Pandas groupby:如何获得字符串的并集 [英] Pandas groupby: How to get a union of strings

问题描述

我有一个这样的数据框:

I have a dataframe like this:

   A         B       C
0  1  0.749065    This
1  2  0.301084      is
2  3  0.463468       a
3  4  0.643961  random
4  1  0.866521  string
5  2  0.120737       !

打电话

In [10]: print df.groupby("A")["B"].sum()

会回来

A
1    1.615586
2    0.421821
3    0.463468
4    0.643961

现在我想对C"列做同样的事情".因为该列包含字符串，所以 sum() 不起作用(尽管您可能认为它会连接字符串).我真正想看到的是每个组的字符串列表或一组，即

Now I would like to do "the same" for column "C". Because that column contains strings, sum() doesn't work (although you might think that it would concatenate the strings). What I would really like to see is a list or set of the strings for each group, i.e.

A
1    {This, string}
2    {is, !}
3    {a}
4    {random}

我一直在努力寻找方法来做到这一点.

I have been trying to find ways to do this.

Series.unique() (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.unique.html) 不起作用，尽管

Series.unique() (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.unique.html) doesn't work, although

df.groupby("A")["B"]

是一个

pandas.core.groupby.SeriesGroupBy object

所以我希望任何系列方法都能奏效.有什么想法吗?

so I was hoping any Series method would work. Any ideas?

推荐答案

In [4]: df = read_csv(StringIO(data),sep='s+')

In [5]: df
Out[5]: 
   A         B       C
0  1  0.749065    This
1  2  0.301084      is
2  3  0.463468       a
3  4  0.643961  random
4  1  0.866521  string
5  2  0.120737       !

In [6]: df.dtypes
Out[6]: 
A      int64
B    float64
C     object
dtype: object

当您应用自己的函数时，不会自动排除非数字列.但是，这比将 .sum() 应用于 groupby

When you apply your own function, there is not automatic exclusions of non-numeric columns. This is slower, though, than the application of .sum() to the groupby

In [8]: df.groupby('A').apply(lambda x: x.sum())
Out[8]: 
   A         B           C
A                         
1  2  1.615586  Thisstring
2  4  0.421821         is!
3  3  0.463468           a
4  4  0.643961      random

sum 默认连接

In [9]: df.groupby('A')['C'].apply(lambda x: x.sum())
Out[9]: 
A
1    Thisstring
2           is!
3             a
4        random
dtype: object

你几乎可以为所欲为

In [11]: df.groupby('A')['C'].apply(lambda x: "{%s}" % ', '.join(x))
Out[11]: 
A
1    {This, string}
2           {is, !}
3               {a}
4          {random}
dtype: object

在整个框架上执行此操作，一次一组.关键是返回一个Series

Doing this on a whole frame, one group at a time. Key is to return a Series

def f(x):
     return Series(dict(A = x['A'].sum(), 
                        B = x['B'].sum(), 
                        C = "{%s}" % ', '.join(x['C'])))

In [14]: df.groupby('A').apply(f)
Out[14]: 
   A         B               C
A                             
1  2  1.615586  {This, string}
2  4  0.421821         {is, !}
3  3  0.463468             {a}
4  4  0.643961        {random}

这篇关于Pandas groupby:如何获得字符串的并集的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！