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Analyze Apple Health data with Python

It is a while since I published my last post here. There are several reasons that I don't write anything, for example, SAP does not publish new features for Analysis for Office or my current project has no special cool new things I can talk about because it is mostly just maintenance and nothing hip. So it was very quiet here, and this is what I want to change. If you follow me on Twitter, you could have seen this post.

So I will write some posts about Python, Data Warehouse Cloud, and some ABAP topics in the near future. This post starts with Python and how to analyze the Apple Health data.

 

You can export the health data from your iPhone and receive a ZIP file that contains an XML file. How you can do this can be found via Google. It is uncomplicated. In my case, my XML file was around 1 GB big, and it contains about 3 million entries until May 2021. So I could not analyze it with Microsoft Excel or Notepad++, and I need only some information out of it, so I tried Python. I work with Python just more than one year, so please be kind if it is not perfectly written code.

First we have to load the XML data. 

from lxml import etree

tree = etree.parse(r'path_to_xml\Export.xml')
root = tree.getroot()
# consider only records no workouts
records = tree.xpath("//Record")

With this code snippet, we load the XML file. We are only interested in the record data, so we read only this and not the workout data. If you are interested in your workout data, I will create another example. The XML path is here //Workouts. The next step is to get the data and put it into a data frame.

import pandas as pd

# fields we want to get
DATETIME_KEYS = ["startDate", "endDate"]
NUMERIC_KEYS = ["value"]
OTHER_KEYS = ["type", "sourceName", "unit"]
ALL_KEYS = DATETIME_KEYS + NUMERIC_KEYS + OTHER_KEYS

# Get all records where as source the apple watch is
df = pd.DataFrame([{key: r.get(key) for key in ALL_KEYS} for r in records if 'Apple' in r.attrib['sourceName']])

Now we have all data in the data frame and can display it with df.tail()

Raw data in a pandas data frame
Raw data in a pandas data frame

In this example I want to focus on my steps, so we filter for the type HKQuantityTypeIdentifierStepCount.

df_steps = df.query('type == "HKQuantityTypeIdentifierStepCount"')

 

Now we have in the new data frame df_steps only the step data. We can now save the data frame to a CSV file, and we can process it further.

df_steps.to_csv("data_health/steps.csv", index=False)

The XML file with almost 1 GB size has become a CSV file with 50 MB. Now you can use Microsoft Excel to open it or process the data further with Python. I found a blog post that also analyzes Apple Health data. I use this blog post to analyze my steps. First, we have to import some libraries.

from datetime import date, datetime, timedelta as td
import pytz
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

After that, I use the conversion for the time column to get time fields I can use for aggregation. In my case, I use the time zone Berlin.

convert_tz = lambda x: x.to_pydatetime().replace(tzinfo=pytz.utc).astimezone(pytz.timezone('Europe/Berlin'))
get_year = lambda x: convert_tz(x).year
get_month = lambda x: '{}-{:02}'.format(convert_tz(x).year, convert_tz(x).month) #inefficient
get_date = lambda x: '{}-{:02}-{:02}'.format(convert_tz(x).year, convert_tz(x).month, convert_tz(x).day) #inefficient
get_day = lambda x: convert_tz(x).day
get_hour = lambda x: convert_tz(x).hour
get_minute = lambda x: convert_tz(x).minute
get_day_of_week = lambda x: convert_tz(x).weekday()

Now we add several columns like the year, month, date, hour, day of the week.

df_steps['startDate'] = pd.to_datetime(df_steps['startDate'])
df_steps['year'] = df_steps['startDate'].map(get_year)
df_steps['month'] = df_steps['startDate'].map(get_month)
df_steps['date'] = df_steps['startDate'].map(get_date)
df_steps['day'] = df_steps['startDate'].map(get_day)
df_steps['hour'] = df_steps['startDate'].map(get_hour)
df_steps['dow'] = df_steps['startDate'].map(get_day_of_week)

With df_steps.tail() we can look into the data frame and it looks like this:

