python - Append a list of arrays as column to pandas Data Frame with same column indices -

i have list of arrays (one-dimensional numpy array) (a_) , list (l_) , want have dataframe them columns. this:

a_: [array([381]), array([376]), array([402]), array([400])...] l_: [1.5,2.34,4.22,...] 

i can by:

df_l = pd.dataframe(l_) df_a = pd.dataframe(a_) df = pd.concat([df_l, df_a], axis=1) 

is there shorter way of doing it? tried use pd.append:

df_l = pd.dataframe(l_) df_l = df_l.append(a_) 

however, because columns indices both 0, adds a_ end of dataframe column, resulting in single column. there this:

l_ = l_.append(a_).reset(columns)

that set new column index appended array? well, not work!

the desired output like:

  0       0 0 1.50    381 1 2.34    376 2 4.22    402  

...

thanks.

suggestion:

df_l = pd.dataframe(l_)  df_1['a_'] = pd.series(a_list, index=df_1.index) 

example #1:

l = list(data) = list(data) data_frame = pd.dataframe(l)  data_frame['a'] = pd.series(a, index=data_frame.index) 

example #2 - same series length (create series , set index same existing data frame):

in [33]: l = list(item item in range(10))  in [34]: = list(item item in range(10,20))  in [35]: data_frame = pd.dataframe(l,columns=['l'])  in [36]: data_frame['a'] = pd.series(a, index=data_frame.index)  in [37]: print data_frame     l   0  0  10 1  1  11 2  2  12 3  3  13 4  4  14 5  5  15 6  6  16 7  7  17 8  8  18 9  9  19 

example #3 - different series lengths (create series , let pandas handle index matching):

in [45]: not_same_length = list(item item in range(50,55))  in [46]: data_frame['nsl'] = pd.series(not_same_length)  in [47]: print data_frame     l    nsl 0  0  10   50 1  1  11   51 2  2  12   52 3  3  13   53 4  4  14   54 5  5  15  nan 6  6  16  nan 7  7  17  nan 8  8  18  nan 9  9  19  nan 

based on comments, looks want join list of lists.i'm assuming in list structure because array() not method in python. following:

in [63]: = [[381],[376], [402], [400]]  in [64]: = [inner_item item in inner_item in item]  in [65]: print  [381, 376, 402, 400] 

then create series using new array , follow steps above add data frame.