将 Pandas GroupBy 输出从 Series 转换为 DataFrame [英] Converting a Pandas GroupBy output from Series to DataFrame
本文介绍了将 Pandas GroupBy 输出从 Series 转换为 DataFrame的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我从这样的输入数据开始
I'm starting with input data like this
df1 = pandas.DataFrame( {
"Name" : ["Alice", "Bob", "Mallory", "Mallory", "Bob" , "Mallory"] ,
"City" : ["Seattle", "Seattle", "Portland", "Seattle", "Seattle", "Portland"] } )
打印时显示如下:
City Name
0 Seattle Alice
1 Seattle Bob
2 Portland Mallory
3 Seattle Mallory
4 Seattle Bob
5 Portland Mallory
分组很简单:
g1 = df1.groupby( [ "Name", "City"] ).count()
并打印产生一个 GroupBy
对象:
and printing yields a GroupBy
object:
City Name
Name City
Alice Seattle 1 1
Bob Seattle 2 2
Mallory Portland 2 2
Seattle 1 1
但我最终想要的是另一个包含 GroupBy 对象中所有行的 DataFrame 对象.换句话说,我想得到以下结果:
But what I want eventually is another DataFrame object that contains all the rows in the GroupBy object. In other words I want to get the following result:
City Name
Name City
Alice Seattle 1 1
Bob Seattle 2 2
Mallory Portland 2 2
Mallory Seattle 1 1
我不太明白如何在 Pandas 文档中完成此操作.欢迎提供任何提示.
I can't quite see how to accomplish this in the pandas documentation. Any hints would be welcome.
推荐答案
g1
这里是一个 DataFrame.不过,它有一个分层索引:
g1
here is a DataFrame. It has a hierarchical index, though:
In [19]: type(g1)
Out[19]: pandas.core.frame.DataFrame
In [20]: g1.index
Out[20]:
MultiIndex([('Alice', 'Seattle'), ('Bob', 'Seattle'), ('Mallory', 'Portland'),
('Mallory', 'Seattle')], dtype=object)
也许你想要这样的东西?
Perhaps you want something like this?
In [21]: g1.add_suffix('_Count').reset_index()
Out[21]:
Name City City_Count Name_Count
0 Alice Seattle 1 1
1 Bob Seattle 2 2
2 Mallory Portland 2 2
3 Mallory Seattle 1 1
或者类似的东西:
In [36]: DataFrame({'count' : df1.groupby( [ "Name", "City"] ).size()}).reset_index()
Out[36]:
Name City count
0 Alice Seattle 1
1 Bob Seattle 2
2 Mallory Portland 2
3 Mallory Seattle 1
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