How to group data, assign values to them, and ungroup them

import pandas as pd

sample = pd.read_csv('./sampledata.csv')
sample.head(3)

	Movement	Island_Type	Island	Distance	Item	Sentence	Subj_id	List	Score
0	WH	whe	non	sh	1	Who thinks that Paul stole the necklace?	1	1	6
1	WH	whe	non	sh	2	Who thinks that Matt chased the bus?	1	1	2
2	WH	whe	non	sh	3	Who thinks that Tom sold the television?	1	1	3

The following code groups the data by two conditions (Island, Distance), assigns the mean and the standard deviation of acceptability scores per group, and ungroups them:

sample['mean_response'] = sample.groupby(['Island','Distance'])['Score'].transform('mean')
sample['sd_answer_z'] = sample.groupby(['Island','Distance'])['Score'].transform('std')

sample.iloc[:5, [2, 3, -3, -2, -1]] #showing only relevant columns

	Island	Distance	Score	mean_response	sd_answer_z
0	non	sh	6	4.585938	1.709295
1	non	sh	2	4.585938	1.709295
2	non	sh	3	4.585938	1.709295
3	non	sh	7	4.585938	1.709295
4	non	sh	2	4.585938	1.709295

How to aggregate data

Now I will aggregate the data based on the two conditions and make a summary dataset that shows the mean, the standard deviation, and the standard error of the mean of each group’s acceptability.

data_summary = sample.groupby(['Island','Distance'])['Score'].agg(['mean','std','sem']).reset_index()
data_summary

	Island	Distance	mean	std	sem
0	isl	lg	2.593750	1.180134	0.104310
1	isl	sh	3.531250	1.128985	0.099789
2	non	lg	3.890625	1.920607	0.169759
3	non	sh	4.585938	1.709295	0.151082