How to use multiple lists of lists to append new rows to a dataframe?

I am trying to use multiple list of lists to add rows to a dataframe.

The error is as follows: IndexError: invalid index to scalar variable.

The code is below:

List_a = [[1,2,3],[4,5,6],[7,8,9],[10,11,12],[13,14,15]]
List_b = [[16,17,18],[19,20,21],[22,23,24],[25,26,27],[28,29,30]]
List_c = [[31,32,33],[34,35,36],[37,38,39],[40,41,42],[43,44,45]]

Some_List = ['0', '1', '2', '3', '4']

first_row = {'A': [0], 'B': [0], 'C': [0]}
All_Rows = pd.DataFrame(first_row)

#Optimized_Trades

for i in range(len(Some_List)):
    for j in range(len(Some_List[i])):
        df_temp = { 'A': List_a[i][j], 'B': List_b[i][j], 'C': List_c[i][j]}
        All_Rows = All_Rows.append(df_temp, ignore_index = True)

All_Trades = All_Trades[1:]
        
display(All_Trades)

Ideally, the final output would be:

A     B      C
1    16     31
2    17     32
3    18     33
4    19     34
5    20     35
6    21     36
7    22     37
8    23     38
9    24     39
10   25     40
11   26     41
12   27     42
13   28     43
14   29     44
15   30     45

Solution 1:

You can use itertools.chain to flatten each list, construct a dictionary with the flattened lists and cast it to a DataFrame:

from itertools import chain
A, B, C = [list(chain.from_iterable(lst)) for lst in [List_a, List_b, List_c]]
out = pd.DataFrame({'A': A, 'B': B, 'C': C})

Output:

     A   B   C
0    1  16  31
1    2  17  32
2    3  18  33
3    4  19  34
4    5  20  35
5    6  21  36
6    7  22  37
7    8  23  38
8    9  24  39
9   10  25  40
10  11  26  41
11  12  27  42
12  13  28  43
13  14  29  44
14  15  30  45

Solution 2:

It seems you need range(3), because length of sublists is 3:

for i in range(len(Some_List)):
    for j in range(3):
        df_temp = { 'A': List_a[i][j], 'B': List_b[i][j], 'C': List_c[i][j]}
        All_Rows = All_Rows.append(df_temp, ignore_index = True)
        

Or you can loop by List_a with enumarate, so inner loop use actual length of sublists:

for i, vals in enumerate(List_a):
    for j, vals1 in  enumerate(vals):
        df_temp = { 'A': List_a[i][j], 'B': List_b[i][j], 'C': List_c[i][j]}
        All_Rows = All_Rows.append(df_temp, ignore_index = True)
        

If need only flatten lists:

L = [List_a, List_b, List_c]

df = pd.DataFrame([[z for y in x for z in y] for x in L], index = ['A','B','C']).T
print (df)
     A   B   C
0    1  16  31
1    2  17  32
2    3  18  33
3    4  19  34
4    5  20  35
5    6  21  36
6    7  22  37
7    8  23  38
8    9  24  39
9   10  25  40
10  11  26  41
11  12  27  42
12  13  28  43
13  14  29  44
14  15  30  45

Solution 3:

EDIT: Based on your fixed expected output, try this -

1. Store all the list of lists as a list l
2. Next, add them directly to a dataframe df which would be a dataframe with 5 columns and 3 rows.
3. Take transpose
4. Use explode on each of the columns
5. Rename the columns

List_a = [[1,2,3],[4,5,6],[7,8,9],[10,11,12],[13,14,15]]
List_b = [[16,17,18],[19,20,21],[22,23,24],[25,26,27],[28,29,30]]
List_c = [[31,32,33],[34,35,36],[37,38,39],[40,41,42],[43,44,45]]

l = [List_a, List_b, List_c]

df = pd.DataFrame(l).T
output = df.explode(list(df.columns))
output.columns = ['A','B','C']
print(output)

    A   B   C
0   1  16  31
0   2  17  32
0   3  18  33
1   4  19  34
1   5  20  35
1   6  21  36
2   7  22  37
2   8  23  38
2   9  24  39
3  10  25  40
3  11  26  41
3  12  27  42
4  13  28  43
4  14  29  44
4  15  30  45