对 Python 中 Pandas 多列的 groupby 操作的含义感到困惑 [英] Confused about meaning of groupby operation with multiple columns with Pandas in Python

问题描述

这是我的数据框，功能:

Here are my dataframe, function:

df = pd.DataFrame({
    'G': 'x x y y'.split(), 
    'C': [1, 2, 1, 2], 
    'D': [2, 2, 1, 1]})

def CD(df):
    return df['C'] * df['D']

这是我的数据框的样子:

Here is what my dataframe looks like:

   G  C  D
0  x  1  2
1  x  2  2
2  y  1  1
3  y  2  1

当我跑步时

df.groupby('G').apply(CD)

我预计它会对 x 和 y 求和得到

I expected that it would sum over x and y to get

   G  C  D
0  x  3  4
1  y  3  2

然后，我希望它乘以 C 和 D 得到

Then, I expected it to multiply C and D to get

x   12
y   6

然而，我得到了

G   
x  0    2
   1    4
y  2    1
   3    2

[2, 4, 1, 2] 的新列看起来与我简单运行时获得的列没有任何不同

That new column of [2, 4, 1, 2] doesn't look any different than what I would have obtained if I simply ran

df['C'] * df['D']

显然，我对 groupby 的作用感到困惑.什么是df.groupby('G').apply(CD)";在我的例子中做什么?

Clearly, I am confused about what groupby does. What is "df.groupby('G').apply(CD)" doing in my example?

推荐答案

Groupby 不做总和.尝试 apply(sum) 并将结果发送到您的函数.

Groupby does not do the sum. Try apply(sum) and sent the results to your function.

>> CD(df.groupby('G')[['C','D']].apply(sum))

G
x    12
y     6
dtype: int64

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