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:
| Index   |         A |         B |         C |         D |         E |         F |         G |         H |         I |         J |
|:--------|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|
| A       |  1        |  0.879024 |  0.778962 |  0.976538 |  0.918813 | -0.409401 | -0.421621 | -0.457868 | -0.368139 | -0.48139  |
| B       |  0.879024 |  1        |  0.624553 |  0.93032  |  0.890223 | -0.315762 | -0.326928 | -0.4535   | -0.302988 | -0.329403 |
| C       |  0.778962 |  0.624553 |  1        |  0.766287 |  0.657157 | -0.202418 | -0.207551 | -0.247894 | -0.252252 | -0.289822 |
| D       |  0.976538 |  0.93032  |  0.766287 |  1        |  0.91906  | -0.376167 | -0.381618 | -0.458128 | -0.352047 | -0.408443 |
| E       |  0.918813 |  0.890223 |  0.657157 |  0.91906  |  1        | -0.617206 | -0.59936  | -0.677281 | -0.570815 | -0.652814 |
| F       | -0.409401 | -0.315762 | -0.202418 | -0.376167 | -0.617206 |  1        |  0.987765 |  0.969062 |  0.977009 |  0.969532 |
| G       | -0.421621 | -0.326928 | -0.207551 | -0.381618 | -0.59936  |  0.987765 |  1        |  0.972715 |  0.965639 |  0.955753 |
| H       | -0.457868 | -0.4535   | -0.247894 | -0.458128 | -0.677281 |  0.969062 |  0.972715 |  1        |  0.961149 |  0.910276 |
| I       | -0.368139 | -0.302988 | -0.252252 | -0.352047 | -0.570815 |  0.977009 |  0.965639 |  0.961149 |  1        |  0.929175 |
| J       | -0.48139  | -0.329403 | -0.289822 | -0.408443 | -0.652814 |  0.969532 |  0.955753 |  0.910276 |  0.929175 |  1        |

## Output:


source: notebook
label: operator: stack
stack_start_idx: 1
stack_end_idx: 10
alternative_label: operator: transpose
operator: stack
stack_start_idx: 1
stack_end_idx: 10