因此,假设我们有一个像这样的DataFrame:
In[1]: operinc_df
Out[1] :
ticker1 ticker2 ticker3
0 0.343573 0.654719 0.246643
1 0.186861 0.219793 0.761056
2 0.417347 0.058368 0.684918
3 0.803177 0.014781 0.896704
4 0.294515 0.488001 0.291187
5 0.402278 0.368005 0.821096
6 0.985514 0.378000 0.929529
7 1.168360 0.729640 0.347064
8 0.025802 1.337121 0.638399
9 0.019182 2.257563 0.041164
我们还有另一个具有相同行数和列数(具有相同名称)的DataFrame:
In[2]: opex_df
Out[2] :
ticker1 ticker2 ticker3
0 1.450770 0.227986 2.243050
1 1.212298 0.406004 1.212320
2 0.918931 0.677043 0.361878
3 0.566981 1.155675 0.295542
4 0.600614 0.872015 1.129760
5 0.470118 0.730027 1.112045
6 1.489904 0.522885 0.475244
7 1.626853 0.142996 0.758590
8 0.290340 1.175891 0.591020
9 1.472838 0.107094 0.715764
我不知道如何创建由fundamentals
和operinc_df
组成的另一个DataFrame opex_df
,使其看起来像下面的DataFrame(可能具有两个索引级别):
In[3]: fundamentals
Out[3] :
operinc_df opex_df
ticker1 0 0.343573 1.450770
ticker1 1 0.186861 1.212298
. . . .
. . . .
. . . .
ticker1 9 0.019182 1.472838
ticker2 0 0.654719 0.227986
ticker2 1 0.219793 0.406004
. . . .
. . . .
. . . .
ticker2 9 2.257563 0.107094
ticker3 0 0.246643 2.243050
ticker3 1 0.761056 1.212320
. . . .
. . . .
. . . .
ticker3 9 0.041164 0.715764
阅读Reshaping dataframes in pandas based on column labels和Create a pandas DataFrame from multiple dicts给了我一些见解(因为我也试图通过首先将原始DataFrame转换为字典,将operinc_df
和opex_df
打包为先用字典理解键,然后再用pandas.DataFrame.from_dict()
尝试创建fundamentals_df
,但是到目前为止,它对我来说还行不通。
您对我如何正确执行此操作有任何想法吗?预先非常感谢。
答案 0 :(得分:4)
您可以合并转置的数据帧,
new_df = pd.concat([operinc_df.T, opex_df.T], axis = 1, keys=['operinc_df', 'opex_df']).stack()
operinc_df opex_df
ticker1 0 0.343573 1.450770
1 0.186861 1.212298
2 0.417347 0.918931
3 0.803177 0.566981
4 0.294515 0.600614
5 0.402278 0.470118
6 0.985514 1.489904
7 1.168360 1.626853
8 0.025802 0.290340
9 0.019182 1.472838
ticker2 0 0.654719 0.227986
1 0.219793 0.406004
2 0.058368 0.677043
3 0.014781 1.155675
4 0.488001 0.872015
5 0.368005 0.730027
6 0.378000 0.522885
7 0.729640 0.142996
8 1.337121 1.175891
9 2.257563 0.107094
ticker3 0 0.246643 2.243050
1 0.761056 1.212320
2 0.684918 0.361878
3 0.896704 0.295542
4 0.291187 1.129760
5 0.821096 1.112045
6 0.929529 0.475244
7 0.347064 0.758590
8 0.638399 0.591020
9 0.041164 0.715764
答案 1 :(得分:0)
您可以这样做:
fundamentals = (operinc_df.stack().rename('operinc_df').to_frame()
.join(opex_df.stack().rename('opex_df'))
.swaplevel().sort_index())
>>> fundamentals
operinc_df opex_df
ticker1 0 0.343573 1.450770
1 0.186861 1.212298
2 0.417347 0.918931
3 0.803177 0.566981
4 0.294515 0.600614
5 0.402278 0.470118
6 0.985514 1.489904
7 1.168360 1.626853
8 0.025802 0.290340
9 0.019182 1.472838
ticker2 0 0.654719 0.227986
1 0.219793 0.406004
2 0.058368 0.677043
3 0.014781 1.155675
4 0.488001 0.872015
5 0.368005 0.730027
6 0.378000 0.522885
7 0.729640 0.142996
8 1.337121 1.175891
9 2.257563 0.107094
ticker3 0 0.246643 2.243050
1 0.761056 1.212320
2 0.684918 0.361878
3 0.896704 0.295542
4 0.291187 1.129760
5 0.821096 1.112045
6 0.929529 0.475244
7 0.347064 0.758590
8 0.638399 0.591020
9 0.041164 0.715764