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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.

Deze oefening maakt deel uit van de cursus

Biomedical Image Analysis in Python

Bekijk cursus

Interactieve oefening met praktijkervaring

Probeer deze oefening door deze voorbeeldcode aan te vullen.

# Print random sample of rows
print(____)
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