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:
| Index        | 3-003   | 5-001   | 6-001   | 7-001   | 11-001 (9 ARUP)   | 12-001 (10 ARUP)   | 13-001   | 17-001 (15 ARUP)   | 18-001 (16 ARUP)   | 17-001   | 18-001   |
|:-------------|:--------|:--------|:--------|:--------|:------------------|:-------------------|:---------|:-------------------|:-------------------|:---------|:---------|
| 0            | 2       | 1       | 8       | 2       | 2                 | 5                  | 3        | 1                  | 1                  | 1        | 1        |
| 2            | 2       | 1       | 7       | 5       | 4                 | 4                  | 1        | 1                  | 1                  | 2        | 1        |
| 4            | 1       | 2       | 9       | 6       | 8                 | 7                  | 4        | 4                  | 1                  | 3        | 3        |
| 6            | 4       | 3       | 7       | 6       | 9                 | 11                 | 5        | 6                  | 1                  | 4        | 3        |
| 8            | 1       | 4       | 6       | 8       | 9                 | 13                 | 6        |                    | 3                  | 7        | 6        |
| 10           | 3       | 7       | 8       | 9       | 12                | 15                 | 7        | 9                  | 4                  |          | 6        |
| EOS          | 1       | 5       | 9       | 8       | 10                | 13                 | 7        | 5                  |                    | 7        | 6        |
| Cotinine(BL) | 151     | 124     | 59      | 29      | 280               | 354                | 159      | 73                 | 30                 | 134      | 39       |
| FagerStrom   | 6       | 7       | 7       | 6       | 7                 | 7                  | 8        | 7                  | 6                  | 6        | 6        |
| Cig/Day      | 11_20   | 11_20   | 0_10    | 0_10    | 0_10              | 11_20              | 11_20    | 11_20              | 0_10               | 11_20    | 0_10     |
| Gender       | F       | M       | M       | M       | M                 | F                  | M        | M                  | M                  | M        | F        |
| Age          | 44      | 26      | 22      | 23      | 29                | 25                 | 37       | 40                 | 22                 | 38       | 29       |
| Weight (lb)  | 137     | 155     | 165     | 174     | 260               | 130                | 206      | 230                | 135                | 175      | 156      |
| Height (")   | 67      | 69.5    | 70      | 68      | 73                | 64                 | 72       | 69                 | 70                 | 69       | 63       |

## Output:


source: notebook
label: operator: transpose
operator: stack
stack_start_idx: 1
stack_end_idx: 6