从Pandas中的GroupBy对象获取所有密钥 [英] Get all keys from GroupBy object in Pandas

问题描述

我正在寻找一种获取GroupBy对象中所有键的列表的方法，但是我似乎无法通过文档或Google来找到一个键.

I'm looking for a way to get a list of all the keys in a GroupBy object, but I can't seem to find one via the docs nor through Google.

肯定有一种通过组的键访问组的方法，如下所示:

There is definitely a way to access the groups through their keys, like so:

df_gb = df.groupby(['EmployeeNumber'])
df_gb.get_group(key)

...所以我认为有一种方法可以访问GroupBy对象中的键列表(或类似键).我正在寻找这样的东西:

...so I figure there's a way to access a list (or the like) of the keys in a GroupBy object. I'm looking for something like this:

df_gb.keys
Out: [1234, 2356, 6894, 9492]

我认为我可以遍历GroupBy对象并以这种方式获取键，但是我认为必须有一种更好的方法.

I figure I could just loop through the GroupBy object and get the keys that way, but I think there's got to be a better way.

推荐答案

您可以通过groupby对象上的属性.groups访问此对象，这将返回一个字典，该字典的键为您提供了组:

You can access this via attribute .groups on the groupby object, this returns a dict, the keys of the dict gives you the groups:

In [40]:
df = pd.DataFrame({'group':[0,1,1,1,2,2,3,3,3], 'val':np.arange(9)})
gp = df.groupby('group')
gp.groups.keys()

Out[40]:
dict_keys([0, 1, 2, 3])

这是groups的输出:

In [41]:
gp.groups

Out[41]:
{0: Int64Index([0], dtype='int64'),
 1: Int64Index([1, 2, 3], dtype='int64'),
 2: Int64Index([4, 5], dtype='int64'),
 3: Int64Index([6, 7, 8], dtype='int64')}

更新

看起来像这样，因为groups的类型是dict，所以当您调用keys时，组顺序不会得到维护:

it looks like that because the type of groups is a dict then the group order isn't maintained when you call keys:

In [65]:
df = pd.DataFrame({'group':list('bgaaabxeb'), 'val':np.arange(9)})
gp = df.groupby('group')
gp.groups.keys()

Out[65]:
dict_keys(['b', 'e', 'g', 'a', 'x'])

如果您致电groups，您会看到订单保持不变:

if you call groups you can see the order is maintained:

In [79]:
gp.groups

Out[79]:
{'a': Int64Index([2, 3, 4], dtype='int64'),
 'b': Int64Index([0, 5, 8], dtype='int64'),
 'e': Int64Index([7], dtype='int64'),
 'g': Int64Index([1], dtype='int64'),
 'x': Int64Index([6], dtype='int64')}

然后保持密钥顺序，对此的一种破解方法是访问每个组的.name属性:

then the key order is maintained, a hack around this is to access the .name attribute of each group:

In [78]:
gp.apply(lambda x: x.name)

Out[78]:
group
a    a
b    b
e    e
g    g
x    x
dtype: object

这不好，因为它没有向量化，但是，如果您已经有一个聚合的对象，则可以获取索引值:

which isn't great as this isn't vectorised, however if you already have an aggregated object then you can just get the index values:

In [81]:
agg = gp.sum()
agg

Out[81]:
       val
group     
a        9
b       13
e        7
g        1
x        6

In [83]:    
agg.index.get_level_values(0)

Out[83]:
Index(['a', 'b', 'e', 'g', 'x'], dtype='object', name='group')

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