Python 如何从pandas中的groupby对象中选择列？
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How to select columns from groupby object in pandas?
提问by
I grouped my dataframe by the two columns below
我按下面的两列对我的数据框进行了分组
df = pd.DataFrame({'a': [1, 1, 3],
'b': [4.0, 5.5, 6.0],
'c': [7L, 8L, 9L],
'name': ['hello', 'hello', 'foo']})
df.groupby(['a', 'name']).median()
and the result is:
结果是：
b c
a name
1 hello 4.75 7.5
3 foo 6.00 9.0
How can I access the namefield of the resulting median (in this case hello, foo)? This fails:
如何访问name结果中位数的字段（在本例中hello, foo）？这失败了：
df.groupby(['a', 'name']).median().name
采纳答案by EdChum
You need to get the index values, they are not columns. In this case level 1
您需要获取索引值，它们不是列。在这种情况下，级别 1
df.groupby(["a", "name"]).median().index.get_level_values(1)
Out[2]:
Index([u'hello', u'foo'], dtype=object)
You can also pass the index name
您还可以传递索引名称
df.groupby(["a", "name"]).median().index.get_level_values('name')
as this will be more intuitive than passing integer values.
因为这比传递整数值更直观。
You can convert the index values to a list by calling tolist()
您可以通过调用将索引值转换为列表 tolist()
df.groupby(["a", "name"]).median().index.get_level_values(1).tolist()
Out[5]:
['hello', 'foo']
回答by cwharland
You can also reset_index()on your groupby result to get back a dataframe with the name column now accessible.
您还可以reset_index()在 groupby 结果上取回名称列现在可访问的数据框。
import pandas as pd
df = pd.DataFrame({"a":[1,1,3], "b":[4,5.5,6], "c":[7,8,9], "name":["hello","hello","foo"]})
df_grouped = df.groupby(["a", "name"]).median().reset_index()
df_grouped.name
0 hello
1 foo
Name: name, dtype: object
If you perform an operation on a single column the return will be a series with multiindex and you can simply apply pd.DataFrameto it and then reset_index.
如果您对单个列执行操作，则返回将是一个具有多pd.DataFrame索引的系列，您可以简单地对其应用然后 reset_index。
回答by proutray
Set as_index = Falseduring groupby
as_index = False在 groupby 期间设置
df = pandas.DataFrame({"a":[1,1,3], "b":[4,5.5,6], "c":[7,8,9], "name":["hello","hello","foo"]})
df.groupby(["a", "name"] , as_index = False).median()
回答by Mina
Using reset_index() after the group by will do the trick:
在 group by 之后使用 reset_index() 可以解决问题：
df = pd.DataFrame({'a': [1, 1, 3],
'b': [4.0, 5.5, 6.0],
'c': ['7L', '8L', '9L'],
'name': ['hello', 'hello', 'foo']})
df.groupby(['a', 'name']).median().reset_index().name
here is the result:
结果如下：
0 hello
1 foo
Name: name, dtype: object
and if you want the list of the values, you can simply:
如果您想要值列表，您可以简单地：
df = pd.DataFrame({'a': [1, 1, 3],
'b': [4.0, 5.5, 6.0],
'c': ['7L', '8L', '9L'],
'name': ['hello', 'hello', 'foo']})
df.groupby(['a', 'name']).median().reset_index().name.values
The result of using values will be a list containing the values for the name column. The code above returns the following list as the results:
使用值的结果将是一个包含名称列值的列表。上面的代码返回以下列表作为结果：
array(['hello', 'foo'], dtype=object)
