Question

我有类似下面的多索引Pandas系列，其中的值由Team，Year和Gender索引。

>>> import pandas as pd
>>> import numpy as np
>>> multi_index=pd.MultiIndex.from_product([['Team A','Team B', 'Team C', 'Team D'],[2015,2016],['Male','Female']], names = ['Team','Year','Gender'])
>>> np.random.seed(0)
>>> df=pd.Series(index=multi_index, data=np.random.randint(1, 10, 16))
>>> df
>>> 
Team    Year  Gender
Team A  2015  Male      6
              Female    1
        2016  Male      4
              Female    4
Team B  2015  Male      8
              Female    4
        2016  Male      6
              Female    3
Team C  2015  Male      5
              Female    8
        2016  Male      7
              Female    9
Team D  2015  Male      9
              Female    2
        2016  Male      7
              Female    8

我的目标是为每个4年/性别组合（2015年男性，男性2016年，女性2015年和女性2016年）获得团队排名顺序的数据框。

我的方法是首先将数据帧取消堆叠，以便由团队编制索引......

>>> unstacked_df = df.unstack(['Year','Gender'])
>>> print unstacked_df
>>> 
>>> 
Year   2015        2016       
Gender Male Female Male Female
Team                          
Team A    6      1    4      4
Team B    8      4    6      3
Team C    5      8    7      9
Team D    9      2    7      8

然后通过循环并对这4列中的每一列进行排序，从索引顺序创建一个数据框......

>>> team_orders = np.array([unstacked_df.sort_values(x).index.tolist() for x in unstacked_df.columns]).T
>>> result = pd.DataFrame(team_orders, columns=unstacked_df.columns)
>>> print result
Year      2015            2016        
Gender    Male  Female    Male  Female
0       Team C  Team A  Team A  Team B
1       Team A  Team D  Team B  Team A
2       Team B  Team B  Team C  Team D
3       Team D  Team C  Team D  Team C

我缺少一种更简单/更好的方法吗？

Answer 1

从您的未堆叠版本开始，您可以使用.argsort()与.apply()对每列进行排名，然后将其用作对索引的查找：

df.unstack([1,2]).apply(lambda x: x.index[x.argsort()]).reset_index(drop=True)

Year      2015            2016        
Gender    Male  Female    Male  Female
0       Team C  Team A  Team A  Team B
1       Team A  Team D  Team B  Team A
2       Team B  Team B  Team C  Team D
3       Team D  Team C  Team D  Team C

编辑：以下是有关其原因的更多信息。只需.argsort()即可获得：

print df.unstack([1,2]).apply(lambda x: x.argsort())

Year   2015        2016       
Gender Male Female Male Female
Team                          
Team A    2      0    0      1
Team B    0      3    1      0
Team C    1      1    2      3
Team D    3      2    3      2

查找位基本上只对每列执行以下操作：

df.unstack([1,2]).index[[2,0,1,3]]

Index([u'Team C', u'Team A', u'Team B', u'Team D'], dtype='object', name=u'Team')

并且.reset_index()摆脱了现在无意义的索引标签。

Pandas为多列

1 个答案: