python - DataFrame's elements as numpy arrays -

i'm trying change dataframe's values this: df['tokens'] = tokens tokens 2-d np.array. expected have column, each element 1-d np.array, found out, each element took first element of correspoding 1-d array. there way store arrays in dataframe's elements?

is want?

in [26]: df = pd.dataframe(np.random.rand(5,2), columns=list('ab'))  in [27]: df out[27]:                   b 0  0.513723  0.886019 1  0.197956  0.172094 2  0.131495  0.476552 3  0.678821  0.106523 4  0.440118  0.802589  in [28]: arr = df.values  in [29]: arr out[29]: array([[ 0.51372311,  0.88601887],        [ 0.19795635,  0.17209383],        [ 0.13149478,  0.47655197],        [ 0.67882124,  0.10652332],        [ 0.44011802,  0.80258924]])  in [30]: df['c'] = arr.tolist()  in [31]: df out[31]:                   b                                           c 0  0.513723  0.886019    [0.5137231110962795, 0.8860188692834928] 1  0.197956  0.172094  [0.19795634688449892, 0.17209383434042336] 2  0.131495  0.476552  [0.13149477867656167, 0.47655196508193576] 3  0.678821  0.106523   [0.6788212365523125, 0.10652331756477551] 4  0.440118  0.802589   [0.44011802077658635, 0.8025892383754725] 

timing 5m rows df:

in [36]: big = pd.concat([df] * 10**6, ignore_index=true)  in [38]: big.shape out[38]: (5000000, 2)  in [39]: arr = big.values  in [40]: %timeit arr.tolist() 1 loop, best of 3: 2.27 s per loop  in [41]: %timeit list(arr) 1 loop, best of 3: 3.62 s per loop