Question

I have a a dataframe with the following structure:

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1152 entries, 0 to 143
Data columns:
cuepos             1152  non-null values
response           1152  non-null values
soa                1152  non-null values
targetpos          1152  non-null values
testorientation    1152  non-null values
dtypes: float64(3), int64(2)

The cuepos column and the targetpos column both contain integer values of either 1 or 2.

I would like to group this data by congruency between cuepos and targetpos. In other words, I would like to produce two groups, one for rows in which cuepos == targetpos and another group for which cuepos != targetpos.

I can’t seem to figure out how I might do this. I looked at using grouping functions, but these seem only to act on a single column… or am I mistaken? Can someone point me in the right direction?

Thanks in advance!
Blz

Answer 1

Note, if you goal is to do group computations, you can do

df.groupby(df.col1 == df.col2).apply(f)

and the result will be keyed by True/False.