将组 ID 取回 Pandas 数据框
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Get group id back into pandas dataframe
提问by beardc
For dataframe
对于数据框
In [2]: df = pd.DataFrame({'Name': ['foo', 'bar'] * 3,
...: 'Rank': np.random.randint(0,3,6),
...: 'Val': np.random.rand(6)})
...: df
Out[2]:
Name Rank Val
0 foo 0 0.299397
1 bar 0 0.909228
2 foo 0 0.517700
3 bar 0 0.929863
4 foo 1 0.209324
5 bar 2 0.381515
I'm interested in grouping by Name and Rank and possibly getting aggregate values
我有兴趣按名称和等级分组并可能获得聚合值
In [3]: group = df.groupby(['Name', 'Rank'])
In [4]: agg = group.agg(sum)
In [5]: agg
Out[5]:
Val
Name Rank
bar 0 1.839091
2 0.381515
foo 0 0.817097
1 0.209324
But I would like to get a field in the original dfthat contains the group number for that row, like
但是我想在原始字段中获取一个df包含该行组号的字段，例如
In [13]: df['Group_id'] = [2, 0, 2, 0, 3, 1]
In [14]: df
Out[14]:
Name Rank Val Group_id
0 foo 0 0.299397 2
1 bar 0 0.909228 0
2 foo 0 0.517700 2
3 bar 0 0.929863 0
4 foo 1 0.209324 3
5 bar 2 0.381515 1
Is there a good way to do this in pandas?
大Pandas有什么好方法可以做到这一点吗？
I can get it with python,
我可以用python得到它，
In [16]: from itertools import count
In [17]: c = count()
In [22]: group.transform(lambda x: c.next())
Out[22]:
Val
0 2
1 0
2 2
3 0
4 3
5 1
but it's pretty slow on a large dataframe, so I figured there may be a better built in pandas way to do this.
但是在大型数据帧上它很慢，所以我认为可能有更好的内置Pandas方式来做到这一点。
回答by DSM
A lot of handy things are stored in the DataFrameGroupBy.grouperobject. For example:
很多方便的东西都存储在DataFrameGroupBy.grouper对象中。例如：
>>> df = pd.DataFrame({'Name': ['foo', 'bar'] * 3,
'Rank': np.random.randint(0,3,6),
'Val': np.random.rand(6)})
>>> grouped = df.groupby(["Name", "Rank"])
>>> grouped.grouper.
grouped.grouper.agg_series grouped.grouper.indices
grouped.grouper.aggregate grouped.grouper.labels
grouped.grouper.apply grouped.grouper.levels
grouped.grouper.axis grouped.grouper.names
grouped.grouper.compressed grouped.grouper.ngroups
grouped.grouper.get_group_levels grouped.grouper.nkeys
grouped.grouper.get_iterator grouped.grouper.result_index
grouped.grouper.group_info grouped.grouper.shape
grouped.grouper.group_keys grouped.grouper.size
grouped.grouper.groupings grouped.grouper.sort
grouped.grouper.groups
and so:
所以：
>>> df["GroupId"] = df.groupby(["Name", "Rank"]).grouper.group_info[0]
>>> df
Name Rank Val GroupId
0 foo 0 0.302482 2
1 bar 0 0.375193 0
2 foo 2 0.965763 4
3 bar 2 0.166417 1
4 foo 1 0.495124 3
5 bar 2 0.728776 1
There may be a nicer alias for for grouper.group_info[0]lurking around somewhere, but this should work, anyway.
grouper.group_info[0]潜伏在某处可能有一个更好的别名，但无论如何这应该有效。
回答by jezrael
Use GroupBy.ngroupfrom pandas 0.20.2+:
GroupBy.ngroup从Pandas 0.20.2+使用：
df["GroupId"] = df.groupby(["Name", "Rank"]).ngroup()
print (df)
Name Rank Val GroupId
0 foo 2 0.451724 4
1 bar 0 0.944676 0
2 foo 0 0.822390 2
3 bar 2 0.063603 1
4 foo 1 0.938892 3
5 bar 2 0.332454 1
回答by Luca Pappalardo
The correct solution is to use grouper.label_info:
正确的解决方案是使用grouper.label_info：
df["GroupId"] = df.groupby(["Name", "Rank"]).grouper.label_info
It automatically associates each row in the dfdataframe to the corresponding group label.
它会自动将df数据框中的每一行与相应的组标签相关联。
