在Pandas中转换与聚合 [英] Transform vs. aggregate in Pandas

问题描述

在对Pandas DataFrame进行分组时，什么时候应该使用transform，什么时候应该使用aggregate?怎么做 它们在实际应用方面有所不同，您选择哪一个 认为更重要?

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?

推荐答案

考虑数据框df

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

groupby是标准用途聚合器

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

也许您希望这些值在整个组中传播，并返回与开始时具有相同索引的内容.
使用transform

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')

agg 用于当您有要针对不同列运行的特定内容或在同一列上运行多个内容的情况.

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']))

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