使用pandas GroupBy.agg()对同一列进行多次聚合 [英] Multiple aggregations of the same column using pandas GroupBy.agg()

问题描述

是否有熊猫内置方法将两个不同的聚合函数f1, f2应用于同一列df["returns"]，而不必多次调用agg()?

Is there a pandas built-in way to apply two different aggregating functions f1, f2 to the same column df["returns"], without having to call agg() multiple times?

示例数据框:

import pandas as pd
import datetime as dt

pd.np.random.seed(0)
df = pd.DataFrame({
         "date"    :  [dt.date(2012, x, 1) for x in range(1, 11)], 
         "returns" :  0.05 * np.random.randn(10), 
         "dummy"   :  np.repeat(1, 10)
}) 

语法上错误但直观上正确的方法是:

The syntactically wrong, but intuitively right, way to do it would be:

# Assume `f1` and `f2` are defined for aggregating.
df.groupby("dummy").agg({"returns": f1, "returns": f2})

很明显，Python不允许重复的键.还有其他方式可以表达对agg()的输入吗?也许元组[(column, function)]的列表可以更好地工作，以允许将多个函数应用于同一列?但是agg()似乎只接受字典.

Obviously, Python doesn't allow duplicate keys. Is there any other manner for expressing the input to agg()? Perhaps a list of tuples [(column, function)] would work better, to allow multiple functions applied to the same column? But agg() seems like it only accepts a dictionary.

除了定义仅在其中应用两个功能的辅助功能之外，是否还有其他解决方法? (无论如何，这如何与聚合一起使用?)

Is there a workaround for this besides defining an auxiliary function that just applies both of the functions inside of it? (How would this work with aggregation anyway?)

推荐答案

您可以简单地将函数作为列表传递:

You can simply pass the functions as a list:

In [20]: df.groupby("dummy").agg({"returns": [np.mean, np.sum]})
Out[20]:         
           mean       sum
dummy                    
1      0.036901  0.369012

或作为字典:

In [21]: df.groupby('dummy').agg({'returns':
                                  {'Mean': np.mean, 'Sum': np.sum}})
Out[21]: 
        returns          
           Mean       Sum
dummy                    
1      0.036901  0.369012

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