python - 列出 Pandas 组中最常见的成员？

Question

我有一个包含这样列的数据框:

        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发起人。我可以使用以下方法列出每个组的大小:

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

这给了我这个:

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

但是如何找到前 10 个最常见的组？如果我这样做:

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

然后我得到一个错误:

AttributeError: 'Series' object has no attribute 'sort_values'

Answer 1

我想你可以使用 Series.nlargest :

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

在docs是注释:

Faster than .sort_values(ascending=False).head(n) for small n relative to the size of the Series object.

示例:

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