I have a following data
0 [[-0.932, 2.443, -1....
1 [[-1.099, 2.140, -1.4...
2 [[-0.985, -1.654, -1....
3 [[-1.339, 2.070, -0....
4 [[-1.119, 2.788, -2....
...
494 [[-0.023, 2.688, -1...
495 [[1.897, 0.0, -2.249,...
496 [[1.538, 2.349, -0.6...
497 [[-0.141, 2.320, -0...
498 [[-0.483, 1.587, -1....
Length: 499, dtype: object
In each row are about 80 lists consisted (list of lists) and I would like to turn them into columns and to get the data:
ID col1 col2 ... col80
1.1.2020 0 -0.932 ...
2.1.2020 0 2.443 ...
3.1.2020 0 -1 ...
1.1.2020 1 -1.099
2.1.2020 1 2.140
3.1.2020 1 -1.4 ...
where the column ID is from the lists indicator (0,1,..,498). The index column (1.1.2020 2.1.2020..) is saved as another object (date). Is this possible and how?
>Solution :
Let’s say you had data like:
import numpy as np
import pandas as pd
ser = pd.Series(np.arange(90).reshape(10, 3, 3).tolist())
0 [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
1 [[9, 10, 11], [12, 13, 14], [15, 16, 17]]
2 [[18, 19, 20], [21, 22, 23], [24, 25, 26]]
3 [[27, 28, 29], [30, 31, 32], [33, 34, 35]]
4 [[36, 37, 38], [39, 40, 41], [42, 43, 44]]
5 [[45, 46, 47], [48, 49, 50], [51, 52, 53]]
6 [[54, 55, 56], [57, 58, 59], [60, 61, 62]]
7 [[63, 64, 65], [66, 67, 68], [69, 70, 71]]
8 [[72, 73, 74], [75, 76, 77], [78, 79, 80]]
9 [[81, 82, 83], [84, 85, 86], [87, 88, 89]]
dtype: object
then I think you can do the bulk of the work like so:
out = ser.explode().apply(pd.Series).reset_index(names="ID")
ID 0 1 2
0 0 0 1 2
1 0 3 4 5
2 0 6 7 8
3 1 9 10 11
4 1 12 13 14
5 1 15 16 17
6 2 18 19 20
7 2 21 22 23
8 2 24 25 26
9 3 27 28 29
10 3 30 31 32
11 3 33 34 35
12 4 36 37 38
13 4 39 40 41
14 4 42 43 44
15 5 45 46 47
16 5 48 49 50
17 5 51 52 53
18 6 54 55 56
19 6 57 58 59
20 6 60 61 62
21 7 63 64 65
22 7 66 67 68
23 7 69 70 71
24 8 72 73 74
25 8 75 76 77
26 8 78 79 80
27 9 81 82 83
28 9 84 85 86
29 9 87 88 89
but you’ll need to rename the columns and change the index yourself (how are you determining those dates?)