新列中的Pandas groupby和agg值

如何解决新列中的Pandas groupby和agg值

import pandas as pd    
df = pd.DataFrame({'company' : [ABC,ABC,XYZ,XYZ],'tin': ['5555','1111','5555','2222']                   
                   })

如果大型数据集中的值相等，我不知道如何按“锡”列分组。

Desirable result:

df = pd.DataFrame({'company' : [ABC,'2222'],'column' : ['text' ABC and XYZ,None,'text' ABC and XYZ,None]

               })

解决方法

我相信您需要：

df1 = pd.DataFrame({ 'tin': ['5555','5555'],'name' : 'AAA,BBB'.split(',')})

print (df1)
    tin name
0  5555  AAA
1  5555  BBB

df2 = pd.DataFrame({'company' : 'ABC,ABC,XYZ,XYZ'.split(','),'tin': ['5555','1111','5555','2222','2222'],AAA,BBB,')})

print (df2)
  company   tin name
0     ABC  5555  AAA
1     ABC  1111  AAA
2     XYZ  5555  AAA
3     XYZ  2222  AAA
4     ABC  5555  BBB
5     ABC  1111  BBB
6     XYZ  5555  BBB
7     XYZ  2222  BBB

首先使用DataFrame.merge进行测试，以测试名为df1的第一个DataFrame与参数indicator=True以及how ='left'是否用于左连接：

df = df2.merge(df1,on=['tin','name'],how='left',indicator=True)
print (df)
  company   tin name     _merge
0     ABC  5555  AAA       both
1     ABC  1111  AAA  left_only
2     XYZ  5555  AAA       both
3     XYZ  2222  AAA  left_only
4     ABC  5555  BBB       both
5     ABC  1111  BBB  left_only
6     XYZ  5555  BBB       both
7     XYZ  2222  BBB  left_only

然后仅按boolean indexing过滤两行：

df = df[df['_merge'].eq('both')]
print (df)
  company   tin name _merge
0     ABC  5555  AAA   both
2     XYZ  5555  AAA   both
4     ABC  5555  BBB   both
6     XYZ  5555  BBB   both

最后两列的总和，并由DataFrame.join分配回来：

s = df.groupby(['tin','name'])['company'].agg(' and '.join).rename('new')
df = df2.join(s,'name'])
print (df)
  company   tin name          new
0     ABC  5555  AAA  ABC and XYZ
1     ABC  1111  AAA          NaN
2     XYZ  5555  AAA  ABC and XYZ
3     XYZ  2222  AAA          NaN
4     ABC  5555  BBB  ABC and XYZ
5     ABC  1111  BBB          NaN
6     XYZ  5555  BBB  ABC and XYZ
7     XYZ  2222  BBB          NaN