Getting index values from pd mean() and std() functions - python-2.7

Question

I'm trying to get the index values from a pd std().
My final objective is to match the index with another df and insert the corresponding values (standard deviations).
(in): df_std['index'] = df_std.index
(out): Index([u'AAPL US Equity', u'QQQ US Equity', u'BRABCBACNPR4 BZ Equity'...dtype='object')
However, I've been unable to add the indexes to the "right" of df_std because of the types: df_std.index is a series while df_std is a df. When I try to do it, a line is added instead of a column:
(in): df_std['index'] = df_std.index
(out):
BRSTNCLF1R25 Govt 64.0864
BRITUBACNPR1 BZ Equity 2.67762
BRSTNCNTB4O9 Govt 48.2419
BRSTNCLF1R74 Govt 64.901
PBR US Equity 0.770755
BRBBASACNOR3 BZ Equity 2.93335
BRSTNCLF1R82 Govt 65.0979
index Index([u'AAPL US Equity', u'QQQ US Equity', u'...
dtype: object
I've already tried converting it df_std.inde to a tuple and to a dataframe.
Thanks!
Edit:
I'm trying to match df_std['index'] with df_final['bloomberg_ticker'] and bring the std values to df_final['std']:
(in): print df_final
(out):
serie tipo tp_cnpjfundo valor id bloomberg_ticker \
0 NaN caixa NaN NaN 0 NaN
1 NaN titpublicos NaN NaN 1 BRSTNCLF1R17 Govt
2 NaN titpublicos NaN NaN 2 BRSTNCLF1R17 Govt
3 NaN titpublicos NaN NaN 3 BRSTNCLF1R25 Govt
(the column 'id' will be deleted later)

Answer 1

Use .reset_index() than assigning if what you have is a dataframe i.e
df_std = df_std.reset_index()
Example :
df = pd.DataFrame([0,1,2,3], index=['a','b','c','d'])
df = df.reset_index()
Output :
index 0
0 a 0
1 b 1
2 c 2
3 d 3
In case what you have is a series, convert that to dataframe then reset_index i.e if df_std is the series you have then
df_std = df_std.to_frame().reset_index()
I think what are trying to do is map the values of series to a specific column so you can use
df = pd.DataFrame({'col':['a','b','c','d','e'],'vales':[5,1,2,4,5]})
s = pd.Series([1,2,3],index=['a','b','c'])
df['new'] = df['col'].map(s)
Output :
col vales new
0 a 5 1.0
1 b 1 2.0
2 c 2 3.0
3 d 4 NaN
4 e 5 NaN
In your case you can use df_final['index'].map(df_std)
For conditional check if the index of series is present int he index column of dataframe then you can use .isin i.e
df['col'].isin(s.index) # Returns the boolen mask
df[df['col'].isin(s.index)] #Returns the dataframe based matched index