Example DataFrame:

import pandas as pd
import dask.dataframe as dd

data = {
    'A': [1, 2, 1, 3, 2, 1],
    'B': ['x', 'y', 'x', 'y', 'x', 'y'],
    'C': [10, 20, 30, 40, 50, 60]
}
pd_df = pd.DataFrame(data)
ddf = dd.from_pandas(pd_df, npartitions=2)

I am working with Dask DataFrames and need to perform a groupby operation efficiently without loading everything into memory or computing multiple times. Here are two inefficient solutions I've tried:

1. Loading everything into memory:

grouped = ddfpute().groupby('groupby_column')
for name, group in grouped:
    # Process each group

This approach loads the entire DataFrame into memory, which defeats the purpose of using Dask.

2. Computing twice:

for name in set(ddf['groupby_column'].unique()pute()):
    group = ddf[ddf['groupby_column'].eq(name)]pute()
    # Process each group

This approach computes the DataFrame twice, which is inefficient.

Question:

How can I efficiently perform a groupby operation on a Dask DataFrame without loading everything into memory or computing multiple times? Is there an updated best practice for this in 2025?

Any help or updated best practices would be greatly appreciated!