Question

我有一个包含这样的列的数据框：

        id                           lead_sponsor lead_sponsor_class
  02837692    Janssen Research & Development, LLC           Industry
  02837679             Aarhus University Hospital              Other
  02837666  Universidad Autonoma de Ciudad Juarez              Other
  02837653         Universidad Autonoma de Madrid              Other
  02837640         Beirut Eye Specialist Hospital              Other

我想找到最常见的主要赞助商。我可以使用以下方式列出每个组的大小：

df.groupby(['lead_sponsor', 'lead_sponsor_class']).size()

给了我这个：

lead_sponsor                              lead_sponsor_class
307 Hospital of PLA                       Other                  1
3E Therapeutics Corporation               Industry               1
3M                                        Industry               4
4SC AG                                    Industry               8
5 Santé                                   Other                  1

但我怎样才能找到十大最常见的群体？如果我这样做：

df.groupby(['lead_sponsor', 'lead_sponsor_class']).size().sort_values(ascending=False).head(10)

然后我收到错误：

AttributeError：'Series'对象没有属性'sort_values'

Answer 1

我认为您可以使用Series.nlargest：

print (df.groupby(['lead_sponsor', 'lead_sponsor_class']).size().nlargest(10))

在docs中备注：

相对于Series对象的大小，小于n的.sort_values（升序=假）.head（n）更快。

样品：

import pandas as pd

df = pd.DataFrame({'id': {0: 2837692, 1: 2837679, 2: 2837666, 3: 2837653, 4: 2837640}, 
                   'lead_sponsor': {0: 'a', 1: 'a', 2: 'a', 3: 's', 4: 's'}, 
                   'lead_sponsor_class': {0: 'Industry', 1: 'Other', 2: 'Other', 3: 'Other', 4: 'Other'}})

print (df)
        id lead_sponsor lead_sponsor_class
0  2837692            a           Industry
1  2837679            a              Other
2  2837666            a              Other
3  2837653            s              Other
4  2837640            s              Other

print (df.groupby(['lead_sponsor', 'lead_sponsor_class']).size())
lead_sponsor  lead_sponsor_class
a             Industry              1
              Other                 2
s             Other                 2
dtype: int64

print (df.groupby(['lead_sponsor', 'lead_sponsor_class']).size().sort_values(ascending=False).head(2))
lead_sponsor  lead_sponsor_class
s             Other                 2
a             Other                 2
dtype: int64

print (df.groupby(['lead_sponsor', 'lead_sponsor_class']).size().nlargest(2))
lead_sponsor  lead_sponsor_class
a             Other                 2
s             Other                 2
dtype: int64

列出Pandas集团中最常见的成员？

1 个答案: