_peloton_id': '<some id hash>',
'id': '<some id hash>',
'image_url': 'https://s3.amazonaws.com/peloton-ride-images/58aa8ebc7d51d09d6513e1a2fab53c4c62c076c6/img_1580922399_a5f1fd0e3a2e48d38ecdd6a3d874820f.png',
'instructor_id': '<some id hash>',
'is_archived': True,
'is_closed_caption_shown': True,
'is_explicit': False,
'is_live_in_studio_only': False,
'language': 'english',
'length': 1940,
'live_stream_id': '<some id hash>-live',
'live_stream_url': None,
'location': 'nyc',
'metrics': ['heart_rate', 'cadence', 'calories'],
'origin_locale': 'en-US',
'original_air_time': 1580919480,
'overall_estimate': 0.9956,
'overall_rating_avg': 0.9956,
'overall_rating_count': 20737,
'pedaling_duration': 1800,
'pedaling_end_offset': 1860,
'pedaling_start_offset': 60,
'rating': 0,
'ride_type_id': '<some id hash>',
'ride_type_ids': ['<some id hash>'],
'sample_vod_stream_url': None,
'scheduled_start_time': 1580920200,
'series_id': '<some id hash>',
'sold_out': False,
'studio_peloton_id': '<some id hash>',
'title': '30 min Power Zone Endurance Ride',
'total_in_progress_workouts': 0,
'total_ratings': 0,
'total_workouts': 32489,
'vod_stream_id': '<some id hash>-vod',
'vod_stream_url': None},
'start_time': 1589642537,
'status': 'COMPLETE',
'strava_id': None,
'timezone': 'America/Los_Angeles',
'title': None,
'total_leaderboard_users': 31240,
'total_work': 322417.21,
'user_id': '<some id hash>',
'workout_type': 'class'}
An example of how you may fetch performance data for a ride
import pprint
conn = pylotoncycle.PylotonCycle(username, password)
workouts = conn.GetRecentWorkouts(5)
for w in workouts:
workout_id = w['id']
resp = conn.GetWorkoutMetricsById(workout_id)
pprint.pprint(resp)
This package is available via pip install.
pip install pylotoncycle
- Lots more to cover. I want to find the right format for pulling in the ride performance data.
- Pull in GPS data for outdoor runs
I'm very happy to take pull requests and fix bugs that come up. But, this is definitely a side project for me.