How To Analyze Apple Health Data With Python

Patrick Loeber
Thumbnail image of the post.

In this article we learn how to analyze Apple Health Data with Python. We learn how to

Get the data

On your iPhone go to Health App -> Profile -> Export data -> Send to your computer.

This should send a file to your Downloads folder. Create a new folder for this project and put the Export.xml file into another subfolder called data. All other files can be ignored here.


We need pandas and matplotlib. Create a new project and install both libraries, e.g. with pip:

$ pip install pandas matplotlib

Import XML in Python

Create a new file (or a jupyter notebook) in the base of your project folder and then import the data:

import xml.etree.ElementTree as ET import pandas as pd import datetime as dt import matplotlib.pyplot as plt"fivethirtyeight") # create element tree object tree = ET.parse('data/Export.xml') # for every health record, extract the attributes root = tree.getroot() record_list = [x.attrib for x in root.iter('Record')]

Create Pandas DataFrame

Create a pandas DataFrame. Then we adjust some data types and value names:

record_data = pd.DataFrame(record_list) # proper type to dates for col in ['creationDate', 'startDate', 'endDate']: record_data[col] = pd.to_datetime(record_data[col]) # value is numeric, NaN if fails record_data['value'] = pd.to_numeric(record_data['value'], errors='coerce') # some records do not measure anything, just count occurences # filling with 1.0 (= one time) makes it easier to aggregate record_data['value'] = record_data['value'].fillna(1.0) # shorter observation names record_data['type'] = record_data['type'].str.replace('HKQuantityTypeIdentifier', '') record_data['type'] = record_data['type'].str.replace('HKCategoryTypeIdentifier', '') record_data.tail()

The record_data looks like this:

alt text

Extract workouts

Extract only the workout data and create another DataFrame for it:

workout_list = [x.attrib for x in root.iter('Workout')] # create DataFrame workout_data = pd.DataFrame(workout_list) workout_data['workoutActivityType'] = workout_data['workoutActivityType'].str.replace('HKWorkoutActivityType', '') workout_data = workout_data.rename({"workoutActivityType": "Type"}, axis=1) # proper type to dates for col in ['creationDate', 'startDate', 'endDate']: workout_data[col] = pd.to_datetime(workout_data[col]) # convert string to numeric workout_data['duration'] = pd.to_numeric(workout_data['duration']) workout_data['totalEnergyBurned'] = pd.to_numeric(workout_data['totalEnergyBurned']) workout_data['totalDistance'] = pd.to_numeric(workout_data['totalDistance']) workout_data.tail()

This will show something like this. (Note: The additional columns device, creationDate, startDate, and endDate are not visible on this image:

alt text

Get the number of workouts

num_workouts = workout_data.shape[0]

Filter workouts by type

def get_workouts(df, workout_type): return df[df["Type"] == workout_type] # to see the different workout types use: print(workout_data.Type.unique()) # ['Running' 'FunctionalStrengthTraining' 'Yoga' # 'HighIntensityIntervalTraining' 'CoreTraining'] running_data = get_workouts(workout_data, "Running")

Get workouts in certain time period

def get_workouts_from_to(df, start, end): start = pd.to_datetime(start, utc=True) end = pd.to_datetime(end, utc=True) workouts = df[df["creationDate"] >= start] workouts = workouts[workouts["creationDate"] <= end] return workouts lower_time =, 1, 1) upper_time =, 1, 1) workouts = get_workouts_from_to(workout_data, lower_time, upper_time) # or relative to the current day today = xdaysago = today - dt.timedelta(days=7) # first_of_month = today - dt.timedelta( - 1) workouts = get_workouts_from_to(workout_data, xdaysago, today)

Get last workout

last_workout = workouts.iloc[[-1]]

Extract heart rate

Extract the heart rate data and put it into a DataFrame. Then we can extract heart rate statistics for certain workouts:

def get_heartrate_for_workout(heartrate, workout): def get_heartrate_for_date(hr, start, end): hr = hr[hr["startDate"] >= start] hr = hr[hr["endDate"] <= end] return hr return get_heartrate_for_date(heartrate, workout["startDate"].item(), workout["endDate"].item()) heartrate_data = record_data[record_data["type"] == "HeartRate"] # Extract heartrate statistics for certain workout last_workout = workouts.iloc[[-1]] heartrate_workout = get_heartrate_for_workout(heartrate_data, last_workout) minh = heartrate_workout["value"].min() maxh = heartrate_workout["value"].max() meanh = heartrate_workout["value"].mean() print(last_workout.Type.item(), minh, maxh, meanh) # HighIntensityIntervalTraining 74.0 176.0 151.2590909090909

