how can i convert time-series data to numpy array keep time order in pandas?

Question

i have below dataframe belows .. and i wanna convert it to numpy array. when i tried.. time order is broken converted to numpy array. may it's because it is time-series data (19:00~0:00:00~07:00:00)

how can i keep time-order convert dataframe to numpy array?

        aaa                                                         \
Date     2015-12-06 2015-12-13 2015-12-20 2015-12-23 2015-12-26 2016-01-03   
Time                                                                         
19:00:00       4.72       8.50       3.87       7.95       1.76       9.82   
19:15:00       4.54       8.00       3.72       8.14       1.74       9.77   
19:30:00       4.44       8.17       3.72       7.99       1.75       9.77   
19:45:00       4.37       7.92       3.28       7.94       1.89       9.61   
20:00:00       4.03       7.54       2.48       7.99       1.98       9.46   
20:15:00       3.74       7.86       3.30       7.68       1.63       9.30   
20:30:00       3.48       8.41       3.52       7.88       1.52       9.22   
20:45:00       3.31       8.52       3.81       7.83       1.54       9.08   
21:00:00       3.17       8.23       3.97       7.96       1.63       9.14   
21:15:00       2.99       8.23       3.37       7.61       1.87       9.14   
21:30:00       2.96       8.26       3.23       7.63       2.03       9.13   
21:45:00       2.69       7.89       3.10       7.34       2.12       9.04   
22:00:00       2.62       7.83       2.94       7.21       2.11       9.04   
22:15:00       2.55       7.78       2.83       7.26       2.39       9.01   
22:30:00       2.49       7.73       2.89       7.15       2.30       9.08   
22:45:00       2.48       7.80       2.79       7.02       2.22       8.92   
23:00:00       2.38       7.71       2.92       7.17       2.43       8.80   
23:15:00       2.23       7.74       3.01       7.24       2.33       8.56   
23:30:00       2.29       7.51       3.10       7.14       2.38       8.32   
23:45:00       2.29       7.31       3.00       6.89       2.10       8.02   
00:00:00       2.17       6.84       2.84       6.89       1.82       7.86   
00:15:00       2.13       6.84       2.65       7.06       1.36       7.95   
00:30:00       2.21       6.78       2.63       6.98       0.92       7.97   
00:45:00       2.19       6.41       2.18       7.08       1.05       7.80   
01:00:00       2.13       6.24       1.56       7.20       0.81       7.73   
01:15:00       2.14       5.90       1.39       7.31       1.01       7.89   
01:30:00       2.13       5.74       1.81       7.58       0.79       7.91   
01:45:00       2.11       5.82       1.60       7.47       1.19       8.02   
02:00:00       1.72       6.01       0.90       7.14       1.27       8.09   
02:15:00       1.94       6.04       1.12       7.33       0.95       8.13   
02:30:00       2.05       6.00       1.44       7.06       1.15       8.15   
02:45:00       1.96       6.03       1.45       6.86       1.05       7.95   
03:00:00       1.63       6.28       1.62       6.85       1.22       7.43   
03:15:00       1.79       6.14       1.41       6.94       1.05       6.97   
03:30:00       1.37       6.03       1.29       6.98       1.27       6.97   
03:45:00       1.44       5.84       1.01       7.29       1.31       6.90   
04:00:00       1.37       5.62       0.92       7.13       1.35       6.77   
04:15:00       1.62       5.75       0.95       7.18       1.21       7.09   
04:30:00       1.64       5.71       1.06       7.18       1.32       7.27   
04:45:00       1.40       5.46       0.79       7.17       1.55       7.35   
05:00:00       1.51       5.48       0.64       6.83       1.42       7.27   
05:15:00       1.46       5.80       0.52       6.58       1.60       7.21   
05:30:00       1.61       5.59       0.35       6.98       1.54       7.13   
05:45:00       1.49       5.28       0.46       6.58       1.58       7.04   
06:00:00       1.55       5.00       0.17       6.35       1.88       7.10   
06:15:00       1.94       4.94      -0.18       6.12       1.94       7.11   
06:30:00       1.45       5.01      -0.31       6.02       1.90       7.14   
06:45:00       1.36       4.90      -0.17       5.83       2.06       7.17   
07:00:00       1.25       4.75       0.20       5.70       2.35       7.18 

Answer 1

You need transpose DataFrame by T and convert to array:

arr = df.T.values

Or first convert to array and then transpose:

arr = df.values.T