Transform vs. aggregate in Pandas

问题:

When grouping a Pandas DataFrame, when should I use transform and when should I use aggregate? How do they differ with respect to their application in practice and which one do you consider more important?

回答1:

consider the dataframe df

df = pd.DataFrame(dict(A=list('aabb'), B=[1, 2, 3, 4], C=[0, 9, 0, 9]))

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groupby is the standard use aggregater

df.groupby('A').mean()

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maybe you want these values broadcast across the whole group and return something with the same index as what you started with.
use transform

df.groupby('A').transform('mean')

df.set_index('A').groupby(level='A').transform('mean')

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agg is used when you have specific things you want to run for different columns or more than one thing run on the same column.

df.groupby('A').agg(['mean', 'std'])

df.groupby('A').agg(dict(B='sum', C=['mean', 'prod']))