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:
| Category/Item     | Column1   | Q1   | Q2   | Q3   | Q4   | Averages   |
|:------------------|:----------|:-----|:-----|:-----|:-----|:-----------|
| Housewares        |           |      |      |      |      |            |
| Coffee Pots       |           | 5.0  | 5.0  | 8.0  | 7.0  | 6.25       |
| Microwaves        |           | 2.0  | 3.0  | 2.0  | 1.0  | 2.0        |
| Popcorn Makers    |           | 1.0  | 3.0  | 2.0  | 1.0  | 1.75       |
| Toasters          |           | 8.0  | 9.0  | 4.0  | 3.0  | 6.0        |
| 0                 |           | 16.0 | 20.0 | 16.0 | 12.0 |            |
|                   |           |      |      |      |      |            |
| Electronics       |           |      |      |      |      |            |
| Cell Phones       |           | 27.0 | 36.0 | 32.0 | 21.0 | 29.0       |
| iPods             |           | 12.0 | 18.0 | 16.0 | 6.0  | 13.0       |
| PS3               |           | 3.0  | 6.0  | 5.0  | 3.0  | 4.25       |
| Stereos           |           | 4.0  | 6.0  | 6.0  | 7.0  | 5.75       |
| TV sets           |           | 14.0 | 12.0 | 15.0 | 6.0  | 11.75      |
| xBox              |           | 2.0  | 7.0  | 4.0  | 2.0  | 3.75       |
| 0                 |           | 62.0 | 85.0 | 78.0 | 45.0 |            |
|                   |           |      |      |      |      |            |
| Sporting Goods    |           |      |      |      |      |            |
| Golf clubs        |           | 5.0  | 4.0  | 3.0  | 5.0  | 4.25       |
| Hockey sticks     |           | 9.0  | 11.0 | 13.0 | 14.0 | 11.75      |
| Ice skates        |           | 5.0  | 2.0  | 3.0  | 8.0  | 4.5        |
| Paintball markers |           | 14.0 | 22.0 | 21.0 | 15.0 | 18.0       |
| Snow Boards       |           | 8.0  | 3.0  | 2.0  | 7.0  | 5.0        |
| 0                 |           | 41.0 | 42.0 | 42.0 | 49.0 |            |

## Output:


source: excel_file
label: operator: subtitle
operator: stack
stack_start_idx: 2
stack_end_idx: 5
alternative_label: operator: stack
stack_start_idx: 2
stack_end_idx: 5
operator: subtitle