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:
| ID           |   Order #1 Quantity | Order #1 Date   | Order #2 Quantity   | Order #2 Date   | Order #3 Quantity   | Order #3 Date   | Order #4 Quantity   | Order #4 Date   |
|:-------------|--------------------:|:----------------|:--------------------|:----------------|:--------------------|:----------------|:--------------------|:----------------|
| Department A |                   1 | 12/23/2020      |                     |                 | 1.0                 | 12/29/2020      |                     |                 |
| Department B |                   1 | 12/23/2020      | 1.0                 | 12/29/2020      |                     |                 |                     |                 |
| Department C |                   2 | 12/23/2020      | 2.0                 | 12/24/2020      | 2.0                 | 12/29/2020      | 1.0                 | 12/30/2020      |
| Department D |                   1 | 12/23/2020      | 1.0                 | 12/29/2020      |                     |                 |                     |                 |
| Department E |                   2 | 12/23/2020      | 2.0                 | 12/29/2020      |                     |                 |                     |                 |
| Department F |                   2 | 12/23/2020      |                     |                 | 1.0                 | 12/29/2020      |                     |                 |

## Output:


source: excel_forum
label: operator: wide_to_long
wide_to_long_start_idx: 1
wide_to_long_end_idx: 8