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:
| Unnamed: 0    | Asian   | Black   | Hispanic (of any race)   | Non-Hispanic White   | Other/international   | Unknown   |
|:--------------|:--------|:--------|:-------------------------|:---------------------|:----------------------|:----------|
| Brown         | 14%     | 6%      | 10%                      | 45%                  | 14%                   | 11%       |
| Columbia      | 15%     | 8%      | 13%                      | 41%                  | 17%                   | 6%        |
| Cornell       | 17%     | 6%      | 10%                      | 46%                  | 13%                   | 10%       |
| Dartmouth     | 14%     | 8%      | 9%                       | 48%                  | 13%                   | 8%        |
| Harvard       | 12%     | 7%      | 9%                       | 45%                  | 22%                   | 6%        |
| Penn          | 19%     | 7%      | 8%                       | 46%                  | 13%                   | 7%        |
| Princeton     | 18%     | 7%      | 8%                       | 49%                  | 15%                   | 3%        |
| Yale          | 15%     | 6%      | 8%                       | 58%                  | 5%                    | 8%        |
| United States | 5%      | 13%     | 17%                      | 63%                  | 4%                    |           |

## Output:


source: web
label: operator: stack
stack_start_idx: 1
stack_end_idx: 6
alternative_label: operator: transpose
operator: stack
stack_start_idx: 0
stack_end_idx: 9