.apply如何在Pandas DataFrame.groupby上工作？ [英] How does .apply work on a Pandas DataFrame.groupby?

问题描述

              Count
League  Result         
EPL     H      16
        D      9
        A      10
Champ   H      67
        D      15
        A      57
        H      87
La Liga D      35
        A      40
        

我对不同联赛的足球结果进行了细分，并计算了结果发生的次数。

I have a breakdown of football results for different leagues and a count of how many times that result occurred.

我想看看主场胜利，平局，客场胜利占比赛总数的百分比。我在下面看到了解决方案：

I want to see the proportion of home wins, draws, away wins as a percentage of the total games played. I have seen a solution to this below:

df.groupby("League").apply(lambda g: (g/g.sum()*100)

乍一看，这是有道理的，但是 g 在这里？我以为是H，D或A计数，然后 g.sum（）求和所有H，D，A计数但是，如果 g 只是一个值，我们如何调用方法 g.sum（）？这里的 g 是什么？

At first glance, this made sense, but what exactly is g here? I assumed it was the H, D or A count and then the g.sum() summed all of the H,D,A counts grouped by each division. But, if g is just a value, how are we calling the method g.sum()? What exactly is g here?

推荐答案

g 是一个DataFrame。由于您将'League'分组，因此您会将DataFrame分为多个单独的块，其中包含'League'。为了说明这一点，我们可以遍历GroupBy对象。

g is a DataFrame. Since you group on 'League' you will split the DataFrame up into separate chunks which contain the unique values of 'League'. To illustrate this, we can iterate over the GroupBy object.

for idx, g in df.groupby('League'):  # `idx` is the unique group key
    print(g, '\n')

---

---

               Count
League Result       
Champ  H          67
       D          15
       A          57
       H          87

               Count
League Result       
EPL    H          16
       D           9
       A          10

                Count
League  Result       
La Liga D          35
        A          40

应用然后将您的函数分别应用于每个DataFrame。调用 g.sum（）将为您提供一个系列，该系列求和该组中的每一列。

The apply then acts to apply your function to each of these DataFrame separately. Calling g.sum() will give you a Series that sums each column within the group.

for idx, g in df.groupby('League'):
    print(g.sum(), '\n')

Count    226
dtype: int64 

Count    35
dtype: int64 

Count    75
dtype: int64 

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