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:
| MONTH              |     JAN |     FEB |     MAR |     APR |     MAY |     JUN |     JUL |     AUG |     SEP |
|:-------------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| MEMBERSHIP FEES    | 9654    | 7521    | 8541    | 8541    | 8541    | 8541    | 8541    | 8541    | 7521    |
| CLUB SHOP          |    0    |  310    |  657    |  594    |  750    |  466    |  395    |  586    |  447    |
| LOCKER RENTALS     |  196.5  |  220    |  205.75 |  205.75 |  315.25 |  205.75 |  205.75 |  205.75 |  205.75 |
| RENT               |  450    |  450    |  450    |  450    |  450    |  450    |  450    |  450    |  450    |
| SALARIES           | 3540.3  | 3540.3  | 3540.3  | 3540.3  | 3540.3  | 3540.3  | 3540.3  | 3540.3  | 3540.3  |
| EQUIPMENT RENTAL   |  750    |  750    |  750    |  750    |  750    |  750    |  750    |  750    |  750    |
| INSURANCE          |  125    |  125    |  125    |  125    |  125    |  125    |  125    |  125    |  125    |
| CLUB SHOP SUPPLIES |    0    | 1015    |  210    |  195    |  374.99 |  348.92 |  831.83 |  450    |  613.98 |
| HEAT-GAS           |  549.53 |  654.28 |  458.61 |  458.61 |  404.2  |  612.26 |  635.09 |  547.9  |  616.85 |
| ELECTRICITY        |  258.36 |  314.9  |  244.77 |  305.61 |  366.07 |  283.21 |  279.64 |  301.9  |  311.99 |
| TELEPHONE          |   65.23 |   58.94 |   55.82 |   55.82 |   71.43 |   60.48 |   59.72 |   69.23 |   56.84 |
| SNOW REMOVAL       |   55    |   45    |   35    |   35    |   15    |   16    |   34    |   26    |   22    |

## Output:


source: excel_file
label: transpose