Q:

How i can transform Dataframe in many temporal feature in Python? 

i have this dataframe:
    Timestamp   DATA0   DATA1   DATA2   DATA3   DATA4   DATA5   DATA6   DATA7
0   1.478196e+09    219 128 220 27  141 193 95  50
1   1.478196e+09    95  237 27  121 90  194 232 137
2   1.478196e+09    193 22  103 217 138 195 153 172
3   1.478196e+09    181 120 186 73  120 239 121 218
4   1.478196e+09    70  194 36  16  81  129 95  217
... ... ... ... ... ... ... ... ... ...
242 1.478198e+09    15  133 112 2   236 81  94  252
243 1.478198e+09    0   123 163 160 13  156 145 32
244 1.478198e+09    83  147 61  61  33  199 147 110
245 1.478198e+09    172 95  87  220 226 99  108 176
246 1.478198e+09    123 240 180 145 132 213 47  60

I need to create a temporal features like this:
    Timestamp   DATA0   DATA1   DATA2   DATA3   DATA4   DATA5   DATA6   DATA7
0   1.478196e+09    219 128 220 27  141 193 95  50
1   1.478196e+09    95  237 27  121 90  194 232 137
2   1.478196e+09    193 22  103 217 138 195 153 172
3   1.478196e+09    181 120 186 73  120 239 121 218
4   1.478196e+09    70  194 36  16  81  129 95  217

Timestamp   DATA0   DATA1   DATA2   DATA3   DATA4   DATA5   DATA6   DATA7
1   1.478196e+09    95  237 27  121 90  194 232 137
2   1.478196e+09    193 22  103 217 138 195 153 172
3   1.478196e+09    181 120 186 73  120 239 121 218
4   1.478196e+09    70  194 36  16  81  129 95  217
5   1.478196e+09    121 69  111 204 134 92  51  190

    Timestamp   DATA0   DATA1   DATA2   DATA3   DATA4   DATA5   DATA6   DATA7
2   1.478196e+09    193 22  103 217 138 195 153 172
3   1.478196e+09    181 120 186 73  120 239 121 218
4   1.478196e+09    70  194 36  16  81  129 95  217
5   1.478196e+09    121 69  111 204 134 92  51  190
6   1.478196e+09    199 132 39  197 159 242 153 104

How can I do this automatically? 
what structure should I use, what functions? 
I was told that the dataframe should become an array of arrays
it's not very clear to me

A:

If I understand it correctly, you want e.g. a list of dataframes, where each dataframe is a progressing slice of the original frame.  This example would give you a list of dataframes:
import pandas as pd

# dummy dataframe
df = pd. DataFrame({'col_1': range(10), 'col_2': range(10)})

# returns slices of size slice_length with step size 1
slice_length = 5
lst = [df.iloc[i:i+slice_length,: ] for i in range(df.shape[0] - slice_length)]

Please note that you are duplicating a lot of data and thus increasing memory usage.  If you merely have to perform an operation on subsequent slices, you should better loop over the dataframe and apply your function.  Even better, if possible, you should try to verctorize your operation, as this will likely make a huge difference in performance. 
EDIT: saving the slices to file:
If you're only interested in saving the slices to file (e.g. in a csv), you don't need to first create a list of all slices (with the associated memory usage).  Instead, loop over the slices (by looping over the starting indices that define each slice), and save each slice to file. 
slice_length = 5

# loop over indices (i.e. slices)
for idx_from in range(df.shape[0] - slice_length):
    # create the slice and write to file
    df.iloc[idx_from: idx_from + slice_length, :].to_csv(f'slice_starting_idx_{idx_from}.csv', sep=';', index=False)

