Python Pandas:使用参数将多个函数传递给agg() [英] Python Pandas: Passing Multiple Functions to agg() with Arguments

问题描述

我正在努力弄清楚如何为熊猫的dataframe.agg()函数结合两种不同的语法.采取以下简单的数据框架:

I'm struggling to figure out how to combine two different syntaxes for pandas' dataframe.agg() function. Take this simple data frame:

df = pd.DataFrame({'A': ['group1', 'group1', 'group2', 'group2', 'group3', 'group3'],
                   'B': [10, 12, 10, 25, 10, 12],
                   'C': [100, 102, 100, 250, 100, 102]})

>>> df
[output]
        A   B    C
0  group1  10  100
1  group1  12  102
2  group2  10  100
3  group2  25  250
4  group3  10  100
5  group3  12  102

我知道您可以将两个函数发送到agg()并获得一个新的数据帧，其中每个函数将应用于每列:

I know you can send two functions to agg() and get a new data frame where each function is applied to each column:

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

[output]
           B                C            
        mean        std  mean         std
A                                        
group1  11.0   1.414214   101    1.414214
group2  17.5  10.606602   175  106.066017
group3  11.0   1.414214   101    1.414214

我知道您可以将参数传递给单个函数:

And I know you can pass arguments to a single function:

df.groupby('A').agg(np.std, ddof=0)

[output]
          B   C
A              
group1  1.0   1
group2  7.5  75
group3  1.0   1

但是有没有一种方法可以传递多个函数以及它们中的一个或两个的参数呢?我希望在文档中找到类似df.groupby('A').agg([np.mean, (np.std, ddof=0)])的内容，但到目前为止还没有运气.有什么想法吗?

But is there a way to pass multiple functions along with arguments for one or both of them? I was hoping to find something like df.groupby('A').agg([np.mean, (np.std, ddof=0)]) in the docs, but so far no luck. Any ideas?

推荐答案

好吧，

Well, the docs on aggregate are in fact a bit lacking. There might be a way to handle this with the correct passing of arguments, and you could look into the source code of pandas for that (perhaps I will later).

但是，您可以轻松做到:

However, you could easily do:

df.groupby('A').agg([np.mean, lambda x: np.std(x, ddof=0)])

它也将正常工作.

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