Plot heart rate for workout

heartrate_workout.plot(x='endDate', y='value', style='r|', markersize=8.5, figsize=(12, 6))
alt text

Put more statistics into DataFrame

def get_hr_for_workout_row(workout, heartrate): def get_hr_for_date(hr, start, end): hr = hr[hr["startDate"] >= start] hr = hr[hr["endDate"] <= end] return hr return get_hr_for_date(heartrate, workout["startDate"], workout["endDate"]) def convert_to_minute_proportion(number): return int(number) + ((number % 1) / 100 * 60) def get_pace_for_workout(workout): if workout["totalDistance"] == 0.0: return 0.0 # pace=min/km pace = workout["duration"] / workout["totalDistance"] return convert_to_minute_proportion(pace) workouts["heartrate"] = workouts.apply(lambda row: get_hr_for_workout_row(row, heartrate_data), axis=1) workouts["hr_mean"] = workouts.apply(lambda row: row['heartrate']["value"].mean(), axis=1) workouts["pace"] = workouts.apply(lambda row: get_pace_for_workout(row), axis=1)

Get statistics

def get_stats(workouts): total_kcal = workouts["totalEnergyBurned"].sum() total_dist = workouts["totalDistance"].sum() total_time = workouts["duration"].sum() total_time_hours = convert_to_minute_proportion(total_time / 60) total_time_mins = convert_to_minute_proportion(total_time) avg_kcal = workouts["totalEnergyBurned"].mean() avg_dist = workouts[workouts["Type"] == "Running"]["totalDistance"].mean() avg_pace = workouts[workouts["Type"] == "Running"]["pace"].mean() avg_time = workouts["duration"].mean() avg_time_hours = convert_to_minute_proportion(avg_time / 60) avg_time_mins = convert_to_minute_proportion(avg_time) print(f"Workout statistics from {lower_time} to {upper_time-dt.timedelta(days=1)}") print(f"{workouts.shape[0]} workouts") print(f"Time: {total_time_mins:.2f} minutes ({total_time_hours:.2f} hours)\nCalories burned: {total_kcal:.2f}kcal\nRunning distance: {total_dist:.2f}km") print("\nAverage per workout:") print(f"Time: {avg_time_mins:.2f} minutes ({avg_time_hours:.2f} hours)\nCalories burned: {avg_kcal:.2f}kcal\nRunning distance: {avg_dist:.2f}km\nRunning pace: {avg_pace:.2f}km/h") get_stats(workouts)
Workout statistics from 2021-01-01 to 2021-12-31 133 workouts Time: 2699.33 minutes (44.60 hours) Calories burned: 25630.65kcal Running distance: 311.67km Average per workout: Time: 20.18 minutes (0.20 hours) Calories burned: 192.71kcal Running distance: 3.90km Running pace: 5.69km/h

Plot workout pie chart

def plot_workouts(workouts): labels = [] slices = [] for wo_type in workouts.Type.unique(): labels.append(wo_type) wo_of_type = workouts[workouts["Type"] == wo_type] num_workouts_of_type = wo_of_type.shape[0] slices.append(num_workouts_of_type) def make_autopct(values): def my_autopct(pct): total = sum(values) val = int(round(pct*total/100.0)) return '{p:.2f}% ({v:d})'.format(p=pct,v=val) return my_autopct plt.figure(figsize=(10, 10)) plt.pie(slices, labels=labels, shadow=True, startangle=90, autopct=make_autopct(slices), wedgeprops={'edgecolor': 'black'}) plt.title("Workouts in 2021") plt.tight_layout() plot_workouts(workouts)
alt text

FREE VS Code / PyCharm Extensions I Use

✅ Write cleaner code with Sourcery, instant refactoring suggestions: Link *

* This is an affiliate link. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you! 🙏

Check out my Courses