[Python] How to efficiently perform pandas groupby and aggregation on large datasets with...

I'm processing a dataset with over 10 thousand rows in pandas, and I need to perform groupby and aggregation operations to summarize the data. However, my system has limited memory (8GB RAM), and the current approach is running into performance issues. I've tried chunking the data and using Dask for parallel processing, but the improvements are minimal. For example, my dataset looks like this:

ID | Value
----|------
1 | 100
2 | 200
1 | 300


and my code of aggregation:

import pandas as pd

data = pd.read_csv('large_file.csv')
result = data.groupby('ID').agg({'Value': 'sum'})


Are there alternative approaches or tools I could use to further optimize the performance of these operations?

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