为什么groupby.apply返回重复级别 [英] why groupby.apply return duplicate level

问题描述

def afun(group):
  aa=len(group)
  group.sort_values(inplace=True)

  return pandas.DataFrame({'score':numpy.arange(aa),'price':group})



df = pandas.DataFrame({
              'stock':numpy.repeat( ['AAPL','GOOG','YHOO'], 3 ),
              'date':numpy.tile( pandas.date_range('5/5/2015', periods=3, freq='D'), 3 ),
              'price':(numpy.random.randn(9).cumsum() + 10) ,
              'price2':(numpy.random.randn(9).cumsum() + 10)})
df = df.set_index(['stock','date'])
agroupDf=df.groupby(level='date') 



tt=agroupDf['price'].apply(afun)

变量tt的值如图所示

我的问题是为什么tt有两列日期"，以及如何避免第二列日期"?

my question is why tt has two columns 'date',and how to avoid the secend column 'date'?

推荐答案

第一个是groupby'date'.第二个是index'日期'.

The first one is the groupby 'date'. The second one is the index 'date'.

改变事物-这次groupby库存:

df       = df.set_index(['date','stock'])
agroupDf = df.groupby(level='stock') 

tt=agroupDf['price'].apply(afun)
tt


                            price  score
stock date       stock                  
AAPL  2015-05-05 AAPL    9.333143      0
      2015-05-06 AAPL    9.680022      1
      2015-05-07 AAPL    9.870889      2
GOOG  2015-05-06 GOOG   10.030032      0
      2015-05-05 GOOG   10.229084      1
      2015-05-07 GOOG   10.571631      2
YHOO  2015-05-07 YHOO    9.996925      0
      2015-05-05 YHOO   10.342180      1
      2015-05-06 YHOO   10.586120      2

我想你想要这个:

df       = df.set_index('stock')
agroupDf = df.groupby('date') 

tt=agroupDf['price'].apply(afun)
tt

                          price  score
date       stock                  
2015-05-05 AAPL   10.414396      0
           GOOG   12.608225      1
           YHOO   12.830496      2
2015-05-06 AAPL   10.428767      0
           GOOG   11.189663      1
           YHOO   11.988177      2
2015-05-07 YHOO   11.202677      0
           AAPL   11.274440      1
           GOOG   11.780654      2

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