将Pandas GroupBy输出从Series转换为DataFrame
我从这样的输入数据开始
df1 = pandas.DataFrame( { "Name" : ["Alice", "Bob", "Mallory", "Mallory", "Bob" , "Mallory"] ,
"City" : ["Seattle", "Seattle", "Portland", "Seattle", "Seattle", "Portland"] } )
打印时显示为:
City Name0 Seattle Alice
1 Seattle Bob
2 Portland Mallory
3 Seattle Mallory
4 Seattle Bob
5 Portland Mallory
分组非常简单:
g1 = df1.groupby( [ "Name", "City"] ).count()
打印产生一个GroupBy
对象:
City NameName 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:
City NameName City
Alice Seattle 1 1
Bob Seattle 2 2
Mallory Portland 2 2
Mallory Seattle 1 1
我在pandas文档中看不到如何完成此操作。任何提示都将受到欢迎。
回答:
g1
这是一个DataFrame
。但是,它具有层次结构索引:
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?
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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