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:
| Column1         | Current daily smoker   | SE   | Smoker, not daily   |
|:----------------|:-----------------------|:-----|:--------------------|
| sex             |                        |      |                     |
| male            | 29.8                   | 2.6  | 1.6                 |
| female          | 17.6                   | 2.0  | 5.1                 |
| residence       |                        |      |                     |
| urban           | 22.1                   | 1.8  | 4.0                 |
| rural           | 23.9                   | 3.0  | 2.8                 |
| income_quintile |                        |      |                     |
| Q1              | 19.7                   | 3.5  | 1.6                 |
| Q2              | 24.5                   | 3.4  | 3.5                 |
| Q3              | 17.6                   | 2.7  | 3.2                 |
| Q4              | 26.2                   | 3.4  | 3.1                 |
| Q5              | 22.6                   | 3.5  | 6.2                 |
| age             |                        |      |                     |
| 18-29           | 33.6                   | 5.3  | 4.5                 |
| 30-44           | 27.6                   | 3.4  | 6.0                 |
| 45-59           | 23.5                   | 2.8  | 2.8                 |
| 60-69           | 15.8                   | 3.2  | 1.5                 |
| 70-79           | 9.8                    | 3.7  | 4.0                 |
| 80+             | 4.6                    | 3.3  | 1.5                 |

## Output:


source: excel_file
label: subtitle