Task: Data-transform-reshape (source dataset: Auto-Tables)

Given a table, predict what python pandas transformation may be needed on the given input table. Valid choices for transformation include: {"stack", "wide_to_long", "transpose", "pivot", "explode", "ffill", "subtitles"}. Note that each of these is a common Python Pandas operator. Some transformations can have parameters. Each transformation and its parameters are describe as follows.
 
- stack: collapse homogeneous cols into rows.
@param (int): stack_start_idx: zero-based starting column index of the homogeneous column-group.
@param (int): stack_end_idx: zero-based ending column index of the homogeneous column-group.
 
- wide_to_long: collapse repeating col-groups into rows.
@param (int): wide_to_long_start_idx: zero-based starting column index of the repeating col-groups.
@param (int): wide_to_long_end_idx: zero-based ending column index of the repeating col-groups.
 
- transpose: convert rows to columns and vice versa.
 
- pivot: pivot repeating row-groups into cols.
@param (int): pivot_row_frequency: frequency of repeating row-groups.
 
- ffill: fill structurally empty cells in tables.
@param (int): ffill_end_idx: zero-based ending column index of the columns to be filled.
 
- explode: convert composite cells into atomic values
@param (int): explode_column_idx: zero-based column index of the column to be exploded.
 
- subtitle: convert table subtitles into a column.
 
OUTPUT the predicted transformation name along with its parameters for the given INPUT table in a JSON, e.g., {"transformation": "stack", "stack_start_idx": 1, "stack_end_idx": 5}. If the input table needs multi-step transformations (e.g, ffill followed by stack), list JSON of each transformation sequentially in a list, e.g., [{"transformation": "ffill", "ffill_end_idx": 1}, {"transformation": "stack", "stack_start_idx": 1, "stack_end_idx": 5}]. No explanation is needed.

## Input:
| Trade         |   19-Oct |   20-Oct |   21-Oct |   22-Oct |   23-Oct |   24-Oct |   25-Oct |   26-Oct |   27-Oct |   28-Oct |   29-Oct |   30-Oct |   31-Oct |   1-Nov |   2-Nov |   3-Nov |   4-Nov |   5-Nov |   6-Nov |   7-Nov |   8-Nov |   9-Nov |   10-Nov |   11-Nov |   12-Nov |   13-Nov |   14-Nov |   15-Nov |
|:--------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|
| Architectural |        5 |        3 |        4 |       15 |       19 |        2 |        5 |       11 |        3 |        9 |        9 |        8 |        6 |       8 |      16 |       6 |      15 |       6 |      16 |       5 |      14 |      20 |       10 |        3 |        1 |        5 |       17 |        8 |
| Mechanical    |        8 |        5 |       11 |       12 |        8 |       14 |        7 |        5 |        9 |        9 |        7 |       11 |       19 |      20 |      11 |      12 |       5 |       5 |      14 |       8 |      13 |      20 |        6 |        9 |       17 |        8 |        6 |        4 |
| Electrical    |       17 |        9 |       14 |        5 |       19 |       18 |       17 |       18 |       10 |        7 |        4 |        2 |       17 |       8 |      20 |       2 |      11 |       6 |      14 |      12 |      16 |       7 |        3 |       15 |       14 |       15 |       14 |        9 |
| ELV           |        6 |        4 |        1 |       16 |        8 |        7 |        6 |        9 |        8 |       20 |        9 |       14 |        6 |       3 |       3 |       4 |      18 |       1 |      20 |       5 |      20 |      15 |        8 |       14 |        4 |       15 |        9 |       15 |

## Output:


source: excel_forum
label: operator: stack
stack_start_idx: 1
stack_end_idx: 28
alternative_label: operator: transpose
operator: stack
stack_start_idx: 1
stack_end_idx: 4