将Pandas GroupBy输出从Series转换为DataFrame [英] Converting a Pandas GroupBy output from Series to DataFrame

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"] } )

打印时显示为:

   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()

并打印出一个GroupBy对象:

                  City  Name
Name    City
Alice   Seattle      1     1
Bob     Seattle      2     2
Mallory Portland     2     2
        Seattle      1     1

但是我最终想要的是另一个DataFrame对象，该对象包含GroupBy对象中的所有行.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

在熊猫文档中，我不太清楚如何做到这一点.任何提示都将受到欢迎.

I can't quite see how to accomplish this in the pandas documentation. Any hints would be welcome.

推荐答案

g1此处是一个DataFrame.但是，它具有层次结构索引:

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)

也许您想要这样的东西?

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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