Raw data steps with date/time fields
Raw data steps with date/time fields

So we can aggregate the steps by date to summarize all records of one day into one single record.

steps_by_date = steps.groupby(['date'])['value'].sum().reset_index(name='Steps')

We can use the new data frame to visualize the result in a line chart diagram. I use the mean value of 30 days to show me an overview. You can change the value to your desire.

steps_by_date['RollingMeanSteps'] = steps_by_date.Steps.rolling(window=30, center=True).mean()
steps_by_date.plot(x='date', y='RollingMeanSteps', title= 'Daily step counts rolling mean over 30 days', figsize=[10, 6])

This is how it looks:

Steps counts rolling mean over 30 days
Steps counts rolling mean over 30 days

As you can see, I have a drop around the COVID-19 start, and as the lockdown in Germany started, I have an increase because we have done a lot of walking with the family. I think this is impressive to see and to analyze for further analysis. So the next step is to get an overview of the weekdays. Therefore, we have to add the weekday to our data frame and visualize it in a diagram.

steps_by_date['date'] = pd.to_datetime(steps_by_date['date'])
steps_by_date['dow'] = steps_by_date['date'].dt.weekday

data = steps_by_date.groupby(['dow'])['Steps'].mean()

fig, ax = plt.subplots(figsize=[10, 6])
ax = data.plot(kind='bar', x='day_of_week')

n_groups = len(data)
index = np.arange(n_groups)
opacity = 0.75

ax.yaxis.grid(True)

plt.suptitle('Average Steps by Day of the Week', fontsize=16)
dow_labels = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
plt.xticks(index, dow_labels, rotation=45)
plt.xlabel('Day of Week', fontsize=12, color='red')
Average Steps by Day of the Week
Average Steps by Day of the Week

The diagram shows what I already know. I do the most steps on the weekend. But I think it is also interesting that I have overall (2019 - 2021) nearly 8000 steps per day. And when I look into 2021, only I have now almost 10,000 steps, even though I am still in my home office since COVID-19 started. Next, I created an overview of my monthly steps. In the monthly chart, we see an increase during the lockdown in Germany, where you could go out for a walk or run. 

df_steps['value'] = pd.to_numeric(df_steps['value'])
total_steps_by_month = df_steps.groupby(['month'])['value'].sum().reset_index(name='Steps')

total_steps_by_month

The total_steps_by_month now has all steps of each month summed up, and this is how it looks like:

Overview monthly steps
Overview monthly steps

After looking into the data, I now want to display it as a chart.

dataset = total_steps_by_month
chart_title = 'Number of Steps per month'

n_groups = len(dataset)
index = np.arange(n_groups)

ax = dataset.plot(kind='line', figsize=[12, 5], linewidth=4, alpha=1, marker='o', color='#6684c1', 
                      markeredgecolor='#6684c1', markerfacecolor='w', markersize=8, markeredgewidth=2)

ax.yaxis.grid(True)
ax.xaxis.grid(True)
ax.set_xticks(index)
ax.set_ylabel('Step Count')
plt.xticks(index, dataset.month, rotation=90)
ax.set_title(chart_title)

plt.show()
Number of Steps per month
Number of Steps per month

The last chart is an overview of my steps per year, which I already knew from the iPhone app Stepz.

Total steps per year
Total steps per year

As I now have my data, I could also save it as a CSV file for later use and analyze the hours when I made my steps. It is interesting what you can do with all of this data.

Conclusion

So that's it. I think it is only the iceberg tip of what you can do with Apple Health data. The next steps are to look into the heart rate and how it developed with my running exercises during the two years and the visualization of GPS data. Maybe someone can provide me with a few tips, so I can improve my Python skills. Is there any good video course or book I should read? Leave a comment below.

author.


Hi,

I am Tobias, I write this blog since 2014, you can find me on Twitter, LinkedInFacebook and YouTube. I work as a Senior Business Warehouse Consultant. In 2016, I wrote the first edition of Analysis Office - The Comprehensive Guide. If you want, you can leave me a PayPal coffee donation. You can also contact me directly if you want.


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