在panda(Python)中删除重复的标题

在panda(Python)中删除重复的标题,python,python-3.x,pandas,python-2.7,dataframe,Python,Python 3.x,Pandas,Python 2.7,Dataframe,基于下面的代码,我正在基于减法将科学和数学结合起来 for f in Science['Name']: Math=(df[df['Name'].str.startswith(f)]) Math['Sub'] = Math['Name'].str.extract(r'(\w*)', expand=False) Field1= pd.merge(Science, Math, left_on='Sub', right_on='Sub') print(

基于下面的代码,我正在基于减法将科学和数学结合起来

for f in Science['Name']:
     Math=(df[df['Name'].str.startswith(f)])
     Math['Sub'] = Math['Name'].str.extract(r'(\w*)', expand=False)
     Field1= pd.merge(Science, Math, left_on='Sub', right_on='Sub')
     
     print(Field1)
上面代码的结果如下所示,但是我想删除重复的标题,它们是Name-x、Name-y、Sub和Name。我将我的预期结果分享如下,我还想将其存储在csv文件中

    Name_x      Name_y    Sub            Name
0  Numbers  Math-Numbers  Math         Math-01
1  Numbers  Math-Numbers  Math         Math-01
2  Numbers  Math-Numbers  Math         Math-01

    Name_x      Name_y    Sub           Name
0  Numbers  Math-Numbers  Math         Math-02
1  Numbers  Math-Numbers  Math         Math-02
2  Numbers  Math-Numbers  Math         Math-02

    Name_x      Name_y    Sub           Name
0  Numbers  Math-Numbers  Math         Math-03
1  Numbers  Math-Numbers  Math         Math-03
2  Numbers  Math-Numbers  Math         Math-03
预期的CSV文件:

    Name_x      Name_y    Sub           Name
0  Numbers  Math-Numbers  Math         Math-01
1  Numbers  Math-Numbers  Math         Math-01
2  Numbers  Math-Numbers  Math         Math-01
0  Numbers  Math-Numbers  Math         Math-02
1  Numbers  Math-Numbers  Math         Math-02
2  Numbers  Math-Numbers  Math         Math-02
0  Numbers  Math-Numbers  Math         Math-03
1  Numbers  Math-Numbers  Math         Math-03
2  Numbers  Math-Numbers  Math         Math-03

您所需要的只是连接生成的数据帧。 i、 e

result = []
for f in Science['Name']:
     Math=(df[df['Name'].str.startswith(f)])
     Math['Sub'] = Math['Name'].str.extract(r'(\w*)', expand=False)
     Field1= pd.merge(Science, Math, left_on='Sub', right_on='Sub')
     result.append(Field1)
out_df = pd.concat(result)
print(out_df)