Identifying potential confounds
Once measures have been extracted, double-check for dependencies within your data. This is especially true if any image parameters (sampling rate, field of view) might differ between subjects, or you pull multiple measures from a single image.
For the final exercises, we have combined demographic and brain volume measures into a pandas DataFrame (df
).
First, you will explore the table and available variables. Then, you will check for correlations between the data.
This exercise is part of the course
Biomedical Image Analysis in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print random sample of rows
print(____)