Selecting values from dataframe based on multiple column values

I have a dataframe in this format:

The objective is to determine which age class benefitted most from being in the treatment group as measured by clocktime. That means i need to somehow group all values for members in each agegroup for both treatment and control conditions and take an average of their clocktimes. Then following that i need to take the difference of the average clocktimes for the subgroups and compare all of these against one another.

Where i am stuck is with filtering the dataframe based on multiple columns simulatneously. I tried using groupby() as follows:

df.groupby(['ageClass','treatment'])['clockTime'].mean()

However I was not able to then calculate the difference in the mean times from the resulting series.

How should I move forward?

You can pivot the table with means you produced

df2 = df.groupby(['ageClass','treatment'])[['clockTime']].mean().reset_index().pivot(columns=['ageClass'], values='clockTime', index='treatment')

ageClass    20  30
treatment       
Control     7862.500000     7826.0
Treatment   7736.333333     8163.0

Then it's easy to find a difference

df2['diff'] = df2[20] - df2[30]

treatment
Control       36.500000
Treatment   -426.666667
Name: diff, dtype: float64

From the groupby you've already done, you can groupby index level 0, i.e. 'ageClass' and then use diff to find the difference between the averages of treatment and control groups for each 'ageClass'. Since diff subtracts the second from the first (and "Control" and "Treatment" are sorted alphabetically), add "-Control" to make it a bit clearer.

s = df.groupby(['ageClass','treatment'])['clockTime'].mean()
out = s.groupby(level=0).diff().dropna().reset_index()
out = out.assign(treatment=out['treatment']+'-Control')

Output:

   ageClass          treatment   clockTime
0        20  Treatment-Control -126.166667
1        30  Treatment-Control  337.000000

From your problem description, I would prescribe ranking. Differences between groups wont tell who benefited the most

s=df.groupby(['ageClass','treatment'])['clockTime'].agg('mean').reset_index()

s['rank']=s.groupby('ageClass')['clockTime'].rank()



    ageClass  treatment    clockTime  rank
0        20    Control  7862.500000   2.0
1        20  Treatment  7736.333333   1.0
2        30    Control  7826.000000   1.0
3        30  Treatment  8163.000000   2.0