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:
| LC Stage   | Process                                                                    | Release   |
|:-----------|:---------------------------------------------------------------------------|:----------|
| Supplier   | 0                                                                          | 0.0       |
|            | Batch in dedicated equipment, frequent cleaning                            | 1.0       |
|            | Batch in dedicated equipment, infrequent cleaning                          | 0.0       |
|            | 1                                                                          | 1.0       |
|            | Other                                                                      | 2.0       |
|            | 2                                                                          |           |
| Producer   | 0                                                                          | 0.0       |
|            | Batch production in dedicated equipment and infrequent cleaning operations | 1.0       |
|            | Batch production in dedicated equipment and frequent cleaning operations   | 0.0       |
|            | 1                                                                          | 2.0       |
|            | 3                                                                          | 0.0       |
|            | 2                                                                          |           |
| User       | 3                                                                          | 0.0       |
|            | Non-dispersive use (industrial point sources)                              | 1.0       |
|            | Wide dispersive use (many small point sources or diffuse releases)         | 2.0       |
|            | 2                                                                          |           |

## Output:


source: excel_file
label: ffill