While fitness trackers include some excellent tools for reviewing health data there are limitations such as overlaying data from different sources when looking for patterns. Below are two examples of graphing health data using IPython’s popular data science libraries (panda and matplotlib).  In these examples I have exported my heart rate variability and resting heart rate data from the last month into two column CSV files. The data is being graphed with the Anaconda IPython ‘qtconsole’ utility.

Installing IPython:

Anaconda provides a simple installer blob which supports Linux, Windows and OS/X. This is probably the easiest  all platform solution to get the core data science tools in place. Packages can be managed with conda after installation.

Once Anaconda is installed you can launch the ‘qtconsole’ within Anaconda Navigator.

Example 1:

Import and Graph Heart Rate Variability (HRV)

Using a CSV file with two columns (Day and HRV) you can see just how easy it is to create a simple plot of the values for the last month. See example1.py