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:
| Race of Farm Operator                  | Type of Adult Worker    |   2003 |
|:---------------------------------------|:------------------------|-------:|
| Black/African American                 | 0                       |    418 |
|                                        | Household               |    300 |
|                                        | 1                       |     97 |
|                                        | 2                       |     21 |
| American Indian/Alaska Native          | 0                       |    574 |
|                                        | 3                       |    379 |
|                                        | 1                       |    185 |
|                                        | 2                       |     10 |
| Asian/Native Hawaiian/Pacific Islander | 0                       |    359 |
|                                        | 3                       |     71 |
|                                        | 1                       |    182 |
|                                        | 2                       |    105 |
| Other (2)                              | 0                       |    288 |
|                                        | 3                       |    231 |
|                                        | 1                       |     57 |
|                                        | 2                       |      0 |
| Total (3)                              | 0                       |   1639 |
|                                        | 3                       |    981 |
|                                        | 1                       |    521 |
|                                        | 2                       |    137 |

## Output:


source: excel_file
label: operator: ffill
ffill_end_idx: 0
operator: explode
explode_column_idx: 0