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:
| Year   |   KPI-JAE |
|:-------|----------:|
| 2000.0 |       2   |
|        |       2.2 |
|        |       1.6 |
|        |       1.8 |
|        |       2   |
|        |       2.2 |
|        |       2.3 |
|        |       2.8 |
|        |       2.7 |
|        |       2.6 |
|        |       2.6 |
|        |       2.7 |
| 2001.0 |       2.8 |
|        |       2.9 |
|        |       2.8 |
|        |       2.6 |
|        |       2.7 |
|        |       2.4 |
|        |       2.6 |
|        |       2.4 |
|        |       2.3 |
|        |       2.5 |
|        |       2.5 |
|        |       2.7 |
| 2002.0 |       2.5 |
|        |       2.1 |
|        |       2.6 |
|        |       2.4 |
|        |       2.6 |
|        |       2.7 |
|        |       2.7 |
|        |       2.3 |
|        |       2.2 |
|        |       2.1 |
|        |       2   |
|        |       1.8 |
| 2003.0 |       1.8 |
|        |       2   |
|        |       1.5 |
|        |       1.6 |
|        |       1.2 |
|        |       0.8 |
|        |       0.7 |
|        |       0.9 |
|        |       0.9 |
|        |       0.8 |
|        |       0.5 |
|        |       0.4 |
| 2004.0 |       0   |
|        |       0   |
|        |       0.3 |
|        |       0.3 |
|        |       0.1 |
|        |       0.3 |
|        |       0.2 |
|        |       0.2 |
|        |       0.5 |
|        |       0.6 |
|        |       1   |
|        |       1   |
| 2005.0 |       0.7 |
|        |       0.7 |
|        |       0.7 |
|        |       0.8 |
|        |       1.1 |
|        |       1.1 |
|        |       1.1 |
|        |       1.3 |
|        |       1.3 |
|        |       1.2 |
|        |       1.1 |
|        |       0.9 |
| 2006.0 |       0.8 |
|        |       1   |
|        |       0.9 |
|        |       0.8 |
|        |       0.7 |
|        |       0.8 |
|        |       0.6 |
|        |       0.4 |
|        |       0.5 |
|        |       0.7 |
|        |       0.8 |
|        |       1   |
| 2007.0 |       1   |
|        |       1.1 |
|        |       1.5 |
|        |       1.4 |
|        |       1.4 |
|        |       1.3 |
|        |       1.4 |
|        |       1.8 |
|        |       1.6 |
|        |       1.4 |
|        |       1.5 |
|        |       1.8 |
| 2008.0 |       1.9 |
|        |       2.2 |
|        |       2.1 |
|        |       2.4 |

## Output:


source: excel_file
label: ffill