>>> random_portafolios

    AAPL weight  MSFT weight  XOM weight  JNJ weight  JPM weight  AMZN weight  GE weight  FB weight T weight
0     0.188478     0.068795    0.141632    0.147974    0.178185     0.040370   0.020516   0.047275  0.166774
1     0.236818     0.008540    0.082680    0.088380    0.453573     0.021001   0.014043   0.089811  0.005155
2     0.179750     0.071711    0.050107    0.089424    0.080108     0.106136   0.155139   0.073487  0.194138
3     0.214392     0.015681    0.034284    0.276342    0.118263     0.002101   0.057484   0.000317  0.281137
4     0.301469     0.099750    0.046454    0.093279    0.020095     0.073545   0.178752   0.146486  0.040168
5     0.132916     0.006199    0.305137    0.032262    0.090356     0.169671   0.205602   0.003686  0.054172

>>> StockReturns.head()

                AAPL      MSFT       XOM      TWTR       JPM      AMZN        GE        FB         T
   Date
  2017-01-04 -0.001164 -0.004356 -0.011069  0.025547  0.001838  0.004657  0.000355  0.015660 -0.005874
  2017-01-05  0.005108  0.000000 -0.014883  0.013642 -0.009174  0.030732 -0.005674  0.016682 -0.002686
  2017-01-06  0.011146  0.008582 -0.000499  0.004681  0.000123  0.019912  0.002853  0.022707 -0.019930
  2017-01-09  0.009171 -0.003170 -0.016490  0.019220  0.000741  0.001168 -0.004979  0.012074 -0.012641
  2017-01-10  0.001049 -0.000335 -0.012829 -0.007429  0.002837 -0.001280 -0.002859 -0.004404  0.000278

def complex_computation():
    WeightedReturns = StockReturns.mul(arr, axis=1)
    ReturnsDaily= WeightedReturns.sum(axis=1)
    mean_retorns_daily = np.mean(ReturnsDaily)
    Returns = ((1+mean_retorns_daily)**252)
    cov_mat =StockReturns.cov()
    cov_mat_annual = cov_mat*252
    Volatility= np.sqrt(np.dot(arr.T, np.dot(cov_mat_annual, arr)))
    return Returns, Volatility

def func(row):
    random_portafolios['Volatility'].append(Volatility)
    Returns, Volatility = complex_computation(row.values)
    return pd.Series({'NewColumn1': Retturns,
                  'NewColumn2': Volatility})

def run_apply(random_portafolios):
    df_result = random_portafolios.apply(func, axis=1)
    return df_result