pandas groupby与dict [英] Pandas groupby with dict

问题描述

是否可以使用字典对列的元素进行分组?

Is it possible to use a dict to group on elements of a column?

例如:

In [3]: df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three','two', 'two', 'one', 'three'],
   ...:          'B' : np.random.randn(8)})
In [4]: df
Out[4]: 
       A         B
0    one  0.751612
1    one  0.333008
2    two  0.395667
3  three  1.636125
4    two  0.916435
5    two  1.076679
6    one -0.992324
7  three -0.593476

In [5]: d = {'one':'Start', 'two':'Start', 'three':'End'}
In [6]: grouped = df[['A','B']].groupby(d)

此(和其他变体)返回一个空的groupby对象.而且我使用.apply的所有方法也都失败了.

This (and other variations) returns an empty groupby object. And my variations on using .apply all fail too.

我想将列A的值与字典的键匹配，并将行放入由值定义的组中.输出看起来像这样:

I'd like to match the values of column A to the keys of the dictionary and put rows into the groups defined by the values. The output would look something like this:

 Start:
           A         B
    0    one  0.751612
    1    one  0.333008
    2    two  0.395667
    4    two  0.916435
    5    two  1.076679
    6    one -0.992324
End:
           A         B
    3  three  1.636125
    7  three -0.593476

推荐答案

来自

From the docs, the dict has to map from labels to group names, so this will work if you put 'A' into the index:

grouped2 = df.set_index('A').groupby(d)
for group_name, data in grouped2:
    print group_name
    print '---------'
    print data

# Output:
End
---------
              B
A              
three -1.234795
three  0.239209

Start
---------
            B
A            
one -1.924156
one  0.506046
two -1.681980
two  0.605248
two -0.861364
one  0.800431

列名和行索引都是标签，而在将'A'放入索引之前，'A'的元素是值.

Column names and row indices are both labels, whereas before you put 'A' into the index, the elements of 'A' are values.

如果索引中还有其他信息使set_index()变得棘手，则可以使用map()创建分组列:

If you have other info in the index that makes doing a set_index() tricky, you can just create a grouping column with map():

df['group'] = df['A'].map(d)
grouped3 = df.groupby('group')

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