Python 熊猫将列添加到 groupby 数据框
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pandas add column to groupby dataframe
提问by Fabio Lamanna
I have this simple dataframe df:
我有这个简单的数据框df：
df = pd.DataFrame({'c':[1,1,1,2,2,2,2],'type':['m','n','o','m','m','n','n']})
my goal is to count values of typefor each c, and then add a column with the size of c. So starting with:
我的目标是计算type每个 的值c，然后添加一个大小为c. 所以开始：
In [27]: g = df.groupby('c')['type'].value_counts().reset_index(name='t')
In [28]: g
Out[28]:
c type t
0 1 m 1
1 1 n 1
2 1 o 1
3 2 m 2
4 2 n 2
the first problem is solved. Then I can also:
第一个问题解决了。然后我还可以：
In [29]: a = df.groupby('c').size().reset_index(name='size')
In [30]: a
Out[30]:
c size
0 1 3
1 2 4
How can I add the sizecolumn directly to the first dataframe? So far I used mapas:
如何将size列直接添加到第一个数据帧？到目前为止，我用作map：
In [31]: a.index = a['c']
In [32]: g['size'] = g['c'].map(a['size'])
In [33]: g
Out[33]:
c type t size
0 1 m 1 3
1 1 n 1 3
2 1 o 1 3
3 2 m 2 4
4 2 n 2 4
which works, but is there a more straightforward way to do this?
哪个有效，但有没有更直接的方法来做到这一点？
采纳答案by EdChum
Use transformto add a column back to the orig df from a groupbyaggregation, transformreturns a Serieswith its index aligned to the orig df:
使用transform从添加一列回原稿DFgroupby聚集，transform返回Series其索引对准原稿DF：
In [123]:
g = df.groupby('c')['type'].value_counts().reset_index(name='t')
g['size'] = df.groupby('c')['type'].transform('size')
g
Out[123]:
c type t size
0 1 m 1 3
1 1 n 1 3
2 1 o 1 3
3 2 m 2 4
4 2 n 2 4
回答by jezrael
Another solution with transformlen:
另一个解决方案：transformlen
df['size'] = df.groupby('c')['type'].transform(len)
print df
c type size
0 1 m 3
1 1 n 3
2 1 o 3
3 2 m 4
4 2 m 4
5 2 n 4
6 2 n 4
Another solution with Series.mapand Series.value_counts:
使用Series.map和的另一种解决方案Series.value_counts：
df['size'] = df['c'].map(df['c'].value_counts())
print (df)
c type size
0 1 m 3
1 1 n 3
2 1 o 3
3 2 m 4
4 2 m 4
5 2 n 4
6 2 n 4
