pandas 使用groupby的计数创建新列

问题描述

我有一个如下所示的df:

id        item        color
01        truck       red
02        truck       red
03        car         black
04        truck       blue
05        car         black

我正在尝试创建一个如下所示的df:

item      color       count
truck     red          2
truck     blue         1
car       black        2

我尝试过

df["count"] = df.groupby("item")["color"].transform('count')

但这不是我要寻找的.

任何指导表示赞赏

解决方案

这不是新列，而是新的DataFrame:

In [11]: df.groupby(["item", "color"]).count()
Out[11]:
             id
item  color
car   black   2
truck blue    1
      red     2

要获得所需的结果，请使用reset_index:

In [12]: df.groupby(["item", "color"])["id"].count().reset_index(name="count")
Out[12]:
    item  color  count
0    car  black      2
1  truck   blue      1
2  truck    red      2

要获取新列"，您可以使用transform:

In [13]: df.groupby(["item", "color"])["id"].transform("count")
Out[13]:
0    2
1    2
2    2
3    1
4    2
dtype: int64

我建议阅读文档的 split-apply-combine部分.

I have a df that looks like the following:

id        item        color
01        truck       red
02        truck       red
03        car         black
04        truck       blue
05        car         black

I am trying to create a df that looks like this:

item      color       count
truck     red          2
truck     blue         1
car       black        2

I have tried

df["count"] = df.groupby("item")["color"].transform('count')

But it is not quite what I am searching for.

Any guidance is appreciated

解决方案

That's not a new column, that's a new DataFrame:

In [11]: df.groupby(["item", "color"]).count()
Out[11]:
             id
item  color
car   black   2
truck blue    1
      red     2

To get the result you want is to use reset_index:

In [12]: df.groupby(["item", "color"])["id"].count().reset_index(name="count")
Out[12]:
    item  color  count
0    car  black      2
1  truck   blue      1
2  truck    red      2

To get a "new column" you could use transform:

In [13]: df.groupby(["item", "color"])["id"].transform("count")
Out[13]:
0    2
1    2
2    2
3    1
4    2
dtype: int64

I recommend reading the split-apply-combine section of the docs.

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