从 Pandas groupby 对象中选择多个组
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Select multiple groups from pandas groupby object
提问by lib
I am experimenting with the groupby features of pandas, in particular
我正在试验Pandas的 groupby 功能，特别是
gb = df.groupby('model')
gb.hist()
Since gb has 50 groups the result is quite cluttered, I would like to explore the result only for the first 5 groups.
由于 gb 有 50 个组，结果非常混乱，我只想探索前 5 个组的结果。
I found how to select a single group with groupsor get_group(How to access pandas groupby dataframe by key), but not how to select multiple groups directly.
The best I could do is :
我找到了如何使用groups或get_group（How to access pandas groupby dataframe by key）选择单个组，但没有找到如何直接选择多个组。我能做的最好的是：
groups = dict(list(gb))
subgroup = pd.concat(groups.values()[:4])
subgroup.groupby('model').hist()
Is there a more direct way?
有没有更直接的方法？
采纳答案by dermen
You can do something like
你可以做类似的事情
new_gb = pandas.concat( [ gb.get_group(group) for i,group in enumerate( gb.groups) if i < 5 ] ).groupby('model')
new_gb.hist()
Although, I would approach it differently. You can use the collections.Counterobject to get groups fast:
虽然，我会以不同的方式处理它。您可以使用该collections.Counter对象快速获取组：
import collections
df = pandas.DataFrame.from_dict({'model': pandas.np.random.randint(0, 3, 10), 'param1': pandas.np.random.random(10), 'param2':pandas.np.random.random(10)})
# model param1 param2
#0 2 0.252379 0.985290
#1 1 0.059338 0.225166
#2 0 0.187259 0.808899
#3 2 0.773946 0.696001
#4 1 0.680231 0.271874
#5 2 0.054969 0.328743
#6 0 0.734828 0.273234
#7 0 0.776684 0.661741
#8 2 0.098836 0.013047
#9 1 0.228801 0.827378
model_groups = collections.Counter(df.model)
print(model_groups) #Counter({2: 4, 0: 3, 1: 3})
Now you can iterate over the Counterobject like a dictionary, and query the groups you want:
现在您可以Counter像字典一样遍历对象，并查询您想要的组：
new_df = pandas.concat( [df.query('model==%d'%key) for key,val in model_groups.items() if val < 4 ] ) # for example, but you can select the models however you like
# model param1 param2
#2 0 0.187259 0.808899
#6 0 0.734828 0.273234
#7 0 0.776684 0.661741
#1 1 0.059338 0.225166
#4 1 0.680231 0.271874
#9 1 0.228801 0.827378
Now you can use the built-in pandas.DataFrame.groupbyfunction
现在您可以使用内置pandas.DataFrame.groupby函数
gb = new_df.groupby('model')
gb.hist()
Since model_groupscontains all of the groups, you can just pick from it as you wish.
由于model_groups包含所有组，您可以根据需要从中选择。
note
笔记
If your modelcolumn contains string values (names or something) instead of integers, it will all work the same - just change the query argument from 'model==%d'%keyto 'model=="%s"'%key.
如果您的model列包含字符串值（名称或其他内容）而不是整数，则它的工作方式相同 - 只需将查询参数从 更改'model==%d'%key为'model=="%s"'%key。
回答by EdChum
It'd be easier to just filter your df first and then perform the groupby:
首先过滤您的 df 然后执行以下操作会更容易groupby：
In [155]:
df = pd.DataFrame({'model':np.random.randint(1,10,100), 'value':np.random.randn(100)})
first_five = df['model'].sort(inplace=False).unique()[:5]
gp = df[df['model'].isin(first_five)].groupby('model')
gp.first()
Out[155]:
value
model
1 -0.505677
2 1.217027
3 -0.641583
4 0.778104
5 -1.037858
回答by firelynx
I don't know of a way to use the .get_group()methodwith more than one group.
我不知道有一种方法可以在多个组中使用该.get_group()方法。
You can however iterate through groups
但是，您可以遍历组
It is still a bit ugly to do this, but here is one solution with iteration:
这样做仍然有点难看，但这里有一个迭代的解决方案：
limit = 5
i = 0
for key, group in gd:
print key, group
i += 1
if i >= limit:
break
You could also do a loop with .get_group(), which imho. is a little prettier, but still quite ugly.
你也可以用.get_group()，imho做一个循环。有点漂亮，但仍然很丑。
for key in gd.groups.keys()[:2]:
print gd.get_group(key)
回答by Shahdab Khatri
def get_groups(group_object):
for i in group_object.groups.keys():
print(f"____{i}____")
display(group_object.get_group(i))
#get all groups by calling this method
get_groups( any_group_which_you_made )
