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:
| Region or group                 |   2011 estimates HDI |   2013 estimates HDI |   2014 estimates HDI |   2015 estimates HDI |
|:--------------------------------|---------------------:|---------------------:|---------------------:|---------------------:|
| Very High Human Development     |                0.889 |                0.905 |                0.89  |                0.892 |
| OECD                            |                0.873 |                0.887 |                0.874 |                0.887 |
| Europe and Central Asia         |                0.751 |                0.771 |                0.738 |                0.756 |
| Latin America and the Caribbean |                0.731 |                0.741 |                0.74  |                0.751 |
| High Human Development          |                0.741 |                0.758 |                0.735 |                0.746 |
| East Asia and the Pacific       |                0.671 |                0.683 |                0.703 |                0.72  |
| World                           |                0.682 |                0.694 |                0.702 |                0.717 |
| Arab states                     |                0.641 |                0.652 |                0.682 |                0.687 |
| Small Island Developing States  |                0.64  |                0.648 |                0.665 |                0.667 |
| Medium Human Development        |                0.63  |                0.64  |                0.614 |                0.631 |
| South Asia                      |                0.548 |                0.558 |                0.588 |                0.621 |
| Sub-Saharan Africa              |                0.463 |                0.475 |                0.502 |                0.523 |
| Least Developed countries       |                0.439 |                0.449 |                0.487 |                0.508 |
| Low Human Development           |                0.456 |                0.466 |                0.493 |                0.497 |

## Output:


source: web
label: operator: stack
stack_start_idx: 1
stack_end_idx: 4